Electronics

18 pages, 1040 KiB

Open AccessArticle

LACTNet: A Lightweight Real-Time Semantic Segmentation Network Based on an Aggregated Convolutional Neural Network and Transformer

by Xiangyue Zhang, Hexiao Li, Jingyu Ru, Peng Ji and Chengdong Wu

Electronics 2024, 13(12), 2406; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122406 - 19 Jun 2024

Abstract

Transformers have demonstrated a significant advantage over CNNs in modeling long-range dependencies, leading to increasing attention being paid towards their application in semantic segmentation tasks. In the present work, a novel semantic segmentation model, LACTNet, is introduced, which synergistically combines Transformer and CNN [...] Read more.

Transformers have demonstrated a significant advantage over CNNs in modeling long-range dependencies, leading to increasing attention being paid towards their application in semantic segmentation tasks. In the present work, a novel semantic segmentation model, LACTNet, is introduced, which synergistically combines Transformer and CNN architectures for the real-time processing of local and global contextual features. LACTNet is designed with a lightweight Transformer, which integrates a specially designed gated convolutional feedforward network, to establish feature dependencies across distant regions. A Lightweight Average Feature Bottleneck (LAFB) module is designed to effectively capture spatial detail information within the features, thereby enhancing segmentation accuracy. To address the issue of spatial feature loss in the decoder, a long skip-connection approach is employed through the designed Feature Fusion Enhancement Module (FFEM), which enhances the integrity of spatial features and the feature interaction capability in the decoder. LACTNet is evaluated on two datasets, achieving a segmentation accuracy of 74.8% mIoU and a frame rate of 90 FPS on the Cityscapes dataset, and a segmentation accuracy of 71.8% mIoU with a frame rate of 126 FPS on the CamVid dataset. Full article

19 pages, 854 KiB

Open AccessArticle

Mitigation of Adversarial Attacks in 5G Networks with a Robust Intrusion Detection System Based on Extremely Randomized Trees and Infinite Feature Selection

by Gianmarco Baldini

Electronics 2024, 13(12), 2405; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122405 - 19 Jun 2024

Viewed by 40

Abstract

IDS are an important tool to mitigate cybersecurity threats in the ICT infrastructures. Preferable properties of the IDSs are the optimization of the attack detection accuracy and the minimization of the computing resources and time. A signification portion of IDSs presented in the [...] Read more.

IDS are an important tool to mitigate cybersecurity threats in the ICT infrastructures. Preferable properties of the IDSs are the optimization of the attack detection accuracy and the minimization of the computing resources and time. A signification portion of IDSs presented in the research literature is based on ML and Deep Learning (DL) elements, but they may be prone to adversarial attacks, which may undermine the overall performance of the IDS algorithm. This paper proposes a novel IDS focused on the detection of cybersecurity attacks in 5G networks, which addresses in a simple but effective way two specific adversarial attacks: (1) tampering of the labeled set used to train the ML algorithm, (2) modification of the features in the training data set. The approach is based on the combination of two algorithms, which have been introduced recently in the research literature. The first algorithm is the ERT algorithm, which enhances the capability of DT and Random Forest (RF) algorithms to perform classification in data sets, which are unbalanced and of large size as IDS data sets usually are (legitimate traffic messages are more numerous than attack related messages). The second algorithm is the recently introduced Infinite Feature Selection algorithm, which is used to optimize the choice of the hyper-parameter defined in the approach and improve the overall computing efficiency. The result of the application of the proposed approach on a recently published 5G IDS data set proves its robustness against adversarial attacks with different degrees of severity calculated as the percentage of the tampered data set samples. Full article

(This article belongs to the Special Issue Machine Learning and Cybersecurity—Trends and Future Challenges)

23 pages, 4380 KiB

Open AccessArticle

Enhancing Multi-Class Attack Detection in Graph Neural Network through Feature Rearrangement

by Hong-Dang Le and Minho Park

Electronics 2024, 13(12), 2404; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122404 - 19 Jun 2024

Viewed by 91

Abstract

As network sizes grow, attack schemes not only become more varied but also increase in complexity. This diversification leads to a proliferation of attack variants, complicating the identification and differentiation of potential threats. Enhancing system security necessitates the implementation of multi-class intrusion detection [...] Read more.

As network sizes grow, attack schemes not only become more varied but also increase in complexity. This diversification leads to a proliferation of attack variants, complicating the identification and differentiation of potential threats. Enhancing system security necessitates the implementation of multi-class intrusion detection systems. This approach enables the categorization of incoming network traffic into distinct intrusion types and illustrates the specific attack encountered within the Internet. Numerous studies have leveraged deep learning (DL) for Network-based Intrusion Detection Systems (NIDS), aiming to improve intrusion detection. Among these DL algorithms, Graph Neural Networks (GNN) stand out for their ability to efficiently process unstructured data, especially network traffic, making them particularly suitable for NIDS applications. Although NIDS usually monitors incoming and outgoing flows in a network, represented as edge features in graph format, traditional GNN studies only consider node features, overlooking edge features. This oversight can result in losing important flow data and diminish the system’s ability to detect attacks effectively. To address this limitation, our research makes several key contributions: (1) Emphasize the significance of edge features for enhancing GNN for multi-class intrusion detection, (2) Utilize port information, which is essential for identifying attacks but often overlooked during training, (3) Reorganize features embedded within the graph. By doing this, the graph can represent close to the actual network, which is the node showing endpoint identification information such as IP addresses and ports; the edge contains information related to flow such as Duration, Number of Packet/s, and Length….; (4) Compared to traditional methods, our experiments demonstrate significant performance improvements on both CIC-IDS-2017 (98.32%) and UNSW-NB15 (96.71%) datasets. Full article

(This article belongs to the Special Issue AI Security and Safety)

14 pages, 1920 KiB

Open AccessArticle

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder

by Haifei Lu, Kedong Zhou and Lei He

Electronics 2024, 13(12), 2403; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122403 - 19 Jun 2024

Viewed by 86

Abstract

Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a [...] Read more.

Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an adaptive denoising autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE’s noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings. Full article

(This article belongs to the Special Issue Recent Advances in Signal Processing and Applications)

19 pages, 1329 KiB

Open AccessArticle

A Fast and Cost-Effective Calibration Strategy of Inter-Stage Residual Amplification Errors for Cyclic-Pipelined ADCs

by Jinge Ma, Yanjin Lyu, Guoao Liu and Yuanqi Hu

Electronics 2024, 13(12), 2402; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122402 - 19 Jun 2024

Viewed by 120

Abstract

Due to nonideal residue amplification, the limited resolution of pipelined analog-to-digital converters (ADCs) has become a popular research topic for ADC designers. High-gain and high-speed amplifiers usually consume too much power for a decent ADC. Hence, this paper proposes a fast and cost-effective [...] Read more.

Due to nonideal residue amplification, the limited resolution of pipelined analog-to-digital converters (ADCs) has become a popular research topic for ADC designers. High-gain and high-speed amplifiers usually consume too much power for a decent ADC. Hence, this paper proposes a fast and cost-effective foreground calibration strategy for cyclic-pipelined ADCs. The calibration strategy compensates for the gain error due to inter-stage residual amplification, which alleviates the DC gain requirement for internal amplifiers. Unlike other digital calibrations, the proposed scheme is implemented with a cyclic-pipelined structure, and only one parameter needs to be calibrated, whose value can be feasibly calculated by the Fix-Point Iteration algorithm. The proposed calibration scheme is implemented in an area-efficient 16-bit, 2 MS/s cyclic-pipelined ADC, fabricated in 180 nm CMOS technology. The ADC is designed and realized by cycling a 6-bit sub-ADC four times with 1-bit redundancy each time. The calibration algorithm manages to recover the sampled data to 93.85 dB spurious free dynamic range (SFDR) even with a 57.8 dB-DC-gain amplifier. The total power consumption of ADC is 17.92 mW and it occupies an active area of 1.8 mm². Full article

(This article belongs to the Collection Analog IC Design—Analog and RF Interface for Electronic Systems)

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20 pages, 4947 KiB

Open AccessArticle

FPGA-Based Acceleration of Polar-Format Algorithm for Video Synthetic-Aperture Radar Imaging

by Dongmin Jeong, Myeongjin Lee, Wookyung Lee and Yunho Jung

Electronics 2024, 13(12), 2401; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122401 - 19 Jun 2024

Viewed by 126

Abstract

This paper presents a polar-format algorithm (PFA)-based synthetic-aperture radar (SAR) processor that can be mounted on a small drone to support video SAR (ViSAR) imaging. For drone mounting, it requires miniaturization, low power consumption, and high-speed performance. Therefore, to meet these requirements, the [...] Read more.

This paper presents a polar-format algorithm (PFA)-based synthetic-aperture radar (SAR) processor that can be mounted on a small drone to support video SAR (ViSAR) imaging. For drone mounting, it requires miniaturization, low power consumption, and high-speed performance. Therefore, to meet these requirements, the processor design was based on a field-programmable gate array (FPGA), and the implementation results are presented. The proposed PFA-based SAR processor consists of both an interpolation unit and a fast Fourier transform (FFT) unit. The interpolation unit uses linear interpolation for high speed while occupying a small space. In addition, the memory transfer is minimized through optimized operations using SAR system parameters. The FFT unit uses a base-4 systolic array architecture, chosen from among various fast parallel structures, to maximize the processing speed. Each unit is designed as a reusable block (IP core) to support reconfigurability and is interconnected using the advanced extensible interface (AXI) bus. The proposed PFA-based SAR processor was designed using Verilog-HDL and implemented on a Xilinx UltraScale+ MPSoC FPGA platform. It generates an image 2048 × 2048 pixels in size within 0.766 s, which is 44.862 times faster than that achieved by the ARM Cortex-A53 microprocessor. The speed-to-area ratio normalized by the number of resources shows that it achieves a higher speed at lower power consumption than previous studies. Full article

(This article belongs to the Special Issue System-on-Chip (SoC) and Field-Programmable Gate Array (FPGA) Design)

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12 pages, 371 KiB

Open AccessArticle

A Hardware Trojan Diagnosis Method for Gate-Level Netlists Based on Graph Theory

by Hongxu Gao, Guangxi Zhai, Zeyu Li, Jia Zhou, Xiang Li and Quan Wang

Electronics 2024, 13(12), 2400; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122400 - 19 Jun 2024

Viewed by 117

Abstract

With the increasing complexity of integrated circuit design, the threat of a hardware Trojan (HT) is becoming more and more prominent. At present, the research mainly focuses on the detection of HTs, but the amount of research on the diagnosis of HTs is [...] Read more.

With the increasing complexity of integrated circuit design, the threat of a hardware Trojan (HT) is becoming more and more prominent. At present, the research mainly focuses on the detection of HTs, but the amount of research on the diagnosis of HTs is very small. The number of existing HT diagnosis methods is generally completed by detecting the HT nodes in the netlist. The main reason is the lack of consideration of the integrity of HTs, so the diagnosis accuracy is low. Based on the above reason, this paper proposes two implanted node search algorithms named layer-by-layer difference search (LDS) and layer-by-layer grouping difference search (LGDS). The LDS algorithm can greatly reduce the search time, and the LGDS algorithm can solve the problem of input node disorder. The two methods greatly reduce the number of nodes sorting and comparing, and therefore the time complexity is lower. Moreover, the relevance between implanted nodes is taken into account to improve the diagnosis rate. We completed experiments on an HT diagnosis; the HT implantation example is from Trust-Hub. The experimental results are shown as follows: (1) The average true positive rate (TPR) of the diagnosis using KNN, RF, or SVM with the LDS or LGDS algorithm is more than 93%, and the average true negative rate (TNR) is 100%. (2) The average proportion of implanted nodes obtained by the LDS or LGDS algorithm is more than 97%. The proposed method has a lower time complexity compared with other existing diagnosis methods, and the diagnosis time is shortened by nearly 75%. Full article

