Recent Advances in Electrical & Electronic Engineering - Volume 14, Issue 3, 2021

Background: Owing to the benefits of software refactoring, the software industry started adopting this practice in the maintenance phase as a means to improve developer’s productivity and software quality. As a result, proposing new techniques for refactoring opportunity identification and sequencing has become the key area of interest for academicians and industry researchers. Objective: This paper aims to perform a review of such existing approaches which are related to software refactoring opportunity identification and sequencing. Methods: We discussed the background concepts of code smells and refactoring and provided their corresponding taxonomies. Moreover, comprehensive literature of several techniques that automatically or semi-automatically identify or prioritize the refactoring opportunities is presented along with considered refactoring activities, optimization algorithms, bad smells, datasets and underlying evaluation approaches. Results: The research in the direction of refactoring opportunity identification and sequencing is highly active and is generally performed by academic researchers. Most of the techniques address Move Method and Extract Class refactoring activities in Java datasets. Conclusion: This paper highlights various open challenges that need further investigation, including lack of dynamic analysis-based approaches, lesser utilization of industrial datasets, nonconsideration of recent optimization algorithms, etc.

Wireless communication is being used in all communication standards. However, with each passing day, the bandwidth scarcity has become a significant concern for the upcoming wireless technologies. In order to address this concern, various techniques based on artificial intelligence have been designed. The basic intelligent radio called cognitive radio has been devised. It works on the basic principle of spectrum sensing and detecting the free frequency for transmission of the secondary user, who is an unlicensed user. This work proposes an efficient technique that has been developed to design cognitive radio based on SDR platform. The frequency updating algorithm has been added for the performance assessment of the proposed technique. The analysis posits that for every 10dB rise in Gaussian Noise, the bit error rate of secondary transmitter and spectrum sensor, cause an increment of 19.59% and 29.39%, respectively. It has been found that spectrum sensor is more prone to noise and that the Gaussian noise degrades the performance of the system. Therefore, it is pertinent that the spectrum sensor should be programmed carefully. This analysis shows that the best range of spectrum sensor under Gaussian noise is 0 to 0.1dB and the bit error rate is within this specified range.

Soil is a major and important natural resource, which not only support human life but also furnish commodities for ecological and economic growth. Ecological risk, such as degradation of soil pose a serious threat to the ecosystem. The high-stress level of heavy metals like chromium, copper, cadmium, etc., produce ecological risks, which include: decrease in the fertility of the soil; reduction in crop yield and degradation of metabolism of living beings, and hence ecological health. The ecological risk associated demands the assessment of heavy metal stress levels in soils. As the rate of stress level of heavy metals is exponentially increasing in recent times, it is apparent to assess or predict heavy metal contamination in soil. The assessment will help the concerned authorities to take corrective as well as preventive measures to enhance the ecological, and hence economic growth. This study reviews the efficient assessment models to predict contamination of soil due to heavy metal.

Background: Code smells are symptoms that something may be wrong in software systems that can cause complications in maintaining software quality. In literature, there exist many code smells and their identification is far from trivial. Thus, several techniques have also been proposed to automate code smell detection in order to improve software quality. Objective: This paper presents an up-to-date review of simple and hybrid machine learning-based code smell detection techniques and tools. Methods: We collected all the relevant research published in this field till 2020. We extracted the data from those articles and classified them into two major categories. In addition, we compared the selected studies based on several aspects like code smells, machine learning techniques, datasets, programming languages used by datasets, dataset size, evaluation approach, and statistical testing. Results: A majority of empirical studies have proposed machine-learning based code smell detection tools. Support vector machine and decision tree algorithms are frequently used by the researchers. Along with this, a major proportion of research is conducted on Open Source Softwares (OSS) such as Xerces, Gantt Project and ArgoUml. Furthermore, researchers pay more attention to Feature Envy and Long Method code smells. Conclusion: We identified several areas of open research like the need for code smell detection techniques using hybrid approaches, the need for employing valid industrial datasets, etc.

Background: In the present scenario, electricity theft is a major problem for the government. This problem affects the Indian economy since GDP goes down due to theft. Electricity is a part of living and its theft affects the common man who indirectly pays for the theft done by someone else in the form of extra charges. Methods: In this paper, we present a novel identification pattern-based energy fault detector, by leveraging the customers' normal and faulty line. The target of this paper was to implement a system to monitor the readings and to find the fault in the power line in real time. Manipulations in readings of the meter are impossible. Results: There is a real time monitoring of meters. Readings could be provided to customers on daily, weekly, monthly or yearly basis. As soon as there occurs a fault, it could be rectified because detection is on a real time basis and further messages can be sent to the customer about the fault. Conclusion: A comparison of developed real time monitoring system is done with GSM meters and analog meters.

