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This paper presents a laminated paper-based Planar Inverted F Antenna (PIFA) for wireless body area network (WBAN) applications. The major challenges like providing adequate thickness, uniformity of surface, and water wet ability associated with paper substrate have been transcended in this antenna design by laminating the substrate with a transparent sheet. The simulated and measured reflection coefficients of the fabricated antenna in free space and on human arm are remarkably good, validating the prototype’s utility. It can operate effectively in GSM, WMTS, GPS, DCS, ISM, WiMAX, HIPERLAN, WLAN, and UWB frequency bands with S11 less than -10 dB and the gain ranging from 0.94 to 9.53 dB (free space) and 1.03 to 6.07 dB (on-body). The electrical size of the proposed antenna is 0.16 λg x 0.12 λg at 0.7 GHz with a group delay below 2.3 ns over the desired frequency bands. The performance characteristics are also investigated on-body and it encompasses most of the desired frequency bands for the body area network. This paper also analyses the specific absorption rate (SAR) of the designed antenna by placing it on a tissue-layered human arm model, which is 0.64 Watt/kg, complying with the SAR safety guidelines.

The advent of digital technology has revolutionized numerous aspects of modern life, including the field of assessment and testing. However, paper tests, despite their seemingly archaic nature, continue to hold a prominent position in various assessment domains. The accessibility, familiarity, security, cost-effectiveness, and versatility of paper tests collectively contribute to their continued prominence. Hence, numerous educational institutions responsible for conducting examinations involving a substantial number of candidates continue to rely on paper tests. Consequently, there arises a demand for the possibility of automated assessment of these tests, aiming to alleviate the burden on teaching staff, enhance objectivity in evaluation, and expedite the delivery of test results. Therefore, diverse software systems have been developed, showcasing the capability to automatically score specific question types. Thus, it becomes imperative to categorize related question types systematically, thereby facilitating a preliminary classification based on the content and format of the questions. This classification serves the purpose of enabling effective comparison among existing software solutions. In this research paper, we present the implementation of such a software system using artificial intelligence techniques, progressively expanding its capabilities to evaluate increasingly complex question types, with the ultimate objective of achieving a comprehensive evaluation of all question types encountered in paper-based tests. The system detailed above demonstrated a recognition success rate of 99.89% on a curated dataset consisting of 734,825 multiple-choice answers. For the matching type, it achieved a recognition success rate of 99.91% on 86,450 answers. In the case of short answer type, the system achieved a recognition success rate of 95.40% on 129,675 answers.

The article deals with the problem of calculating the volume calendar plan of a paper mill. The presented mathematical model and methods make it possible to schedule paper production orders between several paper machines (PM) to even their loading, devise cutting plans for each winder and arrange the order of their implementation. When forming cutting plans, orders are grouped in accordance with such parameters as grammage, roll diameter, core diameter, product type and number of layers. Deadlines and volumes in customer orders are taken into account. The cutting plans for each winder account for the allowable roll width limits and the maximum number of knives. To find the optimal schedule, a combination of the following criteria is used: minimal trim loss, minimal changes to the knives’ setup and smooth transitions by product grades. Solution algorithms are presented that use a combination of the simplex method, the column generation, the branch and bound methods, the greedy algorithm and the local search procedure. We tested the solution approach on real production data from a paper mill in European Russia and obtained the production sequence that better matches deadlines in customer orders compared to the plan devised manually by production planners.

Objectively evaluating representative papers in a specific scientific research field is of great significance to the development of academia and scientific research institutions. Representative papers on achievements in scientific research can reflect the academic level and research characteristics of researchers and research institutions. The existing research methods are mainly based on external feature indicators and citation analysis methods, and the method of combining artificial intelligence is in its infancy. From the perspective of scientific research institutions, this paper proposes a graph autoencoder framework based on heterogeneous networks for the measurement of paper impact, named GAEPIM. Specifically, we propose two versions of GAEPIM based on a graph convolutional network and graph transformer network. The models rank papers in a specific research field and find the most representative papers and their scientific institutions. The proposed framework constructs a heterogeneous network of papers, institutions, and venues and simultaneously analyzes the semantic information of papers and the heterogeneous network structural information. Finally, based on the complex network information diffusion model, the proposed method performs better than several widely used baseline methods.

Due to the fact that the quality of the received signal is adversely affected by the beam squint phenomenon, which is especially pertinent to the millimeter wave (mmwave) bands, many studies have been utilised by other researchers to provide light on some of the challenges that come with analysing this type of occurrence. Squint causes several issues, the most important of which are its detrimental effects on gain, line of sight, angle of arrival, progressive phase, usable bandwidth, and fading effect. As a result of these obstacles, the advantages of adopting a high-frequency band such as mmwave in modern wireless communication systems are severely limited. Squint-phenomena-related difficulties, such as decreased channel capacity, increased bit error rate (BER), and lowered quality of services, may have a substantial detrimental impact on channel performance. The squint phenomenon and associated issues become more pressing with the expansion of the frequency spectrum and the deviation of the arrival angle from boresight. The purpose of this article is to provide a comprehensive overview of the relevant literature and to compare and contrast various methods in order to identify the most fruitful lines of inquiry for future research.

In order to maximize the available data rate and spectrum utilization efficiency, a high-throughput satellite communication system adopts the full spectrum reuse scheme, which will cause serious co-frequency interference. In this paper, a forward link model, considering the effects of free space loss, rainfall attenuation, and beam gain, is established, and the classical low-complexity of the zero-forcing precoding algorithm is improved in order to solve the serious co-frequency interference. Moreover, the regularized zero-forcing precoding algorithm considering the influence of system noise is studied, and a low complexity regularized zero-forcing dirty paper precoding algorithm is proposed, whose basic principle is to sort users based on the principle of channel maximum norm selection and practical application scenarios. Simulation results show that it can encode users sequentially, according to the channel conditions, to maximize the SINR (signal-to-interference-plus-noise ratio) and increase the throughput of the system.

