arXiv subjects

Fabrice Labeau

Publications and source records attributed to Fabrice Labeau.

35 records · Page 2Linked to original sources

arXiv2014

Medically Relevant Criteria used in EEG Compression for Improved Post-Compression Seizure Detection

Biomedical signals aid in the diagnosis of different disorders and abnormalities. When targeting lossy compression of such signals, the medically relevant information that lies within the data should maintain its accuracy and thus its reliability. In fact, signal models that are inspired by the bio-physical properties of the signals at hand allow for a compression that preserves more naturally the clinically significant features of these signals. In this paper, we illustrate this through the example of EEG signals; more specifically, we analyze three specific lossy EEG compression schemes. These schemes are based on signal models that have different degrees of reliance on signal production and physiological characteristics of EEG. The resilience of these schemes is illustrated through the performance of seizure detection post compression.

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arXiv2014

EEG Compression of Scalp Recordings based on Dipole Fitting

A novel technique for Electroencephalogram (EEG) compression is proposed in this article. This technique models the intrinsic dependency inherent between the different EEG channels. It is based on dipole fitting that is usually used in order to find a solution to the classic problems in EEG analysis: inverse and forward problems. The suggested compression system uses dipole fitting as a first building block to provide an approximation of the recorded signals. Then, (based on a smoothness factor,) appropriate coding techniques are suggested to compress the residuals of the fitting process. Results show that this technique works well for different types of recordings and is even able to provide near- lossless compression for event-related potentials.

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arXiv2013

Outage Minimization via Power Adaptation and Allocation for Truncated Hybrid ARQ

In this work, we analyze hybrid ARQ (HARQ) protocols over the independent block fading channel. We assume that the transmitter is unaware of the channel state information (CSI) but has a knowledge about the channel statistics. We consider two scenarios with respect to the feedback received by the transmitter: i) ''conventional'', one-bit feedback about the decoding success/failure (ACK/NACK), and ii) the multi-bit feedback scheme when, on top of ACK/NACK, the receiver provides additional information about the state of the decoder to the transmitter. In both cases, the feedback is used to allocate (in the case of one-bit feedback) or adapt (in the case of multi-bit feedback) the power across the HARQ transmission attempts. The objective in both cases is the minimization of the outage probability under long-term average and peak power constraints. We cast the problems into the dynamic programming (DP) framework and solve them for Nakagami-m fading channels. A simplified solution for the high signal-to-noise ratio (SNR) regime is presented using a geometric programming (GP) approach. The obtained results quantify the advantage of the multi-bit feedback over the conventional approach, and show that the power optimization can provide significant gains over conventional power-constant HARQ transmissions even in the presence of peak-power constraints.

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arXiv2013

Formulation and Steady-state Analysis of LMS Adaptive Networks for Distributed Estimation in the Presence of Transmission Errors

This article presents the formulation and steady-state analysis of the distributed estimation algorithms based on the diffusion cooperation scheme in the presence of errors due to the unreliable data transfer among nodes. In particular, we highlight the impact of transmission errors on the least-mean squares (LMS) adaptive networks. We develop the closed-form expressions of the steady-state mean-square deviation (MSD) which is helpful to assess the effects of the imperfect information flow on on the behavior of the diffusion LMS algorithm in terms of the steady-state error. The model is then validated by performing Monte Carlo simulations. It is shown that local and global MSD curves are not necessarily monotonic increasing functions of the error probability. We also assess sufficient conditions that ensure mean and mean-square stability of diffusion LMS strategies in the presence of transmission errors. Moreover, issues such as scalability in the sense of network size and regressor size, spatially correlated observations, as well as the effect of the distribution of the noise variance are studied. While the proposed theoretical framework is general in the sense that it is not confined to a particular source of error during information diffusion, for practical reasons we additionally study a specific scenario where errors occur at the medium access control (MAC) level. We develop a model to quantify the MAC-level transmission errors according to the network topology and system parameters for a set of nodes employing a backoff procedure to access the channel. To overcome the problem of unreliable data exchange, we propose an enhanced combining rule that can be deployed in order to improve the performance of diffusion estimation algorithms by using the knowledge of the properties of the transmission errors.

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arXiv2013

Systematic DFT Frames: Principle, Eigenvalues Structure, and Applications

Motivated by a host of recent applications requiring some amount of redundancy, frames are becoming a standard tool in the signal processing toolbox. In this paper, we study a specific class of frames, known as discrete Fourier transform (DFT) codes, and introduce the notion of systematic frames for this class. This is encouraged by a new application of frames, namely, distributed source coding that uses DFT codes for compression. Studying their extreme eigenvalues, we show that, unlike DFT frames, systematic DFT frames are not necessarily tight. Then, we come up with conditions for which these frames can be tight. In either case, the best and worst systematic frames are established in the minimum mean-squared reconstruction error sense. Eigenvalues of DFT frames and their subframes play a pivotal role in this work. Particularly, we derive some bounds on the extreme eigenvalues DFT subframes which are used to prove most of the results; these bounds are valuable independently.