(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)

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17 pages, 6524 KiB

Open AccessArticle

Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning

by Almir Carlos dos Santos Júnior, Alexandre Jean René Serres, George Victor Rocha Xavier, Edson Guedes da Costa, Georgina Karla de Freitas Serres, Antonio Francisco Leite Neto, Itaiara Félix Carvalho, Luiz Augusto Medeiros Martins Nobrega and Pavlos Lazaridis

Electronics 2024, 13(12), 2399; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122399 - 19 Jun 2024

Viewed by 142

Abstract

This work presents a methodology for the generation and classification of phase-resolved partial discharge (PRPD) patterns based on the use of a printed UHF monopole antenna and signal conditioning circuit to reduce hardware requirements. For this purpose, the envelope detection technique was applied. [...] Read more.

This work presents a methodology for the generation and classification of phase-resolved partial discharge (PRPD) patterns based on the use of a printed UHF monopole antenna and signal conditioning circuit to reduce hardware requirements. For this purpose, the envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric discs with internal cavities in an oil cell, a potential transformer and tip–tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as a reference. After the acquisition of conditioned UHF signals, a digital signal filtering threshold technique was used, and peaks of partial discharge envelope pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector machine and decision tree algorithms. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work. Full article

(This article belongs to the Special Issue Advances in RF, Analog, and Mixed Signal Circuits)

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21 pages, 1914 KiB

Open AccessArticle

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

by Hanan Saleh Alhaji, Yuksel Celik and Sanjay Goel

Electronics 2024, 13(12), 2398; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122398 - 19 Jun 2024

Viewed by 104

Abstract

The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant [...] Read more.

The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence, Machine Learning, Deep Learning, and Explainable AI (XAI))

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18 pages, 1877 KiB

Open AccessArticle

Geometry Optimization of Stratospheric Pseudolite Network for Navigation Applications

by Yi Qu, Sheng Wang, Hui Feng and Qiang Liu

Electronics 2024, 13(12), 2397; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122397 - 19 Jun 2024

Viewed by 118

Abstract

A stratospheric pseudolite (SP) is a pseudolite installed on a stratospheric airship. A stratospheric pseudolite network (SPN) is composed of multiple SPs, which shows promising potential in navigation applications because of its station-keeping capability, long service duration, and flexible deployment. Most traditional research [...] Read more.

A stratospheric pseudolite (SP) is a pseudolite installed on a stratospheric airship. A stratospheric pseudolite network (SPN) is composed of multiple SPs, which shows promising potential in navigation applications because of its station-keeping capability, long service duration, and flexible deployment. Most traditional research about SPN geometry optimization has centered on geometric dilution of precision (GDOP). However, previous research rarely dealt with the topic of how SPN geometry configuration not only affects its GDOP, but also affects its energy balance. To obtain an optimal integrated performance, this paper employs the proportion of energy consumption in energy production as an indicator to assess SPN energy status and designs a composite indicator including GDOP and energy status to assess SPN geometry performance. Then, this paper proposes an SPN geometry optimization algorithm based on gray wolf optimization. Furthermore, this paper implements a series of simulations with an SPN composed of six SPs in a specific service area. Simulations show that the proposed algorithm can obtain SPN geometry solutions with good GDOP and energy balance performance. Also, simulations show that in the supposed scenarios and the specific area, a higher SP altitude can improve both GDOP and energy balance, while a lower SP latitude can improve SPN energy status. Full article

(This article belongs to the Special Issue Advances in Social Bots)

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26 pages, 10104 KiB

Open AccessArticle

Validation Scores to Evaluate the Detection Capability of Sensor Systems Used for Autonomous Machines in Outdoor Environments

by Magnus Komesker, Christian Meltebrink, Stefan Ebenhöch, Yannick Zahner, Mirko Vlasic and Stefan Stiene

Electronics 2024, 13(12), 2396; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122396 - 19 Jun 2024

Viewed by 124

Abstract

The characterization of the detection capability assumes significance when the reliable monitoring of the region of interest by a non-contact sensor is a safety-relevant function. This paper introduces new validation scores that evaluate the detection capability of non-contact sensors intended to be applied [...] Read more.

The characterization of the detection capability assumes significance when the reliable monitoring of the region of interest by a non-contact sensor is a safety-relevant function. This paper introduces new validation scores that evaluate the detection capability of non-contact sensors intended to be applied to outdoor machines. The scores quantify, in terms of safety, the suitability of the sensor for the intended implementation in an environmental perception system of (highly) automated machines. This was achieved by developing an extension to the new Real Environment Detection Area (REDA) method and linking the methodology with the sensor standard IEC/TS 62998-1. The extension includes point-by-point and statistic-based error evaluation which leads to the Usability-Score, Availability-Score, and Reliability-Score. By applying the principle in the agricultural sector using ISO 18497 and linking this with data from a real outdoor test stand, it was possible to show that the validation scores offer a generic approach to quantify the detection capability and express this in a machine manufacturer-oriented manner. The findings of this study have significant implications for the advancement of safety-related sensor systems integrated into machines operating in complex environments. In order to achieve full implementation, it is necessary to define in the standards which score is required for each Performance Level (PL). Full article

(This article belongs to the Special Issue Intelligent Sensor Systems Applied in Smart Agriculture)

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15 pages, 3000 KiB

Open AccessArticle

Enabling Self-Practice of Digital Audio–Tactile Maps for Visually Impaired People by Large Language Models

by Chanh Minh Tran, Nguyen Gia Bach, Phan Xuan Tan, Eiji Kamioka and Manami Kanamaru

Electronics 2024, 13(12), 2395; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122395 - 19 Jun 2024

Viewed by 128

Abstract

Digital audio–tactile maps (DATMs) on touchscreen devices provide valuable opportunities for people who are visually impaired (PVIs) to explore the spatial environment for engaging in travel activities. Existing solutions for DATMs usually require extensive training for the PVIs to understand the feedback mechanism. [...] Read more.

