نشریه صنایع الکترونیک
سال پنجم شماره 2 (پیاپی 17، تابستان 1393)

VANETs are a class of Mobile Ad hoc Networks (MANETs) that characterized by their high mobility and frequent topology changes. According to the previous research، clustering can be used in VANETs to partition the network into smaller groups of moving vehicles. In this paper، we propose a novel 2-layer dynamic and stable clustering technique combining the features of static and dynamic clustering methods. In order to have dissemination and collection of messages، the proposed scheme uses Agents. Agents learn from the environment in which they are deployed، and accordingly، performs its task in collaboration with the other agents. Cluster head is selected among the cluster members based on a new stability metric derived from connectivity degree، relative average speed، time to leave the road intersection and quality of static cluster head connectivity. Cluster head predicts future association of cluster members based on mobility patterns، in two scenarios. Performance of the proposed scheme is evaluated using extensive simulation with respect to the various metrics such as the average cluster head duration، the average number of clusters changed per vehicle، the average cluster lifetime and the clustering overheads. The simulation shows that our technique has better results and provides more stable cluster structure compared to the two existing techniques.

Junctionless field effect transistors (JL-FETs) are novel structures which have simplified the fabrication process and have appropriate electronic characteristics. By applying junctionless concept to Carbon nanotube field effect transistors (CNTFETs), we have investigated the electronic characteristics of a JL-CNTFET, and compared its characteristics to a conventional CNTFET. Electronic parameters such as subthreshold swing, Ion/Ioff ratio and output characteristic are simulated for both JL-CNTFET and C-CNTFET. Sensitivity of Ion/Ioff ratio to CNT’s chirality and doping density of channel also are investigated. We have simulated the JL-CNTFET structure using the self-consistent solution of Poisson–Schr?dinger equations, within the non-equilibrium Green’s function (NEGF) formalism.

Micro grids are interlocking networks of distributed energy systems (sources and loads) that can work connected to the network or separated from it. According to various loads in terms of nutrition priorities, consumers can help micro grid’s control center in optimizing the exploitation of the network and powering critical loads by offering theirremovable or interruptible load with different prices. Response to demands plays an important rule in electricity market for making balance between supply and demand. Overall consumer prices can be very useful in reducing exploiting costs especially when the market prices are high. Meanwhile, with this method consumers can avoid paying high costs for their little importance loads.In this article it is tried that the exploiting of micro grid being done with the presence of demand’s response, thus while increasing social welfare, the load’s curve be flat in an acceptable level. With this purposes the problem is a Multipurpose objective function based on non-linear programming (NLP), that for solving the problem the GAMS optimizing software and for Multipurpose optimizing, the limitation proviso? , will be used. The suggested algorithm will be implemented on the micro grid Busbar type 17. Obtained results shows the ability of suggested algorithm in improving the micro grid’s operation.

Malicious PDF files have been applied to compromise computer security in recent years because of their portablity and popularity. In addition, common anti malware products are ineffective against new malicious PDF files and adding an intelligent complimentary technique to them is essential. In this paper, we propsed a novel approach for malicious PDF files detection. At first, a set of unique static features are extracted from PDF structure. Then, by applying association rule mining, frequent patterns are extracted from them and an effective classifier is built with them. We analyzed about 6000 malicious PDF and 6000 benign PDF and show that the proposed system achieved better results compared to other related works. Furthermore, this method has robustness against evasion and can be applied to anti malware products with low overhead.

Frequency spectrum estimation is one of topics in Cognitive radios. In this paper, to estimate the frequency spectrum of the solution of Bayesian equations, assuming a nonlinear system with distributed noise according particle filter based algorithm used to reduce computation, a method is developed with resampling. In this way, Primary user signal is first estimated, then the random noise in the range where the primary user, particles, and predicted the next candidate on the similarity between the target model and candidate model particles, weighted begins. Using a particle filter algorithm base on resampling, candidate particles are pushed towards higher particle weight and the frequency spectrum occupied or not occupied by the primary user is estimated. The limit for a secondary user with an estimate of the frequency spectrum is characterized by a spectrum holes without causing nuisance or interference to the primary users of the frequency spectrum to use. Simulation results show that there is the number of optimum particles in the sample are more particle filter precision of the estimates will be more accurate user frequency band. According to studies carried out in this paper, the proposed method compared to other methods for measuring range with more speed and processing time is less. Also, the simulation results can be compared in some ways to reduce errors and increase the speed measuring range of the other methods mentioned.

Unique features and the possibility of easy dissemination of acoustic signals in underwater environment provide the ability to identify and track the underwater targets. From applications of acoustic signals in passive defense, the following can be named: use of sonar for diver detection in order to prevent the diver’s entrance to harbor equipment plants, protection from the coastal power and so on….for this goal, correct diver detection from other underwater targets such as: dolphins and fish schools is important. In this paper, for diver detection, two classification algorithms, support vector machine (SVM) and neural network, have been used. For this work, two features, target strength (TS) and speed have been used. Finally the results of these algorithms have been evaluated based on classification and detection accuracy and time. The simulations indicate that, MLP neural network algorithm has the best result.

In this paper, a novel image encryption algorithm based on quantum chaotic map, nearest-neighboring coupled-map lattices (NCML) and permutation- diffusion architecture is proposed. First, keystream generated by quantum chaotic map is used to permute the pixels of R, G and B components at the same time and make the three components affect one other. Next, perform the random circular shift operation to the result of the permuted pixels to rearrange bits of each pixel. Finally, the new algorithm employs keystreams generated by the nearest-neighboring coupled-map lattices to diffuse the relationship between the cipher-image and the plain-image. In order to generate the initial conditions and parameters of the chaotic maps, 128-bit long external secret key is used. The image encryption is performed with just one iteration; in some of the methods, obtaining the appropriate values for tests requires a number of iterations. This means that the proposed method is faster than other encryption processes. The results of several experimental analyses about randomness, sensitivity and correlation of the cipher-images show that the proposed algorithm has high security level and high sensitivity which can be adopted for network security and secure communications.