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Electronics
Volume 12
Issue 20
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Electronics, Volume 12, Issue 20 (October-2 2023) – 194 articles

Cover Story (view full-size image): In the era of autonomous vehicles (AVs), pedestrian safety at crosswalks is a critical concern. AVs excel in standard traffic conditions, but challenges arise in unique environments like 'naked streets' where traditional traffic infrastructure is absent. This study introduces a novel solution to enhance pedestrian safety in shared spaces, featuring the Smart Pole Interaction Unit (SPIU) in conjunction with an external Human–Machine Interface (eHMI). Our research demonstrates that SPIU streamlines safe decision-making for pedestrians at crosswalks, reducing cognitive loads and the need for eHMI when interacting with multiple AVs, thus improving overall safety and efficiency in shared spaces. View this paper
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26 pages, 8013 KiB  
Article
A Proposed Single-Input Multi-Output Battery-Connected DC–DC Buck–Boost Converter for Automotive Applications
by Hakan Tekin, Göknur Setrekli, Eren Murtulu, Hikmet Karşıyaka and Davut Ertekin
Electronics 2023, 12(20), 4381; https://doi.org/10.3390/electronics12204381 - 23 Oct 2023
Cited by 6 | Viewed by 2491
Abstract
In the realm of electric vehicles (EVs), achieving diverse direct current (DC) voltage levels is essential to meet varying electrical load demands. This requires meticulous control of the battery voltage, which must be adjusted in line with specific load characteristics. Therefore, the integration [...] Read more.
In the realm of electric vehicles (EVs), achieving diverse direct current (DC) voltage levels is essential to meet varying electrical load demands. This requires meticulous control of the battery voltage, which must be adjusted in line with specific load characteristics. Therefore, the integration of a well-designed power converter circuit is crucial, as it plays a pivotal role in generating different DC voltage outputs. In this study, we also consider the incorporation of two additional doubler/divider circuits at the end of the proposed converter, further enhancing its capacity to produce distinct DC voltage levels, thus increasing its versatility. The standout feature of the proposed converter lies in its remarkable ability to amplify DC voltages significantly. For instance, when the input battery voltage is set at 48 VDC with a duty cycle (D) of 0.8, the resulting output demonstrates a remarkable augmentation, producing voltages 18, 36, and 72 times higher than the input voltage. Conversely, with a reduced D of 0.2 while maintaining the input voltage at 48 VDC, the converter yields diminished voltages of 0.1875, 0.375, and 0.75 times the initial voltage. This adaptability, based on the parameterization of D, underscores the converter’s ability to cater to a wide range of voltage requirements. To oversee the intricate operations of this versatile converter, a high-speed DSP-based controller system is employed. It utilizes the renowned PID approach, known for its proficiency in navigating complex, nonlinear systems. Experimental results validate the theoretical and simulation findings, reaffirming the converter’s practical utility in EV applications. The study introduces a simple control mechanism with a single power switch, high efficiency for high-power applications, wide voltage range, especially with VDC and VMC cells, and continuous current operation for the load in CCM mode. This study underscores the significance of advanced power conversion systems in shaping the future of electric transportation. Full article
(This article belongs to the Special Issue Application and Design of Power Electronics Converters for Electric Vehicle Charging)
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14 pages, 5487 KiB  
Article
A Novel Complex-Valued Hybrid Neural Network for Automatic Modulation Classification
by Zhaojing Xu, Shunhu Hou, Shengliang Fang, Huachao Hu and Zhao Ma
Electronics 2023, 12(20), 4380; https://doi.org/10.3390/electronics12204380 - 23 Oct 2023
Cited by 2 | Viewed by 1420
Abstract
Currently, dealing directly with in-phase and quadrature time series data using the deep learning method is widely used in signal modulation classification. However, there is a relative lack of methods that consider the complex properties of signals. Therefore, to make full use of [...] Read more.
Currently, dealing directly with in-phase and quadrature time series data using the deep learning method is widely used in signal modulation classification. However, there is a relative lack of methods that consider the complex properties of signals. Therefore, to make full use of the inherent relationship between in-phase and quadrature time series data, a complex-valued hybrid neural network (CV-PET-CSGDNN) based on the existing PET-CGDNN network is proposed in this paper, which consists of phase parameter estimation, parameter transformation, and complex-valued signal feature extraction layers. The complex-valued signal feature extraction layers are composed of complex-valued convolutional neural networks (CNN), complex-valued gate recurrent units (GRU), squeeze-and-excite (SE) blocks, and complex-valued dense neural networks (DNN). The proposed network can improve the extraction of the intrinsic relationship between in-phase and quadrature time series data with low capacity and then improve the accuracy of modulation classification. Experiments are carried out on RML2016.10a and RML2018.01a. The results show that, compared with ResNet, CLDNN, MCLDNN, PET-CGDNN, and CV-ResNet models, our proposed complex-valued neural network (CVNN) achieves the highest average accuracy of 61.50% and 62.92% for automatic modulation classification, respectively. In addition, the proposed CV-PET-CSGDNN has a significant improvement in the misjudgment situation between 64QAM, 128QAM, and 256QAM compared with PET-CGDNN on RML2018.01a. Full article
(This article belongs to the Special Issue Recent Advances and Challenges of Satellite and Aerial Communication Networks)
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26 pages, 6872 KiB  
Article
Implementing Real-Time DOV Compensation: A Practical Approach Using a MLP on an NPU Embedded in an INS
by Hyunseok Kim, Hyungsoo Kim, Yunhyuk Choi, Yunchul Cho and Chansik Park
Electronics 2023, 12(20), 4379; https://doi.org/10.3390/electronics12204379 - 23 Oct 2023
Viewed by 1078
Abstract
This paper explores the impact of gravity disturbances on INS accuracy and presents a method for real-time compensation during the navigation process. By utilizing data from the precise gravity model, EGM2008, a novel approach to compensate for the DOV in real time on [...] Read more.
