1. J.K. Pollard, S.Rohman, M.Fry. A web-based mobile medical monitoring system
2. A.Tucker. Computing Curricula 2001 and IDAACS
3. G.Mastronardi, M.Castellano, F.Marino. Steganography Effects in Various Formats of Images. A Preliminary Study
4. E.Koutsoukos, E.Angelopoulos, A.Maillis, C.Stefanis. An analytical approach to the neuronal mechanisms underlying the memory storage in the brain. (Experimental study)
5. C.Kosmatopoulos, N.Tsagourias. Using a look-up table for code execution in small microcontrollers system with dash memory
6. H.Laichour, S.Maouche, R.Mandiau. Traffic control assistance in connection nodes: Multi-Agent Applications in Urban Transport Systems
7. H.Yamamoto, T.Sano, S.Hasebe. Automatic Microinjection System using Stereoscopic Microscope
8. S.Demirsoy, R.Beck, I.Kale, A.Dempster. New recursive-DCT implementations with Goertzel filters
9. J.L.Nunez, S.Jones. X-MatchPRO: A High-Performance Full-Duplex Lossless Data Compressor on a ProASIC FPGA
10. T. Sobh, R. Mihali, A. Rosca, B. Ghimire, K. Vovk, G. Gosine, P. Batra, A. Singh, S. Pathak. Case studies in Web-Controlled  Devices and Remote Manipulation
11. S.Gorlatch, H.Kehbel. VisPar: A Visual Tool for Designing Parallel Programs
12. A.Aiello, D.Grimaldi, S.Rapuano. GMSK Neural Network Based Demodulator
13. C.Triki, L.Grandinetti. Computational Grids to Solve Large Scale Optimization Problems with Uncertain Data
14. V.Golovko, Y.Savitsky. Neural Network Approach of Lyapunov Index Determination for Chaotic Processes
15. H.Kanoh, A.Hosokawa. Learning of biological behaviour by classifier
16. G.Setlak. Fuzzy Neural Networks in Intelligent Manufacturing Systems
17. A.Doudkin, A.Machnev, A. Selikhanovich. Iterative Algorithm for Discrete Orthogonal Transformation of Signals in Base of  Two-dimensional Functions
18. M.Dyvak. Tolerance Estimations of Parameters Set Of Static System in Class of Multidimensional Ellipsoids

        A hospital-wide mobile medical monitoring system is described. Telemetry devices are attached to patients to acquire, store and process continuous data about their state of health.
        Medical staff may examine real-time graphical information and make comparisons with historical data. Parameters may be set to cause an automatic alert to portable devices held by appropriate staff if a patient requires urgent observation.
        The system uses the services provided by Internet and Intranet to allow remote supervision and consultation.
        A hardware/software prototype has been constructed to demonstrate real-time data acquisition, wireless transmission/reception and connection to the World-Wide Web.
        The real-time, supervisory and remote-teaching aspects of the software system are being designed and are in the process of implementation.

        This paper discusses the relationship between recent advances in curricula in computer science in the US and intelligent data acquisition/advanced computer systems (IDAACS) research and development. The recent Computing Curricula 2001 Draft Report is used as a primary source of information about current developments in curriculum standards in the US.

        In this paper the effects of steganography in different image formats (BMP, GIF, JPEG and DWT-coded) are studied. With respect to these formats, we try to give an answer to the following questions: “how many bits of noise (i.e. the textual secret message) can be injected without perceptually deteriorating the quality of the image?” and “how and where to inject these bits in order to achieve the best trade-off in terms of length of the textual message and preserved quality of the image?”.

        In the present in vivo experimental study, the dynamical properties of the electroencephalographic (EEG) activity recorded in the CA1 area and the dentate gyrous of rat hippocampus were investigated prior to the induction and during the maintenance phase of Long Term Potentiation (LTP). Our findings suggest that this form of brain plasticity can be reflected by the complexity of spontaneous EEG activity, as it is expressed by the reduction of the correlation dimension D2, indicates that different functional states of the brain are governed by different degrees of functional complexity.

