2012
Luengo, David; Monzon, Sandra; Artés-Rodríguez, Antonio
Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs Artículo de revista
En: Neurocomputing, vol. 87, pp. 62–78, 2012.
Resumen | Enlaces | BibTeX | Etiquetas: Line orientation clustering, Linear blind inverse problems, MIMO systems, Neyman–Pearson hypothesis test, Sparse signals
@article{Luengo2012b,
title = {Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs},
author = {David Luengo and Sandra Monzon and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P43_2012_Novel Fast Random Search Clustering Algorithm for Mixing Matrix Identification in MIMO Linear Blind Inverse Problems with Sparse Inputs.pdf
http://www.sciencedirect.com/science/article/pii/S0925231212000744},
year = {2012},
date = {2012-01-01},
journal = {Neurocomputing},
volume = {87},
pages = {62--78},
abstract = {In this paper we propose a novel fast random search clustering (RSC) algorithm for mixing matrix identification in multiple input multiple output (MIMO) linear blind inverse problems with sparse inputs. The proposed approach is based on the clustering of the observations around the directions given by the columns of the mixing matrix that occurs typically for sparse inputs. Exploiting this fact, the RSC algorithm proceeds by parameterizing the mixing matrix using hyperspherical coordinates, randomly selecting candidate basis vectors (i.e. clustering directions) from the observations, and accepting or rejecting them according to a binary hypothesis test based on the Neyman\textendashPearson criterion. The RSC algorithm is not tailored to any specific distribution for the sources, can deal with an arbitrary number of inputs and outputs (thus solving the difficult under-determined problem), and is applicable to both instantaneous and convolutive mixtures. Extensive simulations for synthetic and real data with different number of inputs and outputs, data size, sparsity factors of the inputs and signal to noise ratios confirm the good performance of the proposed approach under moderate/high signal to noise ratios.},
keywords = {Line orientation clustering, Linear blind inverse problems, MIMO systems, Neyman\textendashPearson hypothesis test, Sparse signals},
pubstate = {published},
tppubtype = {article}
}
In this paper we propose a novel fast random search clustering (RSC) algorithm for mixing matrix identification in multiple input multiple output (MIMO) linear blind inverse problems with sparse inputs. The proposed approach is based on the clustering of the observations around the directions given by the columns of the mixing matrix that occurs typically for sparse inputs. Exploiting this fact, the RSC algorithm proceeds by parameterizing the mixing matrix using hyperspherical coordinates, randomly selecting candidate basis vectors (i.e. clustering directions) from the observations, and accepting or rejecting them according to a binary hypothesis test based on the Neyman–Pearson criterion. The RSC algorithm is not tailored to any specific distribution for the sources, can deal with an arbitrary number of inputs and outputs (thus solving the difficult under-determined problem), and is applicable to both instantaneous and convolutive mixtures. Extensive simulations for synthetic and real data with different number of inputs and outputs, data size, sparsity factors of the inputs and signal to noise ratios confirm the good performance of the proposed approach under moderate/high signal to noise ratios.
Oquendo, Maria A; Baca-García, Enrique; Artés-Rodríguez, Antonio; Perez-Cruz, Fernando; Galfalvy, H C; Blasco-Fontecilla, Hilario; Madigan, D; Duan, N
Machine Learning and Data Mining: Strategies for Hypothesis Generation Artículo de revista
En: Molecular psychiatry, vol. 17, no 10, pp. 956–959, 2012, ISSN: 1476-5578.
Resumen | Enlaces | BibTeX | Etiquetas: Artificial Intelligence, Biological, Data Mining, Humans, Mental Disorders, Mental Disorders: diagnosis, Mental Disorders: therapy, Models
@article{Oquendo2012,
title = {Machine Learning and Data Mining: Strategies for Hypothesis Generation},
author = {Maria A Oquendo and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez and Fernando Perez-Cruz and H C Galfalvy and Hilario Blasco-Fontecilla and D Madigan and N Duan},
url = {http://www.ncbi.nlm.nih.gov/pubmed/22230882},
issn = {1476-5578},
year = {2012},
date = {2012-01-01},
journal = {Molecular psychiatry},
volume = {17},
number = {10},
pages = {956--959},
abstract = {Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an \'{o}rganic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.},
keywords = {Artificial Intelligence, Biological, Data Mining, Humans, Mental Disorders, Mental Disorders: diagnosis, Mental Disorders: therapy, Models},
pubstate = {published},
tppubtype = {article}
}
Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an órganic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.
Reyes-Guerrero, J C; Murillo-Fuentes, Juan Jose; Olmos, Pablo M
Remote Detection of Interfered Downlinks in Wireless Cellular Systems Artículo de revista
En: Transactions on Emerging Telecommunications Technologies, vol. 23, no 5, pp. 444–453, 2012, ISSN: 21613915.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Reyes-Guerrero2012,
title = {Remote Detection of Interfered Downlinks in Wireless Cellular Systems},
author = {J C Reyes-Guerrero and Juan Jose Murillo-Fuentes and Pablo M Olmos},
url = {http://doi.wiley.com/10.1002/ett.2501},
issn = {21613915},
year = {2012},
date = {2012-01-01},
journal = {Transactions on Emerging Telecommunications Technologies},
volume = {23},
number = {5},
pages = {444--453},
abstract = {This work provides a novel technological solution to jamming in wireless systems, particularly to remotely detect interfered communications in a cellular network. The new system is focused on the detection of a failure in a link between a base station and a fixed wireless terminal located in a residential or business area. It has an important impact in security systems based on wireless terminals to transmit an alarm to a central station. In these systems, non-authorised people can prevent the transmission of the alarm by using a short-range jammer. The main advantage of this proposal is that it is non-intrusive; that is, no modification is needed in the base station, and no protocol modification is performed in the terminal. The detection is implemented in an external unit developed on a software-defined radio platform. The novel system proposed is valid for any cellular system and operator. In this work, we focus on its implementation in the GSM/GPRS system to illustrate its benefits and outline the method for Universal Mobile Telecommunications System. We describe the results of some experiments where the system successfully detects the presence of a short-range jammer in a real scenario.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This work provides a novel technological solution to jamming in wireless systems, particularly to remotely detect interfered communications in a cellular network. The new system is focused on the detection of a failure in a link between a base station and a fixed wireless terminal located in a residential or business area. It has an important impact in security systems based on wireless terminals to transmit an alarm to a central station. In these systems, non-authorised people can prevent the transmission of the alarm by using a short-range jammer. The main advantage of this proposal is that it is non-intrusive; that is, no modification is needed in the base station, and no protocol modification is performed in the terminal. The detection is implemented in an external unit developed on a software-defined radio platform. The novel system proposed is valid for any cellular system and operator. In this work, we focus on its implementation in the GSM/GPRS system to illustrate its benefits and outline the method for Universal Mobile Telecommunications System. We describe the results of some experiments where the system successfully detects the presence of a short-range jammer in a real scenario.
Olmos, Pablo M; Salamanca, Luis; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
On the Design of LDPC-Convolutional Ensembles Using the TEP Decoder Artículo de revista
En: IEEE Communications Letters, vol. 16, no 5, pp. 726–729, 2012, ISSN: 1089-7798.
Resumen | Enlaces | BibTeX | Etiquetas: belief propagation decoding, binary erasure channel, channel capacity, Complexity theory, convolutional codes, convolutional LDPC codes, Decoding, design, Error analysis, finite-length analysis, Iterative decoding, LDPC-convolutional ensemble design, LDPCC code decoding, low-density parity-check convolutional code, parity check codes, tree-expectation propagation decoder, tree-structured expectation propagation, window-sliding scheme
@article{Olmos2012b,
title = {On the Design of LDPC-Convolutional Ensembles Using the TEP Decoder},
author = {Pablo M Olmos and Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6168872},
issn = {1089-7798},
year = {2012},
date = {2012-01-01},
journal = {IEEE Communications Letters},
volume = {16},
number = {5},
pages = {726--729},
abstract = {Low-density parity-check convolutional (LDPCC) codes asymptotically achieve channel capacity under belief propagation (BP) decoding. In this paper, we decode LDPCC codes using the Tree-Expectation Propagation (TEP) decoder, recently proposed as an alternative decoding method to the BP algorithm for the binary erasure channel (BEC). We show that, for LDPCC codes, the TEP decoder improves the BP solution with a comparable complexity or, alternatively, it allows using shorter codes to achieve similar error rates. We also propose a window-sliding scheme for the TEP decoder to reduce the decoding latency.},
keywords = {belief propagation decoding, binary erasure channel, channel capacity, Complexity theory, convolutional codes, convolutional LDPC codes, Decoding, design, Error analysis, finite-length analysis, Iterative decoding, LDPC-convolutional ensemble design, LDPCC code decoding, low-density parity-check convolutional code, parity check codes, tree-expectation propagation decoder, tree-structured expectation propagation, window-sliding scheme},
pubstate = {published},
tppubtype = {article}
}
Low-density parity-check convolutional (LDPCC) codes asymptotically achieve channel capacity under belief propagation (BP) decoding. In this paper, we decode LDPCC codes using the Tree-Expectation Propagation (TEP) decoder, recently proposed as an alternative decoding method to the BP algorithm for the binary erasure channel (BEC). We show that, for LDPCC codes, the TEP decoder improves the BP solution with a comparable complexity or, alternatively, it allows using shorter codes to achieve similar error rates. We also propose a window-sliding scheme for the TEP decoder to reduce the decoding latency.
Luengo, David; Martino, Luca
Efficient Random Variable Generation: Ratio of Uniforms and Polar Rejection Sampling Artículo de revista
En: Electronics Letters, vol. 48, no 6, pp. 326–327, 2012, ISSN: 00135194.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Luengo2012bb,
title = {Efficient Random Variable Generation: Ratio of Uniforms and Polar Rejection Sampling},
author = {David Luengo and Luca Martino},
url = {http://digital-library.theiet.org/content/journals/10.1049/el.2012.0206},
issn = {00135194},
year = {2012},
date = {2012-01-01},
journal = {Electronics Letters},
volume = {48},
number = {6},
pages = {326--327},
abstract = {Monte Carlo techniques, which require the generation of samples from some target density, are often the only alternative for performing Bayesian inference. Two classic sampling techniques to draw independent samples are the ratio of uniforms (RoU) and rejection sampling (RS). An efficient sampling algorithm is proposed combining the RoU and polar RS (i.e. RS inside a sector of a circle using polar coordinates). Its efficiency is shown in drawing samples from truncated Cauchy and Gaussian random variables, which have many important applications in signal processing and communications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Monte Carlo techniques, which require the generation of samples from some target density, are often the only alternative for performing Bayesian inference. Two classic sampling techniques to draw independent samples are the ratio of uniforms (RoU) and rejection sampling (RS). An efficient sampling algorithm is proposed combining the RoU and polar RS (i.e. RS inside a sector of a circle using polar coordinates). Its efficiency is shown in drawing samples from truncated Cauchy and Gaussian random variables, which have many important applications in signal processing and communications.
Luengo, David; Martino, Luca
Almost rejectionless sampling from Nakagami-m distributions (m≥1) Artículo de revista
En: Electronics Letters, vol. 48, no 24, pp. 1559–1561, 2012, ISSN: 0013-5194.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Luengo2012ab,
title = {Almost rejectionless sampling from Nakagami-m distributions (m≥1)},
author = {David Luengo and Luca Martino},
url = {http://digital-library.theiet.org/content/view.action?itemId=http://iet.metastore.ingenta.com/content/journals/10.1049/el.2012.3513\&view=\&itemType=http://pub2web.metastore.ingenta.com/ns/Article?itemId=http://iet.metastore.ingenta.com/content/journals/10.1049/el.2012.3513\&view=\&itemType=http://pub2web.metastore.ingenta.com/ns/Article},
issn = {0013-5194},
year = {2012},
date = {2012-01-01},
journal = {Electronics Letters},
volume = {48},
number = {24},
pages = {1559--1561},
publisher = {IET Digital Library},
abstract = {The Nakagami-textitm distribution is widely used for the simulation of fading channels in wireless communications. A novel, simple and extremely efficient acceptance-rejection algorithm is introduced for the generation of independent Nakagami-textitm random variables. The proposed method uses another Nakagami density with a half-integer value of the fading parameter},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Nakagami-textitm distribution is widely used for the simulation of fading channels in wireless communications. A novel, simple and extremely efficient acceptance-rejection algorithm is introduced for the generation of independent Nakagami-textitm random variables. The proposed method uses another Nakagami density with a half-integer value of the fading parameter
López-Castromán, Jorge; Leiva-Murillo, Jose M; Blasco-Fontecilla, Hilario; Garcia-Nieto, R; Morant-Ginestar, C; Blanco, Carlos; Artés-Rodríguez, Antonio; Baca-García, Enrique
P-1266 - Dimensional Schizophrenia: not an Easy Transition Artículo de revista
En: European Psychiatry, vol. 27, pp. 1, 2012.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Lopez-Castroman2012,
title = {P-1266 - Dimensional Schizophrenia: not an Easy Transition},
author = {Jorge L\'{o}pez-Castrom\'{a}n and Jose M Leiva-Murillo and Hilario Blasco-Fontecilla and R Garcia-Nieto and C Morant-Ginestar and Carlos Blanco and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
url = {http://www.sciencedirect.com/science/article/pii/S0924933812754330},
year = {2012},
date = {2012-01-01},
journal = {European Psychiatry},
volume = {27},
pages = {1},
abstract = {Recently, several authors have argued in favor of extending the less common clinical phenotype of schizophrenia to a vulnerability phenotype of schizophrenia in the general population. It has been proposed that high levels in any of four different symptom dimensions (affective, psychosis, negative and cognitive) would lead to clinical assessment, identification of correlated symptoms in other dimensions and finally, the diagnosis of schizophrenia. Being so, we would expect to find such a dimensional pattern in the previous diagnoses of schizophrenic patients. We examined previous contacts of a large cohort of patients diagnosed, according to the International Classification of Diseases (ICD-10), with schizophrenia (n=26,163) in public mental health centers of Madrid (Spain) from 1980 to 2008. Of those patients, 56.7% received another diagnosis prior to schizophrenia. Non-schizophrenia diagnoses within the category of ‘schizophrenia, schizotypal and delusional disorders’ were common (F2; 40.0%). The other most frequent prior diagnoses were ‘neurotic, stress-related and somatoform disorders’ (F4; 47.3%), ‘mood disorders’ (F3; 41.4%), and ‘disorders of adult personality and behavior’ (F6; 20.8%). We then examined the probability of progression to schizophrenia, considering also time proximity. The strongest associations were between several F2 spectrum diagnoses with schizophrenia. However, some affective disorders (F3x) were also linked with schizophrenia but anxiety (F4) or personality disorders (F6) were not. Our findings support two of the previously described dimensions (psychotic, affective) in the development of schizophrenia. Several limitations of the dimensional model will be discussed in view of these findings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recently, several authors have argued in favor of extending the less common clinical phenotype of schizophrenia to a vulnerability phenotype of schizophrenia in the general population. It has been proposed that high levels in any of four different symptom dimensions (affective, psychosis, negative and cognitive) would lead to clinical assessment, identification of correlated symptoms in other dimensions and finally, the diagnosis of schizophrenia. Being so, we would expect to find such a dimensional pattern in the previous diagnoses of schizophrenic patients. We examined previous contacts of a large cohort of patients diagnosed, according to the International Classification of Diseases (ICD-10), with schizophrenia (n=26,163) in public mental health centers of Madrid (Spain) from 1980 to 2008. Of those patients, 56.7% received another diagnosis prior to schizophrenia. Non-schizophrenia diagnoses within the category of ‘schizophrenia, schizotypal and delusional disorders’ were common (F2; 40.0%). The other most frequent prior diagnoses were ‘neurotic, stress-related and somatoform disorders’ (F4; 47.3%), ‘mood disorders’ (F3; 41.4%), and ‘disorders of adult personality and behavior’ (F6; 20.8%). We then examined the probability of progression to schizophrenia, considering also time proximity. The strongest associations were between several F2 spectrum diagnoses with schizophrenia. However, some affective disorders (F3x) were also linked with schizophrenia but anxiety (F4) or personality disorders (F6) were not. Our findings support two of the previously described dimensions (psychotic, affective) in the development of schizophrenia. Several limitations of the dimensional model will be discussed in view of these findings.