This paper explores the impact of gravity disturbances on INS accuracy and presents a method for real-time compensation during the navigation process. By utilizing data from the precise gravity model, EGM2008, a novel approach to compensate for the DOV in real time on the INS’s built-in NPU was introduced. This method predicts gravity disturbances while traveling for platforms on both land and water, utilizing the MLP technique. To predict these gravity disturbances, four distinct MLP models, MLP1~MLP4, were designed and their supervised learning results were compared using HMSE and RMSE. This comparative analysis allowed us to identify that the MLP4 model exhibited the best performance. In order to validate the proposed method, MLP4 was implemented inside the NPU and the measured execution time was 1.041 ms. The field test was conducted with real-time execution of the MLP4 model on the NPU of the INS. The results of this field test clearly demonstrated the effectiveness of the proposed approach in enhancing position accuracy. Over the course of a 2 h field test, it was evident that employing the proposed method improved position accuracy by a notable 27%. Full article
(This article belongs to the Section Systems & Control Engineering)
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19 pages, 4236 KiB  
Article
Improved Leakage Detection and Recognition Algorithm for Residual Neural Networks Based on Transfer Learning
by Liangliang Li, Yu Chen, Zhengxiang Ma, Xinling Wen, Jiabao Pang and Weitao Yuan
Electronics 2023, 12(20), 4378; https://doi.org/10.3390/electronics12204378 - 23 Oct 2023
Cited by 1 | Viewed by 1194
Abstract
Due to the lack of other component information in traditional magnetic leakage signal defects and the low accuracy of prediction methods, this paper proposes an improved residual network for magnetic leakage detection defect recognition method that predicts defect size and different detection speeds. [...] Read more.
Due to the lack of other component information in traditional magnetic leakage signal defects and the low accuracy of prediction methods, this paper proposes an improved residual network for magnetic leakage detection defect recognition method that predicts defect size and different detection speeds. A new defect diagnosis method based on ResNet18 on the Convolutional Neural Network (CNN) is proposed in this study. This method transfers the pre-trained ResNet18 network and replaces the activation function in the transferred network structure. It extracts features from transformed two-dimensional images obtained by converting the original experimental signals and signals with added noise, removing the influence of manual features. The results demonstrated that the improved ResNet18 network model, after transfer learning, achieved 100% prediction accuracy for all 10,000 grayscale images generated with defect lengths of 50 mm; width of 2 mm; and depths of 2 mm, 4 mm, 6 mm, and 8 mm. Moreover, the prediction accuracies for the quasi-static, slow, compensated fast, and fast scanning speeds were 99.20%, 98.50%, 93.30%, and 94.00%, respectively, for defect depths of 2 mm, 4 mm, 6 mm, and 8 mm. These accuracies surpass those of other models, demonstrating the significant improvement in prediction accuracy achieved by this method. Full article
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18 pages, 6202 KiB  
Article
YG-SLAM: GPU-Accelerated RGBD-SLAM Using YOLOv5 in a Dynamic Environment
by Yating Yu, Kai Zhu and Wangshui Yu
Electronics 2023, 12(20), 4377; https://doi.org/10.3390/electronics12204377 - 23 Oct 2023
Cited by 3 | Viewed by 1649
Abstract
Traditional simultaneous localization and mapping (SLAM) performs well in a static environment; however, with the abrupt increase of dynamic points in dynamic environments, the algorithm is influenced by a lot of meaningless information, leading to low precision and poor robustness in pose estimation. [...] Read more.
Traditional simultaneous localization and mapping (SLAM) performs well in a static environment; however, with the abrupt increase of dynamic points in dynamic environments, the algorithm is influenced by a lot of meaningless information, leading to low precision and poor robustness in pose estimation. To tackle this problem, a new visual SLAM algorithm of dynamic scenes named YG-SLAM is proposed, which creates an independent dynamic-object-detection thread and adds a dynamic-feature-point elimination step in the tracking thread. The YOLOv5 algorithm is introduced in the dynamic-object-detection thread for target recognition and deployed on the GPU to speed up image frame identification. The optic-flow approach employs an optic flow to monitor feature points and helps to remove the dynamic points in different dynamic objects based on the varying speeds of pixel movement. While combined with the antecedent information of object detection, the system can eliminate dynamic feature points under various conditions. Validation is conducted in both TUM and KITTI datasets, and the results illustrate that YG-SLAM can achieve a higher accuracy in dynamic indoor environments, with the maximum accuracy augmented from 0.277 m to 0.014 m. Meanwhile, YG-SLAM requires less processing time than other dynamic-scene SLAM algorithms, indicating its positioning priority in dynamic situations. Full article
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31 pages, 13108 KiB  
Article
Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism
by Konstantinos Mountzouris, Isidoros Perikos and Ioannis Hatzilygeroudis
Electronics 2023, 12(20), 4376; https://doi.org/10.3390/electronics12204376 - 23 Oct 2023
Cited by 4 | Viewed by 3283
Abstract
Speech emotion recognition (SER) is an interesting and difficult problem to handle. In this paper, we deal with it through the implementation of deep learning networks. We have designed and implemented six different deep learning networks, a deep belief network (DBN), a simple [...] Read more.
Speech emotion recognition (SER) is an interesting and difficult problem to handle. In this paper, we deal with it through the implementation of deep learning networks. We have designed and implemented six different deep learning networks, a deep belief network (DBN), a simple deep neural network (SDNN), an LSTM network (LSTM), an LSTM network with the addition of an attention mechanism (LSTM-ATN), a convolutional neural network (CNN), and a convolutional neural network with the addition of an attention mechanism (CNN-ATN), having in mind, apart from solving the SER problem, to test the impact of the attention mechanism on the results. Dropout and batch normalization techniques are also used to improve the generalization ability (prevention of overfitting) of the models as well as to speed up the training process. The Surrey Audio–Visual Expressed Emotion (SAVEE) database and the Ryerson Audio–Visual Database (RAVDESS) were used for the training and evaluation of our models. The results showed that the networks with the addition of the attention mechanism did better than the others. Furthermore, they showed that the CNN-ATN was the best among the tested networks, achieving an accuracy of 74% for the SAVEE database and 77% for the RAVDESS, and exceeding existing state-of-the-art systems for the same datasets. Full article
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering)
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16 pages, 5457 KiB  
Article
Research on the Method of Hypergraph Construction of Information Systems Based on Set Pair Distance Measurement
by Jing Wang, Siwu Lan, Xiangyu Li, Meng Lu, Jingfeng Guo, Chunying Zhang and Bin Liu
Electronics 2023, 12(20), 4375; https://doi.org/10.3390/electronics12204375 - 23 Oct 2023
Cited by 1 | Viewed by 1521
Abstract
As a kind of special graph of structured data, a hypergraph can intuitively describe not only the higher-order relation and complex connection mode between nodes but also the implicit relation between nodes. Aiming at the limitation of traditional distance measurement in high-dimensional data, [...] Read more.
