Showing papers on "Multidimensional signal processing published in 1993"

Array Signal Processing: Concepts and Techniques

Abstract: 1. Introduction 2. Signals in Space and Time 3. Apertures and Arrays 4. Conventional Array Processing 5. Detection Theory 6. Estimation Theory 7. Adaptive Array Processing 8. Tracking Appendices References List of Symbols Index.

Higher-Order Spectra Analysis: A Nonlinear Signal Processing Framework

Abstract: 1. Definitions and Properties of Cumulants and Cumulant Spectra. 2. Polyspectra of Deterministic Signals. 3. Conventional Methods for the Estimation of Polyspectra. 4. Nonminimum Phase Signal Reconstruction. 5. Polycepstra. 6. Parameter Estimation. 7. Adaptive Signal Processing. 8. Detection and Characterization of Nonlinearities.

Digital Signal Processing: A Practical Approach

Abstract: From the Publisher: Now in its second edition, Digital Signal Processing offers modern coverage of the fundamentals, implementation and applications of digital signal processing techniques from a practical point of view. The past ten years have seen a significant growth in DSP applications throughout all areas of technology—and this growth is expected well into the next millennium. This book covers a number of DSP techniques that are of particular relevance to industry such as adaptive filtering and multirate processing. The emphasis throughout the book is on the practical aspects of DSP. Chapter topics include analogue I/O interface for real-time DSP systems, discrete transform, the z-transform and its applications in signal processing, correlation and convolution, a framework for digital filter design, finite impulse response (FIR) filter design, design of infinite impulse response (IIR) digital filters, multirate digital signal processing, adaptive digital filters, spectrum estimation and analysis, general and special purpose hardware for DSP, and finite word length effects in fixed point DSP systems and solutions. A reference of DSP techniques for industry professionals.

Signal processing with fractional lower order moments: stable processes and their applications

Abstract: Non-Gaussian statistical signal processing is important when signals and/or noise deviate from the ideal Gaussian model. Stable distributions are among the most important non-Gaussian models. They share defining characteristics with the Gaussian distribution, such as the stability property and central limit theorems, and in fact include the Gaussian distribution as a limiting case. To help engineers better understand the stable models and develop methodologies for their applications in signal processing. A tutorial review of the basic characteristics of stable distributions and stable signal processing is presented. The emphasis is on the differences and similarities between stable signal processing methods based on fractional lower-order moments and Gaussian signal processing methods based on second-order moments. >

Signal processing with higher-order spectra

Abstract: The strengths and limitations of correlation-based signal processing methods are discussed. The definitions, properties, and computation of higher-order statistics and spectra, with emphasis on the bispectrum and trispectrum are presented. Parametric and nonparametric expressions for polyspectra of linear and nonlinear processes are described. The applications of higher-order spectra in signal processing are discussed. >

The Electrical Engineering Handbook

Abstract: Part 1 Circuits: passive components voltage and current sources linear circuit analysis passive signal processing nonlinear circuits Laplace transform state variables, W-K Chen the z-transform, RC Dorf and Z Wan t-pi equivalent networks transfer functions of filters frequency response stability analysis Part 2 Signal processing: digital signal processing speech signal processing spectral estimation and modelling multidimensional signal processing VLSI for signal processing acoustic signal processing neural networks Part 3 Electronics: semiconductors semiconductor manufacturing integrated circuits surface mount technologies operational amplifiers amplifiers computer-aided circuit simulation active filters power electronics optoelectrics D/A and A/D converters thermal management of electronics Part 4 Electromagnetics: electromagnetic fields magnetism and magnetic fields wave propagation antennas microwave devices compatibility lightwave solid state circuits three-dimensional analysis computational electromagnetics Part 5 Electrical effects and devices: electroacoustic devices surface acoustic wave filters ultrasound ferroelectric and piezoelectric materials piezoresistivity the Hall effect superconductivity pyroelectric materials and devices dielectrics and insulators sensors magneto-optics smart materials Part 6 Energy: conventional power generation distributed power generation transmission power transformers energy distribution electrical machines energy management Part 7 Communications: broadcasting digital communication optical communication networks B-ISDN information theory satellites and aerospace personal and office phase locked loop telemetry computer-aided design and analysis of communication systems Part 8 Digital devices: logic elements memory devices logical devices microprocessors displays data acquisition testing Part 9 Computer engineering: organization programming memory systems input and output software engineering computer graphics computer networks fault tolerance knowledge engineering parallel processors operating systems computer security computer reliability Part 10 Systems: control systems robotics aerospace systems command, control and communications (c3) industrial systems man-machine systems vehicular systems industrial illuminating systems instruments navigation systems Part 11 Biomedical systems bioelectricity biomedical sensors bioelectronics and instruments medical imaging rehabilitation engineering biocomputing safety and risk control issues

