Electronics

29 pages, 14671 KiB

Open AccessArticle

Object Detection Based on an Improved YOLOv7 Model for Unmanned Aerial-Vehicle Patrol Tasks in Controlled Areas

by Dewei Zhao, Faming Shao, Li Yang, Xiannan Luo, Qiang Liu, Heng Zhang and Zihan Zhang

Electronics 2023, 12(23), 4887; https://doi.org/10.3390/electronics12234887 - 04 Dec 2023

Cited by 2 | Viewed by 1468

When working with objects on a smaller scale, higher detection accuracy and faster detection speed are desirable features. Researchers aim to endow drones with these attributes in order to improve performance when patrolling in controlled areas for object detection. In this paper, we [...] Read more.

When working with objects on a smaller scale, higher detection accuracy and faster detection speed are desirable features. Researchers aim to endow drones with these attributes in order to improve performance when patrolling in controlled areas for object detection. In this paper, we propose an improved YOLOv7 model. By incorporating the variability attention module into the backbone network of the original model, the association between distant pixels is increased, resulting in more effective feature extraction and, thus, improved model detection accuracy. By improving the original network model with deformable convolution modules and depthwise separable convolution modules, the model enhances the semantic information extraction of small objects and reduces the number of model parameters to a certain extent. Pretraining and fine-tuning techniques are used for training, and the model is retrained on the VisDrone2019 dataset. Using the VisDrone2019 dataset, the improved model achieves an mAP₅₀ of 52.3% on the validation set. Through the visual comparative analysis of the detection results in our validation set, we find that the model shows a significant improvement in detecting small objects compared with previous iterations. Full article

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33 pages, 8632 KiB

Open AccessArticle

ATS-YOLOv7: A Real-Time Multi-Scale Object Detection Method for UAV Aerial Images Based on Improved YOLOv7

by Heng Zhang, Faming Shao, Xiaohui He, Weijun Chu, Dewei Zhao, Zihan Zhang and Shaohua Bi

Electronics 2023, 12(23), 4886; https://doi.org/10.3390/electronics12234886 - 04 Dec 2023

Viewed by 1264

Abstract

The objects in UAV aerial images have multiple scales, dense distribution, and occlusion, posing considerable challenges for object detection. In order to address this problem, this paper proposes a real-time multi-scale object detection method based on an improved YOLOv7 model (ATS-YOLOv7) for UAV [...] Read more.

The objects in UAV aerial images have multiple scales, dense distribution, and occlusion, posing considerable challenges for object detection. In order to address this problem, this paper proposes a real-time multi-scale object detection method based on an improved YOLOv7 model (ATS-YOLOv7) for UAV aerial images. First, this paper introduces a feature pyramid network, AF-FPN, which is composed of an adaptive attention module (AAM) and a feature enhancement module (FEM). AF-FPN reduces the loss of deep feature information due to the reduction of feature channels in the convolution process through the AAM and FEM, strengthens the feature perception ability, and improves the detection speed and accuracy for multi-scale objects. Second, we add a prediction head based on a transformer encoder block on the basis of the three-head structure of YOLOv7, improving the ability of the model to capture global information and feature expression, thus achieving efficient detection of objects with tiny scales and dense occlusion. Moreover, as the location loss function of YOLOv7, CIoU (complete intersection over union), cannot facilitate the regression of the prediction box angle to the ground truth box—resulting in a slow convergence rate during model training—this paper proposes a loss function with angle regression, SIoU (soft intersection over union), in order to accelerate the convergence rate during model training. Finally, a series of comparative experiments are carried out on the DIOR dataset. The results indicate that ATS-YOLOv7 has the best detection accuracy (mAP of 87%) and meets the real-time requirements of image processing (detection speed of 94.2 FPS). Full article

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19 pages, 6364 KiB

Open AccessArticle

Circuit Techniques for Immunity to Process, Voltage, and Temperature Variations in the Attachable Fractional Divider

by Atsushi Motozawa, Yasuyuki Hiraku, Yoshitaka Hirai, Naoaki Hiyama, Yusuke Imanaka and Fukashi Morishita

Electronics 2023, 12(23), 4885; https://doi.org/10.3390/electronics12234885 - 04 Dec 2023

Viewed by 679

Abstract

In the automotive industry, system-on-chips are crucial for managing weak radio waves from space, known as satellite signals. Integer-N phase-locked loops have played a vital role in the operation of system-on-chips in recent history. Their clock frequencies are carefully designed to prevent electromagnetic [...] Read more.

In the automotive industry, system-on-chips are crucial for managing weak radio waves from space, known as satellite signals. Integer-N phase-locked loops have played a vital role in the operation of system-on-chips in recent history. Their clock frequencies are carefully designed to prevent electromagnetic interference. However, as global navigation satellite system becomes more prevalent, integer-N phase-locked loops face new challenges in generating clocks within the shrinking frequency bands due to large frequency steps determined using a reference clock. To address it, replacing integer-N phase-locked loops with fractional-N phase-locked loops is required. This topic has not been discussed extensively, but it is a practical issue that requires consideration due to its potential impact on development costs. This is why we developed an attachable fractional divider. Our developed divider can efficiently transform integer-N phase-locked loops into fractional-N phase-locked loops, achieving low jitter degradation of 0.35 ps_rms and a low fractional spur of −69.3 dBc. Thanks to its attachable design, it expedites time-to-market. Regarding mass production, ensuring immunity to process, voltage, and temperature variations is a significant concern. We introduce the circuit techniques employed in the developed fractional divider for immunity to process, voltage, and temperature variations. Subsequently, we provide a comprehensive set of measurement results. The frequency differences over process variations in fractional-N mode is 6.14 ppm. Power supply and temperature dependances are extremely small in spread-spectrum clocking mode. This article illustrates that the developed fractional divider enhances both time-to-market and product reliance. Full article

(This article belongs to the Section Circuit and Signal Processing)

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16 pages, 730 KiB

Open AccessArticle

Revisiting Hard Negative Mining in Contrastive Learning for Visual Understanding

by Hao Zhang, Zheng Li, Jiahui Yang, Xin Wang, Caili Guo and Chunyan Feng

Electronics 2023, 12(23), 4884; https://doi.org/10.3390/electronics12234884 - 04 Dec 2023

Viewed by 1253

Abstract

Efficiently mining and distinguishing hard negatives is the key to Contrastive Learning (CL) in various visual understanding tasks. By properly emphasizing the penalty of hard negatives, Hard Negative Mining (HNM) can improve the CL performance. However, there is no method to quantitatively analyze [...] Read more.

