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    Using decision trees to model an emotional attention mechanism

    , Article Frontiers in Artificial Intelligence and Applications ; Volume 171, Issue 1 , Volume 171, Issue 1 , 2008 , Pages 374-385 ; 09226389 (ISSN); 9781586038335 (ISBN) Zadeh, S. H ; Bagheri Shouraki, S ; Halavati, R ; Sharif University of Technology
    IOS Press  2008
    Abstract
    There are several approaches to emotions in AI, most of which are inspired by human emotional states and their arousal mechanisms. These approaches usually use high-level models of human emotions that are too complex to be directly applicable in simple artificial systems. It seems that a new approach to emotions, based on their functional role in information processing in mind, can help us to construct models of emotions that are both valid and simple. In this paper, we will try to present a model of emotions based on their role in controlling the attention. We will evaluate the performance of the model and show how it can be affected by some structural and environmental factors. © 2008 The... 

    High-level modeling approach for analyzing the effects of traffic models on power and throughput in mesh-based NoCs

    , Article Proceedings of the IEEE International Frequency Control Symposium and Exposition, 4 January 2008 through 8 January 2008, Hyderabad ; 2008 , Pages 415-420 ; 0769530834 (ISBN); 9780769530833 (ISBN) Koohi, S ; Mirza Aghatabar, M ; Hessabi, S ; Pedram, M ; VLSI Society of India ; Sharif University of Technology
    2008
    Abstract
    Traffic models exert different message flows in a network and have a considerable effect on power consumption through different applications. So a good power analysis should consider traffic models. In this paper we present power and throughput models in terms of traffic rate parameters for the most popular traffic models, i.e. Uniform, Local, HotSpot and First Matrix Transpose (FMT) as a permutational traffic model. We also select Mesh topology as the most prominent NoC topology and validate the presented models by comparing our results against simulation results from Synopsys Power Compiler and Modelsim From the comparison, we show that our modeling approach leads to average error of 2%...