Higher-order spectral analysis

Abstract: Absltacl The purpose of this keynote lecture of the Signal Analysis Track is U) present the motivation behind he use of higher-order spectra (HOS) in signal processing as well as the definitions, properties, and biomedica1 signal processing applications of higher-order spectra. This lecture will also emphasize the state of science of the higher-order spectra field, especially as it applies to non-stadonary signal analysis.

Multi-channel signal separation by decorrelation

Abstract: Identification of an unknown system and recovery of the input signals from observations of the outputs of an unknown multiple-input, multiple-output linear system are considered. Attention is focused on the two-channel case, in which the outputs of a 2*2 linear time invariant system are observed. The approach consists of reconstructing the input signals by assuming that they are statistically uncorrelated and imposing this constraint on the signal estimates. In order to restrict the set of solutions, additional information on the true signal generation and/or on the form of the coupling systems is incorporated. Specific algorithms are developed and tested. As a special case, these algorithms suggest a potentially interesting modification of Widrow's (1975) least-squares method for noise cancellation, where the reference signal contains a component of the desired signal. >

Vector directional filters-a new class of multichannel image processing filters

Abstract: Vector directional filters (VDF) for multichannel image processing are introduced and studied. These filters separate the processing of vector-valued signals into directional processing and magnitude processing. This provides a link between single-channel image processing where only magnitude processing is essentially performed, and multichannel image processing where both the direction and the magnitude of the image vectors play an important role in the resulting (processed) image. VDF find applications in satellite image data processing, color image processing, and multispectral biomedical image processing. Results are presented here for the case of color images, as an important example of multichannel image processing. It is shown that VDF can achieve very good filtering results for various noise source models. >

Optimal and Adaptive Signal Processing

Abstract: Designed for undergraduate and postgraduate students in electrical engineering, as well as for practising scientists and engineers, this text covers the theory of optimal and adaptive signal processing using examples and computer simulations drawn from a wide range of applications including speech and audio communications, reflection seismology and sonar systems. The material is presented without a heavy reliance on mathematics, and focuses on one-dimensional and array procession results, as well as a wide range of adaptive filtre algorithms and implementations. Topics discussed include random signals and optimal processing, adaptive signal processing with the LMS algorithm, applications of adaptive filtering, algorithm and structures for adaptive filtering, spectral analysis and array signal processing.

Oversampling in the Gabor scheme

Abstract: A method for calculating the coefficients of the Gabor expansion in the case of oversampling is presented. The method is based on the concept of frames and utilizes the Zak transform. As such, the Zak transform highlights the meaning and importance of frames in the context of oversampling, and other aspects of signal representation by means of nonorthogonal bases. >

A general framework for frequency domain multi-channel signal processing

Abstract: The authors provide a general framework for performing processing of stationary multichannel (MC) signals that is linear shift-invariant within channel and shift varying across channels. Emphasis is given to the restoration of degraded signals. It is shown that, by utilizing the special structure of semiblock circulant and block diagonal matrices, MC signal processing can be easily carried out in the frequency domain. The generalization of many frequency-domain single-channel (SC) signal processing techniques to the MC case is presented. It is shown that in MC signal processing each frequency component of a signal and system is presented, respectively, by a small vector and a matrix (of size equal to the number of channels), while in SC signal processing each frequency component in both cases is a scalar. >

Sound analysis and resynthesis using correlograms

Abstract: A system for reconstructing a signal waveform from a correlogram is based upon the recognition that the information in each channel of the correlogram is equivalent to the magnitude of the Fourier transform of a signal. By estimating a signal on the basis of its Short-Time Fourier Transform Magnitude, each channel of information from a cochlear model can be reconstructed. Once this information is retrieved, a signal waveform can be resynthesized through inversion of the cochlear model. The process for reconstructing the cochlear model data can be optimized with the use of techniques for improving the initial estimate of the signal from the magnitude of its Fourier Transform, and by employing information that is known apriori about the signal during the estimation process, such as the characteristics of sound signals.

