Scientia Iranica
Volume:15 Issue: 6, 2008

Fractional-pixel accuracy Motion Estimation (ME) has been shown to result in higher quality reconstructed image sequences in hybrid video coding systems. However, the higher quality is achieved by notably increased Motion Field (MF) bitrate and more complex computations. In this paper, new half-pixel block matching ME algorithms are proposed to improve the rate-distortion characteristics of low bitrate video communications. The proposed methods tend to decrease the required video bandwidth, while improving the motion compensation quality. The key idea is to put a deeper focus on the search origin of the ME process, based on center-bias characteristics of low bitrate video MFs. To employ the bene ts of Mesh-based ME (MME), the introduced algorithms are also examined in the framework of a fast MME scheme. Experimental results show the eciency of the proposed schemes, especially when employed in the MME approach, so that a reduction of more than 20% in the MF bitrate is achieved when employing typical QCIF formatted image sequences. Keywords: Motion Estimation (ME); Block matching; Mesh-based ME; Half-pixel accuracy ME; Low bitrate communications; Video coding.

This paper presents the measured impedance at the relaying point in the presence of a series connected Flexible Alternating Current Transmission System (FACTS) device, i.e. Static Synchronous Series Compensator (SSSC). The presence of SSSC on a transmission line has a great in uence on the tripping characteristic of distance relays. The distance relay tripping characteristic itself depends on power system structural and pre-fault operational conditions and, especially, the ground fault resistance. In the presence of SSSC, its controlling parameters, as well as the connection point of the instrument transformers of distance relay a ect the tripping characteristic. Here, measured impedance at the relaying point is calculated, due to the concerned parameters. Keywords: Distance protection; Fault resistance; FACTS devices; Tripping characteristic; SSSC.

Providing the stability of any rate allocation algorithm is a challenging issue in current high-speed networks. Some researchers, such as Kelly, Massoulie, Vinnicombe and Johari, have shown the stability of their rate-based rate allocation algorithms using di erent approaches. Some other researchers have investigated the stability of the second-order, rate-based, rate allocation algorithms under some simplifying constraints. Mo et al. have proved the stability of the rstorder, window-based rate allocation algorithms, using control theory concepts, for a wide range of fairness criteria. In the current work, the stability property of a second-order, high-speed and window-based rate allocation strategy has been investigated using the Lyapunov approach. Simulation results verify the stability of the proposed method under a general network scenario.

In this investigation, a novel approach is presented to nd the optimum locations and capacity of Flexible AC Transmission Systems (FACTS) devices in a power system using a fuzzy multi-objective function. Maximising the fuzzy satisfaction allows the optimization algorithm to simultaneously consider the multiple objectives of the network to obtain active power loss reduction; i.e., new FACTS devices cost reduction, robustifying the security margin against voltage collapse, network loadability enhancement and a voltage deviation reduction of the power system. A Genetic Algorithm (GA) optimization technique is then implemented to solve the fuzzy multi-objective problem. Operational and control constraints, as well as load constraints, are considered for optimum device allocation. Also, an estimated annual load pro le has been utilized in a Sequential Quadratic Programming (SQP) optimization sub-problem to nd the optimum location and capacity of FACTS devices, accurately. A Thyristor Controlled Series Compensator (TCSC) and a Static Var Compensator (SVC) are utilized as series and shunt FACTS devices in this study. The Fars regional electric network is selected as a practical system to validate the performance and e ectiveness of the proposed method. Keywords: FACTS devices allocation; Multi-objective optimization; Genetic algorithm; Fuzzy.

Let f''m: m 2 Mg be a generalized frame in Hilbert space H with frame bounds 0 < A  B < 1 and the analysis operator T: H! L2 (). The paper studies the relation between (space) redundancy (TH)? and (norm) redundancy A. Also، in case dimH < 1، the e ect of the redundancies on the reduction of the total energy of noise is studied. Keywords: Frame; Generalized frames; Noise; Redundancy.

Sucient conditions for the boundedness and regularity of a function, whose partial derivatives satisfy a certain set of equations, are presented. Energy methods are used to establish these results. The asymptotic behavior of the gradient toward a constant function is also investigated. Keywords: Boundedness; Regularity; Asymptotic behavior; Nonlinear ODE; Hessian; Gradient.

