Journal of Electrical and Computer Engineering Innovations
Volume:6 Issue: 2, Summer-Autumn 2018

Vehicular Ad-Hoc Networks can enhance road safety and enable drivers to avoid different threats. Safety applications, mobile commerce, and other information services are among different available services that are affected by dynamic topology, vehicle’s speed and node misbehaving. Dynamic topology makes the route unstable and unreliable. So, improving the throughput and performance of VANET through reliable and stable routes with low overhead are among the important goals in this context. Verifying all issues related to the reliable routing, different effective internal, external and environmental factors on route reliability are led to a new security framework in this paper. Black-hole attack and its effects, as the most well-known attack in wireless networks, along with presenting a secure routing protocol are other achievements of this paper. The proposed protocol uses a trust management system to detect and neutralize this type of attack. Simulation results show that the presented trust-based framework can increase the reliability of the networks by decreasing the effect of the malicious nodes in the routing process.

Wireless Sensor Networks (WSNs) are a specific category of wireless ad-hoc networks where their performance is highly affected by application, life time, storage capacity, processing power, topology changes, the communication medium and bandwidth. These limitations necessitate an effective data transport control in WSNs considering quality of service, energy efficiency, and congestion control. Congestion is a critical issue in wireless networks. Congestion in WSNs badly effects loss rate, channel quality, link utilization, number of retransmissions, traffic flow, network life time, delay, energy and throughput. Due to the dominant role of WSNs in recent technologies, it is necessary to design more efficient congestion control algorithms. In this paper a comprehensive review of different congestion control schemes in WSNs is provided. In particular, different congestion control schemes are classified based on the way congestion is detected, notified and mitigated. Furthermore, congestion mitigation algorithms are classified. Also, different performance metrics are used to compare congestion control algorithms. Finally, the current work attempts to provide specific directives to design and develop novel congestion control schemes.

The accuracy of target position detection in IR seeker depends on the accuracy of tracking error signal (TES) extraction from seeker Field of View (FOV). The type of reticle inside the seeker determines the output modulation signal that carries the TES. In this paper, the stationary wagon wheel reticle is used, which makes the type of the output signal as FM modulation in the linear region of FOV, but the signal will be distorted by changing the radius of target image spot (TIS) and in the nonlinear region of FOV. Firstly, we applied the Hilbert transform algorithm for the first time in this field and compared it with the conventional algorithm in the linear region of FOV to decrease the effect of changing the radius of TIS. Secondly, we presented a new method in the nonlinear region to extract the TES. The results show improvement in TES accuracy extraction in the linear and nonlinear region over the FOV for different radii of TIS.

Routing and data aggregation are two important techniques for reducing communication cost of wireless sensor networks (WSNs). To minimize communication cost, routing methods can be merged with data aggregation techniques. Compressive sensing (CS) is one of the effective techniques for aggregating network data, which can reduce the cost of communication by reducing the amount of routed data to the sink. Spatio-temporal CS (STCS), with the use of spatial and temporal correlation of sensor readings, can increase the compression rate in WSNs, thereby reducing the cost of communication. In this paper, a new method of STCS technique based on the geographic adaptive fidelity (GAF) protocol is proposed which can effectively reduce the communication cost and energy consumption in WSNs. In the proposed method, temporal data is obtained from random selection of temporal readings of cluster head (CH) sensors located in virtual cells in the clustered sensors area and spatial data will be formed from the data readings of CHs located on the routes. Accordingly, a new structure of sensing matrix will be created. The results show that the proposed method as compared to the method proposed in [29], which is the most similar method in the literature, reduces energy consumption in the range of 22% to 43% in various scenarios which were implemented based on the number of required measurements at the sink (M) and the number of measurements in the routes (m_r).

This study presents the importance of reversible logic in designing of high performance and low power consumption digital circuits. In our research, the various forms of sequential reversible circuits such as D, T, SR and JK flip-flops are investigated based on carbon nanotube field-effect transistors. All reversible flip-flops are simulated in two voltages, 0.3 and 0.5 Volt. Our results show that the proposed structures have achieved a significant improvement in terms of the number of reversible gates, quantum cost, number of constant inputs, number of garbage outputs, delay and average power consumption. Hence, all these criteria in the second proposed D flip-flop are improved 83.3%, 77.27%, 66.6%, 80%, 83.3%, and 99.9%, respectively, and for T flip-flop are reduced 33.3%, 73.68%, 66.6%, 80%, 33.3%, and 82%, respectively. Also, the maximum reduction for the mentioned parameters in the SR flip-flop are 66.6%, 68.16%, 33.3%, 75%, 65.65%, and 60.46%, respectively. Finally, the JK flip-flop parameters are respectively improved by 20%, 52%, 0%, 25%, 20%, and 81%. The Hspice_H-2013.03-SP2 software was used to simulate these circuits and the 32nm CNTFET technology (the standard Stanford spice model).

Search protocols are among the main applications of RFID systems. Since a search protocol should be able to locate a certain tag among many tags, not only it should be secure against RFID threats but also it should be affordable. In this article, an RFID-based search protocol will be presented. We use an encryption technique that is referred to as authenticated encryption in order to boost the security level, which can provide confidentiality and integrity, simultaneously. Furthermore, since the proposed protocol belongs to the lightweight protocols category, it is appropriate for applications that require many tags and costs must be low. In terms of the security, the analysis results give a satisfactory security level and it is robust against different RFID threats like replay, traceability and impersonation attacks. Using Ouafi-Phan model, BAN and AVISPA, we also checked the security correctness of the suggested protocol.