Efficiently mining and distinguishing hard negatives is the key to Contrastive Learning (CL) in various visual understanding tasks. By properly emphasizing the penalty of hard negatives, Hard Negative Mining (HNM) can improve the CL performance. However, there is no method to quantitatively analyze the penalty strength of hard negatives, which makes training difficult to converge. In this paper, we propose a method for measuring and controlling the penalty strength. We first define a penalty strength metric to provides a quantitative analysis tool for HNM. Then, we propose a Triplet loss with Penalty Strength Control (T-PSC), which can balance the penalty strength of hard negatives and the difficulty of model optimization. In order to verify the effectiveness of the proposed T-PSC method in different modalities, we applied it to two visual understanding tasks: Image–Text Retrieval (ITR) for multi-model processing, and Temporal Action Localization (TAL) for video processing. T-PSC can be applied to existing ITR and TAL models in a plug-and-play manner without any changes. Experiments combined with existing models show that a reasonable control of the penalty strength can speed up training and improve the performance on higher-level tasks. Full article

(This article belongs to the Section Artificial Intelligence)

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21 pages, 5228 KiB

Open AccessEssay

Convolution Power Ratio Based on Single-Ended Protection Scheme for HVDC Transmission Lines

by Guangqiang Peng, Lixin Chen, Jiyang Wu, Huimin Jiang, Zhijie Wang and Haifeng Li

Electronics 2023, 12(23), 4883; https://doi.org/10.3390/electronics12234883 - 04 Dec 2023

Viewed by 630

Abstract

In order to solve the problems of insufficient abilities to withstand transition resistance under remote faults and difficulties in identifying internal and external faults for HVDC transmission line protection, a new single-ended protection scheme based on time-domain convolutional power was proposed. In this [...] Read more.

In order to solve the problems of insufficient abilities to withstand transition resistance under remote faults and difficulties in identifying internal and external faults for HVDC transmission line protection, a new single-ended protection scheme based on time-domain convolutional power was proposed. In this scheme, the ratio of time-domain convolution power at different frequencies is used to detect internal and external faults, and the long window convolution power is used to form the pole selection criteria. Due to the integration of transient power fault characteristics at high and low frequencies, this scheme amplifies the characteristic differences between internal and external faults caused by DC line boundaries and has a strong ability to withstand transition resistance. Based on PSCAD/EMTDC, simulation verification was conducted on the Yunnan–Guangzhou ±800 kV HVDC project. The results show that the proposed single-ended protection scheme can effectively identify fault poles, as well as internal and external faults. It has strong resistance to transition resistance and certain anti-interference ability and has strong adaptability to DC line boundaries, which meets the protection requirements of HVDC transmission systems for high speed, selectivity and reliability. Full article

(This article belongs to the Special Issue Situational Awareness and Protection Technologies for Low-Carbon Economic Operation of New Power Systems)

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25 pages, 13882 KiB

Open AccessArticle

Localization Method for Underwater Robot Swarms Based on Enhanced Visual Markers

by Qingbo Wei, Yi Yang, Xingqun Zhou, Chuanzhi Fan, Quan Zheng and Zhiqiang Hu

Electronics 2023, 12(23), 4882; https://doi.org/10.3390/electronics12234882 - 04 Dec 2023

Cited by 1 | Viewed by 919

Abstract

In challenging tasks such as large-scale resource detection, deep-sea exploration, prolonged cruising, extensive topographical mapping, and operations within intricate current regions, AUV swarm technologies play a pivotal role. A core technical challenge within this realm is the precise determination of relative positions among [...] Read more.

In challenging tasks such as large-scale resource detection, deep-sea exploration, prolonged cruising, extensive topographical mapping, and operations within intricate current regions, AUV swarm technologies play a pivotal role. A core technical challenge within this realm is the precise determination of relative positions among AUVs within the cluster. Given the complexity of underwater environments, this study introduces an integrated and high-precision underwater cluster positioning method, incorporating advanced image restoration algorithms and enhanced underwater visual markers. Utilizing the Hydro-Optical Image Restoration Model (HOIRM) developed in this research, image clarity in underwater settings is significantly improved, thereby expanding the attenuation coefficient range for marker identification and enhancing it by at least 20%. Compared to other markers, the novel underwater visual marker designed in this research elevates positioning accuracy by 1.5 times under optimal water conditions and twice as much under adverse conditions. By synthesizing the aforementioned techniques, this study has successfully developed a comprehensive underwater visual positioning algorithm, amalgamating image restoration, feature detection, geometric code value analysis, and pose resolution. The efficacy of the method has been validated through real-world underwater swarm experiments, providing crucial navigational and operational assurance for AUV clusters. Full article

(This article belongs to the Special Issue Autonomous Navigation of Unmanned Maritime Vehicles)

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6 pages, 182 KiB

Open AccessEditorial

Recent Advances in Motion Planning and Control of Autonomous Vehicles

by Bai Li, Xiaoming Chen, Tankut Acarman, Xiaohui Li and Youmin Zhang

Electronics 2023, 12(23), 4881; https://doi.org/10.3390/electronics12234881 - 04 Dec 2023

Viewed by 1604

Abstract

An autonomous vehicle operates without human intervention, marking advancements in navigating structured urban roads and unstructured environments [...] Full article

(This article belongs to the Special Issue Recent Advances in Motion Planning and Control of Autonomous Vehicles)

16 pages, 10083 KiB

Open AccessReview

Triboelectric Nanogenerator-Based Electronic Sensor System for Food Applications

by Yutong Wang, Weifeng Jin, Langhong Wang and Zhiyuan Zhu

Electronics 2023, 12(23), 4880; https://doi.org/10.3390/electronics12234880 - 04 Dec 2023

Viewed by 1159

Abstract

Triboelectric nanogenerators (TENGs) have garnered significant attention due to their ability to efficiently harvest energy from the surrounding environment and from living organisms, as well as to enable the efficient utilization of various materials, such as organic polymers, metals, and inorganic compounds. As [...] Read more.

