انجمن کامپیوتر ایران

Asynchronous protocols exhibit various noise robustness and when used in GALS NoC links, they can directly affect the signal integrity. In this paper we study the noise robustness of two well-known asynchronous protocols, namely Dual-Rail (DRP) and Bundled-Data (BDP) in the GALS NoC links, and subsequently confirm our claims through simulations. We apply an enhanced version of BDP and DRP to 32/64 parallel line links, show results in terms of noise robustness using global interconnect features, specified in the ITRS roadmap for 32nm technology. The simulation results for two thousand random generated inputs show that the number and the amplitude of noise glitches over ‘0’ state lines as well as the required threshold voltage needed for avoiding errors in BDP link are much lower than in DRP's. Therefore, BDP links can present better signal integrity features and have less overhead compared to DRP's, employing only some simple noise reduction techniques and more timing adjustment effort.

Discriminative subspace analysis is a popular approach for a variety of applications. There is a growing interest in subspace learning techniques for face recognition. Principal component analysis (PCA) and eigenfaces are two important subspace analysis methods have been widely applied in a variety of areas. However, the excessive dimension of data space often causes the curse of dimensionality dilemma, expensive computational cost, and sometimes the singularity problem. In this paper, a new supervised discriminative subspace analysis is presented by encoding face image as a high order general tensor. As face space can be considered as a nonlinear submanifold embedded in the tensor space, a decomposition method called Tucker tensor is used which can effectively decomposes this sparse space. The performance of the proposed method is compared with that of eigenface, Fisherface, tensor LPP, and ORO4×2 on ORL and Weizermann databases. Conducted experimental results show the superiority of the proposed method.

Being able to predict the coming seizure can impressively improve the quality of the patients' lives since they can be warned to avoid doing risky activities via a prediction system. Here, a locally linear neuro fuzzy model is used to predict the EEG time series. Subsequently, this model is utilized in accompany with Singular Spectrum Analysis for prediction. Afterward, an information theoretic criterion is used to select a reliable subset of input variables which contain more information about the target signal. Comparison of three mentioned methods on one hand shows that SSA enables our prediction model to extract the main patterns of the EEG signal and highly improves the prediction accuracy. On the other hand, applying the method of channel selection to the model yields more accurate prediction. It is shown that fusion of some certain signals provides more information about the target and considerably improves the prediction ability.

In this paper, we present a new measure for evaluating similarity changes in a multi agent system. The similarity measure of the agents changes during the learning process. The similarity differences are because of any composition or decomposition of some agent sets. The presented measure, defines the changes of homogeneity of agents by composition and decomposition. The utility of the metrics is demonstrated in the experimental evaluation of multi agent foraging. The results show that while the similarity difference gets a positive value, the performance grow rapidly.

In this paper, a new fuzzy fading memory (FFM) is developed in order to aid a modified input estimation (MIE) technique and enhance its performance in tracking high maneuvering targets. The MIE has been introduced recently and performs well in tracking low and medium maneuvering targets. However, due to some modeling errors, the accuracy of this tracker may be seriously degraded in presence of high maneuvers. To cope with this difficulty, an intelligent approach based on FFM is presented in this paper. Simulation results prove the efficiency of the proposed method in tracking high maneuvering targets.

Uncertainty reasoning has been identified as an important and challenging issue in the database research. Many logic frameworks have been proposed to represent and reason about uncertainty in deductive databases. On the basis of the way in which uncertainties are associated with the facts and rules in programs, the approaches of these frameworks have been classified into “annotation based (AB)” and “implication based (IB).” When extending both frameworks with certainty constraints, they become equivalent in terms of expressive power. In this paper, we propose a uniform environment to evaluate and experiment with logic programs in AB and IB frameworks at the same time. We also extend the existing query processing to handle certainty constraints and we carry out experiments to evaluate its performance. Our experiments and results indicate that the proposed techniques yield tools that are capable to reason with uncertainty.