Electronics

In this paper, a solid-state plasma (SSP) metamaterial for an analog of the electromagnetically induced transparency phenomenon is designed and investigated. This electromagnetically induced transparency metamaterial has the ability to interact with both incident electric and magnetic fields, and its low-loss characteristics, slow-wave effect, band reconfigurability, and polarization-insensitive characteristics are researched and explored. According to the tunable SSP, we have successfully implemented two modes of operation (mode 1 and mode 2) by whether the SSP resonance unit is excited or not. Low-loss characteristics and polarization-insensitive properties are achieved by rotating the split-ring resonator (SRR) by 180° in the plane and rotating the overall plane framework 90° to form a three-dimensional structure. After that, the maximum group delay of 261.51 ps and 785.09 ps as well as the delay bandwidth product of 17.51 and 62.96 at mode 1 and mode 2, respectively, are discussed respectively. This indicates a good slow-wave effect as well as a high efficiency of communication devices. After all, in mode 1, a transmission peak at 0.541 THz is observed for a transmission ratio of 92.05%; and in mode 2, a transmission peak at 0.741 THz is observed for a transmission ratio of 93.01%, resulting in a bandwidth shift of 0.2 THz. Due to the uniqueness of the developed metamaterial, it holds potential for a wide range of applications in slow-wave devices, modulators, sensors, and communications equipment. Full article

With the combined call for increased network throughput and security comes the need for high-bandwidth, unconditionally secure systems. Through the combination of true random number generators (TRNGs) for unique seed values, and four-dimensional Lorenz hyperchaotic systems implemented on a Stratix 10 Intel FPGA, we are able to implement 60 MB/s encryption/decryption schemes with 0% data loss on an unconditionally secure system with the NIST standard using less than 400 mW. Further, the TRNG implementation allows for unique encryption outputs for similar images while still enabling proper decryption. Histogram and adjacent pixel analysis on sample images demonstrate that without the key, it is not possible to extract the plain text from the encrypted image. This encryption scheme was implemented via PCIe for testing and analysis. Full article

This paper presents a system applied to unmanned aerial vehicles based on Robot Operating Systems (ROSs). The study addresses the challenges of efficient object detection and real-time target tracking for unmanned aerial vehicles. The system utilizes a pruned YOLOv4 architecture for fast object detection and the SiamMask model for continuous target tracking. A Proportional Integral Derivative (PID) module adjusts the flight attitude, enabling stable target tracking automatically in indoor and outdoor environments. The contributions of this work include exploring the feasibility of pruning existing models systematically to construct a real-time detection and tracking system for drone control with very limited computational resources. Experiments validate the system’s feasibility, demonstrating efficient object detection, accurate target tracking, and effective attitude control. This ROS-based system contributes to advancing UAV technology in real-world environments. Full article

Point cloud semantic segmentation is of utmost importance in practical applications. However, most existing methods have evolved to be incredibly intricate, leading to a rise in complexity that has made them increasingly impractical for real-world utilization. The escalating complexity of these methods has resulted in a deterioration in their efficiency and ease of implementation, making them less suitable for use in time-sensitive and resource-constrained environments. Towards this issue, we propose an efficient and lightweight segmentation method, able to achieve a remarkable performance in terms of both segmentation accuracy, training speed, and space consumption. Specifically, we first propose to adopt random sampling to replace the original one to obtain more efficiency. Moreover, a lightweight decoding module and an improved bilateral enhancement (BAE) module are developed to further improve the performance. The proposed method achieved a 73.6% and 60.7% mIoU on the S3DIS and Semantickitti datasets, respectively. In the future, the random sampling and the proposed BAE module can be adopted in a more concise and lightweight network to achieve faster and more-accurate point cloud segmentation. Full article

LiDAR has become a vital sensor for autonomous driving scene understanding. To meet the accuracy and speed of LiDAR point clouds semantic segmentation, an efficient model ACPNet is proposed in this paper. In the feature extraction stage, the backbone is constructed with asymmetric convolutions, so the skeleton of the square convolution kernel is enhanced, which leads to greater robustness to target rotation. Moreover, a contextual feature enhancement module is designed to extract richer contextual features. During training, global scaling and global translation are performed to enrich the diversity of datasets. Compared with the baseline network PolarNet, the mIoU of ACPNet on the SemanticKITTI, SemanticPOSS and nuScenes datasets are improved by 5.1%, 1.6% and 2.9%, respectively. Meanwhile, the speed of ACPNet is 14 FPS, which basically meets the real-time requirements in autonomous driving scenarios. The experimental results show that ACPNet significantly improves the performance of LiDAR point cloud semantic segmentation. Full article