As a kind of special graph of structured data, a hypergraph can intuitively describe not only the higher-order relation and complex connection mode between nodes but also the implicit relation between nodes. Aiming at the limitation of traditional distance measurement in high-dimensional data, a new method of hypergraph construction based on set pair theory is proposed in this paper. By means of dividing the relationship between data attributes, the set pair connection degree between samples is calculated, and the set pair distance between samples is obtained. Then, on the basis of set pair distance, the combination technique of k-nearest neighbor and ε radius is used to construct a hypergraph, and high-dimensional expression and hypergraph clustering are demonstrated experimentally. By performing experiments on different datasets on the Kaggle open-source dataset platform, the comparison of cluster purity, the Rand coefficient, and normalized mutual information are shown to demonstrate that this distance measurement method is more effective in high-dimensional expression and exhibits a more significant performance improvement in spectral clustering. Full article
(This article belongs to the Special Issue Intelligent Analysis and Security Calculation of Multisource Data)
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18 pages, 711 KiB  
Article
A Low-Cost High-Throughput C-Band Communication System for CubeSats
by Rafał Krenz, Paweł Sroka, Michał Sybis, Ilia Zainutdinov and Krzysztof Wesołowski
Electronics 2023, 12(20), 4374; https://doi.org/10.3390/electronics12204374 - 23 Oct 2023
Viewed by 1523
Abstract
This paper presents the physical layer of a proprietary broadband communication system for CubeSats. The system operates in the C band (5.8 GHz), delivering at least 10 Mbps of the net user throughput. Operation at low elevation angles (and therefore low SNRs) and [...] Read more.
This paper presents the physical layer of a proprietary broadband communication system for CubeSats. The system operates in the C band (5.8 GHz), delivering at least 10 Mbps of the net user throughput. Operation at low elevation angles (and therefore low SNRs) and high Doppler shifts is made possible thanks to a sophisticated synchronization subsystem. The system can be adapted to propagation conditions experienced during a given visibility window by changing the signal bandwidth and coding rate. It is implemented using Software Defined Radio (SDR) technology. The system will be used in two missions that are scheduled for 2023 and 2024 and are planned in cooperation with the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” and SatRev S.A. Full article
(This article belongs to the Special Issue Recent Advances and Challenges of Satellite and Aerial Communication Networks)
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15 pages, 3631 KiB  
Article
Dynamic Temperature Compensation of Pressure Sensors in Migratory Bird Biologging Applications
by Jinlu Xie, Zhitian Li and Xudong Zou
Electronics 2023, 12(20), 4373; https://doi.org/10.3390/electronics12204373 - 22 Oct 2023
Cited by 2 | Viewed by 1452
Abstract
This article proposes an improved dynamic quantum particle swarm optimization (DQPSO) algorithm to optimize a radial basis function (RBF) neural network for temperature compensation of pressure sensors used in tracking and monitoring wild migratory birds. The algorithm incorporates a temperature-pressure fitting model that [...] Read more.
This article proposes an improved dynamic quantum particle swarm optimization (DQPSO) algorithm to optimize a radial basis function (RBF) neural network for temperature compensation of pressure sensors used in tracking and monitoring wild migratory birds. The algorithm incorporates a temperature-pressure fitting model that includes temperature rate of change and gradient reference terms. It also includes a loss function that considers fitting accuracy and complexity, thereby improving the robustness of the sensor for complex temperature variations. The calibration experiments revealed that after implementation, the average absolute error of the pressure sensor output during dynamic temperature changes was reduced from 145.3 Pa to 20.2 Pa. This reduction represents an 86% improvement over the commercial polynomial compensation method, and the DQPSO approach significantly outperformed traditional feedforward network models. Finally, the algorithm was deployed and verified in an embedded environment for low-power, high-precision, real-time pressure compensation during the tracking and monitoring of wild migratory birds. Full article
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18 pages, 867 KiB  
Article
A Multiscale Neighbor-Aware Attention Network for Collaborative Filtering
by Jianxing Zheng, Tengyue Jing, Feng Cao, Yonghong Kang, Qian Chen and Yanhong Li
Electronics 2023, 12(20), 4372; https://doi.org/10.3390/electronics12204372 - 22 Oct 2023
Viewed by 1159
Abstract
Most recommender systems rely on user and item attributes or their interaction records to find similar neighbors for collaborative filtering. Existing methods focus on developing collaborative signals from only one type of neighbors and ignore the unique contributions of different types of neighbor [...] Read more.
Most recommender systems rely on user and item attributes or their interaction records to find similar neighbors for collaborative filtering. Existing methods focus on developing collaborative signals from only one type of neighbors and ignore the unique contributions of different types of neighbor views. This paper proposes a multiscale neighbor-aware attention network for collaborative filtering (MSNAN). First, attribute-view neighbor embedding is modeled to extract the features of different types of neighbors with co-occurrence attributes, and interaction-view neighbor embedding is leveraged to describe the fine-grained neighborhood behaviors of ratings. Then, a matched attention network is used to identify different contributions of multiscale neighbors and capture multiple types of collaborative signals for overcoming sparse recommendations. Finally, we make the rating prediction by a joint learning of multi-task loss and verify the positive effect of the proposed MSNAN on three datasets. Compared with traditional methods, the experimental results of the proposed MSNAN not only improve the accuracy in MAE and RMSE indexes, but also solve the problem of poor performance for recommendation in sparse data scenarios. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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21 pages, 1861 KiB  
Article
Learning-Based Collaborative Computation Offloading in UAV-Assisted Multi-Access Edge Computing
by Zikun Xu, Junhui Liu, Ying Guo, Yunyun Dong and Zhenli He
Electronics 2023, 12(20), 4371; https://doi.org/10.3390/electronics12204371 - 22 Oct 2023
Cited by 2 | Viewed by 1328
Abstract
Unmanned aerial vehicles (UAVs) have gained considerable attention in the research community due to their exceptional agility, maneuverability, and potential applications in fields like surveillance, multi-access edge computing (MEC), and various other domains. However, efficiently providing computation offloading services for concurrent Internet of [...] Read more.
