نشریه مهندسی برق و مهندسی کامپیوتر ایران
سال هجدهم شماره 3 (پیاپی 58، پاییز 1399)

Increasing the dimension of the databases have involved the attribute reduction as a critical issue in data mining that it searches to find a subset of attributes with the most effectiveness on the hidden patterns. In the current years, the rough set theory has been considered by researchers as one of the most effective and efficient tools to the reduction. In this paper, the soccer league competition algorithm is modified and adopted to solve the attribute reduction problem for the first time. The ability to escape the local optimal, the ability to use the information distributed by players in the search space, the rapid convergence to the optimal solutions, and the low algorithm’s parameters were the motivation of considering the algorithm in the current research. The proposed ideas to modify the algorithm consist of utilizing the total power of fixed and saved players in calculating the power of each team, considering the combination of continuous and discrete structures for each player, proposing a novel discretization method, providing a hydraulic analysis appropriate to the research problem for evaluating each player, designing correction in Imitation and Provocation operators based on the challenges in their original version. The proposed ideas are performed on small, medium and large data sets from UCI and the experimental results are compared with the state-of-the-art algorithms. This comparison shows that the competitive advantages of the proposed algorithm over the investigated algorithms.

Packet classification plays a critical role in improving the performance of many network devices including routers, firewalls and intrusion detection systems. Due to the increasing number of classification rules, high traffic volume and high bandwidth network links, designing an efficient packet classifier becomes more challenging. Packet classification algorithms that use static data structure do not consider the pattern of the incoming traffic in optimizing their search mechanism. Therefore, we use some statistical characteristics of the incoming traffic to propose a traffic aware data structure. Since most Internet traffic volume belong to long-live flows, the majority of the packets are matched to the rules in a few sub trees. To take the advantage of this feature, AVL tree data structure is served for storing classification rules where the upper and lower limits of the rule-set are used as nodes. Our evaluation have shown that with increasing the skewness of data packets, the average number of memory accesses are significantly decreased compared to the basic case. Finally, evaluation results show that the traffic-aware packet classification with high frequency rules can decrease more than 40% of the average number of memory accesses and consequently the lookup time.

In this article, we propose a novel Semi-Supervised Ensemble classifier using Confidence Based Selection metric, named SSE-CBS. The proposed approach uses labeled and unlabeled data, which aims at reacting to different types of concept drift. SSE-CBS combines an accuracy-based weighting mechanism known from block-based ensembles with the incremental nature of Hoeffding Tree. The proposed algorithm is experimentally compared to the state-of-the-art stream methods, including supervised, semi-supervised, single classifiers, and block-based ensembles in different drift scenarios. Out of all the compared algorithms, SSE-CBS outperforms other semi-supervised ensemble approaches. Experimental results show that SSE-CBS can be considered suitable for scenarios, involving many types of drift in limited labeled data.

The performance of an Analog/Digital (A/D) converter, various aspects like general architecture of the converter, architecture of the building blocks or design of the blocks can be improved. The comparator block is a fundamental block in data converters. Due to contradicting design purposes, circuit constraints and necessities, design of comparators and obtaining best circuit performance are complicated and challenging. Such challenges in circuit design necessitate presenting approaches which not only satisfy all the objectives but also, they are cost effective in terms of time and cost. One of the approaches which has recently attracted attentions is the heuristic algorithms based intelligent Methods. Inclined Planes system Optimization algorithm (IPO) is a novel heuristic algorithm inspired by dynamic movement of the objects on frictionless inclined planes. But despite its remarkable ability for exploration and exploitation of the search space, its standard model has complex relationships with many structural parameters that often confuse the user in choosing the effective values for them.In this paper, IPO algorithm is simplified to present a heuristic algorithm (called SIPO) and its efficiency in optimization of 10 standard benchmarks has been evaluated. Then, a multi-objective version of the proposed algorithm (called MOSIPO) for design and optimization of double tail comparator is presented and its efficiency in optimization of double tail comparator has been evaluated and compared with popular multi-objective intelligent methods. The results clearly demonstrate the improved performance and superiority of SIPO and MOSIPO compared to the other methods.

