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User-Item matrix (UI matrix) has been widely used in recommendation systems for data representation. However, as the amount of users and items increases, UI matrix becomes very sparse, which leads to unsatisfactory performance in traditional recommendation algorithms. To address this problem, in this paper, a rating prediction method with low sensitivity to sparse datasets is proposed. This method...
This paper deals with linear algebra operations on Graphics Processing Unit (GPU) with complex number arithmetic using double precision. An analysis of their uses within iterative Krylov methods is presented to solve acoustic problems. Numerical experiments performed on a set of acoustic matrices arising from the modelisation of acoustic phenomena inside a car compartment are collected, and outline...
Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing (CS) in many applications such as Radar imaging and sparse channel estimation. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted...
The High Performance Conjugate Gradient (HPCG) benchmark, proposed recently in 2013, has drawn increasingly more attention from both academia and industry. Unlike the High Performance Linpack (HPL) benchmark, which has a very high computation-to-communication ratio, HPCG contains both neigh boring and global communication that may severely degrade the parallel performance. To reduce the communication...
One of the goals in the study of infectious disease is to construct a reliable predictive model on the pathogen-host interactome. Conventional methods on the construction of model consider the problem as a binary classification problem. However, most databases only consist of detected interactions and lack of negative results. Thus, as compare to binary classification, this situation is closer to...
Soybean is one of the most important crops for food, feed and bio-energy world-wide. The study of soybean phenotypic variation at different geographical locations can help the understanding of soybean domestication, population structure of soybean, and the conservation of soybean biodiversity. We investigate if soybean varieties can be identified that they differ from other varieties on multiple traits...
In order to better fuse the CT and MR images, based on the classical image fusion method, an image feature extraction and fusion algorithm based on K-SVD is presented. The images are sparse representation. The images are divided into blocks via the sliding window. The dictionary is compiled the column vectors. The redundant dictionary is learned by the K-singular value decomposition (K-SVD) algorithm...
The generalized minimum residual (GMRES) method is a popular method for solving a large-scale sparse nonsymmetric linear system of equations. On modern computers, especially on a large-scale system, the communication is becoming increasingly expensive. To address this hardware trend, a communication-avoiding variant of GMRES (CA-GMRES) has become attractive, frequently showing its superior performance...
In large scale wireless sensor network (WSN) energy reservation is crucial, as in such an environment sensors cannot be periodically maintain. Therefore we investigate the opportunity to reduce the power consumption by reducing the data rate traffic of the network. This is done utilizing either data correlation and sparsity in one dimension or the spatial sparsity among clustered sensor nodes. We...
This contribution deals with the direction-of-arrival estimation of narrowband signals in near-field scenarios using compressed sensing strategies. The considerations relate to a single snapshot of the signal impinging on a sensor array. For the estimation a near-field formulation of the array manifold vector is used. This approach also allows to draw conclusion on the distance between the array and...
The partial least squares (PLS) is designed for prediction problems when the number of predictors is larger than the number of training samples. PLS is based on latent components that are linear combinations of all of the original predictors, it automatically employs all predictors regardless of their relevance. This will degrade its performance and make it difficult to interpret the result. In this...
In this paper we propose a linear MPC scheme for embedded systems based on the dual fast gradient algorithm for solving the corresponding control problem. We establish computational complexity guarantees for the MPC scheme by appropriately deriving tight convergence estimates of order O(1/k2) for an average primal sequence generated by our proposed numerical optimization algorithm. We also show that...
The paper studies radar waveform and receiver filter design for the detection of multiple extended targets using a compressed sensing approach. A multiple-input multiple output (MIMO) radar system is considered and threshold based detection is used to indicate the presence or absence of each target sought in the presence of noise. The detection performance is illustrated with numerical results obtained...
Blind Source Separation (BSS) of underdetermined mixture has acquired a huge attention in signal processing environment, even though it is very much difficult to separate the underlying sources. The difficulty in source separation arise due to the mixing of large number of source signals in time and frequency, and propagation of it to one or more sensors through air. The objective in BSS is to identify...
The area of compressed sensing has developed a lot and is of high interest in the last few years because it provides a solid and promising method to exactly recover signals by sampling at very low rate compared to traditional rates. It has been proved that the topic can be applied to almost all signal processing area which deals with sparse signals. But most of the works done in compressed sensing...
In this paper, a novel computationally efficient algorithm is proposed to achieve high resolution for inverse synthetic aperture radar (ISAR) imaging in the compressive sensing (CS) framework, which is based on dichotomous coordinate descent (DCD) iterations, homotopy, and non-convex regularization. Since traditional CS based methods have to assume that unknown scatterers exactly lie on the pre-discretized...
Channel estimation provides channel state information (CSI) for equalizing channel distortion and demodulating received signals. As a consequence, this technique takes an important part in modern wireless communications systems. Comparing with conventional pilot-aided channel estimators, compressive sensing based channel estimation methods can exploit the sparse property of the wireless channel and...
In this paper we present two algorithms for performing sparse matrix-dense vector multiplication (known as SpMV operation). We show parallel (multicore) version of algorithm, which can be efficiently implemented on the contemporary multicore architectures. Next, we show distributed (so-called multinodal) version targeted at high performance clusters. Both versions are thoroughly tested using different...
Allreduce is a basic building block for parallel computing. Our target here is "Big Data" processing on commodity clusters (mostly sparse power-law data). Allreduce can be used to synchronize models, to maintain distributed datasets, and to perform operations on distributed data such as sparse matrix multiply. We first review a key constraint on cluster communication, the minimum efficient...
Most of the efforts in the FPGA community related to sparse linear algebra focus on increasing the degree of internal parallelism in matrix-vector multiply kernels. We propose a parametrisable dataflow architecture presenting an alternative and complementary approach to support acceleration of banded sparse linear algebra problems which benefit from building a Krylov subspace. We use banded structure...
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