Advanced Digital Signal Processing: Theory and Applications

Abstract: Discrete-Time Signals and Systems Frequency-Domain Signal and System Analysis The Discrete Fourier Transform Digital Filter Design Stochastic Processes State Variable Analysis Multirate Signal Processing Wiener Filtering Adaptive Filtering: Gradient Descent Algorithms Adaptive Filtering: Recursive Least Squares Power Spectrum Estimation Appendix: Mathematical Review References

Signal Processing: A Mathematical Approach

Abstract: "Signal Processing" uses a wide variety of mathematical tools. This book provides the necessary mathematical background and tools to understand and employ signal processing techniques in an applied environment. The author addresses Fourier series and transforms in one and several variables, applications to acoustic and electromagnetic propagation models, transmission and emission tomography and image reconstruction, optimization techniques, high resolution methods, and more. This book serves as a comprehensive reference for mathematical methods in signal processing, and can be used as a text for undergraduate courses in applied mathematics and electrical engineering.

Mathematics of Multidimensional Fourier Transform Algorithms

Abstract: The main emphasis of this book is the development of algorithms for processing multi-dimensional digital signals, particularly algorithms for multi-dimensional Fourier transforms, in a form that is convenient for writing highly efficient code on a variety of vector and parallel computers. The rapidly increasing power of computing chips, the increased availability of vector and array processors, and the increased size of data sets to be analyzed make code-writing a difficult task. By emphasizing the unified basis for the various approaches to multidimensional Fourier transforms, this book also clarifies how to exploit the differences in optimizing implementations.

Step-scanning interferometer with digital signal processing

Abstract: A novel step-scan FT-IR spectrometer incorporating a digital signal processor for demodulation of the detector signal is described. The potential advantages of this method of signal processing are discussed and illustrated. The instrument is based on a commercial cube-corner interferometer which has been modified by replacement of the drive motor with a stepper motor-micrometer and piezoelectric transducer combination. The interferometer retardation is feedback controlled by a 48650 personal computer, which also controls the digital signal processor and collects spectral data. More than one phase modulation frequency can be imposed simultaneously, allowing for a multiplex advantage in photoacoustic depth profiling. Digital signal processing allows for simultaneous demodulation of multiple frequencies which would normally require several lock-in amplifiers. Data that illustrate the feasibility of these concepts are presented. The suitability of this instrument for double-modulation step-scan FT-IR measurements such as polymer stretching and electrochemically modulated step-scan FT-IR is also discussed.

The role of integer matrices in multidimensional multirate systems

Abstract: Various theoretical issues in multidimensional (m-D) multirate signal processing are formulated and solved. In the problems considered, the decimation matrix and the expansion matrix are nondiagonal, so that extensions of 1-D results are nontrivial. The m-D polyphase implementation technique for rational sampling rate alterations, the perfect reconstruction properties for the m-D delay-chain systems, and the periodicity matrices of decimated m-D signals (both deterministic and statistical) are treated. The discussions are based on several key properties of integer matrices, including greatest common divisors and least common multiples. These properties are reviewed. >

Modeling multidimensional data and control flow

Abstract: Multidimensional (M-D) signal processing is a key component of most real-time signal and data processing VLSI systems in industry. Handling the M-D nature of data in an efficient way is crucial to arrive at acceptable system implementations. This aspect of design has often been ignored up to now in high-level synthesis. In this paper, a polyhedral based model is presented for the linear, piecewise linear and data dependent signal indexing as occurring in practical M-D signal processing applications. The model features a mathematical description of dependencies between individual operations and signal instances of M-D signals for all algorithms that contain signal indexing specified by a mixture of indexing by iterators and M-D signals embedded in the data flow. The exact modeling of M-D signal indexing is especially essential for deriving alternative control flow structures for a given data flow specification. Exploration of various control flow structures allows one to arrive at an efficient large-scale memory organization during high level synthesis of architectures, both in terms of storage locations and access order. >

