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اكثر محققين الگوريتم بهينه سازي كلوني مورچه ها بصورت نرم افزاري اين الگوريتم را پياده سازي مي كنند و تحقيقات كمي در زمينه پياده سازي الگوريتم كلوني مورچه ها به صورت سخت افزار انجا م گرفته است . دراين مقاله الگوريتم بهينه سازي كلوني مورچه ها را با تغييراتي به صور تي ارائه مي شود كه بتواند به صورت تراشه اي در تكنولوژي استاندارد CMOS پياده سازي شود . در اين كار ماتريس فرمون بر روي سطح تراشه منتقل مي شود و مورچه ها از بالا به پائين در طول ماتريس فرمون حركت مي كنند . در نهايت بعد از چندين بار حركت مورچه ها درطول ماتريس فرمون به سمت مسيربهينه همگرا مي شوند . نتايج حاصل از شبيه سازي نشان دهنده صحت عملكرد الگوريتم پيشنهادي مي باشد.

نياز به جداسازي خودكار گفتار از موسيقي در كاربردهايي نظير سيستمهاي بازيابي مستندات صوتي از اهميت زيادي برخوردار است. در گذشته روشهاي متعددي براي جداسازي مورد استفاده قرار گرفته است. در اين مقاله از 11 ويژگي سيگنال صوتي به عنوان ويژگي هاي متمايز كننده و از الگوريتمهاي تكاملي برنامهنويسي ژنتيك و سيستم تشخيص ايمني مصنوعي كه از عملكرد سيستمهاي زيستي موجود در طبيعت الهام گرفته شدهاند، براي توليد مدل جداكننده استفاده شده است. نتايج حاصل نشاندهندي كارايي بالاي دو الگوريتم برنامهنويسي ژنتيك و سيستم تشخيص ايمني مصنوعي براي دسته بندي گفتار از موسيقي است. داده هاي موسيقي و گفتار در شرايط تميز و نيز شرايط نويزي مورد استفاده قرار گرفته و كارائي روشهاي پيشنهادي براي تشخيص گفتار از موسيقي در شرايط نويزي بررسي شده است. همچنين اين مقاله تاثير كاهش حجم داده هاي آموزشي با استفاده از روش چند يسازي برداري و نيز قابليت برنامه نويسي ژنتيك در انتخاب ويژگيهاي مناسبتر را مورد ارزيابي قرار داده است.

در اين مقاله يك الگوريتم مبتني بر گراف براي تركيب سرويس ها ارائه شده است. اگر چه كارهاي متفاوتي در رابطه با تركيب سرويس ها موجود مي باشد اما در اين كارها تاكيد بر جنبه هاي مختلفي از تركيب سرويس ها مانند چگونگي بيان سرويس ها، چگونگي تطبيق ميان سرويس ها، ارائه معماري براي تركيب سرويس ها و... مي باشد. در اين مقاله تاكيد ما بر خود الگوريتم تركيب، درستي آن و توليد همه جواب ها مي باشد. الگوريتم ارائه شده براساس درخواست كاربر به ساخت گراف ارتباطي ميان سرويس ها مي پردازد و با جستجو در گراف سرويس هاي تركيبي مورد انتظار كاربر را پيدا مي كند. گراف ميان سرويس ها مي تواند داراي گره ها و يال هاي فراوان و حلقه هاي متعدد باشد و نگهداري و بروز رساني آن، بدليل تغييرات در سرويس ها، مشكل است. الگوريتم ما گراف ميان سرويس ها را به صورت يك گراف مستقيم بدون حلقه و متناسب با هر درخواست كاربر به گونه اي ايجاد مي كند كه تمامي سرويسهاي تركيبي كه ميتوانند پاسخ كاربر باشند را دارا باشد. پس از ايجاد اين گراف مستقيم بدون حلقه ما با يك الگوريتم مسيريابي بازگشتي و با استفاده از يك پشته سرويس هاي تركيبي را از آن استخراج مي كنيم.

