Electronics

The success of businesses is now mostly dependent on e-learning methods as these methods are a rapidly growing innovative technology. Blockchain technology has also been considered to have the ability to change businesses. Therefore, this research aims to explore the direct influence of e-learning on the effectiveness of privacy and security in electronics manufacturing. This study also examines the considerable mediating role of the adoption of blockchain technology between e-learning and privacy and security. Furthermore, the current research investigates how digital orientation moderates the association between e-learning and privacy and security. For the collection of data, the cross-sectional research design and random sampling technique were used, and data were gathered from employees of electronics manufacturing firms in Pakistan through questionnaires. The working response rate of the study was 70%. The findings proved that e-learning plays a considerable role in boosting the privacy and security of electronics manufacturers. The results also demonstrate that the adoption of blockchain technology mediates and digital orientation moderates the link between e-learning and privacy and security. This study adds to the better understanding of management by presenting the significant role of e-learning and blockchain technology in improving the efficiency of privacy and security for electronics manufacturing firms. Full article

The demand for eye-safe 1550 nm pulsed semiconductor laser-emission modules is increasing in the field of active laser detection, owing to their long range and high precision. The high power and narrow pulse of these modules can significantly improve the distance and accuracy of active-laser detection. Here, we propose an equivalent circuit model of a multiquantum-well laser based on the structure of a laser device. We developed a design method for 1550 nm pulsed semiconductor laser-emission modules according to the equivalent circuit model of an InGaAlAs laser. In this method, the module design was divided into laser chip and laser-driver levels for optimization and simulation. At the chip level, a high-output power laser chip with optimal cavity length and optical facet coating coefficients was obtained. At the laser-driver level, the model was applied to a drive circuit to provide direct narrow optical pulses. Finally, a laser-emission module was fabricated based on the optimal design results. In addition to the power-current features of the actual laser, the critical voltage of the emission module and laser pulses were tested. By comparing the test and simulation results, the effectiveness of the proposed method was confirmed. Full article

As technology continues to advance, virtual reality (VR) video services are able to provide an increasingly realistic video experience. VR applications are limited, since the creation of an immersive experience requires processing and delivery of incredibly huge amounts of data. A potential technique to decrease the operation time for VR, as well as its energy use, is mobile edge computing (MEC). In this study, we develop a VR network in which several MEC servers can supply field-of-view (FOV) files to a VR device in order to satisfy the transmission requirements of VR video service and improve the quality of the experience. In this way, the projection process from 2D FOV to 3D FOV and the cached data is possible on an MEC server or a VR device. A cooperative computational offloading and caching strategy is developed as a decision matrix to reduce transmission requirements based on the service time constraint requirement. The VR video service mechanism is examined through the decision matrix. The trade-off between communication, caching, and computation (3C trade-off) is further implemented by means of a closed equation for the decision matrix. Results from simulations show that the suggested technique can perform close to optimally compared to alternative opposing methods. Full article

In the field of software engineering, applying language models to the token sequence of source code is the state-of-the-art approach to building a code recommendation system. When applying language models to source code, it is difficult for state-of-the-art language models to deal with the data inconsistency problem, which is caused by the free naming conventions of source code. It is common for user-defined variables or methods with similar semantics in source code, to have different names in different projects. This means that a model trained on one project may encounter many words the model has never seen before during another project. Those freely named variables or functions in the code will bring difficulties to the processes of training and prediction and cause a data inconsistency problem between projects. However, we discover that the syntax tree of source code has hierarchical structures. This code structure has strong regularity in different projects and can be used to combat data inconsistency. In this paper, we propose a novel Hierarchical Language Model (HLM) to improve the robustness of the state-of-the-art recurrent language model, in order to be able to deal with data inconsistency between training and testing. The newly proposed HLM takes the hierarchical structure of the code tree into consideration to predict code. The proposed HLM method generates the embedding for each sub-tree according to hierarchies and collects the embedding of each sub-tree in context, to predict the next piece of code. The experiments on inner-project and cross-project datasets indicate that the newly proposed HLM method performs better than the state-of-the-art recurrent language model in dealing with the data inconsistency between training and testing, and achieves an average improvement in prediction accuracy of 11.2%. Full article

