Maxime Colas

Secure digital communication using discrete-time chaos synchronization

Abstract In this paper we propose some secure digital communication schemes using discrete chaotic systems. In our approach a message is encrypted at the transmitter using chaotic modulation. Next, the driving signal synchronizes the receiver using discrete observer design or drive-response concept. Finally, by reverting the coding procedure the transmitted message is reconstructed. To demonstrate the efficiency of our communication schemes a modified Henon’s map is considered as an illustrative example.

FFT-Based BP Decoding of General LDPC Codes Over Abelian Groups

We introduce a wide class of low-density parity-check (LDPC) codes, large enough to include LDPC codes over finite fields, rings, or groups, as well as some nonlinear codes. A belief-propagation decoding procedure with the same complexity as for the decoding of LDPC codes over finite fields is also presented. Moreover, an encoding procedure is developed

Blind source separation: a new pre-processing tool for rotating machines monitoring?

Blind source separation (BSS) is a general signal processing method, which consists of recovering from a finite set of observations recorded by sensors, the contributions of different physical sources independently of the propagation medium and without any a priori knowledge of the sources. Recently, these methods paved a new way for the monitoring or the diagnosis of mechanical systems in a working environment. Actually, we show that BSS allows recovering the vibratory information issued from a single rotating machine working in a noisy environment by freeing the sensor signal from the contribution of other working machines. In that way, BSS can be used as a pre-processing step for rotating machine fault detection and diagnosis.

Turbo decision aided reconstruction of clipping noise in coded OFDM

High peak average to power ratio (PAPR) is one of the main drawback of the OFDM systems currently used in high rate communication standards. One way to reduce PAPR consists in clipping the amplitude of the OFDM signals introducing an additional noise that degrades the system performances. This work presents two iterative reconstruction algorithms of the clipped samples in coded OFDM systems in presence of channel noise. These two algorithms combine, a classical DAR procedure with an iterative decoding of the channel code in a turbo-like way.

Turbo decision aided receivers for clipping noise mitigation in coded OFDM

Orthogonal frequency division multiplexing (OFDM) is the modulation technique used in most of the high-rate communication standards. However, OFDM signals exhibit high peak average to power ratio (PAPR) that makes them particularly sensitive to nonlinear distortions caused by high-power amplifiers. Hence, the amplifier needs to operate at large output backoff, thereby decreasing the average efficiency of the transmitter. One way to reduce PAPR consists in clipping the amplitude of the OFDM signal introducing an additional noise that degrades the overall system performance. In that case, the receiver needs to set up an algorithm that compensates this clipping noise. In this paper, we propose three new iterative receivers with growing complexity and performance that operate at severe clipping: the first and simplest receiver uses a Viterbi algorithm as channel decoder whereas the other two implement a soft-input soft-output (SISO) decoder. Each soft receiver is analyzed through EXIT charts for different mappings. Finally, the performances of the receivers are simulated on both short time-varying channel and AWGN channel.

A multitime-frequency approach for detection and classification of neighboring instantaneous frequency laws in a noisy environment

Recently, time-varying higher order spectra (TVHOS) have been defined to analyze nonlinear nonstationary signals. In this letter, a new detection/classification algorithm based on TVHOS is proposed and applied to frequency-modulated (FM) signals disrupted by multiplicative and additive noise. Simulations indicate the superiority of this approach for classification purpose compared with the classical Wigner Spectrum scheme.

WSN sensing coverage based on correlation

The coverage problem is one of the most concerns in Wireless Sensor Networks (WSN). Many coverage algorithms are found in literature suppose that nodes are localizable or rely on a specific probability distribution to be located. In this paper, we assume that the nodes are randomly distributed, and their location is unknown. A new test for hole detection in the coverage of a domain is presented and illustrated through an example. Our method is based on the variance estimation of the field to be sensed, considered as a Gaussian process as well as on a translation of this variance into a simplicial complex. Topological characteristics are then computed on this complex to detect coverage holes.

Secure digital communication using chaotic modulation with feedback

In this note we propose a new chaotic modulation schema with output feedback. It is shown that the receiver can synchronize with the transmitter with any desired rate. Moreover, this approach can be applied to a large class of chaotic systems and provides higher security against an intruder. The robustness of our scheme with respect to noise is illustrated and compared, to the symmetric chaos shift keying method.

Multitime frequency classifiers for neighboring modulations in a multiplicative noise environment

This communication presents two new classification algorithms based on time varying higher order statistics (TVHOS). These two algorithms benefit from the advantageous localization properties provided by TVHOS4 for instantaneous frequency laws disrupted by multiplicative noise. The classification scheme used is a bank of several normalized TVHOS4 correlators. Simulations illustrate successful performance of the classification algorithms for different situations and especially if the SNR is higher than -7 dB.

Time varying higher order statistics for FM signals analysis and decision

Abstract Time varying higher order spectra (TVHOS) are designed for non-Gaussian non-stationary signal processing. However, these complex representations must be well understood to allow some interesting improvements despite their complexity growth. This paper presents the common TVHOS representations and provides an analysis of the “cross-terms” linked to their non-linear structure. Some of these representations are used for FM signal analysis and decision in a context of multiplicative and additive noise. Two new decision schemes using 4th order moment and cumulant representations are presented and their performances, compared with the equivalent 2nd approach (based on Wigner spectrum), are discussed.