Showing papers on "Multidimensional signal processing published in 1997"

Using wavelet transforms for ECG characterization. An on-line digital signal processing system

Abstract: The rapid and objective measurement of timing intervals of the electrocardiogram (ECG) by automated systems is superior to the subjective assessment of ECG morphology. The timing interval measurements are usually made from the onset to the termination of any component of the EGG, after accurate detection of the QRS complex. This article describes a real-time system that uses wavelet transforms to overcome the limitations of other methods of detecting QRS and the onsets and offsets of P- and T-waves. Wavelet transformation is briefly discussed, and detection methods and hardware and software aspects of the system are presented, as well as experimental results.

Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection

Abstract: The utilization of signal processing techniques in nondestructive testing, especially in ultrasonics, is widespread. Signal averaging, matched filtering, frequency spectrum analysis, neural nets, and autoregressive analysis have all been used to analyze ultrasonic signals. The Wavelet Transform (WT) is the most recent technique for processing signals with time-varying spectra. Interest in wavelets and their potential applications has resulted in an explosion of papers; some have called the wavelets the most significant mathematical event of the past decade. In this work, the Wavelet Transform is utilized to improve ultrasonic flaw detection in noisy signals as an alternative to the Split-Spectrum Processing (SSP) technique. In SSP, the frequency spectrum of the signal is split using overlapping Gaussian passband filters with different central frequencies and fixed absolute bandwidth. A similar approach is utilized in the WT, but in this case the relative bandwidth is constant, resulting in a filter bank with a self-adjusting window structure that can display the temporal variation of the signal's spectral components with varying resolutions. This property of the WT is extremely useful for detecting flaw echoes embedded in background noise. The detection of ultrasonic pulses using the wavelet transform is described and numerical results show good detection even for signal-to-noise ratios (SNR) of -15 dB. The improvement in detection was experimentally verified using steel samples with simulated flaws.

Computing the discrete-time 'analytic' signal via FFT

Abstract: Starting with a real-valued N-point discrete-time signal, frequency-domain algorithms are provided for computing (1) the complex-valued standard N-point discrete time 'analytic' signal of the same sample rate, (2) the complex-valued decimated N/2-point discrete-time 'analytic' signal of half the original sample rate, and (3) the complex-valued interpolated NM-point discrete-time 'analytic' signal of M times the original sample rate. Special adjustment of transform end points is shown to generate proper discrete-time 'analytic' signals.

Fractional wavelet transform.

Abstract: The wavelet transform, which has had a growing importance in signal and image processing, has been generalized by association with both the wavelet transform and the fractional Fourier transform. Possible implementations of the new transformation are in image compression, image transmission, transient signal processing, etc. Computer simulations demonstrate the abilities of the novel transform. Optical implementation of this transform is briefly discussed.

Signal Processing Systems: Theory and Design

Abstract: Signals systems signal transforms convolutional type forms of systems finite recursive representations algorithms stability of dynamical systems applications.

High Accuracy Evaluation of the Finite Fourier Transform Using Sampled Data

Abstract: Many system identification and signal processing procedures can be done advantageously in the frequency domain. A required preliminary step for this approach is the transformation of sampled time domain data into the frequency domain. The analytical tool used for this transformation is the finite Fourier transform. Inaccuracy in the transformation can degrade system identification and signal processing results. This work presents a method for evaluating the finite Fourier transform using cubic interpolation of sampled time domain data for high accuracy, and the chirp z-transform for arbitrary frequency resolution. The accuracy of the technique is demonstrated in example cases where the transformation can be evaluated analytically. Arbitrary frequency resolution is shown to be important for capturing details of the data in the frequency domain. The technique is demonstrated using flight test data from a longitudinal maneuver of the F-18 High Alpha Research Vehicle.

Efficient algorithm for discrete sinc interpolation

Abstract: A new computing method for discrete-signal sinc interpolation suitable for use in image and signal processing and the synthesis of holograms is described. It is shown to be superior to the commonly used zero-padding interpolation method in terms of interpolation accuracy, flexibility, and computational complexity.

