The Experts below are selected from a list of 82944 Experts worldwide ranked by ideXlab platform
Tsachy Weissman - One of the best experts on this subject based on the ideXlab platform.
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Universality of logarithmic loss in Lossy compression
2015 IEEE International Symposium on Information Theory (ISIT), 2015Co-Authors: Albert No, Tsachy WeissmanAbstract:We establish two strong senses of universality of logarithmic loss as a distortion criterion in Lossy compression: For any fixed length Lossy compression problem under an arbitrary distortion criterion, we show that there is an equivalent Lossy compression problem under logarithmic loss. In the successive refinement problem, if the first decoder operates under logarithmic loss, we show that any discrete memoryless source is successively refinable under an arbitrary distortion criterion for the second decoder.
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ISIT - Universality of logarithmic loss in Lossy compression
2015 IEEE International Symposium on Information Theory (ISIT), 2015Co-Authors: Albert No, Tsachy WeissmanAbstract:We establish two strong senses of universality of logarithmic loss as a distortion criterion in Lossy compression: For any fixed length Lossy compression problem under an arbitrary distortion criterion, we show that there is an equivalent Lossy compression problem under logarithmic loss. In the successive refinement problem, if the first decoder operates under logarithmic loss, we show that any discrete memoryless source is successively refinable under an arbitrary distortion criterion for the second decoder.
Ming Yu - One of the best experts on this subject based on the ideXlab platform.
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generalized Lossy microwave filter coupling matrix synthesis and design using mixed technologies
IEEE Transactions on Microwave Theory and Techniques, 2008Co-Authors: Vahid Miraftab, Ming YuAbstract:This paper presents a generalized direct synthesis technique for Lossy reciprocal and nonsymmetrical microwave filters. The procedure handles multiple complex coupling among resonators and finite-Q resonators. A coupling matrix model with complex entries is used as the basis for the analysis. New mathematical definitions are introduced to find the necessary properties of the complex coupling matrix. Synthesis verification examples include symmetrical, asymmetrical and different loss level Lossy functions. A Lossy four-pole Chebyshev filter in Ku-band has been designed, fabricated and tested using mixed combline and microstrip technologies for the first time. This design has the advantage of having the signal passing through at least two resonators for any possible signal path, which eliminates unwanted source to load coupling to a great extent.
Sergio Verdu - One of the best experts on this subject based on the ideXlab platform.
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universal Lossy compression under logarithmic loss
International Symposium on Information Theory, 2017Co-Authors: Yanina Y Shkel, Maxim Raginsky, Sergio VerduAbstract:Universal Lossy source coding with the logarithmic loss distortion criterion is studied. Bounds on the non-asymptotic fundamental limit of fixed-length universal coding with respect to a family of distributions are derived. These bounds generalize the well-known minimax bounds for universal lossless source coding. The asymptotic behavior of the resulting optimization problem is studied for a family of i.i.d. sources with a finite alphabet size, and is characterized up to a constant. The redundancy of memoryless sources behaves like k/2 log n, where n is the blocklength and k is the number of degrees of freedom in the parameter space. The impact of the coding rate is on the constant term: higher compression rate effectively reduces the volume of the parameter uncertainty set.
Albert No - One of the best experts on this subject based on the ideXlab platform.
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Universality of Logarithmic Loss in Fixed-Length Lossy Compression
Entropy, 2019Co-Authors: Albert NoAbstract:We established a universality of logarithmic loss over a finite alphabet as a distortion criterion in fixed-length Lossy compression. For any fixed-length Lossy-compression problem under an arbitrary distortion criterion, we show that there is an equivalent Lossy-compression problem under logarithmic loss. The equivalence is in the strong sense that we show that finding good schemes in corresponding Lossy compression under logarithmic loss is essentially equivalent to finding good schemes in the original problem. This equivalence relation also provides an algebraic structure in the reconstruction alphabet, which allows us to use known techniques in the clustering literature. Furthermore, our result naturally suggests a new clustering algorithm in the categorical data-clustering problem.