        An innovative method of code size reduction, in applications which use a small microcontroller with dash memory, is presented in this paper. Code size reduction is of major importance in this type of microcomputer systems, because of the limited available resources. By the proposed method, certain decision structures, which are relevant to the current state of the system, are replaced with a look-up table and a simple calculation algorithm. A detailed description of this approach is presented in this work.

        In this paper, we propose a model for the transport system based on a Hierarchical Multi-Agent System (HMAS). This model can be used in order to design a Decision-Support System (DSS) to facilitate the work of the regulator by proposing him various regulation actions in every connection node (SARC: Systeme d’Aide e la Regulation des Correspondances). An optimal organization of the connections is suggested, in normal or disturbed conditions, to avoid loss of time for the passengers. Our model includes a supervisor agent (interface), a connection agent (decision) for each connection node and an acquisition agent (perception) for each regulation station. In our research, we have used Multi-Agent Systems (MAS) which adapt themselves perfectly to the distributed systems as in the urban transport networks.

        In this paper, we describe a visual feedback system using a stereoscopic microscope that controls a micromanipulator so that a needle head may pierce a target as much length as desired. At first, we developed an image processing algorithm for the tip of needle head to touch the target. Secondarily, we developed an algorithm for prediction of the tip position of the needle head within the target. By performing a preoperation, the shape of the needle head is preserved as a reference pattern. When the needle head piercing the target, the shape of the needle head within the target is predicted by pattern matching. Thus, we developed a microinjection system that axially pierces the target. Experimental results show that the proposed system may be useful in micromanipulation such as microinjection to brain areas in neuroanatomy.

        In this paper we report on a new recursive DCT architecture that is more efficient in terms of area and power in comparison to recently published recursive DCT architectures. Our approach here employs Type A, B and C Goertzel filters. These three different realizations of Goertzel filters together with multiplier-less implementation of loop multiplications are used so as to reduce the area, the multiplier delay, and undesirable transitions hence the power consumption. The newly proposed DCT structure has been compared with conventional recursive implementations at different transform lengths to observe that there are potential savings both in area and power.

        This paper presents the full-duplex architecture of the X-MatchPRO lossless data compressor and its highly integrated implementation in a non-volatile reprogrammable ProASIC FPGA. The X-MatchPRO architecture offers a data independent throughput of 100 Mbytes/s and simultaneous compression/decompression for a combine full-duplex performance of 200 Mbytes/s clocking at 25 MHz. Both compression and decompression channels fit into a single A500K130 ProASIC FPGA with a typical compression ratio that halves the original uncompressed data. This device is specifically targeted to enhance the performance of Gbit/s data networks and storage applications where it can double the performance of the original system.

        The concept of distance learning has been more and more articulated during the past few years and is expected to shortly turn into a practical education system within current high level learning institutions. The chances are that distance learning would transparently extend colleges and institutes of education, and could plausibly overtake and turn into a preferred choice of higher education, especially for adult and working students. In a supportive effort towards faster distance learning implementation and consideration, we are presenting through this material a sequence of projects that have been developed at University of Bridgeport and can serve very well in the process of distance learning education ranging from simple "hobby" style training to professional guidance material. The projects have an engineering / laboratory flavor, are part of an ongoing work of the faculty and students of the Computer Science and Engineering Department of the university, and are being presented in an arbitrary order, topics ranging from vision and sensing to engineering design, scheduling, remote control and operation.

        We describe VisPar – a new visual tool intended to support the programmer in the process of designing complex parallel applications. The novel features of the tool are as follows: support of both task and data parallelism and mixture thereof, use of analytical cost models for performance prediction, systematic program design by optimizing transformations, and visualization of the design process. We demonstrate the usage of VisPar on a relevant case study – the practically used Jpeg compression algorithm – and report on the current status of the tool implementation.

