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In recent years, nonnegative matrix factorization (NMF) attracts much attention in machine learning and signal processing fields due to its interpretability of data in a low dimensional subspace. For clustering problems, symmetric nonnegative matrix factorization (SNMF) as an extension of NMF factorizes the similarity matrix of data points directly and outperforms NMF when dealing with nonlinear data...
In this paper, a novel algorithm for bandwidth reduction in adaptive distributed learning is introduced. We deal with diffusion networks, in which the nodes cooperate with each other, by exchanging information, in order to estimate an unknown parameter vector of interest. We seek for solutions in the framework of set theoretic estimation. Moreover, in order to reduce the required bandwidth by the...
Acquisition is one of the essential stages in the DSSS signal processing. The conventional acquisition methods may degrade when there is a code phase offset which in the worst case may be a quarter of one chip in this stage. A new acquisition method by using weighting adjustment is proposed to solve this problem. In this paper, we take GPS C/A code as an example to introduce this method. The performance...
The paper studies the effect of noise on the asymptotic properties of high dimensional consensus (HDC). HDC offers a unified framework to study a broad class of distributed algorithms with applications to average consensus, leader-follower dynamics in multi-agent networks and distributed sensor localization. We show that under a broad range of perturbations, including inter-sensor communication noise,...
Due to long calculation time and slow convergence speed, ant colony system (ACS) cannot be used directly in wireless sensor networks (WSN). In this paper, we present an improved ACS algorithm based on Altitude Information (AI) and Ant Withdrawal (AW), named ACSA (an improved ACS algorithm with AI). The concept of AI is defined, and the design philosophy, algorithm realization and performance simulation...
This paper introduces higher dimensional consensus, a framework to capture a number of different, but, related distributed, iterative, linear algorithms of interest in sensor networks. We show that, by suitably choosing the iteration matrix of the higher dimensional consensus, we can capture, besides the standard average-consensus, a broad range of applications, including sensor localization, leader-follower,...
Data aggregation is an essential paradigm for energy efficient routing in energy constraint wireless sensor networks (WSN). Data aggregation in WSN can be treated as searching for the Minimum Steiner Tree (MST) including source nodes and sink node. In this paper, we propose a Data-aggregation Algorithm based on Adaptive Ant Colony System (AACS) algorithm. In this algorithm, Directed Diffusion (DD)...
This paper addresses adaptive channel estimation for time-varying mobile wireless channels with nonstationary statistics. We presents a reduced complexity adaptive channel estimator based on a set membership filtering approach known as the Optimal Bounding Ellipsoid (OBE) algorithm. To exploit time and frequency domain correlation properties of the channel in an efficient low-complexity way, we allow...
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