ICASSP99 Physics-Based Signal Processing

Physics-Based Signal Processing
Home Full List of Titles 1: Speech Processing CELP Coding Large Vocabulary Recognition Speech Analysis and Enhancement Acoustic Modeling I ASR Systems and Applications Topics in Speech Coding Speech Analysis Low Bit Rate Speech Coding I Robust Speech Recognition in Noisy Environments Speaker Recognition Acoustic Modeling II Speech Production and Synthesis Feature Extraction Robust Speech Recognition and Adaptation Low Bit Rate Speech Coding II Speech Understanding Language Modeling I 2: Speech Processing, Audio and Electroacoustics, and Neural Networks Acoustic Modeling III Lexical Issues/Search Speech Understanding and Systems Speech Analysis and Quantization Utterance Verification/Acoustic Modeling Language Modeling II Adaptation /Normalization Speech Enhancement Topics in Speaker and Language Recognition Echo Cancellation and Noise Control Coding Auditory Modeling, Hearing Aids and Applications of Signal Processing to Audio and Acoustics Spatial Audio Music Applications Application - Pattern Recognition & Speech Processing Theory & Neural Architecture Signal Separation Application - Image & Nonlinear Signal Processing 3: Signal Processing Theory & Methods I Filter Design and Structures Detection Wavelets Adaptive Filtering: Applications and Implementation Nonlinear Signals and Systems Time/Frequency and Time/Scale Analysis Signal Modeling and Representation Filterbank and Wavelet Applications Source and Signal Separation Filterbanks Emerging Applications and Fast Algorithms Frequency and Phase Estimation Spectral Analysis and Higher Order Statistics Signal Reconstruction Adaptive Filter Analysis Transforms and Statistical Estimation Markov and Bayesian Estimation and Classification 4: Signal Processing Theory & Methods II, Design and Implementation of Signal Processing Systems, Special Sessions, and Industry Technology Tracks System Identification, Equalization, and Noise Suppression Parameter Estimation Adaptive Filters: Algorithms and Performance DSP Development Tools VLSI Building Blocks DSP Architectures DSP System Design Education Recent Advances in Sampling Theory and Applications Steganography: Information Embedding, Digital Watermarking, and Data Hiding Speech Under Stress Physics-Based Signal Processing DSP Chips, Architectures and Implementations DSP Tools and Rapid Prototyping Communication Technologies Image and Video Technologies Automotive Applications / Industrial Signal Processing Speech and Audio Technologies Defense and Security Applications Biomedical Applications Voice and Media Processing Adaptive Interference Cancellation 5: Communications, Sensor Array and Multichannel Source Coding and Compression Compression and Modulation Channel Estimation and Equalization Blind Multiuser Communications Signal Processing for Communications I CDMA and Space-Time Processing Time-Varying Channels and Self-Recovering Receivers Signal Processing for Communications II Blind CDMA and Multi-Channel Equalization Multicarrier Communications Detection, Classification, Localization, and Tracking Radar and Sonar Signal Processing Array Processing: Direction Finding Array Processing Applications I Blind Identification, Separation, and Equalization Antenna Arrays for Communications Array Processing Applications II 6: Multimedia Signal Processing, Image and Multidimensional Signal Processing, Digital Signal Processing Education Multimedia Analysis and Retrieval Audio and Video Processing for Multimedia Applications Advanced Techniques in Multimedia Video Compression and Processing Image Coding Transform Techniques Restoration and Estimation Image Analysis Object Identification and Tracking Motion Estimation Medical Imaging Image and Multidimensional Signal Processing Applications I Segmentation Image and Multidimensional Signal Processing Applications II Facial Recognition and Analysis Digital Signal Processing Education Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	Interpolation And The Chirp Transform: DSP Meets Optics Authors: David C. Munson Jr., Orhan Arikan, Page (NA) Paper number 3025 Abstract: This paper considers the problem of interpolating a signal from one uniformly-spaced grid to another, where the grid spacings may be related by an arbitrary, irrational factor. Noting that interpolation is the digital equivalent of magnification, we begin by reviewing optical systems for magnification