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Underwater acoustic (UWA) channel is a complex time-space- and frequency-variant channel, which is one of the most difficult wireless communication channels so far. Coherent communication has become a hotspot in high speed underwater acoustic communication. To achieve the low bit error rate and high data transmission rate, the channel equalization technique must be introduced for coherent underwater...
The study of the synchronization problems in wireless sensor networks (WSNs) is increasingly significant. Some researchers found that the network topology has an important impact on the convergence performance of the synchronization in WSNs. According to the feature of WSNs, we use random geometric graph (RGG) to describe the model of WSNs. And the degree distribution in RGG is similar to binomial...
The performance of phase-locked loop (PLL) severely affects the system's stability in a three-phase rectifier. Traditional methods tend to adopt the idea of single synchronous coordinate transformation, which implements the phase synchronization through controlling the q-axis voltage to zero. A novel three-phase PLL is proposed in this paper based on this idea. In order to obtain better convergence...
In this paper, we probe into the influence of network structure on the convergence rate of agents in multi-agent systems, especially for the position and number of nodes of network on it. We discover some results of which kinds of networks are conductive to the faster convergence rate of agents in multi-agent systems. Firstly, if there is no ring in different networks where the number of nodes are...
Smart antenna system is very useful to increase the capacity, coverage and quality of communication system. Smart antenna combines the antenna array with digital signal processing capability which tracks the mobile user by focusing radiation in a specific desired user direction and at the same time forming null toward interference. This paper represents the performance analysis of Data reusing least...
In this paper, we study a convex optimization problem that arises in a network where multiple agents cooperatively optimize the sum of nonsmooth but Lipschitz continuous functions, subject to a convex and compact constraint set. Under the additional constraint that each agent can only transmit quantized information, we develop a distributed quantized gradient-free algorithm for solving the multi-agent...
We study the convergence properties of a general inertial first-order proximal splitting algorithm for solving nonconvex nonsmooth optimization problems. Using the Kurdyka-Łojaziewicz (KL) inequality we establish new convergence rates which apply to several inertial algorithms in the literature. Our basic assumption is that the objective function is semialgebraic, which lends our results broad applicability...
We present a novel distributed probabilistic bisection algorithm using social learning with application to target localization. Each agent in the network first constructs a query about the target based on its local information and obtains a noisy response. Agents then perform a Bayesian update of their beliefs followed by an averaging of the log beliefs over local neighborhoods. This two stage algorithm...
In this paper, we study the randomized distributed coordinate descent algorithm with quantized updates. In the literature, the iteration complexity of the randomized distributed coordinate descent algorithm has been characterized under the assumption that machines can exchange updates with an infinite precision. We consider a practical scenario in which the messages exchange occurs over channels with...
We consider distributed optimization problems where nodes in a connected network collaboratively minimize the sum of their locally known convex costs subject to a common (vector-valued) optimization variable. In this paper, we present a mechanism to significantly improve the computational and communication efficiency of some recently proposed first and second order distributed methods for solving...
In order to improve the searching speed and the quality of global optimal solution, we propose an improved algorithm based on Artificial Bee Colony(ABC) algorithm, which can search the space by stochastic optimization and dynamic regulation (named SRABC). Firstly, the improved algorithm can update the next location of ABC algorithm, which can perfect the correlation for the bee colony. Secondly, we...
This paper addresses the speech enhancement problem with adaptive filtering algorithms. We propose a new dual forward blind source separation (FBSS) algorithm based on the use of the recursive least square algorithm to update the cross-filters of the forward structure. This algorithm inherits the good characteristics of the combination between the FBSS and the good properties of the RLS algorithm...
One of the challenging issues in a distributed computing system is to reach on a decision with the presence of so many faulty nodes. These faulty nodes may update the wrong information, provide misleading results and may be nodes with the depleted battery power. Consensus algorithms help to reach on a decision even with the faulty nodes. Every correct node decides some values by a consensus algorithm...
In this paper, a new concept of iterative learning control (ILC), namely consolidated ILC (CILC), is introduced. An iterative learning control system is subject to data interrupt when the system output is restricted within a certain area. Data interrupt means that the system output is interrupted at a certain moment due to output restrictions. The output data from this moment to the end of the trail...
This paper analyzes the performance of off-line secondary path modeling algorithms for narrowband active noise control (ANC) systems. The accuracy and convergence rate of adaptive system identification will be degraded by disturbance. A delayless filterbank is utilized to partition the full-band excitation and error signals in order to reduce the order of the secondary-path model and increase the...
The conventional algorithms such as least mean square (LMS) and normalized LMS (NLMS) exhibit degraded performance for colored signal as input in the presence of impulsive noise. To circumvent this, the family of affine projection sign algorithm (APSA) has been introduced for improved performance under impulsive noise interference. Further, to obtain significant reduction in the computations while...
The adaptive feedback canceller (AFC) based on the least mean square (LMS) or normalized LMS (NLMS) algorithm shows poor convergence for sparse impulse response, since they do not consider the characteristics of the impulse response. The acoustic feedback path of the hearing aid exhibits sparse characteristics consisting of few active coefficients and many non active (near to zero) coefficient values...
The widely used least mean square (LMS) and normalized LMS (NLMS) algorithm for the development of adaptive feedback canceller in hearing aid exhibits poor convergence in the presence of colored signal. However, more often the input signals available to the hearing aid are colored in nature, which results in degraded performance of the feedback canceller and loss of speech intelligibility. The affine...
The adaptive feedback canceller (AFC) based on the least mean square (LMS) or normalized LMS (NLMS) algorithm exhibits poor convergence and high computational complexity when used for modelling an impulse response with large number of coefficients. Also, its convergence rate degrades for colored signal as input. The usage of frequency domain LMS (FLMS) algorithm for developing AFC results in enhanced...
In this paper we introduce an Modified Clipped LMS (MCLMS) algorithm with a variable step size. In the MCLMS algorithm two parameters, the step size and the threshold control the convergence rate of the adaptive filter coefficients and also determine the final mean-square error. The computational complexity decreased dramatically by a large threshold. However, this selection results in a low convergence...
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