Application of wavelets in Doppler ultrasound

Abstract: Provides an introduction into wavelets and illustrates their application with two examples. The wavelet transform provides the analyst with a scaleable time‐frequency representation of the signal, which may uncover details not evidenced by conventional signal processing techniques. The signals used in this paper are Doppler ultrasound recordings of blood flow velocity taken from the internal carotid artery and the femoral artery. Shows how wavelets can be used as an alternative signal processing tool to the short time Fourier transform for the extraction of the time‐frequency distribution of Doppler ultrasound signals. Implements wavelet‐based adaptive filtering for the extraction of maximum blood velocity envelopes in the post processing of Doppler signals.

Digital image enhancement

Abstract: Digital image enhancement apparatus for processing an input sampled image signal having an input sample rate to generate an output, enhanced, image signal at the input sample rate comprises one or more filters for filtering and sample rate up-converting the input image signal to generate a detail signal at an intermediate sample rate higher than the input sample rate and for generating an intermediate image signal, the intermediate image signal being a sample rate up-converted version of the input image signal; a detecting circuit for detecting whether a combination of the detail signal and the intermediate image signal goes beyond predefined limits on the signal level of the output image signal and, if so, for modifying the detail signal so that a combination of the detail signal and the intermediate image signal would not go beyond the predefined limits on the signal level of the output image signal; a sample rate down-converter for converting the detail signal to a down-converted detail signal at the input sample rate; and a combiner for combining the input image signal with the down-converted detail signal at the input sample rate, to generate the output image signal.

Multidimensional Signal Processing

Abstract: A one-dimension a 1 w 1 ndow 1s chosen from the 1 arge cat a 1 og of those available primarily due to its leakage-resolution tradeoff (LRT>. Is it possible to generalize a 1-D window to higher dimensions such that the window's 1-D properties are homogeneously preserved? If we require that the window be continuous and bounded the answer is usually no. Bounded (projection window) general 1zations do exist for the Parzen and TukeyHann1ng windows. The resulting windows, however, are very close to that window obtained by simply rotating the 1-D window into two dimensions. IKTROOUCTION When choosing from the large catalog of standard one-dimensional windows [1-2], one is largely motivated by the window's leakage-resolution tradeoff (LRT). Is 1t possible to generalize these windows to two and higher dimensions such that the 1-D window properties are preserved in each 1-0 slice? If we require these multidimensional windows to be bounded and continuous, the answer is usually negativ~ In the two cases considered 1n this correspondence where bounded two dimensional generalizations do exist, the resulting windows are close to those obtained by the rotation generalization of 1-D windows [3]. A short review of the outer product and rotation of 1-D window generalization methods is given in the next section. In both cases, the LRT is altered in the transformation. In order to homogeneously maintain the 1-D window properties, the higher dimension window must be chosen so that its projection onto one dimension results in the 1-D window. Unfortunately, this requires unbounded generalizations in many cases of interest. The Parzen and Tukey-Hamm1 ng windows are the exceptions. For the discrete case, bounded projection windows can be formed such that desired LRT is preserved inhomogeneously at a number of angular orientations. ·I I PRELIHINABIES There are a wealth of one-dimensional windows with various 1eat<.ageresolut1on tradeoffs. A one-dimensional window, w1 has finite extent: (where IT Ct> = 1 for It I~ 1/2. and is zero elsewhere>, fs normalized with and is even function, i.e., The spectrum of a window is defined by wl ( w) = t!l (t)exp(-j w t)dt The area of a window 1$ = W1(0) The magnitude of a typical window spectrum is shown in Figure 1. For good resolution, the main lobe width, 6, should be small, and for minimal spectra 1 1 eakage, the norma 1 i zed side 1 obe magn 1tude, o , shou 1 d a 1 so be small. Invariably, however, decreasing one of these parameters increases the other. A two dimensional window w2ct1, t 2>, with spectrum fX> f:'2ctl' t 2 > exp [-JJdt 1dt 2 oo -:oo fs commonly generated from a 1-0 counterpart by either the outer product or window rotation techniques [3). The outer product window is and the rotated window, initially suggested by Huang [4J, fs In either case, if w1 1s a "good" window, then so is w2• For certain applications, (e.g. "good" filter design) such dimensional generalizations are acceptable. In other cases, such as spectral estimation, a smal 1 perturbation in window shape can significantly alter results [SJ. Both the outer product and the rotated window s1gn1ffcantly alter the LRT of the corresponding 1-0 window. To illustrate the effects of outer product and rotational dimensional generalization, we choose a boxcar window

Multichannel filters for image processing

Abstract: New filters for multichannel image processing are introduced and analysed. The proposed methodology constitutes a unifying and powerful framework for multichannel signal processing. The new filters use fuzzy membership functions based on different distance measures among the image vectors to adapt to local data in the image. Fuzzy aggregators are utilized to determine the weights in the proposed filter structure. The special case of colour image processing is studied as an important example of multichannel signal processing. Simulation results indicate that the new filters are computationally attractive and have excellent performance.