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arXiv2013

Extended Subspace Error Localization for Rate-Adaptive Distributed Source Coding

A subspace-based approach for rate-adaptive distributed source coding (DSC) based on discrete Fourier transform (DFT) codes is developed. Punctured DFT codes can be used to implement rate-adaptive source coding, however they perform poorly after even moderate puncturing since the performance of the subspace error localization degrades severely. The proposed subspace-based error localization extends and improves the existing one, based on additional syndrome, and is naturally suitable for rate-adaptive distributed source coding architecture.

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arXiv2013

Wide Band Time-Correlated Model for Wireless Communications under Impulsive Noise within Power Substation

The installation of wireless technologies in power substations requires characterizing the impulsive noise produced by the high-voltage equipment. Substation impulsive noise might interfere with classic wireless communications and none of the existing models can reliably represent this noise in wide band. Previous studies have shown that impulsive noise is characterized by series of damped oscillations with the amplitude, the duration and the occurrence times of the impulses that are random. All these characteristics make this noise time-correlated and the partitioned Markov chain remains an efficient model that can ensure the correlation between the samples. In this study, we propose to design a partitioned Markov chain to generate an impulsive noise that is similar to the noise measured in existing substations, in time and frequency domains. We configure our Markov chain to produce the impulses with the damped oscillation effect, then, we determine the probability transition matrix and the distribution of each state of the Markov chain. Finally, we generate noise samples and we study the distribution of the impulsive noise characteristics. Our Markov chain model can replicate the correlation between the measured noise samples; also the distributions of the noise characteristics are similar in the simulations and the measurements.

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arXiv2013

Non-Adaptive Distributed Compression in Networks

In this paper, we discuss non-adaptive distributed compression of inter-node correlated real-valued messages. To do so, we discuss the performance of conventional packet forwarding via routing, in terms of the total network load versus the resulting quality of service (distortion level). As a better alternative for packet forwarding, we briefly describe our previously proposed one-step Quantized Network Coding (QNC), and make motivating arguments on its advantage when the appropriate marginal rates for distributed source coding are not available at the encoder source nodes. We also derive analytic guarantees on the resulting distortion of our one-step QNC scenario. Finally, we conclude the paper by providing a mathematical comparison between the total network loads of one-step QNC and conventional packet forwarding, showing a significant reduction in the case of one-step QNC.

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arXiv2013

Wyner-Ziv Coding in the Real Field Based on BCH-DFT Codes

We show how real-number codes can be used to compress correlated sources and establish a new framework for distributed lossy source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible to model correlation between continuous-valued sources more realistically and compensate for the quantization error when the sources are completely correlated. We focus on the asymmetric case, i.e., lossy source coding with side information at the decoder, also known as Wyner-Ziv coding. The encoding and decoding procedures are described in detail for discrete Fourier transform (DFT) codes, both for syndrome- and parity-based approaches. We also extend the parity-based approach to the case where the transmission channel is noisy and perform distributed joint source-channel coding in this context. The proposed system is well suited for low-delay communications. Furthermore, the mean-squared reconstruction error (MSE) is shown to be less than or close to the quantization error level, the ideal case in coding based on binary codes.

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arXiv2012

Quantized Network Coding for Correlated Sources

Non-adaptive joint source network coding of correlated sources is discussed in this paper. By studying the information flow in the network, we propose quantized network coding as an alternative for packet forwarding. This technique has both network coding and distributed source coding advantages, simultaneously. Quantized network coding is a combination of random linear network coding in the (infinite) field of real numbers and quantization to cope with the limited capacity of links. With the aid of the results in the literature of compressed sensing, we discuss theoretical and practical feasibility of quantized network coding in lossless networks. We show that, due to the nature of the field it operates on, quantized network coding can provide good quality decoding at a sink node with the reception of a reduced number of packets. Specifically, we discuss the required conditions on local network coding coefficients, by using restricted isometry property and suggest a design, which yields in appropriate linear measurements. Finally, our simulation results show the achieved gain in terms of delivery delay, compared to conventional routing based packet forwarding.