Unmanned aerial vehicles (UAVs) have gained considerable attention in the research community due to their exceptional agility, maneuverability, and potential applications in fields like surveillance, multi-access edge computing (MEC), and various other domains. However, efficiently providing computation offloading services for concurrent Internet of Things devices (IOTDs) remains a significant challenge for UAVs due to their limited computing and communication capabilities. Consequently, optimizing and managing the constrained computing, communication, and energy resources of UAVs are essential for establishing an efficient aerial network infrastructure. To address this challenge, we investigate the collaborative computation offloading optimization problem in a UAV-assisted MEC environment comprising multiple UAVs and multiple IODTs. Our primary objective is to obtain efficient offloading strategies within a multi-heterogeneous UAV environment characterized by limited computing and communication capabilities. In this context, we model the problem as a multi-agent markov decision process (MAMDP) to account for environmental dynamics. We employ a multi-agent deep deterministic policy gradient (MADDPG) approach for task offloading. Subsequently, we conduct simulations to evaluate the efficiency of our proposed offloading scheme. The results highlight significant improvements achieved by the proposed offloading strategy, including a notable increase in the system completion rate and a significant reduction in the average energy consumption of the system. Full article
(This article belongs to the Special Issue Celebrating the 100th Anniversary of Yunnan University—Securing Mobile Edge Computing: Challenges and Solutions for Edge Architectures)
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22 pages, 2826 KiB  
Article
Research on Resource Allocation of Autonomous Swarm Robots Based on Game Theory
by Zixiang He, Yi Sun and Zhongyuan Feng
Electronics 2023, 12(20), 4370; https://doi.org/10.3390/electronics12204370 - 22 Oct 2023
Cited by 2 | Viewed by 1198
Abstract
To address the issue of resource allocation optimization in autonomous swarm robots during emergency situations, this paper abstracts the problem as a two-stage extended game. In this game, participants are categorized as either resource-providing robots or resource-consuming robots. The strategies of the resource-providing [...] Read more.
To address the issue of resource allocation optimization in autonomous swarm robots during emergency situations, this paper abstracts the problem as a two-stage extended game. In this game, participants are categorized as either resource-providing robots or resource-consuming robots. The strategies of the resource-providing robots involve resource production and pricing, whereas the strategies of the resource-consuming robots consist of determining the quantity to be purchased based on resource pricing. In the first stage of the game, the resource-providing robots use the Cournot game to determine the resource production according to market supply and demand conditions; in the second stage of the game, the resource-providing robots and the resource-consuming robots play the price game and establish the utility function of the swarm robots to seek the optimal pricing and the optimal purchasing strategy of the swarm robots. After the mathematical derivation, this paper demonstrates the existence of a single Nash equilibrium in the constructed game. Additionally, the inverse distributed iterative search algorithm solves the game’s optimal strategy. Finally, simulation verifies the game model’s validity. This study concludes that the designed game mechanism enables both sides to reach equilibrium and achieve optimal resource allocation. Full article
(This article belongs to the Special Issue Advanced Technologies in Autonomous Robotic System)
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17 pages, 1285 KiB  
Article
PD-PAn: Prefix- and Distribution-Preserving Internet of Things Traffic Anonymization
by Xiaodan Gu and Kai Dong
Electronics 2023, 12(20), 4369; https://doi.org/10.3390/electronics12204369 - 21 Oct 2023
Viewed by 1033
Abstract
One of the features of network traffic in Internet of Things (IoT) environments is that various IoT devices periodically communicate with their vendor services by sending and receiving packets with unique characteristics through private protocols. This paper investigates semantic attacks in IoT environments. [...] Read more.
One of the features of network traffic in Internet of Things (IoT) environments is that various IoT devices periodically communicate with their vendor services by sending and receiving packets with unique characteristics through private protocols. This paper investigates semantic attacks in IoT environments. An IoT semantic attack is active, covert, and more dangerous in comparison with traditional semantic attacks. A compromised IoT server actively establishes and maintains a communication channel with its device, and covertly injects fingerprints into the communicated packets. Most importantly, this server not only de-anonymizes other IPs, but also infers the machine states of other devices (IPs). Traditional traffic anonymization techniques, e.g., Crypto-PAn and Multi-View, either cannot ensure data utility or is vulnerable to semantic attacks. To address this problem, this paper proposes a prefix- and distribution-preserving traffic anonymization method named PD-PAn, which generates multiple anonymized views of the original traffic log to defend against semantic attacks. The prefix relationship is preserved in the real view to ensure data utility, while the IP distribution characteristic is preserved in all the views to ensure privacy. Intensive experiments verify the vulnerability of the state-of-the-art techniques and effectiveness of PD-PAn. Full article
(This article belongs to the Special Issue Privacy and Security for IoT Devices)
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11 pages, 7790 KiB  
Communication
Design Techniques for L-C-L T-Type Wideband CMOS Phase Shifter with Suppressed Phase Error
by Seongjin Jang and Changkun Park
Electronics 2023, 12(20), 4368; https://doi.org/10.3390/electronics12204368 - 21 Oct 2023
Viewed by 1287
Abstract
In this study, we designed a K-band CMOS switch-type phase shifter. Equivalent circuits of shift and pass modes were analyzed to minimize phase errors in a wide frequency range. In particular, the impedance inside the equivalent circuit of the pass mode was analyzed [...] Read more.
In this study, we designed a K-band CMOS switch-type phase shifter. Equivalent circuits of shift and pass modes were analyzed to minimize phase errors in a wide frequency range. In particular, the impedance inside the equivalent circuit of the pass mode was analyzed to derive a frequency region in which the equivalent circuit of the pass mode becomes an L-C-L structure. Based on the fact that equivalent circuits in shift and pass modes can be regarded as L-C-L structures beyond a specific frequency, a design methodology of the wideband phase shifter was proposed through slope adjustment of the phase according to the frequency of each of the two modes. To verify the feasibility of the proposed design methodology, a 20°-bit phase shifter was designed through a 65 nm RFCMOS process. As a result of the measurement at 21.5 GHz to 40.0 GHz, the phase error was within 0.87°. Full article
(This article belongs to the Special Issue RF, Microwave, and Millimeter Wave Devices and Circuits and Their Applications)
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18 pages, 3154 KiB  
Article
A Methodology and Open-Source Tools to Implement Convolutional Neural Networks Quantized with TensorFlow Lite on FPGAs
by Dorfell Parra, David Escobar Sanabria and Carlos Camargo
Electronics 2023, 12(20), 4367; https://doi.org/10.3390/electronics12204367 - 21 Oct 2023
Cited by 4 | Viewed by 1615
Abstract
Convolutional neural networks (CNNs) are used for classification, as they can extract complex features from input data. The training and inference of these networks typically require platforms with CPUs and GPUs. To execute the forward propagation of neural networks in low-power devices with [...] Read more.