Evaluation of fractional, lower-order statistics-based detection algorithms on real radar sea-clutter data

Abstract: Alpha-stable distributions have recently been recognised in the signal processing community as simple, yet accurate, two-parameter statistical models for signals and noises that contain an impulsive component of various degrees of severity. On the basis of this finding, several signal processing problems have been addressed and solved within the framework of alpha-stable distributions and with the use of fractional, lower-order moments. The authors attempt to popularise these new signal processing tools within the radar community. In particular, they evaluate the goodness-of-fit of alpha-stable models in the radar environment and test the performance of new signal processing algorithms for signal detection on real radar sea-clutter data. They also include in the paper a brief review of the key ideas of signal processing with alpha-stable distributions, as well as a large number of references to the literature for further probing.

Wavelet-based hybrid neurosystem for classifying a signal or an image represented by the signal in a data system

Abstract: The present invention relates to a system and a method for signal classification. The system comprises a sensor array for receiving a series of input signals such as acoustic signals, pixel-based image signal (such as from infrared images detectors), light signals, temperature signals, etc., a wavelet transform module for transforming the input signals so that characteristics of the signals are represented in the form of wavelet transform coefficients and an array of hybrid neural networks for classifying the signals into multiple distinct categories and generating a classification output signal. The hybrid neural networks each comprise a location neural network for processing data embedded in the frequency versus time location segment of the output of the transform module, a magnitude neural network for processing magnitude information embedded in the magnitude segment of the output of the transform module, and a classification neural network for processing the outputs from the location and magnitude neural networks. A method for processing the signal using the system of the present invention is also described.

Multi-Dimensional Signal Processin Using an Algebraically Extended Signal Representation

Abstract: Many concepts that are used in multi-dimensional signal processing are derived from one-dimensional signal processing. As a consequence, they are only suited to multi-dimensional signals which are intrinsically one-dimensional. We claim that this restriction is due to the restricted algebraic frame used in signal processing, especially to the use of the complex numbers in the frequency domain. We propose a generalization of the two-dimensional Fourier transform which yields a quaternionic signal representation. We call this transform quaternionic Fourier transform (QFT). Based on the QFT, we generalize the conceptions of the analytic signal, Gabor filters, instantaneous and local phase to two dimensions in a novel way which is intrinsically two-dimensional. Experimental results are presented.

Statistical Digital Signal Processing and Modeling

Abstract: (1997). Statistical Digital Signal Processing and Modeling. Technometrics: Vol. 39, No. 3, pp. 335-336.

Wavelets for biomedical signal processing

Abstract: In this paper, we will discuss the general idea of the wavelet representation, in its continuous and discrete versions, as well as in terms of a multiresolution approximation. In addition, the general expression for the affine class, and the relationship between the affine and Cohen's classes are presented. Also, the shift-scale invariant class is defined. This class basically combines the properties of both classes. Finally a recent development, namely, the use of unitary transformations in both Cohen's and the affine classes, with the consequent generation of even more specific tools for signal analysis will be discussed.

The homomorphic analytic signal

Abstract: This paper introduces an alternative form of analytic signal that is formed using homomorphic signal processing techniques. It may be generated for signals that demonstrate redundancy in their pole-zero distributions, such as phase signals and real signals. The analytic signal is formed by manipulating the positions of the signal's nonminimum phase poles and zeros to create a minimum phase signal. A factorization of real signals is presented that demonstrates the properties of the homomorphic analytic signal, and a new definition of instantaneous frequency is developed. Examples are given to verify the theory and comparisons with the linear analytic signal, and instantaneous frequency are made.

