Electronics

A Wireless Sensor Network (WSN) is a group of autonomous sensors that are distributed geographically. However, sensor nodes in WSNs are battery-powered, and the energy drainage is a significant issue. The clustering approach holds an imperative part in boosting the lifespan of WSNs. This approach gathers the sensors into clusters and selects the cluster heads (CHs). CHs accumulate the information from the cluster members and transfer the data to the base station (BS). Yet, the most challenging task is to select the optimal CHs and thereby to enhance the network lifetime. This article introduces an optimal cluster head selection framework using hybrid opposition-based learning with the gray wolf optimization algorithm. The hybrid technique dynamically trades off between the exploitation and exploration search strategies in selecting the best CHs. In addition, the four different metrics such as energy consumption, minimal distance, node centrality and node degree are utilized. This proposed selection mechanism enhances the network efficiency by selecting the optimal CHs. In addition, the proposed algorithm is experimented on MATLAB (2018a) and validated by different performance metrics such as energy, alive nodes, BS position, and packet delivery ratio. The obtained results of the proposed algorithm exhibit better outcome in terms of more alive nodes per round, maximum number of packets delivery to the BS, improved residual energy and enhanced lifetime. At last, the proposed algorithm has achieved a better lifetime of ≈20%, ≈30% and ≈45% compared to grey wolf optimization (GWO), Artificial bee colony (ABC) and Low-energy adaptive clustering hierarchy (LEACH) techniques. Full article

The paper develops a methodology for the online built-in self-testing of deep neural network (DNN) accelerators to validate the correct operation with respect to their functional specifications. The DNN of interest is realized in the hardware to perform in-memory computing using non-volatile memory cells as computational units. Assuming a functional fault model, we develop methods to generate pseudorandom and structured test patterns to detect hardware faults. We also develop a test-sequencing strategy that combines these different classes of tests to achieve high fault coverage. The testing methodology is applied to a broad class of DNNs trained to classify images from the MNIST, Fashion-MNIST, and CIFAR-10 datasets. The goal is to expose hardware faults which may lead to the incorrect classification of images. We achieve an average fault coverage of 94% for these different architectures, some of which are large and complex. Full article

The problem of traffic congestion caused by the fast-growing travel demands has been getting serious in urban areas. Meanwhile, the future of urban mobility has been foreseen as being electric, shared, and autonomous. Accordingly, the routing and charging strategies for fleets of shared autonomous electric vehicles (SAEVs) need to be carefully addressed to cope with the characteristics of the rideshare service operation of the SAEV fleets. In the literature, much work has been done to develop various traffic control strategies for alleviating the problem in urban traffic congestion. However, little research has proposed effective solutions that integrate the route of charging strategies for SAEV fleets with the urban traffic congestion problem. In this regard, this work presents an integrated framework that tackles the route and charging of SAEV fleets as well as the urban traffic congestion prevention issues. Notably, our contribution in this work not only proposes a joint solution for the problems of the urban traffic congestion control and rideshare dispatch of SAEV fleets, but also fills the gap of the routing and charging strategies for mixed privately owned EVs (PEV) and SAEV fleets in the literature. A general optimization framework is formulated, and effective heuristics are proposed to tackle the above-mentioned problems in this work. The feasibility and effectiveness of the proposed algorithms were evaluated through four different scenarios in the simulation. After applying the proposed algorithms, the traffic volumes of the oversaturated main arterial road were diverted to other less busy road sections, and the traveling times of EV passengers were decreased by 28% during peak periods. The simulation results reveal that the proposed algorithms not only provide a practical solution to prevent the problem in urban traffic congestion during rush hours, but also shorten the travel times of EV passengers effectively. Full article

Considering the significance of an energy efficient, delay tolerant and reliable communication protocol for underwater acoustic wireless sensor network (UWSN), this paper proposes a novel systematic random linear network coding (SRLNC) based transmission system examined over a robust statistical UWSN channel model. The derived statistical channel model deals with both the small-scale fading primarily caused by scattering and small wavelength changes and large-scale fading introduced due to node dislocation in the underwater acoustic medium. The proposed SRLNC transmission-based routing approach has been applied over the proposed underwater acoustic (statistical) channel model, and respective performance assessment has been conducted in terms of throughput, energy efficiency, delay and computational complexity by varying network condition parameters. The contributions such as low coefficient vector and Galois filed, low redundant message requirements, computationally efficient pre-coding scheme, iterative buffer flush and enhanced FEC based decoding make the SRLNC based routing protocol sufficiently robust to enable reliable, energy-efficient and delay resilient communication over UWSN. The proposed SRLNC based UWSN routing protocol and its efficacy over dynamic channel conditions affirm that it can be a potential solution for QoS-oriented mission critical underwater communication purposes. Full article

