In this paper, for the modern intelligent video surveillance, we introduce an optimizing motion detection algorithm aim at overcoming the flaw of conventional background subtraction algorithm. We combine adaptive background model in HSV color space with moving object segmentation based on fuzzy clustering to extract moving objects from frame. The adaptive background model is able to restoring t...

In this work, we firstly propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners. Then, by leveraging the powerful capacity of convolutional neural networks, we propose an end-to-end architecture which jointly learns to extract visual features and produce binary hash codes. Our novel network de...

Design and implementation of data warehouses remain delicate tasks which are led by experts. Nevertheless it would be interesting to allow the users to define and to build themselves their system through a simple and flexible process. In particular, in the field of production systems, exists the need to integrate data from various sources and to analyze them in order to extract knowledge for op...

With the wide development of databases in general and data warehouses in particular, it is important to reduce the tasks that a database administrator must perform manually. The idea of using data mining techniques to extract useful knowledge for administration from the data themselves has existed for some years. However, little research has been achieved. The aim of this study is to search for...

In this paper we consider the problem of adapting parameters of the algorithm used for extraction of features. Typical speech recognition systems use a sequence of modules to extract features which are then used for recognition. We present a method to adapt the parameters in these modules under a variety of criteria, e.g maximum likelihood, maximum mutual information. This method works under th...

The availability of huge amount of data leads to the need of powerful data analysis tool to extract useful knowledge. To manage data analysis on large data sets researchers have long been concerned with statistical and data mining tools. Disease diagnosis is one of the major applications where data mining tools are showing successful results. Heart disease is the major cause of the death all ov...

Cathode materials are crucial to improved battery performance, in part because there are not yet materials that can maintain high power and stable cycling with a capacity comparable to that of anode materials. Our parameter-free, gradient-corrected model for electron-positron correlations predicts that spectroscopies based on positron annihilation can be deployed to study the effect of lithium ...

In this paper, a neural network solution to extract independent components from nonlinearly mixed signals is proposed. Firstly, a structurally constrained mixing model is introduced to extend the recently proposed mono-nonlinearity mixing model, allowing that different nonlinear distortion are applied to each source signal. Based on this nonlinear mixing model, a novel demixing system character...

An ensemble of biological shapes can be represented and analyzed with a dense set of point correspondences. In previous work, optimal point placement was determined by optimizing an information theoretic criterion that depends on relative spatial locations on different shapes combined with pairwise Euclidean distances between nearby points on the same shape. These choices have prevented such me...

Achieving scalable parallelism from general programs was not successful to this point. To extract parallelism from programs has become the key focus of interest on multi-core CPUs. There are many techniques and programming models such as MPI, CUDA and OpenMP adopted in order to exploit more performance. But there is an urge to find the best parallel programming techniques for the benefit of per...