The Neural Engineering Utility with adaptive algorithms (NEUWAA) is a machine-based intelligence system for Automatic Test Equipment, which integrates various technologies in an adaptive Fault-Detection Environment. C...
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Direction-of-arrival (DOA) estimation based on subspace methods has collected much interest over a few decades, and adaptive DOA estimation with rapidly changing parameters will be necessary for wireless communication...
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The paper is devoted to present an analysis of the impact of higher order statistics (HOS) in adaptive blind source separation criteria. Despite the well known fact that they are necessary to provide source separation...
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adaptive algorithms play an important role in many acoustic applications including acoustic echo cancellation. The most popular adaptive algorithm used for echo cancelation is the least mean square (LMS) algorithm and...
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At CERN we have ramped up a program to investigate space charge effects in the LHC pre-injectors with high brightness beams and long storage times. This is in view of the LIU upgrade project [1] for these accelerators...
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We introduce a novel Gravity Search adaptive algorithm, which replicates the movement of a ball descending along the inner surface of a bowl filled with liquid. By choosing the appropriate viscosity of the liquid, gra...
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ISBN:
(纸本)9781467300797
We introduce a novel Gravity Search adaptive algorithm, which replicates the movement of a ball descending along the inner surface of a bowl filled with liquid. By choosing the appropriate viscosity of the liquid, gravity, and the mass of the ball, the movement of the ball can be controlled to achieve the desired performance. The physical law of conservation of energy makes the algorithm very stable. We also show that the Normalized LMS algorithm is a special form of the proposed Gravity Search adaptive algorithm. Simulation results comparing the performance of Normalized LMS algorithm and the Gravity Search adaptive algorithm are provided. The results show that the Gravity Search adaptive algorithm has superior immunity to the random perturbation of the error signal compared to the Normalized LMS algorithm.
In this paper we revisit the fixed-confidence identification of the Pareto optimal set in a multi-objective multi-armed bandit model. As the sample complexity to identify the exact Pareto set can be very large, a rela...
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Tensor-train (TT) decomposition has been an efficient tool to find low order approximation of large-scale, high-order tensors. Existing TT decomposition algorithms are either of high computational complexity or operat...
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ISBN:
(数字)9789082797053
ISBN:
(纸本)9781728150017
Tensor-train (TT) decomposition has been an efficient tool to find low order approximation of large-scale, high-order tensors. Existing TT decomposition algorithms are either of high computational complexity or operating in batch-mode, hence quite inefficient for (near) real-time processing. In this paper, we propose a novel adaptive algorithm for TT decomposition of streaming tensors whose slices are serially acquired over time. By leveraging the alternating minimization framework, our estimator minimizes an exponentially weighted least-squares cost function in an efficient way. The proposed method can yield an estimation accuracy very close to the error bound. Numerical experiments show that the proposed algorithm is capable of adaptive TT decomposition with a competitive performance evaluation on both synthetic and real data.
In this paper, we propose an algorithm to efficiently diagnose large-scale clustered failures. The algorithm, Cluster-MAX-COVERAGE (CMC), is based on greedy approach. We address the challenge of determining faults wit...
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In this paper, we propose an algorithm to efficiently diagnose large-scale clustered failures. The algorithm, Cluster-MAX-COVERAGE (CMC), is based on greedy approach. We address the challenge of determining faults with incomplete symptoms. CMC makes novel use of both positive and negative symptoms to output a hypothesis list with a low number of false negatives and false positives quickly. CMC requires reports from about half as many nodes as other existing algorithms to determine failures with 100% accuracy. Moreover, CMC accomplishes this gain significantly faster (sometimes by two orders of magnitude) than an algorithm that matches its accuracy. Furthermore, we propose an adaptive algorithm called adaptive-MAX-COVERAGE (AMC) that performs efficiently during both kinds of failures, i.e., independent and clustered. During a series of failues that include both independent and clustered, AMC results in a reduced number of false negatives and false positives.
We study the multi-armed bandit (MAB) problem with composite and anonymous feedback. In this model, the reward of pulling an arm spreads over a period of time (we call this period as reward interval) and the player re...
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