In this paper, a study on vehicle lateral motion control using an in-wheel motor (IWM) based on tire cornering stiffness estimation is presented. The main purpose of this paper is to develop a lateral motion control that can be implemented considering practical issues in real-world vehicle applications such as driver comfort, high availability, and stable control accuracy. The proposed lateral motion controller for yaw rate tracking is intended to improve vehicle cornering agility. In this paper, we develop a model-based controller with a feedforward control term to accomplish this. In particular, a change in tire cornering stiffness according to the size of the tire slip angle is reflected to improve control accuracy. Finally, the Weighted Least Square (WLS) allocation method optimally distributes the IWM torque to each wheel. Simulation studies confirm that some evaluation factors are improved in terms of cornering performance compared to conventional control algorithms. Full article

The next generation of wireless systems has benefits in terms of spectrum and energy inefficiencies by exploiting two promising techniques including Non-Orthogonal Multiple Access (NOMA) and Reconfigurable Intelligent Surfaces (RIS). The scenario of two legitimate users existing together with an eavesdropper is worth examining in terms of secure matter while enabling machine learning tools at the base station for expected improvement. The base station deals with a highly complicated algorithm to adjust parameters against the attack of eavesdroppers and to better improve the secure performance of mobile users. This paper suggests a better solution to allow the base station to predict performance at destinations to adjust necessary parameters such as power allocation coefficients properly. To this end, we propose a deep neural network (DNN)-based approach which also leverages the benefits of aerial RIS to achieve predictable performance and significant secure performance improvement could be enhanced. We first derive the formulations for security outage probability (SOP) in closed-form expressions and analyze the strictly positive secrecy capacity (SPSC), which are crucial performance metrics to determine how the systems are against the existence of eavesdroppers. Such eavesdroppers intend to overhear signal transmission dedicated to intended users and incur degraded system performance. The numerical simulations are expected to evaluate how the machine learning tool works with the traditional computation of system performance metrics which is able to be verified by comparing with the Monte-Carlo method. Our numerical simulations demonstrate that the design of a higher number of meta-surface elements at the RIS, as well as a higher signal-to-noise ratio (SNR) levels at the base station, are key parameters to achieving improved security performance for users. For detailed guidelines of the RIS-NOMA aided system, we provide a table of parameters samples resulting in secure performance as expected. Full article

With the increasing importance of reduced-energy wireless communication in 5G networks, maximizing the energy efficiency of device-to-device (D2D) communication has gained a lot of research interest. But so far, the methods have only been able to address the single-cell scenario. This paper proposes an algorithm to improve the non-convex problem in underlaid D2D communication for multiple band scenarios. In 5G wireless communications, device to device is one of the methods used for achieving better energy efficiency. It also reduces the throughput latency. The optimization problem is formulated with a derivative algorithm and proposed modified derivative algorithm. Both the algorithms are compared, and this comparison shows that the modified derivative algorithm is more efficient than the derivative algorithm. Full article

Identifying the type of vehicle on the road is a challenging task, especially in the natural environment with all its complexities, such that the traditional architecture for object detection requires an excessively large amount of computation. Such lightweight networks as MobileNet are fast but cannot satisfy the performance-related requirements of this task. Improving the detection-related performance of small networks is, thus, an outstanding challenge. In this paper, we use YOLOv5s as the backbone network to propose a large-scale convolutional fusion module called the ghost cross-stage partial network (G_CSP), which can integrate large-scale information from different feature maps to identify vehicles on the road. We use the convolutional triplet attention network (C_TA) module to extract attention-based information from different dimensions. We also optimize the original spatial pyramid pooling fast (SPPF) module and use the dilated convolution to increase the capability of the network to extract information. The optimized module is called the DSPPF. The results of extensive experiments on the bdd100K, VOC2012 + 2007, and VOC2019 datasets showed that the improved YOLOv5s network performs well and can be used on mobile devices in real time. Full article