2011
Vazquez-Vilar, Gonzalo; López-Valcarce, Roberto; Sala, Josep
Multiantenna spectrum sensing exploiting spectral a priori information Artículo de revista
En: IEEE Transactions on Wireless Communications, vol. 10, no 12, pp. 4345-4355, 2011, ISSN: 1536-1276.
@article{twc11,
title = {Multiantenna spectrum sensing exploiting spectral a priori information},
author = {Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce and Josep Sala},
doi = {10.1109/TWC.2011.101211.110665},
issn = {1536-1276},
year = {2011},
date = {2011-12-01},
journal = {IEEE Transactions on Wireless Communications},
volume = {10},
number = {12},
pages = {4345-4355},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Vazquez-Vilar, Gonzalo; López-Valcarce, Roberto
Spectrum sensing exploiting guard bands and weak channels Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 59, no 12, pp. 6045-6057, 2011, ISSN: 1053-587X.
@article{tsp11,
title = {Spectrum sensing exploiting guard bands and weak channels},
author = {Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce},
doi = {10.1109/TSP.2011.2167615},
issn = {1053-587X},
year = {2011},
date = {2011-12-01},
journal = {IEEE Transactions on Signal Processing},
volume = {59},
number = {12},
pages = {6045-6057},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ramirez, David; Vazquez-Vilar, Gonzalo; Lopez-Valcarce, Roberto; Via, Javier; Santamaria, Ignacio
Detection of rank-P Signals in Cognitive Radio Networks With Uncalibrated Multiple Antennas Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 59, no 8, pp. 3764-3774, 2011, ISSN: 1053-587X.
@article{ramirez11,
title = {Detection of rank-P Signals in Cognitive Radio Networks With Uncalibrated Multiple Antennas},
author = {David Ramirez and Gonzalo Vazquez-Vilar and Roberto Lopez-Valcarce and Javier Via and Ignacio Santamaria},
doi = {10.1109/TSP.2011.2146779},
issn = {1053-587X},
year = {2011},
date = {2011-08-01},
journal = {IEEE Transactions on Signal Processing},
volume = {59},
number = {8},
pages = {3764-3774},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Vazquez, Manuel A; Miguez, Joaquin
A Per-Survivor Processing Receiver for MIMO Transmission Systems With One Unknown Channel Order Per Output Artículo de revista
En: IEEE Transactions on Vehicular Technology, vol. 60, no 9, pp. 4415–4426, 2011, ISSN: 0018-9545.
Resumen | Enlaces | BibTeX | Etiquetas: Channel estimation, communication channel, Complexity theory, dynamic programming, frequency-selective MIMO channel, frequency-selective multiple-input multiple-output, maximum likelihood detection, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO channel impulse response coefficient, MIMO communication, MIMO transmission system, multipath channels, mutiple-input–multiple-output (MIMO), per-survivor processing receiver, Receiving antennas, Signal processing algorithms, time-selective MIMO channel, Transmitting antennas, Viterbi algorithm
@article{Vazquez2011,
title = {A Per-Survivor Processing Receiver for MIMO Transmission Systems With One Unknown Channel Order Per Output},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P31_2011_A Per-Survivor Processing Receiver for MIMO Transmission Systems With One Unknown Channel Order Per Output.pdf http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6032763},
issn = {0018-9545},
year = {2011},
date = {2011-01-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {60},
number = {9},
pages = {4415--4426},
abstract = {The order of a communications channel is the length of its impulse response. Recently, several works have tackled the problem of estimating the order of a frequency-selective multiple-input-multiple-output (MIMO) channel. However, all of them consider a single order, despite the fact that a MIMO channel comprises several subchannels (specifically, as many as the number of inputs times the number of outputs), each one possibly with its own order. In this paper, we introduce an algorithm for maximum-likelihood sequence detection (MLSD) in frequency- and time-selective MIMO channels that incorporates full estimation of the MIMO channel impulse response (CIR) coefficients, including one channel order per output. Simulation results following the analytical derivation of the algorithm suggest that the proposed receiver can achieve significant improvements in performance when transmitting through a MIMO channel that effectively comprises subchannels of different lengths.},
keywords = {Channel estimation, communication channel, Complexity theory, dynamic programming, frequency-selective MIMO channel, frequency-selective multiple-input multiple-output, maximum likelihood detection, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO channel impulse response coefficient, MIMO communication, MIMO transmission system, multipath channels, mutiple-input\textendashmultiple-output (MIMO), per-survivor processing receiver, Receiving antennas, Signal processing algorithms, time-selective MIMO channel, Transmitting antennas, Viterbi algorithm},
pubstate = {published},
tppubtype = {article}
}
The order of a communications channel is the length of its impulse response. Recently, several works have tackled the problem of estimating the order of a frequency-selective multiple-input-multiple-output (MIMO) channel. However, all of them consider a single order, despite the fact that a MIMO channel comprises several subchannels (specifically, as many as the number of inputs times the number of outputs), each one possibly with its own order. In this paper, we introduce an algorithm for maximum-likelihood sequence detection (MLSD) in frequency- and time-selective MIMO channels that incorporates full estimation of the MIMO channel impulse response (CIR) coefficients, including one channel order per output. Simulation results following the analytical derivation of the algorithm suggest that the proposed receiver can achieve significant improvements in performance when transmitting through a MIMO channel that effectively comprises subchannels of different lengths.
Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Tree-Structured Expectation Propagation for Decoding Finite-Length LDPC Codes Artículo de revista
En: IEEE Communications Letters, vol. 15, no 2, pp. 235–237, 2011, ISSN: 1089-7798.
Resumen | Enlaces | BibTeX | Etiquetas: belief propagation decoder, BP algorithm, BP decoder, code graph, communication complexity, computational complexity, Decoding, finite-length analysis, finite-length low-density parity-check code, LDPC code, LDPC decoding, parity check codes, radiowave propagation, stopping set, TEP algorithm, TEP decoder, tree-structured expectation propagation
@article{Olmos2011c,
title = {Tree-Structured Expectation Propagation for Decoding Finite-Length LDPC Codes},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5682215},
issn = {1089-7798},
year = {2011},
date = {2011-01-01},
journal = {IEEE Communications Letters},
volume = {15},
number = {2},
pages = {235--237},
abstract = {In this paper, we propose Tree-structured Expectation Propagation (TEP) algorithm to decode finite-length Low-Density Parity-Check (LDPC) codes. The TEP decoder is able to continue decoding once the standard Belief Propagation (BP) decoder fails, presenting the same computational complexity as the BP decoder. The BP algorithm is dominated by the presence of stopping sets (SSs) in the code graph. We show that the TEP decoder, without previous knowledge of the graph, naturally avoids some fairly common SSs. This results in a significant improvement in the system performance.},
keywords = {belief propagation decoder, BP algorithm, BP decoder, code graph, communication complexity, computational complexity, Decoding, finite-length analysis, finite-length low-density parity-check code, LDPC code, LDPC decoding, parity check codes, radiowave propagation, stopping set, TEP algorithm, TEP decoder, tree-structured expectation propagation},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose Tree-structured Expectation Propagation (TEP) algorithm to decode finite-length Low-Density Parity-Check (LDPC) codes. The TEP decoder is able to continue decoding once the standard Belief Propagation (BP) decoder fails, presenting the same computational complexity as the BP decoder. The BP algorithm is dominated by the presence of stopping sets (SSs) in the code graph. We show that the TEP decoder, without previous knowledge of the graph, naturally avoids some fairly common SSs. This results in a significant improvement in the system performance.
Asheghan, Mohammad Mostafa; Miguez, Joaquin; Hamidi-Beheshti, Mohammad Taghi; Tavazoei, Mohammad Saleh
Robust Outer Synchronization between two Complex Networks with Fractional Order Dynamics Artículo de revista
En: Chaos (Woodbury, N.Y.), vol. 21, no 3, pp. 033121, 2011, ISSN: 1089-7682.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Asheghan2011,
title = {Robust Outer Synchronization between two Complex Networks with Fractional Order Dynamics},
author = {Mohammad Mostafa Asheghan and Joaquin Miguez and Mohammad Taghi Hamidi-Beheshti and Mohammad Saleh Tavazoei},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P32_2011_Robust Outer Synchronization between two Complex Networks with Fractional Order Dynamics.pdf
http://scitation.aip.org/content/aip/journal/chaos/21/3/10.1063/1.3629986},
issn = {1089-7682},
year = {2011},
date = {2011-01-01},
journal = {Chaos (Woodbury, N.Y.)},
volume = {21},
number = {3},
pages = {033121},
publisher = {AIP Publishing},
abstract = {Synchronization between two coupled complex networks with fractional-order dynamics, hereafter referred to as outer synchronization, is investigated in this work. In particular, we consider two systems consisting of interconnected nodes. The state variables of each node evolve with time according to a set of (possibly nonlinear and chaotic) fractional-order differential equations. One of the networks plays the role of a master system and drives the second network by way of an open-plus-closed-loop (OPCL) scheme. Starting from a simple analysis of the synchronization error and a basic lemma on the eigenvalues of matrices resulting from Kronecker products, we establish various sets of conditions for outer synchronization, i.e., for ensuring that the errors between the state variables of the master and response systems can asymptotically vanish with time. Then, we address the problem of robust outer synchronization, i.e., how to guarantee that the states of the nodes converge to common values when the parameters of the master and response networks are not identical, but present some perturbations. Assuming that these perturbations are bounded, we also find conditions for outer synchronization, this time given in terms of sets of linear matrix inequalities (LMIs). Most of the analytical results in this paper are valid both for fractional-order and integer-order dynamics. The assumptions on the inner (coupling) structure of the networks are mild, involving, at most, symmetry and diffusivity. The analytical results are complemented with numerical examples. In particular, we show examples of generalized and robust outer synchronization for networks whose nodes are governed by fractional-order Lorenz dynamics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Synchronization between two coupled complex networks with fractional-order dynamics, hereafter referred to as outer synchronization, is investigated in this work. In particular, we consider two systems consisting of interconnected nodes. The state variables of each node evolve with time according to a set of (possibly nonlinear and chaotic) fractional-order differential equations. One of the networks plays the role of a master system and drives the second network by way of an open-plus-closed-loop (OPCL) scheme. Starting from a simple analysis of the synchronization error and a basic lemma on the eigenvalues of matrices resulting from Kronecker products, we establish various sets of conditions for outer synchronization, i.e., for ensuring that the errors between the state variables of the master and response systems can asymptotically vanish with time. Then, we address the problem of robust outer synchronization, i.e., how to guarantee that the states of the nodes converge to common values when the parameters of the master and response networks are not identical, but present some perturbations. Assuming that these perturbations are bounded, we also find conditions for outer synchronization, this time given in terms of sets of linear matrix inequalities (LMIs). Most of the analytical results in this paper are valid both for fractional-order and integer-order dynamics. The assumptions on the inner (coupling) structure of the networks are mild, involving, at most, symmetry and diffusivity. The analytical results are complemented with numerical examples. In particular, we show examples of generalized and robust outer synchronization for networks whose nodes are governed by fractional-order Lorenz dynamics.
Delgado-Gómez, David; Aguado, David; Lopez-Castroman, Jorge; Santacruz, Carlos; Artés-Rodríguez, Antonio
Improving Sale Performance Prediction Using Support Vector Machines Artículo de revista
En: Expert Systems with Applications, vol. 38, no 5, pp. 5129–5132, 2011.
Resumen | Enlaces | BibTeX | Etiquetas: Recruitment process, Sale performance prediction, Support vector machines
@article{Delgado-Gomez2011a,
title = {Improving Sale Performance Prediction Using Support Vector Machines},
author = {David Delgado-G\'{o}mez and David Aguado and Jorge Lopez-Castroman and Carlos Santacruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P41_2011_Improving Sale Performance Prediction Using Support Vector Machines.pdf
http://www.sciencedirect.com/science/article/pii/S0957417410011322},
year = {2011},
date = {2011-01-01},
journal = {Expert Systems with Applications},
volume = {38},
number = {5},
pages = {5129--5132},
abstract = {In this article, an expert system based on support vector machines is developed to predict the sale performance of some insurance company candidates. The system predicts the performance of these candidates based on some scores, which are measurements of cognitive characteristics, personality, selling skills and biodata. An experiment is conducted to compare the accuracy of the proposed system with respect to previously reported systems which use discriminant functions or decision trees. Results show that the proposed system is able to improve the accuracy of a baseline linear discriminant based system by more than 10% and that also exceeds the state of the art systems by almost 5%. The proposed approach can help to reduce considerably the direct and indirect expenses of the companies.},
keywords = {Recruitment process, Sale performance prediction, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
In this article, an expert system based on support vector machines is developed to predict the sale performance of some insurance company candidates. The system predicts the performance of these candidates based on some scores, which are measurements of cognitive characteristics, personality, selling skills and biodata. An experiment is conducted to compare the accuracy of the proposed system with respect to previously reported systems which use discriminant functions or decision trees. Results show that the proposed system is able to improve the accuracy of a baseline linear discriminant based system by more than 10% and that also exceeds the state of the art systems by almost 5%. The proposed approach can help to reduce considerably the direct and indirect expenses of the companies.
Delgado-Gómez, David; Blasco-Fontecilla, Hilario; Alegria, AnaLucia A; Legido-Gil, Teresa; Artés-Rodríguez, Antonio; Baca-García, Enrique
Improving the Accuracy of Suicide Attempter Classification Artículo de revista
En: Artificial Intelligence in Medicine, vol. 52, no 3, pp. 165–168, 2011.