Digital audio–tactile maps (DATMs) on touchscreen devices provide valuable opportunities for people who are visually impaired (PVIs) to explore the spatial environment for engaging in travel activities. Existing solutions for DATMs usually require extensive training for the PVIs to understand the feedback mechanism. Due to the shortage of human resources for training specialists, as well as PVIs’ desire for frequent practice to maintain their usage skills, it has become challenging to widely adopt DATMs in real life. This paper discusses the use of large language models (LLMs) to provide a verbal evaluation of the PVIs’ perception, which is crucial for the independent practice of DATM usage. A smartphone-based prototype providing DATMs of simple floor plans was developed for a preliminary investigation. The evaluation results have proven that the interaction with the LLM could help the participants better understand the DATMs’ content and could vividly replicate them by drawings. Full article

(This article belongs to the Special Issue Human-Robot Interaction and Collaboration for Effective Solutions to Real-World Problems)

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38 pages, 7363 KiB

Open AccessArticle

CMAF: Context and Mobility-Aware Forwarding Model for V-NDN

by Elídio Tomás da Silva, Joaquim Macedo and António Costa

Electronics 2024, 13(12), 2394; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122394 - 19 Jun 2024

Viewed by 141

Abstract

Content dissemination in Vehicular Ad hoc Networks (VANET) is a challenging topic due to the high mobility of nodes, resulting in the difficulty of keeping routing tables updated. State-of-the-art proposals overcome this problem by avoiding the management of routing tables but resort to [...] Read more.

Content dissemination in Vehicular Ad hoc Networks (VANET) is a challenging topic due to the high mobility of nodes, resulting in the difficulty of keeping routing tables updated. State-of-the-art proposals overcome this problem by avoiding the management of routing tables but resort to the so-called table of neighbors (NT) from which a next-hop is selected. However, NTs also require updating. For this purpose, some solutions resort to broadcasting beacons. We propose a Context- and Mobility-Aware Forwarding (CMAF) strategy that resorts to a Short-Term Mobility Prediction—STMP—algorithm, for keeping the NT updated. CMAF is based in Named Data Networking (NDN) and works in two modes, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2V CMAF leverages the overheard packets to extract mobility information used to manage NT and feed the STMP algorithm. V2I CMAF also uses a controlled and less frequent beaconing, initially from the Road-Side Units (RSUs), for a further refinement of the predictions from STMP. Results from extensive simulations show that CMAF presents superior performance when compared to the state of the art. In both modes, V2V and V2I (with one beacon broadcast every 10 s) present 5–10% higher Interest Satisfaction Ratio (ISR) than those of CCLF for the same overhead, at a cost of 1 s of increased Interest Satisfaction Delay (ISD). Moreover, the number of retransmissions of CMAF is also comparatively low for relatively the same number of hops. Compared to VNDN and Multicast, CMAF presents fewer retransmissions and

10 %

to

45 %

higher ISR with an increased overhead of about

20 %

. Full article

(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering)

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16 pages, 446 KiB

Open AccessArticle

Evaluating Public Library Services in Taiwan through User-Generated Content: Analyzing Google Maps Reviews

by Chao-Chen Chen and Chen-Chi Chang

Electronics 2024, 13(12), 2393; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122393 - 19 Jun 2024

Viewed by 188

Abstract

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback’s significant yet underexplored potential in understanding public library patronage across Taiwan’s six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review [...] Read more.

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback’s significant yet underexplored potential in understanding public library patronage across Taiwan’s six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with social network analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan’s National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience. Full article

(This article belongs to the Topic Innovation, Communication and Engineering)

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25 pages, 5138 KiB

Open AccessArticle

Game-Theory-Based Design and Analysis of a Peer-to-Peer Energy Exchange System between Multi-Solar-Hydrogen-Battery Storage Electric Vehicle Charging Stations

by Lijia Duan, Yujie Yuan, Gareth Taylor and Chun Sing Lai

Electronics 2024, 13(12), 2392; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122392 - 19 Jun 2024

Viewed by 218

Abstract

As subsidies for renewable energy are progressively reduced worldwide, electric vehicle charging stations (EVCSs) powered by renewable energy must adopt market-driven approaches to stay competitive. The unpredictable nature of renewable energy production poses major challenges for strategic planning. To tackle the uncertainties stemming [...] Read more.

As subsidies for renewable energy are progressively reduced worldwide, electric vehicle charging stations (EVCSs) powered by renewable energy must adopt market-driven approaches to stay competitive. The unpredictable nature of renewable energy production poses major challenges for strategic planning. To tackle the uncertainties stemming from forecast inaccuracies of renewable energy, this study introduces a peer-to-peer (P2P) energy trading strategy based on game theory for solar-hydrogen-battery storage electric vehicle charging stations (SHS-EVCSs). Firstly, the incorporation of prediction errors in renewable energy forecasts within four SHS-EVCSs enhances the resilience and efficiency of energy management. Secondly, employing game theory’s optimization principles, this work presents a day-ahead P2P interactive energy trading model specifically designed for mitigating the variability issues associated with renewable energy sources. Thirdly, the model is converted into a mixed integer linear programming (MILP) problem through dual theory, allowing for resolution via CPLEX optimization techniques. Case study results demonstrate that the method not only increases SHS-EVCS revenue by up to 24.6% through P2P transactions but also helps manage operational and maintenance expenses, contributing to the growth of the renewable energy sector. Full article

(This article belongs to the Special Issue Hydrogen and Fuel Cells: Innovations and Challenges)

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7 pages, 3918 KiB

Open AccessArticle

Effects of Fe Contamination on the Reliability of Gate Oxide Integrity in Advanced CMOS Technology

by Fan Wang, Minghai Fang, Peng Yu, Wenbin Zhou, Kaiwei Cao, Zhen Xie, Xiangze Liu, Feng Yan and Xiaoli Ji

Electronics 2024, 13(12), 2391; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122391 - 19 Jun 2024

Viewed by 195

Abstract

Fe contamination has always been one of the most critical issues in the integrated circuit (IC) industry due to its catastrophic effect on device reliability and electrical characteristics. With complementary metal oxide semiconductor (CMOS) technology scaling down, this issue has been attracting more [...] Read more.