Through-silicon-via (TSV)-based coaxial line techniques can reduce the high-frequency loss due to the low resistivity in the silicon substrate and thus can improve the efficiency of vertical signal transmission. Moreover, a TSV-based coaxial structure allows easily realizing the impedance matching in RF/microwave systems for excellent electrical performance. However, due to the limitations of existing available dielectric materials and the difficulties and challenges in the manufacturing process, ideal coaxial TSVs are not easy to obtain, and thus, the achieved electrical performance might be unexpected. In order to increase the flexibility of designing and manufacturing TSV-based coaxial structures and to better evaluate the fabricated devices, modeling and analysis theories of the corresponding high-frequency electrical performance are proposed in the paper. The theories are finally well validated using the finite-element simulation results, hereby providing guiding rules for selecting materials and improving manufacturing techniques in the practical process, so as to optimize the high-frequency performance of the TSV structures.

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

Radiofrequency (RF) power amplifiers (PAs) are important elements of modern communication systems. The most important components in PAs are the transistors, which are operated under large-signal regimes in such applications. Designing and optimizing PAs are challenging tasks that demand the highest accuracy on large-signal measurements at both device and circuit levels. Large-signal power transistor characterization demands the development and utilization of high-frequency and low-frequency load-pull systems. Furthermore, the static and dynamic characterization of two-input PAs requires the development of new measurement systems that consist of an nonlinear vector network analyzer (NVNA) and an arbitrary waveform generators (AWG). This paper reviews the research activities, achievements, and current research goals at CICESE Research Center in Mexico in the large-signal microwave metrology field.

Paper recommendation systems are important for alleviating academic information overload. Such systems provide personalized recommendations based on implicit feedback from users, supplemented by their subject information, citation networks, etc. However, such recommender systems face problems like data sparsity for positive samples and uncertainty for negative samples. In this paper, we address these two issues and improve upon them from the perspective of metric learning. The algorithm is modeled as a push–pull loss function. For the positive sample pull-out operation, we introduce a context factor, which accelerates the convergence of the objective function through the multiplication rule to alleviate the data sparsity problem. For the negative sample push operation, we adopt an unbiased global negative sample method and use an intermediate matrix caching method to greatly reduce the computational complexity. Experimental results on two real datasets show that our method outperforms other baseline methods in terms of recommendation accuracy and computational efficiency. Moreover, our metric learning method that introduces context improves by more than 5% over the element-wise alternating least squares method. We demonstrate the potential of metric learning in addressing the problem of implicit feedback recommender systems with positive and negative sample imbalances.

The fabrication of underwater devices is necessary for the exploration of water environments and interactions in the Human–Computer Interaction (HCI) field. However, there are fewer approaches to support prototyping used in water environments. The existing prototype methods lack systematic waterproof treatments and provide insufficient software for balance and buoyancy analysis. To address these limitations, we present ElectroPaper, a new approach for the design and fabrication of prototypes used in water environments (surface or beneath) with paper-based electronic interfaces with a crease layer, hardware distribution layer, and hollow-out layer to support physical properties, such as waterproofing, foldability, and conformability. The approach includes a computational design tool for assisting in balance analysis, three-dimensional (3D) model unfolding, and circuit drawing. We describe the design and fabrication process and provide several example applications to illustrate the feasibility and utility of our approach. ElectroPaper provides an inexpensive and effective medium for the fabrication of customized digital prototypes for water environment use.

This paper discusses some of the design choices underlying the development of watt-level integrated Doherty power amplifiers in the K and Ka band, focusing on compound semiconductor technologies. The key aspect of on-chip power combination is discussed, presenting and comparing some of the possible alternatives. Then, the impact on the achievable bandwidth and performance of different parameters is quantified, adopting an approximate analysis, which focuses on the Doherty output combiner and allows estimating the non-linear performance of the amplifier thanks to some simplifying assumptions, without requiring a full, non-linear model of the active devices. Two sample GaAs and GaN technologies are compared first, considering parameters that are representative of the currently available commercial processes, and then several power combination strategies are analyzed, adopting the GaN technology, which is currently the only one that allows achieving the power levels required by the applications directly on chip. Finally, some hints as to the impact of the output parasitic effects of the transistors on the presented analysis are given.

The newly developed research area of inkjet-printed radio frequency (RF) electronics on cellulose-based and synthetic paper substrates is introduced in this paper. This review paper presents the electrical properties of the paper substrates, the printed silver nanoparticle-based thin films, the dielectric layers, and the catalyst-based metallization process. Numerous inkjet-printed microwave passive/ative systems on paper, such as a printed radio frequency identification (RFID) tag, an RFID-enabled sensor utilizing carbon nanotubes (CNTs), a substrate-integrated waveguide (SIW), fully printed vias, an autonomous solar-powered beacon oscillator (active antenna), and artificial magnetic conductors (AMC), are discussed. The reported technology could potentially act as the foundation for true “green” low-cost scalable wireless topologies for autonomous Internet-of-Things (IoT), bio-monitoring, and “smart skin” applications.

Undeniably, the Internet of Things (IoT) ecosystem continues to evolve at a breakneck pace, exceeding all growth expectations and ubiquity barriers. From sensor to cloud, this giant network keeps breaking technological bounds in several domains, and wireless sensor nodes (motes) are expected to be predominant as the number of IoT devices grows towards the trillions. However, their future in the IoT ecosystem still seems foggy, where several challenges, such as (i) device’s connectivity, (ii) intelligence at the edge, (iii) security and privacy concerns, and (iv) growing energy needs, keep pulling in opposite directions. This prospective paper offers a succinct and forward-looking review of recent trends, challenges, and state-of-the-art solutions of low-end IoT motes, where reconfigurable computing technology plays a key role in tomorrow’s IoT devices.

