2018
Koch, Tobias; Vazquez-Vilar, Gonzalo
A Rigorous Approach to High-Resolution Entropy-Constrained Vector Quantization Artículo de revista
En: IEEE Transactions on Information Theory, vol. 64, no 4, pp. 2609-2625, 2018, ISSN: 0018-9448.
Enlaces | BibTeX | Etiquetas: Distortion, Distortion measurement, Entropy, Entropy constrained, high resolution, Probability density function, quantization, Rate-distortion, Rate-distortion theory, Vector quantization
@article{koch-TIT2018a,
title = {A Rigorous Approach to High-Resolution Entropy-Constrained Vector Quantization},
author = {Tobias Koch and Gonzalo Vazquez-Vilar},
doi = {10.1109/TIT.2018.2803064},
issn = {0018-9448},
year = {2018},
date = {2018-04-01},
journal = {IEEE Transactions on Information Theory},
volume = {64},
number = {4},
pages = {2609-2625},
keywords = {Distortion, Distortion measurement, Entropy, Entropy constrained, high resolution, Probability density function, quantization, Rate-distortion, Rate-distortion theory, Vector quantization},
pubstate = {published},
tppubtype = {article}
}
2008
Vila-Forcen, J E; Artés-Rodríguez, Antonio; Garcia-Frias, J
Compressive Sensing Detection of Stochastic Signals Proceedings Article
En: 2008 42nd Annual Conference on Information Sciences and Systems, pp. 956–960, IEEE, Princeton, 2008, ISBN: 978-1-4244-2246-3.
Resumen | Enlaces | BibTeX | Etiquetas: Additive white noise, AWGN, compressive sensing detection, dimensionality reduction techniques, Distortion measurement, Gaussian noise, matrix algebra, Mutual information, optimized projections, projection matrix, signal detection, Signal processing, signal reconstruction, Stochastic processes, stochastic signals, Support vector machine classification, Support vector machines, SVM
@inproceedings{Vila-Forcen2008,
title = {Compressive Sensing Detection of Stochastic Signals},
author = {J E Vila-Forcen and Antonio Art\'{e}s-Rodr\'{i}guez and J Garcia-Frias},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4558656},
isbn = {978-1-4244-2246-3},
year = {2008},
date = {2008-01-01},
booktitle = {2008 42nd Annual Conference on Information Sciences and Systems},
pages = {956--960},
publisher = {IEEE},
address = {Princeton},
abstract = {Inspired by recent work in compressive sensing, we propose a framework for the detection of stochastic signals from optimized projections. In order to generate a good projection matrix, we use dimensionality reduction techniques based on the maximization of the mutual information between the projected signals and their corresponding class labels. In addition, classification techniques based on support vector machines (SVMs) are applied for the final decision process. Simulation results show that the realizations of the stochastic process are detected with higher accuracy and lower complexity than a scheme performing signal reconstruction first, followed by detection based on the reconstructed signal.},
keywords = {Additive white noise, AWGN, compressive sensing detection, dimensionality reduction techniques, Distortion measurement, Gaussian noise, matrix algebra, Mutual information, optimized projections, projection matrix, signal detection, Signal processing, signal reconstruction, Stochastic processes, stochastic signals, Support vector machine classification, Support vector machines, SVM},
pubstate = {published},
tppubtype = {inproceedings}
}