Fe contamination has always been one of the most critical issues in the integrated circuit (IC) industry due to its catastrophic effect on device reliability and electrical characteristics. With complementary metal oxide semiconductor (CMOS) technology scaling down, this issue has been attracting more attention. In this paper, the impact of Fe impurity on the reliability of gate oxide integrity (GOI) in advanced CMOS technology is investigated. Intentional contamination of polysilicon gates was conducted in both boron- and phosphorus-doped devices. Failure analysis of the gate oxide was conducted with high-resolution transmission electron microscopy (HRTEM) and the energy dispersive X-ray (EDX) technique. The experimental results disclose that the properties of PMOS are much more sensitive to Fe contamination than those of NMOS. It is suggested that the reason for the above phenomena is that Fe precipitates at the PMOS gate/oxide interface but dissolves uniformly in the NMOS poly gate due to lower formation energy of the FeB pair (0.65 eV) in PMOS than that of the P4-Fe cluster (3.2 eV) in NMOS. Full article

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18 pages, 7979 KiB

Open AccessArticle

Evaluation of Two Digital Wound Area Measurement Methods Using a Non-Randomized, Single-Center, Controlled Clinical Trial

by Lorena Casanova-Lozano, David Reifs-Jiménez, Maria del Mar Martí-Ejarque, Ramon Reig-Bolaño and Sergi Grau-Carrión

Electronics 2024, 13(12), 2390; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122390 - 18 Jun 2024

Viewed by 263

Abstract

A prospective, single-center, non-randomized, pre-marketing clinical investigation was conducted with a single group of subjects to collect skin lesion images. These images were subsequently utilized to compare the results obtained from a traditional method of wound size measurement with two novel methods developed [...] Read more.

A prospective, single-center, non-randomized, pre-marketing clinical investigation was conducted with a single group of subjects to collect skin lesion images. These images were subsequently utilized to compare the results obtained from a traditional method of wound size measurement with two novel methods developed using Machine Learning (ML) approaches. Both proposed methods automatically calculate the wound area from an image. One method employs a two-dimensional system with the assistance of an external calibrator, while the other utilizes an Augmented Reality (AR) system, eliminating the need for a physical calibration object. To validate the correlation between these methods, a gold standard measurement with digital planimetry was employed. A total of 67 wound images were obtained from 41 patients between 22 November 2022 and 10 February 2023. The conducted pre-marketing clinical investigation demonstrated that the ML algorithms are safe for both the intended user and the intended target population. They exhibit a high correlation with the gold standard method and are more accurate than traditional methods. Additionally, they meet the manufacturer’s expected use. The study validated the performance, safety, and usability of the implemented methods as a valuable tool in the measurement of skin lesions. Full article

(This article belongs to the Special Issue Artificial Intelligence and Signal Processing: Circuits and Systems)

17 pages, 2100 KiB

Open AccessArticle

An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage

by Ming-Jui Wu, Shao-Chun Tseng, Yan-Chin Gau and Wei-Siang Ciou

Electronics 2024, 13(12), 2389; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122389 - 18 Jun 2024

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Abstract

Chronic kidney disease–mineral and bone disorder (CKD-MBD) frequently occurs in hemodialysis patients and is a common cause of osteoporosis. Regular dual-energy X-ray absorptiometry (DXA) scans are used to monitor these patients, but frequent, cost-effective, and low-dose alternatives are needed. This study proposes an [...] Read more.

Chronic kidney disease–mineral and bone disorder (CKD-MBD) frequently occurs in hemodialysis patients and is a common cause of osteoporosis. Regular dual-energy X-ray absorptiometry (DXA) scans are used to monitor these patients, but frequent, cost-effective, and low-dose alternatives are needed. This study proposes an automatic CKD-MBD assessment model using histogram equalization and a squeeze-and-excitation block-based residual U-Net (SER-U-Net) with hand diagnostic radiography for preliminary classification. The process involves enhancing image contrast with histogram equalization, extracting features with the SE-ResNet model, and segmenting metacarpal bones using U-Net. Ultimately, a correlation analysis is carried out between the calculated dual metacarpal cortical percentage (dMCP) and DXA T-scores. The model’s performance was validated by analyzing clinical data from 30 individuals, achieving a 93.33% accuracy in classifying bone density compared to DXA results. This automated method provides a rapid, effective tool for CKD-MBD assessment in clinical settings. Full article

(This article belongs to the Special Issue Deep Learning Technology for Biomedical Signals and Images Applications)

21 pages, 4375 KiB

Open AccessArticle

Sounds of History: A Digital Twin Approach to Musical Heritage Preservation in Virtual Museums

by Changman Zou, Sang-Yong Rhee, Lin He, Dayang Chen and Xiaofei Yang

Electronics 2024, 13(12), 2388; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122388 - 18 Jun 2024

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Abstract

Musical cultural heritage, as an important component of cultural heritage, possesses significant cultural value and inheritance significance. With the development of society and the passage of time, these precious traditional musical cultural heritages inevitably face the dilemma of gradual depletion or even disappearance. [...] Read more.