Reducing undesirable effects of an emphasis processing operation performed on a moving image by adding a noise signal to a decoded uncompressed signal

Abstract: A moving image decoding method and a moving image decoding apparatus for reproducing a natural image from a digital image signal, even when it is viewed on a TV monitor in which emphasis processing is performed. A moving image signal is encoded using a predetermined prediction image signal, the encoded signal is subjected to predetermined processing, the signal resulting from the processing is quantized, and a moving image signal, which is variable length encoded from the quantized signal, is decoded. Noise is adaptively added to the decoded moving image signal in accordance with a luminance signal of the decoded moving image signal. In this way, unnatural deteriorations caused by digital compression can be made less prominent, so that a natural image can be reproduced from a digital image signal even when it is viewed on a TV monitor in which emphasis processing is performed.

Modeling and decomposition of HRV signals with wavelet transforms

Abstract: The authors use a wavelet transform to build a simulated model of an HRV signal and to create an algorithm for HRV signal decomposition. They review the characteristics of HRV signals and discuss an improved integral pulse frequency modulation model for the simulation of these signals. They also present results that show how their model approximates a real HRV signal.

System level memory optimization for hardware-software co-design

Abstract: Application studies in the areas of image and video processing systems indicate that between 50 and 80% of the area cost in (application-specific) architectures for real-time multi-dimensional signal processing (RMSP) is due to data storage and transfer of array signals. This is true for both singleand multi-processor realizations, both customized and (embedded) programmable targets. This paper has two main contributions. First, to reduce this dominant cost, we propose to address the system-level storage organization for the multi-dimensional signals as a first step in the overall methodology to map these applications, before the hardware/software partitioning decision. Secondly, we demonstrate the usefulness of this novel approach based on a realistic test vehicle, namely a quad-tree based image coding application.

Loudness-controlled processing of acoustic signals

Abstract: With the method acoustic signals, e.g. in hearing aids, are processed in loudness-controlled manner in such a way that the loudness subjectively received by the hearing impaired person again always corresponds to the loudness received by listeners with normal hearing. Signal processing takes place without Fourier transformation and without subdivision of the signal into subband signals in iterative manner and completely in the time domain. This eliminates the disadvantage of unacceptably long signal delay times of known methods and permits a practical use. The apparatus for performing the method contains a processing stage (4) for the iterative calculation of a loudness-characteristic control quantity (ψ) and a correcting filter stage (7) controlled in time-dependent manner therewith. Compared with known methods, the inventive method requires only drastically reduced processing resources, which can mainly be attributed to the particularly efficient and unconventional implementation of the processing stages.

A signal processing method utilizing an eigenvector corresponding to the maximum eigenvalue of an autocorrelation matrix of received signals for an antenna array system

Abstract: This invention provides a signal processing method for enhancing the communication quality and increasing the communication capacity by reducing the effects of interference and noises with the nice beam pattern. The signal processing method provides a beam pattern by computing an eigenvector corresponding to the maximum eigenvalue of an autocorrelation matrix of received signals in an antenna array system. The inventive signal processing method introduces a simplified computational technique for generating a nice beam pattern having its maximum gain along the direction of the wanted signal and maintaining the gain toward the direction of the interfering signals in as low a level as possible.

Atomic decompositions of audio signals

Abstract: Signal modeling techniques ranging from basis expansions to parametric approaches have been applied to audio signal processing Motivated by the fundamental limitations of basis expansions for representing arbitrary signal features and providing means for signal modifications, we consider decompositions in terms of functions that are both signal-adaptive and parametric in nature Granular synthesis and sinusoidal modeling can be viewed in this light; we interpret these approaches as signal-adaptive expansions in terms of time-frequency atoms that are highly correlated to the fundamental signal structures This leads naturally to a discussion of the matching pursuit algorithm for deriving decompositions using over complete dictionaries of time-frequency atoms; specifically, we compare expansions using Gabor atoms and damped sinusoids Such decompositions identify important signal features and provide parametric representations that are useful for signal coding and analysis-modification-synthesis

Distortion removal apparatus, method for determining coefficient for the same, and processing speaker system, multi-processor, and amplifier including the same