Triboelectric nanogenerators (TENGs) have garnered significant attention due to their ability to efficiently harvest energy from the surrounding environment and from living organisms, as well as to enable the efficient utilization of various materials, such as organic polymers, metals, and inorganic compounds. As a result, TENGs represent an emerging class of self-powered devices that can power small sensors or serve as multifunctional sensors themselves to detect a variety of physical and chemical stimuli. In this context, TENGs are expected to play a pivotal role in the entire process of food manufacturing. The rapid development of the Internet of Things and sensor technology has built a huge platform for sensor systems for food testing. TENG-based sensor data provide novel judgment and classification features, offering a fast and convenient means of food safety detection. This review comprehensively summarizes the latest progress in the application of TENGs in the food field, mainly involving food quality testing, food monitoring, food safety, and agricultural production. We also introduce different TENG-based, self-powered devices for food detection and improvement from the perspective of material strategies and manufacturing solutions. Finally, we discuss the current challenges and potential opportunities for future development of TENGs in the food field. We hope that this work can provide new insights into the structural and electronic design of TENGs, thereby benefiting environmental protection and food health. Full article

(This article belongs to the Special Issue Material/Circuit Design and Energy Analysis for Complex Electronic Systems)

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16 pages, 2486 KiB

Open AccessArticle

“Canalvoltaico” in Emilia-Romagna, Italy: Assessing Technical, Economic, and Environmental Feasibility of Suspended Photovoltaic Panels over Water Canals

by Valentino Solfrini, Riccardo Farneti, Jessica Rossi, Augusto Bianchini, Matteo Morolli and Ivan Savini

Electronics 2023, 12(23), 4879; https://doi.org/10.3390/electronics12234879 - 04 Dec 2023

Viewed by 696

Abstract

Solar energy has become an increasingly important part of the global energy mix. In Italy, the photovoltaic power installed has grown by 40% since 2015, which raises the issue of land use and occupation. A viable alternative, already experienced in India, is placing [...] Read more.

Solar energy has become an increasingly important part of the global energy mix. In Italy, the photovoltaic power installed has grown by 40% since 2015, which raises the issue of land use and occupation. A viable alternative, already experienced in India, is placing solar panels on the top of water canals (Canal-Top—in Italian, “Canalvoltaico”). It is a relatively new and innovative approach to solar energy installation that offers several advantages including the potential to generate renewable energy without occupying additional land, reduce water evaporation from canals, and improve water quality by reducing algae growth. The article explores various Canal-Top solar projects over the world; then, a feasible application in the Italian region “Emilia-Romagna” is discussed, evaluating two potential construction designs. The primary aim is to establish a capital expenditure cost framework, offering reference values currently lacking in the extant literature and industry studies pertaining to Italy. Moreover, the study addresses additional key factors, including water savings, maintenance considerations, and safety implications. Full article

(This article belongs to the Collection Transition to Industry 4.0 in Emerging Domains: Methodology and Case Studies)

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15 pages, 1961 KiB

Open AccessArticle

Accumulation and Elimination: A Hard Decision-Based Multi-User Interference Cancellation Method in Satellite Communication System

by Haohan Li, Yongjia Jin, Deguang Zhao, Xuhui Ding and Kai Yang

Electronics 2023, 12(23), 4878; https://doi.org/10.3390/electronics12234878 - 04 Dec 2023

Viewed by 783

Abstract

With the increasing number of users in the Medium-Orbit (MEO) satellite communication system, multi-access interference (MAI) has become an important factor that restricts the reliability and capacity of the system. Additionally, the low carrier-power-to-noise-density ratio (

C / N_{0}

) resulting from [...] Read more.

With the increasing number of users in the Medium-Orbit (MEO) satellite communication system, multi-access interference (MAI) has become an important factor that restricts the reliability and capacity of the system. Additionally, the low carrier-power-to-noise-density ratio (

C / N_{0}

) resulting from long-distance transmission poses a significant concern. The parallel interference cancellation (PIC) algorithm, utilized within the paradigm of multi-user detection (MUD), exhibits the capability to effectively mitigate the impact of MAI within the same system. Simultaneously, coherent accumulation serves as a means to substantially enhance the correct detection probability (

P_{c d}

) at low

C / N_{0}

. In this study, a signal acquisition method for multi-user spread spectrum satellite receivers is proposed, which employs interference cancellation and coherent accumulation as its core mechanisms. Furthermore, we introduce a power estimation method based on the outcomes of signal acquisition, which can be integrated into the signal reconstruction module of PIC. Finally, we implement the aforementioned algorithms in both simulation and hardware platforms. Remarkably, we observe that when the interference-to-signal ratio (ISR) caused by MAI equals 20 dB, the improved algorithm attains a maximum

P_{c d}

of 0.95 within the high signal-to-noise ratio (SNR) region, closely approaching the theoretical limit for the bit error rate (BER). The experimental results prove the effectiveness and feasibility of the acquisition algorithm. In summary, the enhanced algorithm holds vast potential for widespread implementation in multi-user spread spectrum communication systems. Full article

(This article belongs to the Section Microwave and Wireless Communications)

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15 pages, 2253 KiB

Open AccessArticle

A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers

by Saeed Anwar, Nick Barnes and Lars Petersson

Electronics 2023, 12(23), 4877; https://doi.org/10.3390/electronics12234877 - 04 Dec 2023

Viewed by 757

Abstract

Fine-grained classifiers collect information about inter-class variations to best use the underlying minute and subtle differences. The task is challenging due to the minor differences between the colors, viewpoints, and structure in the same class entities. The classification becomes difficult and challenging due [...] Read more.

Fine-grained classifiers collect information about inter-class variations to best use the underlying minute and subtle differences. The task is challenging due to the minor differences between the colors, viewpoints, and structure in the same class entities. The classification becomes difficult and challenging due to the similarities between the differences in viewpoint with other classes and its own. This work investigates the performance of landmark traditional CNN classifiers, presenting top-notch results on large-scale classification datasets and comparing them against state-of-the-art fine-grained classifiers. This paper poses three specific questions. (i) Do the traditional CNN classifiers achieve comparable results to fine-grained classifiers? (ii) Do traditional CNN classifiers require any specific information to improve fine-grained ones? (iii) Do current traditional state-of-the-art CNN classifiers improve the fine-grained classification while utilized as a backbone? Therefore, we train the general CNN classifiers throughout this work without introducing any aspect specific to fine-grained datasets. We show an extensive evaluation on six datasets to determine whether the fine-grained classifier can elevate the baseline in their experiments. We provide ablation studies regarding efficiency, number of parameters, flops, and performance. Full article

(This article belongs to the Section Artificial Intelligence)

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15 pages, 1522 KiB

Open AccessArticle

Research on Orderly Charging Strategy for Electric Vehicles Based on Electricity Price Guidance and Reliability Evaluation of Microgrid

by Zhipeng Weng, Jinghua Zhou, Xiaotong Song and Liuming Jing

Electronics 2023, 12(23), 4876; https://doi.org/10.3390/electronics12234876 - 04 Dec 2023

Viewed by 894

Abstract

With the increasing use of electric vehicles (EVs), EVs will be widely connected to the microgrid in the future. However, the influence of the disorderly charging behavior of EVs on the stable and reliable operation of the power grid cannot be ignored. To [...] Read more.

