Time-Evolution of the Power Spectrum of the Black Hole X-ray Nova XTE J1550-564

We have studied the time evolution of the power spectrum of XTE J1550-564, using X-ray luminosity time series data obtained by the Rossi X-Ray Timing Explorer satellite. A number of important practical fundamental issues arise in the analysis of these data, including dealing with time-tagged event data, removal of noise from a highly non-stationary signal, and comparison of different time-frequency distributions. We present two new methods to understand the time frequency variations, and compare them to the dynamic power spectrum of Homan et al. All of the approaches provide evidence that the QPO frequency varies in a systematic way during the time evolution of the signal.Comment: 4 pages, 3 figures; 2001 IEEE - EURASIP Workshop on Nonlinear Signal and Image Processing (June 3-6, 2001), and to appear in the proceeding

Nonlinear Signal and Image Processing—a special issue in honour of Giovanni L. Sicuranza on his seventy-fifth birthday

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Interface Roughness Effects in Ultra-Thin Tunneling Oxides

Advanced MOSFET for ULSI and novel silicon-based devices require the use of ultrathin tunneling oxides where non-uniformity is often present. We report on our theoretical study of how tunneling properties of ultra-thin oxides are affected by roughness at the silicon/oxide interface. The effect of rough interfacial topography is accounted for by using the Planar Supercell Stack Method (PSSM) which can accurately and efficiently compute scattering properties of 3D supercell structures. Our results indicate that while interface roughness effects can be substantial in the direct tunneling regime, they are less important in the Fowler-Nordheim regime

Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.Comment: Accepted for publication at IEEE Journal of Biomedical and Health Informatic

Biologically-Inspired Acoustic Wear Analysis

We report on a novel method of acoustic wear analysis using spectral classification based on a model of mammalian audition. This approach uses biologically-inspired pre-processing filters that give a multi- resolution representation of the sound timbre. A Tree Structured Vector Quantizer (TSVQ) is used to create a classification tree based on the wear labels. We have obtained encouraging results for tools of different diameters, cutting different materials. Trees trained on one kind of data seem to generalize well to new data sets. The research and scientific content in this material have been accepted for the Nonlinear Signal and Image Processing (NSIP) 2001 Conference at Baltimore, MD, 2001 </Center

Multivariate texture discrimination using a principal geodesic classifier

A new texture discrimination method is presented for classification and retrieval of colored textures represented in the wavelet domain. The interband correlation structure is modeled by multivariate probability models which constitute a Riemannian manifold. The presented method considers the shape of the class on the manifold by determining the principal geodesic of each class. The method, which we call principal geodesic classification, then determines the shortest distance from a test texture to the principal geodesic of each class. We use the Rao geodesic distance (GD) for calculating distances on the manifold. We compare the performance of the proposed method with distance-to-centroid and knearest neighbor classifiers and of the GD with the Euclidean distance. The principal geodesic classifier coupled with the GD yields better results, indicating the usefulness of effectively and concisely quantifying the variability of the classes in the probabilistic feature space

Editorial

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