Convolutional neural networks (CNNs) are used for classification, as they can extract complex features from input data. The training and inference of these networks typically require platforms with CPUs and GPUs. To execute the forward propagation of neural networks in low-power devices with limited resources, TensorFlow introduced TFLite. This library enables the inference process on microcontrollers by quantizing the network parameters and utilizing integer arithmetic. A limitation of TFLite is that it does not support CNNs to perform inference on FPGAs, a critical need for embedded applications that require parallelism. Here, we present a methodology and open-source tools for implementing CNNs quantized with TFLite on FPGAs. We developed a customizable accelerator for AXI-Lite-based systems on chips (SoCs), and we tested it on a Digilent Zybo-Z7 board featuring the XC7Z020 FPGA and an ARM processor at 667 MHz. Moreover, we evaluated this approach by employing CNNs trained to identify handwritten characters using the MNIST dataset and facial expressions with the JAFFE database. We validated the accelerator results with TFLite running on a laptop with an AMD 16-thread CPU running at 4.2 GHz and 16 GB RAM. The accelerator’s power consumption was 11× lower than the laptop while keeping a reasonable execution time. Full article
(This article belongs to the Topic Machine Learning in Internet of Things)
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31 pages, 699 KiB  
Article
Adaptive Load Balancing for Dual-Mode Communication Networks in the Power Internet of Things
by Kunpeng Xu, Zheng Li, Yunyi Yan, Hongguang Dai, Xianhui Wang, Jinlei Chen and Zesong Fei
Electronics 2023, 12(20), 4366; https://doi.org/10.3390/electronics12204366 - 21 Oct 2023
Viewed by 1434
Abstract
As an important part of the power Internet of Things, the dual-mode communication network that combines the high-speed power line carrier (HPLC) mode and high-speed radio frequency (HRF) mode is one of the hot directions in current research. Since non-uniform transmission demands for [...] Read more.
As an important part of the power Internet of Things, the dual-mode communication network that combines the high-speed power line carrier (HPLC) mode and high-speed radio frequency (HRF) mode is one of the hot directions in current research. Since non-uniform transmission demands for power consumption information can lead to link congestion among nodes, improving the network load-balancing performance becomes a critical issue. Therefore, this paper proposes a load-balancing routing algorithm for dual-mode communication networks, which is achieved in dual-mode communication networks by adding alternate paths and proxy coordinator (PCO) node election mechanism. Simulation results show that the proposed algorithm achieves the load-balanced distribution of power consumption information transmission. The proposed scheme reduces the delay and packet loss rate, as well as improving the throughput of dual-mode communication compared to existing routing algorithms. Full article
(This article belongs to the Special Issue Physical Layer Security in Future IoT Networks: Theories, Technologies, and Applications)
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15 pages, 7961 KiB  
Article
MCD-Yolov5: Accurate, Real-Time Crop Disease and Pest Identification Approach Using UAVs
by Lianpeng Li, Hui Zhao and Ning Liu
Electronics 2023, 12(20), 4365; https://doi.org/10.3390/electronics12204365 - 20 Oct 2023
Cited by 2 | Viewed by 1648
Abstract
As the principal factor affecting global food production, accurate identification of agricultural pests and diseases is crucial in ensuring a sustainable food supply. However, existing methods lack sufficient performance in terms of accuracy and real-time detection of multiple pests and diseases. Accordingly, accurate, [...] Read more.
As the principal factor affecting global food production, accurate identification of agricultural pests and diseases is crucial in ensuring a sustainable food supply. However, existing methods lack sufficient performance in terms of accuracy and real-time detection of multiple pests and diseases. Accordingly, accurate, efficient, and real-time identification of a wide range of pests and diseases is challenging. To address this, we propose an MCD-Yolov5 with a fusion design that combines multi-layer feature fusion (MLFF), convolutional block attention module CBAM, and detection transformer (DETF). In this model, we optimize the MLFF design to realize the dynamic adjustment of feature weights of the input feature layer to (1) find an appropriate distribution of feature information proportion for the detection task, (2) enhance detection speed by efficiently extracting effective images and effective features through CBAM, and (3) improve feature extraction capability through DETF to compensate for the accuracy problem of multiple pest detection. In addition, we established an unmanned aerial vehicle system (UAV) for crop pest and disease detection to assist in detection and prevention. We validate the performance of the proposed method through an established UAV platform, and five indicators are employed to quantify the performance. MCD-Yolov5 can detect pests and diseases with a large improvement in detection accuracy and detection efficiency, obtaining an 88.12% accuracy. The proposed method and system provide an idea for the effective identification of pests and diseases. Full article
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15 pages, 5027 KiB  
Article
Consideration of FedProx in Privacy Protection
by Tianbo An, Leyu Ma, Wei Wang, Yunfan Yang, Jingrui Wang and Yueren Chen
Electronics 2023, 12(20), 4364; https://doi.org/10.3390/electronics12204364 - 20 Oct 2023
Cited by 2 | Viewed by 1781
Abstract
As federated learning continues to increase in scale, the impact caused by device and data heterogeneity is becoming more severe. FedProx, as a comparison algorithm, is widely used as a solution to deal with system heterogeneity and statistical heterogeneity in several scenarios. However, [...] Read more.
As federated learning continues to increase in scale, the impact caused by device and data heterogeneity is becoming more severe. FedProx, as a comparison algorithm, is widely used as a solution to deal with system heterogeneity and statistical heterogeneity in several scenarios. However, there is no work that comprehensively investigates the enhancements that FedProx can bring to current secure federation algorithms in terms of privacy protection. In this paper, we combine differential privacy and personalized differential privacy with FedProx, propose the DP-Prox and PDP-Prox algorithms under different privacy budget settings and simulate the algorithms on multiple datasets. The experiments show that the proposed algorithms not only significantly improve the convergence of the privacy algorithms under different heterogeneity conditions, but also achieve similar or even better accuracy than the baseline algorithm. Full article
(This article belongs to the Special Issue Security and Privacy Preservation in Big Data Age)
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14 pages, 2966 KiB  
Article
A Multi-Modal Retrieval Model for Mathematical Expressions Based on ConvNeXt and Hesitant Fuzzy Set
by Ruxuan Li, Jingyi Wang and Xuedong Tian
Electronics 2023, 12(20), 4363; https://doi.org/10.3390/electronics12204363 - 20 Oct 2023
Viewed by 1493
Abstract
Mathematical expression retrieval is an essential component of mathematical information retrieval. Current mathematical expression retrieval research primarily targets single modalities, particularly text, which can lead to the loss of structural information. On the other hand, multimodal research has demonstrated promising outcomes across different [...] Read more.
