انجمن کامپیوتر ایران

انتقال جريانهاي ويديويي در شبکههاي حسگر، چالش ه اي زيادي به همراه دارد. يکي از اين چالشها احتمال زي اد وقوع ازدحام است که به دليل بالا بودن نرخ و خاصيت انفجاري جريانهاي وي ديويي و همچنين محدوديت منابع در شبکههاي حسگر پديد ميآي د. در اي ن مقاله، يک روش کنترل ازدحام جديد براي اين نوع شبکهها معرفي شده است. اين روش هم محدوديتهاي موجود در شبکهه اي حسگر مانند محدوديت انرژي و حجم بافر کم گرهها و هم محدوديتهاي کاربردهاي ويديويي مانند حساسيت به تاخير و تغييرات آن را در نظر گرفته است . روش پيشنهادي از تفاوتهايي که ميان اولوي ت بسته ه اي وي ديويي وجود دارد، براي کنترل ازدحام استفاده ميکند. نت ايج ارزيابي که با شبيه ساز NS2 و ابزار Evalvid انجام شده، نشان ميدهد که استفاده از اين روش علاوه بر بالا بردن کيفيت ويديوي دريافتي، انرژ ي کمتري از گرهها را نيز مصرف ميکند.

ترجمه ماش يني مي تواند د ر سطوح مختلف پردازش زبان انجام شود كه هر چه عمق تحل يل بي شتر باشد (سطوح بالاتر ) به پردازش پيچيده تر و نت يجه مطلوب تر منجر خواهد شد . ما در ا ي ن مقاله به صورت مختصر نتيجه پياده ساز ي ي ك مترجم ماش يني از زبان فارس ي به انگل يسي را شرح خواهيم داد كه از سطح معنايي براي ترجمه استفاده مي كند. مترجم توليد شده مبتن ي بر قاعده بوده و گرامرهاي مورد استفاده در آن براي هر دو زبان انگليسي و فارسي بر اساس تئوري گرامري HPSG بنا شده است . معنا در اين مترجم توسط ساختار معنايي MRS نمايش داده مي شود.

بازشناسي زبان، تشخيص زبان يك گفتار يا گويش تلفني ضبط شده مي باشد كه امروزه با توسعه استفاده از فناوري اطلاعات در صنايع مختلف كاربردهاي آن روز به روز افزايش مي يابد . در اين مقاله براي دسته بندي زبان يك گويش تلفني از روش هاي ،PRLM ،GMM PPRLM و PPR استفاده مي شود. آزمايش ها بر روي دادگان تلفني OGI-MLTS انجام شده است. بهترين نتيجه بدست آمده مربوط به روش PPR است كه با دقت 87/91% داده هاي 45 ثانيه اي و با دقت %79/48 داده هاي 10 ثانيه اي زبان فارسي و انگليسي را از هم تشخيص مي دهد. بهترين راندمان بدست آمده براي تشخيص همزمان 10 زبان به كمك روش GMM برابر 59/94% است.

متناسب با کاربرده اي مختلف ، پروتکلهاي گوناگوني بر اي کنترل دسترسي به کانال در شبکهه اي حسگر ز يرآب ي طراح ي شده است. ولي تا بحال ي ک پروتکل خوشه اي مناسب بر اي کاربرده اي مانيتورينگ بلادرنگ توسعه نيافته است. يکي از معمار ي پروتکل ه اي کارا براي کاربردهاي مذکور در شبکههاي خشکي LEACH است . در اين مقاله خواهيم ديد که به کار بردن پروتکل LEACH اصلي در زير آب به دليل مشکلات محيطي اعم از تأخير انتشار و واريانس اين تأخير، متداول بودن خرابي گرهها و خطاي بيت بالا در ارتباطات صو تي عملاً شبکه را از فعاليت بازميدارد. ولي ميتوان با اعمال تغ ييرات ي شامل گستردهتر کردن مراحل فاز پيکربندي و ارسال بستة کنترل ي از سو ي سرخوشه براي گرههاي سالم، کارايي اين پروتکل را از منظر بهره وري استفاده از پهناي باند تا ۶۰ % بازي ابي کرد و پيوست گي اطلاعات را در بيشتر از % ۹۹ زمان تضمين کرد.