With the increasing use of electric vehicles (EVs), EVs will be widely connected to the microgrid in the future. However, the influence of the disorderly charging behavior of EVs on the stable and reliable operation of the power grid cannot be ignored. To address these challenges, the charging load characteristic model is established to describe the charging behavior of EVs. Then, an EVs orderly charging strategy based on electricity price guidance is proposed, and the goal is to minimize the peak–valley difference ratio and the total cost of EV charging. The result shows that, compared with disorderly charging, the EV orderly charging strategy based on electricity price guidance proposed in this paper can effectively reduce the peaking and valley difference ratio of load, reduce user’s charging costs, and optimize the reliability level of the microgrid. Full article

(This article belongs to the Special Issue Advanced Technologies for Renewable Energy Systems and Their Applications)

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21 pages, 8100 KiB

Open AccessArticle

Evaluation of Contactless Identification Card Immunity against a Current Pulse in an Adjacent Conductor

by Peter Vestenický, Marián Hruboš and Eduard Kolla

Electronics 2023, 12(23), 4875; https://doi.org/10.3390/electronics12234875 - 03 Dec 2023

Viewed by 855

Abstract

This paper analyses the possibility of damaging and destroying an identification chip of the Mifare type in a frequently used contactless identification card of size ID-1, following the standard ISO/IEC 7810 (i.e., with dimensions 85.60 × 53.98 × 0.76 mm), using the magnetic [...] Read more.

This paper analyses the possibility of damaging and destroying an identification chip of the Mifare type in a frequently used contactless identification card of size ID-1, following the standard ISO/IEC 7810 (i.e., with dimensions 85.60 × 53.98 × 0.76 mm), using the magnetic field of an adjacent conductor in which a current pulse of a defined shape and amplitude is flowing. For analysis purposes, the nonlinear current–voltage characteristic of the Mifare chip voltage limiter was measured and approximated, and the mutual inductance of the straight conductor and the rectangle coil antenna in the card was calculated. Next, a mathematical analysis was conducted based on the description of the equivalent electrical circuit by the differential equations. The results of the mathematical analysis were verified by a simulation in the free simulation software Micro-Cap 12. The peak value of the current pulse that can damage the Mifare chip was measured by a combination wave generator. Based on these measurements and the chip characteristics, the energy capable of destroying the chip was calculated. The characteristics of chip damage were determined using a comparison of the resonant characteristics of undamaged and damaged RFID cards with Mifare chips. Full article

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19 pages, 481 KiB

Open AccessArticle

An Efficient Bit-Based Approach for Mining Skyline Periodic Itemset Patterns

by Yanzhi Li and Zhanshan Li

Electronics 2023, 12(23), 4874; https://doi.org/10.3390/electronics12234874 - 03 Dec 2023

Viewed by 735

Abstract

Periodic itemset patterns (PIPs) are widely used in predicting the occurrence of periodic events. However, extensive redundancy arises due to a large number of patterns. Mining skyline periodic itemset patterns (SPIPs) can reduce the number of PIPs and guarantee the accuracy of prediction. [...] Read more.

Periodic itemset patterns (PIPs) are widely used in predicting the occurrence of periodic events. However, extensive redundancy arises due to a large number of patterns. Mining skyline periodic itemset patterns (SPIPs) can reduce the number of PIPs and guarantee the accuracy of prediction. The existing SPIP mining algorithm uses FP-Growth to generate frequent patterns (FPs), and then identify SPIPs from FPs. Such separate steps lead to a massive time consumption, so we propose an efficient bit-based approach named BitSPIM to mine SPIPs. The proposed method introduces efficient bitwise representations and makes full use of the data obtained in the previous steps to accelerate the identification of SPIPs. A novel cutting mechanism is applied to eliminate unnecessary steps. A series of comparative experiments were conducted on various datasets with different attributes to verify the efficiency of BitSPIM. The experiment results demonstrate that our algorithm significantly outperforms the latest SPIP mining approach. Full article

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14 pages, 5359 KiB

Open AccessFeature PaperArticle

UHF Textronic RFID Transponder with Bead-Shaped Microelectronic Module

by Piotr Jankowski-Mihułowicz, Mariusz Węglarski, Patryk Pyt, Kacper Skrobacz and Karol Karpiński

Electronics 2023, 12(23), 4873; https://doi.org/10.3390/electronics12234873 - 03 Dec 2023

Viewed by 891

Abstract

The idea of novel antennas and matching circuits, developed for radio frequency identification (RFID) passive transponders, and made on textile substrates, is presented in this paper. By manufacturing an RFID transponder by the means used in every clothing factory, we developed the concept [...] Read more.

The idea of novel antennas and matching circuits, developed for radio frequency identification (RFID) passive transponders, and made on textile substrates, is presented in this paper. By manufacturing an RFID transponder by the means used in every clothing factory, we developed the concept of RFIDtex tags, which, as textronic devices, make a new significant contribution to the Internet of Textile Things (IoTT). The main feature of the device consists of the use of an uncommon inductively coupled system as the antenna feed element. The antenna is sewn/embroidered with a conductive thread, and the microelectronic module with an RFID chip is made in the form of a bead, using standard electronic technology. Finally, the construction of the RFIDtex tag is developed for easy implementation in production lines in the garment industry. The proposed inductive coupling scheme has not been considered anywhere, so far. The developed transponder is dedicated to operating in RFID systems of the ultra-high frequency band (UHF). The numerical calculations confirmed by the experimental results clearly indicate that the proposed coupling system between the antenna and the microelectronic module works properly and the RFIDtex device can operate correctly within a distance of several meters. The proposed design is based on the authors’ patent on the textronic RFID transponder (patent no PL 231291 B1). Full article

(This article belongs to the Special Issue Advances in Passive RFID: From UHF to THz)

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12 pages, 420 KiB

Open AccessArticle

Improving Medical Entity Recognition in Spanish by Means of Biomedical Language Models

by Aitana Villaplana, Raquel Martínez and Soto Montalvo

Electronics 2023, 12(23), 4872; https://doi.org/10.3390/electronics12234872 - 02 Dec 2023

Viewed by 939

Abstract

Named Entity Recognition (NER) is an important task used to extract relevant information from biomedical texts. Recently, pre-trained language models have made great progress in this task, particularly in English language. However, the performance of pre-trained models in the Spanish biomedical domain has [...] Read more.

