انجمن کامپیوتر ایران

This paper presents an efficient algorithm for adaptive binarization of degraded document images. Document binarization algorithms suffer from poor and variable contrast in document images. We propose a contrast independent binarization algorithm that does not require any parameter setting by user. Therefore, it can handle various types of degraded document images. The proposed algorithm involves two consecutive stages. At the first stage, independent of contrast between foreground and background, some parts of each character are extracted and in the second stage, the gray level of foreground and background are locally estimated. For each pixel, the average of estimated foreground and background gray levels is defined as threshold. After extensive experiments, the proposed binarization algorithm demonstrate superior performance against four well-know binarization algorithms on a set of degraded document images captured with camera.

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.