Musical cultural heritage, as an important component of cultural heritage, possesses significant cultural value and inheritance significance. With the development of society and the passage of time, these precious traditional musical cultural heritages inevitably face the dilemma of gradual depletion or even disappearance. In the digital age, effectively protecting and inheriting these musical cultural heritages has become an urgent problem to be addressed. Therefore, this paper proposes an application method based on digital twin technology, exploring how to protect and inherit musical cultural heritages through digital twin technology. By leveraging digital twin technology, a virtual museum dedicated to showcasing the richness and historical connotations of music cultures is created, preserving and simulating the soundscapes of historical music eras. Through the integration of audio archives, 3D modeling, and interactive displays, users can immerse themselves in the experience of historical music in the digital space. This paper evaluates the feasibility and cultural preservation value of this digital music history museum through the creation of music digital twin technology instances and user survey feedback and discusses the prospects of digital twins in the field of musical cultural heritage. Full article

(This article belongs to the Special Issue Metaverse and Digital Twins, 2nd Edition)

19 pages, 8244 KiB

Open AccessArticle

Offloading Strategy Based on Graph Neural Reinforcement Learning in Mobile Edge Computing

by Tao Wang, Xue Ouyang, Dingmi Sun, Yimin Chen and Hao Li

Electronics 2024, 13(12), 2387; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122387 - 18 Jun 2024

Viewed by 137

Abstract

In the mobile edge computing (MEC) architecture, base stations with computational capabilities are subject to service coverage limitations, and the mobility of devices leads to dynamic changes in their connections, directly impacting the offloading decisions of agents. The connections between base stations and [...] Read more.

In the mobile edge computing (MEC) architecture, base stations with computational capabilities are subject to service coverage limitations, and the mobility of devices leads to dynamic changes in their connections, directly impacting the offloading decisions of agents. The connections between base stations and mobile devices, as well as the connections between base stations themselves, are abstracted into an MEC structural diagram due to the difficulty of deep reinforcement learning (DRL) in capturing the complex relationships between nodes and their multi-order neighboring nodes in the graph; decisions solely generated by DRL have limitations. To address this issue, this study proposes a hierarchical mechanism strategy based on Graph Neural Reinforcement Learning (M-GNRL) under multiple constraints. Specifically, the MEC structural graph constructed with the current device as an observation point aggregates to learn node features, thus comprehensively considering the contextual information of nodes, and the learned graph information serves as the environment for deep reinforcement learning, effectively integrating a graph neural network (GNN) with DRL. In the M-GNRL strategy, edge features from GNN are introduced into the architecture of the DRL network to enhance the accuracy of agents’ decision-making. Additionally, this study proposes an updated algorithm to obtain graph data that change with observation points. Comparative experiments demonstrate that the M-GNRL algorithm outperforms other baseline algorithms in terms of system cost and convergence performance. Full article

(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)

13 pages, 1897 KiB

Open AccessArticle

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

by Xiaoyu Lan, Jingyi Hu, Yudi Zhang, Shuang Ma and Ye Tian

Electronics 2024, 13(12), 2386; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122386 - 18 Jun 2024

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Abstract

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive [...] Read more.

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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1 pages, 163 KiB

Open AccessCorrection

Correction: Wu et al. Accumulatively Increasing Sensitivity of Ultrawide Instantaneous Bandwidth Digital Receiver with Fine Time and Frequency Resolution for Weak Signal Detection. Electronics 2022, 11, 1018

by Chen Wu, Taiwen Tang, Janaka Elangage and Denesh Krishnasamy

Electronics 2024, 13(12), 2385; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122385 - 18 Jun 2024

Viewed by 98

Abstract

In the original publication [...] Full article

7 pages, 2944 KiB

Open AccessCommunication

Impact of Al Alloying/Doping on the Performance Optimization of HfO₂-Based RRAM

by Huikai He, Xiaobo Yuan, Wenhao Wu, Choonghyun Lee, Yi Zhao and Zongfang Liu

Electronics 2024, 13(12), 2384; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122384 - 18 Jun 2024

Viewed by 196

Abstract

Al alloying/doping in HfO₂-based resistive random-access memory (RRAM) has been proven to be an effective method for improving the low-resistance state (LRS) retention. However, a detailed understanding of Al concentration on oxygen vacancy migration and resistive switching (RS) behaviors still needs [...] Read more.

Al alloying/doping in HfO₂-based resistive random-access memory (RRAM) has been proven to be an effective method for improving the low-resistance state (LRS) retention. However, a detailed understanding of Al concentration on oxygen vacancy migration and resistive switching (RS) behaviors still needs to be included. Herein, the impact of Al concentration on the RS properties of the TiN/Ti/HfAlO/TiN RRAM devices is addressed. Firstly, it is found that the forming voltage, SET voltage, and RESET voltage can be regulated by varying the Al doping concentration. Moreover, we have demonstrated that the device with 15% Al shows the minimum cycle-to-cycle variability (CCV) and superior endurance (over 10⁶). According to density-functional theory (DFT) calculations, it is found that the increased operation voltage, improved uniformity, and improved endurance are attributed to the elevated migration barrier of oxygen vacancy through Al doping. In addition, LRS retention characteristics of the TiN/Ti/HfAlO/TiN devices with different Al concentrations are compared. It is observed that the LRS retention is greatly enhanced due to the suppressed lateral diffusion process of oxygen vacancy through Al doping. This study demonstrates that Al alloying/doping greatly affects the RS behaviors of HfO₂-based RRAM and provides a feasible way to improve the RS properties through changing the Al concentration. Full article

(This article belongs to the Special Issue Advanced CMOS Devices and Applications, 2nd Edition)

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15 pages, 7899 KiB

Open AccessArticle

RN-YOLO: A Small Target Detection Model for Aerial Remote-Sensing Images

by Ke Wang, Hao Zhou, Hao Wu and Guowu Yuan

Electronics 2024, 13(12), 2383; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122383 - 18 Jun 2024

Viewed by 218

Abstract

Accurately detecting targets in remote-sensing images is crucial for the military, urban planning, and resource exploration. There are some challenges in extracting detailed features from remote-sensing images, such as complex backgrounds, large-scale variations, and numerous small targets. This paper proposes a remote-sensing target [...] Read more.

