Vector quantization (VQ) is a popular signal compression method. In the framework of VQ, fast search method is one of the key issues because it is the time bottleneck for VQ applications. in order to speed up VQ encod...
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Vector quantization (VQ) is a popular signal compression method. In the framework of VQ, fast search method is one of the key issues because it is the time bottleneck for VQ applications. in order to speed up VQ encoding process, how to construct some lower dimensional feature vectors for a k-dimensional original vector so as to measure the distortion between any vectors lightly becomes important. To reduce the dimension for approximately representing a k-dimensional vector, the multi-resolution concept is a natural consideration. By introducing a pyramid data structure, the multi-resolution concept used in fast VQ encoding includes two aspects, which are (1) a multi-resolution distortion check method and (2) a multi-resolution distortion computation method. Some fast search methods that are based on a 4-pixel-merging (4-PM) mean pyramid data structure [Lin, S.J., Chung, K.L., Chang, L.C. 2001. An improved search algorithm for vector quantization using mean pyramid structure. Pattern Recognition Lett. 22 (3/4) 373] and a 2-pixel-merging (2-PM) sum pyramid data structure [Pan, Z., Kotani, K., Ohmi, T. 2004. An improved fast encoding algorithm for vector quantization using 2-pixel-merging sum pyramid data structure. Pattern Recognition Lett. 25 (3) 459] have already been proposed. Both of them realized the multi-resolution concept by using a multi-resolution distortion check method. However, both of them ignored the multi-resolution distortion computation method, which can also be guaranteed by the multi-resolution concept if a recursive computation way is introduced. In principle, a multi-resolution distortion computation method can completely reuse the obtained computation result that is already executed at a lower resolution level so that Do waste to it will occur at all. This paper aims at improving the search efficiency of the previous work [Pan, Z., Kotani, K., Ohmi, T. 2004. An improved fast encoding algorithm for vector quantization using 2-pixel-merging sum p
Let u be a function defined on a spherical triangulation Delta of the unit sphere S. In this paper, we study a recursive method for the construction of a Hermite spline interpolant u(k) of class l(k) and degree 4k + I...
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Let u be a function defined on a spherical triangulation Delta of the unit sphere S. In this paper, we study a recursive method for the construction of a Hermite spline interpolant u(k) of class l(k) and degree 4k + I on S, defined by some data scheme D-k(u). We show that when the data sets D-r(u) are nested, i.e., Dr-l (u) subset of D-r (u), l <=. r <=. k, the spline function u(k) can be decomposed as a sum of k + I simple elements. This decomposition leads to the construction of a new and interesting basis of a space of Hermite spherical splines. The theoretical results are illustrated by some numerical examples. (c) 2007 Elsevier B.V. All rights reserved.
Exact recursive formulas are derived for the state probabilities in priority queueing systems (preemptive and non-preemptive). The derivation is based only on the general structure of the generating function involved,...
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Exact recursive formulas are derived for the state probabilities in priority queueing systems (preemptive and non-preemptive). The derivation is based only on the general structure of the generating function involved, and thus is simpler and more general than previous methods. Furthermore, applications of the method to other queueing systems are discussed.
Several aspects related to the performance of wireless networks have been traditionally modeled with queueing theory. This paper presents a recursive procedure to find the steady state probabilities in an M/M/1/B queu...
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ISBN:
(纸本)9781538608722
Several aspects related to the performance of wireless networks have been traditionally modeled with queueing theory. This paper presents a recursive procedure to find the steady state probabilities in an M/M/1/B queue from the steady state probabilities of an M/M/1/(B-1) queue. The procedure provides an alternative approach for the computation of state probabilities and provides better insight on how those probabilities change when the buffer size increases. A formula is developed for computing the relative decrease in the state probabilities when the buffer size is increased by one.
Hidden-Markov Models (henceforth abbreviated to HMMs), taken in their most general acception, that is including models in which the state space of the hidden chain is continuous have become a widely used class of stat...
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ISBN:
(纸本)0780394038
Hidden-Markov Models (henceforth abbreviated to HMMs), taken in their most general acception, that is including models in which the state space of the hidden chain is continuous have become a widely used class of statistical models with applications in diverse areas such as communications, engineering, bioinformatics. econometrics and many more. This contribution focus oil the computation of derivatives of the log-likelihood and proposes a (comparatively) simple and general framework. based oil the use of Fisher and Louis identities, to obtain recursive equations for computing the score and observed information matrix. This approach is thought to be simpler than (although equivalent to) the solution provided by the so-called sensitivity equations. It is based oil the original remark that recursive smoothers for HMMs are also available for some functionals of the hidden states which do not reduce to sum functionals. This view of the problem also suggests ways in which these exact equations could be approximated using sequential Monte Carlo methods.