This paper presents the application of reinforcement learning in automatic analog IC design. In this work, the Multi-Objective approach by Learning Automata is evaluated for accommodating required functionalities and performance specifications considering optimal minimizing of MOSFETs area and power consumption for two famous CMOS op-amps. The results show the ability of the proposed method to optimize aforementioned objectives, compared with three MO well-known algorithms (including Particle Swarm Optimization, Inclined Planes system Optimization, and Genetic Algorithm). So that for a two-stage CMOS op-amp, it is obtained 560.42 μW power and 72.825 〖μm〗^2 area, and power 214.15 μW and area 13.76 〖μm〗^2 for a single-ended folded-cascode op-amp. In addition to evaluating the Pareto-fronts obtained based on Overall Non-dominated Vector Generation and Spacing criteria, in terms of Total Optimality Index, MOLA for both cases has been able to have the best performance between the applied methods, and other researches with values of -25.683 and -34.16 dB, respectively.

In this work, porous nanoparticles of ferrite cobalt were prepared by dissolving CoCl2.6H2O and FeCl3 in ethylene glycol in a hydrothermal process. Using ethylene glycol instead of DI water as a solvent would cause to provide porous structure of ferrite cobalt. 0.05 ml of colloidal fluid of fabricated nanostructure was injected on interdigitated electrodes (IDE) on a printed circuit board (PCB) substrate by a drop casting process. Morphological and structural characterizations of structure were investigated by X-ray diffraction and scanning electron microscopy and the obtained results of analyses show the porous nanostructure of the material. Sensor's performance in detection of gas vapors was evaluated in different temperatures which has the best response (20.38% for 100ppm methanol vapors) for methanol vapors at room temperature. Performance of sensor in selection of methanol vapors, chemical stability and repeatability of that, makes it useful to profit it in different fields and industries.

Suitable scheming as well as appropriate pricing of demand response (DR) programs are two important issues being encountered by system operator. Assigning proper values could have effects on creating more incentives and raising customers’ participation level as well as improving technical and economical characteristics of the power system.
Here, time of use (TOU) as an important scheme of DR is linearly introduced based on the concepts of self and cross price elasticity indices of load demand. In this paper, in order to construct an effective TOU program, a combined optimization model over the operation cost and customers’ benefit is proposed based on the security-constrained unit commitment problem (SCUC). Supplementary constraints are provided at each load point with 24-hour energy consumption requirement along with DR limitations. GAMS software is used to execute the proposed method in which CPLEX solver finds the optimal solution.
IEEE 24-bus test system has been employed to investigate the different features of the presented method. By varying DR potential in the system, TOU rates are determined and then their impacts on the customers' electricity bill, operation cost, and reserve cost as well as load profile of the system are analyzed. In addition, the effect of network congestion as a technical limitation is studied. The obtained results demonstrate the effectiveness and applicability of the proposed method.

This paper presents a new optimization problem in the field of linkage reconfiguration. This is the problem of minimizing moving parts of a given robot arm for positioning the end effector of the given robot arm at the given target point as well as minimizing the movement of the movable parts. Initially, the formal modeling is accomplished by minimizing the movement problem. At this time, a criterion which called AM</em> (Arithmetic Measure) is introduced, and this criterion is used to quantify the motion of the linkage. Afterward, it is indicated that the presented problem is an NP-Hard problem. Consequently, a greedy heuristic algorithm is presented to minimize movement of the robot''s moving components. After identifying the moving components and the movement of these parts, an algorithm is provided to determine the final configuration of the robot arm. The mentioned algorithm solves the problem by mapping the robot arm with an arbitrary number of links to a robot with a single link or two links. The proposed heuristic approach requires  time using  space.

Big data referred to huge datasets with high number of objects and high number of dimensions. Mining and extracting big datasets is beyond the capability of conventional data mining algorithms including clustering algorithms, classification algorithms, feature selection methods and etc. Clustering, which is the process of dividing the data points of a dataset into different groups (clusters) based on their similarities and dissimilarities, is an unsupervised learning method discovers useful information and hidden patterns from raw data. K-means yet is an efficient clustering algorithm but it suffers from some drawbacks. It has a tendency to converge to a local optimum point, its output result depends on its initial value of cluster centers and it is unable in finding the number of clusters. In this research a new clustering method for big datasets is introduced based on Particle Swarm Optimization (PSO) algorithm. PSO is a heuristic algorithm with high ability in searching the solution space and finding the global optimum point. The proposed method is a two-stage algorithm which first searches the solution space for proper number of clusters and then searches to find the position of the centroids. Its performance is evaluated on 13 synthetics and a biological microarray dataset. Finally, 2 real big mobility datasets, are investigated and analyzed using the proposed clustering method.

There are numerous applications for image registration (IR). The main purpose of the IR is to find a map between two different situation images. In this way, the main objective is to find this map to reconstruct the target image as optimum as possible. Needless to say, the IR task is an optimization problem. As the optimization method, although the evolutionary ones are sometimes more effective in escaping the local minima, their speed is not emulated the mathematical ones at all. In this paper, we employed a mathematical framework based on the Newton method. This framework is suitable for any efficient cost function. Yet we used the sum of square difference (SSD). We also provided an effective strategy in order to avoid sticking in the local minima. As one of the fundamental drawback of mathematical-based optimization methods, local minima have been managed properly by our new strategy. Scale and rotation – as two variables of the problem- have been treated solely in a different iteration. In other words, in every iteration, one of these variables is actually the only variable of the problem as the other one is considered constant. Simulation results indicate the effectiveness of the proposed model.