NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, A- MUHANDISI -I BARQ
Year:2011 | Volume:9 | Issue:3|Papers Count:7

Artificial Intelligence (AI) Techniques (such as learning) are used widely in agent-based systems. However, current research does not address a software engineering view on these techniques that support all the software development process. In this paper, we focus on requirement analysis -as the first step of the software development process and present techniques and tools to cover this shortage. In this regard, we provide a set of stable analysis patterns for learning capability of the agents. Stable analysis patterns are a set of meta-classes and their relations to analyze a specific issue in a domain-independent manner. Using stable analysis concepts, namely Enduring Business Themes (EBT), Business Objects (BO) and Industrial Objects (IO), these patterns represent the conceptual model of the learning. In this paper, we also apply these patterns on two case studies to investigate their applicability. These patterns are used as guidelines during analysis of learning. The main advantage of applying the stable analysis patterns in comparison with conventional analysis methods is modeling the knowledge of the learning analysis in addition to the ordinary classes of the domain. In addition, they generate more stable models via considering different levels of abstraction in the analysis.

Automatic reading of carpet patterns Requires To find the original colors of the pattern in a scanned image. It includes detecting of pattern lines and reducing the number of colors in the image. Color reduction is done in two steps: Finding the best pallet and mapping the image colors to the pallet colors. The accuracy of color reduction is so important that it may be required to ask for user intervention. The purpose of this study is to provide a new method in automatic color reduction with high accuracy. To achieve this target, reinforcement learning method is used which yields a 98% accuracy. This is a new method in color reduction and no one has used it yet. This method is defined with respect to the application and the amount of color reduction is such that does not degrade the accuracy. Therefore, the resulting pallet has more colors comparing to the original one. In the work reported in this article, first the grid lines of the pattern are detected. Then a single color is assigned to each box of the grid. After these steps, through the reinforcement learning method the color reduction is carried out. The results obtained from applying the proposed algorithm on some sample images are reported and discussed.

Gaussian Mixture Model (GMM) is a simple and effective method for statistical modeling of the feature space which is widely used in spoken language recognition systems and EM algorithm is used for training the parameters of this model. In this paper, considering the weakness of GMM models, a new model named PAW-GMM is proposed. In this model, the power of each component of GMM in discriminating one language from the others is considered for determining the weights of components. Since PAW-GMM considers the discriminating property of GMM components, it could increase the accuracy of language recognition systems. Also one of the problems of GMM-PSK-SVM which is one of the best GMM models is the high complexity especially for high number of languages. Therefore UBM-PSK-SVM is proposed that has the same accuracy as GMM-PSK-SVM but lower complexity. Experiments on four languages of OGI corpus show the efficiency of the proposed techniques.

Time reversal method has been recently considered with great interest due to its ability of the receiver complexity mitigation in the UWB communication systems. However, the channel imperfection (Imperfect CSI) has the destroyed effects on the time-reversed UWB communication system performance. In this paper, at first the BER equations have been calculated in the TiR-UWB systems with the simple matched filter receiver in an imperfect CSI scenario. Then, a two-stage algorithm is proposed to improve the TiR-UWB in such conditions. First stage of mentioned algorithm provides the pre-filter coefficients derivation based on MMSE criteria via channel estimation error covariance matrix and then, an iterative routine is obtained in second stage via the simple matched filter receiver based on the derived coefficients in first stage. Finally, exhaustive simulations are done to demonstrate the performance advantage attained by the improved algorithm. As an especial case, the TiR-UWB system performance is improved by the proposed algorithm in 3 steps.

Control of transmitted active power is an important issue in operation and management of power systems especially in congestion or fault conditions. In these situations, Thyristor Controlled Series Capacitor (TCSC) is used to continuous control and increase the transmitted power due to these facts that TCSC can act dynamically and is able to stable the system during fault conditions. In this paper, the transmitted power is controlled in the ten megawatt span by using the TCSC. For this purpose, various controllers such as PID, fuzzy and Adaptive Network-based Fuzzy Interface System (ANFIS) are designed to continuous control of the transmitted power. Simulation results evaluate advantages and disadvantages these controllers. ANFIS controller is designed by open loop method which has a good transient response. However, it has a large steady state error and is very sensitive to the variations in system. Fuzzy and ANFIS controllers are combined to remove these defects. The simulation results verify the advantages of the fuzzy-ANFIS controller with respect to the other designed controllers.

Steganography is the art of hidden writing and secret communication. The goal of steganography is to hide the presence of information in other information. steganalysis is the art and science of detecting messages hidden using steganography. Co-occurrence matrix is the matrix containing information about the relationship between values of adjacent pixel in an image. In this paper, we extract features from Gray Level Co-occurrense Matrix (GLCM) that are difference between cover image (image without hidden information) and stego image (image with hidden information).In the proposed algorithm, first, we use a combined method of steganography based on both location and conversion to hide the information in the image. Then, using GLCM matrix properties, we investigate some difference values in the GLCM of the cover and stego images. We can extract features that were different between cover and stego images. Features are used for training neural network. This algorithm was tested on 800 standard image databases and it can detect 83% of stego images.