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Despite a decade of activity in the development of soft vector processors for FPGAs, high-level language support remains thin. We attribute this problem to a design method in which the high-level vector programming interface is only really considered once the processor architecture has been perfected, by which point the designer may be committed to the time-consuming development of a complicated compiler...
An increasing number of MPI applications are being ported to take advantage of the compute power offered by GPUs. Data movement continues to be the major bottleneck on GPU clusters, more so when data is non-contiguous, which is common in scientific applications. The existing techniques of optimizing MPI data type processing, to improve performance of non-contiguous data movement, handle only certain...
Soft vector processors can augment and extend the capability of embedded hard vector processors in FPGA-based SoCs such as the Xilinx Zynq. We develop a compiler framework and an auto-tuning runtime that optimizes and executes data-parallel computation either on the scalar ARM processor, the embedded NEON engine or the Vectorblox MXP soft vector processor as appropriate. We consider computational...
As an optimal method for sequence alignment, the Smith-Waterman (SW) algorithm is widely used. Unfortunately, this algorithm is computationally demanding, especially for long sequences. This has motivated the investigation of its acceleration on a variety of high-performance computing platforms. However, most work in the literature is only suitable for short sequences. In this paper, we present SWAPHI-LS,...
In Recommender system we have similarity search as a key part for making efficient recommendations. Similarity search have always been a tough task in a high dimensional space. Locality Sensitive Hashing which is most suitable for extracting data in a high dimensional data (Multimedia data). The Idea of locality sensitive hashing is that it decreases the high dimensional data to low dimensions using...
In this paper we present a novel framework for writer-independent on-line signature verification. This framework utilises a dynamic time warping-based dichotomy transformation and a writer-specific dissimilarity normalisation technique, in order to obtain a robust writer-independent signature representation in dissimilarity space. Support vector machines are utilised for signature modelling and verification...
Due to availability of reliable and low cost devices, range maps (depth maps) are extensively used in many applications. Recent advances in human-computer interaction enabled us to interact with computers in intuitive and friendly way. In this paper, we propose a novel approach for recognizing static hand gestures using depth information captured from Photon Mixing Device (PMD) cameras. We segment...
This study investigates the degree that the performance of a likelihood ratio (LR)-based forensic text comparison (FTC) system improves by using logistic-regression fusion on LRs that were separately estimated by three different procedures, involving lexical features, word-based N-grams and character-based N-grams. This study uses predatory chatlog messages. The number of words used for modelling...
This paper considers the problem of modeling complex motions of pedestrians in a crowded environment. A number of methods have been proposed to predict the motion of a pedestrian or an object. However, it is still difficult to make a good prediction due to challenges, such as the complexity of pedestrian motions and outliers in a training set. This paper addresses these issues by proposing a robust...
Following recent works on HRI for UAVs, we present a gesture recognition system which operates on the video stream recorded from a passive monocular camera installed on a quadcopter. While many challenges must be addressed for building a real-time vision-based gestural interface, in this paper we specifically focus on the problem of user personalization. Different users tend to perform the same gesture...
We continue the Coxeter spectral study of finite connected loop-free edge-bipartite graphs ?, with m+2 = 3 vertices (a class of signed graphs), started in [SIAM J. Discrete Math., 27 (2013), 827-854] by means of the complex Coxeter spectrum specc ? ? C and presented in our talks given in SYNASC12 and SYNASC13. Here, we study non-negative edge-bipartite graphs of corank two, in the sense that the symmetric...
This study aims classification of phosphorus magnetic resonance spectroscopic imaging (31P-MRSI) data of human brain tumors using machine-learning algorithms. The metabolite peak intensities and ratios were estimated for brain tumor and healthy 31P MR spectra acquired at 3T. The spectra were classified based on metabolite characteristics using logistic regression and support vector machine. This study...
Day to day variability and non-stationarity caused by changes in subject motivation, learning and behavior pose a challenge in using local field potentials (LFP) for practical Brain Computer Interfaces. Pattern recognition algorithms require that the features possess little to no variation from the training to test data. As such models developed on one day fail to represent the characteristics on...
Intrapartum fetal surveillance for early detection of fetal acidosis in clinical practice focuses on reducing neonatal morbidity via early detection. It is the subject of on going research studies attempting notably to improve detection performance by reducing false positive rate. In that context, the present contribution tailors to fetal heart rate variability analysis a graph-based dimensionality...
The maximum likelihood linear regression (MLLR) technique is a well-known approach to parameter adaptation in hidden Markov model (HMM)-based systems. In this paper, we propose the maximum penalized likelihood kernel regression (MPLKR) approach as a novel adaptation technique for HMM-based speech synthesis. The proposed algorithm performs a nonlinear regression between the mean vector of the base...
Emotional Polarity Classification is an important task in Sentiment Analysis area. It is applied in many real problems such as reviews of consumer products and services, financial markets, and forensic analysis. The scientists from the areas of text mining and nature language processing have studied how to solve emotional polarity classification problem. They used a variety of methods, from simple...
Oncogene is a kind of inherent genes exists in humans' cells. It has been recognized as a genetic disease, if the cells activated, it can make a person carcinogenesis. So, the research of digging out the useful information from gene chip is very hot in modern society. The sample size is small, high dimension, nonlinear which causes the 'dimension disaster', so dimensionality reduction becomes the...
In this paper, we propose a new fast parallel sparse matrix-vector multiplication (SpMV) algorithm on GPU platforms. The new algorithm, called segSpMV, is based on the compressed sparse row (CSR) format and can be applied to wide computational applications with both structured and unstructured matrices. The SpMV operation has very low computing to communication ratio and is bandwidth-limited. The...
Malware is widely used to disrupt computer operation, gain access to users' computer systems or gather sensitive information. Nowadays, malware is a serious threat of the Internet. Extensive analysis of data on the Web can significantly improve the results of malware detection. However malware analysis has to be supported by methods capable of events correlation and cross-layer correlation detection,...
Point of interest (POI) categorization is the task of finding of categories of POIs within a document. Because the documents that possess POIs have clue words for identifying POI categories, the task can be solved as document classification. However, this approach misses two crucial factors for identifying the category of a POI. First, the approach pays no attention to onomastic information, even...
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