Named Entity Recognition (NER) is an important task used to extract relevant information from biomedical texts. Recently, pre-trained language models have made great progress in this task, particularly in English language. However, the performance of pre-trained models in the Spanish biomedical domain has not been evaluated in an experimentation framework designed specifically for the task. We present an approach for named entity recognition in Spanish medical texts that makes use of pre-trained models from the Spanish biomedical domain. We also use data augmentation techniques to improve the identification of less frequent entities in the dataset. The domain-specific models have improved the recognition of name entities in the domain, beating all the systems that were evaluated in the eHealth-KD challenge 2021. Language models from the biomedical domain seem to be more effective in characterizing the specific terminology involved in this task of named entity recognition, where most entities correspond to the "concept" type involving a great number of medical concepts. Regarding data augmentation, only back translation has slightly improved the results. Clearly, the most frequent types of entities in the dataset are better identified. Although the domain-specific language models have outperformed most of the other models, the multilingual generalist model mBERT obtained competitive results. Full article

(This article belongs to the Special Issue Natural Language Processing and Information Retrieval, 2nd Edition)

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15 pages, 2049 KiB

Open AccessEditor’s ChoiceArticle

A Multimodal Late Fusion Framework for Physiological Sensor and Audio-Signal-Based Stress Detection: An Experimental Study and Public Dataset

by Vasileios-Rafail Xefteris, Monica Dominguez, Jens Grivolla, Athina Tsanousa, Francesco Zaffanela, Martina Monego, Spyridon Symeonidis, Sotiris Diplaris, Leo Wanner, Stefanos Vrochidis and Ioannis Kompatsiaris

Electronics 2023, 12(23), 4871; https://doi.org/10.3390/electronics12234871 - 02 Dec 2023

Cited by 1 | Viewed by 1385

Abstract

Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities [...] Read more.

Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities is of great interest. For this cause, public datasets are necessary so that the different proposed solutions can be comparable. In this work, a publicly available multimodal dataset for stress detection is introduced, including physiological signals and speech cues data. The physiological signals include electrocardiograph (ECG), respiration (RSP), and inertial measurement unit (IMU) sensors equipped in a smart vest. A data collection protocol was introduced to receive physiological and audio data based on alterations between well-known stressors and relaxation moments. Five subjects participated in the data collection, where both their physiological and audio signals were recorded by utilizing the developed smart vest and audio recording application. In addition, an analysis of the data and a decision-level fusion scheme is proposed. The analysis of physiological signals includes a massive feature extraction along with various fusion and feature selection methods. The audio analysis comprises a state-of-the-art feature extraction fed to a classifier to predict stress levels. Results from the analysis of audio and physiological signals are fused at a decision level for the final stress level detection, utilizing a machine learning algorithm. The whole framework was also tested in a real-life pilot scenario of disaster management, where users were acting as first responders while their stress was monitored in real time. Full article

(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)

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12 pages, 6418 KiB

Open AccessArticle

Phase-Noise Characterization in Stable Optical Frequency Transfer over Free Space and Fiber Link Testbeds

by Peter Barcik, Jan Hrabina, Martin Cizek, Zdenek Kolka, Petr Skryja, Lenka Pravdova, Ondrej Cip, Lucie Hudcova, Ondrej Havlis and Josef Vojtech

Electronics 2023, 12(23), 4870; https://doi.org/10.3390/electronics12234870 - 02 Dec 2023

Viewed by 864

Abstract

Time and frequency metrology depends on stable oscillators in both radio-frequency and optical domains. With the increased complexity of the highly precise oscillators also came the demand for delivering the oscillators’ harmonic signals between delocalized sites for comparison, aggregation, or other purposes. Besides [...] Read more.

Time and frequency metrology depends on stable oscillators in both radio-frequency and optical domains. With the increased complexity of the highly precise oscillators also came the demand for delivering the oscillators’ harmonic signals between delocalized sites for comparison, aggregation, or other purposes. Besides the traditional optical fiber networks, free-space optical links present an alternative tool for disseminating stable sources’ output. We present a pilot experiment of phase-coherent optical frequency transfer using a free-space optical link testbed. The experiment performed on a 30 m long link demonstrates the phase-noise parameters in a free-space optical channel under atmospheric turbulence conditions, and it studies the impact of active MEMS mirror stabilization of the received optical wave positioning on the resulting transfer’s performance. Our results indicate that a well-configured MEMS mirror beam stabilization significantly enhances fractional frequency stability, achieving the−14th-order level for integration times over 30 s. Full article

(This article belongs to the Special Issue Optical Communications and RF Technologies in Sensor Networks and Multimedia Applications)

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24 pages, 1921 KiB

Open AccessArticle

Knowledge-Aware Graph Self-Supervised Learning for Recommendation

by Shanshan Li, Yutong Jia, You Wu, Ning Wei, Liyan Zhang and Jingfeng Guo

Electronics 2023, 12(23), 4869; https://doi.org/10.3390/electronics12234869 - 02 Dec 2023

Viewed by 1034

Abstract

Collaborative filtering (CF) based on graph neural networks (GNN) can capture higher-order relationships between nodes, which in turn improves recommendation performance. Although effective, GNN-based methods still face the challenges of sparsity and noise in real scenarios. In recent years, researchers have introduced graph [...] Read more.

Collaborative filtering (CF) based on graph neural networks (GNN) can capture higher-order relationships between nodes, which in turn improves recommendation performance. Although effective, GNN-based methods still face the challenges of sparsity and noise in real scenarios. In recent years, researchers have introduced graph self-supervised learning (SSL) techniques into CF to alleviate the sparse supervision problem. The technique first augments the data to obtain contrastive views and then utilizes the mutual information maximization to provide self-supervised signals for the contrastive views. However, the existing approaches based on graph self-supervised signals still face the following challenges: (i) Most of the works fail to effectively mine and exploit the supervised information from the item knowledge graph, resulting in suboptimal performance. (ii) Existing data augmentation methods are unable to fully exploit the potential of contrastive learning, because they primarily focus on the contrastive view of data structure changes and neglect the adjacent relationship among users and items. To address these issues, we propose a novel self-supervised learning approach, namely Knowledge-aware Graph Self-supervised Learning (KGSL). Specifically, we calculate node similarity based on semantic relations between items in the knowledge graph to generate a semantic-based item similarity graph. Then, the self-supervised learning contrast views are generated from both the user–item interaction graph and the item similarity graph, respectively. Maximization of the information from these contrastive views provides additional self-supervised signals to enhance the node representation capacity. Finally, we establish a joint training strategy for the self-supervised learning task and the recommendation task to further optimize the learning process of KGSL. Extensive comparative experiments as well as ablation experiments are conducted on three real-world datasets to verify the effectiveness of KGSL. Full article

(This article belongs to the Section Networks)

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21 pages, 12486 KiB

Open AccessFeature PaperArticle

KULF-TT53: A Display-Specific Turntable-Based Light Field Dataset for Subjective Quality Assessment

by Kamran Javidi, Maria G. Martini and Peter A. Kara

Electronics 2023, 12(23), 4868; https://doi.org/10.3390/electronics12234868 - 02 Dec 2023

Cited by 1 | Viewed by 1061

Abstract

Light field datasets enable researchers to conduct both objective and subjective quality assessments, which are particularly useful when acquisition equipment or resources are not available. Such datasets may vary in terms of capture technology and methodology, content, quality characteristics (e.g., resolution), and the [...] Read more.