In order to optimize space laser communication networking, the 2:1 transmissions for all reflectors on a multi-address transceiver can be removed. However, without the 2:1 transmissions, the reflectors exhibit doubled optical coupling effect in rotation, which reduces the numerical calculation precision of the attitudes of the reflectors’ lines-of-sight (LOS). In the present study, using Snell’s law of reflection and Euler’s theorem, a mathematical model for the attitudes of the LOS of multiple reflectors without 2:1 transmissions was established, and a method was proposed for an indirect numerical calculation of the attitudes of the LOS by creating a Snell transformation matrix. This method eliminates the influence of the doubled optical coupling effect on the numerical solution of the line-of-sight attitudes of multi-reflectors. Compared to the direct numerical calculation method using the angular velocity, the calculation precisions achieved using the proposed method exhibited improvements of 3, 5, and 3 orders of magnitude in the three conical motions, respectively. The indirect calculation method and the numerical calculation model proposed in the present study, therefore, provide a theoretical basis for applying reflectors without 2:1 transmissions to multi-address transceivers. Full article

This paper assessed the small-signal stability performance of a multi-converter-based direct current microgrid (DCMG). The oscillation and potential interactions between critical modes are evaluated. First, the complete analytical model of the DCMG is developed with the converter and associated controllers. Three methodologies, impedance scanning, eigenvalue analysis, and time-domain simulation, along with the fast Fourier transform (FFT) analysis, have been used to comprehensively investigate the oscillations and interactions. The simulation results show inherent weak modes, with a wide range of oscillations in the studied DCMG, which may destabilize the system under disturbances. Based on the sensitivity analysis, controller gains and DC-link capacitance are identified as the most critical parameters and substantially influence the weak modes leading to oscillations, interactions, and resonance. Finally, the performance of the various control synthesis methods is compared. This examination would help the researchers, planning, and design engineers to design and stably operate a multi converter-based DC microgrid. Full article

Eectromechanical systems built by Simulink or Ptolemy have been widely used in industry fields, such as autonomous systems and robotics. It is an urgent need to ensure the safety and security of those systems. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art testing tools employ model-checking techniques or search-based methods to generate test cases. Traditional search-based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model-checking techniques such as symbolic execution have limited performance when dealing with nonlinear elements and complex loops. Recently, coverage guided fuzzing technologies are known to be effective for test case generation, due to their high efficiency and impressive effects over complex branches of loops. In this paper, we apply fuzzing methods to improve model testing and demonstrate the effectiveness. The fuzzing methods aim to cover more program branches by mutating valuable seeds. Inspired by this feature, we propose a novel integration technology SPsCGF, which leverages bounded model checking for symbolic execution to generate test cases as initial seeds and then conduct fuzzing based upon these worthy seeds. Over the evaluated benchmarks which consist of industrial cases, SPsCGF could achieve 8% to 38% higher model coverage and 3x-10x time efficiency compared with the state-of-the-art works. Full article

Cloud desktop represents an outstanding product in the domain of cloud computing, which refers to the desktop cloud, desktop virtualization and virtual desktop. Cloud desktop explores the virtualization technology to concentrate computing resources, which delivers traditional computer desktops (operating system interfaces) or applications deployed in the pooled computing resources to polymorphic terminals through the Internet. As a distinctive product of cloud computing, cloud desktop has been a hot topic since its inception. Today, the virtualized resource pool of cloud computing achieves the elastic and dynamic expansion of resources, which brings the desktop system from an independent personal computer to a centralized physical server. Consequently, the great improvement in basic network conditions makes it possible to transmit high-quality desktops over the network. There are two key factors for cloud desktops, one of which is the virtualization technology on the server side and the other one, which is the transmission protocol of cloud desktops. The cloud desktop transmission protocol mainly completes the transmission of graphics, images and audio from the server to the user terminal. The transmission of input information from the user terminal, called DaaS (Desktop-as-a-Service), includes the input information of peripherals such as a mouse, keyboard, printer and so on. The efficiency of the transmission protocol determines the basic delivery capability of the cloud desktop, while the bearer protocol and graphics and image processing methods in the transmission protocol determine the interactive experience of the cloud desktop. Different protocols have their characteristics and applicable space. This paper spies on application and transport layer communication protocols to meet DaaS communication requirements. This paper describes the internal mechanism of various transport protocols applicable to a cloud desktop from the principle level and points out the pros and cons and the current application environment. It can be seen that these methods solve the transmission efficiency of burst traffic, improve user experience and reduce bandwidth consumption, which are the development direction of transmission protocols. Full article

