@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}

}

@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 \& numerical data, Ambulatory Care: utilization, Child, Diagnosis-Related Groups, Female, General, General: statistics \& numerical data, General: utilization, Health Care Costs, Health Care Costs: statistics \& numerical data, Health Services Accessibility, Health Services Accessibility: statistics \& numeri, Health Services Needs and Demand, Health Services Needs and Demand: statistics \& 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 \& numerical data, Sex Distribution, Spain, Spain: epidemiology, Utilization Review, Utilization Review: statistics \& numerical data},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{RamirezSantamariaScharf-2024-Passivedetectionofrandomsignal,

title = {Passive detection of a random signal common to multi-sensor reference and surveillance arrays},

author = {D. Ram\'{i}rez and I. Santamar\'{i}a and L. L. Scharf},

doi = {10.1109/TVT.2024.3366757},

issn = {0018-9545},

journal = {IEEE Trans. Vehicular Techn.},

volume = {73},

number = {7},

pages = {10106\textendash10117},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bonilla-EscribanoRamirezBaca-Garcia-2023-Multidimensionalvariabilityinecologicalassessments,

title = {Multidimensional variability in ecological assessments predicts two clusters of suicidal patients},

author = {P. Bonilla-Escribano and D. Ram\'{i}rez and Enrique Baca-Garc\'{i}a and P. Courtet and A. Art\'{e}s-Rodr\'{i}guez and Jorge L\'{o}pez-Castrom\'{a}n},

doi = {10.1038/s41598-023-30085-1},

issn = {2045-2322},

journal = {Scientific Reports},

volume = {13},

number = {3546},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{XiaoHuangRamirez-2023-Covariancematrixrecoveryfromone-bitb,

title = {Covariance matrix recovery from one-bit data with non-zero quantization thresholds: Algorithm and performance analysis},

author = {Y. -H. Xiao and L. Huang and D. Ram\'{i}rez and C. Qian and H. C. So},

doi = {10.1109/TSP.2023.3325664},

issn = {1053-587X},

journal = {IEEE Trans. Signal Process.},

volume = {71},

pages = {4060\textendash4076},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PerezViaVielva-2022-OnlinedetectionandSNRestimationb,

title = {Online detection and SNR estimation in cooperative spectrum sensing},

author = {J. P\'{e}rez and J. V\'{i}a and L. Vielva and D. Ram\'{i}rez},

doi = {10.1109/TWC.2021.3113089},

issn = {1536-1276},

journal = {IEEE Trans. Wireless Comm.},

volume = {21},

number = {4},

pages = {2521\textendash2533},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{XiaoRamirezSchreier-2022-One-bittargetdetectionincollocatedb,

title = {One-bit target detection in collocated MIMO Radar and performance degradation analysis},

author = {Y. -H. Xiao and D. Ram\'{i}rez and P. J. Schreier and C. Qian and L. Huang},

doi = {10.1109/TVT.2022.3178285},

issn = {0018-9545},

journal = {IEEE Trans. Vehicular Techn.},

volume = {71},

number = {9},

pages = {9363\textendash9374},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{RamirezMarquesSegarra-2021-Graph-signalreconstructionandblinddeconvolution,

title = {Graph-signal reconstruction and blind deconvolution for structured inputs},

author = {D. Ram\'{i}rez and A. G. Marques and S. Segarra},

doi = {10.1016/j.sigpro.2021.108180},

issn = {0165-1684},

journal = {Signal Process. (Special issue on Processing and Learning over Graphs)},

volume = {188},

pages = {108180},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Moreno-MunozRamirezArtes-Rodriguez-2021-Change-pointdetectioninhierarchicalcircadian,

title = {Change-point detection in hierarchical circadian models},

author = {P. Moreno-Mu\~{n}oz and D. Ram\'{i}rez and A. Art\'{e}s-Rodr\'{i}guez},

doi = {10.1016/j.patcog.2021.107820},

issn = {0031-3203},

journal = {Pattern Recognition},

volume = {113},

pages = {107820},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{GargSantamariaRamirez-2019-Subspaceaveragingandorderdetermination,

title = {Subspace averaging and order determination for source enumeration},

author = {V. Garg and I. Santamar\'{i}a and D. Ram\'{i}rez and L. L. Scharf},

doi = {10.1109/TSP.2019.2912151},

issn = {1053-587X},

journal = {IEEE Trans. Signal Process.},

volume = {67},

number = {11},

pages = {3028\textendash3041},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{EguizabalLameiroRamirez-2019-Sourceenumerationinpresenceof,

title = {Source enumeration in the presence of colored noise},

author = {A. Eguizabal and C. Lameiro and D. Ram\'{i}rez and P. J. Schreier},

doi = {10.1109/LSP.2019.2895548},

issn = {1070-9908},

journal = {IEEE Signal Process. Lett.},

volume = {26},

number = {3},

pages = {475\textendash479},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Guzman2015b,

title = {Cooperative optical wireless transmission for improving performance in indoor scenarios for visible light communications},

author = {Borja Genoves Guzman and Alejandro Lancho Serrano and V\'{i}ctor P. Gil Jimenez},

doi = {10.1109/tce.2015.7389772},

issn = {1558-4127},

journal = {IEEE Trans. Consumer Electron.},

volume = {61},

number = {4},

pages = {393--401},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jimenez2017,

title = {Learning Mobile Communications Standards through Flexible Software Defined Radio Base Stations},

author = {Victor P. Gil Jimenez and Alejandro Lancho Serrano and Borja Genoves Guzman and Ana Garcia Armada},

doi = {10.1109/mcom.2017.1601219},

issn = {0163-6804},

journal = {IEEE Commun. Mag.},

volume = {55},

number = {5},

pages = {116--123},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bl\'{a}zquez-S\'{a}nchez2025,

title = {Automated web-based typing of Clostridioides difficile ribotypes via MALDI-TOF MS},

author = {Mario Bl\'{a}zquez-S\'{a}nchez and Alejandro Guerrero-L\'{o}pez and Ana Candela and Albert Belenguer-Llorens and Jos\'{e} Miguel Moreno and Carlos Sevilla-Salcedo and Mar\'{i}a S\'{a}nchez-Cueto and Manuel J. Arroyo and Mark Guti\'{e}rrez-Pareja and Vanessa G\'{o}mez-Verdejo and Pablo M. Olmos and Luis Mancera and Patricia Mu\~{n}oz and Mercedes Mar\'{i}n and Luis Alcal\'{a} and David Rodr\'{i}guez-Temporal and Bel\'{e}n Rodr\'{i}guez-S\'{a}nchez},

doi = {10.1186/s12859-025-06200-6},

issn = {1471-2105},

journal = {BMC Bioinformatics},

volume = {26},

number = {1},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}