Accurately detecting targets in remote-sensing images is crucial for the military, urban planning, and resource exploration. There are some challenges in extracting detailed features from remote-sensing images, such as complex backgrounds, large-scale variations, and numerous small targets. This paper proposes a remote-sensing target detection model called RN-YOLO (YOLO with RepGhost and NAM), which integrates RepGhost and a normalization-based attention module (NAM) based on YOLOv8. Firstly, NAM is added to the feature extraction network to enhance the capture capabilities for small targets by recalibrating receptive fields and strengthening information flow. Secondly, an efficient RepGhost_C2f structure is employed in the feature fusion network to replace the C2f module, effectively reducing the parameters. Lastly, the WIoU (Wise Intersection over Union) loss function is adopted to mitigate issues such as significant variations in target sizes and difficulty locating small targets, effectively improving the localization accuracy of small targets. The experimental results demonstrate that compared to the YOLOv8s model, the RN-YOLO model reduces the parameter count by 13.9%. Moreover, on the DOTAv1.5, TGRS-HRRSD, and RSOD datasets, the detection accuracy ([email protected]:.95) of the RN-YOLO model improves by 3.6%, 1.2%, and 2%, respectively, compared to the YOLOv8s model, showcasing its outstanding performance and enhanced capability in detecting small targets. Full article

(This article belongs to the Special Issue Applications of Computer Vision, Volume II)

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11 pages, 3136 KiB

Open AccessArticle

Anti-Resonant Hollow-Core Fibers with High Birefringence and Low Loss for Terahertz Propagation

by Yuhang Du, Dinghao Zhou, Ruizhe Zhang, Jingkai Zhou and Hui Zou

Electronics 2024, 13(12), 2382; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122382 - 18 Jun 2024

Viewed by 205

Abstract

A new type of anti-resonant hollow-core fiber for terahertz waveguides is proposed. By introducing central support pillars and an elliptical structure, the fiber achieves high birefringence while maintaining low confinement loss and low material absorption loss. The fiber structure is optimized through simulation [...] Read more.

A new type of anti-resonant hollow-core fiber for terahertz waveguides is proposed. By introducing central support pillars and an elliptical structure, the fiber achieves high birefringence while maintaining low confinement loss and low material absorption loss. The fiber structure is optimized through simulation using the finite element method. The optimized fiber exhibits a birefringence of up to 1.22 × 10⁻² at a frequency of 1 THz, with a confinement loss of 8.34 × 10⁻⁶ dB/cm and a material absorption loss of 7.17 × 10⁻³ dB/cm. Furthermore, when the bending radius of the fiber is greater than 12 cm, the bending loss of the anti-resonant optical fiber at 1 THz is less than 1.36 × 10⁻⁴ dB/cm, demonstrating good bending resistance and high practical value. It is expected to play a significant role in optical communication systems. Full article

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18 pages, 936 KiB

Open AccessArticle

Linux IoT Malware Variant Classification Using Binary Lifting and Opcode Entropy

by Jayanthi Ramamoorthy, Khushi Gupta, Narasimha K. Shashidhar and Cihan Varol

Electronics 2024, 13(12), 2381; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122381 - 18 Jun 2024

Viewed by 221

Abstract

Binary function analysis is fundamental in understanding the behavior and genealogy of malware. The detection, classification, and analysis of Linux IoT malware and its variants present significant challenges due to the wide range of architectures supported by the Linux IoT platform. This study [...] Read more.

Binary function analysis is fundamental in understanding the behavior and genealogy of malware. The detection, classification, and analysis of Linux IoT malware and its variants present significant challenges due to the wide range of architectures supported by the Linux IoT platform. This study concentrates on static analysis using binary lifting techniques to extract and analyze Intermediate Representation (IR) opcode sequences. We introduce a set of statistical entropy-based features derived from these IR opcode sequences, establishing a practical and straightforward methodology for machine learning classification models. By exclusively analyzing function metadata and opcode entropy, our architecture-agnostic approach not only efficiently detects malware but also classifies its variants with a high degree of accuracy, achieving an F1 score of 97%. The proposed approach offers a robust alternative for enhancing malware detection and variant identification frameworks for IoT devices. Full article

(This article belongs to the Special Issue Machine Learning for Cybersecurity: Threat Detection and Mitigation)

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10 pages, 859 KiB

Open AccessArticle

Phase-Slip Based SQUID Used as a Photon Switch in Superconducting Quantum Computation Architectures

by Hu Zhao, Xiaoyu Wu, Wenlong Li, Xudong Fang and Tiefu Li

Electronics 2024, 13(12), 2380; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122380 - 18 Jun 2024

Viewed by 214

Abstract

The photon storage time in a superconducting coplanar waveguide (CPW) resonator is contingent on the loaded quality factor, primarily dictated by the input and output capacitance of the resonator. The phase-slip based superconducting quantum interference device (PS-SQUID) comprises two phase-slip (PS) junctions connected [...] Read more.

The photon storage time in a superconducting coplanar waveguide (CPW) resonator is contingent on the loaded quality factor, primarily dictated by the input and output capacitance of the resonator. The phase-slip based superconducting quantum interference device (PS-SQUID) comprises two phase-slip (PS) junctions connected in series with a superconducting island in between. The PS-SQUID can manifest nonlinear capacitance behavior, with the capacitance finetuned by the gate voltage to minimize the impact of magnetic field noise as much as possible. By substituting the coupling capacitance of the CPW resonator with the PS-SQUID, the loaded quality factor of the resonator can be changed by three orders, thus, we get a microwave photon switch in superconducting quantum computation architectures. Furthermore, by regulating the loaded quality factors, the coupling strength between the CPW and superconducting quantum circuits can be controlled, enabling the ability to manipulate stationary qubits and flying qubits. Full article

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25 pages, 1150 KiB

Open AccessArticle

Multi-Relation Extraction for Cybersecurity Based on Ontology Rule-Enhanced Prompt Learning

by Fei Wang, Zhaoyun Ding, Kai Liu, Lehai Xin, Yu Zhao and Yun Zhou

Electronics 2024, 13(12), 2379; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122379 - 18 Jun 2024

Viewed by 426

Abstract

In the domain of cybersecurity, available annotated data are often scarce, especially for Chinese cybersecurity datasets, often necessitating the manual construction of datasets. The scarcity of samples is one of the challenges in researching cybersecurity, especially for the “no-relation” class. Since the annotation [...] Read more.

