The paper is presenting theoretical analytical model and computer simulation of electromagnetic transient process in a transformer. Transformer parameters in a selected transitional process have been analyzed. Theoretical model refers to an energetic transformer with concentrated parameters with consideration of parameters of mutual inductance M. Simulation was performed on a personal computer using the software program MATLAB SIMULINK. The computer simulation confirmed the possibility of transitional process analysis in transformer’s windings with concentrated parameters.

In the estimation problems, rather than the white observation noise, there are cases where the observation noise is modeled by the colored noise process. In the observation equation, the observed value y(k) is given as a sum of the signal z(k)=Hx(k) and the colored observation noise v_c(k). In this paper, the observation equation is converted to the new observation equation for the white observation noise. In accordance with the observation equation for the white observation noise, this paper proposes new RLS Wiener estimation algorithms for the fixed-point smoothing and filtering estimates in linear discrete-time wide-sense stationary stochastic systems. The RLS Wiener estimators require the following information: (a) the system matrix for the state vector x(k); (b) the observation matrix H; (c) the variance of the state vector x(k); (d) the system matrix for the colored observation noise v_c(k); (e) the variance of the colored observation noise.

Functional decomposition is an important and powerful technique in the logic synthesis. The ternary matrix cover approach is one of the existing methods of this type. This method is also used in decomposition of a system of completely specified Boolean functions. Before constructing the desired superposition, it needs to encode a table. There is a trivial encoding method. But to find a better solution, it is important to use a special approach, because the result of the encoding has a direct influence on the obtained functions. In this paper, an efficient algorithm to encode this table is presented. It uses the approach connected with the assembling Boolean hyper cube method. The proposed algorithm is explained in details with an example. The benefits and impacts of the suggested technique are also discussed.

Most image processing algorithms are inherently parallel, so multithreading processors are suitable in such applications. In huge image databases, image processing takes very long time for run on a single core processor because of single thread execution of algorithms. Graphical Processors Units (GPU) is more common in most image processing applications due to multithread execution of algorithms, programmability and low cost. In this paper we implement color based image retrieval system in parallel using Compute Unified Device Architecture (CUDA) programming model to run on GPU. The main goal of this research work is to parallelize the process of color based image retrieval through color moments; also whole process is much faster than normal. Our work uses extensive usage of highly multithreaded architecture of multi-cored GPU. An efficient use of shared memory is needed to optimize parallel reduction in CUDA. We evaluated the retrieval of the proposed technique using Recall, Precision, and Average Precision measures. Experimental results showed that parallel implementation led to an average speed up of 6.305×over the serial implementation when running on a NVIDIA GPU GeForce 610M. The average Precision and the average Recall of presented method are 53.84% and 55.00% respectively.

Method for object motion characteristic estimation based on wavelet Multi-Resolution Analysis: MRA is proposed. With moving pictures, the motion characteristics, direction of translation, roll/pitch/yaw rotations can be estimated by MRA with an appropriate support length of the base function of wavelet. Through simulation study, method for determination of the appropriate support length of Daubechies base function is clarified. Also it is found that the proposed method for object motion characteristics estimation is validated.

In wireless sensor networks, it is crucial to design and employ energy-efficient communication protocols, since nodes are battery-powered and thus their lifetimes are limited. Such constraints combined with a great number of applications used in these networks, pose many challenges (limited energy, low security…) to the design and management of wireless sensor networks. These challenges necessitate a great attention. In this paper, we present a new version of Directed Diffusion routing protocol which provides both security and energy efficiency together in wireless sensor networks.

A MANET (Mobile Ad hoc NETwork) is an interconnection of mobile devices by wireless links forming a dynamic topology without much physical network infrastructure such as routers, servers, access points/cables or centralized administration. Routing is a mechanism of exchanging data between the source node and the destination node. Several protocols are used to perform routing the information from the source node to the destination node. The main aim of this paper is to explore the working principles of each unipath routing protocol. The unipath routing protocols are divided into Table-Driven (Proactive), On-demand (Reactive), Hybrid routing protocols.

In this paper we present a logical circuits design for approximate content matching implemented as finite state machines (FSM). As network speed increases the software based network intrusion detection and prevention systems (NIDPS) are lagging behind requirements in throughput of so called deep package inspection - the most exhaustive process of finding a pattern in package payloads. Therefore, there is a demand for hardware implementation. Approximate content matching is a special case of content finding and variations detection used by "evasion" techniques. In this research we will enhance the k-differentiate problem with "ability" to detect a generalized Levensthein edit distance i.e. transposition of two neighboring characters. The proposed designs are based on automata theory using the concept of state reduction and complexity minimization. The main objective is to present the feasibility of the hardware design and the trade-off between the simple next state and output functions of NFA and reduced number of required memory elements (flip-flops) of DFA.

As we know, the datasets related to social networks are increasing. There are different procedures to analyze these types of datasets; one of these procedures is clustering which makes communities of social data. Random walk is a process which can find communities in a network, in other words when a random walk is used, it scans the nodes in some steps; it begins with an initial node and based on a random process progresses to neighboring nodes. In this paper an algorithm is proposed which aims to finding communities in a way that modularity factor increases, for this goal, random walks with random local search agent are combined. Experimental results show that the proposed method gives better modularity in comparison with other algorithms.

A novel image encryption scheme based on chaotic system is proposed. The proposed encryption scheme utilizes one tent map to generate a pseudo-random sequence and then shift the bits of the expanding 0-1 image circularly so as to shuffle the image gray values. To make the encryption scheme resist differential attack efficiently, generalized Arnold maps and Bernoulli shift maps are applied to produce two pseudo-random gray value sequences and then diffuse the gray values bi-directionally. The bit circular shift process and diffusion processes greatly confuse the statistical nature between plain-images and cipher-images. Security analyses including key sensitivity analysis, key space analysis, statistical analysis, differential attack analysis and information entropy analysis are performed. All the experimental results demonstrate that the proposed image encryption scheme possesses large key space to frustrate brute-force attack efficiently and can resist statistical attack, differential attack, etc.

Fault localization, a central part of network fault management, is a process of deducing the exact source of a failure from a set of observed failure indications. in the network, end systems and hosts communicate through routers and links connecting them. When a link or a router faces with a fault, the information sent through these components will be damaged. Hence, faulty components in a network need to be detected and repaired to sustain the health of the network. In this paper we introduce an end to end method that detect and repair the faulty components in the network. The proposed method is a heuristic algorithm that uses the embedded information retrieved from disseminated data over the network to detect and repair faulty components. Simulation results show that our heuristic scheme only requires testing a very small set of network components to detect and repair all faults in the network.

Both fuzzy logic and sliding mode can compensate the steady-state error of proportional-derivative (PD) method. This paper presents parallel sliding mode optimization for fuzzy PD management. The asymptotic stability of fuzzy PD management with first-order sliding mode optimization in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding-mode is guaranteed.