Resumen | Enlaces | BibTeX | Etiquetas: Barratt’s impulsiveness scale, Boosting, International personality disorder evaluation scre, Suicide prediction, Support vector machines
@article{Delgado-Gomez2011b,
title = {Improving the Accuracy of Suicide Attempter Classification},
author = {David Delgado-G\'{o}mez and Hilario Blasco-Fontecilla and AnaLucia A Alegria and Teresa Legido-Gil and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
url = {http://www.sciencedirect.com/science/article/pii/S0933365711000595},
year = {2011},
date = {2011-01-01},
journal = {Artificial Intelligence in Medicine},
volume = {52},
number = {3},
pages = {165--168},
abstract = {OBJECTIVE Psychometrical questionnaires such as the Barrat’s impulsiveness scale version 11 (BIS-11) have been used in the assessment of suicidal behavior. Traditionally, BIS-11 items have been considered as equally valuable but this might not be true. The main objective of this article is to test the discriminative ability of the BIS-11 and the international personality disorder evaluation screening questionnaire (IPDE-SQ) to predict suicide attempter (SA) status using different classification techniques. In addition, we examine the discriminative capacity of individual items from both scales. MATERIALS AND METHODS Two experiments aimed at evaluating the accuracy of different classification techniques were conducted. The answers of 879 individuals (345 SA, 384 healthy blood donors, and 150 psychiatric inpatients) to the BIS-11 and IPDE-SQ were used to compare the classification performance of two techniques that have successfully been applied in pattern recognition issues, Boosting and support vector machines (SVM) with respect to linear discriminant analysis, Fisher linear discriminant analysis, and the traditional psychometrical approach. RESULTS The most discriminative BIS-11 and IPDE-SQ items are “I am self controlled” (Item 6) and “I often feel empty inside” (item 40), respectively. The SVM classification accuracy was 76.71% for the BIS-11 and 80.26% for the IPDE-SQ. CONCLUSIONS The IPDE-SQ items have better discriminative abilities than the BIS-11 items for classifying SA. Moreover, IPDE-SQ is able to obtain better SA and non-SA classification results than the BIS-11. In addition, SVM outperformed the other classification techniques in both questionnaires.},
keywords = {Barratt’s impulsiveness scale, Boosting, International personality disorder evaluation scre, Suicide prediction, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
OBJECTIVE Psychometrical questionnaires such as the Barrat’s impulsiveness scale version 11 (BIS-11) have been used in the assessment of suicidal behavior. Traditionally, BIS-11 items have been considered as equally valuable but this might not be true. The main objective of this article is to test the discriminative ability of the BIS-11 and the international personality disorder evaluation screening questionnaire (IPDE-SQ) to predict suicide attempter (SA) status using different classification techniques. In addition, we examine the discriminative capacity of individual items from both scales. MATERIALS AND METHODS Two experiments aimed at evaluating the accuracy of different classification techniques were conducted. The answers of 879 individuals (345 SA, 384 healthy blood donors, and 150 psychiatric inpatients) to the BIS-11 and IPDE-SQ were used to compare the classification performance of two techniques that have successfully been applied in pattern recognition issues, Boosting and support vector machines (SVM) with respect to linear discriminant analysis, Fisher linear discriminant analysis, and the traditional psychometrical approach. RESULTS The most discriminative BIS-11 and IPDE-SQ items are “I am self controlled” (Item 6) and “I often feel empty inside” (item 40), respectively. The SVM classification accuracy was 76.71% for the BIS-11 and 80.26% for the IPDE-SQ. CONCLUSIONS The IPDE-SQ items have better discriminative abilities than the BIS-11 items for classifying SA. Moreover, IPDE-SQ is able to obtain better SA and non-SA classification results than the BIS-11. In addition, SVM outperformed the other classification techniques in both questionnaires.
Miguez, Joaquin; Crisan, Dan; Djuric, Petar M
On the Convergence of Two Sequential Monte Carlo Methods for Maximum a Posteriori Sequence Estimation and Stochastic Global Optimization Artículo de revista
En: Statistics and Computing, vol. 23, no 1, pp. 91–107, 2011, ISSN: 0960-3174.
Resumen | Enlaces | BibTeX | Etiquetas: Global optimization, MAP sequence estimation, Sequential Monte Carlo, State space models
@article{Miguez2011,
title = {On the Convergence of Two Sequential Monte Carlo Methods for Maximum a Posteriori Sequence Estimation and Stochastic Global Optimization},
author = {Joaquin Miguez and Dan Crisan and Petar M Djuric},
url = {http://www.researchgate.net/publication/225447686_On_the_convergence_of_two_sequential_Monte_Carlo_methods_for_maximum_a_posteriori_sequence_estimation_and_stochastic_global_optimization},
issn = {0960-3174},
year = {2011},
date = {2011-01-01},
journal = {Statistics and Computing},
volume = {23},
number = {1},
pages = {91--107},
abstract = {This paper addresses the problem of maximum a posteriori (MAP) sequence estimation in general state-space models. We consider two algorithms based on the sequential Monte Carlo (SMC) methodology (also known as particle filtering). We prove that they produce approximations of the MAP estimator and that they converge almost surely. We also derive a lower bound for the number of particles that are needed to achieve a given approximation accuracy. In the last part of the paper, we investigate the application of particle filtering and MAP estimation to the global optimization of a class of (possibly non-convex and possibly non-differentiable) cost functions. In particular, we show how to convert the cost-minimization problem into one of MAP sequence estimation for a state-space model that is “matched” to the cost of interest. We provide examples that illustrate the application of the methodology as well as numerical results.},
keywords = {Global optimization, MAP sequence estimation, Sequential Monte Carlo, State space models},
pubstate = {published},
tppubtype = {article}
}
This paper addresses the problem of maximum a posteriori (MAP) sequence estimation in general state-space models. We consider two algorithms based on the sequential Monte Carlo (SMC) methodology (also known as particle filtering). We prove that they produce approximations of the MAP estimator and that they converge almost surely. We also derive a lower bound for the number of particles that are needed to achieve a given approximation accuracy. In the last part of the paper, we investigate the application of particle filtering and MAP estimation to the global optimization of a class of (possibly non-convex and possibly non-differentiable) cost functions. In particular, we show how to convert the cost-minimization problem into one of MAP sequence estimation for a state-space model that is “matched” to the cost of interest. We provide examples that illustrate the application of the methodology as well as numerical results.
Lopez-Castroman, Jorge; Perez-Rodriguez, Mercedes M; Jaussent, Isabelle; Alegria, AnaLucia A; Artés-Rodríguez, Antonio; Freed, Peter; Guillaume, Sébastien; Jollant, Fabrice; Leiva-Murillo, Jose M; Malafosse, Alain; Oquendo, Maria A; de Prado-Cumplido, Mario; Saiz-Ruiz, Jeronimo; Baca-García, Enrique; Courtet, Philippe
Distinguishing the Relevant Features of Frequent Suicide Attempters Artículo de revista
En: Journal of psychiatric research, vol. 45, no 5, pp. 619–625, 2011, ISSN: 1879-1379.
Resumen | Enlaces | BibTeX | Etiquetas: Adult, Attempted, Attempted: psychology, Attempted: statistics & numerical data, Female, France, Humans, Interview, Male, Middle Aged, Prevalence, Probability, Psychiatric Status Rating Scales, Psychological, Risk Factors, ROC Curve, Spain, Suicide
@article{Lopez-Castroman2011,
title = {Distinguishing the Relevant Features of Frequent Suicide Attempters},
author = {Jorge Lopez-Castroman and Mercedes M Perez-Rodriguez and Isabelle Jaussent and AnaLucia A Alegria and Antonio Art\'{e}s-Rodr\'{i}guez and Peter Freed and S\'{e}bastien Guillaume and Fabrice Jollant and Jose M Leiva-Murillo and Alain Malafosse and Maria A Oquendo and Mario de Prado-Cumplido and Jeronimo Saiz-Ruiz and Enrique Baca-Garc\'{i}a and Philippe Courtet},
url = {http://www.tsc.uc3m.es/~antonio/papers/P39_2011_Distinguishing the Relevant Features of Frequent Suicide Attempters.pdf
http://www.ncbi.nlm.nih.gov/pubmed/21055768},
issn = {1879-1379},
year = {2011},
date = {2011-01-01},
journal = {Journal of psychiatric research},
volume = {45},
number = {5},
pages = {619--625},
abstract = {BACKGROUND: In spite of the high prevalence of suicide behaviours and the magnitude of the resultant burden, little is known about why individuals reattempt. We aim to investigate the relationships between clinical risk factors and the repetition of suicidal attempts. METHODS: 1349 suicide attempters were consecutively recruited in the Emergency Room (ER) of two academic hospitals in France and Spain. Patients were extensively assessed and demographic and clinical data obtained. Data mining was used to determine the minimal number of variables that blinded the rest in relation to the number of suicide attempts. Using this set, a probabilistic graph ranking relationships with the target variable was constructed. RESULTS: The most common diagnoses among suicide attempters were affective disorders, followed by anxiety disorders. Risk of frequent suicide attempt was highest among middle-aged subjects, and diminished progressively with advancing age of onset at first attempt. Anxiety disorders significantly increased the risk of presenting frequent suicide attempts. Pathway analysis also indicated that frequent suicide attempts were linked to greater odds for alcohol and substance abuse disorders and more intensive treatment. CONCLUSIONS: Novel statistical methods found several clinical features that were associated with a history of frequent suicide attempts. The identified pathways may promote new hypothesis-driven studies of suicide attempts and preventive strategies.},
keywords = {Adult, Attempted, Attempted: psychology, Attempted: statistics \& numerical data, Female, France, Humans, Interview, Male, Middle Aged, Prevalence, Probability, Psychiatric Status Rating Scales, Psychological, Risk Factors, ROC Curve, Spain, Suicide},
pubstate = {published},
tppubtype = {article}
}
BACKGROUND: In spite of the high prevalence of suicide behaviours and the magnitude of the resultant burden, little is known about why individuals reattempt. We aim to investigate the relationships between clinical risk factors and the repetition of suicidal attempts. METHODS: 1349 suicide attempters were consecutively recruited in the Emergency Room (ER) of two academic hospitals in France and Spain. Patients were extensively assessed and demographic and clinical data obtained. Data mining was used to determine the minimal number of variables that blinded the rest in relation to the number of suicide attempts. Using this set, a probabilistic graph ranking relationships with the target variable was constructed. RESULTS: The most common diagnoses among suicide attempters were affective disorders, followed by anxiety disorders. Risk of frequent suicide attempt was highest among middle-aged subjects, and diminished progressively with advancing age of onset at first attempt. Anxiety disorders significantly increased the risk of presenting frequent suicide attempts. Pathway analysis also indicated that frequent suicide attempts were linked to greater odds for alcohol and substance abuse disorders and more intensive treatment. CONCLUSIONS: Novel statistical methods found several clinical features that were associated with a history of frequent suicide attempts. The identified pathways may promote new hypothesis-driven studies of suicide attempts and preventive strategies.
Tuia, D; Verrelst, J; Alonso, L; Perez-Cruz, Fernando; Camps-Valls, Gustavo
Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation Artículo de revista
En: IEEE Geoscience and Remote Sensing Letters, vol. 8, no 4, pp. 804–808, 2011, ISSN: 1545-598X.
Resumen | Enlaces | BibTeX | Etiquetas: Biological system modeling, Biomedical imaging, Biophysical parameter estimation, chlorophyll content estimation, Estimation, fractional vegetation cover, geophysical image processing, hyperspectral compact high-resolution imaging spec, image resolution, leaf area index, model inversion, multioutput support vector regression method, nonparametric biophysical parameter estimation, Parameter estimation, regression, regression analysis, Remote sensing, remote sensing biophysical parameter estimation, remote sensing image, single-output support vector regression method, spectrometers, Support vector machines, support vector regression (SVR), Vegetation mapping
@article{Tuia2011,
title = {Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation},
author = {D Tuia and J Verrelst and L Alonso and Fernando Perez-Cruz and Gustavo Camps-Valls},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5735189},
issn = {1545-598X},
year = {2011},
date = {2011-01-01},
journal = {IEEE Geoscience and Remote Sensing Letters},
volume = {8},
number = {4},
pages = {804--808},
abstract = {This letter proposes a multioutput support vector regression (M-SVR) method for the simultaneous estimation of different biophysical parameters from remote sensing images. General retrieval problems require multioutput (and potentially nonlinear) regression methods. M-SVR extends the single-output SVR to multiple outputs maintaining the advantages of a sparse and compact solution by using an $epsilon$-insensitive cost function. The proposed M-SVR is evaluated in the estimation of chlorophyll content, leaf area index and fractional vegetation cover from a hyperspectral compact high-resolution imaging spectrometer images. The achieved improvement with respect to the single-output regression approach suggests that M-SVR can be considered a convenient alternative for nonparametric biophysical parameter estimation and model inversion.},
keywords = {Biological system modeling, Biomedical imaging, Biophysical parameter estimation, chlorophyll content estimation, Estimation, fractional vegetation cover, geophysical image processing, hyperspectral compact high-resolution imaging spec, image resolution, leaf area index, model inversion, multioutput support vector regression method, nonparametric biophysical parameter estimation, Parameter estimation, regression, regression analysis, Remote sensing, remote sensing biophysical parameter estimation, remote sensing image, single-output support vector regression method, spectrometers, Support vector machines, support vector regression (SVR), Vegetation mapping},
pubstate = {published},
tppubtype = {article}
}
This letter proposes a multioutput support vector regression (M-SVR) method for the simultaneous estimation of different biophysical parameters from remote sensing images. General retrieval problems require multioutput (and potentially nonlinear) regression methods. M-SVR extends the single-output SVR to multiple outputs maintaining the advantages of a sparse and compact solution by using an $epsilon$-insensitive cost function. The proposed M-SVR is evaluated in the estimation of chlorophyll content, leaf area index and fractional vegetation cover from a hyperspectral compact high-resolution imaging spectrometer images. The achieved improvement with respect to the single-output regression approach suggests that M-SVR can be considered a convenient alternative for nonparametric biophysical parameter estimation and model inversion.
Santiago-Mozos, Ricardo; Perez-Cruz, Fernando; Artés-Rodríguez, Antonio
Extended Input Space Support Vector Machine Artículo de revista
En: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, vol. 22, no 1, pp. 158–163, 2011, ISSN: 1941-0093.
