نشریه مهندسی برق و مهندسی کامپیوتر ایران
سال شانزدهم شماره 4 (پیاپی 49، زمستان 1397)

The advancement of technology in the area of wireless sensor networks and the ability to use the Internet Protocol in small objects with limited resources (such as sensors) has changed the Internet landscape. How to communicate and how to exchange information is one of the challenges of the Internet world of things. 6LoWPAN and CoAP standards for using web protocols in low-loss and low-power sensor networks (LLNs) are presented. The 6LoWPAN / CoAP protocol stack allows access to the sensor network through web protocols. This will facilitate the development of applications on the sensor network and access to them by the Internet. Each layer stack of the 6LoWPAN / CoAP protocol imposes overhead on interchange messages, and data overload in multichannel networks exacerbates energy consumption. In this paper, a method for reducing the overhead imposed on small and medium packets in multi-step networks based on 6LoWPAN / CoAP is presented using the scheduling and aggregation of CoAP packets on sensor nodes. In order to achieve the research objectives, measures such as the classification of CoAP requests / responses in terms of network priority (maximum allowed delay detection), scheduling and aggregation of incoming messages on sensor nodes (based on the maximum allowed delay of each), and opening messages aggregated in the destination , It has been done. The evaluation results of the proposed method indicate a reduction of energy consumption and network traffic for applications such as monitoring, in multi-step networks based on the 6LoWPAN/ CoAP protocol stack.

In many applications of wireless mesh networks, due to the lack of access to a permanent source of energy and the use of battery and energy harvesting equipment, energy sustainable design is very important. Duty-cycle adjustment, putting the node into sleep mode in some parts of the working period, is a method for energy saving and sustainability assurance. In this case, to exchange data between neighboring nodes, protocols for sleep scheduling are needed. In some applications of these networks, such as video surveillance applications, it is necessary to collect data from different parts of the network. Tree topology is a good option for these applications. A simple method for coordinating sleep in a tree topology is the TIME-SPLIT algorithm, at which the working time of each node is evenly divided among its children.
The proposed TIME-SPLIT scheduling algorithm does not consider the node energy limitations. In this paper, we have added the nodes duty-cycle constraint in the TIME-SPLIT algorithm to guarantee energy sustainability in tree-based wireless mesh networks. In situations where the energy status of the children is different, equal division of time leads to network inefficiency. To improve network efficiency and throughput, we provide two scheduling algorithms that take into account the conditions of the children's energy and traffic. In the first proposed algorithm, the time division is performed in relation to the duty-cycle of the children of each node. In the second algorithm, the time division is dynamically and in proportion to the traffic of the children, and the connection acceptance is more precisely performed based on its energy consumption during its lifespan. The simulation results performed by the NS3 network simulator show that in energy and tree structure imbalance conditions, where children of a node have different energy or sub tree, the proposed methods significantly (more than about 60%) increase the network’s total delivered traffic.

Pattern matching is one of possible methods proposed for estimating the WCET of the loops. If the loop matches with the proposed pattern, the number of iterations is calculated using an equation. In fact, the derivation of counter values for all iterations is thus avoided. A shortcoming of pattern matching methods is its excessive dependence upon patterns. It is dependent upon location, frequency and how to change in value of the counter and structure and place of counter tester. In order to reduce dependence upon patterns, loop flow can be modeled in two sets of symbolic expressions indicating iteration conditions and changes in value of counters. Based upon these expressions, the number of possible values that could be assigned to the loop control variables during the loop execution is computed as the worst-case estimation of the number of loop iterations. But the estimate presented in this method is greater than the actual value and there is overestimation. In this paper, the variables whose values are equal on the different paths and this value is accounted as an iteration, are detected and are considered in the estimations. This will reduce the overestimation. The evaluations are showed that the proposed method is effective and efficient and has less overestimation.

Today, opinion mining is one the most important applications of natural language processing which requires special methods to process documents due to the high volume of comments produced. Since the users’ opinions on social networks and e-commerce websites constitute an evolving stream, the application of traditional non-incremental classification algorithm for opinion mining leads to the degradation of the classification model as time passes.
Moreover, because the users’ comments are massive, it is not possible to label enough comments to build training data for updating the learned model. Another issue in incremental opinion mining is the concept drift that should be supported to handle changing class distributions and evolving vocabulary.
In this paper, a new incremental method for polarity detection is proposed which with the application of stream-based active learning selects the best documents to be labeled by experts and updates the classifier. The proposed method is capable of detecting and handling concept drift using a limited labeled data without storing the documents. We compare our method with the state of the art incremental and non-incremental classification methods using credible datasets and standard evaluation measures. The evaluation results show the effectiveness of the proposed method for polarity detection of opinions.