The power of grid technology in aggregating autonomous resources owned by several organizations into a single virtual system has made it popular in compute-intensive and data-intensive applications. Complex and dynamic nature of grid makes failure of users’ jobs fairly probable. Furthermore, traditional methods for job failure recovery have proven costly and thus a need to shift toward proactive and predictive management strategies is necessary in such systems. In this paper, an innovative effort is made to predict the futurity of jobs submitted to a production grid environment (AuverGrid). By analyzing grid workload traces and extracting patterns describing common failure characteristics, the success or failure status of jobs during 6 months of AuverGrid activity was predicted with around 96% accuracy. The quality of services on grid can be improved by integrating the result of this work into management services like scheduling and monitoring.

The discovery of suitable web services for a given task is one of the major operations in SOA architecture, and researches are being done to automate this step. For the large amount of available Web services that can be expected in real-world settings, the computational costs of automated discovery based on semantic matchmaking become important. To make a discovery engine a reliable software component, we must aim at minimizing both the mean and the variance of the duration of the discovery task. For this, we present an extension for discovery engines in SWS environments that exploit structural knowledge and previous discovery results for reducing the search space of consequent discovery operations. Our prototype implementation shows significant improvements when applied to the Stanford SWS Challenge scenario and dataset.

A hybrid feature and decision level information fusion architecture is proposed for human emotion recognition from facial expression and speech prosody. An active buffer stores the most recent information extracted from face and speech. This buffer allows fusion of asynchronous information through keeping track of individual modality updates. The contents of the buffer will be fused at feature level; if their respective update times are close to each other. Based on the classifiers’ reliability, a decision level fusion block combines results of the unimodal speech and face based systems and the feature level fusion based classifier. Experimental results on a database of 12 people show that the proposed fusion architecture performs better than unimodal classification, pure feature level fusion and decision level fusion.

In this paper we propose a new integration method for multi-feature object tracking in a particle filter framework. We divide particles into separate clusters. All particles within a cluster measure a specific feature. The number of particles within a cluster is in proportion to the reliability of associated feature. We do a compensation stage which neutralizes the effect of particles weights mean within a cluster. Compensation stage balances the concentration of particles around local maximal. So, particles are distributed more effectively in the scene. Proposed method provides both effective hypothesis generation and effective evaluation of hypothesis. Experimental results over a set of real-world sequences demonstrate better performance of our method compared to the common methods of feature integration.

This paper introduces a revisited hybrid algorithm for function approximation. In this paper, a simple and fast learning algorithm is proposed, which automates structure and parameter identification simultaneously based on input-target samples. First, without need of clustering, the initial structure of the network with the specified number of rules is established, and then a training process based on the error of other training samples is applied to obtain a more precision model. After the network structure is identified, an optimization learning, based on the criteria error, is performed to optimize the obtained parameter set of the premise parts and the consequent parts. At the end, comprehensive comparisons are made with other approaches to demonstrate that the proposed algorithm is superior in term of compact structure, convergence speed, memory usage and learning efficiency.

This paper estimates and segments the moving objects based on center of mass model to decrease the search window and to provide a new algorithm, which achieves an accurate and rapid tracking. Furthermore, a novel method is proposed to update the template size adaptively by using estimation and segmentation of moving objects. The estimated results of moving target is transformed to wavelet domain and target tracking is performed in that domain. To improve the algorithm center of mass model is performed in wavelet domain. By using kalman predictor and thresholding method, a new approach is presented for object tracking failure and recovery.

Human can determine optimal behaviors which depend on the ability to make planned and adaptive decisions. In this paper, we have proposed a predictive structure based on neuropsychological evidences to model human decision making process by concentrating on the role of frontal brain regions which are responsible for predictive control of human behavior. We have considered a model-based reinforcement learning framework to implement the relations between these brain areas. Finally, we have designed an experimental test to compare the function of model with human behavior in a maze task. Our results reveal that there is more than reward and punishment in human behavior, and considering higher cognitive functions such as prediction will help to have more reliable models which could better describe human behavior.