Resumen | Enlaces | BibTeX | Etiquetas: Algorithms, Artificial Intelligence, Automated, Automated: standards, Computer Simulation, Computer Simulation: standards, Neural Networks (Computer), Pattern recognition, Problem Solving, Software Design, Software Validation
@article{Santiago-Mozos2011,
title = {Extended Input Space Support Vector Machine},
author = {Ricardo Santiago-Mozos and Fernando Perez-Cruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P38_2011_Extended Input Space Support Vector Machine.pdf
http://www.ncbi.nlm.nih.gov/pubmed/21095866},
issn = {1941-0093},
year = {2011},
date = {2011-01-01},
journal = {IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council},
volume = {22},
number = {1},
pages = {158--163},
abstract = {In some applications, the probability of error of a given classifier is too high for its practical application, but we are allowed to gather more independent test samples from the same class to reduce the probability of error of the final decision. From the point of view of hypothesis testing, the solution is given by the Neyman-Pearson lemma. However, there is no equivalent result to the Neyman-Pearson lemma when the likelihoods are unknown, and we are given a training dataset. In this brief, we explore two alternatives. First, we combine the soft (probabilistic) outputs of a given classifier to produce a consensus labeling for K test samples. In the second approach, we build a new classifier that directly computes the label for K test samples. For this second approach, we need to define an extended input space training set and incorporate the known symmetries in the classifier. This latter approach gives more accurate results, as it only requires an accurate classification boundary, while the former needs an accurate posterior probability estimate for the whole input space. We illustrate our results with well-known databases.},
keywords = {Algorithms, Artificial Intelligence, Automated, Automated: standards, Computer Simulation, Computer Simulation: standards, Neural Networks (Computer), Pattern recognition, Problem Solving, Software Design, Software Validation},
pubstate = {published},
tppubtype = {article}
}
In some applications, the probability of error of a given classifier is too high for its practical application, but we are allowed to gather more independent test samples from the same class to reduce the probability of error of the final decision. From the point of view of hypothesis testing, the solution is given by the Neyman-Pearson lemma. However, there is no equivalent result to the Neyman-Pearson lemma when the likelihoods are unknown, and we are given a training dataset. In this brief, we explore two alternatives. First, we combine the soft (probabilistic) outputs of a given classifier to produce a consensus labeling for K test samples. In the second approach, we build a new classifier that directly computes the label for K test samples. For this second approach, we need to define an extended input space training set and incorporate the known symmetries in the classifier. This latter approach gives more accurate results, as it only requires an accurate classification boundary, while the former needs an accurate posterior probability estimate for the whole input space. We illustrate our results with well-known databases.
Plata-Chaves, Jorge; Lazaro, Marcelino
Closed-Form Error Exponent for the Neyman–Pearson Fusion of Dependent Local Decisions in a One-Dimensional Sensor Network Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 59, no 3, pp. 1239–1254, 2011, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Plata-Chaves2011b,
title = {Closed-Form Error Exponent for the Neyman\textendashPearson Fusion of Dependent Local Decisions in a One-Dimensional Sensor Network},
author = {Jorge Plata-Chaves and Marcelino Lazaro},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5654602},
issn = {1053-587X},
year = {2011},
date = {2011-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {59},
number = {3},
pages = {1239--1254},
abstract = {We consider a distributed detection system formed by a large number of local detectors and a data fusion center that performs a Neyman-Pearson fusion of the binary quantizations of the sensor observations. In the analyzed two-stage detection system the local decisions are taken with no kind of cooperation among the devices and they are transmitted to the fusion center over an error free parallel access channel. In addition, the sensors are randomly deployed along a straight line, and the corresponding sensor spacings are drawn independently from a common probability density function (pdf). For both hypothesis, H0 and H1, depending on the correlation structure of the observed phenomenon the local decisions might be dependent. In the case of being dependent, their correlation structure is modelled with a one-dimensional Markov random field with nearest neighbor dependency and binary state space. Under this scenario, we first derive a closed-form error exponent for the Neyman-Pearson fusion of the local decisions when the involved data fusion center only knows the distribution of the sensor spacings. Second, based on a single parameter that captures the mean correlation strength among the local decisions, some analytical properties of the error exponent are investigated. Finally, we develop a physical model for the conditional probabilities of the Markov random fields that might be present under each hypothesis. Using this model we characterize the error exponent for two well-known models of the sensor spacing: i) equispaced sensors with failures, and ii) exponentially spaced sensors with failures.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We consider a distributed detection system formed by a large number of local detectors and a data fusion center that performs a Neyman-Pearson fusion of the binary quantizations of the sensor observations. In the analyzed two-stage detection system the local decisions are taken with no kind of cooperation among the devices and they are transmitted to the fusion center over an error free parallel access channel. In addition, the sensors are randomly deployed along a straight line, and the corresponding sensor spacings are drawn independently from a common probability density function (pdf). For both hypothesis, H0 and H1, depending on the correlation structure of the observed phenomenon the local decisions might be dependent. In the case of being dependent, their correlation structure is modelled with a one-dimensional Markov random field with nearest neighbor dependency and binary state space. Under this scenario, we first derive a closed-form error exponent for the Neyman-Pearson fusion of the local decisions when the involved data fusion center only knows the distribution of the sensor spacings. Second, based on a single parameter that captures the mean correlation strength among the local decisions, some analytical properties of the error exponent are investigated. Finally, we develop a physical model for the conditional probabilities of the Markov random fields that might be present under each hypothesis. Using this model we characterize the error exponent for two well-known models of the sensor spacing: i) equispaced sensors with failures, and ii) exponentially spaced sensors with failures.
2010
Vazquez-Vilar, Gonzalo; Mosquera, Carlos; Jayaweera, Sudharman K
Primary User Enters the Game: Performance of Dynamic Spectrum Leasing in Cognitive Radio Networks Artículo de revista
En: IEEE Transactions on Wireless Communications, vol. 9, no 12, pp. 3625-3629, 2010, ISSN: 1536-1276.
@article{twc2010,
title = {Primary User Enters the Game: Performance of Dynamic Spectrum Leasing in Cognitive Radio Networks},
author = {Gonzalo Vazquez-Vilar and Carlos Mosquera and Sudharman K Jayaweera},
doi = {10.1109/TWC.2010.101310.101056},
issn = {1536-1276},
year = {2010},
date = {2010-12-01},
journal = {IEEE Transactions on Wireless Communications},
volume = {9},
number = {12},
pages = {3625-3629},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Jayaweera, Sudharman K; Vazquez-Vilar, Gonzalo; Mosquera, Carlos
Dynamic Spectrum Leasing (DSL): A New Paradigm for Spectrum Sharing in Cognitive Radio Networks Artículo de revista
En: IEEE Transactions on Vehicular Technology, vol. 59, no 5, pp. 2328-2339, 2010, ISSN: 0018-9545.
@article{tvt2010,
title = {Dynamic Spectrum Leasing (DSL): A New Paradigm for Spectrum Sharing in Cognitive Radio Networks},
author = {Sudharman K Jayaweera and Gonzalo Vazquez-Vilar and Carlos Mosquera},
doi = {10.1109/TVT.2010.2042741},
issn = {0018-9545},
year = {2010},
date = {2010-06-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {59},
number = {5},
pages = {2328-2339},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Perez-Cruz, Fernando; Kulkarni, S R
Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks Artículo de revista
En: IEEE Signal Processing Letters, vol. 17, no 4, pp. 355–358, 2010, ISSN: 1070-9908.
Resumen | Enlaces | BibTeX | Etiquetas: communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks
@article{Perez-Cruz2010,
title = {Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks},
author = {Fernando Perez-Cruz and S R Kulkarni},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5395679},
issn = {1070-9908},
year = {2010},
date = {2010-01-01},
journal = {IEEE Signal Processing Letters},
volume = {17},
number = {4},
pages = {355--358},
abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for sensor network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.},
keywords = {communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {article}
}
We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for sensor network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.
Carballo, Juan J; Baca-García, Enrique; Blanco, Carlos; Perez-Rodriguez, Mercedes M; Jimenez-Arriero, Miguel A; Artés-Rodríguez, Antonio; Rynn, Moira; Shaffer, David; Oquendo, Maria A
Stability of Childhood Anxiety Disorder Diagnoses: a Follow-Up Naturalistic Study in Psychiatric Care Artículo de revista
En: European child & adolescent psychiatry, vol. 19, no 4, pp. 395–403, 2010, ISSN: 1435-165X.
Resumen | Enlaces | BibTeX | Etiquetas: Adolescent, Ambulatory Care, Ambulatory Care: utilization, Anxiety Disorders, Anxiety Disorders: diagnosis, Anxiety Disorders: epidemiology, Catchment Area (Health), Child, Cohort Studies, Female, Follow-Up Studies, Humans, International Classification of Diseases, Male, Mental Health Services, Mental Health Services: utilization, Preschool, Prospective Studies, Severity of Illness Index, Spain, Spain: epidemiology
@article{Carballo2010,
title = {Stability of Childhood Anxiety Disorder Diagnoses: a Follow-Up Naturalistic Study in Psychiatric Care},
author = {Juan J Carballo and Enrique Baca-Garc\'{i}a and Carlos Blanco and Mercedes M Perez-Rodriguez and Miguel A Jimenez-Arriero and Antonio Art\'{e}s-Rodr\'{i}guez and Moira Rynn and David Shaffer and Maria A Oquendo},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19826859},
issn = {1435-165X},
year = {2010},
date = {2010-01-01},
journal = {European child \& adolescent psychiatry},
volume = {19},
number = {4},
pages = {395--403},
abstract = {Few studies have examined the stability of major psychiatric disorders in pediatric psychiatric clinical populations. The objective of this study was to examine the long-term stability of anxiety diagnoses starting with pre-school age children through adolescence evaluated at multiple time points. Prospective cohort study was conducted of all children and adolescents receiving psychiatric care at all pediatric psychiatric clinics belonging to two catchment areas in Madrid, Spain, between 1 January, 1992 and 30 April, 2006. Patients were selected from among 24,163 children and adolescents who received psychiatric care. Patients had to have a diagnosis of an ICD-10 anxiety disorder during at least one of the consultations and had to have received psychiatric care for the anxiety disorder. We grouped anxiety disorder diagnoses according to the following categories: phobic disorders, social anxiety disorders, obsessive-compulsive disorder (OCD), stress-related disorders, and \"{o}ther" anxiety disorders which, among others, included generalized anxiety disorder, and panic disorder. Complementary indices of diagnostic stability were calculated. As much as 1,869 subjects were included and had 27,945 psychiatric/psychological consultations. The stability of all ICD-10 anxiety disorder categories studied was high regardless of the measure of diagnostic stability used. Phobic and social anxiety disorders showed the highest diagnostic stability, whereas OCD and \"{o}ther" anxiety disorders showed the lowest diagnostic stability. No significant sex differences were observed on the diagnostic stability of the anxiety disorder categories studied. Diagnostic stability measures for phobic, social anxiety, and \"{o}ther" anxiety disorder diagnoses varied depending on the age at first evaluation. In this clinical pediatric outpatient sample it appears that phobic, social anxiety, and stress-related disorder diagnoses in children and adolescents treated in community outpatient services may have high diagnostic stability.},
keywords = {Adolescent, Ambulatory Care, Ambulatory Care: utilization, Anxiety Disorders, Anxiety Disorders: diagnosis, Anxiety Disorders: epidemiology, Catchment Area (Health), Child, Cohort Studies, Female, Follow-Up Studies, Humans, International Classification of Diseases, Male, Mental Health Services, Mental Health Services: utilization, Preschool, Prospective Studies, Severity of Illness Index, Spain, Spain: epidemiology},
pubstate = {published},
tppubtype = {article}
}
Few studies have examined the stability of major psychiatric disorders in pediatric psychiatric clinical populations. The objective of this study was to examine the long-term stability of anxiety diagnoses starting with pre-school age children through adolescence evaluated at multiple time points. Prospective cohort study was conducted of all children and adolescents receiving psychiatric care at all pediatric psychiatric clinics belonging to two catchment areas in Madrid, Spain, between 1 January, 1992 and 30 April, 2006. Patients were selected from among 24,163 children and adolescents who received psychiatric care. Patients had to have a diagnosis of an ICD-10 anxiety disorder during at least one of the consultations and had to have received psychiatric care for the anxiety disorder. We grouped anxiety disorder diagnoses according to the following categories: phobic disorders, social anxiety disorders, obsessive-compulsive disorder (OCD), stress-related disorders, and öther" anxiety disorders which, among others, included generalized anxiety disorder, and panic disorder. Complementary indices of diagnostic stability were calculated. As much as 1,869 subjects were included and had 27,945 psychiatric/psychological consultations. The stability of all ICD-10 anxiety disorder categories studied was high regardless of the measure of diagnostic stability used. Phobic and social anxiety disorders showed the highest diagnostic stability, whereas OCD and öther" anxiety disorders showed the lowest diagnostic stability. No significant sex differences were observed on the diagnostic stability of the anxiety disorder categories studied. Diagnostic stability measures for phobic, social anxiety, and öther" anxiety disorder diagnoses varied depending on the age at first evaluation. In this clinical pediatric outpatient sample it appears that phobic, social anxiety, and stress-related disorder diagnoses in children and adolescents treated in community outpatient services may have high diagnostic stability.
Delgado-Gómez, David; Sukno, Federico; Aguado, David; Santacruz, Carlos; Artés-Rodríguez, Antonio
Individual Identification Using Personality Traits Artículo de revista
En: Journal of Network and Computer Applications, vol. 33, no 3, pp. 293–299, 2010, ISSN: 10848045.
Resumen | Enlaces | BibTeX | Etiquetas: Biometrics, Personality traits, Psychometrics, Samejima's model
@article{Delgado-Gomez2010,
title = {Individual Identification Using Personality Traits},
author = {David Delgado-G\'{o}mez and Federico Sukno and David Aguado and Carlos Santacruz and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.sciencedirect.com/science/article/pii/S1084804509001453},
issn = {10848045},
year = {2010},
date = {2010-01-01},
journal = {Journal of Network and Computer Applications},
volume = {33},
number = {3},
pages = {293--299},
abstract = {In this article, a pioneer study is conducted to evaluate the possibility of identifying people through their personality traits. The study is conducted using the answers of a population of 734 individuals to a collection of 206 items. These items aim at measuring five common different personality traits usually called the big five. These five levels are neuroticism, extraversion, agreeableness, conscientiousness and openness. The traits are estimated using the widely used Samejima's model and then used to discriminate the individuals. Results point biometrics using personality traits as a new promising biometric modality.},
keywords = {Biometrics, Personality traits, Psychometrics, Samejima's model},
pubstate = {published},
tppubtype = {article}
}
In this article, a pioneer study is conducted to evaluate the possibility of identifying people through their personality traits. The study is conducted using the answers of a population of 734 individuals to a collection of 206 items. These items aim at measuring five common different personality traits usually called the big five. These five levels are neuroticism, extraversion, agreeableness, conscientiousness and openness. The traits are estimated using the widely used Samejima's model and then used to discriminate the individuals. Results point biometrics using personality traits as a new promising biometric modality.
