Monique P. Fargues

A hierarchical approach to the classification of digital modulation types in multipath environments

This study presents a hierarchical approach to the classification of digital modulation schemes of types [2,4,8]-PSK, [2,4,8]-FSK and [16,64,256]-QAM in various SNR levels and multipath propagation channel conditions. A tree-based classification approach is selected as it leads to a relatively simple overall scheme with a few higher-order statistics parameters selected as discriminating features. Back propagation neural network decision units are adopted at each tree node to offer the flexibility needed to cope with varying propagation environments for all schemes considered, except for specific MQAM types, which are discriminated via equalization algorithms. Extensive simulations are conducted in various multipath environments, and show overall classification performances to be strongly affected by the amount of multipath distortion and noise in the transmission channels.

Wavelet-based detection of frequency hopping signals

We investigate the application of wavelet transforms in the detection and estimation of spread spectrum frequency hopping signals. The technique developed makes only two basic assumptions of a minimum hopping time and a minimum frequency hopping differential. The approach is based on the phase information of the temporal correlation function and the resulting discrete wavelet transform of the phase information is used to estimate the hopping time of frequency hopping signals. Results show the proposed scheme is robust to additive white Gaussian noise degradations for SNR levels of 3 dB and above.

A Dual Mode Human-Robot Teleoperation Interface Based on Airflow in the Aural Cavity

Robot teleoperation systems have been limited in their utility due to the need for operator motion, lack of portability and limitation to singular input modalities. In this article, the design and construction of a dual-mode human—machine interface system for robot teleoperation addressing all these issues is presented. The interface is capable of directing robotic devices in response to tongue movement and/or speech without insertion of any device in the vicinity of the oral cavity. The interface is centered on the unique properties of the human ear as an acoustic output device. Specifically, we present: (1) an analysis of the sensitivity of human ear canals as acoustic output device; (2) the design of a new sensor for monitoring airflow in the aural canal; (3) pattern recognition procedures for recognition of both speech and tongue movement by monitoring aural flow across several human test subjects; and (4) a conceptual design and simulation of the machine interface system. We believe this work will lay the foundation for a new generation of human machine interface systems for all manner of robotic applications.

Tracking moving sources using the rank-revealing QR factorization

The author investigates an updating scheme for the rank revealing QR (RRQR) algorithm described earlier by T.F. Chan (see Linear Algebr. Appl., vol.88, no 89, p.67-82 1987) and applies it to the direction of arrival problem. This technique allows for tracking of moving sources by taking advantage of the simplicity of the regular QR updating scheme and the rank-revealing property of the RRQR factorization. Subspace methods and the RRQR technique are reviewed. It is shown that the RRQR algorithm can be used to update signal and noise subspaces from the noise-free correlation matrix. Experimental results and comparisons with eigen-based signal and noise subspaces are presented.<<ETX>>

Investigations in the numerical behavior of the adaptive rank-revealing QR factorization

We present a tracking procedure based on the rank-revealing QR (RRQR) factorization and investigate its numerical properties by applying it to the direction-of-arrival problem. We address numerical issues raised by the related work proposed earlier by Prasad et al. (1991), and we compare the performance of the proposed algorithm to that obtained using an EVD-based technique.

Feature extraction of intra-pulse modulated signals using time-frequency analysis

Time-frequency/wavelet decompositions and pattern recognition schemes are used to extract the parameters of linear and hyperbolic chirps. The study first compares the robustness to additive noise distortions of eleven different transformations applied in connection with the Radon transform to estimate linear chirp parameters. Second, it proposes an iterative scheme to estimate hyperbolic chirp parameters from the time-frequency image.

Comparing wavelet transforms and AR modeling as feature extraction tools for underwater signal classification

This study investigates the application of orthogonal, non-orthogonal wavelet-based procedures, and AR modeling as feature extraction techniques to classify several classes of underwater signals consisting of sperm whale, killer whale, gray whale, pilot whale, humpback whale, and underwater earthquake data. A two-hidden-layers backpropagation neural network is used for the classification procedure. The performances obtained using the two wavelet-based schemes are compared with those obtained using reduced-rank AR modeling tools. Results show that the non-orthogonal undecimated A-trous implementation with multiple voices leads to the highest classification rate of 96.7%.

Uncooled Infrared Imaging Face Recognition Using Kernel-Based Feature Vector Selection

This study considers an approximation to the Generalized Discriminant Analysis (GDA) and its applications to an uncooled infrared image face recognition problem. We consider the feature vector selection approach recently proposed by Baudat and Anouar, and combine it with the Linear Discriminant Analysis method (FVS-LDA). The resulting scheme is applied to the fifty-subject uncooled IR face database developed locally in an earlier project for comparison purposes. Identification and verification experiments are reported and compared to those obtained with the GDA implementation. Results indicate that similar recognition performances may be obtained when using well- tuned FVS parameters for a significantly reduced computational effort.

Lossless compression using the adaptive discrete cosine transform

This paper describes a technique using the adaptive discrete cosine transform for lossless waveform data compression. The technique is a variation on a two-stage lossless method that was developed by one of the authors. The earlier work employed an adaptive frequency sampling filter as part of an adaptive differential pulse code modulation-type (ADPCM) scheme to be applied to the output of a lossless successive difference operation to output a integer residual sequence for transmission, storage, or further encoding. The present work replaces the adaptive frequency sampling filter with an adaptive discrete cosine transform. The method remains lossless, unlike most ADPCM schemes.

Adaptive discrete cosine transform

The theory and performance of the adaptive discrete cosine transform filter is examined. The discrete cosine transform filter is a realization of an FIR filter as the cascade of an all-zero FIR filter with a bank of IIR digital resonators. Each bank has a single magnitude and a single phase. The result of such a realization is that each coefficient can be directly identified with an amplitude or a phase of the transfer function at a particular frequency when run as an adaptive filter. The update method examined is the LMS algorithm, with the coefficients being adapted as the magnitudes and phases at a full range of frequencies.