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This paper presents a novel method for adaptive filtering of functional magnetic resonance imaging (fMRI) time-series. The method progressively reduces noise from the fMRI time courses based on selective spatial averaging of the underlying voxels. A new similarity measure is proposed to assign the weights of the averaging kernel. The performance of the proposed method is verified by its application...
Multi-core architecture provides more on-chip parallelism and powerful computational capability. It helps virtualization achieve scalable performance. KVM (kernel based virtual machine) is different from other virtualization solutions which can make use of the Linux kernel components such as completely fair scheduler (CFS). However, CFS treats the KVM threads as normal tasks without considering about...
Graphic Processing Unit (GPU), with many light-weight data-parallel cores, can provide substantial parallel computing power to accelerate several general purpose applications. Both the AMD and NVIDIA corps provide their specific high performance GPUs and software platforms. As the floating-point computing capacity increases continually, the problem of ``memory-wall'' becomes more serious, especially...
Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained...
This paper presents a new neural network architecture kernel principal component neural network (KPCNN) trained by threshold accepting based training algorithm with different kernels like polynomial, sigmoid and Gaussian and its application to bankruptcy prediction in banks. KPCNN is a non linear version of the PCNN proposed elsewhere. In this architecture, dimensionality reduction is taken care of...
We present an adaptive smoothing scheme for denoising functional magnetic resonance imaging (fMRI) data using weighted average filtering. A novel metric is proposed that assigns the weights of the smoothing kernel on the basis of similarity of the voxels under the smoothing kernel with the voxel under consideration as well as a reference time course. Pearson's coefficient of correlation is used as...
In this paper, we investigate discrete finite impulse response (FIR) filtering of images, while harnessing the powerful computational resources of next-generation GPUs. These novel platforms exhibit a massive data parallel architecture with an advanced SIMT execution model and thread management, to enable designers to better cope with the infamous memory wall, i.e. the growing gap between the cost...
NVIDIA CUDA and ATI Stream are the two major general-purpose GPU (GPGPU) computing technologies. We implemented RankBoost, a web relevance ranking algorithm, on both NVIDIA CUDA and ATI Stream platforms to accelerate the algorithm and illustrate the differences between these two technologies. It shows that the performances of GPU programs are highly dependent on the utilization of GPU's hardware memory...
Data De-duplication has become a commodity component in data-intensive storage systems. But compared with other traditional storage paradigms, de-duplication system achieves elimination of data duplications or redundancies at the cost of bringing several additional layers or function components into the I/O path, and these additional components are either CPU-intensive or I/O intensive, largely hindering...
Suffix array is a simpler and compact alternative to the suffix tree, lexicographic name construction is the fundamental building block in suffix array construction process. This paper depicts the design issues of first data parallel implementation of the lexicographic name construction algorithm on a commodity multiprocessor GPU using the Compute Unified Device Architecture (CUDA) platform, both...
Counting sort is a simple, stable and efficient sort algorithm with linear running time, which is a fundamental building block for many applications. This paper depicts the design issues of a data parallel implementation of the count sort algorithm on a commodity multiprocessor GPU using the Compute Unified Device Architecture (CUDA) platform, both from NVIDIA Corporation. The full parallel version...
As virtualization technology is used widely in cloud computing, there are more and more interactive workloads being deployed on virtual machine (VM) environment. Although improving interactive performance has been heavily studied in operating system area, in consolidated VM environment, the improvements of guest OS are usually offset by the more coarse-grained VM scheduler, which may cause poor interactive...
Solving complex convection-diffusion equations is very important to many practical mathematical and physical problems. After the finite difference discretization, most of the time for equations solution is spent on sparse linear equation solvers. In this paper, our goal is to solve 2D Nonlinear Unsteady Convection-Diffusion Equations by accelerating an iterative algorithm named Jacobi-preconditioned...
All sections of the society need to benefit from the strides in Information Technology, more so the differently enabled. This paper presents a Novel solution for a text read out OCR system adapted for the visually challenged. A paper of text from Malayalam magazines, newspapers, books or journals placed on a flatbed scanner would be recognized and read out as text. Selected text could also be printed...
Recent technological developments made available various many-core hardware platforms. For example, a SIMD-like hardware architecture became easily accessible for many users who have their computers equipped with modern NVIDIA GPU cards with CUDA technology. In this paper we redesign the maximal accepting predecessors algorithm for LTL model checking in terms of matrix-vector product in order to accelerate...
Graphics processing units (GPUs) have been widely used to accelerate algorithms that exhibit massive data parallelism or task parallelism. When such parallelism is not inherent in an algorithm, computational scientists resort to simply replicating the algorithm on every multiprocessor of a NVIDIA GPU, for example, to create such parallelism, resulting in embarrassingly parallel ensemble runs that...
Feature selection is a process to select a subset of original features. It can improve the efficiency and accuracy by removing redundant and irrelevant terms. Feature selection is commonly used in machine learning, and has been wildly applied in many fields. we propose a new feature selection method. This is an integrative hybrid method. It first uses Affinity Propagation and SVM sensitivity analysis...
Radar emitter identification has attracted increasing interests in the last decade. The class-dependent method in to optimize time-frequency kernel of ambiguity function (AF) needs to rank kernel points in the whole AF plane and is sensitive to sampling data length. In this paper, an ambiguity function zero-slice based feature optimization algorithm is proposed for radar emitter recognition. It efficiently...
This paper proposes a local polynomial modeling approach and bandwidth selection algorithm for estimating time-varying linear models (TVLM). The time-varying coefficients of a TVLM are modeled locally by polynomials and estimated using least-squares estimation with a kernel having a certain bandwidth or support. Asymptotic behavior of the proposed estimator is established and it shows that there exists...
Virtual network is an important approach to support multiple legacy applications running unmodified in distributed virtual computing environments. A virtual networking approach called VirNet is proposed in this paper. VirNet can build multiple customized and isolated virtual networks simultaneously on the same physical hosts in the network, and it enables existing distributed applications written...
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