Koch, Tobias; Lapidoth, Amos
Gaussian Fading Is the Worst Fading Artículo de revista
En: IEEE Transactions on Information Theory, vol. 56, no 3, pp. 1158–1165, 2010, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, channel capacity, channels with memory, Distribution functions, ergodic fading processes, Fading, fading channels, flat fading, flat-fading channel capacity, Gaussian channels, Gaussian fading, Gaussian processes, H infinity control, high signal-to-noise ratio (SNR), Information technology, information theory, multiple-input single-output fading channels, multiplexing gain, noncoherent, noncoherent channel capacity, peak-power limited channel capacity, Signal to noise ratio, signal-to-noise ratio, single-antenna channel capacity, spectral distribution function, time-selective, Transmitters
@article{Koch2010a,
title = {Gaussian Fading Is the Worst Fading},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5429105},
issn = {0018-9448},
year = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {56},
number = {3},
pages = {1158--1165},
abstract = {The capacity of peak-power limited, single-antenna, noncoherent, flat-fading channels with memory is considered. The emphasis is on the capacity pre-log, i.e., on the limiting ratio of channel capacity to the logarithm of the signal-to-noise ratio (SNR), as the SNR tends to infinity. It is shown that, among all stationary and ergodic fading processes of a given spectral distribution function and whose law has no mass point at zero, the Gaussian process gives rise to the smallest pre-log. The assumption that the law of the fading process has no mass point at zero is essential in the sense that there exist stationary and ergodic fading processes whose law has a mass point at zero and that give rise to a smaller pre-log than the Gaussian process of equal spectral distribution function. An extension of these results to multiple-input single-output (MISO) fading channels with memory is also presented.},
keywords = {Additive noise, channel capacity, channels with memory, Distribution functions, ergodic fading processes, Fading, fading channels, flat fading, flat-fading channel capacity, Gaussian channels, Gaussian fading, Gaussian processes, H infinity control, high signal-to-noise ratio (SNR), Information technology, information theory, multiple-input single-output fading channels, multiplexing gain, noncoherent, noncoherent channel capacity, peak-power limited channel capacity, Signal to noise ratio, signal-to-noise ratio, single-antenna channel capacity, spectral distribution function, time-selective, Transmitters},
pubstate = {published},
tppubtype = {article}
}
The capacity of peak-power limited, single-antenna, noncoherent, flat-fading channels with memory is considered. The emphasis is on the capacity pre-log, i.e., on the limiting ratio of channel capacity to the logarithm of the signal-to-noise ratio (SNR), as the SNR tends to infinity. It is shown that, among all stationary and ergodic fading processes of a given spectral distribution function and whose law has no mass point at zero, the Gaussian process gives rise to the smallest pre-log. The assumption that the law of the fading process has no mass point at zero is essential in the sense that there exist stationary and ergodic fading processes whose law has a mass point at zero and that give rise to a smaller pre-log than the Gaussian process of equal spectral distribution function. An extension of these results to multiple-input single-output (MISO) fading channels with memory is also presented.
Martino, Luca; Miguez, Joaquin
Generalized Rejection Sampling Schemes and Applications in Signal Processing Artículo de revista
En: Signal Processing, vol. 90, no 11, pp. 2981–2995, 2010.
Resumen | Enlaces | BibTeX | Etiquetas: Adaptive rejection sampling, Gibbs sampling, Monte Carlo integration, Rejection sampling, sensor networks, Target localization
@article{Martino2010a,
title = {Generalized Rejection Sampling Schemes and Applications in Signal Processing},
author = {Luca Martino and Joaquin Miguez},
url = {http://www.sciencedirect.com/science/article/pii/S0165168410001866},
year = {2010},
date = {2010-01-01},
journal = {Signal Processing},
volume = {90},
number = {11},
pages = {2981--2995},
abstract = {Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become very popular in signal processing over the last years. However, in many problems of practical interest these techniques demand procedures for sampling from probability distributions with non-standard forms, hence we are often brought back to the consideration of fundamental simulation algorithms, such as rejection sampling (RS). Unfortunately, the use of RS techniques demands the calculation of tight upper bounds for the ratio of the target probability density function (pdf) over the proposal density from which candidate samples are drawn. Except for the class of log-concave target pdf's, for which an efficient algorithm exists, there are no general methods to analytically determine this bound, which has to be derived from scratch for each specific case. In this paper, we introduce new schemes for (a) obtaining upper bounds for likelihood functions and (b) adaptively computing proposal densities that approximate the target pdf closely. The former class of methods provides the tools to easily sample from a posteriori probability distributions (that appear very often in signal processing problems) by drawing candidates from the prior distribution. However, they are even more useful when they are exploited to derive the generalized adaptive RS (GARS) algorithm introduced in the second part of the paper. The proposed GARS method yields a sequence of proposal densities that converge towards the target pdf and enable a very efficient sampling of a broad class of probability distributions, possibly with multiple modes and non-standard forms. We provide some simple numerical examples to illustrate the use of the proposed techniques, including an example of target localization using range measurements, often encountered in sensor network applications.},
keywords = {Adaptive rejection sampling, Gibbs sampling, Monte Carlo integration, Rejection sampling, sensor networks, Target localization},
pubstate = {published},
tppubtype = {article}
}
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become very popular in signal processing over the last years. However, in many problems of practical interest these techniques demand procedures for sampling from probability distributions with non-standard forms, hence we are often brought back to the consideration of fundamental simulation algorithms, such as rejection sampling (RS). Unfortunately, the use of RS techniques demands the calculation of tight upper bounds for the ratio of the target probability density function (pdf) over the proposal density from which candidate samples are drawn. Except for the class of log-concave target pdf's, for which an efficient algorithm exists, there are no general methods to analytically determine this bound, which has to be derived from scratch for each specific case. In this paper, we introduce new schemes for (a) obtaining upper bounds for likelihood functions and (b) adaptively computing proposal densities that approximate the target pdf closely. The former class of methods provides the tools to easily sample from a posteriori probability distributions (that appear very often in signal processing problems) by drawing candidates from the prior distribution. However, they are even more useful when they are exploited to derive the generalized adaptive RS (GARS) algorithm introduced in the second part of the paper. The proposed GARS method yields a sequence of proposal densities that converge towards the target pdf and enable a very efficient sampling of a broad class of probability distributions, possibly with multiple modes and non-standard forms. We provide some simple numerical examples to illustrate the use of the proposed techniques, including an example of target localization using range measurements, often encountered in sensor network applications.
Djuric, Petar M; Miguez, Joaquin
Assessment of Nonlinear Dynamic Models by Kolmogorov–Smirnov Statistics Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 58, no 10, pp. 5069–5079, 2010, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: Cumulative distributions, discrete random variables, dynamic nonlinear models, Electrical capacitance tomography, Filtering, filtering theory, Iron, Kolmogorov-Smirnov statistics, Kolomogorov–Smirnov statistics, model assessment, nonlinear dynamic models, nonlinear dynamical systems, Permission, predictive cumulative distributions, predictive distributions, Predictive models, Random variables, Robots, statistical analysis, statistical distributions, statistics, Telecommunication control
@article{Djuric2010a,
title = {Assessment of Nonlinear Dynamic Models by Kolmogorov\textendashSmirnov Statistics},
author = {Petar M Djuric and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5491124},
issn = {1053-587X},
year = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {58},
number = {10},
pages = {5069--5079},
abstract = {Model assessment is a fundamental problem in science and engineering and it addresses the question of the validity of a model in the light of empirical evidence. In this paper, we propose a method for the assessment of dynamic nonlinear models based on empirical and predictive cumulative distributions of data and the Kolmogorov-Smirnov statistics. The technique is based on the generation of discrete random variables that come from a known discrete distribution if the entertained model is correct. We provide simulation examples that demonstrate the performance of the proposed method.},
keywords = {Cumulative distributions, discrete random variables, dynamic nonlinear models, Electrical capacitance tomography, Filtering, filtering theory, Iron, Kolmogorov-Smirnov statistics, Kolomogorov\textendashSmirnov statistics, model assessment, nonlinear dynamic models, nonlinear dynamical systems, Permission, predictive cumulative distributions, predictive distributions, Predictive models, Random variables, Robots, statistical analysis, statistical distributions, statistics, Telecommunication control},
pubstate = {published},
tppubtype = {article}
}
Model assessment is a fundamental problem in science and engineering and it addresses the question of the validity of a model in the light of empirical evidence. In this paper, we propose a method for the assessment of dynamic nonlinear models based on empirical and predictive cumulative distributions of data and the Kolmogorov-Smirnov statistics. The technique is based on the generation of discrete random variables that come from a known discrete distribution if the entertained model is correct. We provide simulation examples that demonstrate the performance of the proposed method.
Perez-Cruz, Fernando; Rodrigues, Miguel R D; Verdu, Sergio
MIMO Gaussian Channels With Arbitrary Inputs: Optimal Precoding and Power Allocation Artículo de revista
En: IEEE Transactions on Information Theory, vol. 56, no 3, pp. 1070–1084, 2010, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: Collaborative work, Equations, fixed-point equation, Gaussian channels, Gaussian noise channels, Gaussian processes, Government, Interference, linear precoding, matrix algebra, mean square error methods, mercury-waterfilling algorithm, MIMO, MIMO communication, MIMO Gaussian channel, minimum mean-square error, minimum mean-square error (MMSE), multiple-input-multiple-output channel, multiple-input–multiple-output (MIMO) systems, Mutual information, nondiagonal precoding matrix, optimal linear precoder, optimal power allocation policy, optimal precoding, optimum power allocation, Phase shift keying, precoding, Quadrature amplitude modulation, Telecommunications, waterfilling
@article{Perez-Cruz2010a,
title = {MIMO Gaussian Channels With Arbitrary Inputs: Optimal Precoding and Power Allocation},
author = {Fernando Perez-Cruz and Miguel R D Rodrigues and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5429131},
issn = {0018-9448},
year = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {56},
number = {3},
pages = {1070--1084},
abstract = {In this paper, we investigate the linear precoding and power allocation policies that maximize the mutual information for general multiple-input-multiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error (MMSE). The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For non-Gaussian inputs, a nondiagonal precoding matrix in general increases the information transmission rate, even for parallel noninteracting channels. Whenever precoding is precluded, the optimal power allocation policy also satisfies a fixed-point equation; we put forth a generalization of the mercury/waterfilling algorithm, previously proposed for parallel noninterfering channels, in which the mercury level accounts not only for the non-Gaussian input distributions, but also for the interference among inputs.},
keywords = {Collaborative work, Equations, fixed-point equation, Gaussian channels, Gaussian noise channels, Gaussian processes, Government, Interference, linear precoding, matrix algebra, mean square error methods, mercury-waterfilling algorithm, MIMO, MIMO communication, MIMO Gaussian channel, minimum mean-square error, minimum mean-square error (MMSE), multiple-input-multiple-output channel, multiple-input\textendashmultiple-output (MIMO) systems, Mutual information, nondiagonal precoding matrix, optimal linear precoder, optimal power allocation policy, optimal precoding, optimum power allocation, Phase shift keying, precoding, Quadrature amplitude modulation, Telecommunications, waterfilling},
pubstate = {published},
tppubtype = {article}
}
In this paper, we investigate the linear precoding and power allocation policies that maximize the mutual information for general multiple-input-multiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error (MMSE). The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For non-Gaussian inputs, a nondiagonal precoding matrix in general increases the information transmission rate, even for parallel noninteracting channels. Whenever precoding is precluded, the optimal power allocation policy also satisfies a fixed-point equation; we put forth a generalization of the mercury/waterfilling algorithm, previously proposed for parallel noninterfering channels, in which the mercury level accounts not only for the non-Gaussian input distributions, but also for the interference among inputs.
Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Joint Nonlinear Channel Equalization and Soft LDPC Decoding with Gaussian Processes Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 58, no 3, pp. 1183–1192, 2010, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian nonlinear classification tool, Bit error rate, Channel Coding, channel equalizers, Channel estimation, Coding, equalisers, equalization, error statistics, Gaussian processes, GPC, joint nonlinear channel equalization, low-density parity-check (LDPC), low-density parity-check channel decoder, Machine learning, nonlinear channel, nonlinear codes, parity check codes, posterior probability estimates, soft LDPC decoding, soft-decoding, support vector machine (SVM)
@article{Olmos2010a,
title = {Joint Nonlinear Channel Equalization and Soft LDPC Decoding with Gaussian Processes},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5290078},
issn = {1053-587X},
year = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {58},
number = {3},
pages = {1183--1192},
abstract = {In this paper, we introduce a new approach for nonlinear equalization based on Gaussian processes for classification (GPC). We propose to measure the performance of this equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate output by the equalizer might be irrelevant to understand the performance of the overall communication receiver. In this sense, GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. In the experimental section, we compare the proposed GPC-based equalizer with state-of-the-art solutions to illustrate its improved performance.},
keywords = {Bayesian nonlinear classification tool, Bit error rate, Channel Coding, channel equalizers, Channel estimation, Coding, equalisers, equalization, error statistics, Gaussian processes, GPC, joint nonlinear channel equalization, low-density parity-check (LDPC), low-density parity-check channel decoder, Machine learning, nonlinear channel, nonlinear codes, parity check codes, posterior probability estimates, soft LDPC decoding, soft-decoding, support vector machine (SVM)},
pubstate = {published},
tppubtype = {article}
}
In this paper, we introduce a new approach for nonlinear equalization based on Gaussian processes for classification (GPC). We propose to measure the performance of this equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate output by the equalizer might be irrelevant to understand the performance of the overall communication receiver. In this sense, GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. In the experimental section, we compare the proposed GPC-based equalizer with state-of-the-art solutions to illustrate its improved performance.
Koch, Tobias; Lapidoth, Amos
On Multipath Fading Channels at High SNR Artículo de revista
En: IEEE Transactions on Information Theory, vol. 56, no 12, pp. 5945–5957, 2010, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: approximation theory, capacity pre-loglog, capacity to loglog, channel capacity, channels with memory, Delay, Fading, fading channels, frequency-selective fading, high signal-to-noise ratio, high SNR, Limiting, multipath, multipath channels, noncoherent, noncoherent multipath fading channel, Receivers, Signal to noise ratio, signal-to-noise ratio, Transmitters
@article{Koch2010b,
title = {On Multipath Fading Channels at High SNR},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5625630},
issn = {0018-9448},
year = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {56},
number = {12},
pages = {5945--5957},
abstract = {A noncoherent multipath fading channel is considered, where neither the transmitter nor the receiver is cognizant of the realization of the path gains, but both are cognizant of their statistics. It is shown that if the delay spread is large in the sense that the variances of the path gains decay exponentially or slower, then capacity is bounded in the signal-to-noise ratio (SNR). For such channels, capacity does not tend to infinity as the SNR tends to infinity. In contrast, if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the SNR. It is further demonstrated that if the number of paths is finite, then at high SNR capacity grows double-logarithmically with the SNR, and the capacity pre-loglog-defined as the limiting ratio of capacity to loglog(SNR) as the SNR tends to infinity-is 1 irrespective of the number of paths. The results demonstrate that at high SNR multipath fading channels with an infinite number of paths cannot be approximated by multipath fading channels with only a finite number of paths. The number of paths that are needed to approximate a multipath fading channel typically depends on the SNR and may grow to infinity as the SNR tends to infinity.},
keywords = {approximation theory, capacity pre-loglog, capacity to loglog, channel capacity, channels with memory, Delay, Fading, fading channels, frequency-selective fading, high signal-to-noise ratio, high SNR, Limiting, multipath, multipath channels, noncoherent, noncoherent multipath fading channel, Receivers, Signal to noise ratio, signal-to-noise ratio, Transmitters},
pubstate = {published},
tppubtype = {article}
}
A noncoherent multipath fading channel is considered, where neither the transmitter nor the receiver is cognizant of the realization of the path gains, but both are cognizant of their statistics. It is shown that if the delay spread is large in the sense that the variances of the path gains decay exponentially or slower, then capacity is bounded in the signal-to-noise ratio (SNR). For such channels, capacity does not tend to infinity as the SNR tends to infinity. In contrast, if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the SNR. It is further demonstrated that if the number of paths is finite, then at high SNR capacity grows double-logarithmically with the SNR, and the capacity pre-loglog-defined as the limiting ratio of capacity to loglog(SNR) as the SNR tends to infinity-is 1 irrespective of the number of paths. The results demonstrate that at high SNR multipath fading channels with an infinite number of paths cannot be approximated by multipath fading channels with only a finite number of paths. The number of paths that are needed to approximate a multipath fading channel typically depends on the SNR and may grow to infinity as the SNR tends to infinity.
