The rapidly increasing need for information communication requires higher speed data transmission over the existing channels. The data rate over these channels is limited mainly by Inter Symbol Interference (ISI). Channel equalizers are used to reduce the effect of ISI. In this paper, a new equalizer based on adaptive neuro-fuzzy inference system is presented. The performance of the proposed equalizer is evaluated for both linear as well as non-linear channels in terms of bit-error rate for different noise powers. Simulation results show that the proposed equalizer has better Bit Error Rate (BER) performance compared to multi-layer perceptron and least mean square equalizers. However, its BER performance is slightly poorer than that of radial basis function network and optimal Bayesian equalizer but is better in terms of structural complexity.

In this paper, experimental setup of three-level cascaded inverter based 380V/΁25kVAR D-STATCOM using decoupled indirect current control method (DICC) is realized. AC and DC side of D- STATCOM is modelled in dq-axis on account of D-STATCOM's controlling. DICC is used for control of D-STATCOM's dq-axis currents independently. Gate pulses for inverter are generated with multilevel sinusoidal pulse width modulation (SPWM) technique. In this study, controller card used for signal processing is dSPACEs DS1103. The control algorithm is prepared by the help of MATLAB/Simulink^® software. This algorithm is converted to C language by using Matlab/Real-Time Workshop and downloaded to DS1103's program memory by dSPACE/Real-Time Interface. Implemented experimental setup is tested by changing reference reactive current (i_qref ) +20A to -20A and obtained results from this test are given.

Currently, the manufacturing process of complex systems such as network-based Systems-on-Chip incurs a considerable amount of failures. This paper presents a class of very low cost routing algorithms to increase the yield and to tolerate permanent faulty links in Networks-on-Chip. These new algorithms are fault tolerant through dynamic reconfiguration when the regular topology is altered by faulty links. Also, the proposed algorithms are the reconfigurable extensions of deterministic routing algorithms and their deadlock freeness is obtained by prohibiting a few turns. To demonstrate the effectiveness of the proposed routing algorithms, the performance, power consumption, and area overheads are evaluated through appropriate simulations and syntheses. The experimental results show that considerable reliability and yield improvements are obtained with only a few percent power and area overheads.

Mach-Zehnder interferometer (MZI)-based wavelength interleaver is an important device for wavelength multiplexing in dense wavelength division multiplexed optical communication systems. An MZI-based wavelength interleaver can be realized by concatenating 2×2 directional couplers through differential delay lines. The importance of the location of the second delay line in a 2-stage MZI-based wavelength interleaver, and its implication on the optimum value of the splitting ratios of the couplers, to achieve a flattop wavelength response, is highlighted. Here a novel method based on phasor notation to estimate the appropriate magnitude and location of the differential phase delay, and its dependence on the splitting ratios of the couplers is presented. The applicability of the method has been illustrated through analytical equations and simulated responses. The approach gives a better intuitive understanding of the working of interleavers, which is crucial in fabricating a practical wavelength interleaver with flattop response.

Performance of an L branch maximal ratio combining receiver has been analyzed in equally correlated Hoyt fading channels. For the combiner output signal-to-noise ratio, analytical expressions for the probability density function, its average value, n^th moment and outage probability have been obtained. Further, for the MRC receiver, expressions for average bit error rate for binary, coherent, and noncoherent modulations have been presented. Both numerical evaluation and computer simulation results have been generated to validate analytically obtained expressions. Numerical results have been plotted to illustrate the effect of channel correlation and L on the receiver performance.

Recent development in three-dimensional (3D) imaging techniques such as magnetic resonance imaging or computed tomography with application in medical science demands the development of appropriate 3D image processing methods. It is therefore of interest to develop methods of image analysis, which would make use of this additional information. This article presents the classification and segmentation of 3D MR human liver images. The described experiments investigate whether it is possible to improve the accuracy of homogenous texture classification with the use of 3D analysis. Classification was performed both for 3D and two-dimensional data samples using Gray Level Co-occurrence Matrices. The proposed segmentation method is based on the 3D network of synchronized oscillators applied for 3D data. The principles of oscillator network operation are described here. The network was tested on sample 3D artificial images, and the segmentation results were compared with those obtained with the use of multilayer feedforward perceptron. It is demonstrated that the advantage of the discussed approach is its resistance to changes of visual image information caused, for example, by noise, that are very often present in biomedical images. The proposed technique was applied for segmentation of 3D liver images, and the sample results are presented and discussed.

