The presented deformable field theory deals with electromagnetic local forces on the basis of field energy density. In this theory, any movement, rigid or deforming, distorts the electromagnetic field continuum. This leads to novel concepts of total and local forces explicitly related to the elastic deformation gradient rather than the classical gradient of the magnetic field. It is shown how the magnetic vector potential, as the magnetic invariant variable, is associated to this deformable field continuum and is, meanwhile, reference-independent. Then, within an adiabatic virtual work, the local magnetic energy derivatives are analytically performed, converging to overall electromagnetic force and stress tensors, including Lorenz, inherent magnetization and strict magnetostriction forces.

This paper presents and utilizes an Improved Particle Swarm Optimization algorithm (IPSO) for reactive power management in restructured power systems. Reactive power procurement is modeled as a Security Constraint Optimal Power Flow (SCOPF), which incorporates a voltage stability problem. This is a major concern in power system control and operation. The model attempts to minimize the cost of reactive power procurement and energy losses as a main objective, while the technical criteria and voltage stability margin, especially, are treated as soft constraints. From a mathematical point of view, the reactive power market can be expressed as a nonlinear non-convex optimization problem with multi-local minima. In most cases, the non-convexity results in a failure of the mathematical-based optimization algorithm to find the global optimum. Thus, the PSO, a powerful heuristic searching algorithm, is developed and implemented to find the global optimum of the reactive power market objective function. The feasibility of the methodology (IPSO) is tested over an IEEE30 bus system, while the obtained simulation results are compared with the gradient-based approach, using General Algebraic Modeling System (GAMS) software, the original PSO and another evolutionary programming called a Genetic Algorithm (GA). The results demonstrate that the IPSO can converge to better feasible solutions with less iteration and can be successfully used for reactive power scheduling in deregulation environments.

In this paper, the operational approach to the Tau method is used for the numerical solution of a nonlinear Fredholm integro-differential equations system and nonlinear ODEs with initial or boundary conditions without linearizing. An efficient error estimation of the approximate solution is also introduced. Some examples are given to clarify the efficiency and high accuracy of the method.

In this paper, two methods are proposed for the detection of a band-limited signal in unknown variance white Gaussian noise. The complex amplitude and the frequency of the signal and the noise variance are assumed as unknown parameters. Using wavelet concepts, an orthonormal, fully-decimated filter-bank is employed to decompose the signal into its subband components. It is shown that, in this process, the noise is also decomposed into orthonormal zero-mean components. In the output, if a band-limited target signal is present, the respective single subband component (or two components in marginal cases) containing the target signal presents a non-zero mean. The presence of a non-zero mean componen (s) in this canonical form is tested using a well-known Generalized Likelihood Ratio (GLR) solution ($F$-test), which is based on the ratio between the output power of one (or two) subband(s) and the average output power of the other subbands (estimating the noise variance). Comparing to a threshold, a Constant False Alarm Rate (CFAR) detector is constructed. Since the target signal's central frequency is unknown, the proper subband(s) is selected as the one(or two) maximizing the $F$-test statistic and a GLR test, namely a Wavelet Detector (WD), is obtained. It turns out that the performance of WD depends on the frequency of the signal. For instance, a lowpass signal is detected better than a bandpass signal by this detector. To overcome this problem, the frequency band, where the signal may exist, is estimated, and the signal is down-converted such that the detection is always accomplished at the lowest subband in the new detector, a Modified WD (MWD). The performance of the proposed methods is evaluated in solving two well-known problems, compared with the existing DFT detector. A sinusoid with unknown amplitude, phase and frequency is detected by these detectors as an approximately band-limited signal. The proposed detectors are also applicable for the detection of a signal composed of a white component and an approximately band limited component. A sinusoid, with unknown phase and frequency and Rayleigh-distributed amplitude, is also detected as such a signal.

The architecture of a hardwired simulator for implementation of a iscrete event-driven simulation of digital systems at the logic level is resented. In the design of this system, attempts have been made to utilize echniques of high performance computing to have a system capable of simulating he digital circuits rapidly. The centralized event-driven simulation algorithm hosen here, has the advantages of being efficient and conceptually traightforward. The high reliability of the simulator has been taken care of hrough a collection of handshake signals between each two of the three main odules.

One of the most important challenges in automatic speech recognition is the case of mismatch between training and test data. Conventional methods for improving recognition robustness seek to eliminate or reduce the mismatch, e.g. enhancement of the speech by adapting the statistical models. Training the model in different situations is another example of these methods. The success with these techniques has been moderate compared to human performance. In this paper, an inspiration from human listeners created the motivation to develop and implement a new bidirectional neural network. This network is capable of modeling the phoneme sequence, using bidirectional connections in an isolated word recognition task. This network can correct the phoneme sequence obtained from the acoustic model to what is learned in the training phase. Acoustic feature vectors are enhanced, based on the inversion techniques in neural networks, by cascading the lexical and the acoustic model. Speech enhancement by this method has a remarkable effect in eliminating mismatch between the training and test data. The efficiency of the lexical model and speech enhancement was observed by a 17.3 percent increase in the phoneme recognition correction ratio.