The conventional machine learning-based method for the prediction of microblogs’ reposting number mainly focuses on the extraction and representation of static features of the source microblogs such as user attributes and content attributes, without taking into account the problem that the microblog propagation network is dynamic. Moreover, it neglects dynamic features such as the change of the spatial and temporal background in the process of microblog propagation, leading to the inaccurate description of microblog features, which reduces the performance of prediction. In this paper, we contribute to the study on microblog propagation trends, and propose a new microblog feature presentation and time-dependent prediction method based on group features, using a reposting number which reflects the scale of microblog reposting to quantitatively describe the spreading effect and trends of the microblog. We extract some dynamic features created in the process of microblog propagation and development, and incorporate them with some traditional static features as group features to make a more accurate presentation of microblog features than a traditional machine learning-based research. Subsequently, based on the group features, we construct a time-dependent model with the LSTM network for further learning its hidden features and temporal features, and eventually carry out the prediction of microblog propagation trends. Experimental results show that our approach has better performance than the state-of-the-art methods. Full article

Although Scratch is the most widely used block-based educational programming language, it is not easy for students to create various types of Scratch programs based on real-life data because it does not provide web scraping capabilities. In this paper, we present novel Scratch blocks for web scraping. Using these blocks, students can not only scrape the contents of HTML elements in a web page by using CSS selectors but also automate their keyboard and mouse in a number of ways, such as by using XPaths, the coordinates of the mouse, input strings, keys, or hot keys. We also present file access blocks that allow students to easily store and retrieve the scraped data in the form of key–value pairs. We conducted two lectures for a total of 15 primary/secondary school (K-12) teachers, allowing them to make ten web scraping example applications. As a result of a survey of the teachers, the proposed web scraping blocks achieved high scores for all evaluation measures. Full article

Automatic classification of benign and malignant breast ultrasound images is an important and challenging task to improve the efficiency and accuracy of clinical diagnosis of breast tumors and reduce the rate of missed and misdiagnosis. The task often requires a large amount of data to train. However, it is difficult to obtain medical images, which contradicts the large amount of data needed to obtain good diagnostic models for training. In this paper, a novel classification model for the classification of breast tumors is proposed to improve the performance of diagnosis models trained by small datasets. The method integrates three features from medical features extracted from segmented images, features selected from the pre-trained ResNet101 output by principal component analysis (PCA), and texture features. Among the medical features that are used to train the naive Bayes (NB) classifier, and the PCA-selected features are used to train the support vector machine (SVM) classifier. Subsequently, the final results of boosting are obtained by weighting the classifiers. A five-fold cross-validation experiment yields an average accuracy of 89.17%, an average precision of 90.00%, and an average AUC value of 0.95. According to the experimental results, the proposed method has better classification accuracy compared to the accuracy obtained by other models trained on only small datasets. This approach can serve as a reliable second opinion for radiologists, and it can also provide useful advice for junior radiologists who do not have sufficient clinical experience. Full article

In order to improve the fault diagnosis accuracy of bearings, an intelligent fault diagnosis method based on Variational Mode Decomposition (VMD), Composite Multi-scale Dispersion Entropy (CMDE), and Deep Belief Network (DBN) with Particle Swarm Optimization (PSO) algorithm—namely VMD-CMDE-PSO-DBN—is proposed in this paper. The number of modal components decomposed by VMD is determined by the observation center frequency, reconstructed according to the kurtosis, and the composite multi-scale dispersion entropy of the reconstructed signal is calculated to form the training samples and test samples of pattern recognition. Considering that the artificial setting of DBN node parameters cannot achieve the best recognition rate, PSO is used to optimize the parameters of DBN model, and the optimized DBN model is used to identify faults. Through experimental comparison and analysis, we propose that the VMD-CMDE-PSO-DBN method has certain application value in intelligent fault diagnosis. Full article