Electronic Engineering and Design Innovations are both academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs [...]

The paper presents the methodology to compute modulo multiplication with the moduli set 2n, 2n−1, 2n+1. In addition to this, designs of the modulo multipliers, namely 2n, 2n−1, and 2n+1 (with n = 4, 8, and 16), have been proposed which are based on half adders, full adders, 4:3 compressor, 7:3 compressor, and the multi-column compressor namely 5,5:4. The gate level design of 4:3 compressor is carried out by solving the truth table using the K-map reduction. To verify the functionalities we have implemented the proposed modulo multipliers using VHDL coding in Xilinx 14.2 design suite. Simulation using Virtex-6 device has been performed to estimate delay, power consumption, and power-delay product (PDP). Moreover, the modulo multipliers are simulated in Cadence RC compiler using 0.18 µm technology to estimate the area. One of the major contributions to the arts of this work is in the partial product reduction stage which utilizes the multi-column 5,5:4 compressor to reduce power and area. The modulo 2n−1 multiplier of operand size 4-bit shows an improvement of 66.34% in terms of area over the best-reported paper. On the other hand, the modulo 2n+1 multiplier of operand size 4-bit shows an improvement of 58.59% terms of in area and the same of operand size 8-bit shows an improvement of 22.72% over the best-reported paper. The proposed algorithms of moduli multiplication are applicable to Booth multiplication of signed numbers.

This paper presents a study on the impact of circuit parameters on the transmission of electrical energy in wireless power transfer systems designed for intelligent sensing devices within the urban electric power Internet of Things (IoT). Relying on the essential principles of resonant mutual inductance models, the paper conducts an analytical investigation into the phenomena of power-frequency splitting characteristics, efficiency-frequency splitting characteristics, and efficacy synchronization characteristics within wireless energy transmission technologies. The investigation includes a detailed analysis of a wireless power transfer system model operating at 100 kHz, delineating how varying circuit parameters influence the system’s efficiency. Via the utilization of graphical software and computational programming for simulation modeling, this research delved into the dynamics between key parameters such as equivalent load and coupling coefficient and their influence on distinct splitting phenomena. This rigorous approach substantiated the validity of the proposed power-frequency and efficiency-frequency splitting characteristics outlined in the study. Based on the analytical results, it is shown that selecting an appropriate equivalent load or utilizing impedance matching networks to adjust the equivalent load to a suitable size is crucial in consideration of the system’s output power, voltage withstand level, and transmission efficiency. The research findings provide a theoretical basis for the design of wireless power supply systems for non-directly buried cable front-end sensing devices.

Radiation hardening of power MOSFETs (metal oxide semiconductor field effect transistors) is of the highest priority for sustaining high-power systems in the space radiation environment. Silicon carbide (SiC)-based power electronics are being investigated as a strong alternative for high power spaceborne power electronic systems. SiC MOSFETs have been shown to be most prone to single-event burnout (SEB) from space radiation. The current knowledge of SiC MOSFET device degradation and failure mechanisms are reviewed in this paper. Additionally, the viability of radiation tolerant SiC MOSFET designs and the modeling methods of SEB phenomena are evaluated. A merit system is proposed to consider the performance of radiation tolerance and nominal electrical performance. Criteria needed for high-fidelity SEB simulations are also reviewed. This paper stands as a necessary analytical review to intercede the development of radiation-hardened power devices for space and extreme environment applications.

Loop unrolling can provide more instruction-level parallelism opportunities for code and enables a greater range of instruction pipeline scheduling. In high-performance very-long-instruction-word (VLIW) digital signal processors (DSPs), there are special registers to address. To further improve the instruction-level parallelism of code for such DSPs by making full use of these registers, in this paper, we propose a more effective loop unrolling approach through extending memory accessing (LUAEMA). In this approach, the final unrolling factor is computed by a model in which every register kind and every memory accessing operation are considered. For basic digital signal processing algorithms, the unrolling factor under the LUAEMA is larger than that under the conventional loop unrolling approach. We also provide the opportunity to reduce the number of instructions in a loop during the code transformation of loop unrolling. The experimental results show that the loop unrolling approach proposed in this paper can achieve an average speedup ratio ranging from 1.14 to 1.81 compared with the conventional loop unrolling approach. For some algorithms, the peak speedup ratio is up to 2.11.

With the rapid development of mobile application technology, the content and forms of comments disseminated on the internet are becoming increasingly complex. Various comments serve as users’ firsthand reference materials for understanding the application. However, some comments contain a significant amount of inappropriate content unrelated to the app itself, such as gambling, loans, pornography, and game account recharging, seriously impacting the user experience. Therefore, this article aims to assist users in filtering out irrelevant and inappropriate messages, enabling them to quickly obtain useful and relevant information. This study focuses on analyzing actual comments on various Chinese apps on the Apple App Store. However, these irrelevant comments exhibit a certain degree of concealment, sparsity, and complexity, which increases the difficulty of detection. Additionally, due to language differences, the existing English research methods exhibit relatively poor adaptability to Chinese textual data. To overcome these challenges, this paper proposes a research method named “blend net”, which combines text adversarial and multi-information fusion detection to enhance the overall performance of the system. The experimental results demonstrate that the method proposed in this paper achieves precision and recall rates both exceeding 98%, representing an improvement of at least 2% compared to existing methods.

Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization.

