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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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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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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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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adaptive algorithms for beam shaping of a phased array antenna and multiple-input multiple-output (MIMO) system gaining importance in today's advanced wireless networks to mitigate interference effects and distort...
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adaptive algorithms for beam shaping of a phased array antenna and multiple-input multiple-output (MIMO) system gaining importance in today's advanced wireless networks to mitigate interference effects and distortion in the receiving signal due to multipath, small scale, and large scale fading effects. This article deals with the development of reconfigurable field programmable gate array (FPGA)-based hardware for smart antenna system to explore parameter dependencies, drawbacks, and relative performance comparison of popular adaptive beamforming and interference suppression algorithms. These are least mean square, recursive least squares (RLS), and sample matrix inversion (SMI) used in real-time under laboratory environment where the existing wireless channel between transmitters and receivers is linear time-varying in nature due to presence of secondary sources giving rise to small-scale fading. For this at first, we propose a novel received signal strength indicator-based procedure to measure the radiation pattern of the antenna under an echoic indoor environment on a reconfigurable and portable FPGA system named wireless open-access research platform (WARP), controllable by generic programming codes over a user-friendly MATLAB interface. For better performance, the SMI algorithm was modified to increase block size rather than block shifting in general SMI. Later a comparative study was performed under varying conditions to observe the best utilization of three adaptive algorithms in beam shaping. In all cases, SMI performs the best with less beam shaping error and faster convergence, validating its use in a real-time fading environment.
In this paper, di_erent numerical methods to calculate the optimum coe_cients in the Volterra Series are introduced to analyze the performance of a Digital Predistorter (DPD) for Power Amplifier (PA) with memory. The ...
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In this paper, di_erent numerical methods to calculate the optimum coe_cients in the Volterra Series are introduced to analyze the performance of a Digital Predistorter (DPD) for Power Amplifier (PA) with memory. The adaptive algorithms used are Least Mean Square (LMS), Normalized LMS (NLMS), Variable Step Size (VSS), and the VSS modified. The parameters in the Volterra Model are typically calculated based on the mean square error criteria, then in this paper we compare alternatives to reduce the complexity, number of operations, and the time in the linearization of PA through DPD measured with the OFDM signal. The simulation results show that the VSS algorithm is faster and e_ective to calculate the parameters in the Volterra model.
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