        In this paper, the pattern recognition characteristics of the Artificial Neural Networks are used to realise a real demodulator for Gaussian Minimum Shift-Keying signals, used in the GSM telecommunications. The demodulator utilises the Learning Vector Quantisation (LVQ) neural network. It offers both greater efficiency in demodulating and less sensitivity to noise. In order to solve the problem regarding input signal synchronisation, a pre-processing phase is organised. The demodulator prototype has been realised by implementing the pre-processing phase and the LVQ neural network on TMS320C30 Digital Signal Processor. The demodulator has been tested according to the European Telecommunication Standard Institute Recommendations.

        In this paper we discuss the use computational grids to solve stochastic optimization problems. These problems are generally difficult to solve and are often characterized by a high number of variables and constraints. Furthermore, for some applications it is required to achieve a real-time solution. Obtaining reasonable results is a difficult objective without the use of high performance computing. Here we present a grid-enabled path-following algorithm and we discuss some experimental results.

        In this paper the new methods and algorithms for chaotic processes identification based on multilayer neural networks are considered. The neural network algorithm of the greatest Lyapunov exponent calculation for a various chaotic time series is offered. The original technique of a Lyapunov spectrum calculation is considered which is based on combining of nonlinear neural network and Gram-Schmidt orthogonalization procedure. The proposed methods are characterized in small size of the initial data set and high speed of calculations in comparison with traditional techniques. The experimental researches of these methods on the basis of Henon and Lorenz chaotic processes demonstrate high conformity of the received results to original meanings.

        The objective of this paper is to investigate biological features of a virtual creature on the computer. It is assumed that the creature does not have a complex judgment, a sophisticated detector and has simple basic actions. We call it a bug. As a lower animal, an insect does not have complex brain, while it is seemed to be able to do complicated works. Specifically complex behaviour of an insect is considered to be based on a reflex action for the external stimuli. The classifier system is used for the generation of bug's behaviour rules in a complicated environment made in the computer. A classifier system is a reflex system, which generates an action soon after receiving a stimulus from the environment. This is quite similar to the feature of lower animal's function of processing the information.
        The bug is released at some spot in the virtual world where baits are randomly located and there are obstacles and enemies. The classifier is rewarded only when the bug succeeds to obtain the bait. The more the bug learned, the more new classifier rules emerge: such as a rule to take a step forward or another rule to change the direction towards bait. At this stage, the bug learns the way to obtain the bait avoiding the obstacles and enemies by itself.
        The results of the observation of simulation experiments show that the chain of the continuous action which consists of multiple classifiers is arising.

        This paper presents fuzzy neural networks, which are an expansion of classical neural networks. These networks can formally represent and process both the qualitative (linguistic) and quantitative information, which usually describe a complex, multidimensional systems or decision making processes. The second part presents the results of tests and a practical implementation of applications for decision support systems based on fuzzy neural networks used for strategic management and determination of product development strategy.

        A parallel algorithm for discrete orthogonal transformation of 2D signals is proposed. The following two main features distinguish it from known algorithms. The number of operations of multiplication and addition is smaller than those used in known algorithms and, besides that, coefficients of transformation is computed at once without executing 1D transformations. The structure of the algorithm is also convenient for VLSI implementation. Contour extraction algorithms are proposed based on discrete 2D Walsh and Haar functions.

        The methods of tolerance ellipsoidal estimations of static systems parameters set are considered. Conditions of tolerance set existence, when the static system are described by interval model are shown and iterative algorithm are developed, which allow to combine procedure of localization model parameters set and tolerance ellipsoidal estimation. Analysis of the task of tolerance set estimations of the linear interval model parameters in the conditions of the base functions of the model identifications are executed. This allow to design the approach of tolerance ellipsoidal estimations of the parameters model for such case.