فهرست مطالب

Information Systems and Telecommunication - Volume:6 Issue:4, 2019
  • Volume:6 Issue:4, 2019
  • تاریخ انتشار: 1398/06/01
  • تعداد عناوین: 7
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  • erwin erwin *, Tomi Kiyatmoko Pages 189-196
    In the diagnosis of retinal disease, Retinal vessels become an important role in determining certain diseases. Retina vessels are an important element with a variety of shapes and sizes, each human blood vessel also can determine the disease with various types, but the feasibility of the pattern of retinal blood vessels is very important for the advanced diagnosis process in medical retina such as detection, identification and classification. Improvement and improvement of image quality in this case is very important by focusing on extracting or segmenting the retinal veins so that parameters such as accuracy, specifications, and sensitivity can be obtained that are better and meet the advanced system. Therefore we conducted experiments in order to develop extraction of retinal images to obtain binary images of retinal vessels in the medical world using Dynamic Threshold and Butterworth Bandpass Filter. Using a database DRIVE Accuracy of 94.77%, sensitivity of 54.48% and specificity of 98.71%.
    Keywords: Butterworth Bandpass Filter, Dynamic Threshold, DRIVE, Retinal Blood Vessels, Segmentation, STARE
  • Davoud Bahrepour *, Negin Maroufi Pages 197-203
    In recent years, reduction of the complementary metal-oxide-semiconductor (CMOS) circuit feature size has caused significant challenges, such as current loss and leakage, and high power consumption. Therefore, further reduction of the size of CMOS technology is not feasible. Quantum-dot cellular automata (QCA) is an emerging technology at the nanoscale, which can utilize for designing computers and very-large-scale integration (VLSI) circuits in the near future. QCA technology makes it possible to design low-power, high-performance, and area-efficient logical circuits. A comparator function is a digital logical function, which compares whether a bit is greater than, smaller than or equal to the other bit or not (half comparator). Full comparator has a third input, which shows the result of the previous step. Half and full comparators play an essential role in CPU architecture. In this paper, a full comparator circuit based on the QCA and a new quantum cost function is proposed. Besides a 2-bit comparator is presented based on the introduced full comparator. Using the new quantum cost function the proposed full comparator design is compared with the previously presented designs in terms of area, delay, and complexity. Comparisons show that the proposed design has less area and delay and therefore, it is more suitable for utilizing in CPU design.
    Keywords: Quantum-Dot Cellular Automata, Full Comparator, Cost Function, QCA Cell, Majority Gate, NOT Gate
  • mohammad rasoul kahrizi *, jahanshah kabudian Pages 204-208
    Speech detection systems are known as a type of audio classifier systems which are used to recognize, detect or mark parts of an audio signal including human speech. Applications of these types of systems include speech enhancement, noise cancellation, identification, reducing the size of audio signals in communication and storage, and many other applications. Here, a novel robust feature named Long-Term Spectral Pseudo-Entropy (LTSPE) is proposed to detect speech and its purpose is to improve performance in combination with other features, increase accuracy and to have acceptable performance. To this end, the proposed method is compared to other new and well-known methods of this context in two different conditions, with uses a well-known speech enhancement algorithm to improve the quality of audio signals and without using speech enhancement algorithm. In this research, the MUSAN dataset has been used, which includes a large number of audio signals in the form of music, speech and noise. Also various known methods of machine learning have been used. As well as Criteria for measuring accuracy and error in this paper are the criteria for F-Score and Equal-Error Rate (EER) respectively. Experimental results on MUSAN dataset show that if our proposed feature LTSPE is combined with other features, the performance of the detector is improved. Moreover, this feature has higher accuracy and lower error compared to similar ones.
    Keywords: Audio Signal Processing, Speech Processing, Speech Activity Detection (SAD), Speech Recognition, Voice Activity Detection (VAD), Robust Feature, LTSPE
  • Ali Harounabadi *, Maral Kolahkaj, Alireza Nikravan shalmani, Rahim Chinipardaz Pages 209-219
    By the advent of some applications in the web 2.0 such as social networks which allow the users to share media, many opportunities have been provided for the tourists to recognize and visit attractive and unfamiliar Areas-of-Interest (AOIs). However, finding the appropriate areas based on user’s preferences is very difficult due to some issues such as huge amount of tourist areas, the limitation of the visiting time, and etc. In addition, the available methods have yet failed to provide accurate tourist’s recommendations based on geo-tagged media because of some problems such as data sparsity, cold start problem, considering two users with different habits as the same (symmetric similarity), and ignoring user’s personal and context information. Therefore, in this paper, a method called “Demographic-Based Context-Aware Collaborative Filtering” (DBCACF) is proposed to investigate the mentioned problems and to develop the Collaborative Filtering (CF) method with providing personalized tourist’s recommendations without users’ explicit requests. DBCACF considers demographic and contextual information in combination with the users' historical visits to overcome the limitations of CF methods in dealing with multi- dimensional data. In addition, a new asymmetric similarity measure is proposed in order to overcome the limitations of symmetric similarity methods. The experimental results on Flickr dataset indicated that the use of demographic and contextual information and the addition of proposed asymmetric scheme to the similarity measure could significantly improve the obtained results compared to other methods which used only user-item ratings and symmetric measures.
