انجمن کامپیوتر ایران

In present study, in order to improve the performance and reduce the amount of power which is dissipated in heterogeneous multicore processors, the ability of detecting the program execution phases is investigated. The program’s execution intervals have been classified in different phases based on their throughput and the utilization of the cores. The results of implementing the phase detection technique are investigated on a single core processor and also on a multi-core processor. To minimize the profiling overhead, an algorithm for the dynamic adjustment of the profiling intervals is presented. It is based on the behavior of the program and reduces the profiling overhead more than three fold. The results are obtained from executing multiprocessor benchmarks on a given processor. In order to show the program phases clearly, throughput and utilization of execution intervals are presented on a scatter plot. The results are presented for both fixed and variable intervals.

Performance of wavelet thresholding methods for speech enhancement is dependent on estimating an exact threshold value in the wavelet sub-bands. In this paper, we propose a new method for more exact estimating the threshold value. We proposed to determine the threshold value based on the symmetric Kullback-Leibler divergence between the probability distributions of noisy speech and noise wavelet coefficients. In the next step, we improved this value using segmental SNR. We used some of TIMIT utterances to assess the performance of the proposed threshold. The algorithm is evaluated using the PESQ score and the SNR improvement. In average, we obtain 2db SNR improvement and a PESQ score increase up to 0.7 in comparison to the conventional wavelet thresholding approaches.

Long-term forecasting of load demand is necessary for the correct operation of electric utilities. There is an on-going attention toward putting new approaches to the task. Recently, Neurofuzzy modeling has played a successful role in various applications over nonlinear time series prediction. This paper presents a neurofuzzy model for long-term load forecasting. This model is identified through Locally Linear Model Tree (LoLiMoT) learning algorithm. The model is compared to a multilayer perceptron and hierarchical hybrid neural model (HHNM). The models are trained and assessed on load data extracted from a North- American electric utility.

This paper presents a new segmentation method for color images. It relies on soft and hard segmentation processes. In the soft segmentation process, a cellular learning automata analyzes the input image and closes together the pixels that are enclosed in each region to generate a soft segmented image. Adjacency and texture information are encountered in the soft segmentation stage. Soft segmented image is then fed to the hard segmentation process to generate the final segmentation result. As the proposed method is based on CLA it can adapt to its environment after some iterations. This adaptive behavior leads to a semi content-based segmentation process that performs well even in presence of noise. Experimental results show the effectiveness of the proposed segmentation method.

Object tracking is one of the major subjects in machine vision and plays a main role in detection of major events in indoor soccer matches. In this paper, a novel approach for tracking the ball and players is proposed. In this method, the ground lines are segmented and eliminated using a fast and effective method. Then, the remaining non-field pixels are considered and labeled as players and the ball. A fast level set contour is used to track players and the ball. The proposed method can track players and the ball in presence of occlusion. Experiments show that the proposed method is robust to occlusion and different field colors.

Due to the changing and dynamic environment of web services, Quality of Service (QoS) becomes a key factor to differentiate service providers. Since current web service standards and technologies suffer from the lack of QoS Management, having architectures capable of supporting QoS verification, selection, negotiation and monitoring is inevitable. Software agents have been recognized as a promising technology for managing web services. Using FIPA compliant Multi Agents we were able to propose a Multi Agents based web service QoS Management Architecture. We also introduced a QoS based web service clustering method which helps us to select the best service that suits user quality preferences.