Showing papers on "Multidimensional signal processing published in 1989"

Discrete-Time Signal Processing

Abstract: For senior/graduate-level courses in Discrete-Time Signal Processing. THE definitive, authoritative text on DSP -- ideal for those with an introductory-level knowledge of signals and systems. Written by prominent, DSP pioneers, it provides thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete-time Fourier Analysis. By focusing on the general and universal concepts in discrete-time signal processing, it remains vital and relevant to the new challenges arising in the field --without limiting itself to specific technologies with relatively short life spans.

Source separation using higher order moments

Abstract: The author presents a simple algebraic method for the extraction of independent components in multidimensional data. Since statistical independence is a much stronger property than uncorrelation, it is possible, using higher-order moments, to identify source signatures in array data without any a priori model for propagation or reception, that is, without directional vector parameterization, provided that the emitting sources are independent with different probability distributions. The author proposes such a blind identification procedure. Source signatures are directly identified as covariance eigenvectors after data have been orthonormalized and nonlinearly weighted. Potential applications to array processing are illustrated by a simulation consisting of a simultaneous range-bearing estimation with a passive array. >

Set membership identification in digital signal processing

Abstract: Set membership (SM) identification refers to a class of techniques for estimating parameters of linear systems or signal models under a priori information that constrains the solutions to certain sets. When data do not help refine these membership sets, the effort of updating the parameter estimates at those points can be avoided. An intuitive development is given, first in one dimension and then in the general case, of an SM algorithm based on least-squares estimation. Two useful versions of the method are described, one of which can be implemented on a systolic array processor. The relationship of the featured SM method to both historical and current developments is discussed. Application to real speech data illustrates the developments. >

The role of lossless systems in modern digital signal processing: a tutorial

Abstract: A self-contained discussion of discrete-time lossless systems and their properties and relevance in digital signal processing is presented. The basic concept of losslessness is introduced, and several algebraic properties of lossless systems are studied. An understanding of these properties is crucial in order to exploit the rich usefulness of lossless systems in digital signal processing. Since lossless systems typically have many input and output terminals, a brief review of multiinput multioutput systems is included. The most general form of a rational lossless transfer matrix is presented along with synthesis procedures for the FIR (finite impulse response) case. Some applications of lossless systems in signal processing are presented. >

An introduction to digital signal processing

Abstract: Signals and Systems Sampled Data and the Z Transform Sinusoidal Response of LSI Systems Couplets and Elementary Filters The Discrete Fourier Transform The Continuous Fourier Integral Transform Application of the Fourier Transform to Digital Signal Processing Digital Filter Design Inverse Filtering and Deconvolution Spectral Factorization Power Spectral Estimation Multidimensional DSP References

Signal reconstruction from multiple correlations: frequency- and time-domain approaches

Abstract: One-dimensional (1-D) ultrashort laser signals cannot be recorded directly, although it is possible to detect their multiple correlations. The reconstruction of 1-D deterministic sampled signals from their multiple correlations is studied. A computationally efficient, fast-Fourier-transform-based, frequency-domain algorithm is described for simultaneously reconstructing the amplitude and the phase of a finite-duration signal. It is shown that, by modeling the Fourier transform of a discrete sequence as a pole-zero rational function, unique (modulo time shifts) signal recovery is possible from any multiple correlation of order greater than 2. The resulting time-domain algorithm uses all the nonredundant 1-D slices of a multiple-correlation sequence and applies to one- or two-sided, finite- or infinite-duration signals. The signal parameters are obtained in closed form by using a set of linear equations. Noise effects are studied theoretically and experimentally through simulated data. Both frequency-and time-domain algorithms are applicable to modeling and interpolation of raster-scanned images.

Signal waveform estimation in sensor array processing

Abstract: The problem of estimating the waveform of narrowband signals impinging on a sensor array is of importance in many engineering applications. A known look-direction for a signal-of-interest (SOI) is usually assumed and a particular performance measure is optimized. To achieve optimality, these methods require the SOI to be uncorrelated with the other signals as well as the additive noise. Herein, the signal parameters (directions) are assumed to be unknown and an optimal estimator of all the signal waveforms is derived. The signal waveforms can be correlated or even coherent. The methods is based on a stochastic signal model and uses optimal signal parameter estimes obtained from the measurements via maximum likelihood estimation or weighted subspace fitting. Based on these estimates, the emitter signal covariance is estimated and a structured maximum a posteriori estimate of the signal waveforms is obtained. Simulations are presented comparing this estimator to the deterministic maximum likelihood estimator and the unstructured stochastic estimator.