Fresia, Maria; Perez-Cruz, Fernando; Poor, Vincent H; Verdu, Sergio
Joint Source and Channel Coding Artículo de revista
En: IEEE Signal Processing Magazine, vol. 27, no 6, pp. 104–113, 2010, ISSN: 1053-5888.
Resumen | Enlaces | BibTeX | Etiquetas: belief propagation, Channel Coding, combined source-channel coding, Decoding, Encoding, graphical model, Hidden Markov models, Iterative decoding, joint source channel coding, JSC coding, LDPC code, low density parity check code, Markov processes, parity check codes, Slepian-Wolf problem, variable length codes
@article{Fresia2010,
title = {Joint Source and Channel Coding},
author = {Maria Fresia and Fernando Perez-Cruz and Vincent H Poor and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5563107},
issn = {1053-5888},
year = {2010},
date = {2010-01-01},
journal = {IEEE Signal Processing Magazine},
volume = {27},
number = {6},
pages = {104--113},
abstract = {The objectives of this article are two-fold: First, to present the problem of joint source and channel (JSC) coding from a graphical model perspective and second, to propose a structure that uses a new graphical model for jointly encoding and decoding a redundant source. In the first part of the article, relevant contributions to JSC coding, ranging from the Slepian-Wolf problem to joint decoding of variable length codes with state-of-the-art source codes, are reviewed and summarized. In the second part, a double low-density parity-check (LDPC) code for JSC coding is proposed. The double LDPC code can be decoded as a single bipartite graph using standard belief propagation (BP) and its limiting performance is analyzed by using extrinsic information transfer (EXIT) chart approximations.},
keywords = {belief propagation, Channel Coding, combined source-channel coding, Decoding, Encoding, graphical model, Hidden Markov models, Iterative decoding, joint source channel coding, JSC coding, LDPC code, low density parity check code, Markov processes, parity check codes, Slepian-Wolf problem, variable length codes},
pubstate = {published},
tppubtype = {article}
}
The objectives of this article are two-fold: First, to present the problem of joint source and channel (JSC) coding from a graphical model perspective and second, to propose a structure that uses a new graphical model for jointly encoding and decoding a redundant source. In the first part of the article, relevant contributions to JSC coding, ranging from the Slepian-Wolf problem to joint decoding of variable length codes with state-of-the-art source codes, are reviewed and summarized. In the second part, a double low-density parity-check (LDPC) code for JSC coding is proposed. The double LDPC code can be decoded as a single bipartite graph using standard belief propagation (BP) and its limiting performance is analyzed by using extrinsic information transfer (EXIT) chart approximations.
Martino, Luca; Miguez, Joaquin
A Generalization of the Adaptive Rejection Sampling Algorithm Artículo de revista
En: Statistics and Computing, vol. 21, no 4, pp. 633–647, 2010, ISSN: 0960-3174.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Martino2010b,
title = {A Generalization of the Adaptive Rejection Sampling Algorithm},
author = {Luca Martino and Joaquin Miguez},
url = {http://link.springer.com/10.1007/s11222-010-9197-9},
issn = {0960-3174},
year = {2010},
date = {2010-01-01},
journal = {Statistics and Computing},
volume = {21},
number = {4},
pages = {633--647},
abstract = {Rejection sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. The adaptive rejection sampling method is an efficient algorithm to sample from a log-concave target density, that attains high acceptance rates by improving the proposal density whenever a sample is rejected. In this paper we introduce a generalized adaptive rejection sampling procedure that can be applied with a broad class of target probability distributions, possibly non-log-concave and exhibiting multiple modes. The proposed technique yields a sequence of proposal densities that converge toward the target pdf, thus achieving very high acceptance rates. We provide a simple numerical example to illustrate the basic use of the proposed technique, together with a more elaborate positioning application using real data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rejection sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. The adaptive rejection sampling method is an efficient algorithm to sample from a log-concave target density, that attains high acceptance rates by improving the proposal density whenever a sample is rejected. In this paper we introduce a generalized adaptive rejection sampling procedure that can be applied with a broad class of target probability distributions, possibly non-log-concave and exhibiting multiple modes. The proposed technique yields a sequence of proposal densities that converge toward the target pdf, thus achieving very high acceptance rates. We provide a simple numerical example to illustrate the basic use of the proposed technique, together with a more elaborate positioning application using real data.
Zoubir, A; Viberg, M; Yang, B; Miguez, Joaquin
Analysis of a Sequential Monte Carlo Method for Optimization in Dynamical Systems Artículo de revista
En: Signal Processing, vol. 90, no 5, pp. 1609–1622, 2010.
Resumen | Enlaces | BibTeX | Etiquetas: Dynamic optimization, Nonlinear dynamics, Nonlinear tracking, Sequential Monte Carlo, Stochastic optimization
@article{Zoubir2010,
title = {Analysis of a Sequential Monte Carlo Method for Optimization in Dynamical Systems},
author = {A Zoubir and M Viberg and B Yang and Joaquin Miguez},
url = {http://www.sciencedirect.com/science/article/pii/S0165168409004708},
year = {2010},
date = {2010-01-01},
journal = {Signal Processing},
volume = {90},
number = {5},
pages = {1609--1622},
abstract = {We investigate a recently proposed sequential Monte Carlo methodology for recursively tracking the minima of a cost function that evolves with time. These methods, subsequently referred to as sequential Monte Carlo minimization (SMCM) procedures, have an algorithmic structure similar to particle filters: they involve the generation of random paths in the space of the signal of interest (SoI), the stochastic selection of the fittest paths and the ranking of the survivors according to their cost. In this paper, we propose an extension of the original SMCM methodology (that makes it applicable to a broader class of cost functions) and introduce an asymptotic-convergence analysis. Our analytical results are based on simple induction arguments and show how the SoI-estimates computed by a SMCM algorithm converge, in probability, to a sequence of minimizers of the cost function. We illustrate these results by means of two computer simulation examples.},
keywords = {Dynamic optimization, Nonlinear dynamics, Nonlinear tracking, Sequential Monte Carlo, Stochastic optimization},
pubstate = {published},
tppubtype = {article}
}
We investigate a recently proposed sequential Monte Carlo methodology for recursively tracking the minima of a cost function that evolves with time. These methods, subsequently referred to as sequential Monte Carlo minimization (SMCM) procedures, have an algorithmic structure similar to particle filters: they involve the generation of random paths in the space of the signal of interest (SoI), the stochastic selection of the fittest paths and the ranking of the survivors according to their cost. In this paper, we propose an extension of the original SMCM methodology (that makes it applicable to a broader class of cost functions) and introduce an asymptotic-convergence analysis. Our analytical results are based on simple induction arguments and show how the SoI-estimates computed by a SMCM algorithm converge, in probability, to a sequence of minimizers of the cost function. We illustrate these results by means of two computer simulation examples.
2009
Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Gaussian Process Regressors for Multiuser Detection in DS-CDMA Systems Artículo de revista
En: IEEE Transactions on Communications, vol. 57, no 8, pp. 2339–2347, 2009, ISSN: 0090-6778.
Resumen | Enlaces | BibTeX | Etiquetas: analytical nonlinear multiuser detectors, code division multiple access, communication systems, Detectors, digital communication, digital communications, DS-CDMA systems, Gaussian process for regressi, Gaussian process regressors, Gaussian processes, GPR, Ground penetrating radar, least mean squares methods, maximum likelihood, maximum likelihood detection, maximum likelihood estimation, mean square error methods, minimum mean square error, MMSE, Multiaccess communication, Multiuser detection, nonlinear estimator, nonlinear state-ofthe- art solutions, radio receivers, Receivers, regression analysis, Support vector machines
@article{Murillo-Fuentes2009,
title = {Gaussian Process Regressors for Multiuser Detection in DS-CDMA Systems},
author = {Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5201027},
issn = {0090-6778},
year = {2009},
date = {2009-01-01},
journal = {IEEE Transactions on Communications},
volume = {57},
number = {8},
pages = {2339--2347},
abstract = {In this paper we present Gaussian processes for Regression (GPR) as a novel detector for CDMA digital communications. Particularly, we propose GPR for constructing analytical nonlinear multiuser detectors in CDMA communication systems. GPR can easily compute the parameters that describe its nonlinearities by maximum likelihood. Thereby, no cross-validation is needed, as it is typically used in nonlinear estimation procedures. The GPR solution is analytical, given its parameters, and it does not need to solve an optimization problem for building the nonlinear estimator. These properties provide fast and accurate learning, two major issues in digital communications. The GPR with a linear decision function can be understood as a regularized MMSE detector, in which the regularization parameter is optimally set. We also show the GPR receiver to be a straightforward nonlinear extension of the linear minimum mean square error (MMSE) criterion, widely used in the design of these receivers. We argue the benefits of this new approach in short codes CDMA systems where little information on the users' codes, users' amplitudes or the channel is available. The paper includes some experiments to show that GPR outperforms linear (MMSE) and nonlinear (SVM) state-ofthe- art solutions.},
keywords = {analytical nonlinear multiuser detectors, code division multiple access, communication systems, Detectors, digital communication, digital communications, DS-CDMA systems, Gaussian process for regressi, Gaussian process regressors, Gaussian processes, GPR, Ground penetrating radar, least mean squares methods, maximum likelihood, maximum likelihood detection, maximum likelihood estimation, mean square error methods, minimum mean square error, MMSE, Multiaccess communication, Multiuser detection, nonlinear estimator, nonlinear state-ofthe- art solutions, radio receivers, Receivers, regression analysis, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
In this paper we present Gaussian processes for Regression (GPR) as a novel detector for CDMA digital communications. Particularly, we propose GPR for constructing analytical nonlinear multiuser detectors in CDMA communication systems. GPR can easily compute the parameters that describe its nonlinearities by maximum likelihood. Thereby, no cross-validation is needed, as it is typically used in nonlinear estimation procedures. The GPR solution is analytical, given its parameters, and it does not need to solve an optimization problem for building the nonlinear estimator. These properties provide fast and accurate learning, two major issues in digital communications. The GPR with a linear decision function can be understood as a regularized MMSE detector, in which the regularization parameter is optimally set. We also show the GPR receiver to be a straightforward nonlinear extension of the linear minimum mean square error (MMSE) criterion, widely used in the design of these receivers. We argue the benefits of this new approach in short codes CDMA systems where little information on the users' codes, users' amplitudes or the channel is available. The paper includes some experiments to show that GPR outperforms linear (MMSE) and nonlinear (SVM) state-ofthe- art solutions.
Mariño, Inés P.; Miguez, Joaquin; Meucci, Riccardo
Monte Carlo Method for Adaptively Estimating the Unknown Parameters and the Dynamic State of Chaotic Systems Artículo de revista
En: Physical Review E, vol. 79, no 5, pp. 056218, 2009, ISSN: 1539-3755.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Marino2009,
title = {Monte Carlo Method for Adaptively Estimating the Unknown Parameters and the Dynamic State of Chaotic Systems},
author = {In\'{e}s P. Mari\~{n}o and Joaquin Miguez and Riccardo Meucci},
url = {http://link.aps.org/doi/10.1103/PhysRevE.79.056218},
issn = {1539-3755},
year = {2009},
date = {2009-01-01},
journal = {Physical Review E},
volume = {79},
number = {5},
pages = {056218},
publisher = {American Physical Society},
abstract = {We propose a Monte Carlo methodology for the joint estimation of unobserved dynamic variables and unknown static parameters in chaotic systems. The technique is sequential, i.e., it updates the variable and parameter estimates recursively as new observations become available, and, hence, suitable for online implementation. We demonstrate the validity of the method by way of two examples. In the first one, we tackle the estimation of all the dynamic variables and one unknown parameter of a five-dimensional nonlinear model using a time series of scalar observations experimentally collected from a chaotic CO2\<math display="inline"\>\<mrow\>\<msub\>\<mrow\>\<mtext\>CO\<mn\>2 laser. In the second example, we address the estimation of the two dynamic variables and the phase parameter of a numerical model commonly employed to represent the dynamics of optoelectronic feedback loops designed for chaotic communications over fiber-optic links.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We propose a Monte Carlo methodology for the joint estimation of unobserved dynamic variables and unknown static parameters in chaotic systems. The technique is sequential, i.e., it updates the variable and parameter estimates recursively as new observations become available, and, hence, suitable for online implementation. We demonstrate the validity of the method by way of two examples. In the first one, we tackle the estimation of all the dynamic variables and one unknown parameter of a five-dimensional nonlinear model using a time series of scalar observations experimentally collected from a chaotic CO2<math display="inline"><mrow><msub><mrow><mtext>CO<mn>2 laser. In the second example, we address the estimation of the two dynamic variables and the phase parameter of a numerical model commonly employed to represent the dynamics of optoelectronic feedback loops designed for chaotic communications over fiber-optic links.
Vazquez, Manuel A; Miguez, Joaquin
Maximum-Likelihood Sequence Detection in Time- and Frequency-Selective MIMO Channels With Unknown Order Artículo de revista
En: IEEE Transactions on Vehicular Technology, vol. 58, no 1, pp. 499–504, 2009, ISSN: 0018-9545.
