This paper presents an overview of the precise models that have been utilized to investigate induction motors under faulty conditions using Artificial Intelligence techniques. A comprehensive explanation of each typic...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
This paper presents an overview of the precise models that have been utilized to investigate induction motors under faulty conditions using Artificial Intelligence techniques. A comprehensive explanation of each typical category is given, as well as the strengths and weaknesses in their capacity to replicate various kinds of flaws using AI Techniques.
Over the past two decades, motor imagery brain-computer interface (MI-BCI) system has been extensively developed. In this system signal processing algorithms are critical to robust operation. In BCI community, however...
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Over the past two decades, motor imagery brain-computer interface (MI-BCI) system has been extensively developed. In this system signal processing algorithms are critical to robust operation. In BCI community, however, there is no comprehensive review of the recent development of signal processing algorithms. Through analyzing the latest papers, signal processing algorithms of pre-processing, feature extraction, feature selection, and classification components are discussed in detail. Besides, post-processing and other existing problems are mentioned. The following key issues are addressed: (1) which components are the key of signalprocessing;(2) which signal processing algorithms are frequently used in each component;(3) which signal processing algorithms attract more attention. This information can be used as reference and guidance for further research.
Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded ...
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Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). signal processing algorithms that can extract ECG signals out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The synthetic textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 trials (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs.
Digital signalprocessing (DSP) is the process of taking a signal and performing an algorithm on it to analyze, modify, or better identify that signal. To take advantage of DSP advances, one must have at least a basic...
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Digital signalprocessing (DSP) is the process of taking a signal and performing an algorithm on it to analyze, modify, or better identify that signal. To take advantage of DSP advances, one must have at least a basic understanding of DSP theory along with an understanding of the hardware architecture designed to support these new advances. There are several programming techniques that maximize the efficiency of the DSP hardware, as well as a few fundamental concepts used to implement DSP software. This tutorial will touch on the fundamental concepts of DSP theory and algorithms and also provide an overview of the implementation and optimization of DSP software.
This paper introduces a systematic quantitative methodology to prototype deterministic recursive DSP algorithms onto multiple programmable signal processors. A scheduling framework that is based upon linear integer pr...
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This paper introduces a systematic quantitative methodology to prototype deterministic recursive DSP algorithms onto multiple programmable signal processors. A scheduling framework that is based upon linear integer programming techniques is used to obtain rate, processor, delay, and communications optimal schedules for a given data flow graph representation of a signalprocessing algorithm. This powerful design synthesis environment facilitates optimal scheduling for randomly connected heterogeneous systems with multiple pipelined functional units and finite resources in VLSI. This framework can also be used in the high-level synthesis of efficient register-transfer level (RTL) VLSI descriptions from behavioral specifications.
In future telecommunications networks, an important role will be played by ''intelligent control'' techniques aimed at selecting and tuning signalprocessing (SP) algorithms, In this paper, we first de...
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In future telecommunications networks, an important role will be played by ''intelligent control'' techniques aimed at selecting and tuning signalprocessing (SP) algorithms, In this paper, we first define the main problems of automatic control of SP algorithms;then, we propose our knowledge-based approach to carry out such a task. In the planning phase, a restricted set of algorithm sequences are selected. This set is used as a raw plan to be refined and corrected in the execution phase, based on quality tests on progressive results. In particular, we focus on the control strategies adopted and describe how expert knowledge is represented and applied to implement such strategies, As an example, the control of low-level image processing is detailed, Results an an image-compression application are also reported,
In this paper, we study the properties of information matrices of the difference operator or the so-called delta (delta) operator-based algorithms for adaptive signalprocessing. We show that the conditioning of a tra...
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In this paper, we study the properties of information matrices of the difference operator or the so-called delta (delta) operator-based algorithms for adaptive signalprocessing. We show that the conditioning of a transformed information matrix in the delta domain is always better than that of the original information matrix in the conventional q domain for ill-conditioned problems. The results obtained in this paper give the explanation of the advantages of using delta operator algorithms for adaptive signalprocessing that have been developed recently. The analysis in this paper also helps to clarify the problem about the effect of Delta in the delta operator algorithms and justify the use of sampling interval as Delta in most cases when the ill conditioning is caused by fast sampling of continuous time systems.
Forms of algorithms that facilitate rapid processing on affiliated systolic arrays are examined. Classes of linear maps A: E/sup /n to E/sup /n that can be computed on p*p arrays at speed. O(p) where p= square root n ...
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Forms of algorithms that facilitate rapid processing on affiliated systolic arrays are examined. Classes of linear maps A: E/sup /n to E/sup /n that can be computed on p*p arrays at speed. O(p) where p= square root n are identified. The array architectures which provide the requisite computational support are proposed. The expansion of arbitrary linear maps in terms of the fast maps is considered. The results include a definitive method for minimal expansions and for best approximations of an a priori order. A detailed comparative example which illustrates the principles in question is also included.
A novel approach to definition of digital signal processing algorithms using bilinear form representation is introduced. The new algorithms are used to calculate power system power and line parameter values based on t...
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A novel approach to definition of digital signal processing algorithms using bilinear form representation is introduced. The new algorithms are used to calculate power system power and line parameter values based on the current and voltage samples. The bilinear form approach provides a convenient methodology for optimal design of digital signal processing algorithms. This feature is utilized to design digital algorithms for power and line parameter measurements with low sensitivity to system frequency change. Several different algorithms are defined and their performance is investigated by testing their sensitivity to system frequency change. Various sampling rates and different data windows are utilized to define several test cases.< >
This work features a stochastic perturbation theoretic approach that can be used to calculate the performance analyses of array signal processing algorithms for the nonasymptotically large, moderate data regimes that ...
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This work features a stochastic perturbation theoretic approach that can be used to calculate the performance analyses of array signal processing algorithms for the nonasymptotically large, moderate data regimes that are appropriate for many practical system applications. As an example, the perturbation method is used to develop the expressions for the moments of the MUSIC null spectrum at moderate SNR for independent data snapshot numbers as low as approximately 5. Theoretical predictions are shown to be in good agreement with empirical statistical simulation results.
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