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Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for intelligent robot. However, existing work neglects the intrinsic relation between different fingers which simultaneously contact the object. In this paper, a joint kernel sparse coding...
In this paper we propose a biometric solution for individual identification based on electroencephalography with classification using local probability centers. In our study, the electroencephalography signals of a subject are recorded from only one active channel Cz with eyes closed and without any external stimulations. The original signals are preprocessed by Haar wavelet transformation; then a...
A novel approach for real-time monitoring of long-term voltage stability using local linear regression and adaptive training database is proposed in this paper. Comparing to previous methods, the proposed local predictive regression model could properly balance the simplicity/transparency and accuracy, and it is also adaptive to the changes of system and operating conditions. The approach is tested...
Sequence analysis plays critical role in bioinformatics, and most applications of which have compute intensive kernels consuming over 70% of total execution time. By exploiting the compute intensive execution stages of popular sequence analysis applications, we present and evaluate a VLSI architecture with a focus on those that target at biological sequences directly, including pairwise alignment,...
We present a locality preserving K-SVD (LP-KSVD) algorithm for joint dictionary and classifier learning, and further incorporate kernel into our framework. In LP-KSVD, we construct a locality preserving term based on the relations between input samples and dictionary atoms, and introduce the locality via nearest neighborhood to enforce the locality of representation. Motivated by the fact that locality-related...
In many applications such as dynamic social network and customer behavioral analysis, the data intrinsically have many dimensions and can be naturally represented as high-order tensors. In this study, a SVM ensemble learning method is proposed for classification using tensor data. The method is used in identifying cross selling opportunities to recommend personalized products and services to customers...
The recent advancement of social media has given users a platform to socially engage and interact with a global population. With millions of images being uploaded onto social media platforms, there is an increasing interest in inferring the emotion and mood display of a group of people in images. Automatic affect analysis research has come a long way but has traditionally focussed on a single subject...
We address the problem of video face retrieval in TV-Series, which searches video clips based on the presence of particular character, given one video clip of his/hers. This is tremendously challenging because on one hand, faces in TV-Series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand retrieval task typically needs highly efficient representation...
We present a novel audiovisual emotion recognition solution using multimodal information fusion based on entropy estimation. Considering the limitations of existing methods, we propose a new dual-level fusion framework which consists of feature level fusion module based on kernel entropy component analysis and score level fusion module based on maximum correntropy criterion. In our system, audio and...
Blood vessel extraction from retinal fundus images is an important task in developing the computer-aided diagnostic system for ophthalmologists. In this paper we have presented an algorithm for extraction of blood vessels of retinal fundus images and comparison of different moment invariants used for the extraction of features for the vessel pixels. The algorithm uses neural networks for distinguishing...
The retinal blood vessels are highly responsible for the detection of retinal pathology such as glucoma, hypertension, arteriosclerosis and diabetes. So the segmentation of retinal blood vessels from their background is a prominent task. The objective of this paper is to present an automatic local entropy thresholding based fast, efficient and accurate retinal blood vessels segmentation method by...
Automatic classification of tropical wood species is becoming more important especially for timber exporting countries due to the considerable economic challenge as a result of fraudulent labelling of timber species at the custom checkpoints. Hence, a reliable automated wood species recognition system is needed to inspect the wood species labelling at the checkpoints. A tropical wood species classification...
Software is rapidly increasing in size and complexity. Static analyses must be designed to scale well if they are to be usable with realistic applications, but prior efforts have often been limited by available memory. We propose a database-backed strategy for large program analysis based on graph algorithms, using a Semantic Web database to manage representations of the program under analysis. Our...
Manually reproducing bugs is time-consuming and tedious. Software maintainers routinely try to reproduce unconfirmed issues using incomplete or no informative bug reports. Consequently, while reproducing an issue, the maintainer must augment the report with information -- such as a reliable sequence of descriptive steps to reproduce the bug -- to aid developers with diagnosing the issue. This process...
Big data and the Internet of Things era continue to challenge computational systems. Several technology solutions such as NoSQL databases have been developed to deal with this challenge. In order to generate meaningful results from large datasets, analysts often use a graph representation which provides an intuitive way to work with the data. Graph vertices can represent users and events, and edges...
To satisfy product quality specifications and reduce operation cost, it is crucial to optimize operating conditions of an industrial process. In this research, a new optimization method based on locally weighted partial least squares (LW-PLS) is proposed to cope with changes in process characteristics and collinearity among process variables in a nonlinear multi-stage process. To solve a nonlinear...
Cloud Computing enabled by virtualization technology exhibits revolutionary change in IT Infrastructure. Hypervisor is a pillar of virtualization and it allows sharing of resources to virtual machines. Vulnerabilities present in virtual machine leveraged by an attacker to launch the advanced persistent attacks such as stealthy rootkit, Trojan, Denial of Service (DoS) and Distributed Denial of Service...
Column-store in-memory databases have received a lot of attention because of their fast query processing response times on modern multi-core machines. Among different database operations, group by/aggregate is an important and potentially costly operation. Moreover, sort-based and hash-based algorithms are the most common ways of processing group by/aggregate queries. While sort-based algorithms are...
The aim of this study is to detect P-wave onset and end of electrocardiograms (ECG). This wave is important for detecting people prone to atrial fibrillation, one of the most frequent heart diseases, but the wave is very difficult to segment accurately because of its small amplitude and the very different shapes it can take. Two different methods are tested for the segmentation : the first one is...
The development of an application of speech processing in a car environment is addressed. The main objective is to provide the user of a vehicular phone with a powerful and friendly bidirectional vocal interface. In particular, the paper focusses on the speech recogniser component of the interface as it was specifically designed and tuned to operate in the very hostile acoustic environment of a moving...
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