2015
Valera, Isabel; Ruiz, Francisco J R; Svensson, Lennart; Perez-Cruz, Fernando
A Bayesian Nonparametric Approach for Blind Multiuser Channel Estimation Proceedings Article
En: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 2766–2770, IEEE, Nice, 2015, ISBN: 978-0-9928-6263-3.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian nonparametric, communication systems, factorial HMM, Hidden Markov models, machine-to-machine, multiuser communication, Receiving antennas, Signal to noise ratio, Transmitters
@inproceedings{Valera2015b,
title = {A Bayesian Nonparametric Approach for Blind Multiuser Channel Estimation},
author = {Isabel Valera and Francisco J R Ruiz and Lennart Svensson and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7362888 http://www.eurasip.org/Proceedings/Eusipco/Eusipco2015/papers/1570096659.pdf},
doi = {10.1109/EUSIPCO.2015.7362888},
isbn = {978-0-9928-6263-3},
year = {2015},
date = {2015-08-01},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
pages = {2766--2770},
publisher = {IEEE},
address = {Nice},
abstract = {In many modern multiuser communication systems, users are allowed to enter and leave the system at any given time. Thus, the number of active users is an unknown and time-varying parameter, and the performance of the system depends on how accurately this parameter is estimated over time. We address the problem of blind joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop a Bayesian nonparametric model based on the Markov Indian buffet process and an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our experimental results show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios.},
keywords = {Bayes methods, Bayesian nonparametric, communication systems, factorial HMM, Hidden Markov models, machine-to-machine, multiuser communication, Receiving antennas, Signal to noise ratio, Transmitters},
pubstate = {published},
tppubtype = {inproceedings}
}
Nazabal, Alfredo; Artés-Rodríguez, Antonio
Discriminative spectral learning of hidden markov models for human activity recognition Proceedings Article
En: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1966–1970, IEEE, Brisbane, 2015, ISBN: 978-1-4673-6997-8.
Resumen | Enlaces | BibTeX | Etiquetas: Accuracy, Bayesian estimation, classify sequential data, Data models, Databases, Discriminative learning, discriminative spectral learning, Hidden Markov models, HMM parameters, Human activity recognition, learning (artificial intelligence), maximum likelihood, maximum likelihood estimation, ML, moment matching learning technique, Observable operator models, sensors, Spectral algorithm, spectral learning, Speech recognition, Training
@inproceedings{Nazabal2015,
title = {Discriminative spectral learning of hidden markov models for human activity recognition},
author = {Alfredo Nazabal and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178314},
doi = {10.1109/ICASSP.2015.7178314},
isbn = {978-1-4673-6997-8},
year = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1966--1970},
publisher = {IEEE},
address = {Brisbane},
abstract = {Hidden Markov Models (HMMs) are one of the most important techniques to model and classify sequential data. Maximum Likelihood (ML) and (parametric and non-parametric) Bayesian estimation of the HMM parameters suffers from local maxima and in massive datasets they can be specially time consuming. In this paper, we extend the spectral learning of HMMs, a moment matching learning technique free from local maxima, to discriminative HMMs. The resulting method provides the posterior probabilities of the classes without explicitly determining the HMM parameters, and is able to deal with missing labels. We apply the method to Human Activity Recognition (HAR) using two different types of sensors: portable inertial sensors, and fixed, wireless binary sensor networks. Our algorithm outperforms the standard discriminative HMM learning in both complexity and accuracy.},
keywords = {Accuracy, Bayesian estimation, classify sequential data, Data models, Databases, Discriminative learning, discriminative spectral learning, Hidden Markov models, HMM parameters, Human activity recognition, learning (artificial intelligence), maximum likelihood, maximum likelihood estimation, ML, moment matching learning technique, Observable operator models, sensors, Spectral algorithm, spectral learning, Speech recognition, Training},
pubstate = {published},
tppubtype = {inproceedings}
}
2014
Valera, Isabel; Ruiz, Francisco J R; Perez-Cruz, Fernando
Infinite Factorial Unbounded Hidden Markov Model for Blind Multiuser Channel Estimation Proceedings Article
En: 2014 4th International Workshop on Cognitive Information Processing (CIP), pp. 1–6, IEEE, Copenhagen, 2014, ISBN: 978-1-4799-3696-0.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian non parametrics, Bayesian nonparametric models, blind multiuser channel estimation, Channel estimation, degrees of freedom, detection problems, dispersive channel model, generative model, Hidden Markov models, HMM, inference algorithm, infinite factorial unbounded hidden Markov model, Markov chain Monte Carlo, Markov processes, MIMO, MIMO communication, MIMO communication systems, multiple-input multiple-output (MIMO), multiple-input multiple-output communication syste, receiver performance, Receivers, Signal to noise ratio, Transmitters, unbounded channel length, unbounded number, user detection
@inproceedings{Valera2014a,
title = {Infinite Factorial Unbounded Hidden Markov Model for Blind Multiuser Channel Estimation},
author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6844506},
isbn = {978-1-4799-3696-0},
year = {2014},
date = {2014-01-01},
booktitle = {2014 4th International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Copenhagen},
abstract = {Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multiuser multiple-input multiple-output (MIMO) communication systems we might not know the number of active users and the channel they face, and assuming maximal scenarios (maximum number of transmitters and maximum channel length) might degrade the receiver performance. In this paper, we propose a Bayesian nonparametric prior and its associated inference algorithm, which is able to detect an unbounded number of users with an unbounded channel length. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each transmitted symbol in a fully blind manner, i.e., without the need of pilot (training) symbols.},
keywords = {Bayes methods, Bayesian non parametrics, Bayesian nonparametric models, blind multiuser channel estimation, Channel estimation, degrees of freedom, detection problems, dispersive channel model, generative model, Hidden Markov models, HMM, inference algorithm, infinite factorial unbounded hidden Markov model, Markov chain Monte Carlo, Markov processes, MIMO, MIMO communication, MIMO communication systems, multiple-input multiple-output (MIMO), multiple-input multiple-output communication syste, receiver performance, Receivers, Signal to noise ratio, Transmitters, unbounded channel length, unbounded number, user detection},
pubstate = {published},
tppubtype = {inproceedings}
}
2012
Florentino-Liaño, Blanca; O'Mahony, Niamh; Artés-Rodríguez, Antonio
Long Term Human Activity Recognition with Automatic Orientation Estimation Proceedings Article
En: 2012 IEEE International Workshop on Machine Learning for Signal Processing, pp. 1–6, IEEE, Santander, 2012, ISSN: 1551-2541.
