انجمن کامپیوتر ایران

In this paper, we study the selection of active sensors in wireless sensor networks (WSN) subject to a limited energy consumption and a given estimation error. A cost function based on spatial correlation is derived for active sensor selection. Accordingly, a new adaptive algorithm is proposed in which the number of active sensors is adaptively determined and the best topology of active set is selected based on the add one sensor node at a time method. Simulation results show that the distortion of the optimum defined cost function is less than suboptimum one. Also, the performance of the proposed algorithm for sensor selection in computational burden is better than the other algorithm.

This paper presents an asynchronous and a synchronous NoC router architecture. The asynchronous scheme is implemented by the help of CSP-Verilog language and the synchronous one is designed employing VHDL language. Their designs are similar except the extra links which are in charge of handshaking processes in asynchronous architecture. According to the experimental results the transition counts of buffer, and switch components in synchronous router are almost 82% and 60% of asynchronous one, respectively. On the other hand, the transition counting of routing unit in asynchronous NoC router is nearly 73% of synchronous one. Power consumption of them are evaluated according to the obtained transition counting. Based on the comparison the power consumption of buffer and switch components are almost same due to their similar structure. However, the power consumption of routing unit component in asynchronous design is lower than synchronous one.

In this paper, we propose a method for selecting the symmetry axis of eyes region from two or more candidates. We propose a region-based deformable template matching from two new defined operations: intensity-based 2-clustering and edge shadowing. The results display the effectiveness of our method for extraction of eye, eyebrow and nose templates. The parameters of these templates can be used as feature vectors in low bit rate transmission. Evaluation of the proposed method on an Iranian database shows the accuracy of 99% for feature region extraction and 86% in average for feature template extraction.

Quantum Evolutionary Algorithm (QEA) is a novel optimization algorithm which uses a probabilistic representation for solution and is highly suitable for combinatorial problems like Knapsack problem. Fractal image compression is a well-known problem which is in the class of NP-Hard problems. Genetic algorithms are widely used for fractal image compression problems, but QEA is not used for this kind of problems yet. This paper uses a novel Functional Sized population Quantum Evolutionary Algorithm for fractal image compression. Experimental results show that the proposed algorithm has a better performance than GA and conventional fractal image compression algorithms.

Increasing the domain of locality by using treeadjoining- grammars (TAG) encourages some researchers to use it as a modeling formalism in their language application. But parsing with a rich grammar like TAG faces two main obstacles: low parsing speed and a lot of ambiguous syntactical parses. We uses an idea of the shallow parsing based on a statistical approach in TAG formalism, named supertagging, which enhanced the standard POS tags in order to employ the syntactical information about the sentence. In this paper, an error-driven method in order to approaching a full parse from the partial parses based on TAG formalism is presented. These partial parses are basically resulted from supertagger which is followed by a simple heuristic based light parser named light weight dependency analyzer (LDA). Like other error driven methods, the process of generation the deep parses can be divided into two different phases: error detection and error correction, which in each phase, different completion heuristics applied on the partial parses. The experiments on Penn Treebank show considerable improvements in the parsing time and disambiguation process.

In this paper we present a new approach for evolving an optimized neural network architecture for a three layer feedforward neural network with a mutation based genetic algorithm. In this study we will optimize the weights and the network architecture simultaneously through a new presentation for the three layer feedforward neural network. The goal of the method is to find the optimal number of neurons and their appropriate weights. This optimization problem so far has been solved by looking at the general architecture of the network but we optimize the individual neurons of the hidden layer. This change results in a search space with much higher resolution and an increased speed of convergence. Evaluation of algorithm by 3 data sets reveals that this method shows a very good performance in comparison to current methods.

اين مقاله مسئله جداسازي كور سيستمهاي طيف گسترده پرش فركانسي (FHSS) را با استفاده از يك المان آرايهاي بدون دانستن الگوي پرش و يا جهت سيگنال ورودي (DOA) براي كاربردهاي زمانحقيقي مورد بررسي قرار ميدهد. در اين مقاله الگوريتم مورد استفاده، الگوريتم تخمين و قطري سازي توام ماتريس كاميولنت (JADE) ميباشد. مزيت اين الگوريتم، سرعت بالاي آن ميباشد كه باعث شده است براي كاربردهاي زمان-حقيقي مناسب باشد. اما مشكل الگوريتم JADE رعايت نكردن ترتيب منابع يا جايگشت آنها به هنگام جداسازي است. براي حل مشكل جايگشت در اين مقاله، از ماتريس همبستگي استفاده ميكنيم.