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No 30
Vol. 8 No. 2
Spring 2020
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An efficient method for simultaneous human body part segmentation and pose estimation is introduced. A conditional random field with a fully-connected graphical model is used. Possible node (image pixel) labels comprise of the human body parts and the background. In the human body skeleton model, the spatial dependencies among body parts are encoded in the definition of pairwise energy functions according to the conditional random fields. Proper pairwise edge potentials between image pixels are defined according to the presence or absence of human body parts that are near to each other. Various Gaussian kernels in position, color, and histogram of oriented gradients spaces are used for defining the pairwise energy terms. Shifted Gaussian kernels are defined between each two body parts that are connected to each other according to the human body skeleton model. As shifted Gaussian kernels impose a high computational cost to the inference, an efficient inference process is proposed by a mean field approximation method that uses high dimensional shifted Gaussian filtering. The experimental results evaluated on the challenging KTH Football, Leeds Sports Pose, HumanEva, and Penn-Fudan datasets show that the proposed method increases the per-pixel accuracy measure for human body part segmentation and also improves the probability of correct parts metric of human body joint locations.
Sara Ershadi-Nasab - Shohreh Kasaei - Esmaeil Sanaei - Erfan Noury - Hassan Hafez-kolahi
DOI : 0
Keywords : Human Body Parts ، Skeleton Model ، Mean Field Approximation ، Pose Estimation ، Segmentation ، Shifted Gaussian kernel
One of the most important problems of text processing systems is the word mismatch problem. This results in limited access to the required information in information retrieval. This problem occurs in analyzing textual data such as news, or low accuracy in text classification and clustering. In this case, if the text-processing engine does not use similar/related words in the same sense, it may not be able to guide you to the appropriate result. Various statistical techniques have been proposed to bridge the vocabulary gap problem; e.g., if two words are used in similar contexts frequently, they have similar/related meanings. Synonym and similar words, however, are only one of the categories of related words that are expected to be captured by statistical approaches. Another category of related words is the pair of an original word in one language and its transliteration from another language. This kind of related words is common in non-English languages. In non-English texts, instead of using the original word from the target language, the writer may borrow the English word and only transliterate it to the target language. Since this kind of writing style is used in limited texts, the frequency of transliterated words is not as high as original words. As a result, available corpus-based techniques are not able to capture their concept. In this article, we propose two different approaches to overcome this problem: (1) using neural network-based transliteration, (2) using available tools that are used for machine translation/transliteration, such as Google Translate and Behnevis. Our experiments on a dataset, which is provided for this purpose, shows that the combination of the two approaches can detect English words with 89.39% accuracy.
Ali Hoseinmardy - Saeedeh Momtazi
DOI : 0
Keywords : Transliteration ، Text processing ، Words Relation ، Neural Network-Based Sequence2Sequence Model ، Google Translate ، Behnevis
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a different characteristic of the brain. Therefore, experts have to analyze each of the images independently. This requires more expertise by doctors and delays the detection and diagnosis time. Multimodal Image Fusion is a process of generating image of high visual quality, by fusing different images. However, it introduces blocking effect, noise and artifacts in the fused image. Most of the enhancement techniques deal with contrast enhancement, however enhancing the image quality in terms of edges, entropy, peak signal to noise ratio is also significant. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widely used enhancement technique. The major drawback of the technique is that it only enhances the pixel intensities and also requires selection of operational parameters like clip limit, block size and distribution function. Particle Swarm Optimization (PSO) is an optimization technique used to choose the CLAHE parameters, based on a multi objective fitness function representing entropy and edge information of the image. The proposed technique provides improvement in visual quality of the Laplacian Pyramid fused MRI and CT images.
Leena Chandrashekar - A Sreedevi Asundi
DOI : 0
Keywords : Glioblastoma ، Laplacian Pyramid ، Image Fusion ، Image Enhancement ، Contrast Limited Adaptive Histogram Equalization ، Particle Swarm Optimization
Today’s, intelligent agent system (IAS) are considered as an important part of people's lives. Therefore, many of organizations try to implement IAS in their mechanism. One of these organizations in Iran is telecommunication Infrastructure Company. Because any implementation need a model which clarify the structural and contextual components, therefore, the current research is conducted to provide a model for developing the necessary infrastructure for implementation of intelligent technologies in the communication and telecommunication mechanisms of Ilam Province. To achieve the goal, a qualitative approach and thematic analysis method were used. The research population consisted of all experts in the field of ICT in Ilam province Infrastructure Communications Company that using purposeful sampling method and relying on theoretical data saturation, 10 of them were selected as sample. Semi-structured interviews were used to collect the data. The data were analyzed through theme analysis. Based on the method, 4 themes, 10 main categories and 153 open codes were extracted. The findings of the study showed that to transform communication mechanisms into intelligent technologies, there must be technological, management, marketing and cultural infrastructure. Technological infrastructure consisted of intelligent software and hardware; management infrastructure consisted of knowledge and belief; marketing infrastructure included attracting intelligent technology to audiences, encouraging ideas, physical and virtual channels; and finally, cultural infrastructure, it was staff training and public awareness.