The Indian domestic telecom sector has seen phenomenal growth over the past few years. The number of mobile phone subscribers in India has already crossed 500 million by December 2009 and increasing at about 15 million subscribers per month. This phenomenal growth also has lead to poor quality of service with frequent call drop outs, and so on, due to heavy traffic congestion, especially in urban areas. The problem is compounded further by the limited amount of spectrum available for mobile cellular networks. This leads to a situation where the mobile service provider has to significantly increase the number of cell sites for capacity enhancement with attendant costs on infrastructure development. The adoption of emerging Next Generation Network techniques coupled with Fixed Mobile Convergence (FMC) could alleviate the problems to a large extent. The FMC concept for spectrum saving is based on the concept of utilization of an existing resource in the form of fixed telephone lines for diverting the traffic on mobile networks in scenarios where such diversion will not affect the user service requirements. The enabling technologies that can lead to FMC have also been identified from user's perspective. A mathematical model has been developed to estimate the amount of spectrum that can be saved through FMC. Parametric analysis has been carried out with respect to the parameters, which influence the spectrum saving. The model has been applied on real life networks to demonstrate the percentage of spectrum that can be saved. The spectrum thus saved can be used for increasing the number of mobile users without any addition to the existing mobile network infrastructure.

A new Hybrid Algorithm (HA) is proposed as a statistical technique for obtaining approximate solutions to combinatorial optimization problems. The proposed algorithm is a combination of Threshold Accepting (TA) and Hamming Scan algorithms. It combines the good methodologies of these algorithms, such as global minimum converging property of TA algorithm and fast convergence rate of Hamming scan algorithm. HA is used to design sixty-phase sequences, which have good autocorrelation properties. Sixty-phase Barker sequences with length up to 18 only were reported in the literature. Whereas in this paper, sixty-phase Barker sequences up to lengths 26 are presented. The merit factors of synthesized sixty-phase Barker sequences are better than well-known binary Barker sequences. The sequences up to length 441 have autocorrelation peak sidelobe values better than well-known Frank codes. The synthesized sequences are promising for practical applications to radar and in communication. These sequences have complex structure and very difficult to detect and analyze by an enemy's electronic support measures (ESMs). The convergence rate of HA is found to be good.

The present article proposes a high-performance architecture for the Two-Step Search algorithm, which is used in half-pixel motion estimation. As motion estimation calls for intense computation on a large number of pixels stored in memory, frequent memory access is involved in this operation. In the present article, an architecture, which is based on an intelligent memory configuration to contain the required large memory bandwidth, has been proposed for implementing the Two-Step Search algorithm for variable block sizes as recommended by H.264 standard. The present architecture has been compared with a reported architecture. It has been found that the proposed architecture can process up to 33% more number of High Definition Tele-Vision frames (of size 1280×720) and also consumes 5% less power by sacrificing only about 1.6% of the total chip area.

Power MOSFETs are important devices in many types of power converters. Considering their main parasitic parameters, the Cdv/dt induced effects that will deteriorate the performance of converters are analyzed. The switching operation process is discussed firstly. Then, an analytical model of power MOSFET is deduced and validated, based on which the Cdv/dt induced effects are simulated with different values of parasitic parameters. Finally, according to the simulation results, the design optimizations are presented, and an experimental prototype is used to validate the proposed approach.

In most of the path-planning applications, the controlled object (mobile robot) is expected to reach its predetermined target by following the shortest path and avoiding the obstacles. This navigation problem is also called optimal obstacle avoidance. In this work, obstacles are assumed to be motionless circles in different sizes. The object is supposed to be a point robot. The two-stage algorithm is proposed to find a numerical solution to the problem. At first stage, the method, which is optimal for one step, is applied iteratively. In every step of the method the first obstacle on the straight line between the current position and the target is assumed to be a single obstacle. The proposed method is realized using geometric representations. Some evaluations are made to prove that the method is convergent. The path obtained at the first stage might not be optimum. However, its length can be used to limit the feasible region through an ellipse, which contains the shortest path. Thus, the reduced search space makes the next stage more efficient and endurable for real-time applications. In the second stage of the algorithm, the elliptic region is meshed with squares, the side length of which is set in agreement with the minimum distance between obstacles. It is prohibited to pass through the squares that intersect obstacles. Thus, by discretization the problem becomes the shortest path problem in a graph, and is solved by applying the Dijkstra's algorithm. The proposed two-stage algorithm is verified with numerical simulations. Obstacles are chosen randomly. A target position is selected and fixed. For different starting points, the algorithm is tested repeatedly. The results show that the proposed algorithm can be applied to find an optimal solution for the obstacle avoidance problem.

In computer vision, the corners of an object play an important role in shape representation and analysis. In this paper, we carried out a dozen popular and most cited corner detection algorithms and studied their performances based on several performance measures proposed by us as well as in the literature over a varied range of images and categorized the corner detectors based on different image types and rank their performances with suitable threshold ranges corresponding to different image types. After obtaining a suitable corner detector for a given type of images, the paper describes a new approach to corner detection in a digital image based on the assumption that corners are those image points with high information content, and hence corners in an image exist in those regions having considerably high-intensity variation. Consequently, a complex corner response function is computed only within those regions with considerable high-intensity variation instead of entire image, reducing the computational cost of the whole procedure. Experiments conducted with the help of a few images showed the efficiency of the technique, both in terms of execution time and false-positive corners.