A Representation Theory for Morphological Image and Signal Processing

Abstract: This paper presents a unifying theory for many concepts and operations encountered in or related to morphological image and signal analysis. This unification requires a set-theoretic methodology, where signals are modeled as sets, systems (signal transformations) are viewed as set mappings, and translation-invariant systems are uniquely characterized by special collections of input signals. This approach leads to a general representation theory, in which any translation-in- variant, increasing, upper semicontinuous system can be represented exactly as a minimal nonlinear superposition of morphological erosions or dilations. In this representation, many similarities and a few differences are observed between systems processing binary or multi- level signals, and continuous-domain or discrete-domain signals. The theory is used to analyze some special cases of image/signal analysis systems, such as morphological filters, median and order-statistic fil- ters, linear filters, and shape recognition transforms. Although the de- veloped theory is algebraic, its prototype operations are well suitable for shape analysis; hence, the results of this study also apply to systems that extract information about the geometrical structure of signals. Zndex Terms-Imagelsignal processing, mathematical morphology, nonlinearllinear filtering, shape analysis, systems representation.

Split-spectrum processing: analysis of polarity threshold algorithm for improvement of signal-to-noise ratio and detectability in ultrasonic signals

Abstract: A polarity thresholding algorithm that has recently been developed for split-spectrum processing for ultrasonic coherent noise reduction is theoretically analyzed to evaluate its performance. The probability density function (PDF) of the output of the algorithm is derived and used to calculate the theoretical signal-to-noise ratio (SNR) enhancement and the receiver operating characteristics. The performance limits of the algorithm are also established. Some experimental results of SNR enhancement obtained with the polarity thresholding algorithm are presented. >

A new quadrature sampling and processing approach

Abstract: A quadrature sampling and array signal processing technique that differs from earlier approaches in that is processes the data before the Hilbert transformation is presented. A fast Fourier transformation (FFT) technique that performs the discrete Fourier transformation (DFT) on the sampled data directly without Hilbert transformation is proposed for frequency-domain signal processing. For array signal processing, the proposed approach does not perform Hilbert transformation prior to signal combining. It features high processing speed, low distortion, and hardware simplicity. Error analyses, performance evaluation, and computer simulation results are included. >

Computational structures for fast implementation of L-path and L-block digital filters

Abstract: One of the major problems in the multi-DSP (digital signal processor) implementation of L-path and L-block digital filters is the hardware complexity-throughput rate tradeoff The author presents computational structures based on the theory of fast algorithms for short linear convolutions, which are suitable for the implementation of these types of digital filters He also compares the performance of the structures with two previously published ones The comparison shows that the schemes proposed here are faster and that the complexity-throughput tradeoffs can easily be controlled by the designer >

Some Aspects of Non-Stationary Signal Processing with Emphasis on Time-Frequency and Time-Scale Methods

Abstract: The analysis and the processing of nonstationary signals call for specific tools which go beyond Fourier analysis. This paper is intended to review most of the Signal Processing methods which have been proposed in this direction. Emphasis is put on time-frequency representations and on their time-scale versions which implicitly make use of “wavelet” concepts. Relationships between Gabor expansion, wavelet transform and ambiguity functions are detailed by considering signal decomposition as a detection-estimation problem. This permits one to make more precise some of the links which exist between time-frequency and time-scale.

Method and apparatus for processing speech signals

Abstract: Processing speech signals applicable to a variety of speech processing including narrowband, mediumband and wideband coding. The speech signal is modified by a normalization process using the envelope of the speech signal such that the modified signal will have more desirable characteristics as seen by the intended processing algorithm. The modification is achieved by a point-by-point division (normalization) of the signal by an amplitudes function, which is obtained from lowpass filtering the magnitude of the signal. Several examples of normalized signal are presented. Application to pitch detection and speech coding are described herein.