The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. The proliferation of IoT devices has resulted in an unprecedented surge of data, presenting formidable challenges concerning efficient processing, meaningful analysis, and informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep-belief networks (DBNs), have demonstrated significant efficacy in mitigating these challenges by furnishing robust tools for learning and extraction of insights from vast and diverse IoT-generated data. This survey article offers a comprehensive and meticulous examination of recent scholarly endeavors encompassing the amalgamation of deep-learning techniques within the IoT landscape. Our scrutiny encompasses an extensive exploration of diverse deep-learning models, expounding on their architectures and applications within IoT domains, including but not limited to smart cities, healthcare informatics, and surveillance applications. We proffer insights into prospective research trajectories, discerning the exigency for innovative solutions that surmount extant limitations and intricacies in deploying deep-learning methodologies effectively within IoT frameworks. Full article

This research investigates the usefulness and efficacy of synthetic ruler images for the development of a deep learning-based ruler detection algorithm. Synthetic images offer a compelling alternative to real-world images as data sources in the development and advancement of computer vision systems. This research aims to answer whether using a synthetic dataset of ruler images is sufficient for training an effective ruler detector and to what extent such a detector could benefit from including synthetic images as a data source. The article presents the procedural method for generating synthetic ruler images, describes the methodology for evaluating the synthetic dataset using trained convolutional neural network (CNN)-based ruler detectors, and shares the compiled synthetic and real ruler image datasets. It was found that the synthetic dataset yielded superior results in training the ruler detectors compared with the real image dataset. The results support the utility of synthetic datasets as a viable and advantageous approach to training deep learning models, especially when real-world data collection presents significant logistical challenges. The evidence presented here strongly supports the idea that when carefully generated and used, synthetic data can effectively replace real images in the development of CNN-based detection systems. Full article

The Internet of Medical Things (IoMT) can automate breast tumor detection and classification with the potential of artificial intelligence. However, the leakage of sensitive data can cause harm to patients. To address this issue, this study proposed an intrauterine breast cancer diagnosis method, namely “Privacy-Embedded Lightweight and Efficient Automated (PLA)”, for IoMT, which represents an approach that combines privacy-preserving techniques, efficiency, and automation to achieve our goals. Firstly, our model is designed to achieve lightweight classification prediction and global information processing of breast cancer by utilizing an advanced IoMT-friendly ViT backbone. Secondly, PLA protects patients’ privacy by federated learning, taking the classification task of breast cancer as the main task and introducing the texture analysis task of breast cancer images as the auxiliary task to train the model. For our PLA framework, the classification accuracy is 0.953, the recall rate is 0.998 for the best, the F1 value is 0.969, the precision value is 0.988, and the classification time is 61.9 ms. The experimental results show that the PLA model performs better than all of the comparison methods in terms of accuracy, with an improvement of more than 0.5%. Furthermore, our proposed model demonstrates significant advantages over the comparison methods regarding time and memory. Full article

Varistors processed from mixtures of certain metal oxides (as additives to the main component, zinc oxide, ZnO), called MOVs, represent the devices most used for overvoltage protection and are integrated into the construction of high-performance surge arresters. The manufacturing process of these powerful electronic devices is crucial for their electronic performance. For manufacturing temperature-related studies, we used two seven-varistor experimental series: one based on two added oxides and the other on five ones. The main goal of these series was to identify the suitable sintering temperature in the case of each chemical composition from the point of view of assessing the most important electric/electronic behavioral parameters. A simple study considering mass losses after the sintering process was carried out in order to provide a brief reference for the manufacturing engineers. Before performing these studies, each varistor was sintered at a different temperature. In order to draw a general set of conclusions about the impact of the sintering pressure on the main electrical and electronic performances, a second activity involved producing two additional smaller varistors series with similar chemical compositions (two main oxides and five main oxides as additives) all processed at two different sintering pressures 4900 N/cm² and 9800 N/cm². The electrical/electronic parameters considered for the assessment are the main current–voltage characteristics, the non-linearity logarithmic coefficient, and the normal operational leakage current. All electrical/electronic behavioral tests were performed according to the IEC standards and regulations for both types of varistor devices (seven different temperatures and two pressure values). We concluded that a sintering temperature of 1300 °C and a pressure of 4900 N/cm² are optimal for both types of varistors (with two and five additives). Full article

Magnetic pulse welding (MPW) is a joining method that uses Lorentz force generated from an electromagnetic field. This method not only has the advantage of not causing thermal deformation of the material and no by-products compared to the method of joining by melting by heat but also enables the joining of dissimilar metals rather than the joining of the same metal. Joining dissimilar metals can reduce the weight of mechanical devices and apply them to various fields. Recent research on MPW has focused on the characteristics of bonding according to the material or structure of metal rather than on pulse power research that generates the main factor of operation. However, in the operation of MPW, a Lorentz force is generated by the induced current caused by the electromotive force created in the flyer tube and the external magnetic field in the actuator. Therefore, it is necessary to analyze and optimize the pulse power to improve reliability and to miniaturize the system to expand the MPW utilization range. In this paper, we analyzed MPW operation according to a section of the pulse power output waveform. A condition for obtaining the maximum current in the flyer tube was proposed, and a plateau-shaped waveform was derived as an ideal output waveform capable of maintaining the Lorentz force. Through analysis, the proposed pulse power device is designed as a pulse-forming network (PFN) that generates a plateau output waveform. The design specification is that the circuit of PFN (type E) is designed so that the output waveform is pulse width 10 (μs) and the maximum output current is 100 (kA), and it is verified by simulation. Full article