Light field datasets enable researchers to conduct both objective and subjective quality assessments, which are particularly useful when acquisition equipment or resources are not available. Such datasets may vary in terms of capture technology and methodology, content, quality characteristics (e.g., resolution), and the availability of subjective ratings. When contents of a light field dataset are visualized on a light field display, the display system matches the received input to its output capabilities through various processes, such as interpolation. Therefore, one of the most straightforward methods to create light field contents for a specific display is to consider its visualization parameters during acquisition. In this paper, we introduce a novel display-specific light field dataset, captured using a DSLR camera and a turntable rig. The visual data of the seven static scenes were recorded twice by using two settings of angular resolution. While both were acquired uniformly within a 53-degree angle, which matches the viewing cone of the display they were captured for, one dataset consists of 70 views per content, while the other of 140. Capturing the contents twice was a more straightforward solution than downsampling, as the latter approach could either degrade the quality or make the FOV size inaccurate. The paper provides a detailed characterization of the captured contents, as well as compressed variations of the contents with various codecs, together with the calculated values of commonly-used quality metrics for the compressed light field contents. We expect that this dataset will be useful for the research community working on light field compression, processing, and quality assessment, for instance to perform subjective quality assessment tests on a display with a 53-degree display cone and to test new interpolation methods and objective quality metrics. In future work, we will also focus on subjective tests and provide relevant results. This dataset is made free to access for the research community. Full article

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22 pages, 4202 KiB

Open AccessArticle

RLARA: A TSV-Aware Reinforcement Learning Assisted Fault-Tolerant Routing Algorithm for 3D Network-on-Chip

by Jiajia Jiao, Ruirui Shen, Lujian Chen, Jin Liu and Dezhi Han

Electronics 2023, 12(23), 4867; https://doi.org/10.3390/electronics12234867 - 02 Dec 2023

Viewed by 814

Abstract

A three-dimensional Network-on-Chip (3D NoC) equips modern multicore processors with good scalability, a small area, and high performance using vertical through-silicon vias (TSV). However, the failure rate of TSV, which is higher than that of horizontal links, causes unpredictable topology variations and requires [...] Read more.

A three-dimensional Network-on-Chip (3D NoC) equips modern multicore processors with good scalability, a small area, and high performance using vertical through-silicon vias (TSV). However, the failure rate of TSV, which is higher than that of horizontal links, causes unpredictable topology variations and requires adaptive routing algorithms to select the available paths dynamically. Most works have aimed at the congestion control for TSV partially 3D NoCs to bypass the TSV reliability issue, while others have focused on the fault tolerance in TSV fully connected 3D NoCs and ignored the performance degradation. In order to adequately improve reliability and performance in TSV fully connected 3D NoC architectures, we propose a TSV-aware Reinforcement Learning Assisted Routing Algorithm (RLARA) for fault-tolerant 3D NoCs. The proposed method can take advantage of both the high throughput of fully connected TSVs and the cost-effective fault tolerance of partially connected TSVs using periodically updated TSV-aware Q table of reinforcement learning. RLARA makes the distributed routing decision with the lowest TSV utilization to avoid the overheating of the TSVs and mitigate the reliability problem. Furthermore, the K-means clustering algorithm is further adopted to compress the routing table of RLARA by exploiting the routing information similarity. To alleviate the inherent deadlock issue of adaptive routing algorithms, the link Q-value from reinforcement learning is combined with the router status based in buffer utilization to predict the congestion and enable RLARA to perform best even under a high traffic load. The experimental results of the ablation study on simulator Garnet 2.0 verify the effectiveness of our proposed RLARA under different fault models, which can perform better than the latest 3D NoC routing algorithms, with up to a 9.04% lower average delay and 8.58% higher successful delivered rate. Full article

(This article belongs to the Section Computer Science & Engineering)

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13 pages, 2277 KiB

Open AccessCommunication

A 22.3-Bit Third-Order Delta-Sigma Modulator for EEG Signal Acquisition Systems

by Qianqian Wang, Fei Liu, Liyin Fu, Qianhui Li, Jing Kang, Ke Chen and Zongliang Huo

Electronics 2023, 12(23), 4866; https://doi.org/10.3390/electronics12234866 - 02 Dec 2023

Viewed by 687

Abstract

This paper presents a high resolution delta-sigma modulator for continuous acquisition of electroencephalography (EEG) signals. The third-order single-loop architecture with a 1-bit quantizer is adopted to achieve 22.3-bit resolution. The effects of thermal noise on the performance of the delta-sigma modulator are analyzed [...] Read more.

This paper presents a high resolution delta-sigma modulator for continuous acquisition of electroencephalography (EEG) signals. The third-order single-loop architecture with a 1-bit quantizer is adopted to achieve 22.3-bit resolution. The effects of thermal noise on the performance of the delta-sigma modulator are analyzed to reasonably allocate the switched-capacitor sizes for optimal signal to noise ratio (SNR) and minimum chip area. The coefficients in feedback path and input path are optimized to avoid the signal distortion under the full-scale input voltage range with almost no increase in total capacitance sizes. Fabricated in 0.5 µm CMOS technology and powered by a 5 V voltage supply, the proposed delta-sigma modulator can achieve 136 dB peak SNR with 16 Hz input and 137 dB dynamic range in 100 Hz signal bandwidth with an oversampling ratio of 512. The modulator dissipates 700 µA. The core chip area is 1.96 mm

^{2}

. The modulator occupies 1.41 mm

^{2}

and the decimator occupies 0.55 mm

^{2}

. Full article

(This article belongs to the Special Issue Design of Mixed Analog/Digital Circuits, Volume 2)

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12 pages, 305 KiB

Open AccessArticle

Mobile Sensoring Data Verification via a Pairing-Free Certificateless Signature Secure Approach against Novel Public Key Replacement Attacks

by Guilin Wang, Hua Shen, Liquan Chen, Jinguang Han and Ge Wu

Electronics 2023, 12(23), 4865; https://doi.org/10.3390/electronics12234865 - 02 Dec 2023

Viewed by 711

Abstract

To achieve flexible sensing coverage with low deployment costs, mobile users need to contribute their equipment as sensors. Data integrity is one of the most fundamental security requirements and can be verified by digital signature techniques. In the mobile crowdsensing (MCS) environment, most [...] Read more.