Signal processing method and sound source data forming apparatus

Abstract: A method for processing a digital signal produced by digitizing an analog signal such as a musical instrument sound signal, and an apparatus for producing sound source data. When the input signal contains a periodically repetitive wave form portion, the fundamental frequency and its high harmonic components of the input signal is extracted by a comb filter prior to signal processing which takes advantage of the periodicity of the input signal. The fundamental frequency or pitch is detected by performing Fourier transform to produce frequency components, phase matching these frequency components and performing inverse Fourier transform. When extracting a repetitive waveform portion or so-called looping domain, such looping domain having the highest similarity in waveform in the vicinity of both ends of the domain is selected. When the bit compression of digital signal data is performed by selecting a filter with blocks each consisting of plural samples as units, a pseudo signal is affixed to the input signal, before the start point of the input signal, which pseudo signal will cause a filter of the lowest order to be selected. The looping domain is set so as to be a whole number multiple of the block which serves as the unit for bit compression, and the parameters of the looping start block are formed on the basis of data of the start and the end blocks. By applying a part or the whole of the signal processing method to a sound source data forming apparatus, sound source data may be formed which is reduced in the looping noise and error caused by data compression and which is of superior sound quality.

Real time processing and analysis of fetal phonocardiographic signals

Abstract: The technique described makes use of an inexpensive, non-invasive phonocardiographic (phono) transducer which facilitates safe long-term patient monitoring. A variable comb filter applied to the frequency domain is used in order to take full advantage of the harmonic content of fetal heart signals. Real time estimation of FHR has been achieved on pre-recorded phono signals lasting eight hours. Recordings with a reasonable signal quality were analysed and some of the results are given. Advanced signal processing techniques followed by Artificial Intelligence (AI) algorithms reduce the number of erroneous estimates during periods of low signal to noise ratio (SNR). The resulting FHR time series is stored on the host computer for further processing, display and parameter extraction.

Trade-off's in the computation of mono- and multi-dimensional DCT's

Abstract: An overview of some alternative algorithms for one- and two-dimensional DCTs (discrete cosine transforms) is given. Operation counts are derived for typical examples useful in image processing. It is shown that it is possible to generalize the 2-D schemes to 3-D DCTs as well. The result is that a 3-D DCT can be obtained from a 3-D DFT (discrete Fourier transform) of the same size on reals at the cost of permutations and O(3/2N/sup 3/) multiplications. The scheme involves rotations on eight output points at a time. Improvements through scaling are discussed, and implementation issues (both in hardware and software) are addressed. >

Digital Signal Processing Design

Abstract: Fundamentals Theory and application of discrete signal processing Basic programming considerations Digital filters Spectral analysis and the FFT General signal processing algorithms Hardware and software device support

Signal abstractions in signal processing software

Abstract: The authors propose and formally define the concept of multilevel signal abstractions as an organizational principle for real-world signal processing software that requires both algorithmic and heuristic techniques. As an example, they have implemented a set of signal abstractions, the extended spectrum, for harmonic spectra. The extended spectrum is shown to be useful in a variety of problems associated with harmonic spectra. For example, the focus in spectral estimation is often on adjusting parameters to maximize the 'peakness' of harmonically related peaks. It is demonstrated that this can be conveniently performed by taking advantage of the multiple abstraction levels of the extended spectrum representation. The extended spectrum can also be used to represent explicitly the evolution of harmonic spectra over time. To illustrate this concept, the authors have implemented a helicopter pitch and power tracking system. >

What we need around here is more aliasing (computer graphics)

Abstract: A brief tutorial is given on what aliasing means. Plots of some relevant functions are shown. Some of the conventional wisdom about aliasing is described, and why that wisdom may not be so wise is explained. Aliasing is actually an image processing phenomenon involving the Fourier transform, convolution and the convolution theorem. >

A signal processor for median-based algorithms

Abstract: A sorter-based processor architecture is introduced for digital signal processing purposes. The processor has been optimized to implement sliding average-type linear structures and three- and five-sample sorting operations. The specialized processor can be used, for example, for the several variations of the finite-impulse-response (FIR) median hybrid (FMH) filters, as well as other types of ranked-order filters and running-sum averaging operations. FMH filters with averaging substructures and window lengths of up to 65 samples can be computed with sampling intervals of less than 20 clock cycles. The 12-bit microprogrammable core processor is designed as a full-custom very large scale integration (VLSI) circuit. Examples of filter implementations show that the sorter-based processor architecture is suitable for several kinds of digital signal processing tasks. >