In this paper, a different approach for finding an approximate solution of the Nonlinear Volterra Integral Equations (NVIE) of the Second Kind is presented. In this approach, the nonlinearity of the kernel has no serious effect on the convergence of the solution. The author's approach is simple and direct for solving the (NVIE). The solution of the original problem is obtained, by converting the problem into an optimal moment problem. The moment problem is modified into one consisting of the minimization of a positive linear functional over a set of Radon measures. Then, an optimal measure is obtained, which is approximated by a finite combination of atomic measures and, by using atomic measures, this one is changed into a semi-infinite dimensional nonlinear programming problem. The latter is approximated by a finite dimensional linear programming problem. Finally, the approximated solution for some examples is found.

In this paper, a new approach for constructing quadratic Lyapunov Krasovskii functionals for a class of nonlinear time-delay systems is developed. The functionals are then used to derive delay-dependent stability conditions. These conditions are sufficient and local. Numerical example shows that the results obtained using the proposed method are less conservative than those obtained by the existing methods.

The proportional fairness criterion, which was first proposed by F.P. Kelly and his colleagues, has a number of properties in allocating user rates. For example, it resembles the AIMD in the TCP-Vegas~[1] in rate allocation to users and there exists a well-established stability analysis in Kelly's work relating to the stability of the rate allocation algorithm. Another outstanding feature is that Kelly et al. try to solve the optimization problem of maximizing the aggregate utility of users in a distributed manner, by decomposing the overall system problem into two subproblems. These subproblems can be solved by the network and individual users by introducing a pricing scheme~[2]. In the current work, a new high-speed second-order rate allocation algorithm has been proposed, which is based on the Jacobi method. The performance of the algorithm, under user arrival and departure and background variable bit-rate traffic, is evaluated, in comparison with the conventional Kelly's algorithm. Simulation results show that the proposed method outperforms that of Kelly in convergence speed. For short-time users, the proposed algorithm assigns more rates than that of Kelly.

In this paper, a design and construction method for an omnidirectional vision system is described, including how to use it on autonomous soccer robots for object detection, localization and, also, collision avoidance in the middle size league of RoboCup. This vision system uses two mirrors, flat and hyperbolic. The flat mirror is used for detecting very close objects around the robot body and the hyperbolic one is used as a global viewing device to construct a world model for the soccer field. This world model contains information about the position and orientation of the robot itself and the position of other objects in a fixed coordinate system. In addition, a fast object detection method is introduced. It reduces the entire search space of an image into a small number of pixels, using a new idea that is called jump points. The objects are detected by examining the color of pixels overlapping these jump points and a few pixels in their neighborhood. Two fast and robust localization methods are introduced, using the angle of several fixed landmarks on the field and the perpendicular borderlines of the field. Borderline detection uses the clustering of candidate points and the Hough transform. In addition, the omnidirectional viewing system is combined with a front view that uses a plain CCD camera. This combination provided a total vision system solution that was tested in the RoboCup 2001 competitions in Seattle USA. Highly satisfactory results were obtained, both in object detection and localization in desired real-time speed.

Pervasive computing, as a new branch in the field of distributed computing, has received wide contribution from different researchers. In this novel computing model, a vast range of computational and communication resources, along with other types of service, are gathered under a single system image based on certain predefined criteria. To create a transparent environment and provide end-users with the illusion of the local availability of multiple resources, some kind of manager is needed to coordinate the tasks and their required resources. The resource management system is mainly responsible for a balanced distribution of available resources among different tasks. Devising efficient resource discovery and dissemination algorithms is, hence, an important step towards preparing the bases for a resource centric management package. In this article, the aim is to provide two algorithms for this problem, using mobile agents. The proposed resource discovery algorithms use two different hierarchical and flat approaches. The simulations show a good performance for both of the proposed models

however, the hierarchical algorithm shows better results, based on some of the introduced factors.

Appropriate sensors are a crucial necessity for the success of recognition systems. Nature has always coevolved sensors and recognition systems and this can also be done in artificially intelligent systems. To get a very fast isolated word speech recognition system for a small embedded speech recognizer, an evolutionary approach has been used to create together the required sensors and appropriate recognition structures. The input sensors are designed and evolved through inspiration by the human auditory system and the classification is done by artificial neural networks. The resulting system is compared with a widely used speech recognition system, and the results are quite satisfactory.

In this paper, reinforcement learning is used in order to model the reputation of buying and selling agents. Two important factors, quality and price, are considered in the proposed model. Each selling agent learns to evaluate the reputation of buying agents, based on their profits for that seller and uses this reputation to dedicate a discount for reputable buying agents. Also, selling agents learn to maximize their expected profits by using reinforcement learning to adjust the quality and price of the products, in order to satisfy the buying agents' preferences. In contrast, buying agents evaluate the reputation of selling agents based on two different factors: Reputation based on quality and price. Therefore, buying agents avoid interacting with disreputable selling agents. In addition, the fact that buying agents can have different priorities on the quality and price of their goods is taken into account. The proposed model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/buying agents that use the proposed algorithms in this paper obtain more satisfaction than the other selling/buying agents.