The received signal strength (RSS) based Wi-Fi fingerprinting method is one of the most potential and easily deployed approaches for a reliable indoor positioning system. However, due to the labor intensive and time-consuming radio map construction process, interpolation is often incorporated. To ensure the interpolated radio map is robust against environmental noise and RSS fluctuations, we propose two novel interpolation methods, termed as DimRed and DimRedClust, for an improved radio map construction. The former performs dimensionality reduction prior to the interpolation while the latter employs both the dimensionality reduction and clustering before interpolating the radio map. For dimensionality reduction, principal component analysis (PCA) or truncated singular value decomposition (TSVD) is adopted to profoundly extract essential features from the RSS data while the K-means algorithm is used to partition the reference points (RPs) into several clusters. Subsequently, the RSS for all virtual points are interpolated via inverse distance weighting (IDW). Numerical results based on the real-world multi-floor multi-building dataset confirm the supremacy of the proposed schemes over the baseline IDW interpolation. Compared to the baseline IDW, the proposed PCA-K-means-IDW, TSVD-K-means-IDW, PCA-IDW, and TSVD-IDW could attain a performance gain in terms of average positioning error of up to 30.17%, 30.93%, 19.33%, and 21.61%, respectively. Full article

As an important component of the movement speed adjustment components of the executive elements in a hydraulic system, the speed-control valve should have good dynamic response ability and dynamic and static flow stability. In the working state, due to the fluctuation of oil supply pressure, load variation, valve spool movement, force variation, etc., the pressure difference between the inlet and outlet of the speed-regulation valve is prone to a step response. Due to the nonlinear relationship between the flow and pressure, the phenomenon of instantaneous fluctuations in the controlled flow occurs, which in turn leads to the unstable phenomenon of the speed of the execution element. Based on the working principle of the internal core components of a speed-control valve with a pressure compensation function, this study establishes a dynamic mathematical model of the valve, which is analyzed using AMEsim software. Aiming at the key problem of sudden changes in the differential pressure steps, the characteristics of a speed-regulation valve are analyzed in relation to the differential pressure increment, spring stiffness, spring preload, and initial opening of the pressure compensator and its viscous damping coefficient. The influence of the above five variables on the flow fluctuation is studied and a reasonable parameter adjustment range is given. The simulation and analysis results provide strong theoretical support for the performance and parameter optimization configuration of the speed-regulation valve. Full article

Malware has recently grown exponentially in recent years and poses a serious threat to individual users, corporations, banks, and government agencies. This can be seen from the growth of Advanced Persistent Threats (APTs) that make use of advance and sophisticated malware. With the wide availability of computer-automated tools such as constructors, email flooders, and spoofers. Thus, it is now easy for users who are not technically inclined to create variations in existing malware. Researchers have developed various defense techniques in response to these threats, such as static and dynamic malware analyses. These techniques are ineffective at detecting new malware in the main memory of the computer and otherwise require considerable effort and domain-specific expertise. Moreover, recent techniques of malware detection require a long time for training and occupy a large amount of memory due to their reliance on multiple factors. In this paper, we propose a computer vision-based technique for detecting malware that resides in the main computer memory in which our technique is faster or memory efficient. It works by taking portable executables in a virtual environment to extract memory dump files from the volatile memory and transform them into a particular image format. The computer vision-based contrast-limited adaptive histogram equalization and the wavelet transform are used to improve the contrast of neighboring pixel and to reduce the entropy. We then use the support vector machine, random forest, decision tree, and XGBOOST machine learning classifiers to train the model on the transformed images with dimensions of 112 × 112 and 56 × 56. The proposed technique was able to detect and classify malware with an accuracy rate of 97.01%. Its precision, recall, and F1-score were 97.36%, 95.65%, and 96.36%, respectively. Our finding shows that our technique in preparing dataset with more efficient features to be trained by the Machine Learning classifiers has resulted in significant performance in terms of accuracy, precision, recall, F1-score, speed and memory consumption. The performance has superseded most of the existing techniques in its unique approach. Full article

