Journal of Computer and Robotics
Volume:1 Issue: 1, Winter and Spring 2008

Content-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. The challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user interaction in the retrieval cycle. Hence, in this paper, an image retrieval system is introduced that provided two kind of query presenting, query by keyword and query by sample image. The proposed system, after the first result retrieval, does an interactive retrieval process semantically based on user's relevance feedbacks and related high level semantic labels to the images semi-automatically. This system can reply different requests in the image retrieval domain based on a hierarchical semantic network and doing a kind of learning process by the feedbacks given by user. According to experiments, the proposed approach concludes acceptable accuracy for retrieval results.

In recent years, size of VLSI circuits is dramatically grown and layout generation of current circuits has become a dominant task in design flow. Standard cell placement is an effective stage of physical design and quality of placement affects directly on the performance, power consumption and signal immunity of design. Placement can be performed analytically or heuristically. Analytical placers generate optimal or near-optimal solution but they are not usable for large circuits due to large computation time. In contrast, Heuristic placers can be used to place large circuits with more poor quality rather than analytical ones. In this paper, a hybrid analytical and heuristic approach for standard-cell placement is proposed. In this approach, cell rows are arranged heuristically but the location of cells inside each row are determined analytically. Experimental results show that general metric of placement (total wire length) is improved by 28.6% and this improvement will be more considerable for more large circuits. However, total wire length reduction is gained with a little computation overhead (about 0.01%).

Optimizing the database queries is one of hard research problems. Exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. The use of evolutionary methods, because of their efficiency and strength, has been changed in to a suitable research area in the field of optimizing the database queries. In this paper, a hybrid evolutionary algorithm has been proposed for solving the optimization of Join ordering problem in database queries. This algorithm uses two methods of genetic algorithm and learning automata synchronically for searching the states space of problem. It has been showed in this paper that by synchronic use of learning automata and genetic algorithms in searching process, the speed of finding an answer has been accelerated and prevented from getting stuck in local minimums. The results of experiments show that hybrid algorithm has dominance over the methods of genetic algorithm and learning automata

Artificial Immune Systems (AIS) can be defined as soft computing systems inspired by immune system of vertebrates. Immune system is an adaptive pattern recognition system. AIS have been used in pattern recognition, machine learning, optimization and clustering. Feature reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and semantic based image retrieval. Unlike other dimensionality reduction methods, feature selectors preserve the original meaning of the features after reduction. In this paper we introduce the capability of AIS for semantic preserving data reduction (SPDR). For this purpose a complete survey is done on artificial immune systems. Then a case study is selected to represent the capability of semantic preserving data reduction of AIS. Experimental results subjectively show and verify the proposed idea.

First Riccati equation with matrix variable coefficients, arising in optimal and robust control approach, is considered. An analytical approximation of the solution of nonlinear differential Riccati equation is investigated using the Adomian decomposition method. An application in optimal control is presented. The solution in different order of approximations and different methods of approximation will be compared respect to accuracy. Then the Hamilton-Jacobi-Belman (HJB) equation, obtained in nonlinear optimal approach, is considered and an analytical approximation of the solution of it using the Adomian decomposition method is presented.

This paper presents, a hybrid method, low-resolution and high-resolution, for Persian page segmentation. In the low-resolution page segmentation, a pyramidal image structure is constructed for multiscale analysis and segments document image to a set of regions. By high-resolution page segmentation, by connected components analysis, each region is segmented to homogeneous regions and identifying them as texts, images, and tables/drawings. The proposed method was experiment with the Persian documents. The result of these tests have shown that the proposed method provide more accurate and speed results.

In this paper, an intelligent controller is applied to control omni-directional robots motion. First, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named LoLiMoT. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. This emotional learning is based on a computational model of limbic system in the mammalian brain. The Brain Emotional Learning Based Intelligent Controller (BELBIC), using the concept of LQR control is adopted for the omni-directional robots. The performance of this multi objective control is illustrated with simulation results based on real world data. This approach can be utilized directly to the robots in the future.

In many autonomous mobile applications, robots must be capable of analyzing motion of moving objects in their environment. Duringmovement of robot the quality of images is affected by quakes of camera which cause high errors in image processing outputs. In thispaper, we propose a novel method to effectively overcome this problem using Neural Networks and Kalman Filtering theory. Thistechnique uses movement parameters of camera to resolve problems caused by error in image processing outputs. The technique issuccessfully applied in the MRL Middle Size Soccer Robots where ball motion detection has an especial importance in their decisionmaking. Experimental results are presented and 2.2% achieved error suggests that the combined approach performs significantly better thantraditional techniques.