A brief study is made of the propagation of errors in linear first-order difference equations. The recursive computation of successive derivatives of θx/x and (cos x)/x is considered as an Illustration. [ABSTRACT FRO...
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A brief study is made of the propagation of errors in linear first-order difference equations. The recursive computation of successive derivatives of θx/x and (cos x)/x is considered as an Illustration. [ABSTRACT FROM AUTHOR]
Fast, accurate, and stable computation of the Clebsch-Gordan (C-G) coefficients is always desirable, for example, in light scattering simulations, the translation of the multipole fields, quantum physics and chemistry...
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Fast, accurate, and stable computation of the Clebsch-Gordan (C-G) coefficients is always desirable, for example, in light scattering simulations, the translation of the multipole fields, quantum physics and chemistry. Current recursive methods for computing the C-G coefficients are often unstable for large quantum numbers due to numerical overflow or underflow. In this paper, we present an improved method, called the sign-exponent recurrence, for the recursive computation of C-G coefficients. The result shows that the proposed method can significantly improve the stability of the computation without losing its efficiency, producing accurate values for the C-G coefficients even with very large quantum numbers. (C) 2020 The Author. Published by Elsevier Ltd.
In-network processing is an efficient way to reduce the transmission cost in wireless sensor networks (WSNs). The in-network processing of many domain-specific computation tasks in WSNs usually requires to losslessly ...
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ISBN:
(纸本)9781510601550
In-network processing is an efficient way to reduce the transmission cost in wireless sensor networks (WSNs). The in-network processing of many domain-specific computation tasks in WSNs usually requires to losslessly distribute the computation of the tasks into the sensor nodes, which is however usually not easy. In this paper we are concerned with such kind of tasks whose computation can only be partitioned into recursive computation mode. To distribute the recursive computations into WSNs, it is required to design an appropriate single in-network processing path, along which the intermediate data is forwarded and updated in the WSNs. We address the recursive computation with constant size of computation result, e.g., distributed least square estimation (D-LSE). Finding the optimal in-network processing path to minimize the total transmission cost in WSNs, is a new problem and seldom studied before. To solve it, we propose a novel routing algorithm called as S-TSP, and compare it with some other greedy algorithms. Extensive simulations are conducted, and the results show the good performance of the proposed S-TSP algorithm.
Aiming at the development of real-time control-performance-oriented fault diagnosis and fault-tolerant control, an online recursive computational approach is proposed in this paper for the closed-loop stability margin...
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ISBN:
(纸本)9781728112138
Aiming at the development of real-time control-performance-oriented fault diagnosis and fault-tolerant control, an online recursive computational approach is proposed in this paper for the closed-loop stability margin of the plug-and-play process monitoring and control architecture (PnP-PMCA). The core of the proposed approach is based on the recursive least square (RLS) technique, which avoids the online computation of the matrix inverse. With the proposed approach in this paper, real-time control-performance-oriented fault diagnosis can be achieved. The correctness and the effectiveness of the proposed approach are demonstrated through the case studies on a DC motor benchmark system.
Machine learning methods have been widely used in different applications, including process control and monitoring. For handling statistical process control (SPC) problems, conventional supervised machine learning met...
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Machine learning methods have been widely used in different applications, including process control and monitoring. For handling statistical process control (SPC) problems, conventional supervised machine learning methods (e.g., artificial neural networks and support vector machines) would have some difficulties. For instance, a training dataset containing both in-control and out-of-control (OC) process observations is required by a supervised machine learning method, but it is rarely available in SPC applications. Furthermore, many machine learning methods work like black boxes. It is often difficult to interpret their learning mechanisms and the resulting decision rules in the context of an application. In the SPC literature, there have been some existing discussions on how to handle the lack of OC observations in the training data, using the one-class classification, artificial contrast, real-time contrast, and some other novel ideas. However, these approaches have their own limitations to handle SPC problems. In this article, we extend the self-starting process monitoring idea that has been employed widely in modern SPC research to a general learning framework for monitoring processes with serially correlated data. Under the new framework, process characteristics to learn are well specified in advance, and process learning is sequential in the sense that the learned process characteristics keep being updated during process monitoring. The learned process characteristics are then incorporated into a control chart for detecting process distributional shift based on all available data by the current observation time. Numerical studies show that process monitoring based on the new learning framework is more reliable and effective than some representative existing machine learning SPC approaches.
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