signalprocessing software is characterized by a heavy emphasis on arithmetic calculations and the lack of complicated control structures, placing specific constraints on which testing techniques are applicable and ho...
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signalprocessing software is characterized by a heavy emphasis on arithmetic calculations and the lack of complicated control structures, placing specific constraints on which testing techniques are applicable and how signalprocessing software can be efficiently tested. In this paper, we analyze the unique characteristics of signalprocessing software from the testing viewpoint and propose applied techniques for tackling the verification challenges of such software. We propose a testing method for the signalprocessing context and provide examples of its application to an FIR and a second order IIR filter. This method extends the applicability of algebraic testing and domain testing methods to signalprocessing software. The developed method applies to linear systems and can be further extended to take nonlinearities in the tested system into account.
This paper investigates the benefits derived by adopting the use of Graphics processing Unit (GPU) parallel programming in the field of biomedical signalprocessing. The differences in execution time when computing th...
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This paper investigates the benefits derived by adopting the use of Graphics processing Unit (GPU) parallel programming in the field of biomedical signalprocessing. The differences in execution time when computing the Correlation Dimension (CD) of multivariate neurophysiological recordings and the Skin Conductance Level (SCL) are reported by comparing several common programming environments. Moreover, as indicated in this study, the combination of parallel programming with special design techniques dealing with memory management issues such as data transfer between device memory and GPU may further accelerate the processing speed. So, the minimization achieved in the time execution by means of proper parallel architecture design may reach a factor of 29 in comparison with pure C language. Therefore, the role of parallel GPU programming environment may be beneficial for numerous biomedical applications within the sphere of biosignalprocessing.
Adaptive signal processing algorithms are often used in order to "track" an unknown time-varying parameter vector. Such algorithms are typically some form of stochastic gradient-descent algorithm. The Widrow...
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Adaptive signal processing algorithms are often used in order to "track" an unknown time-varying parameter vector. Such algorithms are typically some form of stochastic gradient-descent algorithm. The Widrow LMS algorithm is apparently the most frequently used. This work develops an upper bound on the norm-squared error between the parameter vector being tracked and the value obtained by the algorithm. The upper bound illustrates the relationship between the algorithm step-size and the maximum rate of variation in the parameter vector. Finally, some simple covariance decay-rate conditions are imposed to obtain a bound on the mean square error.
For airborne Earth observation applications, there is a special interest in lightweight, cost effective, imaging sensors of high resolution. The combination of frequency modulated continuous wave (FMCW) technology and...
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For airborne Earth observation applications, there is a special interest in lightweight, cost effective, imaging sensors of high resolution. The combination of frequency modulated continuous wave (FMCW) technology and synthetic aperture radar (SAR) techniques can lead to such a sensor. In this paper, a developed algorithm for SAR imaging that takes into account the special characteristics of FMCW signals is presented. Constrains for the validity of the stop and go approximation are shown. A demonstrator system has been built at Delft University of Technology and some results from the last airborne campaign are presented.
Presented in this paper is comparative analysis regarding the resolution of CO and MSK algorithms of radar mapping at the side looking. The analysis displays that at q ges 0 MSK algorithm has better resolution than SF...
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Presented in this paper is comparative analysis regarding the resolution of CO and MSK algorithms of radar mapping at the side looking. The analysis displays that at q ges 0 MSK algorithm has better resolution than SF, but its signalnoise ratio at the output is worse.
MAT2DSP is a MATLAB toolbox, currently under development, whose function is to estimate the implementation requirements of algorithms specified in the form of a MATLAB program. This toolbox is aimed at providing resea...
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MAT2DSP is a MATLAB toolbox, currently under development, whose function is to estimate the implementation requirements of algorithms specified in the form of a MATLAB program. This toolbox is aimed at providing researchers developing advanced signal and image processingalgorithms, a quick and convenient way of estimating what would be needed to implement their algorithm on a specified processor; MAT2DSP analyzes the user program and generates reports on its computational requirements.
Natural gas and carbon monoxide domestic detectors available in the market present serious deficiencies in terms of selectivity. Their problems result in a high percentage of false alarms. A possible solution of this ...
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Natural gas and carbon monoxide domestic detectors available in the market present serious deficiencies in terms of selectivity. Their problems result in a high percentage of false alarms. A possible solution of this problem is to improve the performance by means of signalprocessing. The authors present the work carried out towards the improvement of signalprocessing in quantitative analysis. in this paper we present the results obtain by some quantitative algorithms together with their application examples focused in the selective detection of methane and carbon.
Statistical signalprocessing is applied to estimate the geometrical and electrical characteristics of narrowband emissive sources. The key issue of the approach is based upon the use of MUSIC algorithm, which is larg...
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ISBN:
(纸本)9798331500016
Statistical signalprocessing is applied to estimate the geometrical and electrical characteristics of narrowband emissive sources. The key issue of the approach is based upon the use of MUSIC algorithm, which is largely used in array processing in far field conditions, to estimate the positions and the characteristics of emissive sources operating in near field conditions. The processing is divided in two phases : detection aimed to define the type of source and the estimation aimed to pinpoint the actual geometrical position of the source.
In this paper, we present a doubly pipelined VLSI Cordic array processor for digital signalprocessing computations. The basic notion of doubly pipelined CORDIC computation will be introduced first. Then, some potenti...
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In this paper, we present a doubly pipelined VLSI Cordic array processor for digital signalprocessing computations. The basic notion of doubly pipelined CORDIC computation will be introduced first. Then, some potential applications to digital signalprocessing problems will be discussed. Specifically, we shall demonstrate how a doubly pipelined CORDIC processor array can be applied to compute discrete Fourier transform and Fast Fourier transform, to implement Lattice filters, to solve Toeplitz systems as well as matrix QR factorizations. It is shown that by adopting a secondary pipelining, about one third hardware can be saved, and sometimes the throughput of the entire CORDIC processor array may be doubled.
algorithms for real-time ambulatory monitoring of physiological signals often run on platforms which have very limited processing power and memory. Therefore, in addition to functionality, it is necessary to carefully...
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algorithms for real-time ambulatory monitoring of physiological signals often run on platforms which have very limited processing power and memory. Therefore, in addition to functionality, it is necessary to carefully consider real-time performance requirements. This is particularly important for physiological monitoring of life threatening conditions such as hemorrhage. In this paper we present a comparison of several algorithms as candidates for implementation on a prehospital ambulatory monitor of hypovolemic states. The algorithms were tested on data from lower body negative pressure (LBNP) experiments that were used to simulate hypovolemic states. We also present a promising real-time wavelet algorithm for heart rate variability (HRV) processing in prehospital ambulatory monitoring applications
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