The constant growth of Internet of Things traffic is ensured by the ongoing evolution of the hierarchy of all hardware links of sensor networks. At the same time, the implementation of the Edge computing ideology regulates the complexity of the “first-mile” section (from the sensors array to the peripheral server). Here, the authors suggest paying attention to the growing share of massive traffic from target sensors in the total traffic of the sensors array. This circumstance makes it expedient to introduce an additional link to the peripheral server for summarizing massive traffic from target sensors. The authors present a sensor network end IoT device (SNEIoTD), implemented grounded on a reliable and cheap Raspberry Pi computing platform, as such a link. The introduction of this SNEIoTD makes it possible to reduce the probability of information loss from the critical infrastructure of a smart city and increase the flexibility of controlling the massive traffic of the first mile. In this context, the urgent task is the reliable control of information transfer from the SNEIoTD environment to a hub, which the authors formalize based on Transmission Control Protocol (TCP). This article proposes a mathematical model of the interaction of the main mechanisms of the TCP in the form of a queuing system. As part of this model, a semi-Markov process of an information transfer with a unified speed is selected and its stationary distribution is analytically formalized. A computationally efficient information technology for determining the TCP Window Size is formulated, taking into account the interaction of TCP mechanisms in the process of massive traffic control. Using the example of TCP Westwood+ protocol modification, it is shown that the results of the application of information technology permit increases in the stability of data transfer under the circumstances of increasing Round-Trip Times. Full article

Cyber–physical systems (CPS) integrate diverse elements developed by various vendors, often dispersed geographically, posing significant development challenges. This paper presents an improved version of our previously developed co-simulation interface based on the non-proprietary Distributed Co-Simulation Protocol (DCP) standard, now optimized for broader hardware platform compatibility. The core contributions include a demonstration of the interface’s hardware-agnostic capabilities and its straightforward adaptability across different platforms. Furthermore, we provide a comparative analysis of our interface against the original DCP. It is validated via various X-in-the-Loop simulations, reinforcing the interface’s versatility and applicability in diverse scenarios, such as distributed real-time executions, verification and validation processes, or Intellectual Property protection. Full article

The frequency synthesizer performs a simple function of generating a desired frequency by manipulating a reference frequency signal, but stable and precise frequency generation is essential for reliable operation in mechanical equipment such as communication, control, surveillance, medical, and commercial fields. Frequency synthesis, which is commonly used in various contexts, has been used in analog and digital methods or hybrid methods. Especially in the field of communication, a precise frequency synthesizer is required for each frequency band, from very low-frequency AF (audio frequency) to high-frequency microwaves. The purpose of this paper is to design and implement a highly reliable frequency synthesizer for application to a railway track circuit systems using AF frequency only with the logic circuit of an FPGA (field programmable gate array) without using a microprocessor. Therefore, the development trend of analog, digital, and hybrid frequency synthesizers is examined, and a method for precise frequency synthesizer generation on the basis of the digital method is suggested. In this paper, the generated frequency generated by applying the digital frequency synthesizer using the ultra-precision algorithm completed by many trials and errors shows the performance of generating the target frequency with an accuracy of more than 99.999% and a resolution of mHz, which is much higher than the resolution of 5 Hz in the previous study. This highly precise AF-class frequency synthesizer contributes greatly to the safe operation and operation of braking and signaling systems when used in transportation equipment such as railways and subways. Full article

The data exchange according to communication protocols used in automation is often based on registers (e.g., Modbus). Values of many variables can be sent in a single frame, provided that they are placed in adjacent registers. If the required registers are not adjacent, it may sometimes be advantageous to transmit more registers than required, along with redundant ones, to minimize the number of frames and the total transmission time. The article analyzes the possibilities of improving time parameters and determining the optimal grouping based on the arrangement of registers. Various existing optimization approaches such as mixed integer linear programming, constraint programming, and a tabu search are analyzed, and several new simple deterministic algorithms (greedy or heuristic rule-based) are proposed. The results obtained were confirmed experimentally. Full article