In this paper, a static task scheduling and mapping heuristic approach to optimize execution time, reliability, power and temperature of multiprocessor systems on chip is presented. This method is proposed based on the list scheduling approach and utilized task replication, dynamic voltage and frequency scaling, and adding cooling slacks to improve reliability, power consumption and temperature to expand the design space and explore the solution set more efficiently. Due to the existing trade-offs among the considered parameters and their optimization, the optimization process is complicated and our proposed method is used the Pareto front generation technique. Moreover, our proposed method, models the objectives comprehensively to consider their dependency. Several experiments are performed to demonstrate the performance and capability of the proposed method in joint optimization of the parameters and extracting the proper solution set. Compared to the previous research, our proposed method outperforms them in optimizing the considered design parameters and its results is 19% better averagely than an efficient studied heuristic method.

Research in the field of video surveillance systems has been improving because of the increasing need for intelligent monitoring, control and management. Given the large amount of data on these intelligent transportation systems, extracting patterns and automatically labeling them is a challenging task. In this paper, a topic model was used to detect and extract traffic patterns at intersections so that visual patterns are transformed into visual words. The input video is first split into clips. Then, the flow characteristics of the clips, which are based on abundant local motion vector information, are computed using optical flow algorithms and converted to visual words. After that, with a non-probabilistic topic model, the traffic patterns are extracted to the designed system by a group sparse topical coding method. These patterns represent visible motion that can be used to describe a scene by answering a behavioral question such as: Where does a vehicle go? The results of the implementation of the proposed method on the QMUL video database show that the proposed method can correctly detect and display meaningful traffic patterns such as turn left, turn right and crossing a roundabout.

Today, human action recognition in still images has become one of the active topics in computer vision and pattern recognition. The focus is on identifying human action or behavior in a single static image. Unlike the traditional methods that use videos or a sequence of images for human action recognition, still images do not involve temporal information. Therefore, still image-based action recognition is more challenging compared to video-based recognition. Given the importance of motion information in action recognition, the Im2flow method has been used to estimate motion information from a static image. To do this, three deep neural networks are combined together, called a three-stream neural network. The proposed structure of this paper, namely the three-stream network, stemmed from the combination of three deep neural networks. The first, second and third networks are trained based on the raw color image, the optical flow predicted by the image, and the human pose obtained in the image, respectively. In other words, in this study, in addition to the predicted spatial and temporal information, the information on human pose is also used for human action recognition due to its importance in recognition performance. Results revealed that the introduced three-stream neural network can improve the accuracy of human action recognition. The accuracy of the proposed method on Willow7 action, Pascal voc2012, and Stanford10 data sets were 91.8%, 91.02%, and 96.97%, respectively, which indicates the promising performance of the introduced method compared to state-of-the-art performance.

The triangulation of the given point set on the 2D-plane is the planar straight-line embedding of the graph whose vertices is exactly and set of its edges is maximal (with the most edge). Two important issues are being explored in this area. a) In how many ways can the given set of points be triangulated? b) Which triangulation is optimal based on the given particular feature? The first problem is an open problem, and except in special cases where it has a closed relation, a polynomial time algorithm for it has not been presented in general. The second problem is NP-HARD when the goal is to find a triangulation whose total edge length is the smallest (MWT). So research has been done to provide heuristic, meta heuristic, or approximation algorithms for it.In this paper, a method is presented in which by constructing the intersection graph obtained from all the line segments obtained from all pairs of points of and then algorithms for generating all maximal independent sets (MIS) of the intersection graph is introduced. Furthermore, an algorithm is introduced for counting the number of maximal independent sets. This approach in which constructing intersection graph and converting any triangulation problem to the maximal independent set problem is a new approach for triangulation problem in both cases (a) and (b). Considering difficulties to design algorithms for problems (a) and (b) because of its geometric natures, all the algorithms that have been proposed so far for problems (a) and (b) can be used to solve the triangulation problems in both cases by the approach proposed in this article. The proposed approach of converting triangulation problem to the MIS problem is a new approach that has never been reported to solve counting the number of triangulations or minimum weight triangulation. Furthermore a heuristic estimation algorithm will be introduced to estimate average number of triangulations on the given point set and the algorithm implementation shows its outputs is near to exact values for some instances.