Showing papers on "Multidimensional signal processing published in 1998"

Computational signal processing with wavelets

Abstract: This unique resource examines the conceptual, computational, and practical aspects of applied signal processing using wavelets. With this book, readers will understand and be able to use the power and utility of new wavelet methods in science and engineering problems and analysis. The text is written in a clear, accessible style avoiding unnecessary abstractions and details. From a computational perspective, wavelet signal processing algorithms are presented and applied to signal compression, noise suppression, and signal identification. Numerical illustrations of these computational techniques are further provided with interactive software (MATLAB code) is available on the world wide web. Topics and Features: * Continuous wavelet and Gabor transforms * Frame-based theory of discretization and reconstruction of analog signals is developed * New and efficient "overcomplete" wavelet transform is introduced and applied * Numerical illustrations with an object-oriented computational perspective using the Wavelet Signal Processing Workstation (MATLAB code) available This book is an excellent resource for information and computational tools needed to use wavelets in many types of signal processing problems. Graduates, professionals, and practitioners in engineering, computer science, geophysics, and applied mathematics will benefit from using the book and software tools.

Fixed-point optimization utility for C and C++ based digital signal processing programs

Abstract: Fixed-point optimization utility software is developed that can aid scaling and wordlength determination of digital signal processing algorithms written in C or C++. This utility consists of two programs: the range estimator and the fixed-point simulator. The former estimates the ranges of floating-point variables for purposes of automatic scaling, and the latter translates floating-point programs into fixed-point equivalents to evaluate the fixed-point performance by simulation. By exploiting the operator overloading characteristics of C++, the range estimation and the fixed-point simulation can be conducted by simply modifying the variable declaration of the original program. This utility is easily applicable to nearly all types of digital signal processing programs including nonlinear, time-varying, multirate, and multidimensional signal processing algorithms. In addition, this software can be used to compare the fixed-point characteristics of different implementation architectures. An optimization example for an 8/spl times/8 inverse discrete cosine transform (IDCT) architecture that conforms to the IEEE standard specifications is presented. The optimized results require 8% fewer gates when compared with the previous best implementation.

Random Processes for Image and Signal Processing

Abstract: Probability theory; random processes; canonical representation; optimal filtering; random models.

Nonlinear and nonstationary signal processing

Abstract: Presents a brief discussion of the need for and evolution to nonlinear and nonstationary signal processing. Applications where these are useful are mentioned.

Signal Analysis and Prediction

Abstract: This book surveys the latest information concerning methods of time-frequency and time-scale signal analysis, higher order statistics in signal processing, selected methods of signal identification, nonlinear modeling by neural networks, fuzzy-rule based systems, and methods of signal prediction and noise rejection. Many chapters include Matlab examples and visualizations.

Signal processing with the sparseness constraint

Abstract: An overview is given of the role of the sparseness constraint in signal processing problems. It is shown that this is a fundamental problem deserving of attention. This is illustrated by describing several applications where sparseness of solution is desired. Lastly, a review is given of the algorithms that are currently available for computing sparse solutions.

Highlights of statistical signal and array processing

Abstract: The Statistical Signal and Array Processing Technical Committee (SSAP-TC) deals with signals that are random and processes an array of signals simultaneously. The field of SSAP represents both solid theory and practical applications. Starting with research in spectrum estimation and statistical modeling, study in this field is always full of elegant mathematical tools such as statistical analysis and matrix theory. The area of statistical signal processing expands into estimation and detection algorithms, time-frequency domain analysis, system identification, and channel modeling and equalization. The area of array signal processing also extends into multichannel filtering, source localization and separation, and so on. This article represents an endeavor by the members of the SSAT-TC to review all the significant developments in the field of SSAP. To provide readers with pointers for further study of the field, this article includes a very impressive bibliography-close to 500 references are cited. This is just one of the indications that the field of statistical signals has been an extremely active one in the signal processing community. The article also introduces the recent reorganization of three technical committees of the Signal Processing Society.

The Nonuniform Discrete Fourier Transform and Its Applications in Signal Processing

Abstract: 1. Introduction. 2. The Nonuniform Discrete Fourier Transform. 3. 1-D Fir Filter Design Using the NDFT. 4. 2-D Fir Filter Design Using the NDFT. 5. Antenna Pattern Synthesis with Prescribed Nulls. 6. Dual-Tone Multi-Frequency Signal Decoding. 7. Conclusions. References. Index.