Abstract: A distortion removal apparatus connected between a signal source and a signal input section of a distortion-generating system, for processing a signal output from the signal source so as to compensate for a distortion component generated in the system is provided. The distortion removal apparatus includes a frame division section for dividing a signal output from the signal source into data streams having a length of N while causing the data streams to partially overlap with one another; a Fourier transform section for performing Fourier transform of the data streams obtained by the frame division section in a time domain into a signal in a frequency domain; a memory section for storing N samples of first coefficients and N×N samples of second coefficients in the frequency domain. The distortion removal apparatus further includes an operation section for removing a distortion component from the output signal from the Fourier transform section by performing an operation based on the first coefficients and the output signal from the Fourier transform section and an operation based on the second coefficients and the output signal from the Fourier transform section; an inverse Fourier transform section for performing inverse Fourier transform of the output signal from the operation section into a signal in the time domain; and a frame synthesis section for sequentially connecting parts of the output signal from the inverse Fourier transform section.

A Digital Signal Processing Laboratory Using the Tms320c30

Abstract: 1. Introduction. 2. The TMS320C30 Digital Signal Processor. 3. Sampling. 4. Waveform Generation. 5. FIR Filter Implementations. 6. IIR Filter Implementations. 7. Fast Fourier Transform 8. Quantization Noise. 9. Adaptive filters. 10. Multirate Signal Processing. 11. DSP Projects. A. Host Data Communications. B. Interface to C Language. C. Matlab Functions. D. Miscellaneous Programs. Bibliography. Index.

The Applications of Wavelets to GPS Signal Processing

Abstract: Wavelet transformation is a new kind of tool of signal analysis, and it can offer time and frequency information of the signal at the same time. Using multiresolution analysis (MRA) method, the details and features of the signal can be extracted rapidly by using fast wavelet transformation algorithms. In this paper, MRA and wavelet transformation are introduced in digital signal processing form to promote the better understanding and implementation of physical meaning and algorithms of the wavelet essential. Based on the Daubechies’s orthonormal wavelet family, real data processing is carried out for GPS measurements. The Applications are focused on the signal denoising, outlier detection, bias separation and data compression, which are important issues for GPS data processing, and the results are promising. Some relevant topics, such as signal identification, and future development of the wavelet signal processing are also mentioned.

Practical solutions for counting scalars and dependences in ATOMIUM-a memory management system for multidimensional signal processing

Abstract: Image and video processing applications involve large multidimensional signals which have to be stored in memory modules. In application-specific architectures for real-time multidimensional signal processing, a significant cost in terms of chip area and power consumption is due to these background memory units. The multidimensional signals are usually modeled in behavioral descriptions with array variables. In the algorithmic specifications of our target applications, many of the array references cover large amounts of scalars. Therefore, the efficient handling of array references in the specifications for image and video processing is crucial for obtaining low cost memory allocation solutions. This paper addresses a central problem which arises when handling the array variables in behavioral specifications: the computation of the number of scalars covered by an array reference. This problem is closely related to the computation of dependences in data-flow analysis. The novel algorithms proposed in this paper are embedded in the ATOMIUM environment-a memory management system for multidimensional signal processing.

Overlap resolution: arithmetic with continuous valued digits in hybrid architectures

Abstract: Overlap resolution signal processing opens up a powerful approach to parallel analog computations with digital accuracy. This new redundant representation of signals, with continuous valued digits, carries the accuracy of analog signal processing beyond the accuracy of the analog circuitry itself. The proposed processing methodology can also be applied to an all digital system. Due to the residue nature of the analog digits, addition is residue-like and resembles carry save structures, yet it is implemented with analog circuitry. Familiar array multiplying structures are applied for multiplication when the multiplicand is overlap resolution and the multiplier is a positional number.

Measuring metastability and its effect on communication signal processing systems

Abstract: New digital communication receivers often oversample an intermediate frequency (IF) or baseband signal at a very high rate (16 to 32 times the IF frequency or bit rate) as a means to demodulate the encoded data and synchronize the recovered clock. Unfortunately, an asynchronous data source can result in metastable errors and its effect on communications system performance has not been analyzed before now. Fortunately, our results demonstrate the vitality of 1-bit quantized IF high-speed digital signal processing. We review a practical measurement technique using a digital sampling oscilloscope that can be easily applied to today's digital signal processing systems. This method is used to collect data on a high-speed digital gate array technology used for a digital communication receiver, and the statistics of packet error rate are calculated. The analysis shows the substantial robustness of fast gate array logic and the importance of selecting the right technology. The technique and analysis presented can be applied to other digital systems.