In compressed sensing (CS), one seeks to down-sample some high-dimensional signals and recover them accurately by exploiting the sparsity of the signals. However, the traditional sparsity assumption cannot be directly satisfied in most practical applications. Fortunately, many signals-of-interest do at least exhibit a low-complexity representation with respect to a certain transformation. Particularly, total variation (TV) minimization is a notable example when the transformation operator is a difference matrix. Presently, many theoretical properties of total variation have not been completely explored, e.g., how to estimate the precise location of phase transitions and their rigorous understanding is still in its infancy. So far, the performance and robustness of the existing algorithm for phase transition prediction of TV model are not satisfactory. In this paper, we design a new approximate message passing algorithm to solve the above problems, called total variation vector approximate message passing (TV-VAMP) algorithm. To be specific, we first consider the problem from the Bayesian perspective, and formulate it as an optimization problem. Then, the vector factor graph for the TV model is constructed based on the formulized problem. Finally, the TV-VAMP algorithm is derived according to the idea of probabilistic inference in machine learning. Compared with the existing algorithm, our algorithm can be applied to a wider range of measurements distributions, including the non-zero-mean Gaussian distribution measurements matrix and ill-conditioned measurements matrix. Furthermore, in experiments with various settings, including different measurement distribution matrices, signal gradient sparsity, and measurement times, the proposed algorithm can almost reach the target mean squared error (−60 dB) with fewer iterations and better fit the empirical phase transition curve. Full article

As an efficient information processing method, reservoir computing (RC) is essential to artificial neural networks (ANNs). Via the Santa Fe time series prediction task, we numerically investigated the effect of the mismatch of some critical parameters on the prediction performance of the RC based on two mutually delay-coupled semiconductor lasers (SLs) with optical injection. The results show that better prediction performance can be realized by setting appropriate parameter mismatch scenarios. Especially for the situation with large prediction errors encountered in the RC with identical laser parameters, a suitable parameter mismatch setting can achieve computing performance improvement of an order of magnitude. Our research is instructive for the hardware implementation of laser-based RC, where the parameter mismatch is unavoidable. Full article

The microwave-driven plasma light source has the feature of the full spectrum, similar to sunlight, and has broad application prospects. Startups of plasma lamps are very difficult since the plasma density and the resonator impedance are changing constantly with the warm-up process and the RF driver can hardly couple the power to the plasma efficiently. This paper presents a startup control method for plasma lamps by using an integrated fractional-N phase-locked loop (PLL) as the RF signal source. An RF control module including a 21 dBm RF source with a band of 430–460 MHz and detection circuits for the RF driver is developed, and the startup process of the plasma lamp is further studied by analyzing the return loss of the resonator under the plasma load. According to the test result, the maximum frequency deviation of the RF control module is 3.3 kHz over the working temperature range of −40–80 °C. The designed RF control module is used to drive a high-power amplifier to start the plasma lamp automatically. It takes 79 s to achieve the stable arc operation from the gas breakdown. The return loss of the resonator is −26 dB, with an incident power of 171 W and reflected power of 418 mW, indicating that the RF driver and the plasma achieve good coupling. Compared with continuous wave, the luminous flux of the lamp powered by RF pulse improves by 18% under the same electric power. This startup control method has stable performance, small temperature drift, and an effective control function for plasma lamps. Full article

Enhanced mobile broadband (eMBB) and Ultra-Reliable and Low-Latency Communications (URLLC) are the two main services in the fifth-generation mobile network. URLLC services have stringent latency and reliability requirements, while eMBB services are designed to provide extremely high data rates for content delivery. The differentiated Quality of Service (QoS) requirements of the two services make their coexistence on the same bandwidth resources a challenging issue. To meet this challenge, we introduce reconfigurable intelligent surface (RIS) technology to assist in solving the resource allocation problem. The problem is formulated as two optimization problems. For the eMBB allocation problem, we jointly optimize the resource block allocation, power allocation, and RIS phase shift matrix and propose a two-stage alternating iterative algorithm to maximize the total traversal capacity of eMBB users. For the URLLC allocation problem, we maximize the URLLC packet reception rate and minimize the amount of eMBB rate loss while ensuring the quality of service for eMBB and URLLC. A heuristic URLLC allocation algorithm based on pre-configured RIS is proposed. Simulation results show that the proposed algorithm can meet the strict delay requirements of URLLC even when the URLLC packet reception rate is nearly 95.5%. Meanwhile, the total loss rate of eMBB service caused by the reuse of URLLC service is less than 6%. Thus, the applicability of the proposed solution to the coexistence problem is demonstrated. Full article