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Finite State Machines (FSM) are widely used computation models for many application domains. These embarrassingly sequential applications with irregular memory access patterns perform poorly on conventional von-Neumann architectures. The Micron Automata Processor (AP) is an in-situ memory-based computational architecture that accelerates non-deterministic finite automata (NFA) processing in hardware...
The ADMM based linear programming (LP) technique shows interesting error correction performance when decoding binary LDPC block codes. Nonetheless, it's applicability to decode LDPC convolutional codes (LDPC-CC) has not been yet investigated. In this paper, a first flooding based formulation of the ADMM-LP for decoding LDPC-CCs is described. In addition, reduced complexity decoding schedules to lessen...
Based on the variable step-size afïîne projection algorithm, the idea of multiple forgetting factors and variable projection order is proposed. On the one hand, the accurate estimation of error energy is realized in order to achieve faster convergence rate and lower final misalignment. On the other hand, the reduction of computational complexity can be achieved. The simulation results show that the...
In massive multiple-input multiple-output (MIMO) mobile system, the computational complexity of signal detection increases exponentially along with the growing number of antennas. For example, the sub-optimal linear detection schemes, such as zero forcing (ZF) detector and minimum mean square error (MMSE) detector, always have to balance the performance and complexity resulted from the large-scale...
Least-squares temporal difference learning (LSTD) has been used mainly for improving the data efficiency of the critic in actor-critic (AC). However, convergence analysis of the resulted algorithms is difficult when policy is changing. In this paper, a new AC method is proposed based on LSTD under discount criterion. The method comprises two components as the contribution: (1) LSTD works in an on-policy...
LDPC codes have been applied in recent communication standards, such as WiFi, WiGig, and 10GBased-T Ethernet as a forward error correction code. However, LDPC codes require a large number of computational complexity for high performances. To solve this problem, various studies have been continuously performed for reducing computational complexity. In this paper, we propose an adaptive forced convergence...
This work examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gradient vector. The selection of which coordinates to use varies randomly from iteration to iteration and from agent to agent across the network....
Digital predistortion (DPD) is an effective power amplifier (PA) linearization technique improving the system energy efficiency. At this point, real-time DPD adaptation is still an open issue due to the high computational complexity during the coefficients estimation procedure. Online censoring approach, which is effective in reducing the redundant data samples, can be applied in the DPD coefficients...
Narrowband blanket jamming is common in satellite navigation systems. To suppress narrowband jamming, this paper proposes an adaptive LMS filtering algorithm in the frequency domain by updating partial weight coefficients. Firstly this paper introduces the model and the implementation means of the algorithm, then performs contrastive analysis on the computational complexity and the convergence speed...
Multiobjective optimization aims to simultaneously optimize two or more objectives for a problem, with multiobjective evolutionary algorithms (MOEAs) having become a popular research topic in evolutionary multiobjective optimization. We first define the multiobjective optimization problem and briefly summarize multiobjective optimization methods based on the evolutionary algorithm. Representative...
The number of crossband filters controls convergence rate and steady-state MSE of LMS algorithm in subband in the short-time Fourier domain, which necessitates a compromise between them due to its fixed value. Therefore, a decision to adaptively control the number of crossband filters is proposed to provide both fast convergence rate and small steady-state MSE. The advantage of the proposed algorithm...
Interactive multiobjective optimization (IMO) methods aim at supporting human decision makers (DMs) to find their most preferred solutions in solving multiobjective optimization problems. Due to the subjectivity of human DMs, human fatigue, or other limiting factors, it is hard to design experiments involving human DMs to evaluate and compare IMO methods. In this paper, we propose a framework of a...
As more than 2.5 quintillion bytes of data are generated every day, the era of big data is undoubtedly upon us. Running analysis on extensive datasets is a challenge. Fortunately, a significant percentage of the data accrued can be omitted while maintaining a certain quality of statistical inference in many cases. Censoring provides us a natural option for data reduction. However, the data chosen...
The hybrid steepest descent method (HSDM) [Yamada, '01] was introduced as a low-computational complexity tool for solving convex variational-inequality problems over the fixed-point set of non-expansive mappings in Hilbert spaces. Motivated by results on decentralized optimization, this study introduces an HSDM variant that extends, for the first time, the applicability of HSDM to affinely constrained...
Kernel least mean square (KLMS) algorithm has been successfully applied in fields of adaptive filtering and online learning due to their ability to solve sequentially nonlinear problems by implicitly mapping the input signal to a high-dimensional reproducing kernel Hilbert space (RKHS). In this paper, we propose a novel adaptive algorithm called KLMS based on conjugate gradient (KLMS-CG), which uses...
The alternating direction method of multipliers (ADMM) is an iterative first order optimization algorithm for solving convex problems such as the ones arising in linear model predictive control (MPC). The ADMM convergence rate depends on a penalty (or step size) parameter that is often difficult to choose. In this paper we present an ADMM prescaling strategy for strongly convex quadratic problems...
The orthogonality principle states that as an adaptive filter approaches to the Weiner solution and error approaches to its minimum value, the correlation between error and input signal approaches to zero. This fact has been exploited in the proposed scheme for the minimizing mean square deviation (MSD) for the tap weights and its estimated value at each iteration. Essentially, this paper proposes...
Affected by relaxation factor ω, for large-scale MIMO uplink, successive over relaxation (SOR) detection involves low-complexity matrix inversion but unstable performance as ω changes. In this paper, a more stable and efficient SOR-based detection, which is nearly unaffected by ω, is proposed. First, the convergence of the proposed method is proved. Both analytic and numerical results have shown that,...
In this paper, a new subband affine projection algorithm is proposed, which is robust against impulsive noise and presents reduced computational complexity. A subband structure composed of sparse filters in a closed-loop error configuration is used, and the update equation is derived by means of a minimum-disturbance approach with a posteriori subband error constraints, employing a cost function that...
Visual saliency detection (VSD) has been attracting increasing attention due to its wide applications in computer visions. In this paper, a visual saliency detection method based on maximum entropy random walk (MEVSD) is proposed. Gaze wandering over images is modeled as a random walk process on a graph, in which the super-pixels and their similarities are regarded as nodes and edges respectively...
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