and "computation" of the chirp Fourier transform. This route suggests several analog schemes for magnification, which can be discretized to produce algorithms for interpolation. We then derive one of these algorithms from first principles, using a digital-signal-processing perspective. The result is an important, but forgotten, algorithm for interpolation first suggested as an application of the chirp-z transform by Rabiner, Schafer, and Rader. Unlike the earlier derivation, our approach is direct -- we do not make use of Bluestein's trick of completing the square. In addition, our approach identifies parameters under user control that can be optimized for best performance. IC993025.PDF (From Author) IC993025.PDF (Rasterized) TOP Estimation of Aircraft Altitude and Altitude Rate with Over-the-Horizon Radar Authors: Michael Papazoglou, Duke University (U.K.) Jeffrey L Krolik, Duke University (U.K.) Page (NA) Paper number 3003 Abstract: In previous work, a matched-field estimate of aircraft altitude which uses multiple over-the-horizon radar dwells was presented. This approach exploited the altitude dependent structure of the micro-multipath rays which result from reflections local to the aircraft. While it was shown that the multi-dwell matched-field estimate is able to accurately estimate altitude while using typical radar parameters, the estimate was derived assuming that the aircraft altitude is constant for the duration of the track. In this paper, a matched-field method for jointly estimating altitude and altitude rate is presented which extends the micro-multipath model to include effects of constant altitude rate on the micro-multipath Doppler frequencies. Simulation results illustrate that altitude and altitude rate can be jointly estimated while achieving an altitude estimation accuracy of +/-2500 feet using 10 radar dwells. IC993003.PDF (From Author) IC993003.PDF (Rasterized) TOP Tumor Treatment By Time-Reversal Acoustics Authors: M. Porter, P. Roux, H. Song, W. Kuperman, Page (NA) Paper number 3026 Abstract: There has been a great deal of work in ocean science over the last 10 years in using acoustic channel models in the signal processing. The goal has been to compensate for the "barbershop" effect in which a SONAR system confuses the true source with its reflections in the acoustic mirrors formed by the ocean surface and bottom. Separately, in medicine, hyperthermia acoustic beams are trained on tumors with the goal of reversing their growth. Our interest is the question of what (if any) lessons from the SONAR experience can be applied to hyperthermia. IC993026.PDF (From Author) IC993026.PDF (Rasterized) TOP Source And Environmental Parameter Estimation Using Electromagnetic Matched Field Processing Authors: Peter Gerstoft, Donald F. Gingras, Page (NA) Paper number 3027 Abstract: Modern signal and array processing methods now incorporate the physics of wave propagation as an integral part of the processing. Matched field processing (MFP) refers to signal and array processing techniques in which, rather than a plane wave arrival model, complex-valued (amplitude and phase) field predictions for propagating signals are used. Matched field processing has been successfully applied in ocean acoustics and electromagnetics. In this paper, source localization performance via MFP is examined in the electromagnetics domain. Specifically, the impact of uncertainty in the a priori knowledge of the underlying physical parameters, atmospheric refractivity vs height, on source localization performance is examined. IC993027.PDF (From Author) IC993027.PDF (Rasterized) TOP Multi-Aspect Target Identification With Wave-Based Matching Pursuits And Continuous Hidden Markov Models Authors: Paul Runkle, Department of Electrical and Computer Engineering, Duke University (U.K.) Lawrence Carin, Department of Electrical and Computer Engineering, Duke University (U.K.) Page (NA) Paper number 3001 Abstract: A wave-based matching-pursuits algorithm is used to parse multi-aspect time-domain backscattering data into its underlying wavefront-resonance constituents, or features. Consequently, the N multi-aspect waveforms under test are mapped into N feature vectors. Target identification is effected by fusing these N vectors in a maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). Algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets. IC993001.PDF (From Author) IC993001.PDF (Rasterized) TOP