Mathematical expression retrieval is an essential component of mathematical information retrieval. Current mathematical expression retrieval research primarily targets single modalities, particularly text, which can lead to the loss of structural information. On the other hand, multimodal research has demonstrated promising outcomes across different domains, and mathematical expressions in image format are adept at preserving their structural characteristics. So we propose a multi-modal retrieval model for mathematical expressions based on ConvNeXt and HFS to address the limitations of single-modal retrieval. For the image modal, mathematical expression retrieval is based on the similarity of image features and symbol-level features of the expression, where image features of the expression image are extracted by ConvNeXt, while symbol-level features are obtained by the Symbol Level Features Extraction (SLFE) module. For the text modal, the Formula Description Structure (FDS) is employed to analyze expressions and extract their attributes. Additionally, the application of the Hesitant Fuzzy Set (HFS) theory facilitates the computation of hesitant fuzzy similarity between mathematical queries and candidate expressions. Finally, Reciprocal Rank Fusion (RRF) is employed to integrate rankings from image modal and text modal retrieval, yielding the ultimate retrieval list. The experiment was conducted on the publicly accessible ArXiv dataset (containing 592,345 mathematical expressions) and the NTCIR-mair-wikipedia-corpus (NTCIR) dataset.The MAP@10 values for the multimodal RRF fusion approach are recorded as 0.774. These substantiate the efficacy of the multi-modal mathematical expression retrieval approach based on ConvNeXt and HFS. Full article
(This article belongs to the Special Issue Natural Language Processing and Information Retrieval)
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15 pages, 4474 KiB  
Article
FCIHMRT: Feature Cross-Layer Interaction Hybrid Method Based on Res2Net and Transformer for Remote Sensing Scene Classification
by Yan Huo, Shuang Gang and Chao Guan
Electronics 2023, 12(20), 4362; https://doi.org/10.3390/electronics12204362 - 20 Oct 2023
Cited by 40 | Viewed by 1550
Abstract
Scene classification is one of the areas of remote sensing image processing that is gaining much attention. Aiming to solve the problem of the limited precision of optical scene classification caused by complex spatial patterns, a high similarity between classes, and a high [...] Read more.
Scene classification is one of the areas of remote sensing image processing that is gaining much attention. Aiming to solve the problem of the limited precision of optical scene classification caused by complex spatial patterns, a high similarity between classes, and a high diversity of classes, a feature cross-layer interaction hybrid algorithm for optical remote sensing scene classification is proposed in this paper. Firstly, a number of features are extracted from two branches, a vision transformer branch and a Res2Net branch, to strengthen the feature extraction capability of the strategy. A novel interactive attention technique is proposed, with the goal of focusing on the strong correlation between the two-branch features, to fully use the complementing advantages of the feature information. The retrieved feature data are further refined and merged. The combined characteristics are then employed for classification. The experiments were conducted by using three open-source remote sensing datasets to validate the feasibility of the proposed method, which performed better in scene classification tasks than other methods. Full article
(This article belongs to the Special Issue Convolutional Neural Networks for Visual Detection, Recognition and Segmentation in Images and Videos)
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17 pages, 7099 KiB  
Review
A Survey of Ultra-Low-Power Amplifiers for Internet of Things Nodes
by Alfio Dario Grasso, Salvatore Pennisi and Chiara Venezia
Electronics 2023, 12(20), 4361; https://doi.org/10.3390/electronics12204361 - 20 Oct 2023
Cited by 1 | Viewed by 1533
Abstract
This paper investigates CMOS operational transconductance amplifier (OTA) design methodologies suitable for Internet of Things nodes. The use of MOS transistors in the subthreshold of the body terminal for signal input or bias, as well as newer inverter- and digital-based techniques, is considered. [...] Read more.
This paper investigates CMOS operational transconductance amplifier (OTA) design methodologies suitable for Internet of Things nodes. The use of MOS transistors in the subthreshold of the body terminal for signal input or bias, as well as newer inverter- and digital-based techniques, is considered. Solutions from the authors’ work are utilized as main case examples. State-of-the-art ultra-low-power OTAs are then thoroughly compared using a data-driven approach. According to the findings, digital- and inverter-based solutions have the lowest area occupation and superior small-signal performance but are inherently susceptible to process, supply, and temperature (PVT) variations. The only “analog” approach suitable for a sub-0.6 V supply is body driving in conjunction with subthreshold bias. It offers competitive large-signal performance and, more importantly, is less sensitive to PVT variations at the expense of silicon area. Full article
(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing)
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16 pages, 2408 KiB  
Article
Random Convolutional Kernels for Space-Detector Based Gravitational Wave Signals
by Ruben Poghosyan and Yuan Luo
Electronics 2023, 12(20), 4360; https://doi.org/10.3390/electronics12204360 - 20 Oct 2023
Viewed by 1752
Abstract
Neural network models have entered the realm of gravitational wave detection, proving their effectiveness in identifying synthetic gravitational waves. However, these models rely on learned parameters, which necessitates time-consuming computations and expensive hardware resources. To address this challenge, we propose a gravitational wave [...] Read more.
Neural network models have entered the realm of gravitational wave detection, proving their effectiveness in identifying synthetic gravitational waves. However, these models rely on learned parameters, which necessitates time-consuming computations and expensive hardware resources. To address this challenge, we propose a gravitational wave detection model tailored specifically for binary black hole mergers, inspired by the Random Convolutional Kernel Transform (ROCKET) family of models. We conduct a rigorous analysis by factoring in realistic signal-to-noise ratios in our datasets, demonstrating that conventional techniques lose predictive accuracy when applied to ground-based detector signals. In contrast, for space-based detectors with high signal-to-noise ratios, our method not only detects signals effectively but also enhances inference speed due to its streamlined complexity—a notable achievement. Compared to previous gravitational wave models, we observe a significant acceleration in training time while maintaining acceptable performance metrics for ground-based detector signals and achieving equal or even superior metrics for space-based detector signals. Our experiments on synthetic data yield impressive results, with the model achieving an AUC score of 96.1% and a perfect recall rate of 100% on a dataset with a 1:3 class imbalance for ground-based detectors. For high signal-to-noise ratio signals, we achieve flawless precision and recall of 100% without losing precision on datasets with low-class ratios. Additionally, our approach reduces inference time by a factor of 1.88. 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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22 pages, 5219 KiB  
Article
Real-Time Implementation of a Frequency Shifter for Enhancement of Heart Sounds Perception on VLIW DSP Platform
by Vincenzo Muto, Emilio Andreozzi, Carmela Cappelli, Jessica Centracchio, Gennaro Di Meo, Daniele Esposito, Paolo Bifulco and Davide De Caro
Electronics 2023, 12(20), 4359; https://doi.org/10.3390/electronics12204359 - 20 Oct 2023
Cited by 2 | Viewed by 1401
Abstract
Auscultation of heart sounds is important to perform cardiovascular assessment. External noises may limit heart sound perception. In addition, heart sound bandwidth is concentrated at very low frequencies, where the human ear has poor sensitivity. Therefore, the acoustic perception of the operator can [...] Read more.