Apparatus and methods for proportional audio compression and frequency shifting

Abstract: Apparatus and methods for audio compression and frequency shifting retain the spectral shape of an audio input signal while compressing and shifting its frequency. The fast Fourier transform of the input signal is generated, to allow processing in the frequency domain. The input audio signal is divided into small time segments, and each is subjected to frequency analysis. Frequency processing includes compression and optional frequency shifting. The inverse fast Fourier transform function is performed on the compressed and frequency shifted spectrum, to compose an output audio signal, equal in duration to the original signal. The output signal is then provided to the listener with appropriate amplification to insure audible speech across the usable frequency range.

Apparatus and methods for image and signal processing

Abstract: An apparatus and methods for efficiently processing signal and image data are described. The invention provides a representation of signal and image data that can be used as a figure of merit to compare and characterize different signal processing techniques. The representation can be used as an intermediate result that is may be subjected to further processing, and/or may be used as a control element for processing operations. As a provider of an intermediate result, the invention can be used as a step in processes for the transduction, storage, enhancement, refinement, feature extraction, compression, coding, transmission, or display of image, audio and other data. The invention improves manipulation of data from intrinsically unpredictable, or partially random sources. The result is a concise coding of the data in a form permitting robust and efficient data processing, a reduction in storage demands, and restoration of original data with minimal error and degradation. The invention provides a system of coding source data derived from the external environment, whether noise-free or contaminated by random components, and regardless of whether the data are represented in its natural state, such as photons, or have been pre-processed.

LabVIEW signal processing

Abstract: Background signal generation signal processing control systems windowing measurement VIs digital filtering curve fitting linear algebra probability and statistics digital filter design toolkit G math toolkit.

Schaum's Outline of Theory and Problems of Digital Signal Processing

Abstract: Signals and Systems. Fourier Analysis. Sampling. The Z-Transform. Transform Analysis of Systems. The DFT. The Fast Fourier Transform. Implementation of Discrete-Time Systems. Filter Design.

Application of an optimal look-up table to sensor data processing

Abstract: This paper presents a new method for calculating a look-up table to evaluate the measurand when a sensor pair is considered. A least mean squares technique has been used to calculate look-up table entries directly from sparse calibration data. The algorithm has been implemented by using the eight-bit MC68HC11 microcontroller; it reduces to a half the maximum value of the approximation error referred to the full scale if compared with the classical methods. A resistive network with end point linearity referred to the full scale of 15% has been used to emulate a sensor pair and the two input signals have been sampled with an 8-bit A/D converter.

Method and apparatus for joint space-time array signal processing

Abstract: Processing methods and associated hardware architectures for the spatio-temporal processing of phased array sensor signals are disclosed. The processing method employs a joint spatio-temporal domain processing approach. The disclosed method is an optimal estimation scheme which exhibits significantly reduced computational burden. Suppression of interference sources at angles-of-arrival other than the desired signal is achieved implicitly. The associated system architecture provides not only the ability to achieve good joint angle-Doppler estimates, but offers the ability to easily trade the relative performance in each domain. The disclosed approach emphasizes the use of multiple hypothesis testing with spatio-temporal whitening filters.

Obtaining order in a world of chaos [signal processing]

Abstract: Measurements of a physical or biological system result in a time series, s(t)=s(t/sub 0/+n/spl tau//sub s/)=s(n) sampled at intervals of /spl tau//sub s/ and initiated at t/sub 0/. When a signal can be represented as a superposition of sine waves with different amplitudes, its characteristics can be adequately described by Fourier coefficients of amplitude and phase. In these circumstances, linear and Fourier based methods for extracting information from the signal are appropriate and powerful. However, the signal may be generated by a nonlinear system. The waveform can be irregular and continuous and broadband in the frequency domain. The signal is noise-like, but is deterministic and may be chaotic. More information than the Fourier coefficients is required to describe the signal. This article describes methods for distinguishing chaotic signals from noise, and how to utilize the properties of a chaotic signal for classification, prediction, and control.

The past, present, and future of image and multidimensional signal processing

Abstract: The field of image and multidimensional signal processing began as a field of strong theoretical framework based on mathematics, statistics, and physics. Later, with advances in computing, memory, and image-sensing technology, techniques developed for image enhancement, still and moving image compression, image understanding gave this field a solid base of practical applications. Furthermore, the exploding growth of the Internet and the ubiquity of images and video, the field of image and multidimensional signal processing is becoming more and more exciting. Topics covered in the article include: multidimensional signal-processing theory, image acquisition, image transforms, image modeling, image enhancement and restoration, image and video analysis, processing, coding, hardware and software implementation issues, and computed imaging.

