Linearly constrained LMS adaptive filter algorithms are considered for digital processing of 50/60 Hz line-frequency signals. The constraints are set such that the primary sinusoidal waveform is guaranteed to pass the...
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Linearly constrained LMS adaptive filter algorithms are considered for digital processing of 50/60 Hz line-frequency signals. The constraints are set such that the primary sinusoidal waveform is guaranteed to pass the filter unaltered, and the adaptation is used to dynamically optimize the noise attenuation properties. In order to reduce the computational complexity of the constrained algorithm, selective coefficient updating is used, and the update formulas are derived accordingly. The approach is efficient in suppressing noise and harmonics in applications such as reactive power estimation and zero-crossing detection.
Adaptive beamforming (ABF) methods typically yield beam patterns that contain regions of high sidelobe gain. This can become a major problem when the platform is mobile with respect to noise sources. Real world ABF sy...
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Adaptive beamforming (ABF) methods typically yield beam patterns that contain regions of high sidelobe gain. This can become a major problem when the platform is mobile with respect to noise sources. Real world ABF systems demand some sort of sidelobe control or stabilisation - one such approach is via penalty function methods. Computational speed is an important issue and their exists a requirement for computationally efficient methods of calculating penalty functions. One such method is described in this paper.
The purpose of the work is to substantiate the possibility traveling wave fault location (TWFL) complex functioning in branched distribution networks of medium voltage class. Traveling waves (TW) are born in the place...
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ISBN:
(数字)9798350386028
ISBN:
(纸本)9798350386035
The purpose of the work is to substantiate the possibility traveling wave fault location (TWFL) complex functioning in branched distribution networks of medium voltage class. Traveling waves (TW) are born in the place of fault. The complex registers the time of TW arrival to the ends of the network in a unified satellite time scale. Then the complex calculates the fault location. The main factor complicating the complex's functioning in branched networks of medium voltage class lines is the presence of a large number of concentrated inhomogeneities in the form of nodes with branches. Analytically and with the help of model calculations in PSCAD program, we can obtain TW transmission coefficient through inhomogeneity nodes at different types of faults. The paper shows the experiments' results. A large number of complex's sensors in several ends of the branched network allows us to determine the time TW registration and the speed of their propagation along the lines between paired combinations complex's sensors. Comparison of the registered propagation speed with the speed of light allows us to separate reliable or accurate registrations of the TW beginning from unreliable registrations. It is shown, that the amplitude of the registered TW and the amplitude of stationary and non-stationary noise at the place of registration determine the reliability of the complex operation. The paper compares the results of TW registration generated inside and outside the area controlled by the complex.
algorithms estimate the parameters of UWB radiothermal signals in single-antenna radiometers with modulation are synthesized and investigated within the bounds of method of maximum likelihood. Based on analysis of the...
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algorithms estimate the parameters of UWB radiothermal signals in single-antenna radiometers with modulation are synthesized and investigated within the bounds of method of maximum likelihood. Based on analysis of the matrix inverse to the Fisher in- formation matrix the potential accuracy of estimates of measured parameters and the sensitivity of radiometric systems are investigated.
In this paper we discuss two kinds of ill-posed problems in signalprocessing, that is, in detail, reconstructing compactly supported signals in the Fourier transform and solving the convolution equation with analytic...
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In this paper we discuss two kinds of ill-posed problems in signalprocessing, that is, in detail, reconstructing compactly supported signals in the Fourier transform and solving the convolution equation with analytic kernel. Having analyzed the essential reason of ill-posedness for these problems, we present some stabilized algorithms, which cure the ill-posedness, to recover the approximate solution. Finally numerical experiments show the efficiency and fast convergence of these algorithms.
This paper presents the application of genetic algorithms to the on-line adaptation of non-linear adaptive filters-adaptive systems applicable to, for example, stochastic signal estimation, system identification and t...
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This paper presents the application of genetic algorithms to the on-line adaptation of non-linear adaptive filters-adaptive systems applicable to, for example, stochastic signal estimation, system identification and the optimization of electronic or optoelectronic signal processors. Given the filter topology, the corresponding filter parameters are estimated using a time-dependent moving error criterion. The genetic algorithm's ability to track temporal changes in the signal statistics is achieved by the use of partial hypermutation. The proposed methodology is applied to the problem of non-linear and non-stationary signal estimation by using the adaptive filter as part of a non-linear prediction error filter. Simulation results for the estimation of autoregressive and bilinear stochastic signal models and a comparison to the least mean squares algorithm are presented demonstrating the suitability of the approach.
I introduce SPIRAL (***), a generator of libraries of linear signal processing algorithms like linear transforms, including the discrete Fourier transform, the discrete cosine transform, filters, or wavelets, as well ...
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I introduce SPIRAL (***), a generator of libraries of linear signal processing algorithms like linear transforms, including the discrete Fourier transform, the discrete cosine transform, filters, or wavelets, as well as applications lke JPEG2000, Viterbi decoding, and SAR image formation algorithms. SPIRAL is a new breed of intelligent compilers that couples domain knowledge, computer architecture modeling, machine learning methodologies, and compiler technology to generate automatically highly tuned SW codes and very efficient HW IP cores. SPIRAL produces automatically C code with embedded vector and parallel instructions for multicore or multiprocessor architectures, or netlists for FPGA implementations. SPIRAL can optimize for or trade among a range of criteria including runtime, power/energy, accuracy, or area. SPIRAL has been benchmarked against expert hand tuned implementations and has been licensed as a tool by INTEL.
This paper describes the signal processing algorithms that can be used for the PMUs operating in a dynamic environment. Dynamic response and rejection to noise, harmonics and interharmonics are discussed and some solu...
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ISBN:
(纸本)9781467391351
This paper describes the signal processing algorithms that can be used for the PMUs operating in a dynamic environment. Dynamic response and rejection to noise, harmonics and interharmonics are discussed and some solutions for improving them, emerging from the technical literature and from some additional tests, are proposed. For example dynamic response can be improved by evaluating the Taylor series as a time function, while the rejection to harmonics by selecting appropriately the observation time.
The purpose of this paper is to present a unified, simplified, and concise, overview of spectral target detection algorithms for hyperspectral imaging applications. We focus on detection algorithms derived using estab...
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The purpose of this paper is to present a unified, simplified, and concise, overview of spectral target detection algorithms for hyperspectral imaging applications. We focus on detection algorithms derived using established statistical techniques and whose performance is predictable under reasonable assumptions about hyperspectral imaging data. The emphasis on a signalprocessing perspective helps to, better understand the strengths and limitations of each algorithm, avoid unrealistic performance expectations, and apply an algorithm properly and sensibly.
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