Auscultation of heart sounds is important to perform cardiovascular assessment. External noises may limit heart sound perception. In addition, heart sound bandwidth is concentrated at very low frequencies, where the human ear has poor sensitivity. Therefore, the acoustic perception of the operator can be significantly improved by shifting the heart sound spectrum toward higher frequencies. This study proposes a real-time frequency shifter based on the Hilbert transform. Key system components are the Hilbert transformer implemented as a Finite Impulse Response (FIR) filter, and a Direct Digital Frequency Synthesizer (DDFS), which allows agile modification of the frequency shift. The frequency shifter has been implemented on a VLIW Digital Signal Processor (DSP) by devising a novel piecewise quadratic approximation technique for efficient DDFS implementation. The performance has been compared with other DDFS implementations both considering piecewise linear technique and sine/cosine standard library functions of the DSP. Piecewise techniques allow a more than 50% reduction in execution time compared to the DSP library. Piecewise quadratic technique also allows a more than 50% reduction in total required memory size in comparison to the piecewise linear. The theoretical analysis of the dynamic power dissipation exhibits a more than 20% reduction using piecewise techniques with respect to the DSP library. The real-time operation has been also verified on the DSK6713 rapid prototyping board by Texas Instruments C6713 DSP. Audiologic tests have also been performed to assess the actual improvement of heart sound perception. To this aim, heart sound recordings were corrupted by additive white Gaussian noise, crowded street noise, and helicopter noise, with different signal-to-noise ratios. All recordings were collected from public databases. Statistical analyses of the audiological test results confirm that the proposed approach provides a clear improvement in heartbeat perception in noisy environments. Full article
(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing)
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14 pages, 312 KiB  
Article
Decentralized Controller Design for Large-Scale Uncertain Discrete-Time Systems with Non-Block-Diagonal Output Matrix
by Danica Rosinová, Ladislav Körösi and Vojtech Veselý
Electronics 2023, 12(20), 4358; https://doi.org/10.3390/electronics12204358 - 20 Oct 2023
Viewed by 913
Abstract
This research paper addresses the challenge of designing a decentralized controller for a discrete-time uncertain polytopic system with a linear large-scale (LSS) structure. Specifically, we investigate this problem in cases where the subsystem’s output matrix lacks a decentralized structure. Firstly, the proposed novel [...] Read more.
This research paper addresses the challenge of designing a decentralized controller for a discrete-time uncertain polytopic system with a linear large-scale (LSS) structure. Specifically, we investigate this problem in cases where the subsystem’s output matrix lacks a decentralized structure. Firstly, the proposed novel procedure of a decentralized controller design transforms the LSS model to have a fully decentralized structure (both input and output matrices are block-diagonal). Then, the robust stability boundary parameter is calculated for the open-loop system. This stability boundary parameter is considered in robust decentralized controller design where an appropriate controller design method is used. The entire process of designing a robust decentralized controller takes place at the subsystem level, and the influence of interaction is considered through the robust stability boundary parameter. Lastly, we present an example of a five-order system comprising two subsystems to show the effectiveness of the new method. Full article
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13 pages, 703 KiB  
Article
Double Consistency Regularization for Transformer Networks
by Yuxian Wan, Wenlin Zhang and Zhen Li
Electronics 2023, 12(20), 4357; https://doi.org/10.3390/electronics12204357 - 20 Oct 2023
Cited by 1 | Viewed by 1304
Abstract
The large-scale and deep-layer deep neural network based on the Transformer model is very powerful in sequence tasks, but it is prone to overfitting for small-scale training data. Moreover, the prediction result of the model with a small disturbance input is significantly lower [...] Read more.
The large-scale and deep-layer deep neural network based on the Transformer model is very powerful in sequence tasks, but it is prone to overfitting for small-scale training data. Moreover, the prediction result of the model with a small disturbance input is significantly lower than that without disturbance. In this work, we propose a double consistency regularization (DOCR) method for the end-to-end model structure, which separately constrains the output of the encoder and decoder during the training process to alleviate the above problems. Specifically, on the basis of the cross-entropy loss function, we build the mean model by integrating the model parameters of the previous rounds and measure the consistency between the models by calculating the KL divergence between the features of the encoder output and the probability distribution of the decoder output of the mean model and the base model so as to impose regularization constraints on the solution space of the model. We conducted extensive experiments on machine translation tasks, and the results show that the BLEU score increased by 2.60 on average, demonstrating the effectiveness of DOCR in improving model performance and its complementary impacts with other regularization techniques. Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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18 pages, 8690 KiB  
Article
Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification
by Pendar Alirezazadeh, Fadi Dornaika and Abdelmalik Moujahid
Electronics 2023, 12(20), 4356; https://doi.org/10.3390/electronics12204356 - 20 Oct 2023
Cited by 1 | Viewed by 1234
Abstract
When considering a large dataset of histopathologic breast images captured at various magnification levels, the process of distinguishing between benign and malignant cancer from these images can be time-intensive. The automation of histopathological breast cancer image classification holds significant promise for expediting pathology [...] Read more.
When considering a large dataset of histopathologic breast images captured at various magnification levels, the process of distinguishing between benign and malignant cancer from these images can be time-intensive. The automation of histopathological breast cancer image classification holds significant promise for expediting pathology diagnoses and reducing the analysis time. Convolutional neural networks (CNNs) have recently gained traction for their ability to more accurately classify histopathological breast cancer images. CNNs excel at extracting distinctive features that emphasize semantic information. However, traditional CNNs employing the softmax loss function often struggle to achieve the necessary discriminatory power for this task. To address this challenge, a set of angular margin-based softmax loss functions have emerged, including angular softmax (A-Softmax), large margin cosine loss (CosFace), and additive angular margin (ArcFace), each sharing a common objective: maximizing inter-class variation while minimizing intra-class variation. This study delves into these three loss functions and their potential to extract distinguishing features while expanding the decision boundary between classes. Rigorous experimentation on a well-established histopathological breast cancer image dataset, BreakHis, has been conducted. As per the results, it is evident that CosFace focuses on augmenting the differences between classes, while A-Softmax and ArcFace tend to emphasize augmenting within-class variations. These observations underscore the efficacy of margin penalties on angular softmax losses in enhancing feature discrimination within the embedding space. These loss functions consistently outperform softmax-based techniques, either by widening the gaps among classes or enhancing the compactness of individual classes. Full article
(This article belongs to the Special Issue Medical Applications of Artificial Intelligence)
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18 pages, 6693 KiB  
Article
Profitability of Hydrogen-Based Microgrids: A Novel Economic Analysis in Terms of Electricity Price and Equipment Costs
by Jesús Rey, Francisca Segura and José Manuel Andújar
Electronics 2023, 12(20), 4355; https://doi.org/10.3390/electronics12204355 - 20 Oct 2023
Cited by 2 | Viewed by 1897
Abstract
The current need to reduce carbon emissions makes hydrogen use essential for self-consumption in microgrids. To make a profitability analysis of a microgrid, the influence of equipment costs and the electricity price must be known. This paper studies the cost-effective electricity price (EUR/kWh) [...] Read more.
