نشریه مهندسی برق و مهندسی کامپیوتر ایران
سال یازدهم شماره 2 (پیاپی 33، زمستان 1392)

This paper defines an optimal architecture for the FPGA using exact methods. In order to achieve this goal, optimal placement and routing solutions are found using the integer linear programming techniques. After redefining the internal architecture of the logic blocks, quantum circuits are partitioned by a heuristic algorithm in order to reach maximum utilization of the resources inside logic blocks and minimum delay of the paths traversed by the q-bits in the circuit.Experimental results show that FPGA architecture modifications can result in the reduction of the delay of critical paths of circuits by up to half in some cases and in a considerable reduction of the number of channels used for routing. Furthermore, the results show that defining the logic blocks with 12 q-bits instead of 4 q-bits can decrease circuits delay and the number of used channels to a large extent.

Word and phrase segmentation is one of the main activities in natural languages processing (NLP). Many programs in NLP need to be preprocessed for extraction of text’s words and distinction phrases. Getting meaningful words with their prefix and suffix is the main and the final goal of segmentation. This activity depends on various natural languages can be easy or hard. Persian is among the languages with complex preprocessing tasks. One of the complexity sources is handling different writing scripts. In written Persian texts, we have two kinds of spaces: short space and white space. Also there are various scripts for writing Persian texts, differing in the style of writing words, using or elimination of spaces within or between words, using various forms of characters and so on.In this paper, we want to suggest a statistical method for phrase segmentation on Persian texts using neural networks due to using in search engines. For this purpose, we use occurrence likelihood of uniwords and biwords in corpus. The suggested algorithm includes four steps and could detect about 89.6% of correct tokens. Experimental results show this method can improve the performance of the usual methods.

Due to the rapid growth of digital libraries, digitizing large documents has become an important topic. In a quite long book, similar characters, sub-words and words will occur many times. In this paper, we propose a sub-word image clustering method for the applications dealing with large uniform documents. We assumed that the whole document is printed in a single font and print quality is not good. To test our method, we created a dataset of all sub-words of a Farsi book. The book has 233 pages with more than 111000 sub-words manually labeled. We use an incremental clustering algorithm. Four simple features are extracted from each sub-word and compared with the corresponding features of each cluster center. If all feature's differences lie within certain thresholds, the sub-word and the winner cluster center are finely compared using a template matching algorithm. In our experiments, we show that all sub-words of the book are recognized with more than 99.7% accuracy by assigning the label of each cluster center to all of its members.

In data stream management systems as long as streams of data arrive to the system, stored queries are executed on these data. Regarding high workload, high processing capacity is required, leading to consider multiple processors to cope with it. Partitioning approach, one of the main methods in multiprocessor real-time scheduling, bind each query to one of processors based on its utilization, ratio of estimated execution time to period, and instances of each query which should be completed under defined deadline can only be executed on specified processor. Each query which could not be assigned to any processor can be split based on utilization of processors and spread among them, causing to get closer to optimum result. This system has been examined with real network data, showing lower miss ratio and higher utilization in comparison to simple partitioning approach.

In this paper a recognition system for Persian words is introduced which utilizes the local higher order of the log-polar image autocorrelation for feature extraction of Persian sub-words. This feature extraction technique brings up leads to a system robustness in cases of writing variations alteration like scaled or rotated handwritings. Also using the log-polar transform, the sub-word image sampling will be performed so that most of acquired samples will be centered in a certain area. The proposed method uses the discrete Hidden Markov’s Model (HMM) as a classifier. Furthermore a net of dictionaries were employed to increase the reliability and precision of the system output. Finally, the Iran-Shahr database is utilized to evaluate the system performance. Comparing the results of the proposed method and other previous methods, proves that a less sensitivity has been achieved by the proposed method about handwriting variations.

Color is an important feature to describe object in visual tracking. Color-based histogram is used to model the object properly and Bhattacharya distance is also used to measure the error between reference and candidate histogram. Particles filter estimate position of target while two-dimension affine transformation is used as state of the system. Considering geometric properties of affine transformation as affine group cause two-dimensional mapping of the object to be closer to the real three-dimensional model. Approximation of optimal importance function of particles filter is obtained from Taylor expansion of Bhattacharya distance. Experiments show the accuracy and stability of the proposed tracker for fast and complex movement of a color target versus the gray level geometric particle filtering algorithm.

Intra vascular imaging is used for extracting more accurate information about the size and characteristics of plaques than coronary angiography. Sometimes shadows appear behind the calcification plaques that it makes some problem to process these images automatically. This paper describes a new approach for shadows region and border detection in Intra Vascular Ultrasound images. In the proposed algorithm, Otsu thresholding is utilized for identification of shadows location and the Active contours without edge is used for shadows border detection. According to experiments conducted on 30 samples, this proposed algorithm can able to detect shadow regions correctly with sensitivity of 86%.