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arXiv2012

Improved Modeling of the Correlation Between Continuous-Valued Sources in LDPC-Based DSC

Accurate modeling of the correlation between the sources plays a crucial role in the efficiency of distributed source coding (DSC) systems. This correlation is commonly modeled in the binary domain by using a single binary symmetric channel (BSC), both for binary and continuous-valued sources. We show that "one" BSC cannot accurately capture the correlation between continuous-valued sources; a more accurate model requires "multiple" BSCs, as many as the number of bits used to represent each sample. We incorporate this new model into the DSC system that uses low-density parity-check (LDPC) codes for compression. The standard Slepian-Wolf LDPC decoder requires a slight modification so that the parameters of all BSCs are integrated in the log-likelihood ratios (LLRs). Further, using an interleaver the data belonging to different bit-planes are shuffled to introduce randomness in the binary domain. The new system has the same complexity and delay as the standard one. Simulation results prove the effectiveness of the proposed model and system.

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arXiv2012

One-Step Quantized Network Coding for Near Sparse Gaussian Messages

In this paper, mathematical bases for non-adaptive joint source network coding of correlated messages in a Bayesian scenario are studied. Specifically, we introduce one-step Quantized Network Coding (QNC), which is a hybrid combination of network coding and packet forwarding for transmission. Motivated by the work on Bayesian compressed sensing, we derive theoretical guarantees on robust recovery in a one-step QNC scenario. Our mathematical derivations for Gaussian messages express the opportunity of distributed compression by using one-step QNC, as a simplified version of QNC scenario. Our simulation results show an improvement in terms of quality-delay performance over routing based packet forwarding.

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arXiv2012

Bayesian Quantized Network Coding via Belief Propagation

In this paper, we propose an alternative for routing based packet forwarding, which uses network coding to increase transmission efficiency, in terms of both compression and error resilience. This non-adaptive encoding is called quantized network coding, which involves random linear mapping in the real field, followed by quantization to cope with the finite capacity of the links. At the gateway node, which collects received quantized network coder packets, minimum mean squared error decoding is performed, by using belief propagation in the factor graph representation. Our simulation results show a significant improvement, in terms of the number of required packets to recover the messages, which can be interpreted as an embedded distributed source coding for correlated messages.

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arXiv2012

Distributed Lossy Source Coding Using Real-Number Codes

We show how real-number codes can be used to compress correlated sources, and establish a new framework for lossy distributed source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible to model correlation between continuous-valued sources more realistically and correct quantization error when the sources are completely correlated. The encoding and decoding procedures are described in detail, for discrete Fourier transform (DFT) codes. Reconstructed signal, in the mean squared error sense, is seen to be better than that in the conventional approach.

cs.IT↗

arXiv2012

Systematic DFT Frames: Principle and Eigenvalues Structure

Motivated by a host of recent applications requiring some amount of redundancy, frames are becoming a standard tool in the signal processing toolbox. In this paper, we study a specific class of frames, known as discrete Fourier transform (DFT) codes, and introduce the notion of systematic frames for this class. This is encouraged by application of systematic DFT codes in distributed source coding using DFT codes, a new application for frames. Studying their extreme eigenvalues, we show that, unlike DFT frames, systematic DFT frames are not necessarily tight. Then, we come up with conditions for which these frames can be tight. In either case, the best and worst systematic frames are established from reconstruction error point of view. Eigenvalues of DFT frames, and their subframes, play a pivotal role in this work.

cs.IT↗

arXiv2012

Restricted Isometry Property in Quantized Network Coding of Sparse Messages

In this paper, we study joint network coding and distributed source coding of inter-node dependent messages, with the perspective of compressed sensing. Specifically, the theoretical guarantees for robust $\ell_1$-min recovery of an under-determined set of linear network coded sparse messages are investigated. We discuss the guarantees for $\ell_1$-min decoding of quantized network coded messages, using the proposed local network coding coefficients in \cite{naba}, based on Restricted Isometry Property (RIP) of the resulting measurement matrix. Moreover, the relation between tail probability of $\ell_2$-norms and satisfaction of RIP is derived and used to compare our designed measurement matrix, with i.i.d. Gaussian measurement matrix. Finally, we present our numerical evaluations, which shows that the proposed design of network coding coefficients result in a measurement matrix with an RIP behavior, similar to that of i.i.d. Gaussian matrix.

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arXiv2012

Quantized Network Coding for Sparse Messages

In this paper, we study the data gathering problem in the context of power grids by using a network of sensors, where the sensed data have inter-node redundancy. Specifically, we propose a new transmission method, calledquantized network coding, which performs linear net-work coding in the field of real numbers, and quantization to accommodate the finite capacity of edges. By using the concepts in compressed sensing literature, we propose to use l1-minimization to decode the quantized network coded packets, especially when the number of received packets at the decoder is less than the size of sensed data (i.e. number of nodes). We also propose an appropriate design for network coding coefficients, based on restricted isometry property, which results in robust l1-min decoding. Our numerical analysis show that the proposed quantized network coding scheme with l1-min decoding can achieve significant improvements, in terms of compression ratio and delivery delay, compared to conventional packet forwarding.

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