The current need to reduce carbon emissions makes hydrogen use essential for self-consumption in microgrids. To make a profitability analysis of a microgrid, the influence of equipment costs and the electricity price must be known. This paper studies the cost-effective electricity price (EUR/kWh) for a microgrid located at ‘’La Rábida Campus’’ (University of Huelva, south of Spain), for two different energy-management systems (EMSs): hydrogen-priority strategy and battery-priority strategy. The profitability analysis is based, on one hand, on the hydrogen-systems’ cost reduction (%) and, on the other hand, considering renewable energy sources (RESs) and energy storage systems (ESSs), on cost reduction (%). Due to technological advances, microgrid-element costs are expected to decrease over time; therefore, future profitable electricity prices will be even lower. Results show a cost-effective electricity price ranging from 0.61 EUR/kWh to 0.16 EUR/kWh for hydrogen-priority EMSs and from 0.4 EUR/kWh to 0.17 EUR/kWh for battery-priority EMSs (0 and 100% hydrogen-system cost reduction, respectively). These figures still decrease sharply if RES and ESS cost reductions are considered. In the current scenario of uncertainty in electricity prices, the microgrid studied may become economically competitive in the near future. Full article
(This article belongs to the Special Issue Energy Harvesting and Storage Technologies)
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19 pages, 10299 KiB  
Article
Pattern Orientation Finder (POF): A Robust, Bio-Inspired Light Algorithm for Pattern Orientation Measurement
by Alessandro Carlini and Michel Paindavoine
Electronics 2023, 12(20), 4354; https://doi.org/10.3390/electronics12204354 - 20 Oct 2023
Viewed by 1138
Abstract
We present the Pattern Orientation Finder (POF), an innovative, bio-inspired algorithm for measuring the orientation of patterns of parallel elements. The POF was developed to obtain an autonomous navigation system for drones inspecting vegetable cultivations. The main challenge was to obtain an accurate [...] Read more.
We present the Pattern Orientation Finder (POF), an innovative, bio-inspired algorithm for measuring the orientation of patterns of parallel elements. The POF was developed to obtain an autonomous navigation system for drones inspecting vegetable cultivations. The main challenge was to obtain an accurate and reliable measurement of orientation despite the high level of noise that characterizes aerial views of vegetable crops. The POF algorithm is computationally light and operable on embedded systems. We assessed the performance of the POF algorithm using images of different cultivation types. The outcomes were examined in light of the accuracy and reliability of the measurement; special attention was paid to the relationship between performance and parameterization. The results show that the POF guarantees excellent performance, even in more challenging conditions. The POF shows high reliability and robustness, even in high-noise contexts. Finally, tests on images from different sectors suggest that the POF has excellent potential for application to other fields as well. Full article
(This article belongs to the Special Issue Machine Vision and 3D Sensing in Smart Agriculture)
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17 pages, 1463 KiB  
Article
Design and Implementation of Automatic Cooling Case Based on High-Power and High-Density Power Supply Array
by Zerui Chen, Hangwei Feng, Guoguang Zhang and Chong Yang
Electronics 2023, 12(20), 4353; https://doi.org/10.3390/electronics12204353 - 20 Oct 2023
Viewed by 1307
Abstract
Multi-board electronic cases with high-density and power modules are widely used in industrial power supply management. Heat dissipation becomes an important factor in the design process in improving case performance and miniaturization requirements. The design of existing small electronic thermal methods ignores high-temperature [...] Read more.
Multi-board electronic cases with high-density and power modules are widely used in industrial power supply management. Heat dissipation becomes an important factor in the design process in improving case performance and miniaturization requirements. The design of existing small electronic thermal methods ignores high-temperature and high-load environment tests without automation control. To solve these problems, a heat dissipation case is designed with a magnesium and aluminum alloy, for intelligent temperature control based on a high-power and high-density power supply array. Based on the extreme miniaturization design principle, a composite heat dissipation mode is adopted based on conduction and supplemented by forced air cooling. The results show that the heat dissipation design in this article can work normally in high-temperature and high-load environment tests. Finally, the existing cooling designs are compared and analyzed. The cooling performance parameters in this article are better than those in the existing case. It contributes to the thermal design of miniaturized electronic cases in power supply management. Full article
(This article belongs to the Special Issue High Power Density Power Electronics)
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12 pages, 3703 KiB  
Article
Blind Super-Resolution Network with Dual-Channel Attention for Images Captured by Sub-Millimeter-Diameter Fiberscope
by Wei Chen, Yi Liu, Jie Zhang, Zhigang Duan, Le Zhang, Xiaojuan Hou, Wenjun He, Yajun You, Jian He and Xiujian Chou
Electronics 2023, 12(20), 4352; https://doi.org/10.3390/electronics12204352 - 20 Oct 2023
Viewed by 1348
Abstract
A blind super-resolution network with dual-channel attention is proposed for images captured by the 0.37 mm diameter sub-millimeter fiberscope. The fiberscope can used in scenarios where other image acquisition devices cannot be applied based on its flexible, soft, and minimally invasive characteristics. However, [...] Read more.
A blind super-resolution network with dual-channel attention is proposed for images captured by the 0.37 mm diameter sub-millimeter fiberscope. The fiberscope can used in scenarios where other image acquisition devices cannot be applied based on its flexible, soft, and minimally invasive characteristics. However, the images have black reticulated noise and only 3000 pixels. To improve image quality, the Butterworth band-stop filter is used to reduce the frequency of the reticulated noise. By optimizing the blind super-resolution model, high-quality images can be reconstructed that do not require a lot of synthetic paired fiberscope image data. Perceptual loss is utilized as a loss function, and channel and spatial attention mechanisms are introduced to the model to enhance the high-frequency detail information of the reconstructed image. In the comparative experiment with other methods, our method showed improvements of 2.25 in peak signal-to-noise ratio (PSNR) and 0.09 in structural similarity (SSIM) based on objective evaluation metrics. The learned perceptual image patch similarity (LPIPS) based on learning was reduced by 0.6. Furthermore, four different methods were used to enhance the resolution of the fiberscope images by a factor of four. The results of this paper improve the information entropy and Laplace clarity by 0.44 and 2.54, respectively, compared to the average of other methods. Validation results show that the approach in this paper is more applicable to sub-millimeter-diameter fiberscopes. Full article
(This article belongs to the Section Computer Science & Engineering)
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