Generative Artificial Intelligence (AI) systems, like ChatGPT, have the potential to perpetuate and amplify cultural biases embedded in their training data, which are predominantly produced by dominant cultural groups. This paper explores the philosophical and technical challenges of detecting and mitigating cultural bias in generative AI, drawing on Plato’s Allegory of the Cave to frame the issue as a problem of limited and distorted representation. We propose a multifaceted approach combining technical interventions, such as data diversification and culturally aware model constraints, with a deeper engagement with the cultural and philosophical dimensions of the problem. Drawing on theories of extended cognition and situated knowledge, we argue that mitigating AI biases requires a reflexive interrogation of the cultural contexts of AI development and a commitment to empowering marginalized voices and perspectives. We claim that controlling cultural bias in generative AI is inseparable from the larger project of promoting equity, diversity, and inclusion in AI development and governance. By bridging philosophical reflection with technical innovation, this paper contributes to the growing discourse on responsible and inclusive AI, offering a roadmap for detecting and mitigating cultural biases while grappling with the profound cultural implications of these powerful technologies.

To accurately assess students’ cognitive state of knowledge points in the learning process within the smart classroom, a knowledge tracing (KT) model based on classroom network characteristic learning engagement and temporal-spatial feature fusion (CL-TSKT) is proposed. First, a classroom network is constructed based on the information of the student ID, seating relationship, student–student interaction, head-up or head-down state, and classroom network characteristics obtained from a smart classroom video. Second, a learning engagement model is established by utilizing the student–student interactions, head-up or head-down state, and classroom network characteristics. Finally, according to the learning engagement model and the knowledge point test data, a parallel temporal attention GRU network is proposed. It is utilized to extract the temporal features of the knowledge points and learning engagement. They are fused to obtain the knowledge point-learning engagement temporal characteristics and their associated attributes. Meanwhile, a CNN is used to extract the knowledge point-knowledge point spatial features. We consider the associative properties of knowledge point-knowledge points from a spatial perspective and fuse the knowledge point-knowledge point spatial features with the knowledge point-learning engagement temporal features. To accurately characterize the cognitive state of the knowledge points and provide effective support for teachers’ accurate and sustainable interventions for learners in the teaching and learning process, this paper conducts extensive experiments on four real datasets. The CL-TSKT model in this paper shows superior performance in all four evaluation metrics, compared with the state-of-the-art KT models.

With the rapid development of science and technology, uncrewed aerial vehicle (UAV) technology has shown a wide range of application prospects in various fields. The accuracy and real-time performance of UAV target detection play a vital role in ensuring safety and improving the work efficiency of UAVs. Aimed at the challenges faced by the current UAV detection field, this paper proposes the Gathering Cascaded Dilated DETR (GCD-DETR) model, which aims to improve the accuracy and efficiency of UAV target detection. The main innovations of this paper are as follows: (1) The Dilated Re-param Block is creatively applied to the dilatation-wise Residual module, which uses the large kernel convolution and the parallel small kernel convolution together and fuses the feature maps generated by multi-scale perception, greatly improving the feature extraction ability, thereby improving the accuracy of UAV detection. (2) The Gather-and-Distribute mechanism is introduced to effectively enhance the ability of multi-scale feature fusion so that the model can make full use of the feature information extracted from the backbone network and further improve the detection performance. (3) The Cascaded Group Attention mechanism is innovatively introduced, which not only saves the computational cost but also improves the diversity of attention by dividing the attention head in different ways, thus enhancing the ability of the model to process complex scenes. In order to verify the effectiveness of the proposed model, this paper conducts experiments on multiple UAV datasets of complex scenes. The experimental results show that the accuracy of the improved RT-DETR model proposed in this paper on the two UAV datasets reaches 0.956 and 0.978, respectively, which is 2% and 1.1% higher than that of the original RT-DETR model. At the same time, the FPS of the model is also improved by 10 frames per second, which achieves an effective balance between accuracy and speed.

The distribution network is a crucial component of the power system as it directly connects to users and serves the purpose of distributing power and balancing the load. With the integration of new energy sources through distributed generation (DG), the distribution network has undergone a transformation from a single power radial network into a complex multi-source network. Consequently, traditional fault location methods have proven inadequate in this new network structure. Therefore, the focus of this paper is to investigate fault location techniques specifically tailored for DG integration into distribution networks. This paper analyzes how fault conditions impact the characteristics of single-phase grounding faults and extracts spectral feature quantities to describe differences in zero-sequence currents under various fault distances. This paper also proposes a fault location method based on centroid frequency and a BPNN (back propagation neural network). The method uses centroid frequency to describe the features of zero-sequence currents; to simulate the mapping relationship between fault conditions and spectral features, BPNN is employed. The mapping relationship differs for different lines and distribution networks. When a line faults, the spectral features are calculated, along with the transition resistance and fault closing angle. The corresponding mapping relationship is then called upon to complete distance measurements. This location method can be applied to various types of distribution lines and fault conditions with high accuracy. Even with insufficient training samples, sample expansion can ensure accuracy in fault distance measurement. We built a distribution network consisting of four feeders with different types and lengths of each line on Simulink and verified the effectiveness of the proposed method by setting different fault conditions. The results suggest that the method has a clear advantage over other frequency domain-based approaches, especially for hybrid lines and feeder lines with branches in distribution networks. Additionally, the method achieves a measurement accuracy within a range of 100 m.

Visual simultaneous localization and mapping (SLAM) algorithms in dynamic scenes will apply the moving feature points to the camera pose’s calculation, which will cause the continuous accumulation of errors. As a target-detection tool, mask R-CNN, which is often used in combination with the former, due to the limited training datasets, easily results in the semantic mask being incomplete and deformed, which will increase the error. In order to solve the above problems, we propose in this paper a visual SLAM algorithm based on an adaptive mask dilation strategy and the dynamic probability of the feature points, named MDP-SLAM. Firstly, we use the mask R-CNN target-detection algorithm to obtain the initial mask of the dynamic target. On this basis, an adaptive mask-dilation algorithm is used to obtain a mask that can completely cover the dynamic target and part of the surrounding scene. Then, we use the K-means clustering algorithm to segment the depth image information in the mask coverage area into absolute dynamic regions and relative dynamic regions. Combined with the epipolar constraint and the semantic constraint, the dynamic probability of the feature points is calculated, and then, the highly dynamic possible feature points are removed to solve an accurate final pose of the camera. Finally, the method is tested on the TUM RGB-D dataset. The results show that the MDP-SLAM algorithm proposed in this paper can effectively improve the accuracy of attitude estimation and has high accuracy and robustness in dynamic indoor scenes.