    Keywords: Decision Support Systems, Data Mining, Context-aware Recommendation, Geo-tagged Photo, Asymmetric Similarity
  • ardalan Ghasemzadeh, Omid R. Speily * Pages 220-227
    With the development of the internet and social networks, the interest on multimedia data, especially digital images, has been increased among scientists. Due to their advantages such as high speed as well as high security and complexity, chaotic functions have been extensively employed in images encryption. In this paper, a modified logistic map function was proposed, which resulted in higher scattering in obtained results. Confusion and diffusion functions, as the two main actions in cryptography, are not necessarily performed respectively, i.e. each of these two functions can be applied on the image in any order, provided that the sum of total functions does not exceed 10. In calculation of sum of functions, confusion has the coefficient of 1 and diffusion has the coefficient of 2. To simulate this method, a binary stack is used. Application of binary stack and pseudo-random numbers obtained from the modified chaotic function increased the complexity of the proposed encryption algorithm. The security key length, entropy value, NPCR and UICA values and correlation coefficient analysis results demonstrate the feasibility and validity of the proposed method. Analyzing the obtained results and comparing the algorithm to other investigated methods clearly verified high efficiency of proposed method.
    Keywords: Encryption, Decryption, Logistic Map, Confusion, Diffusion
  • meisam Yadollahzadeh tabari *, Ali A Pouyan Pages 228-235
    The application of mobile ad hoc networks (MANET) in emergency and critical cases needs a precise and formal performance evaluation of these networks. Traditional simulation-based performance evaluators like NS-2 and OPNET usually need a considerable time for producing high level performance metrics. Also there is no theoretical background for mentioned simulators, too. In this research, we propose a framework for performance evaluation of mobile ad hoc networks. The presented framework points to the network layer of MANETs using SRN (Stochastic Reward Nets) modeling tool as variation of generalized stochastic Petri net (GSPN). Based on decomposition technique it encompasses two separate models: one for analysis of data flowing process and the other for modeling routing process ; supposing AODV as a routing protocol that is worked out. To verify the presented model, an equivalence-based method is applied. The proposed SRN model has been quantified by deriving two performances metrics as Packet Delivery Ratio (PDR) and End-to-end Delay. Both metrics are also compared to the value obtained from NS-2 simulator versus different number of nodes and four packet generation rates. The results show the obtained values from presented SRN model well matched to the values generated from NS-2 simulator with a considerable lesser execution time.
    Keywords: Mobile ad Hoc Network, Stochastic Reward Net, Performance Evaluation, Modeling
  • Abolfazl Toroghi Haghighat, Amir Masoud Rahmani, Monireh Hosseini Sayadnavard Pages 236-244
    Dynamic Virtual Machine (VM) consolidation is an effective manner to reduce energy consumption and balance the resource load of physical machines (PMs) in cloud data centers that guarantees efficient power consumption while maintaining the quality of service requirements. Reducing the number of active PMs using VM live migration leads to prevent inefficient usage of resources. However, high frequency of VM consolidation has a negative effect on the system reliability and we need to deal with the trade-off between energy consumption and system reliability. In recent years many research work has been done to optimize energy management using power management techniques. Although these methods are very efficient from the point of view of energy management, but they ignore the negative impact on the system reliability. In this paper, a novel approach is proposed to achieve a reliable VM consolidation method. In this way, a Markov chain model is designed to determine the reliability of PMs and then it has been prioritized PMs based on their CPU utilization level and reliability status. Two algorithms are presented to determining source and destination servers. The efficiency of our proposed approach is validated by conducting extensive simulations. The results of the evaluation clearly show that the proposed approach significantly improve energy consumption while avoiding the inefficient VM migrations.
    Keywords: Cloud Computing, VM Consolidation, Energy Efficiency, Reliability, Markov Chain