Resumen | Enlaces | BibTeX | Etiquetas: Acceleration, Activity recognition, automatic orientation estimation, biomedical equipment, Estimation, Gravity, Hidden Markov models, human daily activity recognition, Humans, Legged locomotion, long term human activity recognition, medical signal processing, object recognition, orientation estimation, sensors, single miniature inertial sensor, time intervals, Vectors, virtual sensor orientation, wearable sensors
@inproceedings{Florentino-Liano2012b,
title = {Long Term Human Activity Recognition with Automatic Orientation Estimation},
author = {Blanca Florentino-Lia\~{n}o and Niamh O'Mahony and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6349789},
issn = {1551-2541},
year = {2012},
date = {2012-01-01},
booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},
pages = {1--6},
publisher = {IEEE},
address = {Santander},
abstract = {This work deals with the elimination of sensitivity to sensor orientation in the task of human daily activity recognition using a single miniature inertial sensor. The proposed method detects time intervals of walking, automatically estimating the orientation in these intervals and transforming the observed signals to a “virtual” sensor orientation. Classification results show that excellent performance, in terms of both precision and recall (up to 100%), is achieved, for long-term recordings in real-life settings.},
keywords = {Acceleration, Activity recognition, automatic orientation estimation, biomedical equipment, Estimation, Gravity, Hidden Markov models, human daily activity recognition, Humans, Legged locomotion, long term human activity recognition, medical signal processing, object recognition, orientation estimation, sensors, single miniature inertial sensor, time intervals, Vectors, virtual sensor orientation, wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
Florentino-Liaño, Blanca; O'Mahony, Niamh; Artés-Rodríguez, Antonio
Human Activity Recognition Using Inertial Sensors with Invariance to Sensor Orientation Proceedings Article
En: 2012 3rd International Workshop on Cognitive Information Processing (CIP), pp. 1–6, IEEE, Baiona, 2012, ISBN: 978-1-4673-1878-5.
Resumen | Enlaces | BibTeX | Etiquetas: Acceleration, Accelerometers, biomechanics, classification algorithm, Gyroscopes, Hidden Markov models, human daily activity recognition, inertial measurement unit, Legged locomotion, miniature inertial sensors, raw sensor signal classification, sensor orientation invariance, sensor orientation sensitivity, sensor placement, sensor position sensitivity, sensors, signal classification, signal transformation, Training, triaxial accelerometer, triaxial gyroscope, virtual sensor orientation
@inproceedings{Florentino-Liano2012a,
title = {Human Activity Recognition Using Inertial Sensors with Invariance to Sensor Orientation},
author = {Blanca Florentino-Lia\~{n}o and Niamh O'Mahony and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6232914},
isbn = {978-1-4673-1878-5},
year = {2012},
date = {2012-01-01},
booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Baiona},
abstract = {This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method reduces sensitivity to the position and orientation of the sensor on the body, which is inherent in traditional methods, by transforming the observed signals to a “virtual” sensor orientation. By means of this computationally low-cost transform, the inputs to the classification algorithm are made invariant to sensor orientation, despite the signals being recorded from arbitrary sensor placements. Classification results show that improved performance, in terms of both precision and recall, is achieved with the transformed signals, relative to classification using raw sensor signals, and the algorithm performs competitively compared to the state-of-the-art. Activity recognition using data from a sensor with completely unknown orientation is shown to perform very well over a long term recording in a real-life setting.},
keywords = {Acceleration, Accelerometers, biomechanics, classification algorithm, Gyroscopes, Hidden Markov models, human daily activity recognition, inertial measurement unit, Legged locomotion, miniature inertial sensors, raw sensor signal classification, sensor orientation invariance, sensor orientation sensitivity, sensor placement, sensor position sensitivity, sensors, signal classification, signal transformation, Training, triaxial accelerometer, triaxial gyroscope, virtual sensor orientation},
pubstate = {published},
tppubtype = {inproceedings}
}