Pitch synchronous spectral analysis scheme for voiced speech

Abstract: The problem of spectrum analysis of harmonic signals which are periodic or at least quasi-periodic, such as human voice, is addressed. An efficient and accurate pitch-synchronized spectral analysis scheme for obtaining the Fourier coefficients of a harmonic signal, sampled at an arbitrary rate above the Nyquist critical rate, is outlined. The pitch is derived from the sampled signal prior to the spectral analysis. The rationale behind the scheme is based on an interpolation of the signal with an upsampling rate that is synchronized with the pitch period of the signal. It is shown that the resulting unsampled sequence is aperiodic, but nevertheless can be decomposed into a periodic signal corrupted by a small, aperiodic, high-frequency noise. The fact that this noise is correlated with the signal is used to obtain a closed-form solution for the desired Fourier coefficients from the noisy values, using the computationally superior fast Fourier transform (FFT) algorithm. The accuracy of the scheme is demonstrated for synthetic speech for which the spectrum is known a priori. The results obtained for real speech signals show better consistency across adjacent frames as compared to conventional methods. >

Equivalence between two-dimensional analytic and real signal Wigner distributions

Abstract: A solution to the problem of undesired cross-terms in the Wigner distribution (WD) of two-dimensional real signals is presented. The solution is to introduce the notion of two-dimensional analytic signal in the WD. The relation between WDs of the two-dimensional real signal and those of its associated two-dimensional analytical signal is discussed and illustrated by an example. >

Digital information coding system which evenly distributes valid input data to digital signal processors operating in parallel

Abstract: A digital signal processing apparatus which is used for the computation of coding image signals or the like and a motion compensative operation method which uses a digital signal processing apparatus. The apparatus comprises a plurality of signal processing means arranged in parallel and control means which assigns loads to the signal processing means so that the signal processing means have even computation volumes. Alternatively, an address generator is provided for each of data sets entered independently. An intermediate check is conducted during the computation for a block which involves a motion compensative operation.

New fast algorithm to compute two-dimensional discrete Hartley transform

Abstract: A new fast algorithm for computing the two-dimensional discrete Hartley transform is presented. This algorithm requires the lowest number of multiplications compared with other related algorithms.

Multiple source location: The signal subspace approach

Abstract: The multiple source location problem is solved for a general array geometry using the principal of least-squares modeling. The incident wavefields may be a mixture of coherent and noncoherent sources that are broadband and/or narrowband. Furthermore, the wavefields may be plane waves and/or spherical waves. An effective algorithmic approach is developed for solving the multiple source location problem in the signal and covariance domains. In each domain, it is necessary to represent a set of vectors as a linear combination of array steering vectors. Using this property, a generic descent algorithmic approach is developed for estimating the wavefield directions of arrival. The success of such iterative approaches is critically dependent on the generation of good estimates to initialize the algorithm. An effective initialization procedure is developed for this purpose. Direction-of-arrival estimates which arise from the proposed procedure are empirically found to be unbiased and low variance. >

A new speech signal scrambling method for secure communications: theory, implementation, and security evaluation

Abstract: A two-dimensional signal-scrambling method implemented by digital signal-processing techniques that eliminate the need for frame synchronization without impairing security is presented. Such techniques include short time Fourier analysis and the filter bank concept. The use of special digital finite-impulse-response filters, which make it possible to implement the system algorithm completely via commercial processor software, is discussed. As a result, the system can be configured with very little hardware. Methods for determining available keyspace and selecting keys are also presented. >

Digital signal processing method

Abstract: of EP0690376A digital signal processing apparatus which is used for the computation of coding image signals or the like and a motion compensative operation method which uses a digital signal processing apparatus. The apparatus comprises a plurality of signal processing means arranged in parallel and control means which assigns loads to the signal processing means so that the signal processing means have even computation volumes. Alternatively, an address generator is provided for each of data sets entered independently. An intermediate check is conducted during the computation for a block which involves a motion compensative operation.

Operator Groups and Ambiguity Functions in Signal Processing

Abstract: The standard ambiguity function in signal processing is well suited for studying signal properties under translations in time and frequency shifts, which belong to the Weyl-Heisenberg group of transformations. Another group, the affine group, is intimately connected with wavelet transforms. We attempt to generalize the connection between target estimation and the properties of group representations and to show the interest of considering still other transformations which arise naturally in detection/estimation problems for moving targets.

New method of computing Wigner-Ville distribution

Abstract: The Wigner-Ville distribution (WVD) is of great significance in time-frequency signal analysis. In the letter we present a new method to evaluate the WVD of a real signal using the fast Hartley transform (FHT). This is compared with the existing fast Fourier transform (FFT) method in terms of computation time. The FHT method presented turns out to be much faster than the FFT method.