Remote sensing data are essentially used for land cover and vegetation classi cation. However, classes of interest are often imperfectly separable in the feature space provided by the spectral data. Application of Neural Networks (NN) to the classi cation of satellite images is increasingly emerging. Without any assumption about the probabilistic model to be made, the networks are capable of forming highly non-linear decision boundaries in the feature space. Training has an important role in the NN. There are several algorithms for training and the Variable Learning Rate (VLR) is one of the fastest. In this paper, a network that focuses on the determination of an optimum learning rate is proposed for the classi cation of satellite images. Di erent networks with the same conditions are used for this and the results showed that a network with one hidden layer with 20 neurons is suitable for the classi cation of IRS-1D satellite images. An optimum learning rate between the ranges of 0.001-0.006 was determined for training the VLR algorithm. This range can be used for training algorithms in which the learning rate is constant.

The solvability of @ operator in a class of hypo-analytic manifolds in complex dimension 2 is studied. Suitable weighted L2 spaces are introduced for establishing an a priori inequality. The regularity of the solutions is shown by using a theory of degenerate elliptic operators, developed by Grusin and Visik. The theorem obtained is a degenerate version for the @ problem in strictly pseudo-convex domains.

In this paper, the Conley index theory is used to examine the Poincare index of an isolated invariant set. Some limiting conditions on a critical point of a planar vector eld are obtained to be an isolated invariant set. As a result, the existence of in nitely many homoclinic orbits for a critical point with the Poincare index greater than one is shown. Keywords: Conley index; Homoclinic orbit; Poincare-Lefchetz duality; Poincare index.

In this paper, a simple and ecient clock boosting mechanism to increase the performance of an adaptive router in Network-on-Chip (NoC) is proposed. One of the most serious disadvantages of a fully adaptive wormhole router is performance degradation due to the routing decision time. The key idea to overcome this shortcoming is the use of di erent clocks in a head it and body its. The simulation results show that the proposed clock boosting mechanism enhances the performance of the original adaptive router by increasing the accepted load and decreasing the average latency in the region of e ective bandwidth. The enhanced throughput of a router results in power saving by reducing the operating frequency of a router for certain communication bandwidth requirements. Keywords: Network-on-Chip (NoC); Interconnection network; Wormhole ow control; Adaptive router; Dynamic Frequency Scaling (DFS); Low power design.

The Fuzzy Logic Congestion Detection (FLCD) algorithm is a recent proposal for congestion detection in IP networks which combines the good characteristics of both traditional Active Queue Management (AQM) algorithms and fuzzy logic based AQM algorithms. The Membership Functions (MFs) of the FLCD algorithm are designed using a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, in order to achieve optimal performance on all the major performance metrics of IP congestion control. The FLCD algorithm achieves better performance when compared to the basic Fuzzy Logic AQM and Random Explicit Marking (REM) algorithms. Since the optimization process is undertaken oine and is based on a single optimization script, the performance of the FLCD algorithm may not be optimal under di erent network conditions, due to the fact that the IP environment is characterized by dynamic trac patterns. This paper proposes two online self-learning and organization structures that enable the FLCD algorithm to learn the system conditions and adjust the fuzzy rule base in accordance with prevailing conditions. The self-organized FLCD algorithm is compared with the unorganized FLCD, the basic Fuzzy Logic AQM and the Adaptive Random Early Detection (RED) algorithms using simulations with dynamic trac patterns. Performance results show that the self-organized FLCD algorithm is more robust than the other algorithms. Compared to the unorganized FLCD, the new scheme improves the UDP trac delay for short round trip times and also reduces packet loss rates. In terms of jitter, fairness and link utilization, it exhibits a similar performance to the unorganized FLCD algorithm. Keywords: Active queue management; Congestion control; Fuzzy logic; Multi-objective particle swarm optimization; Pareto set.

The aim of the minimization analysis of network attack graphs (NAGs) is to nd a minimum critical set of exploits so that by preventing them an intruder cannot reach his goal using any attack scenario. This problem is, in fact, a constrained optimization problem. In this paper, a binary particle swarm optimization algorithm, called SwarmNAG, is presented for the minimization analysis of large-scale network attack graphs. A penalty function method with a time-varying penalty coecient is used to convert the constrained optimization problem into an unconstrained problem. Also, a time-varying velocity clamping, a greedy mutation operator and a local search heuristic are used to improve the overall performance of the algorithm. The performance of the SwarmNAG is compared with that of an approximation algorithm for the minimization analysis of several large-scale network attack graphs. The results of the experiments show that the SwarmNAG outperforms the approximation algorithm and nds a critical set of exploits with less cardinality. Keywords: Particle swarm optimization; Constrained optimization; Penalty function method; Local search; Network attack graph.