International Journal Information and Communication Technology Research
Volume:14 Issue: 4, Autumn 2022

In this paper, we investigate a non-orthogonal multiple access (NOMA)-based underlay multi-channel cognitive device-to-device (D2D) communications and efficiently exploit a resource management scheme for the investigated model. A two-stage solution is used in which sub-channels (SCs) and powers are jointly assigned to the D2Ds and transmitters, respectively, employing a convex optimization method to achieve the optimal parameters. We show that throughput of the D2D users can be maximized by the proposed strategy, subject to controlling total transmission power, interference power, and minimum rate requirements. We study the performance of the network by increasing the number of PUs and SCs. Moreover, minimum rate requirement and maximum allowed interference at the PUs versus sum rate of the SU transceivers is investigated. The simulation results present insights about the impact of the optimal power and SC allocations.

Nowadays, the energy consumption of wireless sensor networks has increased dramatically due to the significant growth of these networks, especially their use in the Internet of Things. Also, reducing the energy consumption in these networks has been considered to protect the environment. Energy consumption in nodes is critical, and many research studies have been conducted to reduce it. Most methods are based on clustering and cluster selection, while this work presents a solution based on managin g nodes' activity. The nodes were scheduled so that almost all of them were active. The energy of all nodes should be consumed equally. The proposed solution was compared with the DSP-SR algorithm. The results demonstrated that the proposed method can work much better than DSP-SR. According to the evaluation, the proposed method had strengths such as optimal energy allocation and almost no dead nodes in the time periods.

Pansharpening is the fusion of panchromatic (PAN) and multispectral (MS) images to obtain a high spectral and spatial resolution image. Various metrics are introduced to assess the performance of different algorithms of pansharpening. This paper proposes a new metric for spectral quality evaluation of fused images. In the proposed method, spectrum vector of each pixel of fused image is compared to corresponding spectrum of reference image. Area of difference between two spectra is measured, and by applying this process to all pixel vectors of the fused image and taking an average over obtained values, spectral distortion of whole image is obtained. To investigate the efficiency of the proposed index, deliberate spectral distortion is applied to fused image and the proposed metric's ability to detect distortion is examined. Experimental results on real remote sensing images demonstrate the superior performance of the proposed metric compared to other existing metrics.

MapReduce algorithm inspired by the map and reduces functions commonly used in functional programming. The use of this model is more beneficial when optimization of the distributed mappers in the MapReduce framework comes into the account. In standard mappers, each mapper operates independently and has no collaborative function or content relationship with other mappers. We propose a new technique to improve performance of the inter-processing tasks in MapReduce functions. In the proposed method, the mappers are connected and collaborated through a shared coordinator with a distributed metadata store called DMDS. In this new structure, a parallel and co-evolutionary genetic algorithm has been used to optimize and match the matrix processes simultaneously. The proposed method uses a genetic algorithm with a parallel and evolutionary executive structure in the mapping process of the mappers program to allocate resources, transfer and store data. The co-evolutionary MapReduce mappers can simplify and optimize relational data processing in the large clusters. MapReduce using a co-evolutionary mapper, provide successful convergence and better performance. Our experimental evaluation shows that collaborative techniques improves performance especially in the big size computations, and dramatically improves processing time across the MapReduce process. Even though the execution time in MapReduce varies with data volume, in the proposed method the overhead processing in low volume data is considerable where in high volume data shows more competitive advantage. In fact, with increasing the data volume, advantage of the proposed method becomes more considerable.

The equalization of histograms is a simple and efficient process for contrast enhancement. This paper presents equalization of the bi-histogram with the level of the entropy-based plateau. In the first step, the input histogram is divided into two separate sub-histograms, using the mean brightness as a primary threshold of total image pixels. The mentioned threshold is updated in a way that it minimizes the different values of discrete entropy between each section. Then, based on the measured plateau value, these sub-histograms are clipped to prevent unnecessary enhancement. Finally, the image intensity is stretched based on the cumulative distribution function. Laboratory results show that this method gives better outcomes of enhancement, especially in the presence of noise, compared to some two-section methods of preserving of brightness of the histogram equalization.

Stance detection aims to identify an author's stance towards a specific topic which has become a critical component in applications such as fake news detection, claim validation, author profiling, etc. However, while the stance is easily detected by humans, machine learning models are falling short of this task. In the English language, due to having large and appropriate e datasets, relatively good accuracy has been achieved in this field, but in the Persian language, due to the lack of data, we have not made significant progress in stance detection. So, in this paper, we present a stance detection dataset that contains 3813 labeled tweets. We provide a detailed description of the newly created dataset and develop deep learning models on it. Our best model achieves a macro-average F1-score of 58%. Moreover, our dataset can facilitate research in some fields in Persian such as cross-lingual stance detection, author profiling, etc.