Resumen | Enlaces | BibTeX | Etiquetas: channel impulse response, channel order estimation, CIR, frequency-selective multiple-input-multiple-output, joint channel and data estimation, maximum likelihood detection, maximum-likelihood sequence detection, MIMO channels, MIMO communication, MLSD, Multiple Input Multiple Output (MIMO), multiple-input–multiple-output (MIMO), per-survivor processing, per-survivor processing (PSP), telecommunication channels, time-selective multiple-input-multiple-output chan
@article{Vazquez2009,
title = {Maximum-Likelihood Sequence Detection in Time- and Frequency-Selective MIMO Channels With Unknown Order},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4510724},
issn = {0018-9545},
year = {2009},
date = {2009-01-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {58},
number = {1},
pages = {499--504},
abstract = {In the equalization of frequency-selective multiple-input-multiple-output (MIMO) channels, it is usually assumed that the length of the channel impulse response (CIR), which is also referred to as the channel order, is known. However, this is not true in most practical situations, and it is a common approach to overestimate the channel order to avoid the serious performance degradation that occurs when the CIR length is underestimated. Unfortunately, the computational complexity of maximum-likelihood sequence detection (MLSD) in frequency-selective channels exponentially grows with the channel order; hence, overestimation can actually be undesirable because it leads to more expensive and inefficient receivers. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per-survivor processing (PSP) methodology; it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. In addition to the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.},
keywords = {channel impulse response, channel order estimation, CIR, frequency-selective multiple-input-multiple-output, joint channel and data estimation, maximum likelihood detection, maximum-likelihood sequence detection, MIMO channels, MIMO communication, MLSD, Multiple Input Multiple Output (MIMO), multiple-input\textendashmultiple-output (MIMO), per-survivor processing, per-survivor processing (PSP), telecommunication channels, time-selective multiple-input-multiple-output chan},
pubstate = {published},
tppubtype = {article}
}
In the equalization of frequency-selective multiple-input-multiple-output (MIMO) channels, it is usually assumed that the length of the channel impulse response (CIR), which is also referred to as the channel order, is known. However, this is not true in most practical situations, and it is a common approach to overestimate the channel order to avoid the serious performance degradation that occurs when the CIR length is underestimated. Unfortunately, the computational complexity of maximum-likelihood sequence detection (MLSD) in frequency-selective channels exponentially grows with the channel order; hence, overestimation can actually be undesirable because it leads to more expensive and inefficient receivers. In this paper, we introduce an algorithm for MLSD that incorporates the full estimation of the MIMO CIR parameters, including its order. The proposed technique is based on the per-survivor processing (PSP) methodology; it admits both blind and semiblind implementations, depending on the availability of pilot data, and is designed to work with time-selective channels. In addition to the analytical derivation of the algorithm, we provide computer simulation results that illustrate the effectiveness of the resulting receiver.
Koch, Tobias; Lapidoth, Amos; Sotiriadis, Paul P
Channels That Heat Up Artículo de revista
En: IEEE Transactions on Information Theory, vol. 55, no 8, pp. 3594–3612, 2009, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: additive noise channel, Capacity per unit cost, channel capacity, channels with memory, cooling, electronic circuits, heat dissipation, heat sinks, high signal-to-noise ratio, high signal-to-noise ratio (SNR), intrinsic thermal noise, low transmit power, network analysis, noise variance, on-chip communication, thermal noise
@article{Koch2009,
title = {Channels That Heat Up},
author = {Tobias Koch and Amos Lapidoth and Paul P Sotiriadis},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5165190},
issn = {0018-9448},
year = {2009},
date = {2009-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {55},
number = {8},
pages = {3594--3612},
abstract = {This paper considers an additive noise channel where the time-A; noise variance is a weighted sum of the squared magnitudes of the previous channel inputs plus a constant. This channel model accounts for the dependence of the intrinsic thermal noise on the data due to the heat dissipation associated with the transmission of data in electronic circuits: the data determine the transmitted signal, which in turn heats up the circuit and thus influences the power of the thermal noise. The capacity of this channel (both with and without feedback) is studied at low transmit powers and at high transmit powers. At low transmit powers, the slope of the capacity-versus-power curve at zero is computed and it is shown that the heating-up effect is beneficial. At high transmit powers, conditions are determined under which the capacity is bounded, i.e., under which the capacity does not grow to infinity as the allowed average power tends to infinity.},
keywords = {additive noise channel, Capacity per unit cost, channel capacity, channels with memory, cooling, electronic circuits, heat dissipation, heat sinks, high signal-to-noise ratio, high signal-to-noise ratio (SNR), intrinsic thermal noise, low transmit power, network analysis, noise variance, on-chip communication, thermal noise},
pubstate = {published},
tppubtype = {article}
}
This paper considers an additive noise channel where the time-A; noise variance is a weighted sum of the squared magnitudes of the previous channel inputs plus a constant. This channel model accounts for the dependence of the intrinsic thermal noise on the data due to the heat dissipation associated with the transmission of data in electronic circuits: the data determine the transmitted signal, which in turn heats up the circuit and thus influences the power of the thermal noise. The capacity of this channel (both with and without feedback) is studied at low transmit powers and at high transmit powers. At low transmit powers, the slope of the capacity-versus-power curve at zero is computed and it is shown that the heating-up effect is beneficial. At high transmit powers, conditions are determined under which the capacity is bounded, i.e., under which the capacity does not grow to infinity as the allowed average power tends to infinity.
Lázaro, Marcelino; González-Olasola, Jonathan
Blind Equalization Using the IRWLS Formulation of the Support Vector Machine Artículo de revista
En: Signal Processing, vol. 89, no 7, pp. 1436–1445, 2009, ISSN: 01651684.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Lazaro2009b,
title = {Blind Equalization Using the IRWLS Formulation of the Support Vector Machine},
author = {Marcelino L\'{a}zaro and Jonathan Gonz\'{a}lez-Olasola},
url = {http://www.sciencedirect.com/science/article/pii/S0165168409000383},
issn = {01651684},
year = {2009},
date = {2009-01-01},
journal = {Signal Processing},
volume = {89},
number = {7},
pages = {1436--1445},
abstract = {In this paper, using a common framework, we propose, analyze, and evaluate several variants of batch algorithms for blind equalization of SISO channels. They are based on the iterative re-weighted least square (IRWLS) solution for the support vector machine (SVM). The proposed methods combine the conventional cost function of the SVM with classical error functions applied to blind equalization: Sato's and Godard's error functions are included in the penalty term of the SVM. The relationship of these batch algorithms with conventional equalization and regularization techniques is analyzed in the paper. Simulation experiments performed over a relevant set of channels show that the proposed equalization methods perform better than traditional cumulant-based methods: they require a lower number of data samples to achieve the same equalization level and convergence ratio.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, using a common framework, we propose, analyze, and evaluate several variants of batch algorithms for blind equalization of SISO channels. They are based on the iterative re-weighted least square (IRWLS) solution for the support vector machine (SVM). The proposed methods combine the conventional cost function of the SVM with classical error functions applied to blind equalization: Sato's and Godard's error functions are included in the penalty term of the SVM. The relationship of these batch algorithms with conventional equalization and regularization techniques is analyzed in the paper. Simulation experiments performed over a relevant set of channels show that the proposed equalization methods perform better than traditional cumulant-based methods: they require a lower number of data samples to achieve the same equalization level and convergence ratio.
Lazaro, Marcelino; Sanchez-Fernandez, Matilde; Artés-Rodríguez, Antonio
Optimal Sensor Selection in Binary Heterogeneous Sensor Networks Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 57, no 4, pp. 1577–1587, 2009, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: binary heterogeneous sensor networks, discrimination performance, Energy scaling, object detection, optimal sensor selection, performance-cost ratio, sensor networks, sensor selection, symmetric Kullback-Leibler divergence, target detection problem, Wireless Sensor Networks
@article{Lazaro2009bb,
title = {Optimal Sensor Selection in Binary Heterogeneous Sensor Networks},
author = {Marcelino Lazaro and Matilde Sanchez-Fernandez and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4749309},
issn = {1053-587X},
year = {2009},
date = {2009-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {57},
number = {4},
pages = {1577--1587},
abstract = {We consider the problem of sensor selection in a heterogeneous sensor network when several types of binary sensors with different discrimination performance and costs are available. We want to analyze what is the optimal proportion of sensors of each class in a target detection problem when a total cost constraint is specified. We obtain the conditional distributions of the observations at the fusion center given the hypotheses, necessary to perform an optimal hypothesis test in this heterogeneous scenario. We characterize the performance of the tests by means of the symmetric Kullback-Leibler divergence, or J -divergence, applied to the conditional distributions under each hypothesis. By formulating the sensor selection as a constrained maximization problem, and showing the linearity of the J-divergence with the number of sensors of each class, we found that the optimal proportion of sensors is ldquowinner takes allrdquo like. The sensor class with the best performance/cost ratio is selected.},
keywords = {binary heterogeneous sensor networks, discrimination performance, Energy scaling, object detection, optimal sensor selection, performance-cost ratio, sensor networks, sensor selection, symmetric Kullback-Leibler divergence, target detection problem, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {article}
}
We consider the problem of sensor selection in a heterogeneous sensor network when several types of binary sensors with different discrimination performance and costs are available. We want to analyze what is the optimal proportion of sensors of each class in a target detection problem when a total cost constraint is specified. We obtain the conditional distributions of the observations at the fusion center given the hypotheses, necessary to perform an optimal hypothesis test in this heterogeneous scenario. We characterize the performance of the tests by means of the symmetric Kullback-Leibler divergence, or J -divergence, applied to the conditional distributions under each hypothesis. By formulating the sensor selection as a constrained maximization problem, and showing the linearity of the J-divergence with the number of sensors of each class, we found that the optimal proportion of sensors is ldquowinner takes allrdquo like. The sensor class with the best performance/cost ratio is selected.
2008
Perez-Cruz, Fernando; Murillo-Fuentes, Juan Jose; Caro, S
Nonlinear Channel Equalization With Gaussian Processes for Regression Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 56, no 10, pp. 5283–5286, 2008, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: Channel estimation, digital communications receivers, equalisers, equalization, Gaussian processes, kernel adaline, least mean squares methods, maximum likelihood estimation, nonlinear channel equalization, nonlinear equalization, nonlinear minimum mean square error estimator, regression, regression analysis, short training sequences, Support vector machines
@article{Perez-Cruz2008c,
title = {Nonlinear Channel Equalization With Gaussian Processes for Regression},
author = {Fernando Perez-Cruz and Juan Jose Murillo-Fuentes and S Caro},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4563433},
issn = {1053-587X},
year = {2008},
date = {2008-01-01},
journal = {IEEE Transactions on Signal Processing},
volume = {56},
number = {10},
pages = {5283--5286},
abstract = {We propose Gaussian processes for regression (GPR) as a novel nonlinear equalizer for digital communications receivers. GPR's main advantage, compared to previous nonlinear estimation approaches, lies on their capability to optimize the kernel hyperparameters by maximum likelihood, which improves its performance significantly for short training sequences. Besides, GPR can be understood as a nonlinear minimum mean square error estimator, a standard criterion for training equalizers that trades off the inversion of the channel and the amplification of the noise. In the experiment section, we show that the GPR-based equalizer clearly outperforms support vector machine and kernel adaline approaches, exhibiting outstanding results for short training sequences.},
keywords = {Channel estimation, digital communications receivers, equalisers, equalization, Gaussian processes, kernel adaline, least mean squares methods, maximum likelihood estimation, nonlinear channel equalization, nonlinear equalization, nonlinear minimum mean square error estimator, regression, regression analysis, short training sequences, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
We propose Gaussian processes for regression (GPR) as a novel nonlinear equalizer for digital communications receivers. GPR's main advantage, compared to previous nonlinear estimation approaches, lies on their capability to optimize the kernel hyperparameters by maximum likelihood, which improves its performance significantly for short training sequences. Besides, GPR can be understood as a nonlinear minimum mean square error estimator, a standard criterion for training equalizers that trades off the inversion of the channel and the amplification of the noise. In the experiment section, we show that the GPR-based equalizer clearly outperforms support vector machine and kernel adaline approaches, exhibiting outstanding results for short training sequences.
Baca-García, Enrique; Perez-Rodriguez, Mercedes M; Basurte-Villamor, Ignacio; Quintero-Gutierrez, Javier F; Sevilla-Vicente, Juncal; Martinez-Vigo, Maria; Artés-Rodríguez, Antonio; del Moral, Antonio Fernandez L; Jimenez-Arriero, Miguel A; de Rivera, Jose Gonzalez L
Patterns of Mental Health Service Utilization in a General Hospital and Outpatient Mental Health Facilities: Analysis of 365,262 Psychiatric Consultations Artículo de revista
En: European archives of psychiatry and clinical neuroscience, vol. 258, no 2, pp. 117–123, 2008, ISSN: 0940-1334.