The steganalysis purpose is to prevent the pursuit of steganography methods for your goals. In steganography, in order to evaluate new ideas, there should be known steganalysis attacks on them, and the results should be compared with other existing methods. One of the most well-known steganalysis methods is CDF method that used in this research.
One of the major challenges in the image steganalysis issue is the large number of extracted features. High-dimensional data sets from two directions reduce steganalysis performance. On the one hand, with the increase in the dimensions of the data, the volume of computing increases, and on the other hand, a model based on high-dimensional data has a low generalization capability and increases probability of overfitting. As a result, reducing the dimensions of the problem can both reduce the computational complexity and improve the steganalysis performance. In this paper, has been tried to combine the concept of the maximum weighted clique problem and edge centrality measure, and to consider the suitability of each feature, to select the most effective features with minimum redundancy as the final features. The simulation results on the SPAM and CC-PEV data showed that the proposed method had a good performance and accurately obtained about 96% in the detection of data embedding in the images, and this method is more accurate than the previously known methods.

Design of low-cost and high-speed datapath is very important for current computing systems. The adders are the essential parts of datapaths in computing systems. Among different types of adders, the carry select adder (CSeA) has a high speed while having the area overhead, as well. A factor influencing the speed of this adder is the incorporated grouping structure dependent to its components' delay. In this paper, at first, the delay and area of different existing CSeA architectures are reduced by utilizing a fast and small multiplexer. Then, a new grouping structure is proposed for more delay reduction based on a delay analysis. Implementation and experimental results show that applying the proposed grouping and modifications on different CSeA architectures leads to a high delay reduction in the add operation compared to the best existing grouping structure. For example, the amount of delay reduction in the investigated 32-bit CSeA architectures is more than 33%. In addition, the average reduction of power-delay-product criterion for 32-bit and 64-bit CSeAs utilizing the proposed grouping equals45% and 35%, respectively, compared to the CSeAs incorporating the current best grouping.

Static contents defined in Web 1.0 were replaced with structured user generated contents by means of Web 2.0. Wikis, Blogs, Social Networks, and Social Bookmarking Systems are some of the examples where users can generate and publish contents. Generating contents by users leads to creation of heterogeneous data which makes computation and algorithms hard to be applied. Web 2.0 benefits hashtags (tags) in order to solve the heterogeneous problem of the contents in which users can label their contents with hashtags. This technique cannot help in microblogging systems such as Twitter because of number of characters in each tweet (140 characters per tweet) and leads the tags or words be truncated or be used in heterogeneous form. In the current paper, a novel method is introduced based on Latent Dirichlet Allocation which can be used for numericalization tweets in a vector namely topic vector (TV). Additionally, TV is used for modeling users’ taste which can improve hashtag recommendation. The proposed method has been tested on 8396744 real tweets in English. The top 1 to 5 hashtags are recommended for each tweet and results show precision more than 20% and recall more than 45%. The improvement applied by TV shows that the most precision is increased from 3% to 32%, and recall from 21% to 46% to the best method tested by the authors.

Integrated modular avionics (IMA) has significantly evolved avionic industry. In this architecture, tasks with different criticality have been integrated into a share hardware in order to reduce the size, weight, power consumption and cost so they commonly use the resources. The industry’s interest in integrating tasks has resulted in introducing mixed-criticality systems. Real time and assurance of executing critical tasks are considered of the two basic needs for these kinds of systems. However, integration of critical and non-critical tasks makes some problems for scheduling executing tasks. On the other hand, reducing energy consumption is another important need as these devices run by batteries. Therefore, the present study aims at satisfying the above mentions needs (real time scheduling and reducing energy consumption) by introducing an innovative energy- aware scheduling approach. The proposed algorithm guarantees executing critical tasks as well as reducing energy consumption by dynamic voltage and frequency scaling (DVFS). The results of simulation showed that energy consumption of the proposed algorithm improved up to 14% in comparison with the similar approaches.