Sampling, Aliasing, and Data Fidelity for Electronic Imaging Systems, Communications, and Data Acquisition

Abstract: Fourier Transform Linear System Theory Sampling Sampling Devices Resampling Reconstruction Reconstructed Signal Appearance System Analysis System Resolution Image Quality Metrics.

Signal processor with local signal behavior

Abstract: A new signal processing method and a signal processing engine which can achieve extremely fast responsiveness to instantaneous changes in the behavior of the signal, and maintain the accuracy of standard harmonic methods. The signal processing engine unifies Nyquist's theorem and Taylor's theorem by means of polynomial approximations using linear operators, e.g. differential and integral operators. The signal processing engine samples the signal at a rate which is n times the band limit of the signal, where n is greater than 2, i.e. greater than the Nyquist rate, produces a digital representation of the sampled signal, and calculates the outputs of linear operators applied to polynomial approximations of the sampled signal. A switch mode power amplifier which incorporates the signal processing method and engine of the overcomes shortcomings of existing switching amplifiers, e.g. class 'D' amplifiers. These shortcomings include: poor handling of highly reactive complex loads (e.g., speakers), usually requiring a duty cycle or feed-back adjustment with the change of the load; poor performance in the upper part of the bandwidth, including numerous switching artifacts; and high distortion, especially in the upper part of the spectrum. These shortcomings are all overcome using the local signal behavior signal processing method and engine of the invention.

Comparison of reduced-rank signal processing techniques

Abstract: This paper compares several reduced-rank signal processing algorithms for adaptive sensor array processing. The comparisons presented here use Monte Carlo analysis to evaluate the algorithmic performance as a function of both rank and sample support when the covariance matrix is unknown and estimated from collected sensor data. The adaptive techniques considered are the principal components algorithm, the cross-spectral metric and the multistage Wiener filter. It is shown that the new multistage Wiener filtering technique provides more robust performance as a function of both rank and sample support.

Methods and apparatus for blind signal separation

Abstract: A set of generalized architectures, frameworks, algorithms, and devices for separating, discriminating, and recovering original signal sources by processing a set of received mixtures and functions of said signals based on processing of the received, measured, recorded or otherwise stored signals or functions thereof. There are multiple criteria that can be used alone or in conjunction with other criteria for achieving the separation and recovery of the original signal content from the signal mixtures. The system of the invention enables the adaptive blind separation and recovery of several unknown signals mixed together in changing interference environments with very minimal assumption on the original signals. The system of this invention has practical applications to non-multiplexed media sharing, adaptive interferer rejection, acoustic sensors, acoustic diagnostics, medical diagnostics and instrumentation, speech, voice, language recognition and processing, wired and wireless modulated communication signal receivers, and cellular communications.

Color image embedding using multidimensional lattice structures

Abstract: This paper describes a robust data embedding scheme which uses noise resilient channel codes based on a multidimensional lattice structure. Compared to prior work in digital watermarking, the proposed scheme can handle a significantly larger quantity of signature data such as gray-scale or color images. A trade-off between the quantity of hidden data and the quality of the watermarked image is achieved by varying the number of quantization levels for the signature, and a scale factor for data embedding. Experimental results on signature recovery from JPEG compressed watermarked images show that good quality reconstruction is possible even when the images are lossy compressed by as much as 85%. Potential applications of this method include, in addition to watermarking, digital data hiding for security and for bit stream control and manipulation.

Detecting system and power-spectral analysis of pulse signals of human body

Abstract: In this paper, in the light of three features of the human pulse signal, a non-contact-type detection system is designed, which can be used to detect pulse signals. The signal processing system suitable for the pulse signals is set up. The power spectra of the four kinds of pulse signals are obtained by using the fast Fourier transform (FFT) and the power-spectral characteristics are analyzed and compared. Several important conclusions drawn in this paper may be of great value in the objective study of the pulse-condition and in the noninvasive diagnosis of the cardiovascular system.

