توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱The Impact of Unreliable Communications on the Steady–State Performance of Incremental RLS Adaptive Networks
اطلاعات انتشار: نوزدهمین کنفرانس مهندسی برق ایران، سال
تعداد صفحات: ۵
Adaptive networks are known as appealing solutions for the distributed estimation problem, when the statistical information of the underlying process of interest is not available. In this paper, we study the effect of unreliable communications (modeled by noisy links) on the performance of incremental recursive leastsquares (RLS) adaptive networks. The motivation for such study stems from this fact that noisy links strongly affect the performance of adaptive networks. We derive theoretical relations in terms of mean–square deviation (MSD), excess mean–square error (EMSE) and meansquare error (MSE), to explain how the noisy links affect the performance of incremental RLS adaptive networks. In our analysis we evaluate the steady–state performance of the individual nodes across the entire network using the spatial–temporal energy conservation argument. The simulation results show that there is a good match between simulations and derived theoretical expressions<\div>

۲Exploiting Observation Quality Information to Enhance the Steady–State Performance of Incremental LMS Adaptive Networks
اطلاعات انتشار: نوزدهمین کنفرانس مهندسی برق ایران، سال
تعداد صفحات: ۵
In this paper, we investigate the effect of observation quality on the steady–state performance of incremental adaptive networks with LMS learning. We exploit the knowledge of observation quality to adjust the step–size parameter in an adaptive network according to nodes observation quality. We formulate the step–size assignment as a constrained optimization problem and then solve it via Lagrange multipliers approach. We show that applying the optimal step sizes in an incremental adaptive network improves its the steady–state performance. The simulation results are also presented to illustrate the derived theoretical results<\div>

۳A Discrete Wavelet Transform Based Algorithm for Usable Speech Extraction
نویسنده(ها): ، ،
اطلاعات انتشار: یازدهمین کنفرانس سراسری سیستم های هوشمند، سال
تعداد صفحات: ۵
Co–channel speech signal is generated when two or more people are talking at the same time over the same channel.Segments of co–channel speech that are still usable for speech processing algorithms are known as usable speech. Usable speech is a context–dependent concept, so we consider usable speech extraction in speaker identification application. So far many algorithms are proposed for usable speech extraction [6–8]. The common feature of all existing algorithms is that they exploit the periodical property of the usable frames, in time domain or in frequency domain. In this paper, we propose a discrete wavelet transform (DWT) based algorithm to exploit the periodical property of speech frames. The proposed algorithm is used in two different cases: first, in the voiced detection algorithm and second, in the usable speech extraction algorithm. To evaluate the proposed algorithm, the simulation results are presented in the paper. The simulation results show that the proposed algorithm has 3% improvement in usable speech detection compared with other algorithms<\div>

۴A Robust Distributed Estimation Algorithm under Alpha–Stable Noise Condition
نویسنده(ها): ، ،
اطلاعات انتشار: Journal of Communication Engineering، چهارم،شماره۲، Autumn ۲۰۱۵، سال
تعداد صفحات: ۱۰
Robust adaptive estimation of unknown parameter has been an important issue in recent years for reliable operation in the distributed networks. The conventional adaptive estimation algorithms that rely on mean square error (MSE) criterion exhibit good performance in the presence of Gaussian noise, but their performance drastically decreases under impulsive noise. In this paper, we propose a robust adaptive estimation algorithm for networks with cyclic cooperation. We model the impulsive noise as the realization of alpha–stable distribution. Here, we move beyond MSE criterion and define the estimation problem in terms of a modified cost function which exploits higher order moments of the error. To derive a distributed and adaptive solution, we first recast the problem as an equivalent form amenable to distributed implementation. Then, we resort to the steepest–descent and statistical approximation to obtain the proposed algorithm. We present some simulations results which reveal the superior performance of the proposed algorithm than the incremental least mean square (ILMS) algorithm in impulsive noise environments.
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