heshmat moradhaseli - jalal Haghighatmonfared
DOI : 0
Keywords : Intelligent Agent System ، Intelligent Technology ، Intelligent Communication ، Technological Infrastructure ، Management Infrastructure ، Marketing Infrastructure
One of the main reasons for switching to the next generation of communication systems is the demand of increasing capacity and network connections. This goal can be achieved using massive multiple input - multiple output (massive-MIMO) systems in combination with Non-orthogonal multiple access (NOMA) technique. NOMA technology uses the successive interference cancellation (SIC) receiver to detect user’s signals which imposes an additional complexity on the system. In this paper, we proposed two methods to reduce the system complexity. The proposed method despite imperfect channel state information (CSI) in the receiver, there is not significantly reduction in the system performance. Since the computation of matrices inverse has a high computational complexity, we used the Neumann series approximation method and the Gauss-Seidel decomposition method to compute matrices inverse in the SIC receiver. Simulation results are provided at the end of the paper in terms of bit error rate (BER) at the receiver which show, these methods have lower computational complexity in comparison with the traditional methods while they cause a slight performance reduction in the SIC receiver. Also, we examined the increasing and decreasing value of imperfect channel state information in the system performance which shows the increasing value of imperfect channel state information, cause a slight performance reduction in SIC receiver.
Nilufar Tutunchi - Afrooz Haghbin - Behrad Mahboobi
DOI : 0
Keywords : Massive MIMO ، NOMA ، Complexity ، SIC ، Neumann;Gauss - Seidel
SDN makes the network programmable, agile, and flexible with data and control traffic separating. This architecture consists of three layers which are application, control and data. The aim of our research is concentrated on the control layer to improve the performance of the network in an autonomic manner. In the first step, we have categorized the performance improvement researches based on network performance improvement solutions proposed in the recent papers. This performance improvement solution clustering is one of our contributions to our paper. The significant contribution in this paper is a novel autonomic SDN-based architecture to ameliorate the performance metrics including blocking probability (BP), delay, jitter, packet loss rate (PLR), and path utilization. Our SDN-based autonomic system consists of three layers (data, autonomic control, and Route learning) to separate the traffics based on deep neural networks (DNN) and to route the flows with the greedy algorithm. The autonomic SDN-based architecture which has proposed in this paper makes better network performance metrics dynamically. Our proposed autonomic architecture will be developed in the POX controller which has developed by python. Mininet is used for simulation and the results are compared with the commonly used SDN named pure SDN in this article. The simulation results show that our structure works better in a full-mesh topology and improves the performance metrics simultaneously. The average performance is improved by about %2.5 in comparison with pure SDN architecture based on the Area Under Curve (AUC) of network performance.
Alireza shirmarz - ali ghaffari
DOI : 0
Keywords : SDN ، Performance ، Autonomic System ، Resource Management ، QoS
Online communities are the most popular interactive environments on the Internet, which provide users with a platform to share their knowledge and expertise. The most important use of online communities in cyberspace is sharing knowledge. These communities are a great place to ask questions and find answers. The important challenges of these communities are the large volume of information and the lack of a method to determine their validity as well as expert finding which attracted a lot of attention in both industry and academia in. Therefore, identifying persons with relevant knowledge on a given topic and ranking them according to their expertise score can help to calculate the accuracy of the comments submitted on the internet. In this research, a model for finding experts and determining their domain expertise level by the aid of statistical calculations and the ant colony algorithm in the MetaFilter online community was presented. The WordNet Dictionary was used to determine the relevance of the user’s questions with the intended domain. The proposed algorithm determines the level of people’s expertise in the intended field by using the pheromone section of the Ant colony algorithm, which is based on the similarity of the questions sent by the users and the shared knowledge of the users from their interactions in the online community
Mojtaba Sharifian - Neda Abdolvand - Saeedeh Rajaee Harandi
DOI : 0
Keywords : Online Communities ، Experts Finding ، Ant Colony Algorithm ، Word Net

About Journal

Affiliated to :ICT Research Institute of ACECR
Manager in Charge :Habibollah Asghari
Editor in Chief :Masood Shafiei
Editorial Board :
Abdolali Abdipour
Mahmoud Naghibzadeh
Zabih Ghasemlooy
Mahmoud Moghavemi
Aliakbar Jalali
Ramazan Ali Sadeghzadeh
Hamidreza Sadegh Mohammadi
Saeed Ghazimaghrebi
Shaban Elahi
Alireza Montazemi
Ali Mohammad Djafari
Rahim Saeidi
Shohreh Kasaei
Mehrnoush Shamsfard
ISSN :2322-1437
eISSN :2345-2773

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