Informatica

This paper proposes the use of a particle filter combined with color, depth information and shape features as an efficient and effective way to deal with tracking a head on the basis of image stream coming from a mobile stereovision camera. The head is modeled in the 2D image domain by an ellipse. The color distribution within interior of the ellipse is represented by a color histogram. The color histogram is dynamically updated over time. The length of the ellipse's minor axis is determined on the basis of depth information. The particles representing the candidate ellipses are weighted in each time step in respect of intensity gradient near the edge of the ellipse and matching score of the color histograms representing the interior of an ellipse surrounding the tracked object and currently analyzed one. The proposed algorithm can track a head reliably in cases of temporal occlusions as well as varying illumination conditions by dealing with multiple hypotheses for the pose. Experimental results obtained on long image sequences show the feasibility of our approach to perform tracking a head undergoing complex changes of shape and appearance against a varying background. The tracker has been evaluated in experiments consisting in face tracking with a real mobile agent.

A quick matrix multiplication algorithm is presented and evaluated on a cluster of networked workstations consisting of Pentium hosts connected together by Ethernet segments. The obtained results confirm the feasibility of using networked workstations to provide fast and low cost solutions to many computationally intensive applications such as large linear algebraic systems. The paper also presents and verifies an accurate timing model to predict the performance of the proposed algorithm on arbitrary clusters of workstations. Through this model the viability of the proposed algorithm can be revealed without the extra effort that would be needed to carry out real testing.

In this paper on basis of the results (Dyomin et al., 2003a) the structure of Shannon information amount in the joint filtering and extrapolation problem of the stochastic processes by continuous‐discrete time memory observations is investigated. For particular class of processes with applying of the general results the problem of optimal transmission over the lag channels is considered and efficiency of filtering and extrapolation receptions under transmission over channels with memory or lag is investigated.

This paper presents an improved method inspired by the recently proposed Pretty‐Simple PAKE (Password Authenticated Key‐Exchange) protocol, which is already a well‐known, robust and simple password authenticated key exchange scheme. In our even more efficient scheme, only the password needs to be negotiated in advance, and the computations have also been simplified. Our scheme is based on the elliptic curve discrete logarithm problem and can gain the benefit from the key block size, speed and security. Since our new scheme is both efficient and fast with a low cost for device storage, it is especially suitable for the wireless network environment.

In this paper, a new digital watermarking method based on vector quantization (VQ) is proposed. In contrast with conventional VQ‐based watermarking schemes, the mean of sub‐blocks is used to train the VQ codebook. In addition, the Anti‐Gray Coding (AGC) technique is employed to enhance the robustness of the proposed watermarking scheme. In this scheme, the secret keys are used to hide the associated information between the original image and the watermark. Then the set of secret keys will be registered to a trusted third party for future verification. Thus, the original image remains unchanged after the watermark is melted into the set of secret keys. Experimental results show that the watermark can survive various possible attacks. Besides that, the size of the secret keys can be reduced.

The JPEG image is the most popular file format in relation to digital images. However, up to the present time, there seems to have been very few data hiding techniques taking the JPEG image into account. In this paper, we shall propose a novel high capacity data hiding method based on JPEG. The proposed method employs a capacity table to estimate the number of bits that can be hidden in each DCT component so that significant distortions in the stego‐image can be avoided. The capacity table is derived from the JPEG default quantization table and the Human Visual System (HVS). Then, the adaptive least‐significant bit (LSB) substitution technique is employed to process each quantized DCT coefficient. The proposed data hiding method enables us to control the level of embedding capacity by using a capacity factor. According to our experimental results, our new scheme can achieve an impressively high embedding capacity of around 20% of the compressed image size with little noticeable degradation of image quality.

We propose a layered Soft IP Customisation (SIPC) model for specifying and implementing system‐level soft IP design processes such as wrapping and customisation. The SIPC model has three layers: (1) Specification Layer for specification of a customisation process using UML class diagrams, (2) Generalisation Layer for representation of a customisation process using the metaprogramming techniques, and (3) Generation Layer for generation of the customised soft IP instances from metaspecifications. UML allows us to specify customisation of soft IPs at a high level of abstraction. Metaprogramming allows us to manage variability in a domain, develop generic domain components, and describe generation of customised component instances. The usage of the SIPC model eases and accelerates reuse, adaptation and integration of the pre‐designed soft IPs into new hardware designs.

Specifics of hidden Markov model‐based speech recognition are investigated. Influence of modeling simple and context‐dependent phones, using simple Gaussian, two and three‐component Gaussian mixture probability density functions for modeling feature distribution, and incorporating language model are discussed. Word recognition rates and model complexity criteria are used for evaluating suitability of these modifications for practical applications. Development of large vocabulary continuous speech recognition system using HTK toolkit and WSJCAM0 English speech corpus is described. Results of experimental investigations are presented.

The mathematical model and methods of calculation of the layout structure of comparator signal circuits with distributed parameters are presented. The algorithm of computer formulation and solving of equations of transfer functions of comparator circuits is provided. Theoretical substantiation of optimizing the micro‐layout of large‐scale integration circuits of parallel subnanosecond analog‐to‐digital converters (ADC) is proposed.

Evolutionary Engineering (EE) is defined to be “the art of using evolutionary algorithms approach such as genetic algorithms to build complex systems”. This paper deals with a neural net based system. It analyses ability of genetically trained neural nets to control Simulated robot arm, witch tries to track a moving object. In difference from classical Approaches neural network learning is performed on line, i.e., in real time. Usually systems are built/evolved, i.e., genetically trained separately of their utilization. That is how it is commonly done. It's a fact that evolution process is heavy on time; that's why Real‐Time approach is rarely taken into consideration. The results presented in this paper show that such approach (Real‐Time EE) is possible. These successful results are essentially due to the “continuity” of the target's trajectory. In EE terms, we express this by the Neighbourhood Hypothesis (NH) concept.