Depthwise separable convolution (DSC) significantly reduces parameter and floating operations with an acceptable loss of accuracy and has been widely used in various lightweight convolutional neural network (CNN) models. In practical applications, however, DSC accelerators based on graphics processing units (GPUs) cannot fully exploit the performance of DSC and are unsuitable for mobile application scenarios. Moreover, low resource utilization due to idle engines is a common problem in DSC accelerator design. In this paper, a high-performance DSC hardware accelerator based on field-programmable gate arrays (FPGAs) is proposed. A highly reusable and scalable multiplication and accumulation engine is proposed to improve the utilization of computational resources. An efficient convolution algorithm is proposed for depthwise convolution (DWC) and pointwise convolution (PWC), respectively, to reduce the on-chip memory occupancy. Meanwhile, the proposed convolution algorithms achieve partial fusion between PWC and DWC, and improve the off-chip memory access efficiency. To maximise bandwidth utilization and reduce latency when reading feature maps, an address mapping method for off-chip accesses is proposed. The performance of the proposed accelerator is demonstrated by implementing MobileNetV2 on an Intel Arria 10 GX660 FPGA by using Verilog HDL. The experimental results show that the proposed DSC accelerator achieves a performance of 205.1 FPS, 128.8 GFLOPS, and 0.24 GOPS/DSP for input images of size $224\times 224\times 3$. Full article

In recent years, commercial and research interest in service robots working in everyday environments has grown. These devices are expected to move autonomously in crowded environments, maximizing not only movement efficiency and safety parameters, but also social acceptability. Extending traditional path planning modules with socially aware criteria, while maintaining fast algorithms capable of reacting to human behavior without causing discomfort, can be a complex challenge. Solving this challenge has involved the development of proactive systems that take into account cooperation (and not only interaction) with the people around them, the determined incorporation of approaches based on Deep Learning, or the recent fusion with skills coming from the field of human–robot interaction (speech, touch). This review analyzes approaches to socially aware navigation and classifies them according to the strategies followed by the robot to manage interaction (or cooperation) with humans. Full article

Time-to-failure (TTF) prediction of bearings is vital to the prognostic and health management of rotating machines. Owing to the shifty degradation trends (DTs) of bearings, it is still difficult to obtain accurate TTF prognostic results. To solve this problem, this paper proposes an online, continuously updated TTF prognostic method based on health indicator (HI) similarity analysis and DT detection. First, multiple degradation features are extracted and fused to construct principal component HI by using dynamic principal component analysis. Next, exponential degradation models are fitted using the HI values for future state prediction. By regarding several HI values as a tested segment, the DT is detected by analyzing the similarity of the tested segment and the fitted curve. Finally, TTF is predicted by extrapolating the DT to hit the estimated failure threshold. Two case studies based on public bearing datasets demonstrate the superiority of the proposed approach over state-of-the-art methods. Full article

Siamese network-based trackers have developed rapidly in the field of visual object tracking recently. Many Siamese network-based trackers currently in use rely on result fusion to combine the classification result map and regression result map. However, these result maps are obtained from the multi-level feature map and are independent of each other. It is inappropriate and flawed to use result fusion. Additionally, classification module and regression module are independent of each other, which leads to feature misalignment. In this paper, we propose a feature-fusion approach that involves fusing similarity response maps using a novel scale attention mechanism and subsequently decoding the features. To reduce the feature misalignment and produce more precise tracking results, we suggest using Classification Supervised Regression Loss (CSRL), to train the model. Experiments conducted on three challenging benchmark datasets show that this method outperforms current models in terms of both performance and efficiency, running at 40 fps. Full article

In this paper, a compact-transmission-line (TL) decoupling and matching network (DMN) for three-element circular arrays is presented. As a result of the miniaturization of wireless terminals, the antenna spacing is too close, leading to large couplings and deteriorating system performance. The DMN consists of an impedance transformation section and a star-shaped neutralization section which eliminates couplings between antennas while occupying a smaller area. The impedance transformation section converts the odd and even mode conductances of the antenna to the impedance of the feeding line, and the neutralization section eliminates the odd and even mode susceptances to complete the decoupling and matching of the antenna. The star-shaped circuit utilizes the area surrounded by the antennas in a more efficient manner than traditional triangle circuits. This facilitates the folding of the TL and the miniaturization of the circuit. A design formula is given for each module of the circuit after it has been analyzed analytically. A decoupling example is simulated and manufactured with the diameter of the area occupied by the DMN less than a quarter wavelength. At the working frequency, the port isolation is increased from 7.6 dB to 33.5 dB. The ECC between ports is reduced from 0.11 to 0.011, which validates the method proposed. Full article