Dealing with the complexity of modern technological non-linear systems and their disturbances is a very challenging issue. Sliding Mode Control (SMC) can deal perfectly with the non-linear systems and their disturbances because its accuracy and stability are very high. In this paper, a brief review of SMC types, SMC methods, and SMC in the field of Fault Tolerant Control (FTC), are provided. It also gives brief details about the reaching phase, sliding phase, and sliding surface, with their advantages and disadvantages. Further, chattering, which is the main drawback of SMC, is discussed and methods to resolve the chattering are also provided. In the end, various types of FTC are discussed and potential use of SMC in the design of FTC has been described. This paper will give comprehensive state-of-the-art to new researchers about the implementation of SMC in the FTC domain for further research.

Background: With the development of the application of GPS satellite, the requirement of anti-jamming and integration for navigation receiving terminal is gradually raised. In terms of anti-jamming performance, because the satellite signal arrives at the ground with weak intensity and is vulnerable to electromagnetic interference, the positioning results are not available. Objective: Therefore, in complex electromagnetic environment, the anti-jamming navigation receiving terminal is needed, and the interference nulling or beam pointing can be achieved by using multi- element antenna array with anti-jamming algorithm. Methods: Accordingly to requirements, the development trend of navigation receiving terminal is from single mode to multi-mode and miniaturization. A miniaturized 7-element anti-jamming antenna array is proposed. It mainly aims at GPS L1 and L2 frequency points. All elements adopt ceramic substrate microstrip antenna. The array elements adopt laminated design to effectively reduce the antenna aperture. The spacing between elements at two frequency points is not greater than half wavelength, which can ensure the back-end anti-jamming algorithm performance, and its performance and characteristics are verified through related experiments and simulations. Results: Anti-jamming algorithm performance, and its performance and characteristics are verified through related experiments and simulations. Conclusion: The designed multi-element antenna can be used in the anti-jamming application.

Background: In state-of-the-art nanometer metal-oxide-semiconductor-field-effect- transistors (MOSFETs), optimization of timing characteristic is one of the major concerns in the design of modern digital integrated circuits. Objective: This study proposes an effective back-gate-biasing technique to comprehensively investigate the timing and its variation due to random dopant fluctuation (RDF) employing Monte Carlo methodology. Methods: To analyze RDF-induced timing variation in a 22-nm complementary metal-oxide semiconductor (CMOS) inverter, an ensemble of 1000 different samples of channel-doping for negative metal-oxide semiconductor (NMOS) and positive metal-oxide semiconductor (PMOS) was reproduced and the input/output curves were measured. Since back-gate bias is technology dependent, we present in parallel results with and without VBG. Results: It is found that the suppression of RDF-induced timing variations can be achieved by appropriately adopting back-gate voltage (VBG) through measurements and detailed Monte Carlo simulations. Consequently, the timing parameters and their variations are reduced and, moreover they are also insensitive to channel doping with back-gate bias. Conclusion: Circuit designers can appropriately use back-gate bias to minimize timing variations and improve the performance of CMOS integrated circuits.

Background: With the further development of the electric Internet of Things (eIoT), IoT devices in the distributed network generate data with different frequencies and types. Objective: Fog platform is located between the smart collected terminal and cloud platform, and the resources of fog computing are limited, which affects the delay of service processing time and response time. Methods: In this paper, an algorithm of fog resource scheduling and load balancing is proposed. First, the fog devices divide the tasks into high or low priority. Then, the fog management nodes cluster the fog nodes through the K-mean+ algorithm and implement the earliest deadline first dynamic (EDFD) task scheduling algorithm and De-REF neural network load balancing algorithm. Results: We use tools to simulate the environment, and the results show that this method has strong advantages in -30% response time, -50% scheduling time, delay, -50% load balancing rate, and energy consumption, which provides a better guarantee for eIoT. Conclusion: Resource scheduling is an important factor affecting system performance. This article mainly addresses the needs of eIoT in terminal network communication delay, connection failure, and resource shortage. A new method of resource scheduling and load balancing is proposed. The evaluation was performed, and it proved that our proposed algorithm has better performance than the previous method, which brings new opportunities for the realization of eIoT.

Background: The day-ahead load forecasting is an essential guideline for power generating, and it is of considerable significance in power dispatch. Objective: Most of the existing load probability prediction methods use historical data to predict a single area, and rarely use the correlation of load time and space to improve the accuracy of load prediction. Methods: This paper presents a method for day-ahead load probability prediction based on spacetime correction. Firstly, the kernel density estimation (KDE) is employed to model the prediction error of the long short-term memory (LSTM) model, and the residual distribution is obtained. The correlation value is then used to modify the time and space dimensions of the test set's partial period prediction values. Results: The experiment selected three years of load data in 10 areas of a city in northern China. The MAPE of the two modified models on their respective test sets can be reduced by an average of 10.2% and 6.1% compared to previous results. The interval coverage of the probability prediction can be increased by an average of 4.2% and 1.8% than before. Conclusion: The test results show that the proposed correction schemes are feasible.