In the domain of cybersecurity, available annotated data are often scarce, especially for Chinese cybersecurity datasets, often necessitating the manual construction of datasets. The scarcity of samples is one of the challenges in researching cybersecurity, especially for the “no-relation” class. Since the annotation process typically focuses only on known relation classes, there are usually no training samples for the “no-relation” class. This poses a zero-shot classification problem, where during the classification process, there is a tendency to classify into a class with a relationship. Zero-shot classification tasks are particularly challenging in this context. Moreover, most relation classification models currently need to traverse all relations to calculate the class with the highest probability. Therefore, the problem of “computational redundancy” is another challenge faced. Thus, how to accurately and efficiently acquire cyberspace knowledge from heterogeneous data sources and address the challenges such as sample scarcity, zero-shot recognition, and computational redundancy is the main focus of this chapter. To address these problems, this chapter designs a multi-relation extraction model based on ontology rule-enhanced prompt learning, which is a parameter-sharing-based multi-task model. By introducing prompt learning, which has shown significant effectiveness in the few-shot domain, this chapter designs prompt templates combining discrete and continuous tokens and uses rule injection in prompt learning to solve the difficulties in zero-shot recognition of “no-relation” and computational redundancy issues, achieving efficient and accurate multi-relation extraction. Specifically, by constructing sub-prompts to achieve an efficient combination of templates, a parameter-sharing structure is used to implement knowledge extraction step by step: The first step constructs entity prompt templates combining discrete and continuous tokens, identifying the classes of two entities based on prompt learning. The second step involves rule injection, identifying whether it belongs to the “no-relation” class based on the combination of sub-prompts; if there is no connection between the classes of two entities, it is classified as “no relation”; if a connection exists, the candidate relation set is filtered out. The third step uses the pre-trained model and vectors from the first step, utilizing prompt learning and rule judgment to determine the relation class from the candidate relation set. Finally, the effectiveness of our model is validated on the general datasets TACRED, ReTACRED, and the cybersecurity dataset constructed in this paper. Full article

(This article belongs to the Special Issue New Insights in Cybersecurity of Information Systems)

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16 pages, 331 KiB

Open AccessArticle

Verifiable Additive Homomorphic Secret Sharing with Dynamic Aggregation Support

by Sinan Wang, Changgen Peng, Xinxin Deng, Zongfeng Peng and Qihong Chen

Electronics 2024, 13(12), 2378; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122378 - 18 Jun 2024

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Abstract

(n, m, t)

-Homomorphic Secret Sharing (HSS) allows n clients to share data secretly to m servers, which compute a function f homomorphically on the received secretly shared data while restricting the input data acquired by a collection of [...] Read more.

(n, m, t)

-Homomorphic Secret Sharing (HSS) allows n clients to share data secretly to m servers, which compute a function f homomorphically on the received secretly shared data while restricting the input data acquired by a collection of t servers to private ones. In Verifiable Homomorphic Secret Sharing (VHSS), if there are partially colluding malicious servers submitting erroneous computation results to the client, such erroneous computation results will be rejected by the client. In traditional static homomorphic secret sharing schemes, once a secret share of raw data is assigned to a group of servers, then all servers in the group must participate in the computation, which means that the computation has to be restarted once some servers fail to perform the task. In order to solve the above problem, we propose the first dynamic homomorphic secret sharing scheme for additive computation in this paper. In our scheme, once some servers fail, there is no need to recalculate the secret sharing but only the need to reissue the index set of servers that perform the computation, Our structure assigns more computation to the servers, which is very useful in real scenarios. In addition, we propose dynamic verifiable homomorphic secret sharing schemes based on the above schemes, which have less computational overhead compared to the existing schemes, although we sacrifice the public verifiability property. Finally, we give a detailed correctness, security, and verifiability analysis of the two proposed schemes and provide the theoretical and experimental evaluation results of the computational overhead. Full article

(This article belongs to the Special Issue Digital Security and Privacy Protection: Trends and Applications)

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11 pages, 2505 KiB

Open AccessArticle

Dynamic Resonant-Inductive Wireless Power Transfer System for Automated Guided Vehicles with Reduced Number of Position Sensors

by Deniss Stepins, Aleksandrs Sokolovs and Janis Zakis

Electronics 2024, 13(12), 2377; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122377 - 18 Jun 2024

Viewed by 250

Abstract

This paper deals with the position detection of automated guided vehicles (AGVs) in dynamic resonant-inductive wireless power transfer (WPT) systems. A position detection is necessary to activate the correct transmitting coil. One of the simplest and most effective approaches for a position detection [...] Read more.

This paper deals with the position detection of automated guided vehicles (AGVs) in dynamic resonant-inductive wireless power transfer (WPT) systems. A position detection is necessary to activate the correct transmitting coil. One of the simplest and most effective approaches for a position detection method is to use optical or magnetic position sensors for each coil. However, due to needing a high number of sensors, this technique is relatively expensive. Therefore, an AGV position detection technique based on a reduced number of optical or magnetic sensors (by a factor of two) is proposed. The proposed detection technique was verified experimentally by using a scaled-down prototype of the dynamic WPT system. The proposed approach can be easily implemented by uploading a specific program code to a microcontroller. The microcontroller with the code developed by us was used for processing data from AGV position detection sensors, activating a suitable transmitting coil and controlling an inverter of the dynamic WPT system. As shown by the experiments, due to the proposed approach for the position detection of AGVs and activation of transmitting coils, the number of the position detection sensors is reduced by a factor of two, leading to reductions in the overall cost and level of complexity of the dynamic WPT system without degrading its performance. Full article

(This article belongs to the Special Issue Advances in Dynamic Wireless Power Transfer for Moving Objects)

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