In a wide area environment, leveraging a unified interface for the management of diverse databases is appealing. Nonetheless, variations in access and operation across heterogeneous databases pose challenges in abstracting a unified access model while preserving specific database operations. Simultaneously, intricate deployment and network conditions in wide-area environments create obstacles for forwarding database requests and achieving high-performance access. To address these challenges, this paper introduces a technology for unified access to heterogeneous databases in wide-area environments, termed Global Virtual Data Space SQL (GvdsSQL). Initially, this paper implements a unified data access mechanism for heterogeneous databases through metadata extraction, abstracts the unified access model, and accomplishes identification and forwarding of fundamental database operations. Secondly, the paper introduces a mechanism for expanding database operations through code generation. This mechanism achieves compatibility for special database operations by injecting rules to generate code. Lastly, this paper implements a multilevel caching mechanism for query results in wide-area databases utilizing semantic analysis. Through intelligent analysis of operation statements, it achieves precise management of cache items, enhancing wide-area access performance. The performance is improved by approximately 35% and 240% compared to similar methods.

Synchrophasor data contain a trove of information on the power system and its dynamics. These measurements have a high potential to unlock our ability to cope with changing system conditions and challenges posed by distributed and intermittent energy sources. While Phasor Measurement Units (PMUs) have seen a large deployment in the grid, their applications are limited by the software platforms that are deployed in control centers to monitor the grid. In this paper, we present an open source toolkit that enables fast prototyping of PMU applications. The toolkit is akin to a software development kit (SDK) for synchrophasor applications, providing a number of functionalities that enable high-level PMU application development within the LabVIEW environment. This Smart-grid Synchrophasor SDK (S3DK) proposes a paradigm based on the concept of distributed applications, which allows development and deployment to be independent of the existing software stack deployed in control centers and to leverage PMU data at any level of a synchrophasor system hierarchy. This paper serves to introduce the S3DK, which is released as open source software to facilitate broader and fast prototyping of synchrophasor applications.

This paper aims to structure and semantically describe the information within the industrial chain by constructing an Industry Chain Knowledge Graph (ICKG), enabling more efficient and intelligent information management and analysis. In more detail, this paper constructs a multi-domain industrial chain dataset and proposes a method that combines the top-down establishment of a semantic expression framework with the bottom-up establishment of a data layer to build an ICKG. In the data layer, a deep learning algorithm based on BERT-BiLSTM-CRF is used to extract industry chain entities from relevant literature and reports. The results indicate that the model can effectively identify industry chain entities. These entities and relationships populate a Neo4j graph database, creating a large-scale ICKG for visual display and aiding cross-domain applications.

In this paper, we dive into the exciting world of wireless communication, focusing on how millimeter-wave technology and Multiple-Input Multiple-Output phased array antennas are shaping the future of 5G and the upcoming 6G technologies. We cover the latest advancements in millimeter-wave and beamforming technologies, emphasizing their role in enhancing network security and efficiency in automotive vehicles through dual radar communication. Our discussion spans the benefits, applications, challenges, and solutions of these technologies individually from millimeter-wave to beamforming technologies and joint radar communications, alongside a look at their theoretical and practical implementations. We emphasize the integration of beamforming technology in joint radar communications for future automotive vehicles and its impact on automotive systems, smart cities, and the Internet of Things (IoT). Looking ahead, we discuss the potential of these technologies to transform future technology landscapes while also addressing the security implications of merging communication and radar capabilities. This paper aims to provide a clear view of the advancements and prospects of millimeter-wave, beamforming, and dual radar communication technologies.

Virtual reality (VR) has good potential to promote technology-enhanced learning. Students can benefit from immersive visualization and intuitive interaction in their learning of abstract concepts, complex structures, and dynamic processes. This paper is interested in evaluating the effects of VR learning games in a Virtual and Augmented Reality Technology-Enhanced Learning (VARTeL) environment within an engineering education setting. A VARTeL flipped classroom is established in the HIVE learning hub at Nanyang Technological University (NTU) Singapore for the immersive and interactive learning. Experiments are designed for the university students conducting the learning, with three interactive and immersive VR games related to science, technology, engineering and mathematics (STEM), i.e., virtual cells, a virtual F1 racing car, and vector geometry. These VR games are a part of the VARTeL apps designed in-house at NTU for STEM education. Quantitative and qualitative analyses are performed. A total of 156 students from Mechanical Engineering participated in the experiment. There are 15 participants selected for an interview after the experiment. Pre-tests and post-tests are performed using two different models, the developed VARTeL and the modified Technology-Rich Outcome-Focused Learning Environment Inventory (TROFLEI), in order to measure the efficiency of the VARTeL environment in Higher Education. Significant improvements of about 24.8% are observed for the post-tests over the pre-tests, which illustrate the effectiveness of the VARTeL for Engineering education. Details of the VR simulation games, methods of data collection, data analyses, as well as the experiment results are discussed. It is observed from the results that all the underlying scales of the modified TROFLEI are above the threshold for the ‘Good’ category, indicating that a very reliable questionnaire is designed in this research. The mean ‘Ideal’ values are about 0.7–2.6% higher than the mean ‘Actual’ values. The limitations of the experiment and future works with recommendations are also presented in this paper.

In this paper, multi-band bandstop filters (BSFs) based on mixed electric and magnetic coupling resonators are proposed. These proposed structures include a multimode resonator based on symmetrical open-circuit branches, including upper- and lower-branch filter circuits. Through this design, the center frequencies of the stopbands can be flexibly and autonomously adjusted. In addition, the filters proposed in this paper have excellent characteristics, such as miniature dimensions and abrupt roll-off skirts. Finally, these tri-band to sext-band bandstop filters were fabricated and the measured results agreed well with the simulated ones. The proposed structures can be applied in the fields of communication, information, and coal automation.

Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for large-scale multi-robot task allocation problems. This paper proposes a novel graph deep-reinforcement-learning-based approach, which solves the problem through learning. The framework leverages the graph sample and aggregate concept as the encoder to extract the node features in the context of the graph, followed by a cross-attention decoder to output the probability that each task is allocated to each robot. A graph normalization technique is also proposed prior to the input, enabling an easy adaption to real-world applications, and a deterministic solution can be guaranteed. The most important advantage of this architecture is the scalability and quick feed-forward character; regardless of whether cases have a varying number of robots or tasks, single depots, multiple depots, or even mixed single and multiple depots, solutions can be output with little computational effort. The high efficiency and robustness of the proposed method are confirmed by extensive experiments in this paper, and various multi-robot task allocation scenarios demonstrate its advantage.

Fast factorized back-projection (FFBP) is a classical fast time-domain technique that has garnered significant success in spotlight synthetic aperture radar (SAR) signal processing. The algorithm’s efficiency has been extended to stripmap SAR through integral aperture determination and full-aperture data block processing while retaining its computational efficiency. However, the above method is only operated in the azimuth direction, and the computing efficiency needs to be urgently improved in the actual processing process. This paper proposes a fast factorized back-projection algorithm for stripmap SAR imaging based on range block division. The echo data are divided into multiple subblocks in the range direction, and FFBP processing is applied separately to each full-aperture subblock, further enhancing computational efficiency. The paper analyzes the algorithm’s principles, underscores the necessity of integral aperture determination and full-aperture data block processing, provides specific implementation steps, and applies the algorithm to point target simulation and experimental data from a vehicle-mounted ice radar. The experiments validate the algorithm’s efficiency in stripmap SAR imaging.

The positioning and velocity accuracy of spraying robots determine the quality of the coating, and the influence of the robotic dynamic characteristics on control precision is significant. This paper presents a method of linearizing dynamic characteristics into feedforward coefficients and designs a dual-loop control system consisting of an inner velocity loop and an outer position loop. The system is divided into three sections: a cascaded section, a feedback section, and a feedforward section. The cascaded section eliminates the nonlinear characteristics of the system; the feedback section ensures the stability of the system; the feedforward section compensates for the internal errors of the system. The main innovation of this paper lies in proposing an offline parameter tuning method, which avoids online parameter adjustments and significantly enhances the real-time performance of the control system. Additionally, this method does not require specific physical information of the system, thus avoiding the cumbersome process of parameter adjustment. The experimental results demonstrate that when facing different high-speed trajectories, the proposed control system exhibits a significant improvement in control accuracy compared to other advanced control schemes.

In this paper, a previously proposed converter to be used for inductive wireless power transmission is modeled and a control strategy is proposed. The converter topology combines into a single stage, two buck converters and a half-bridge converter to feed a resonant stage. This simple and symmetrical topology is straightforward to design; only a buck converter and a parallel resonant tank must be specified. It would be desirable for the converter to feed a wide range of loads and be robust under input voltage variations. These objectives can not be attained with a linear model and control. For this reason, in this paper, a nonlinear converter model is derived step by step and controller strategy is developed without relying on system linearization. The proposed controller does not measure the output but only its peak value. This can be conducted because it takes advantage of the square current pulse fed into a resonant tank; it outputs an approximately sinusoidal signal. The control strategy is completed with a scheme to build the required pulse at the input of the resonant tank. The resulting nonlinear controller has a fast closed-loop performance; furthermore, it is robust under parameter uncertainty, and load and input voltage variations. Despite its features, the controller is fairly simple to implement.

In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider’s operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate.

Traffic flow prediction is essential for smart city management and planning, aiding in optimizing traffic scheduling and improving overall traffic conditions. However, due to the correlation and heterogeneity of traffic data, effectively integrating the captured temporal and spatial features remains a significant challenge. This paper proposes a model spatial–temporal fusion gated transformer network (STFGTN), which is based on an attention mechanism that integrates temporal and spatial features. This paper proposes an attention mechanism-based model to address these issues and model complex spatial–temporal dependencies in road networks. The self-attention mechanism enables the model to achieve long-term dependency modeling and global representation of time series data. Regarding temporal features, we incorporate a time embedding layer and a time transformer to learn temporal dependencies. This capability contributes to a more comprehensive and accurate understanding of spatial–temporal dynamic patterns throughout the entire time series. As for spatial features, we utilize DGCN and spatial transformers to capture both global and local spatial dependencies, respectively. Additionally, we propose two fusion gate mechanisms to effectively accommodate to the complex correlation and heterogeneity of spatial–temporal information, resulting in a more accurate reflection of the actual traffic flow. Our experiments on three real-world datasets illustrate the superior performance of our approach.

The purpose of the paper is to propose a new implementation of the PID (proportional–integral–derivative) algorithm in digital hardware. The proposed structure is optimized for cost. It follows a serialized, rather than parallel, scheme. It uses only one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequentially cyclic manner. The proposed circuit operates on standard single-precision (32-bit) floating-point numbers. It implements an extended PID formula, containing a non-ideal derivative component, and weighting coefficients, which enable reducing the influence of setpoint changes in the proportional and derivative components. The circuit was implemented in a Cyclone V FPGA (Field-Programmable Gate Array) device from Intel, Santa Clara, CA, USA. The proper operation of the circuit was verified in a simulation. For the specific implementation, which is reported in the paper, the sampling period of 516 ns was obtained, which means that the proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on single-precision floating-point numbers. However, the presented solution is much more efficient in terms of cost. It uses 1173 LUT (Look-up Table) blocks, 1026 registers, and 1 DSP (Digital Signal Processing) block, i.e., about 30% of logic resources required by comparable solutions.

