The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform

Steve Hranilovic - One of the best experts on this subject based on the ideXlab platform.

  • capacity bounds for wireless optical intensity channels with gaussian noise
    IEEE Transactions on Information Theory, 2010
    Co-Authors: A.a. Farid, Steve Hranilovic
    Abstract:

    Lower and upper bounds on the capacity of wireless optical intensity pulse amplitude modulation channels under nonnegativity and average optical power constraints are derived. A lower bound is derived based on Source Entropy maximization over a family of discrete nonuniform distributions with equally spaced mass points. A closed form for the maxentropic discrete input distribution is provided. Compared to previously reported bounds, the derived lower bound is tight at both low and high signal-to-noise ratios (SNRs). In addition, a closed-form upper bound is derived based on signal space geometry via a sphere packing argument. The proposed bound is tight at low SNRs and incurs a small gap to the channel capacity at high SNRs. The derived bounds asymptotically describe the optical intensity channel capacity at low SNRs, where a majority of such links operate.

  • ICC - Capacity of Optical Intensity Channels with Peak and Average Power Constraints
    2009 IEEE International Conference on Communications, 2009
    Co-Authors: A.a. Farid, Steve Hranilovic
    Abstract:

    The design and analysis of capacity-approaching input signalling for optical intensity channels are presented. Both peak and average optical power constraints are considered in the analysis. The capacity-achieving distribution for this channel is discrete with a finite number of mass points. In practice, finding this distribution requires solving a complex non-linear optimization at every SNR. In this work, we present a closed form discrete capacity-approaching distribution derived via Source Entropy maximization. The computation of this distribution is substantially less complex than previous optimization approaches and can be easily computed for different SNRs. The information rates using the derived maxentropic distribution are shown to be negligibly far away from the channel capacity found by non-linear optimization in the SNR range -6 to 6 dB.

  • ISIT - Upper and Lower Bounds on the Capacity of Wireless Optical Intensity Channels
    2007 IEEE International Symposium on Information Theory, 2007
    Co-Authors: A.a. Farid, Steve Hranilovic
    Abstract:

    Improved upper and lower bounds on the capacity of wireless optical intensity channels under non-negativity and average optical power constraints are derived. We consider intensity modulated/direct detection (IM/DD) channels with pulse amplitude modulation (PAM). Utilizing the signal space geometry and a sphere packing argument, an upper bound is derived. Compared to previous work, the derived upper bound is tighter at low signal-to-noise ratios. In addition, a lower bound is derived based on Source Entropy maximization over discrete distributions. The proposed distribution provides a tighter lower bound compared to previous continuous distributions. The derived bounds asymptotically describe the capacity of PAM optical intensity channels at both low and high SNR.

A.a. Farid - One of the best experts on this subject based on the ideXlab platform.

  • capacity bounds for wireless optical intensity channels with gaussian noise
    IEEE Transactions on Information Theory, 2010
    Co-Authors: A.a. Farid, Steve Hranilovic
    Abstract:

    Lower and upper bounds on the capacity of wireless optical intensity pulse amplitude modulation channels under nonnegativity and average optical power constraints are derived. A lower bound is derived based on Source Entropy maximization over a family of discrete nonuniform distributions with equally spaced mass points. A closed form for the maxentropic discrete input distribution is provided. Compared to previously reported bounds, the derived lower bound is tight at both low and high signal-to-noise ratios (SNRs). In addition, a closed-form upper bound is derived based on signal space geometry via a sphere packing argument. The proposed bound is tight at low SNRs and incurs a small gap to the channel capacity at high SNRs. The derived bounds asymptotically describe the optical intensity channel capacity at low SNRs, where a majority of such links operate.

  • ICC - Capacity of Optical Intensity Channels with Peak and Average Power Constraints
    2009 IEEE International Conference on Communications, 2009
    Co-Authors: A.a. Farid, Steve Hranilovic
    Abstract:

    The design and analysis of capacity-approaching input signalling for optical intensity channels are presented. Both peak and average optical power constraints are considered in the analysis. The capacity-achieving distribution for this channel is discrete with a finite number of mass points. In practice, finding this distribution requires solving a complex non-linear optimization at every SNR. In this work, we present a closed form discrete capacity-approaching distribution derived via Source Entropy maximization. The computation of this distribution is substantially less complex than previous optimization approaches and can be easily computed for different SNRs. The information rates using the derived maxentropic distribution are shown to be negligibly far away from the channel capacity found by non-linear optimization in the SNR range -6 to 6 dB.

  • ISIT - Upper and Lower Bounds on the Capacity of Wireless Optical Intensity Channels
    2007 IEEE International Symposium on Information Theory, 2007
    Co-Authors: A.a. Farid, Steve Hranilovic
    Abstract:

    Improved upper and lower bounds on the capacity of wireless optical intensity channels under non-negativity and average optical power constraints are derived. We consider intensity modulated/direct detection (IM/DD) channels with pulse amplitude modulation (PAM). Utilizing the signal space geometry and a sphere packing argument, an upper bound is derived. Compared to previous work, the derived upper bound is tighter at low signal-to-noise ratios. In addition, a lower bound is derived based on Source Entropy maximization over discrete distributions. The proposed distribution provides a tighter lower bound compared to previous continuous distributions. The derived bounds asymptotically describe the capacity of PAM optical intensity channels at both low and high SNR.