To achieve flexible sensing coverage with low deployment costs, mobile users need to contribute their equipment as sensors. Data integrity is one of the most fundamental security requirements and can be verified by digital signature techniques. In the mobile crowdsensing (MCS) environment, most sensors, such as smartphones, are resource-limited. Therefore, many traditional cryptographic algorithms that require complex computations cannot be efficiently implemented on these sensors. In this paper, we study the security of certificateless signatures, in particular, some constructions without pairing. We notice that there is no secure pairing-free certificateless signature scheme against the super adversary. We also find a potential attack that has not been fully addressed in previous studies. To handle these two issues, we propose a concrete secure construction that can withstand this attack. Our scheme does not rely on pairing operations and can be applied in scenarios where the devices’ resources are limited. Full article

(This article belongs to the Special Issue Data Privacy and Cybersecurity in Mobile Crowdsensing)

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12 pages, 283 KiB

Open AccessArticle

A Novel Unsupervised Outlier Detection Algorithm Based on Mutual Information and Reduced Spectral Clustering

by Yuehua Huang, Wenfen Liu, Song Li, Ying Guo and Wen Chen

Electronics 2023, 12(23), 4864; https://doi.org/10.3390/electronics12234864 - 02 Dec 2023

Cited by 1 | Viewed by 818

Abstract

Outlier detection is an essential research field in data mining, especially in the areas of network security, credit card fraud detection, industrial flaw detection, etc. The existing outlier detection algorithms, which can be divided into supervised methods and unsupervised methods, suffer from the [...] Read more.

Outlier detection is an essential research field in data mining, especially in the areas of network security, credit card fraud detection, industrial flaw detection, etc. The existing outlier detection algorithms, which can be divided into supervised methods and unsupervised methods, suffer from the following problems: curse of dimensionality, lack of labeled data, and hyperparameter tuning. To address these issues, we present a novel unsupervised outlier detection algorithm based on mutual information and reduced spectral clustering, called MISC-OD (Mutual Information and reduced Spectral Clustering—Outlier Detection). MISC-OD first constructs a mutual information matrix between features, then, by applying reduced spectral clustering, divides the feature set into subsets, utilizing the LOF (Local Outlier Factor) for outlier detection within each subset and combining the outlier scores found within each subset. Finally, it outputs the outlier score. Our contributions are as follows: (1) we propose a novel outlier detection method called MISC-OD with high interpretability and scalability; (2) numerous experiments on 18 benchmark datasets demonstrate the superior performance of the MISC-OD algorithm compared with eight state-of-the-art baselines in terms of ROC (receiver operating characteristic) and AP (average precision). Full article

(This article belongs to the Section Artificial Intelligence)

14 pages, 3470 KiB

Open AccessArticle

A Novel Relocalization Method-Based Dynamic Steel Billet Flaw Detection and Marking System

by Hongxing Zhou, Juan Chen, Qinghan Hu, Xue Zhao and Zhiqing Li

Electronics 2023, 12(23), 4863; https://doi.org/10.3390/electronics12234863 - 02 Dec 2023

Viewed by 802

Abstract

In the current steel production process, occasional flaws within the billet are somewhat inevitable. Overlooking these flaws can compromise the quality of the resulting steel products. To address and mark these flaws for further handling, Magnetic Particle Testing (MT) in conjunction with machine [...] Read more.

In the current steel production process, occasional flaws within the billet are somewhat inevitable. Overlooking these flaws can compromise the quality of the resulting steel products. To address and mark these flaws for further handling, Magnetic Particle Testing (MT) in conjunction with machine vision is commonly utilized. This method identifies flaws on the billet’s surface and subsequently marks them via a device, eliminating the need for manual intervention. However, certain processes, such as magnetic particle cleaning, require substantial spacing between the vision system and the marking device. This extended distance can lead to shifts in the billet position, thereby potentially affecting the precision of flaw marking. In response to this challenge, we developed a detection-marking system consisting of 2D cameras, a manipulator, and an integrated 3D camera to accurately pinpoint the flaw’s location. Importantly, this system can be integrated into active production lines without causing disruptions. Experimental assessments on dynamic billets substantiated the system’s efficacy and feasibility. Full article

(This article belongs to the Special Issue Challenging Methods and Applications for Smart Measurement Using Machine Vision)

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27 pages, 2768 KiB

Open AccessArticle

Switching Capacitor Filter with Multiple Functions, Adjustable Bandwidth in the Range of 5 Hz–10 kHz

by Fan He, Yubo Yuan, Jinjin Xiao, Zehao Han, Yingchun Fu and Shuilong Huang

Electronics 2023, 12(23), 4862; https://doi.org/10.3390/electronics12234862 - 01 Dec 2023

Viewed by 961

Abstract

This article proposes a second-order switch-capacitor filter that integrates low-pass, high-pass, band-pass, band-stop, and all-pass, and achieves flexible bandwidth adjustment of the filter through clock rate and capacitance ratio. The final filter design consists of two completely independent second-order switch-capacitor filter channels, and [...] Read more.

This article proposes a second-order switch-capacitor filter that integrates low-pass, high-pass, band-pass, band-stop, and all-pass, and achieves flexible bandwidth adjustment of the filter through clock rate and capacitance ratio. The final filter design consists of two completely independent second-order switch-capacitor filter channels, and a 4-order Butterworth low-pass filter is designed through two-stage cascades. The two completely independent second-order switch-capacitor filters are integrated on a single chip and manufactured using the Huahong BCD350GE high-voltage 24 V process. The measurement results indicate that the proposed switch-capacitor filter achieves various functional filtering characteristics and achieves a bandwidth of 5 Hz to 10 kHz. The chip area is 5.1 × 3.1 mm

^{2}

, powered by a dual power supply of ± 5 V, and the power consumption is 80 mW. Full article

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

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14 pages, 3276 KiB

Open AccessArticle

Research on the Signal Noise Reduction Method of Fish Electrophysiological Behavior Based on CEEMDAN with Improved Wavelet Thresholding

by Jingfei Meng, Weiming Cai, Siyi Ou, Jian Zhao, Shengli Fan and Bicong Zheng

Electronics 2023, 12(23), 4861; https://doi.org/10.3390/electronics12234861 - 01 Dec 2023

Viewed by 830

Abstract

Electrophysiological signals are one of the key ways that fish convey information and govern movement. Changes in physiological electrical signals may indirectly reflect changes in fish sensory thresholds and locomotor behavior. The acquisition of physiological electrical signals in fish is more susceptible than [...] Read more.