A fast filter-bank for adaptive Fourier analysis

Abstract: Adaptive Fourier analyzers have been developed for measuring periodic signals with unknown or changing fundamental frequency. Typical applications are vibration measurements and active noise control related to rotating machinery and calibration equipment that can avoid the changes of the line frequency by adaptation. Higher frequency applications have limitations since the computational complexity of these analyzers are relatively high as the number of the harmonic components to be measured (or suppressed) is usually above 50. In this paper, based on the concept of transformed domain signal processing, a fast filter-bank structure is proposed to reduce the above computational complexity. The first step of the suggested solution is the application of the filter-bank version of the fast Fourier transform or any other fast transformations that convert input data into the transformed domain. These fast transform structures operate as single-input multiple-output filter-banks, however, they can not be adapted since their efficiency is due to their special symmetry. As a second step, the adaptation of the filter-bank is performed at the transform structure's output by adapting a simple linear combiner to the fundamental frequency of the periodic signal to be processed.

Wavelet Signal Processing

Abstract: A basic motivation behind transform methods is the idea that some sorts of processing are better (or perhaps only possibly) achieved in the transform domain rather than in the original signal domain, In this sense, the utility of a transform is measured by its ability to facilitate desired signal processing tasks in the transform domain via algorithms that are digitally tractable, computationally efficient, concise, and noise robust. The efficacy of general wavelet transforms comes from the fact that wavelet domain algorithms exhibit all of these benefits when dealing with signals that are characterized by their time—frequency behavior. This chapter explores applications of overcomplete wavelet transforms in problems of data compression, noise suppression, digital communication, and signal identification.

Classification of rotated and scaled textures using HMHV spectrum estimation and the Fourier-Mellin transform

Abstract: In the framework of image modeling for texture analysis, we propose the combination of the new parametric 2-D spectrum estimation method called HMHV (harmonic mean horizontal vertical) and the Fourier-Mellin transform This latter technique allows the calculation of a set of texture descriptors from a 2-D spectrum estimate which is invariant under rotation and scaling A comparison of the HMHV and the "standard" parametric HM (harmonic mean) methods on synthetic and natural stochastic textures shows that the HMHV method presents almost no spurious peaks and is quite isotropic By performing the classification of a set of 60 images divided in 12 texture classes, descriptors computed with the HMHV method provide better results than those computed with the HM method

Fundamentals of Acoustic Signal Processing

Abstract: Introduction. Discrete Expression of Signals. z-Transform. Transfer Function and Frequency Response Function of Linear Systems. Discrete Fourier Transform. Transfer Function Models and Wave Equations. Statistical Models for Acoustical Transfer Functions. Deconvolution and Inverse Filters. Linear Equations, Inverse Filters, and Signal Analysis. Index.

System analysis and signal processing: with emphasis on the use of MATLAB

Abstract: 1. Getting Started in Matlab and an Introduction to Systems and Signal Processing. 2. Impulse Functions, Impulse Responses, and Convolution. 3. The Steady State Response of Analogue Networks to Sinusoids and to the complex exponential EJWT. 4. Phasors. 5. Line Spectra and the Fourier Series. 6. Spectral Density Functions and the Fourier Transform. 7 The Sampling and Digitization of Signals. 8. The Discrete Fourier Transform. 9. The Fast Fourier Transform and Some Applications. 10. The Steady State Response of Analogue Systems By Consideration of the 11. Natural Responses, Transients and Stability. 12. The Laplace Transform. 13. Synthesis of Analogue Filters. 14. An Introduction to Digital Networks and the Z-Transform. 15. Synthesis of Digital Filters. 16. Correlation. 17. Processing Techniques for Bandpass Signals. Index.

Anytime algorithms in embedded signal processing systems 1

Abstract: In this paper a family of so-called anytime signal processing algorithms is introduced to improve the overall performance of larger scale embedded digital signal processing (DSP) systems. In such systems there are cases where due to abrupt changes within the environment and/or the processing system temporal shortage of computational power and/or loss of some data may occnr. It is an obvious requirement that even in such situations the actual processing should be continued to insure appropriate performance. This means that signal processing of somewhat simpler complexity should provide outputs of acceptable quality to continue the operation of the complete embedded system. The accuracy of the processing will be temporarily lower but possibly still enough to produce data for qualitative evaluations and supporting decisions. Consequently anytime algorithms should provide short response time and be very flexible with respect to the available input information and computational power. The paper presents such algorithms based on the re-configurable version of the recursive signal transformer structure described in [1].