Hidetoshi Yokoo - One of the best experts on this subject based on the ideXlab platform.

  • ISITA - On the stationary distribution of asymmetric numeral systems
    2016
    Co-Authors: Hidetoshi Yokoo
    Abstract:

    The Asymmetric Numeral Systems (ANS) are a family of Entropy coders for information Sources with a finite alphabet, developed by J. Duda as an alternative to arithmetic coding. We have already proposed an approximation formula to the stationary distribution of the states in a special version (ABS) of ANS. We show that our previous result in ABS, which deals only with binary Sources, can be applied to ANS as it is. The previous approximation holds regardless of the alphabet size or Source parameters. We prove in a similar way to our previous one that the rate of ANS asymptotically attains the Source Entropy.

  • ISIT - On the stationary distribution of Asymmetric Binary Systems
    2016 IEEE International Symposium on Information Theory (ISIT), 2016
    Co-Authors: Hidetoshi Yokoo
    Abstract:

    This paper proposes an approximation to the stationary distribution of the states in Duda's ABS Entropy coder. While arithmetic coders represent a codeword by an interval of numbers, the ABS encoder represents its inner state by a single number. This paper proves that the proposed approximation to the state distribution converges to the true stationary distribution in the limit of a parameter of ABS. This leads to a rigorous proof of the fact that the rate of ABS asymptotically attains the Source Entropy.

  • CPM - A Dynamic Data Structure for Reverse Lexicographically Sorted Prefixes
    Combinatorial Pattern Matching, 1999
    Co-Authors: Hidetoshi Yokoo
    Abstract:

    This paper proposes a simple data structure, called a prefix list, which maintains all prefixes of a string in reverse lexicographic order. It can be on-line incrementally constructed in time and space linear in the string length. It is strongly related to suffix trees and suffix arrays, and may share applications with these existing structures. A suffix array can be built via the corresponding prefix list in linear time. Particular applications of the prefix list lie in Source-coding problems that require on-line right-to-left string matching. We apply the prefix list to on-line estimation of Source Entropy and to context-based symbol-ranking text compression algorithms.

Gadiel Seroussi - One of the best experts on this subject based on the ideXlab platform.

  • Sequential prediction and ranking in universal context modeling and data compression
    IEEE Transactions on Information Theory, 1997
    Co-Authors: Marcelo Weinberger, Gadiel Seroussi
    Abstract:

    Most state-of-the-art lossless image compression schemes use prediction followed by some form of context modeling. This might seem redundant at first, as the contextual information used for prediction is also available for building the compression model, and a universal coder will eventually learn the "predictive" patterns of the data. In this correspondence, we provide a format justification to the combination of these two modeling tools, by showing that a combined scheme may result in faster convergence rate to the Source Entropy. This is achieved via a reduction in the model cost of universal coding. In deriving the main result, we develop the concept of sequential ranking, which can be seen as a generalization of sequential prediction, and we study its combinatorial and probabilistic properties.

  • On the interaction between universal context modeling and prediction
    Proceedings of 1994 IEEE International Symposium on Information Theory, 1
    Co-Authors: Marcelo Weinberger, Gadiel Seroussi
    Abstract:

    Investigates the use of prediction as a means of reducing the model cost in lossless data compression. The authors show that its combination with a universal code may result in a faster convergence rate to the Source Entropy. >

Niels De Vreede - One of the best experts on this subject based on the ideXlab platform.

  • The Spammed Code Offset Method
    IEEE Transactions on Information Forensics and Security, 2014
    Co-Authors: Boris Skoric, Niels De Vreede
    Abstract:

    Helper data schemes are a security primitive used for privacy-preserving biometric databases and physical unclonable functions. One of the oldest known helper data schemes is the code offset method (COM). We propose an extension of the COM: the helper data are accompanied by many instances of fake helper data that are drawn from the same distribution as the real one. While the adversary has no way to distinguish between them, the legitimate party has more information and can see the difference. We use a low-density parity check code in order to improve the efficiency of the legitimate party's selection procedure. Our construction provides a new kind of tradeoff: more effective use of the Source Entropy, at the price of increased helper data storage. We give a security analysis in terms of Shannon Entropy and order-2 Renyi Entropy. We also propose a variant of our scheme in which the helper data list is not stored but pseudorandomly generated, changing the tradeoff to Source Entropy utilization versus computation effort.

  • The spammed code offset method
    IACR Cryptology ePrint Archive, 2013
    Co-Authors: Boris Skoric, Niels De Vreede
    Abstract:

    Helper data schemes are a security primitive used for privacy-preserving biometric databases and Physical Unclonable Functions. One of the oldest known helper data schemes is the Code Offset Method (COM). We propose an extension of the COM: the helper data is accompanied by many instances of fake helper data that are drawn from the same distribution as the real one. While the adversary has no way to distinguish between them, the legitimate party has more information and {\em can} see the difference. We use an LDPC code in order to improve the efficiency of the legitimate party's selection procedure. Our construction provides a new kind of trade-off: more effective use of the Source Entropy, at the price of increased helper data storage. We give a security analysis in terms of Shannon Entropy and order-2 Renyi Entropy. We also propose a variant of our scheme in which the helper data list is not stored but pseudorandomly generated, changing the trade-off to Source Entropy utilization vs. computation effort. Keywords: PUF, helper data, code offset construction, code offset method, fuzzy extractor, secure sketch