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Universality of logarithmic loss in Lossy compression
2015 IEEE International Symposium on Information Theory (ISIT), 2015Co-Authors: Albert No, Tsachy WeissmanAbstract:We establish two strong senses of universality of logarithmic loss as a distortion criterion in Lossy compression: For any fixed length Lossy compression problem under an arbitrary distortion criterion, we show that there is an equivalent Lossy compression problem under logarithmic loss. In the successive refinement problem, if the first decoder operates under logarithmic loss, we show that any discrete memoryless source is successively refinable under an arbitrary distortion criterion for the second decoder.
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ISIT - Universality of logarithmic loss in Lossy compression
2015 IEEE International Symposium on Information Theory (ISIT), 2015Co-Authors: Albert No, Tsachy WeissmanAbstract:We establish two strong senses of universality of logarithmic loss as a distortion criterion in Lossy compression: For any fixed length Lossy compression problem under an arbitrary distortion criterion, we show that there is an equivalent Lossy compression problem under logarithmic loss. In the successive refinement problem, if the first decoder operates under logarithmic loss, we show that any discrete memoryless source is successively refinable under an arbitrary distortion criterion for the second decoder.
Dat Tran - One of the best experts on this subject based on the ideXlab platform.
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ICPR - Impact of Lossy data compression techniques on EEG-based pattern recognition systems
2018 24th International Conference on Pattern Recognition (ICPR), 2018Co-Authors: Binh Nguyen, Wanli Ma, Dat TranAbstract:Electroencephalogram (EEG) data compression has been used to reduce the space for storage and speed up the data circulation. Albeit Lossy compression techniques achieve a much higher compression ratio than lossless ones, they introduce the loss of information in reconstructed data, which may affect to the performance of EEG-based pattern recognition systems. In this paper, we investigate the impact of Lossy compression techniques on the performance of EEG-based pattern recognition systems including seizure recognition and person recognition. Our experiments are conducted on two public databases using two different EEG Lossy compression techniques. Experimental results show that the recognition performance is not significantly reduced when using Lossy techniques at high compression ratios.
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SOCO-CISIS-ICEUTE - A Study of Combined Lossy Compression and Person Identification on EEG Signals
Advances in Intelligent Systems and Computing, 2018Co-Authors: Binh Nguyen, Wanli Ma, Dat TranAbstract:Biometric information extracted from electroencephalogram (EEG) signals is being used increasingly in person identification systems thanks to several advantages, compared to traditional ones such as fingerprint, face and voice. However, one of the major challenges is that a huge amount of EEG data needs to be processed, transmitted and stored. The use of EEG compression is therefore becoming necessary. Although the Lossy compression technique gives a higher Compression Ratio (CR) than lossless ones, they introduce the loss of information in recovered signals, which may affect to the performance of EEG-based person identification systems. In this paper, we investigate the impact of Lossy compression on EEG data used in EEG-based person identification systems. Experimental results demonstrate that in the best case, CR could achieve up to 70 with minimal loss of person identification performance, and using EEG Lossy compression is feasible compared to using lossless one.
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Impact of Lossy data compression techniques on EEG-based pattern recognition systems
2018 24th International Conference on Pattern Recognition (ICPR), 2018Co-Authors: Binh Nguyen, Dat TranAbstract:Electroencephalogram (EEG) data compression has been used to reduce the space for storage and speed up the data circulation. Albeit Lossy compression techniques achieve a much higher compression ratio than lossless ones, they introduce the loss of information in reconstructed data, which may affect to the performance of EEG-based pattern recognition systems. In this paper, we investigate the impact of Lossy compression techniques on the performance of EEG-based pattern recognition systems including seizure recognition and person recognition. Our experiments are conducted on two public databases using two different EEG Lossy compression techniques. Experimental results show that the recognition performance is not significantly reduced when using Lossy techniques at high compression ratios.
