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Error Weighted Hashing (EWH) is a fast algorithm for Approximate k-Nearest neighbour search in Hamming space. It is more efficient than traditional LocalitySensitive Hashing algorithm (LSH) since it generates shorterlist of strings for finding the exact distance from the query. Wehave parallelized the EWH algorithm using Cuda and OpenMP.Speedup of 44 times on a 16 core GPU and 16 core CPUmachine was...
this present paper deals with the exhaustive review of literature based on different algorithms for design of high speed digital signal processor. To make an efficient and effective processor the features like pipelining, parallelism and hazard handling capabilities are used. High speed multipliers, divider and adders are prime requirement for DSP operations. The multiplier has been designed using...
Spectrum sensing is one of the most mandatory mechanisms of Cognitive Radio (CR) to find the unused Spectrum. Spectrum sensing has renewed on a very active scrutiny area over recent years. As it is one of the most important tasks performed using cognitive radio and the implementation of the other functions of the cognitive radio is based on the spectrum sensing. So it is mandatory for cognitive radio...
Now a day's plenty of research work is going in the field of network security. Because of large data size and real time constraint. So most of the cryptographic algorithms are more suitable only for text. The main problem of image security is the encryption result is insensitive with respective to the plain text. In this paper trellis algorithm is combined with DNA sequence to provide more security...
In this letter, we present a novel speech separation scheme using two microphones. We divide the inter-phase information into sub-segments and statistic these directional segments. Then we construct the objective function by convolving the statistics information with a low pass filter. By the decreasing gradient algorithm and ideal binary mask, we obtain the separated speeches. The method is valid...
The radio signals, possessing the so-named ideal auto — correlation function (ACF), resembling the Dirac delta — function (pulse), are widely applied in many technical areas. Due to this reason the methods for design of such signals have been intensively researched during the past sixty years. With regard to this situation in the paper a new algorithm for synthesis of families of binary phase manipulated...
Compare with the infra-ray light gaze tracking systems, the visible light gaze tracking (VLGT) design provides new applications to consumer electronics. However, the VLGT suffers from the technical difficulties of accommodating various illumination conditions and unstable image features. These system design issues lead to the problem of low accuracy in estimating iris center location and high computational...
Efficient online algorithms are developed to perform dictionary learning (DL) for the features lifted to a high-dimensional space via nonlinear mapping. Inspired by recent works on batch kernelized DL with promising performance for real-world learning tasks, two kernel DL formulations are put forth, amenable to online processing. The first formulation aims at faithfully representing the high-dimensional...
This paper deals with the problem of recovering a sparse unknown signal from a set of observations. The latter are obtained by convolution of the original signal and corruption with additive noise. We tackle the problem by minimizing a least-squares fit criterion penalized by a Geman-McClure like potential. The resulting criterion is a rational function, which makes it possible to formulate its minimization...
We consider non-differentiable convex optimization problems that vary continuously in time and we propose algorithms that sample these problems at specific time instances and generate a sequence of converging near-optimal decision variables. This sequence converges up to a bounded error to the solution trajectory of the time-varying non-differentiable problems. We illustrate through analytical examples...
We develop a decentralized algorithm for stochastic composite optimization problems, combining ideas from consensus-based multi-agent optimization and the celebrated mirror descent algorithm. When the composite regularization term is strongly convex, the proposed method is shown to converge at a rate of O(1/k) where k is the number of iterations executed. This is known to be the best possible rate...
Linear filtering methods are well known and have been successfully applied in system identification and equalization problems. However, they become unpractical when the number of parameters to estimate is very large. The recently proposed assumption of system separability allows the development of computationally efficient alternatives to classic adaptive methods in this scenario. In this work, we...
We study nonconvex distributed optimization in multi-agent networks. We introduce a novel algorithmic framework for the distributed minimization of the sum of a smooth (possibly nonconvex) function-the agents' sum-utility-plus a convex (possibly nonsmooth) regularizer. The proposed method hinges on successive convex approximation (SCA) techniques while leveraging dynamic consensus as a mechanism to...
Recovering sparse signals from noisy underdetermined linear measurements has been a concerning problem in the signal processing community. Lasso has been put forward to handle this problem well, yet recent research reveals that replacing ℓ1 norm with some non-convex functions leads to better recovery performance. In this paper, based on majorization-minimization and proximal operator, we propose a...
This paper presents a transform domain fragile watermarking technique based on Fermat number transform (FNT). This transform domain is very simple and efficient in terms of computational complexity and speed. FNT is a true digital transform, which makes the watermarking method free from round-off errors. In this method, the image is divided into non-overlapping blocks and FNT is applied on each block...
The research of the high-precision sound source localization system which is based on time difference of arrival (TDOA) of different sensors positioning system has increasingly become popular research. The outcome of the traditional generalized cross-correlation function (GCC) time delay estimation algorithm which is under the condition of the intense noise, has an apparent error, therefore, we can't...
In this study, a complex-valued adaptive filter algorithm based on Lyapunov stability theory is presented to solve a system identification problem in the complex domain. The performance of the proposed complex-valued Lyapunov adaptive filter (CLAF) algorithm is improved for the complex-valued system identification problem by integrating the LST into the filter optimization cost. The performance of...
This paper proposes Volterra RLS (VRLS) adaptive filter for estimation of harmonic parameters and decaying DC component in presence of white Gaussian noise. VRLS shows good error convergence rate under high non-stationary and noisy conditions which is quite essential to estimate power quality disturbances. VRLS algorithm is an extension of Forgetting factor RLS (FFRLS) with the help of Volterra expansion...
Human brain behaves differently with the different task conditions presented. This is due to the cognitive load that each task condition imposes. In this article we are looking for the difference in cognitive load that each subject faces when subjected to different task conditions. We will try to differentiate the two category of subjects i.e. alcoholic and controls on the basis of the cognitive demand...
This paper describes an acoustical investigation on Thai speech segmentation using a combination of average level crossing rate (ALCR) and root-mean-square (RMS) energy. Simple and easy to compute, ALCR information alone was successfully used in an automatic speech segmentation system for English. However, ALCR has never been applied to Thai. As a result, the objective of the study is to apply ALCR...
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