In this paper, we present a preliminary study that proposes to improve surface electromyography (sEMG)-based hand gesture recognition through optimizing parameters and sliding voting classifiers. Targeting the high-performing myoelectric control system, the traditional methods for hand gesture recognition still need to further improve the classification accuracy and utilization rate for sEMG signals. Therefore, the proposed method first optimizes parameters to reduce redundant information by selecting the proper values for the window length, the overlapping rate, the number of channels, and the features of sEMG signals. In addition, the random forest (RF) classifier is an advanced classifier for sEMG-based hand gesture recognition. To further improve classification performance, this paper proposes a sliding voting random forest (SVRF) classifier which can reduce potential pseudo decisions made by the RF classifier. Finally, experiments were conducted using two sEMG datasets, named DB2 and DB4, from the NinaPro database, as well as self-collected data. The results illustrate a certain improvement in classification accuracy based on the optimized values for window length, overlapping rate, number of channels, and features of sEMG signals. And the SVRF classifier can significantly improve performance with higher accuracy compared with the traditional linear discriminate analysis (LDA), k-nearest neighbors (KNN), support vector machine (SVM), and RF classifiers.

Today, as data is easily exposed through various channels, such as storing data in cloud services or exchanging data through a SNS (Social Network Service), related privacy issues are receiving a significant amount of attention. In addition, for data that are more sensitive to personal information, such as medical images, more attention should be paid to privacy protection. De-identification is a common method for privacy protection. Typically, it is a method of deleting or masking individual identifiers and omitting quasi-identifiers such as birth dates. In the case of images, de-identification is performed by mosaic processing or applying various effects. In this paper, we present a method of de-identifying an image by encrypting only some of the data in the JPEG (Joint Photograph Experts Group) image format, one of the most common image compression formats, so that the entire image cannot be recognized. The purpose of this paper is to protect images by encrypting only small parts, and not the entire image. This work is suitable for the fast and safe transmission and verification of high-capacity images. We have shown that images can be de-identified by encrypting data from the DHT (Define Huffman Table) segment among the JPEG header segments. Through experiments, we confirmed that that these images could not be identified after encrypting only a minimal portion, compared to previous studies that encrypted entire images, and the encryption speed and decryption speed were also faster and more effective than the results of previous studies. A model was implemented to de-identify images using AES-256 (Advanced Encryption Standard-256) and symmetric key encryption algorithm in the Huffman tables of JPEG headers, resulting in the ability to render entire images unidentifiable quickly and effectively.

The onset of cloud computing brought with it an adoption of containerization—a lightweight form of virtualization, which provides an easy way of developing and deploying solutions across multiple environments and platforms. This paper describes the current use of containers and complementary technologies in software development and the benefits it brings. Certain applications run into obstacles when deployed on the cloud due to the latency it introduces or the amount of data that needs to be processed. These issues are addressed by edge intelligence. This paper describes edge intelligence, the deployment of artificial intelligence close to the data source, the opportunities it brings, along with some examples of practical applications. We also discuss some of the challenges in the development and deployment of edge intelligence solutions and the possible benefits of applying containerization in edge intelligence.

Carbon nanotubes have excellent electrical properties and can be used as a new generation of semiconductor materials. This paper presents a compact model for carbon nanotube field-effect transistors (CNTFETs). The model uses a semi-empirical approach to model the current–voltage properties of CNTFETs with gate lengths exceeding 100 nm. This study introduces an innovative approach by proposing physical parametric reference lengths (Lref), which facilitate the integration of devices of varying sizes into a unified modeling framework. Furthermore, this paper develops models for the bipolar properties of carbon nanotube devices, employing two distinct sets of model parameters for enhanced accuracy. The model offers a comprehensive analysis of the different capacitances occurring between the electrodes within the device. The simulation of the model shows good agreement with the experimental measurements, confirming the model’s validity. The model is implemented in the Verilog-A hardware description language, with the circuit being subsequently constructed and subjected to simulations via the HSPICE tool. The CNTFET-based inverter exhibits a gain of 7.022 and a delay time of 16.23 ps when operated at a voltage of 1.2 V.

The purpose of this paper is to explore the implementation of retrieval-augmented generation (RAG) technology with open-source large language models (LLMs). A dedicated web-based application, PaSSER, was developed, integrating RAG with Mistral:7b, Llama2:7b, and Orca2:7b models. Various software instruments were used in the application’s development. PaSSER employs a set of evaluation metrics, including METEOR, ROUGE, BLEU, perplexity, cosine similarity, Pearson correlation, and F1 score, to assess LLMs’ performance, particularly within the smart agriculture domain. The paper presents the results and analyses of two tests. One test assessed the performance of LLMs across different hardware configurations, while the other determined which model delivered the most accurate and contextually relevant responses within RAG. The paper discusses the integration of blockchain with LLMs to manage and store assessment results within a blockchain environment. The tests revealed that GPUs are essential for fast text generation, even for 7b models. Orca2:7b on Mac M1 was the fastest, and Mistral:7b had superior performance on the 446 question–answer dataset. The discussion is on technical and hardware considerations affecting LLMs’ performance. The conclusion outlines future developments in leveraging other LLMs, fine-tuning approaches, and further integration with blockchain and IPFS.