This study proposes a new integrated approach to the motion control of autonomous vehicles, which differs from the conventional method of treating planning and tracking tasks as separate or hierarchical components. By means of the proposed approach we can reduce the side effects on the performance of autonomous vehicles under challenging driving circumstances. To this end, our approach processes both of the aforementioned tasks asynchronously and simultaneously utilizes a multi-threaded architecture to enhance control performance. Meanwhile, the behavior planning feature is integrated into the path-tracking module. Then, a linear parameter-varying model predictive control is deployed for trajectory tracking of autonomous vehicles and compared with the linear model predictive control method. Finally, the control performance of the proposed approach was evaluated through simulation trials on urban roads with placed obstacles. The outcomes revealed that the suggested framework satisfies the processing rate and high-precision criteria, while safely avoiding obstacles, indicating that it is a promising control strategy for real-world applications. Full article

This paper addresses the problem regarding the optimal placement and sizing of distribution static synchronous compensators (D-STATCOMs) in electrical distribution networks via a stochastic mixed-integer convex (SMIC) model in the complex domain. The proposed model employs a convexification technique based on the relaxation of hyperbolic constraints, transforming the nonlinear mixed-integer programming model into a convex one. The stochastic nature of renewable energy and demand is taken into account in multiple scenarios with three different levels of generation and demand. The proposed SMIC model adds the power transfer losses of the D-STATOMs in order to size them adequately. Two objectives are contemplated in the model with the aim of minimizing the annual installation and operating costs, which makes it multi-objective. Three simulation cases demonstrate the effectiveness of the stochastic convex model compared to three solvers in the General Algebraic Modeling System. The results show that the proposed model achieves a global optimum, reducing the annual operating costs by 29.25, 60.89, and 52.54% for the modified IEEE 33-, 69-, and 85-bus test systems, respectively. Full article

Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm. Full article

In 5G scenarios, the dynamic resource allocation of network slicing is crucial for quality-of-service (QoS) guaranteed under fluctuating traffic demands in rapidly changing communication environments. In this paper, we propose a novel QoS guaranteed joint resource allocation framework for NR with supplementary uplink (SUL) called QGJRA-SUL, where three parameters of SUL admission, TDD pattern, and band slicing scheme are jointly optimized. The framework is driven by a well-designed deep reinforcement learning agent. By combining the activation functions tanh and softmax, the agent can jointly optimize three parameters at the same time. Under the original problem of QoS satisfaction rate maximization, we introduce the load unbalance degree of slices into the reward function as a penalty term. The simulation results show that the framework can guarantee the QoS satisfaction rate well and balance the load of slices. QGJRA-SUL can accommodate 15% more user equipments (UEs) with the same QoS satisfaction rate than that of a traditional single-band solution without SUL, and achieve a 73% increase in the performance of load balancing than that without a load balancing mechanism near the full load. Full article

Infrared small target detection, especially under low SCR conditions and complex backgrounds, is still a challenging research task. Considering the scale change caused by small targets rapidly moving, in this paper, a nonparametric regression-based multi-scale gradient correlation filtering (MGCF) detection method is proposed. First, a nonparametric regression method is applied to calculate the gradient of each point. Then, based on the unique gradient characteristics of small targets, a multi-scale gradient correlation (MGC) template is designed to distinguish small targets from clutter. After that, a multi-scale gradient correlation filtering method is proposed to enhance the target intensity and suppress clutter. At last, based on the obtained filtering response, an adaptive threshold segmentation method is adopted to extract real small targets. Experimental results demonstrate that the proposed method can fully improve the signal-to-clutter ratio (SCR) of small targets under different complex backgrounds. Moreover, compared with other baseline methods, the proposed method exhibits excellent detection performance. Full article

The essence of font style transfer is to move the style features of an image into a font while maintaining the font’s glyph structure. At present, generative adversarial networks based on convolutional neural networks play an important role in font style generation. However, traditional convolutional neural networks that recognize font images suffer from poor adaptability to unknown image changes, weak generalization abilities, and poor texture feature extractions. When the glyph structure is very complex, stylized font images cannot be effectively recognized. In this paper, a deep deformable style transfer network is proposed for artistic font style transfer, which can adjust the degree of font deformation according to the style and realize the multiscale artistic style transfer of text. The new model consists of a sketch module for learning glyph mapping, a glyph module for learning style features, and a transfer module for a fusion of style textures. In the glyph module, the Deform-Resblock encoder is designed to extract glyph features, in which a deformable convolution is introduced and the size of the residual module is changed to achieve a fusion of feature information at different scales, preserve the font structure better, and enhance the controllability of text deformation. Therefore, our network has greater control over text, processes image feature information better, and can produce more exquisite artistic fonts. Full article