Recent activation and electrophysiological studies have demonstrated that sound recognition and localization are processed in two distinct cortical networks that are each present in both hemispheres. Sound recognition and/or localization may be, however, disrupted by purely unilateral damage, suggesting that processing within one hemisphere may not be sufficient or may be disturbed by the contra lateral lesion. Based on the results of our research on acute and chronic lesions, patterns of recovery and functional imaging, evidence will be presented, which support the latter hypothesis.

To identify different radar techniques, a matrix algorithm is proposed in this paper. A systolic array is designed to do the algorithm in real time manner. Homogeneous Submatrix Systolic multiplier (HSSM) method has been used in this design. Modeling and simulation results show the processor could effectively identify different radar techniques.

This paper gives a new application of DEA to evaluate the scheduling solutions of Parallel processing. It evaluates the scheduling solutions of Parallel processing using the non-convex DEA model, FDH model. By introducing each solution of Parallel processing scheduling as a DMU with some relevant inputs and outputs this paper shows that how the most efficient schedule(s) can be identified.

There are different variants of Particle Swarm Optimization (PSO) algorithm such as Adaptive Particle Swarm Optimization (APSO) and Particle Swarm Optimization with an Aging Leader and Challengers (ALC-PSO). These algorithms improve the performance of PSO in terms of finding the best solution and accelerating the convergence speed. However, these algorithms are computationally intensive. The goal of this paper is high performance implementations of Traditional PSO (TPSO), APSO and ALCPSO using CUDA technology. We have implemented these three algorithms on both central processing unit (CPU) and graphics processing unit (GPU) in order to analyze and improve their performance and reduce their computational times. We have achieved speedups up to 14. 5x, 31x, and 152x, for GPUTPSO, GPU-ALCPSO, and GPU-APSO, respectively. In addition, different number of threads has been chosen in order to find an appropriate number of threads per block for both APSO and ALC-PSO algorithms. Our experimental results show that the best choice for number of threads per block depends on the number of existing variables and constants in each algorithm and the number of registers per multiprocessor.

This paper presents an investigation into the development of stresses in anchorage zone in prestressed post-tensioned concrete beam using the finite element analysis. A finite element computer code on the platform of a supercomputer PARAM 10000 is developed and employed for the present study. A Parallel algorithm for matrix inversion method is developed and implemented in the presented finite element code. Concentric and eccentric prestressing forces are applied for prestressing of the beams. Effect of Poisson’s ratio over the bursting tensile force developed in the anchorage zone is studied and an equation to compute the magnitude of bursting tensile force by incorporating the effect of Poisson’s ratio is proposed. Development of spalling zone is also investigated and it was found that due to eccentric loading, stresses of higher magnitude were developed in this zone.Supercomputer PARAM 10000 is used to carry out present analysis and time reduction has been achieved by using multiple processors of the supercomputer PARAM 10000. At the end the performance of the developed Parallel code is studied and presented.

We present an algorithm for NVIDIA CUDA platform based on Smith-Waterman algorithm for sequence alignment problem. CUDA is a new programming language which is very similar to the standard C language, with some extensions. By using the Smith-Waterman algorithm which is used to find similarity between two sequences by making a scoring matrix; this application tries to find the similarity between a sequence which called query sequence and sequences in a database file. In this program, each thread is used for calculating one scoring matrix between the query sequence and one of the sequences in database. The algorithm utilizes small but fast shared memory which is inside the GPU for holding four intermediate columns in each cycle for each thread. If the database is large enough, it can keep the GPU in full working order and results in better speedup in comparison to CPU. To demonstrate the performance, we used a high-end CPU, Intel Core 2 Due E6600 and a mid-end GPU, GeForce 8600GT and saw the speedup in about twenty times more than CPU.

The mathematical modeling of the acoustic wave propagation in seawater is the basis for realizing goals such as, underwater communication, seabed mapping, advanced fishing, oil and gas exploration, marine meteorology, positioning and explore the unknown targets within the water. However, due to the existence of various physical phenomena in the water environment and the various conditions governing the sea environment, such as, noise, sea state, depth, substrate, temperature and salinity, various methods have been proposed for the propagation of acoustic wave, which results in a high computational complexity in the numerical solution of acoustic wave propagation. Since the high speed of computing is a key factor in most real-time applications, in this study, a Parallel algorithm for the implementation of computerized underwater acoustic wave propagation is presented, using the Gaussian Beamtracing method. In order to compare the performance of the proposed Parallel algorithm with respect to the serial mode, the results are reported in figure and table for several different scenarios. The simulation results indicate a much higher rate of Parallel algorithm than the serial mode despite its very precise accuracy.