Abstract—In this paper we discussed about utilizing methods and techniques of semantic web in the Intrusion Detection Systems. To extract semantic relations between computer attacks and intrusions in a Distributed Intrusion Detection System, we use ontology. Protégé software is our selected software for building ontology. In addition, we utilized Jena framework to make interaction between MasterAgent and attacks ontology. Our Distributed Intrusion Detection System is a network which contains some IDSagents and a special MasterAgent. MasterAgent contains our proposed attacks ontology. Every time a IDSagent detects an attack or new suspected condition, it sends detection’s report for MasterAgent. Therefore, it can extract the semantic relationship among computer attacks and suspected situations in the network with proposed ontology. Finally, the experience shows that the pruposed system reduced the rate of false positive and false negative.

Wireless sensor networks are limited in energy. Any routing protocol used in wireless sensor networks should take into consideration the time sensitive nature of the traffic in such networks, along with the amount of energy left for each sensor. In this paper we present an energy aware packet delivery mechanism for probabilistic Quality of Service (QoS) guarantee in wireless sensor networks. Each node takes routing decisions based on geographic progress towards the destination sink, required end-to-end total reaching probability, delay at the candidate forwarding node and residual energy. The simulation results demonstrate that the proposed protocol effectively improves the energy usage efficiency of the sensor nodes, maximizing the lifetime of the entire sensor network, while keeping guaranteed QoS.

A user of Brain Computer Interface (BCI) system must be able to control external computer devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. There are problems associated with classification of different BCI tasks. In this paper we propose the use of chaotic indices of the BCI. We use largest Lyapunov exponent, mutual information, correlation dimension and minimum embedding dimension as the features for the classification of EEG signals which have been released by BCI Competition IV. A multi-layer Perceptron classifier and a KMSVM( support vector machine classifier based on kmeans clustering) is used for classification process, which lead us to an accuracy of 95.5%, for discrimination between two motor imagery tasks.

Classification is a well known task in data mining and machine learning that aims to predict the class of items as accurately as possible. A well planned data classification system makes essential data easy to find. An object is classified into one of the categories called classes according to the features that well separated the classes. Actually, classification maps an object to its classification label. Many researches used different learning algorithms to classify data; neural networks, decision trees, etc. In this paper, a new classification approach based on cooperative game is proposed. Cooperative game is a branch of game theory consists of a set of players and a characteristic function which specifies the value created by different subsets of the players in the game. In order to find classes in classification process, objects can be imagine as the players in a game and according to the values which obtained by these players, classes will be separated. This approach can be used to classify a population according to their contributions. In the other words, it applies equally to different types of data. Through out this paper, a special case in medical diagnosis was studied. 304 samples taken from human leukemia tissue consists of 17 attributes which determine different CD markers related to leukemia were analyzed. These samples collected from different types of leukemia at Iran Blood Transfusion Organization (IBTO). Obtained results demonstrate that cooperative game is very promising to use directly for classification.

Two popular and better performing approaches to language Identification (LID) are Phone Recognition followed by Language Modeling (PRLM) and Parallel PRLM. In this paper, we report several improvements in Phone Recognition which reduces error rate in PRLM and PPRLM based LID systems. In our previous paper, we introduced APRLM approach that reduces error rate for about 1.3% in LID tasks. In this paper, we suggest other solution that overcomes APRLM. This new LID approach is named Generalized PRLM or GPRLM. Several language identification experiments were conducted and the proposed improvements were evaluated using OGI-MLTS corpus. Our results show that GPRLM overcomes PPRLM and APRLM about 2.5% and 1.2% respectively in two language classification tasks.

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.