Electrophysiological signals are one of the key ways that fish convey information and govern movement. Changes in physiological electrical signals may indirectly reflect changes in fish sensory thresholds and locomotor behavior. The acquisition of physiological electrical signals in fish is more susceptible than in mammals to the effects of surface mucus and water noise, thereby reducing signal quality. In this study, a noise reduction method for electrophysiological behavioral signals in fish was proposed, namely the decomposition of the original EMG signal into multiple intrinsic mode components using CEEMDAN. To choose the signal-dominated IMF, noise-dominated IMF, and pure IMF, mutual correlation function characteristic analysis is done on each IMF and the original signal. The signal-dominated IMF is then filtered using the improved wavelet thresholding approach. Finally, the wavelet threshold filtered signal-dominated IMF with pure IMF was reconstructed into the processed fish EMG signal. It is demonstrated that the algorithm proposed in this paper improves the SNR by 3.1977 dB and reduces the RMSE by 0.0235 when compared to the traditional wavelet threshold denoising. The denoising method proposed in this paper can effectively improve the signal quality and provides an effective tool for the in-depth analysis of fish behavior from the perspective of physiological electrical signals. Full article

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27 pages, 7822 KiB

Open AccessArticle

Specific Point in Time Excitation Control Method for Spatial Multi-Degree-of-Freedom Systems under Continuous Operation

by Shengtao Zhang and Yixiao Qin

Electronics 2023, 12(23), 4860; https://doi.org/10.3390/electronics12234860 - 01 Dec 2023

Viewed by 655

Abstract

The port container gantry crane studied in this paper is a four-degree-of-freedom spatial continuous system. In actual work, in order to make the container transfer smoothly, the response of the whole system needs to be accurately predicted and timely adjusted. The whole system [...] Read more.

The port container gantry crane studied in this paper is a four-degree-of-freedom spatial continuous system. In actual work, in order to make the container transfer smoothly, the response of the whole system needs to be accurately predicted and timely adjusted. The whole system is divided into rotary mechanism, lifting mechanism, lifting trolley mechanism, and big cart mechanism for detailed analysis. By constructing the field transfer matrix, a one-dimensional wave equation of continuous system and the Lagrange equation with redundant parameters, the response of each subsystem is solved precisely. The results of the study found that in some periods, the swing of the container was too large. In order to improve the safety and stability of transmission, an active control method of specific point in time excitation (SPE) is proposed for the first time. This method predicts the swing amplitude of the container in advance using the response results of the numerical model. When the set response interval is exceeded, the external excitation intervention can effectively inhibit the moving range of the container in the transit process. Finally, the results are compared with the simulation model to achieve the experimental purpose. It is in line with the expected experimental effect. Full article

(This article belongs to the Topic Cooperative Localization, Optimization and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)

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23 pages, 1017 KiB

Open AccessArticle

A Deep Learning Approach for Speech Emotion Recognition Optimization Using Meta-Learning

by Lara Toledo Cordeiro Ottoni, André Luiz Carvalho Ottoni and Jés de Jesus Fiais Cerqueira

Electronics 2023, 12(23), 4859; https://doi.org/10.3390/electronics12234859 - 01 Dec 2023

Cited by 1 | Viewed by 1784

Abstract

Speech emotion recognition (SER) is widely applicable today, benefiting areas such as entertainment, robotics, and healthcare. This emotional understanding enhances user-machine interaction, making systems more responsive and providing more natural experiences. In robotics, SER is useful in home assistance devices, eldercare, and special [...] Read more.

Speech emotion recognition (SER) is widely applicable today, benefiting areas such as entertainment, robotics, and healthcare. This emotional understanding enhances user-machine interaction, making systems more responsive and providing more natural experiences. In robotics, SER is useful in home assistance devices, eldercare, and special education, facilitating effective communication. Additionally, in healthcare settings, it can monitor patients’ emotional well-being. However, achieving high levels of accuracy is challenging and complicated by the need to select the best combination of machine learning algorithms, hyperparameters, datasets, data augmentation, and feature extraction methods. Therefore, this study aims to develop a deep learning approach for optimal SER configurations. It delves into the domains of optimizer settings, learning rates, data augmentation techniques, feature extraction methods, and neural architectures for the RAVDESS, TESS, SAVEE, and R+T+S (RAVDESS+TESS+SAVEE) datasets. After finding the best SER configurations, meta-learning is carried out, transferring the best configurations to two additional datasets, CREMA-D and R+T+S+C (RAVDESS+TESS+SAVEE+CREMA-D). The developed approach proved effective in finding the best configurations, achieving an accuracy of 97.01% for RAVDESS, 100% for TESS, 90.62% for SAVEE, and 97.37% for R+T+S. Furthermore, using meta-learning, the CREMA-D and R+T+S+C datasets achieved accuracies of 83.28% and 90.94%, respectively. Full article

(This article belongs to the Special Issue Machine Learning for Signals Processing)

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25 pages, 4224 KiB

Open AccessReview

A Comprehensive Survey on Wi-Fi Sensing for Human Identity Recognition

by Pengsong Duan, Xianguang Diao, Yangjie Cao, Dalong Zhang, Bo Zhang and Jinsheng Kong

Electronics 2023, 12(23), 4858; https://doi.org/10.3390/electronics12234858 - 01 Dec 2023

Viewed by 1138

Abstract

In recent years, Wi-Fi sensing technology has become an emerging research direction of human–computer interaction due to its advantages of low cost, contactless, illumination insensitivity, and privacy preservation. At present, Wi-Fi sensing research has been expanded from target location to action recognition and [...] Read more.

In recent years, Wi-Fi sensing technology has become an emerging research direction of human–computer interaction due to its advantages of low cost, contactless, illumination insensitivity, and privacy preservation. At present, Wi-Fi sensing research has been expanded from target location to action recognition and identity recognition, among others. This paper summarizes and analyzes the research of Wi-Fi sensing technology in human identity recognition. Firstly, we overview the history of Wi-Fi sensing technology, compare it with traditional identity-recognition technologies and other wireless sensing technologies, and highlight its advantages for identity recognition. Secondly, we introduce the steps of the Wi-Fi sensing process in detail, including data acquisition, data pre-processing, feature extraction, and identity classification. After that, we review state-of-the-art approaches using Wi-Fi sensing for single- and multi-target identity recognition. In particular, three kinds of approaches (pattern-based, model-based, and deep learning-based) for single-target identity recognition and two kinds of approaches (direct recognition and separated recognition) for multi-target identity recognition are introduced and analyzed. Finally, future research directions are discussed, which include transfer learning, improved multi-target recognition, and unified dataset construction. Full article

(This article belongs to the Special Issue Energy-Efficient Wireless Solutions for 6G/B6G)

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