Signal Processing and Its Applications

Abstract: Signal processing for linear instrumental systems with noise - a general theory with illustrations from optical imaging and light scattering problems, M. Bertero and E.R. Pike boundary implication results in parameter space, N.K. Bose sampling of bandlimited signals - fundamental results and some extensions, J.L. Brown Jr localization of sources in a sector - algorithms and statistical analysis, K. Buckley and X-L Xu the signal subspace direction-of-arrival algorithm, J.A. Cadzow digital differentiators, S.C. Dutta Roy and B. Kumar VLSI in signal processing, A. Ghouse constrained beam forming and adaptive algorithm, L.C. Godara bispectral speckle interferometry to reconstruct extended objects from turbulence-degraded telescope images, D.M. Goodman et al multi-dimensional signal processing, K. Hirano on the assessment of visual communication, F.O. Huck et al VLSI implementations of number theoretic concepts with applications in signal processing, G.A. Jullien et al decision-level neural net sensor fusion, R.Y. Levine and T.S. Khuon statistical algorithms for non-causal Gauss Markov fields, J.M.F. Moura and N. Balram subspace methods for directions-of-arrival estimation, A. Paulraj et al closed form solution to the estimates of directions of arrival using data from an array of sensors, C.R. Rao and B. Zhou high-resolution direction-finding, S.V. Schell and W.A. Gardner multiscale signal processing techniques - a review, A.H. Tewfik et al sampling theorens and wavelets, G.G. Walter image and video coding research, J.W. Woods fast algorithms for structured matrices in signal processing, A.E. Yagle.

A VLSI based MIMD architecture of a multiprocessor system for real-time video processing applications

Abstract: A MIMD based multiprocessor architecture for real-time video processing applications consisting of identical bus connected processing elements has been developed. Each processing element contains a RISC processor for controlling and data-dependent tasks and a Low Level Coprocessor for fast processing of convolution-type video processing tasks. To achieve efficient parallel processing of video input signals, the architecture supports independent processing of overlapping image segments. Running at a clock rate of 40 MHz, a single processing element provides a peak performance of 640 Mega arithmetic operations per second (MOPS). For the real-time processing of basic video processing tasks like 3×3 FIR-filter, 8×8 2D-DCT and motion estimation, a single processing element provides a sufficient computational rate for video signals with Common Intermediate Format (CIF) at a frame rate up to 30 Hz. For hybrid source coding of CIF video signals at a frame rate of 30 Hz a multiprocessor system consisting of six processing elements is required. A linear speedup of the multiprocessor system compared to a single processing element is achieved. A VLSI implementation of a processing element in 0.8 µm CMOS technology is under development.

Image processor for processing high definition image signal and standard image signal and disk apparatus therefor

Abstract: An image signal processor for processing both high definition image signal with a high resolution and standard image signal with a resolution lower than that of the high definition image signal in which the high definition image signal is decomposed into a plurality of subband signals and the standard image signal, when input, is converted into a subband signal having a low subband. Each subband signal is encoded and recorded in a predetermined area of a disk. The subband signal recorded is read out and decoded for each subband and the low subband signal is reproduced as the standard image signal by converting the width of the low subband signal into the width of the standard image signal.

Signal processing system and processing method

Abstract: PURPOSE: To attain high-speed signal processing by modulating an inputted analog signal to form a 1-bit high-speed sampling signal in which quantization noise is concentrated to high frequencies when a spectrum of quantization noises is controlled while taking a human audible sense into account so as to input the high-speed sampling signal to the system where an original analog signal is decoded. CONSTITUTION: A 1-bit high speed sampling system is made up of an emphasis section 1, a ΣΔ modulation section 2, a signal processing section 3, a decoding section 4 and a de-emphasis section 5. The emphasis section 1 and the de- emphasis section 5 are used to reduce a high frequency component of an acoustic signal to reduce quantization noise, the modulation section 2 is used to execute 2nd - 7th-degree of ΣΔ modulation, the processing section 3 is a section to record and transmit an original signal corresponding to satellite communication, and a decoding section 4 is a conventional D/A converter. Through the constitution above, in the case of 4-channels, for example, 1-bit A/D converter sections 2a-2d are used to convert the inputted signal into digital signals Sa-Sd and they are fed to 1-bit D/A converter sections 4a-4d via the processing section 3, in which the signals are decoded. COPYRIGHT: (C)1994,JPO&Japio

A new method for solving broadband matching problems

Abstract: Keywords: Non-Linear Signal Processing Reference LANOS-CONF-1993-012 Record created on 2004-12-03, modified on 2016-08-08