Resumen | Enlaces | BibTeX | Etiquetas: 80 and over, Adolescent, Adult, Age Distribution, Aged, Ambulatory Care, Ambulatory Care: statistics &amp; numerical data, Ambulatory Care: utilization, Child, Diagnosis-Related Groups, Female, General, General: statistics &amp; numerical data, General: utilization, Health Care Costs, Health Care Costs: statistics &amp; numerical data, Health Services Accessibility, Health Services Accessibility: statistics &amp; numeri, Health Services Needs and Demand, Health Services Needs and Demand: statistics &amp; num, Hospitals, Humans, Male, Mental Disorders, Mental Disorders: classification, Mental Disorders: diagnosis, Mental Disorders: epidemiology, Mental Disorders: therapy, Mental Health Services, Mental Health Services: economics, Mental Health Services: utilization, Middle Aged, Outcome and Process Assessment (Health Care), Preschool, Psychiatry, Psychiatry: economics, Psychiatry: statistics &amp; numerical data, Sex Distribution, Spain, Spain: epidemiology, Utilization Review, Utilization Review: statistics &amp; numerical data
@article{Baca-Garcia2008,
title = {Patterns of Mental Health Service Utilization in a General Hospital and Outpatient Mental Health Facilities: Analysis of 365,262 Psychiatric Consultations},
author = {Enrique Baca-Garc\'{i}a and Mercedes M Perez-Rodriguez and Ignacio Basurte-Villamor and Javier F Quintero-Gutierrez and Juncal Sevilla-Vicente and Maria Martinez-Vigo and Antonio Art\'{e}s-Rodr\'{i}guez and Antonio Fernandez L del Moral and Miguel A Jimenez-Arriero and Jose Gonzalez L de Rivera},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17990050},
issn = {0940-1334},
year = {2008},
date = {2008-01-01},
journal = {European archives of psychiatry and clinical neuroscience},
volume = {258},
number = {2},
pages = {117--123},
abstract = {PURPOSE: Mental health is one of the priorities of the European Commission. Studies of the use and cost of mental health facilities are needed in order to improve the planning and efficiey of mental health resources. We analyze the patterns of mental health service use in multiple clinical settings to identify factors associated with high cost. SUBJECTS AND METHODS: 22,859 patients received psychiatric care in the catchment area of a Spanish hospital (2000-2004). They had 365,262 psychiatric consultations in multiple settings. Two groups were selected that generated 80% of total costs: the medium cost group (N = 4,212; 50% of costs), and the high cost group (N = 236; 30% of costs). Statistical analyses were performed using univariate and multivariate techniques. Significant variables in univariate analyses were introduced as independent variables in a logistic regression analysis using "high cost" (\>7,263$) as dependent variable. RESULTS: Costs were not evenly distributed throughout the sample. 19.4% of patients generated 80% of costs. The variables associated with high cost were: age group 1 (0-14 years) at the first evaluation, permanent disability, and ICD-10 diagnoses: Organic, including symptomatic, mental disorders; Mental and behavioural disorders due to psychoactive substance use; Schizophrenia, schizotypal and delusional disorders; Behavioural syndromes associated with physiological disturbances and physical factors; External causes of morbidity and mortality; and Factors influencing health status and contact with health services. DISCUSSION: Mental healthcare costs were not evenly distributed throughout the patient population. The highest costs are associated with early onset of the mental disorder, permanent disability, organic mental disorders, substance-related disorders, psychotic disorders, and external factors that influence the health status and contact with health services or cause morbidity and mortality. CONCLUSION: Variables related to psychiatric diagnoses and sociodemographic factors have influence on the cost of mental healthcare.},
keywords = {80 and over, Adolescent, Adult, Age Distribution, Aged, Ambulatory Care, Ambulatory Care: statistics \&amp; numerical data, Ambulatory Care: utilization, Child, Diagnosis-Related Groups, Female, General, General: statistics \&amp; numerical data, General: utilization, Health Care Costs, Health Care Costs: statistics \&amp; numerical data, Health Services Accessibility, Health Services Accessibility: statistics \&amp; numeri, Health Services Needs and Demand, Health Services Needs and Demand: statistics \&amp; num, Hospitals, Humans, Male, Mental Disorders, Mental Disorders: classification, Mental Disorders: diagnosis, Mental Disorders: epidemiology, Mental Disorders: therapy, Mental Health Services, Mental Health Services: economics, Mental Health Services: utilization, Middle Aged, Outcome and Process Assessment (Health Care), Preschool, Psychiatry, Psychiatry: economics, Psychiatry: statistics \&amp; numerical data, Sex Distribution, Spain, Spain: epidemiology, Utilization Review, Utilization Review: statistics \&amp; numerical data},
pubstate = {published},
tppubtype = {article}
}
PURPOSE: Mental health is one of the priorities of the European Commission. Studies of the use and cost of mental health facilities are needed in order to improve the planning and efficiey of mental health resources. We analyze the patterns of mental health service use in multiple clinical settings to identify factors associated with high cost. SUBJECTS AND METHODS: 22,859 patients received psychiatric care in the catchment area of a Spanish hospital (2000-2004). They had 365,262 psychiatric consultations in multiple settings. Two groups were selected that generated 80% of total costs: the medium cost group (N = 4,212; 50% of costs), and the high cost group (N = 236; 30% of costs). Statistical analyses were performed using univariate and multivariate techniques. Significant variables in univariate analyses were introduced as independent variables in a logistic regression analysis using "high cost" (>7,263$) as dependent variable. RESULTS: Costs were not evenly distributed throughout the sample. 19.4% of patients generated 80% of costs. The variables associated with high cost were: age group 1 (0-14 years) at the first evaluation, permanent disability, and ICD-10 diagnoses: Organic, including symptomatic, mental disorders; Mental and behavioural disorders due to psychoactive substance use; Schizophrenia, schizotypal and delusional disorders; Behavioural syndromes associated with physiological disturbances and physical factors; External causes of morbidity and mortality; and Factors influencing health status and contact with health services. DISCUSSION: Mental healthcare costs were not evenly distributed throughout the patient population. The highest costs are associated with early onset of the mental disorder, permanent disability, organic mental disorders, substance-related disorders, psychotic disorders, and external factors that influence the health status and contact with health services or cause morbidity and mortality. CONCLUSION: Variables related to psychiatric diagnoses and sociodemographic factors have influence on the cost of mental healthcare.
Perez-Cruz, Fernando; Murillo-Fuentes, Juan Jose
Digital Communication Receivers Using Gaussian Processes for Machine Learning Artículo de revista
En: EURASIP Journal on Advances in Signal Processing, vol. 2008, no 1, pp. 1–13, 2008, ISSN: 1687-6172.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Perez-Cruz2008d,
title = {Digital Communication Receivers Using Gaussian Processes for Machine Learning},
author = {Fernando Perez-Cruz and Juan Jose Murillo-Fuentes},
url = {http://asp.eurasipjournals.com/content/2008/1/491503},
issn = {1687-6172},
year = {2008},
date = {2008-01-01},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2008},
number = {1},
pages = {1--13},
publisher = {Springer},
abstract = {We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function of the received symbols for discrete inputs. GPR can be presented as a nonlinear MMSE estimator and thus capable of achieving optimal performance from MMSE viewpoint. Also, the design of digital communication receivers can be viewed as a detection problem, for which GPC is specially suited as it assigns posterior probabilities to each transmitted symbol. We explore the suitability of GPs as nonlinear digital communication receivers. GPs are Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which can then be set optimally. GPs outperform state-of-the-art nonlinear machine learning approaches that prespecify their hyperparameters or rely on cross validation. We illustrate the advantages of GPs as digital communication receivers for linear and nonlinear channel models for short training sequences and compare them to state-of-the-art nonlinear machine learning tools, such as support vector machines.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function of the received symbols for discrete inputs. GPR can be presented as a nonlinear MMSE estimator and thus capable of achieving optimal performance from MMSE viewpoint. Also, the design of digital communication receivers can be viewed as a detection problem, for which GPC is specially suited as it assigns posterior probabilities to each transmitted symbol. We explore the suitability of GPs as nonlinear digital communication receivers. GPs are Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which can then be set optimally. GPs outperform state-of-the-art nonlinear machine learning approaches that prespecify their hyperparameters or rely on cross validation. We illustrate the advantages of GPs as digital communication receivers for linear and nonlinear channel models for short training sequences and compare them to state-of-the-art nonlinear machine learning tools, such as support vector machines.
Leiva-Murillo, Jose M; Salcedo-Sanz, Sancho; Gallardo-Antolín, Ascensión; Artés-Rodríguez, Antonio
A Simulated Annealing Approach to Speaker Segmentation in Audio Databases Artículo de revista
En: Engineering Applications of Artificial Intelligence, vol. 21, no 4, pp. 499–508, 2008.
Resumen | Enlaces | BibTeX | Etiquetas: Audio indexing, information theory, Simulated annealing, Speaker segmentation
@article{Leiva-Murillo2008c,
title = {A Simulated Annealing Approach to Speaker Segmentation in Audio Databases},
author = {Jose M Leiva-Murillo and Sancho Salcedo-Sanz and Ascensi\'{o}n Gallardo-Antol\'{i}n and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.sciencedirect.com/science/article/pii/S0952197607000954},
year = {2008},
date = {2008-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {21},
number = {4},
pages = {499--508},
abstract = {In this paper we present a novel approach to the problem of speaker segmentation, which is an unavoidable previous step to audio indexing. Mutual information is used for evaluating the accuracy of the segmentation, as a function to be maximized by a simulated annealing (SA) algorithm. We introduce a novel mutation operator for the SA, the Consecutive Bits Mutation operator, which improves the performance of the SA in this problem. We also use the so-called Compaction Factor, which allows the SA to operate in a reduced search space. Our algorithm has been tested in the segmentation of real audio databases, and it has been compared to several existing algorithms for speaker segmentation, obtaining very good results in the test problems considered.},
keywords = {Audio indexing, information theory, Simulated annealing, Speaker segmentation},
pubstate = {published},
tppubtype = {article}
}
In this paper we present a novel approach to the problem of speaker segmentation, which is an unavoidable previous step to audio indexing. Mutual information is used for evaluating the accuracy of the segmentation, as a function to be maximized by a simulated annealing (SA) algorithm. We introduce a novel mutation operator for the SA, the Consecutive Bits Mutation operator, which improves the performance of the SA in this problem. We also use the so-called Compaction Factor, which allows the SA to operate in a reduced search space. Our algorithm has been tested in the segmentation of real audio databases, and it has been compared to several existing algorithms for speaker segmentation, obtaining very good results in the test problems considered.
Vazquez, Manuel A; Bugallo, Monica F; Miguez, Joaquin
Sequential Monte Carlo Methods for Complexity-Constrained MAP Equalization of Dispersive MIMO Channels Artículo de revista
En: Signal Processing, vol. 88, no 4, pp. 1017–1034, 2008.
Resumen | Enlaces | BibTeX | Etiquetas: joint channel and data estimation, Multiple Input Multiple Output (MIMO), Sequential Monte Carlo (SMC)
@article{Vazquez2008b,
title = {Sequential Monte Carlo Methods for Complexity-Constrained MAP Equalization of Dispersive MIMO Channels},
author = {Manuel A Vazquez and Monica F Bugallo and Joaquin Miguez},
url = {http://www.sciencedirect.com/science/article/pii/S0165168407003763},
year = {2008},
date = {2008-01-01},
journal = {Signal Processing},
volume = {88},
number = {4},
pages = {1017--1034},
abstract = {The ability to perform nearly optimal equalization of multiple input multiple output (MIMO) wireless channels using sequential Monte Carlo (SMC) techniques has recently been demonstrated. SMC methods allow to recursively approximate the a posteriori probabilities of the transmitted symbols, as observations are sequentially collected, using samples from adequate probability distributions. Hence, they are a class of online (adaptive) algorithms, suitable to handle the time-varying channels typical of high speed mobile communication applications. The main drawback of the SMC-based MIMO-channel equalizers so far proposed is that their computational complexity grows exponentially with the number of input data streams and the length of the channel impulse response, rendering these methods impractical. In this paper, we introduce novel SMC schemes that overcome this limitation by the adequate design of proposal probability distribution functions that can be sampled with a lesser computational burden, yet provide a close-to-optimal performance in terms of the resulting equalizer bit error rate and channel estimation error. We show that the complexity of the resulting receivers grows polynomially with the number of input data streams and the length of the channel response, and present computer simulation results that illustrate their performance in some typical scenarios.},
keywords = {joint channel and data estimation, Multiple Input Multiple Output (MIMO), Sequential Monte Carlo (SMC)},
pubstate = {published},
tppubtype = {article}
}
The ability to perform nearly optimal equalization of multiple input multiple output (MIMO) wireless channels using sequential Monte Carlo (SMC) techniques has recently been demonstrated. SMC methods allow to recursively approximate the a posteriori probabilities of the transmitted symbols, as observations are sequentially collected, using samples from adequate probability distributions. Hence, they are a class of online (adaptive) algorithms, suitable to handle the time-varying channels typical of high speed mobile communication applications. The main drawback of the SMC-based MIMO-channel equalizers so far proposed is that their computational complexity grows exponentially with the number of input data streams and the length of the channel impulse response, rendering these methods impractical. In this paper, we introduce novel SMC schemes that overcome this limitation by the adequate design of proposal probability distribution functions that can be sampled with a lesser computational burden, yet provide a close-to-optimal performance in terms of the resulting equalizer bit error rate and channel estimation error. We show that the complexity of the resulting receivers grows polynomially with the number of input data streams and the length of the channel response, and present computer simulation results that illustrate their performance in some typical scenarios.
2007
Leiva-Murillo, Jose M; Artés-Rodríguez, Antonio
Maximization of Mutual Information for Supervised Linear Feature Extraction Artículo de revista
En: IEEE Transactions on Neural Networks, vol. 18, no 5, pp. 1433–1441, 2007, ISSN: 1045-9227.
Resumen | Enlaces | BibTeX | Etiquetas: Algorithms, Artificial Intelligence, Automated, component-by-component gradient-ascent method, Computer Simulation, Data Mining, Entropy, Feature extraction, gradient methods, gradient-based entropy, Independent component analysis, Information Storage and Retrieval, information theory, Iron, learning (artificial intelligence), Linear discriminant analysis, Linear Models, Mutual information, Optimization methods, Pattern recognition, Reproducibility of Results, Sensitivity and Specificity, supervised linear feature extraction, Vectors
@article{Leiva-Murillo2007,
title = {Maximization of Mutual Information for Supervised Linear Feature Extraction},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4298118},
issn = {1045-9227},
year = {2007},
date = {2007-01-01},
journal = {IEEE Transactions on Neural Networks},
volume = {18},
number = {5},
pages = {1433--1441},
publisher = {IEEE},
abstract = {In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.},
keywords = {Algorithms, Artificial Intelligence, Automated, component-by-component gradient-ascent method, Computer Simulation, Data Mining, Entropy, Feature extraction, gradient methods, gradient-based entropy, Independent component analysis, Information Storage and Retrieval, information theory, Iron, learning (artificial intelligence), Linear discriminant analysis, Linear Models, Mutual information, Optimization methods, Pattern recognition, Reproducibility of Results, Sensitivity and Specificity, supervised linear feature extraction, Vectors},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.
0000
Pradier, Melanie F.; Hyland, Stephanie L.; Stark, Stefan G.; Lehmann, Kjong; Vogt, Julia E.; Perez-Cruz, Fernando; Rätsch, Gunnar
A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types Artículo de revista En preparación
En: En preparación.
Enlaces | BibTeX | Etiquetas: Bayesian non parametrics
@article{FPerez18c,
title = {A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types},
author = {Melanie F. Pradier and Stephanie L. Hyland and Stefan G. Stark and Kjong Lehmann and Julia E. Vogt and Fernando Perez-Cruz and Gunnar R\"{a}tsch},
doi = {https://doi.org/10.1101/623215},
keywords = {Bayesian non parametrics},
pubstate = {forthcoming},
tppubtype = {article}
}
Barrigon, Maria Luisa; Romero-Medrano, Lorena; Moreno-Muñoz, Pablo; Porras-Segovia, Alejandro; Lopez-Castroman, Jorge; Courtet, Philippe; Artés-Rodríguez, Antonio; Baca-Garcia, Enrique
One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study Artículo de revista
En: J Med Internet Res, vol. 25, pp. e43719, 0000, ISSN: 1438-8871.
Resumen | Enlaces | BibTeX | Etiquetas: e-health; m-health; Ecological Mometary Asssessment; risk prediction; sensor monitoring; suicidal; suicide attempt; suicide
@article{info:doi/10.2196/43719,
title = {One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study},
author = {Maria Luisa Barrigon and Lorena Romero-Medrano and Pablo Moreno-Mu\~{n}oz and Alejandro Porras-Segovia and Jorge Lopez-Castroman and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garcia},
url = {http://www.ncbi.nlm.nih.gov/pubmed/37656498},
doi = {10.2196/43719},
issn = {1438-8871},
journal = {J Med Internet Res},
volume = {25},
pages = {e43719},
abstract = {Background: Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. Objective: We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation. Methods: We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested. Results: During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy. Conclusions: We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.},
keywords = {e-health; m-health; Ecological Mometary Asssessment; risk prediction; sensor monitoring; suicidal; suicide attempt; suicide},
pubstate = {published},
tppubtype = {article}
}
Background: Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach. Objective: We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation. Methods: We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested. Results: During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy. Conclusions: We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.