The M-estimates of multivariate location and scatter are a class of robust alternatives to the sample mean vector and sample covariance matrix. They also have applications to bounded influence regression. There are a ...
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The M-estimates of multivariate location and scatter are a class of robust alternatives to the sample mean vector and sample covariance matrix. They also have applications to bounded influence regression. There are a number of problems, though, which need to be resolved before they can become widely applicable. This paper addresses the problems of existence, uniqueness and computation of the M-estimates for finite sample sizes.
In this paper, we study the problem of one-unit linear Independent Component Analysis (ICA) without whitening. The FastICA algorithm is arguably the most popular algorithm for solving the whitened one-unit linear ICA ...
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ISBN:
(纸本)9781424427093
In this paper, we study the problem of one-unit linear Independent Component Analysis (ICA) without whitening. The FastICA algorithm is arguably the most popular algorithm for solving the whitened one-unit linear ICA problem. Although a modified FastICA has been already proposed to solve the non-whitened one-unit linear ICA problem, there is unfortunately no known analysis regarding its effectiveness and efficiency. In this work, the non-whitened FastICA algorithm is revisited and analyzed in the framework of geometric optimization algorithms. In this paper, a conjugate gradient (CG) algorithm for the non-whitened one-unit linear ICA problem is developed as well. Local convergence properties of both algorithms are discussed. Finally, local convergence performance of the algorithms is investigated by several numerical experiments.
We propose a method that combines fixed point algorithms with a neural network to optimize jointly discrete and continuous variables in millimeter-wave communication systems, so that the users' rates are allocated...
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ISBN:
(纸本)9781728157672
We propose a method that combines fixed point algorithms with a neural network to optimize jointly discrete and continuous variables in millimeter-wave communication systems, so that the users' rates are allocated fairly in a well-defined sense. In more detail, the discrete variables include user-access point assignments and the beam configurations, while the continuous variables refer to the power allocation. The beam configuration is predicted from user-related information using a neural network. Given the predicted beam configuration, a fixed point algorithm allocates power and assigns users to access points so that the users achieve the maximum fraction of their interference-free rates. The proposed method predicts the beam configuration in a "one-shot" manner, which significantly reduces the complexity of the beam search procedure. Moreover, even if the predicted beam configurations are not optimal, the fixed point algorithm still provides the optimal power allocation and user-access point assignments for the given beam configuration.
This paper presents the digital design of a STATCOM based voltage and frequency regulator for self excited induction generator (SEIG) feeding linear and nonlinear loads. The SEIG have inherent poor voltage and frequen...
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ISBN:
(纸本)9781479931781
This paper presents the digital design of a STATCOM based voltage and frequency regulator for self excited induction generator (SEIG) feeding linear and nonlinear loads. The SEIG have inherent poor voltage and frequency regulation. The voltage and frequency of the SEIG system depend upon the load current and power factor of the load for constant power operation with fixed excitation capacitor employing unregulated turbines. The performance of SEIG is largely affected by load harmonics. A current controlled voltage source inverter working as STATCOM is used for harmonic elimination, load balancing and it provides reactive power compensation. A DC chopper with dump load is connected across DC bus capacitor to regulate varying consumer load. The control algorithm has been co-simulated with processor in the loop (PIL) using TMS320F2812 fixedpoint DSP. The transient behaviour of SEIG-STATCOM system at different operating conditions such as application and removal of balanced, unbalanced and nonlinear load is investigated.
This article aims to show that the classes of enriched nonexpansive mappings due to Berinde, the mappings satisfying Suzuki-(KC)-condition due to Karapinar and Tas and generalized nonexpansive mappings due to Hardy an...
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This article aims to show that the classes of enriched nonexpansive mappings due to Berinde, the mappings satisfying Suzuki-(KC)-condition due to Karapinar and Tas and generalized nonexpansive mappings due to Hardy and Rogers do not imply each other. As an application of our main results, a common solution of the nonlinear fractional differential equations is approximated. Further, we establish some weak and strong convergence results for two Suzuki-(KC) mappings to approximate common fixedpoints by using S-type iterative algorithm in uniformly convex Banach spaces. For the valuation of our results, a couple of nontrivial numerical examples are also presented.
The physical limitations of medical imaging devices together with the adverse effect of measurement noises tend to reduce the resolution and contrast of resulting diagnostic images. As a result, there is a need to pre...
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ISBN:
(纸本)9781424441280
The physical limitations of medical imaging devices together with the adverse effect of measurement noises tend to reduce the resolution and contrast of resulting diagnostic images. As a result, there is a need to preprocess the images before their interpretation by a medical practitioner. The present study is concerned with the case in which the images of interest are degraded by convolutional blur and Poisson noises. Such a situation is prevalent in many imaging modalities including PET, SPECT and confocal microscopy. To alleviate the image degradation, there exist a range of solution methods which are based on the principles originating from the fixed-pointalgorithm of Richardson and Lucy (RL). In this paper, we extend the RL algorithm to incorporate a constraint that requires the image of interest to be sparsely represented in the domain of a suitable linear transform. In this case, the positivity of the reconstructed image and its representation coefficients is ensured by using a positive valued dictionary of "representation atoms". The superiority of the proposed algorithm over some alternative reconstruction methods has been established through a series of numerical experiments.
This paper presents an algorithm for computing approximations to a certain subset of Pareto optimal allocations in a public goods economy. Consumers are partitioned into a number of exogenous governmental jurisdiction...
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Steady-state solutions of a piecewise-linear oscillator under multi-forcing frequencies are obtained using the fixed point algorithm (FPA). Stability analysis is also performed using the same technique. For the period...
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Maximum correntropy criteria (MCC) has been exhibited a robustness against impulse noise by applying various area of signal process. MCC has been shown to be a rather robust adaption principle for adaptive system trai...
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ISBN:
(纸本)9781509012855
Maximum correntropy criteria (MCC) has been exhibited a robustness against impulse noise by applying various area of signal process. MCC has been shown to be a rather robust adaption principle for adaptive system training in the presence of heavy-tailed non Gaussian noises. fixed point algorithms converge to the optimum solution more quickly for fixed signals. In this paper shows convergence of a Dynamic Harmonic Balance algorithms with sufficient condition and comparison between fixed point algorithm and Dynamic Harmonic Balance algorithm.
This study compares an existing method with a novel approach for state estimation of Max-Plus Linear systems with bounded uncertainties. Traditional stochastic filtering does not apply to this system class, despite co...
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This study compares an existing method with a novel approach for state estimation of Max-Plus Linear systems with bounded uncertainties. Traditional stochastic filtering does not apply to this system class, despite computable posterior probability density function (PDF) support. Existing literature suggests a limited scalability disjunctive approach using difference-bound matrices. To overcome this, we study an alternative method recently investigated in Mufid et al. (2022) using Satisfiability Modulo Theory (SMT) techniques, which are known to be NP-hard. We propose a concise method that utilizes a pseudo-polynomial time algorithm using max-plus algebra. We evaluate its efficiency against SMT techniques through numerical experiments involving sparse matrix multiplications for enhanced computational speed.
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