Multisystem adaptable type signal processing and recording/reproducing apparatus

Abstract: When the low bit rate coding is effected in a recording system 1A, a signal is coded and recorded in a hierarchical structure. On the reproduction side, the type of a recorded signal (the low bit rate coded signal of the hierarchical structure, non-hierarchical structure, conventional analog recording or the like) is determined by a determination system 1E and the operation modes of a digital signal processing system 1G and an analog signal processing system 1F are set. The analog signal processing system processes a reproduced signal when it is analog signal. The digital signal processing system processes a reproduced signal when it is a digital signal or when a reproduced output from the analog signal processing system is processed in a digital manner. Further, hierarchical decoding G30 and inverse converting G40 are provided, a signal processed in the hierarchical structure can be restored to an original form, and the restored reproduced signal can be processed by use of system conversion G60 and converted to a desired system and output. As a result, various types of input video signals can be recorded in a preset form and various forms of recorded video signals can be reproduced and output in a desired form. Further, an analog recorded signal can be reproduced and output after converted to a desired form.

Signal processing incorporating signal tracking, estimation,and removal processes using a maximum a posteriori algorithm, and sequential signal detection

Abstract: A system and method for automating signal tracking, estimation of signal parameters, and extraction of signals from sonar data to detect weaker signals. A maximum a posteriori (MAP) algorithm processing provides a track output of the signal which is used as a guide or template to provide optimal spectral integration on an unstable or frequency varying line. The present invention includes track integration, parameter estimation, signal track normalization, and sequential signal detection. The present invention partitions the input band into frequency subwindows. For each subwindow, the strongest signal is tracked, its parameters are estimated, and then the signal is normalized (removed) from the subwindow. This is repeated until the entire subwindow set is processed. Then the subwindows, now with their strongest signals removed, are recombined to form one input band. This aggregated procedure represents one processing pass. In the next pass, the entire above procedure is repeated with either the same or new subwindow boundaries. This continues until a predetermined number of passes is completed. Sequential signal detection is provided from one data frame to the next, a problem that is beyond the capability of conventional systems and methods for tracking frequency lines of unknown frequency modulation and amplitude.

Coherent signal power detector using higher-order statistics

Abstract: A signal detection system divides a data sampling run into blocks and perms a fast Fourier transform on each block, sorting results by frequency. Combinations of results of the fast Fourier transform corresponding to each frequency are processed to derive a test statistic which is unbiased by Gaussian noise while including such combinations of results of the fast Fourier transform which would be redundant over other combinations. Information concerning the frequency behavior of the signal derived in the course of detection, is accomplished with increased sensitivity.

Cellular logic array processing techniques for high-throughput image processing systems

Abstract: This paper describes certain image processing techniques within the framework ofcellular automata andnormal algorithms for high-throughput data processing. The central idea on which these techniques have been developed is that a digital image can be treated as acellular automaton configuration, and an image processing operation, as anevolution of the automaton due to an updating rule that describes a relational attribute among the pixel values in a specific neighbourhood. Filtering operations on digital images, like that of thinning, edge detection segmentation, erosion and dilation are modelled and realized using cellular automata.

Non-uniform PRI pulse-Doppler radar

Abstract: Nonuniform pulse repetition interval (PRI) pulse-Doppler waveforms are processed by replacing the fast Fourier transform algorithm in standard pulse-Doppler processors with a more general discrete Fourier transform. Clutter rejection is a problem because the well-understood techniques of amplitude windowing are not available. However, processing weights with controllable properties can be synthesized by other means. Several applications most amenable to the strengths and limitations of nonuniform PRI waveforms are described.

Multisystem signal processing and recording/reproducing apparatus

Abstract: When the low bit rate coding is effected in a recording system 1A, a signal is coded and recorded in a hierarchical structure. On the reproduction side, the type of a recorded signal (the low bit rate coded signal of the hierarchical structure, non-hierarchical structure, conventional analog recording or the like) is determined by a determination system 1E and the operation modes of a digital signal processing system 1G and an analog signal processing system 1F are set. The analog signal processing system processes a reproduced signal when it is analog signal. The digital signal processing system processes a reproduced signal when it is a digital signal or when a reproduced output from the analog signal processing system is processed in a digital manner. Further, hierarchical decoding means G30 and inverse converting means G40 are provided, a signal processed in the hierarchical structure can be restored to an original form, and the restored reproduced signal can be processed by use of system conversion means G60 and converted to a desired system and output. As a result, various types of input video signals can be recorded in a preset form and various forms of recorded video signals can be reproduced and output in a desired form. Further, an analog recorded signal can be reproduced and output after converted to a desired form.