نشریه مهندسی برق و مهندسی کامپیوتر ایران
سال شانزدهم شماره 2 (پیاپی 47، تابستان 1397)

Multiplication is one of the important computations required for different signal processing applications especially regarding voice and image. However, the multipliers as digital circuits are susceptible to different environmental effects such as noises. In this paper, a new approach is proposed for designing a 32-bit floating-point multiplier which can operate in two operational modes, normal and fault-tolerant, dependent to the environmental conditions. In the fault-tolerant mode, by reducing the normal precision and accepting a negligible error in the output, a portion of preliminary circuit is released which is used for redundant computations in order to detect or correct errors. This way, two multiplier architectures with error detection or correction capability are proposed that have a beneficial reliability against different types of permanent and transient faults. The implementation results show that in the fault-tolerant mode, maintaining 13 bits instead of 23 bits for the mantissa will be enough to achieve an error detecting multiplier, and maintaining 11 bits will be enough to achieve an error correcting multiplier with acceptable area and power overheads (from 12% to 26%) while their precisions are enough for most applications.

This research has been prepared in the field of linkage structures and their movements. A linkage is a set of line segments that can be interconnected via their ends, which exhibits numerous usages in modeling robot arms. To date, various domain-specific languages have been introduced in the field of robot movements. In spite that some of the capabilities of these languages combined with general-purpose languages can be used to describe and create the movements of these linkages and their structures, yet they cannot be considered domain-specific languages for explaining the linkages and their movements. The domain-specific languages are programs that raise the level of abstraction, the ability to understand better, accelerating the development and requires less effort to learn relevant knowledge that will provide the same advantages. So like all software, have levels such as analysis, design, implementation, testing, maintenance and support. Therefore, in this paper, we attempt to analyze and design a domain-specific language and describe and create linkage movements and their structures. By using this domain specific language, there is no limit to the definition of simple linkages in terms of it’s number. Also, by defining the modules and their sequential and parallel combinations, the final movements of the linkages are generated, and by using the features of the language, the terms needed to start or terminate each final movements are defined. Applying this kind of attitude to specific general-purpose modeling, in addition to providing ease in defining the structure of linkages and diversity in their initial definition, allows for the coordination and collaboration of multiple robots to perform a single task and then implemented in the next step.

Heterogeneous networks, including macrocell and femtocell, cause to increase network capacity. Also, they improve quality of offers services to users in cellular networks. Common subchannel allocation among different tier users, make cross-tier interference among users. Since macrocell users have priority to femtocell ones, presence of femtocell users should not prevent macrocell users to access minimum quality-of-service. In this paper, a power control and subchannel allocation scheme in downlink transmission an orthogonal frequency division multiple access (OFDMA) based two tier of macrocell and femtocell is proposed, aiming the maximization of femtocell users total data rate, in which the minimum QOS for all macrocell users and femtocell delay-sensitive users is observed. In macrocell tier, two different problems are considered. The first problem aim to maximizing the total threshold of tolerable cross-tier interference for macrocell users and the second problem’s goal is minimizing the macrocell’s total transmission power. For the femtocell tier, maximizing the users total data rate is the objective. Hungrian method, an assignment optimization method, is used for solving the first problem in macrocell tier. Moreover, in order to solve the second problem a heuristic method for subchannel allocation is proposed and dual Lagrange method is used for power control. In addition, in order to solve the problem for femtocell tier, a heuristic method is used for subchannel allocation. Subsequently, a dual Lagrange method which is one of the convex optimization problem solver is used, so that we can control the power. Finally, an extend simulations are performed to validate the performance of the proposed method.

A Hyper Spherical Search (HSS) optimization algorithm based on chaos theory is proposed that resolves the weakness of the standard HSS optimization algorithm including the speed of convergence and the sequential increment in the number of algorithm iterations to achieve the optimal solution. For this, in the particle initiation and search steps of the proposed algorithm, random values used in the standard algorithm are replaced with the values of two mappings, Chebyshev and Liebovitch, that makes the results of the proposed algorithm definite and decreases their standard deviation. The simulation results on the standard benchmark functions show that the proposed algorithm not only has faster convergence, but also acts as a more accurate search algorithm to find the optimal solution in comparison to standard hyper spherical search algorithm and some other optimization algorithms such as genetic, particle swarm, and harmony search algorithm.

Reinforcement learning is atype of machine learning methods in which the agent uses its transactions with the environment to recognize the environment and to improve its behavior.One of the main problems of standard reinforcement learning algorithms like Q-learning is that they are not able to solve large scale problems in a reasonable time. Acquiring skills helps to decompose the problem to a set of sub-problems and to solve it with hierarchical methods. In spite of the promising results of using skills in hierarchical reinforcement learning, it has been shown in some previous studies that based on the imposed task, the effect of skills on learning performance can be quite positive. On the contrary, if they are not properly selected, they can increase the complexity of problem-solving. Hence, one of the weaknesses of previous methods proposed for automatically acquiring skills is the lack of a systematic evaluation method for each acquired skill. In this paper, we propose new methods based on graph clustering for subgoal extraction and acquisition of skills. Also, we present new criteria for evaluating skills, with the help of which, inappropriate skills for solving the problem are eliminated. Using these methods in a number of experimental environments shows a significant increase in learning speed.

In order to increase throughput and spectral efficiency in a heterogeneous network including a macro and a femtocell, we propose a combined offloading with In-Band Full Duplex (IBFD) scheme in this paper. Traffic offloading to the femtocell is deployed to transmit network users traffic to a macrocell base station in the uplink. In other words, all or a part of the traffic is offloaded to the femtocell and then transmitted to the macrocell, while the rest of traffic is transmitted to the macrocell directly. In the femtocell, we deploy and investigate IBFD technology, i.e., simultaneous transmit and receive traffic in one frequency band. Furthermore, in order to improve throughput of the network, we propose several scheduling schemes to transmit traffic. Finally, optimal number and position of users who use IBFD or do not use it, are determined. We propose a heuristic solution to find optimal position of IBFD users. Simulation results verify the network throughput improvement and power consumption reduction.

Wireless sensor networks have limitations such as processing power, storage resources, and time delay in data transfer to the cloud. The cloud computing by the development of cloud-based services to the edge of the network reduces traffic and delays, so these types of networks are used in many systems, such as medical care, wearable devices, transportation systems and smart cities. Task scheduling techniques in fog computing are considered to be NP-hard issues. Applications require resources to run. Network fog devices are close to the sensors and the cloud and have the required processing power to run the applications. Each fog device can be used to run resource allocation policies. In this paper, we present an optimized Knapsack-based method optimized by symbiotic organism search to allocate resources appropriately to tasks in fog network. The proposed method is simulated in the iFogsim as a developed library from Cloudsim for fog computing. The results indicate improvement in energy consumption, resource consumption, and execution cost of the network. The proposed method is better than FCFS and Knapsack methods.

In this paper, we propose a self-learning single image super-resolution. In our proposed method, adjacent pixels information in smooth area is used. Low and high-resolution pyramids are built by applying up-sampling and down-sampling techniques on input image, as training data. In training phase, we apply support vector regression (SVR) to model the relationship between the pair of low and high-resolution images. For each patch in the low-resolution image, sparse representation is extracted as a feature vector. In this paper, in order to reduce the edge blurring effects, we first separate edge pixels from non-edge pixels. In the smooth area, because of the similar colors around the each pixel, the center pixel value is determined by considering the reconstructed adjacent pixels. Experimental results show that the proposed method is quantitatively and qualitatively outperform the competitive super-resolution approaches.