Studying how objects are positioned is vital for improving technologies like robots, cameras, and virtual reality. In our earlier papers, we introduced a bio-inspired artificial visual system for orientation detection, demonstrating its superiority over traditional systems with higher recognition rates, greater biological resemblance, and increased resistance to noise. In this paper, we propose a hardware-based orientation detection system (ODS). The ODS is implemented by a multiple dendritic neuron model (DNM), and a neuronal pruning scheme for the DNM is proposed. After performing the neuronal pruning, only the synapses in the direct and inverse connections states are retained. The former can be realized by a comparator, and the latter can be replaced by a combination of a comparator and a logic NOT gate. For the dendritic function, the connection of synapses on dendrites can be realized with logic AND gates. Then, the output of the neuron is equivalent to a logic OR gate. Compared with other machine learning methods, this logic circuit circumvents floating-point arithmetic and therefore requires very little computing resources to perform complex classification. Furthermore, the ODS can be designed based on experience, so no learning process is required. The superiority of ODS is verified by experiments on binary, grayscale, and color image datasets. The ability to process data rapidly owing to advantages such as parallel computation and simple hardware implementation allows the ODS to be desirable in the era of big data. It is worth mentioning that the experimental results are corroborated with anatomical, physiological, and neuroscientific studies, which may provide us with a new insight for understanding the complex functions in the human brain.

The fluctuation and intermittency of wind power generation seriously affect the stability and security of power grids. Aiming at smoothing wind power fluctuations, this paper proposes a flywheel–battery hybrid energy storage system (HESS) based on optimal variational mode decomposition (VMD). Firstly, the grid-connected power and charging–discharging power of the HESS are determined based on the sliding average algorithm. Secondly, the VMD algorithm, optimized using long short-term memory (LSTM), is used to decompose the hybrid energy storage power (HESP) into a series of sub-modes with frequencies from low to high. Then, the state of charge of the battery energy storage system and the speed of the flywheel energy storage system are monitored in real time, and the primary power of the HESS is modified according to the rules formulated by fuzzy control. Finally, through a simulation example, it is concluded that the method meets the requirements of smoothing wind power fluctuations and gives full play to the characteristics of energy storage battery and flywheel energy storage to ensure the stable operation of the energy storage system. The method presented in this paper can provide a reference for HESP configuration and control operation strategy formulation.

Defect pattern analysis of wafer bin maps (WBMs) is an important means of identifying process problems. Recently, automated analysis methods using machine learning or deep learning have been studied as alternatives to manual classification by engineers. In this paper, we propose a method to improve the feature extraction performance of defect patterns by transforming the polar coordinate system instead of the existing WBM image input. To reduce the variability of the location representation, defect patterns in the Cartesian coordinate system, where the location of the distributed defect die is not constant, were converted to a polar coordinate system. The CNN classifier, which uses polar coordinate transformed input, achieved a classification accuracy of 91.3%, which is 4.8% better than the existing WBM image-based CNN classifier. Additionally, a tree-structured classifier model that sequentially connects binary classifiers achieved a classification accuracy of 94%. The method proposed in this paper is also applicable to the defect pattern classification of WBMs consisting of different die sizes than the training data. Finally, the paper proposes an automated pattern classification method that uses individual classifiers to learn defect types and then applies ensemble techniques for multiple defect pattern classification. This method is expected to reduce labor, time, and cost and enable objective labeling instead of relying on subjective judgments of engineers.

The video game Factorio is a popular real-time strategy game that hosts a variety of gameplay scenarios analogous to real-world problems such as factory building, pipeline optimization, and conveyor belt logistics. It also considers environmental costs such as pollution and energy consumption. This game focuses on automation and resource management within the context of creating and maintaining factories. Players are challenged to design efficient factories, automate production, control energy consumption, and balance resource utilization, all while defending against environmental challenges such as pollution and hostile enemies. Factorio is a complex software; however, studies have demonstrated its ability to enhance student satisfaction when integrated as an educational module at the university level. This paper aims to utilize the power of Petri Net theory and the practical tool GPenSIM to model and simulate several Factorio pipelines and score them against one another, exploring which pipeline configuration is optimal. The primary objective is to make a mathematical model (Petri Net) to better understand the game’s complex mechanisms, identify bottlenecks within the system, and explore opportunities for optimization within the network of modules. The insights gained from this paper may find practical applications in real-world scenarios.

Mobile phone backplanes are an important part of mobile phones, and are often affected by a wide range of factors during the manufacturing process, resulting in defects of various scales and similar backgrounds. Therefore, accurately identifying these defects is crucial for improving mobile phone quality. To address this challenge, this paper proposes a multi-scale and dynamic attention fusion UNet (MDAF-UNet) model. The model innovatively combines normal convolution with dilated convolution. This allows the model to capture subtle features of defects and to perceive a larger range of feature variations. Moreover, an improved attention mechanism is introduced in this paper. It fuses channel attention and spatial attention, and dynamically adjusts the feature fusion strategy with learnable weights. This allows the model to increase the attention of important features and improve the effectiveness of feature representation. Experimental results on a publicly available dataset show that the MDAF-UNet model achieves 66.9% Mean Intersection over Union (MIoU), outperforming other state-of-the-art models. This result provides an effective solution to the mobile phone backplane defect segmentation problem.

Typically, the current and speed loop closure of servo motor of the parallel platform is accomplished with incremental PI regulation. The control method has strong robustness, but the parameter tuning process is cumbersome, and it is difficult to achieve the optimal control state. In order to further optimize the performance, this paper proposes a double-loop control structure based on fuzzy integral and neuron proportional integral (FI-NPI). The structure makes full use of the control advantages of the fuzzy controller and integrator to improve the performance of speed closed-loop control. And through the feedforward branch, the speed error is used as the teacher signal for neuron supervised learning, which improves the effect of current closed-loop control. Through comparative simulation experiments, this paper verifies that the FI-NPI controller has a faster dynamic response speed than the traditional PI controller. Finally, in this paper, the FI-NPI controller is implemented in C language in the servo-driven lower computer, and the speed closed-loop test of the BLDC motor is carried out. The experimental results show that the FI-NPI double-loop controller is better than the traditional double-PI controller in performance indicators such as convergence rate and RMSE, which confirms that the FI-NPI double-loop controller is more suitable for BLDC servo control.