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Moving object detection is very important for video surveillance. In this paper, we present a new real time motion detection algorithm that is based on the integration of accumulative optical flow and double background filtering method (long-term background and short-term background) to achieve better performance. The accumulative optical flow method is used to obtain and keep a stable background image to cope with variations on environmental changing conditions and the double background filtering method is used to eliminate the background information and separate the moving object from it. The biggest advantage of this algorithm is that it does not need to learn the background model from hundreds of images and can handle quick image variations without prior knowledge about the object size and shape. The algorithm has high capability of anti-interference and preserves high accurate rate detection at the same time. The effectiveness of the proposed algorithm for motion detection is demonstrated in a simulation environment and the evaluation results are reported in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a recently developed fiber tracking algorithm to be used with diffusion tensor (DT) fields acquired via magnetic resonance imaging (MRI) is improved and applied to real brain DT-MR images. The method performs satisfactorily in regions where branching and crossing fibers exist and offers the capability of reporting a probability value for the computed tracts. This certainty figure takes into account both the anisotropy and the information provided by all the eigenvectors and eigenvalues of the diffusion matrix at each voxel. In previous papers of the authors, a simpler algorithm was applied only to elementary synthetic DT-MR images. As now presented, this algorithm is now adequately used with more intricate synthetic images and is applied to real white matter DT-MR images with successful results. A novel neural network is used to estimate the main parameters of the algorithm. Numerical experiments show a performance gain over previous approaches, specially with respect to convergence and computational load. The tracking of white matter fibers in the human brain will improve the diagnosis and treatment of many neuronal diseases. [ABSTRACT FROM AUTHOR]

The recent boom in wireless communications has led to a wide range of new applications. Wireless positioning is an emerging technology which can provide accurate locations for indoor environments when satellite based positioning systems are not available. In this paper, a Session Initiation Protocol (SIP) based system architecture for wireless positioning is described and an overview of how this can be used in overall system design is provided. New simulation results show that SIP is effective as a network signaling protocol for indoor wireless positioning. [ABSTRACT FROM AUTHOR]

Our work explores the use of several text categorization techniques for classification of manufacturing quality defect and service shop data sets into fixed categories. Although our work was in the area of manufacturing quality the technique is applicable to free form, short text summaries of data that may be stored in a database, file, or document. We refer to these types of text as "technical text passages". These summaries may not follow standard grammar conventions; they commonly contain abbreviations, technical phrases, misspelled words and industry specific acronyms. Typical types of text to be classified include aircraft engine repair shop findings, industrial manufacturing quality problems and corrective actions, and standardization of attributes in a bill-of-materials. In this paper, we will present our results in using machine learning and rule based algorithms to categorize text. Our results show that the rules based approach is as good as several machine learning approaches. For example, using Support Vector Machine algorithms we were able to achieve 82% accuracy on validation set, using 1,645 training samples and 823 validation samples. Each category had 50 or more samples. Using rule-based approach we were able to achieve 80% accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to Arabic letter recognition is proposed. The system is based on the classified vector quantization (CVQ) technique employing the minimum distance classifier. To prove the robustness of the CVQ system, its performance is compared to that of a standard artificial neural network (ANN)-based solution. In the CVQ system, each input letter is mapped to its class using the minimum Euclidean distance. Simulation results are provided and show that the CVQ system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions. [ABSTRACT FROM AUTHOR]

Pose problem is a big challenge for applying face recognition technology under real world conditions. In this paper, appearance based approach was proposed to recognize face across front and non-frontal view images by reconstructing frontal view features. Statistical learning method based on sample images is applied to find transformation matrix which encapsulated general knowledge of pose transition in feature subspace, therefore, different view feature vectors constituted linear equations and transformation matrix can be solved from the equations by least square (LS) approach. Experimental results on popular FERET and CMU databases showed that the proposed method could cope with the head rotation roughly within half profile view. Compared with model based approaches, this method is not dependent on heavy computation and has merit of easy implementing in live conditions. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to develop image watermarking algorithms which are portable to a variety of applications such as copyright protection, fingerprinting and identification. Therefore, we require that the watermark be binary and be not only detectable but also extractable. The embedding technique is based on the parent-child structure of the multiwavelet transform called "triple tree" and this technique does not require the original image in the watermark extraction. The experimental results show that the watermark. [ABSTRACT FROM AUTHOR]

This paper presents a new method for the signature analysis of mechanical watch movements. Contrary to the existing method, it analyzes the time-frequency features of mechanical watch movement through a combination of reassigned scalogram and Finite Element Analysis (FEA). By mapping the signal into a 2D domain of time and frequency, the reassigned scalogram reveals the frequency components of the shocks at different time. On the other hand, FEA gives the modal frequencies of the movements. By comparing the frequency components at different shocks to the modal frequencies of the movement, possible malfunctions of the movement can then be detected. A number of demonstration examples are included. [ABSTRACT FROM AUTHOR]

This paper presents a multi step-size monic normalization equation-error linear filter. We also extend the idea to nonlinear adaptive filter and derive a multi step-size monic normalization equation-error bilinear filter (MSS MNEEBF). The algorithms enjoy fast convergence behavior and can remove biased estimates associated with conventional equation-error adaptive filters. Simulation results validate the usefulness of our algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes two models of adding relations to a linking pin type organization structure where every pair of siblings in a complete K-ary tree of height H is adjacent: (i) a model of adding an edge between two nodes with the same depth N and (ii) a model of adding edges between every pair of nodes with the same depth N. For each of the two models, an optimal depth N* is obtained by maximizing the total shortening path length which is the sum of shortening lengths of shortest paths between every pair of all nodes. [ABSTRACT FROM AUTHOR]

This paper proposes a new discrete-time queue analytical model based on BLUE algorithm in order to determine the network congestion in preliminary stages. We compare the original BLUE, which has been implemented in Java, with our proposed analytical model with regards to different performance measures (average queue length, throughput, average queueing delay and packet loss probability). The comparison results show that the proposed discrete-time queue analytical model outperforms BLUE algorithm in terms of throughput and packet loss probability. Moreover, the proposed model maintains the throughput performance regardless whether the amount of the traffic load is light or heavy. Furthermore, we calculate the packet dropping probability function for our analytical model and the BLUE algorithm in order to decide which algorithm drops fewer packets. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study for evaluting the decision support value if queueing network (QN) based simulation models for information system design performance. For illustration, queueing network simulation models have been extracted corresponding to three annotated design alternatives of a selected case study. The design alternatives are produced using logical requirements of the selected system. The performance of each alternative is then predicted using quantifiable parameters considering the dynamics of the system such as service time, waiting time and number of entities waiting in the system. In particular, results have shown that the first alternative performs better than the other two in terms of the selected parameters. In general, the case study revealed that QN-based simulation models alternatives in terms of selected parameters and under given assumptions. This is also means that the use of simulation may lead ti better designed information systems. [ABSTRACT FROM AUTHOR]