This paper presents a low-power, high-resolution reconfigurable hybrid ADC for bio-electrical signal processing. The proposed ADC contains a SAR ADC for the most significant bit (MSB) and a single-slope ADC for the least significant bit (LSB). To solve the issue of exponentially increasing sampling speed based on the resolution of the single-slope ADC, the SAR ADC is designed to be reconfigurable with a resolution of 8–10-bit, while the single-slope ADC is configured with a resolution of 4-bit. To achieve this resolution reconfiguration, the bit shifting method is proposed and implemented with reconfigurable SAR logic circuit and 4-bit single-slope digital ramp generator. Measurement results demonstrate the power consumption of 34.0 uW, which includes analog power of 23.8 uW and digital power of 10.2 uW, INL/DNL of ±3.5 LSB and −1.0/+2.5 LSB. The ENOB and FoM are measured to be 10.8 bits and 53 fJ/step, respectively. Full article

Sharding technology, which divides a network into multiple disjoint groups so that transactions can be processed in parallel, is applied to blockchain systems as a promising solution to improve Transactions Per Second (TPS). This paper considers the Optimal Blockchain Sharding (OBCS) problem as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL)-based methods, where the action space must be discretized to increase processability. However, the discretization degrades the quality of the solution since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and provide dynamic sharding strategies based on them. The Branching Dueling Q-Network Blockchain Sharding (BDQBS) algorithm is designed for discrete action spaces. Compared with traditional DRL algorithms, the BDQBS overcomes the drawbacks of high action space dimensions and difficulty in training neural networks. And it improves the performance of the blockchain system by 1.25 times. We also propose a sharding control algorithm based on the Parameterized Deep Q-Networks (P-DQN) algorithm, i.e., the Parameterized Deep Q-Networks Blockchain Sharding (P-DQNBS) algorithm, to efficiently handle the discrete–continuous hybrid action space without the scalability issues. Also, the method can effectively improve the TPS by up to $28\%$. Full article

Smart grids incorporate diverse power equipment used for energy optimization in intelligent cities. This equipment may use Internet of Things (IoT) devices and services in the future. To ensure stable operation of smart grids, cybersecurity of IoT is paramount. To this end, use of cryptographic security methods is prevalent in existing IoT. Non-cryptographic methods such as radio frequency fingerprinting (RFF) have been on the horizon for a few decades but are limited to academic research or military interest. RFF is a physical layer security feature that leverages hardware impairments in radios of IoT devices for classification and rogue device detection. The article discusses the potential of RFF in wireless communication of IoT devices to augment the cybersecurity of smart grids. The characteristics of a deep learning (DL)-aided RFF system are presented. Subsequently, a deployment framework of RFF for smart grids is presented with implementation and regulatory aspects. The article culminates with a discussion of existing challenges and potential research directions for maturation of RFF. Full article

The increasing demand for efficient and safe transportation systems has led to the development of autonomous vehicles and vehicle platooning. Truck platooning, in particular, offers numerous benefits, such as reduced fuel consumption, enhanced traffic flow, and increased safety. In this paper, we present a drone-based decentralized framework for truck platooning in highway monitoring scenarios. Our approach employs multiple drones, which communicate with the trucks and make real-time decisions on whether to form a platoon or not, leveraging Model Predictive Control (MPC) and Unscented Kalman Filter (UKF) for drone formation control. The proposed framework integrates a simple truck model in the existing drone-based simulation, addressing the truck dynamics and constraints for practical applicability. Simulation results demonstrate the effectiveness of our approach in maintaining the desired platoon formations while ensuring collision avoidance and adhering to the vehicle constraints. This innovative drone-based truck platooning system has the potential to significantly improve highway monitoring efficiency, traffic management, and safety. Our drone-based truck platooning system is primarily designed for implementation in highway monitoring and management scenarios, where its enhanced communication and real-time decision-making capabilities can significantly contribute to traffic efficiency and safety. Future work may focus on field trials to validate the system in real-world conditions and further refine the algorithms based on practical feedback and evolving vehicular technologies. Full article

In order to analyze the interference mechanisms of indirect lightning effects on a low-noise amplifier (LNA), a circuit model of the LNA was constructed based on the advanced design system 2020 (ADS 2020) software. Lightning pulse injection simulations were conducted to explore the influence of lightning pulses on the performance of the LNA. A pin injection test was performed to investigate the interference and damage threshold of the LNA. A protective circuit incorporating the transient voltage suppressor (TVS) and Darlington structure was designed through simulation, employing the ADS 2020 for the LNA. The research findings reveal that the interference threshold for the LNA is 60 V, while the damage threshold is determined to be 100 V. The protective circuit demonstrates a measured insertion loss of 0.1 dB, a response time of 1.5 ns, and a peak output voltage of 20 V. The research results indicate that the protective circuit can effectively reduce the impact of lightning’s indirect effects on the LNA. In the future, we will continue the design work of the protective circuit and proceed with physical fabrication and experimental validation. Full article