نشریه محاسبات نرم
سال هفتم شماره 1 (پیاپی 13، بهار و تابستان 1397)

Petri net is one of the most popular methods in modeling and evaluation of concurrent and event-based systems. Different tools have been created to support modeling and simulation of different extensions of Petri net in different applications. Each tool supports some extensions and some features. In this work a Petri net based modeling and evaluation tool is presented that not only supports different Petri net extensions but also provides some features not supported before. Automatic model construction, steady state analysis of different extensions including constant timed Petri net, run time reconstruction, learning and  feature, and automatic solution search are some specific features of the proposed tool. In addition to introducing the model, its capabilities  are shown through some examples.

In this paper, of digital error correction algorithm in of capacitor mismatch error and finite and nonlinear gain of Op-Amp has increased significantly by the use of DLMS, an evolutionary search algorithm. To this end, a 16-bit pipelined analog to digital converter was modeled. The obtained digital model is FIR filter with 16 adjustable weights. To adjust of , was divided into three stages and in each stage, the number of filter weights by DLMS algorithm and totally the error correction algorithm is converged through 3000 repetitions in three stages. The DLMS algorithm was simulated using synthesizable RTL code in Verilog HDL and may be implemented. The division of the error correction algorithm into three stages led to improve the error correction and reduce the power consumption. Moreover, an optimum MDAC circuit has been proposed for designing pipelined converter and based on this circuit the error correction algorithm has been designed.

Internet of things (IoT) is a network with a wide range of smart and physical objects and gadgets that can exchange information with each other. IOT introduces a variety of services that human life is dependent on its secure and accessible activities. These networks face numerous problems in terms of energy consumption and reliable communication to send the correct data. Moreover, the characteristics of these networks, such as the dynamism of their topology and the presence of energy constraints due to their wireless property led to challenges in the routing problem. The AOMDV protocol is one of the multi-path protocols that has been designed to maintain the balance of traffic load, management and control of energy consumption of nodes, delay deduction and etc. It uses several different paths to send it has a weakness in terms of the rate of package loss. In this article, an improvement of AOMDV called EEA_IOT is presented where the routes with high energy are selected for sending packets. To this end, the format of the AOMDV packets and some fields are added to so that the energy criterion is considered for routing in IOT. To demonstrate the efficiency of the proposed method, it is compared to AOMDV and SMG through two different scenarios in terms of packet loss rate and residual energy. The results indicate a better performance of the proposed method.

One of the main challenges of increasing information in the new era, is to find information of interest in the mass of data. This important matter has been considered in the design of many sites that interact with users. Recommender systems have been considered to resolve this issue and have tried to help users to achieve their desired information; however, they face limitations. One of the most important challenges that they face is cold-start problem, which is raised when a new user/item entered into the system, while no previous information is available for it. The lack of previous knowledge of the new user/item, will causes the system fails generating its suggestions normally. In this paper, to solve the problem of cold-start user/item a new method is presented using combining content-based models and collaborative filtering. Moreover, demographic data is used to recommend the nearest items to cold-start users/items' interests. Compared to existing methods, the evaluation results show that the proposed method reduces the MAE and RMSE error.

Copy-move forgery is a particular type of distortion where a part or portions of one image is/are copied to other parts of the same image. This type of manipulation is done to hide a particular part of the image or to copy one or more objects into the same image. There are several methods for detecting copy-move forgery, including block-based and key point-based methods. In this paper, a method for detecting copy-move forgery is presented based on hybrid features such as SIFT, KAZE, HOG, and Zernike descriptors. The experimental results of the combinational of these descriptors on the forged images show that this type of combination increases the accuracy of detection than using single and double descriptors; but this high accuracy has the cost of computational time. To reduce the computational time, a Genetic Algorithm (GA) was used to optimize the descriptors and analysis algorithm of the principal component was applied to reduce the dimensions of the features. The results show that the HOG descriptor with a precision of 93.59 has better performance than the other descriptors to detect parts. Combined the four descriptors, the performance of the system is improved by about three percentage in compared with HOG, and reaches a precision of 96.29. The GA as the selector of the best features (six features) achieved the detection rate of 96.94. Using the principal component analysis, the dimensions of the features were reduced to 35, with a detection rate of 94.63.

With the development of technology, the use of wireless telecommunication networks for the various affairs is essential. These networks are one of the safest and most widely used networks, for instance, in medical care and remote patient monitoring. What matters is the quality of service in these networks. The purpose of this paper is to increase packet transduction in a wireless body area network by assuming that the use of time-based access protocol (TDMA) method is effective in increasing the accuracy of packets and increasing the quality of network service. Therefore, by presenting a protocol in the link layer of the Internet of Things, some of the parameters of the quality of service were improved. Compared with other methods, the proposed protocol yielded better results in terms of latency, energy consumption and throughput. The proposed algorithm improvement was 23.67% in the packet delay, 27.12% in the energy consumption, and 10% in throughput.