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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Khan, Rehan Saeed, Umer Koo, Insoo University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of School of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9798331510756

Sensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. Wireless Sensor Networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial Network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection. © 2025 IEEE.

关键词： Gallium nitride

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Model Uncertainty Estimation Based on a Dual-Module Neural Network Architecture 23

Model Uncertainty Estimation Based on a Dual-Module Neural N...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Guo, Weiqing Zhang, Tianlun Peng, Dunlu Wang, Debby D. School of Science and Technology Hong Kong Metropolitan Universit Hong Kong Hong Kong School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798331528041

In recent years, deep learning has achieved remarkable success in various fields, including biology and medicine. However, the interpretability and robustness still face challenges, as erroneous predictions can lead to serious consequences in critical applications. This paper introduces a new framework for estimating model uncertainty that can enhance the decision-making process. Compared to existing uncertainty estimation methods like Bayesian neural networks and Monte Carlo dropout, this paper provides a more direct way to model uncertainty by directly estimating the output variance through a dual-module neural network architecture. Without the need for multiple forward passes or complex Bayesian inference, it can reduce computational intensity. In specific, this framework consists of a prediction module for input processing and an uncertainty module for estimating prediction uncertainty. We demonstrate the efficiency of our method through applications in medical image segmentation and protein-ligand binding affinity prediction. By incorporating model uncertainty estimation, the proposed framework not only improves the accuracy of deep learning models, but also significantly reduces the computational burden which is especially beneficial for processing large datasets. © 2024 IEEE.

关键词： Prediction models

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Enhancement-Mode Multichannel MoS2 Transistor with Spacer engineering and Design-technology Co-Optimization Based on the 8" Platform

Enhancement-Mode Multichannel MoS2 Transistor with Spacer En...

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2024 IEEE International Electron Devices Meeting, IEDM 2024

作者： Zhu, Jiadi Yao, Aijia Wu, Peng Jiao, Yixuan Park, Ji-Hoon Jiang, Jianfeng Yang, Tilo H. Gupta, Ayush S. Cheema, Suraj S. Kong, Jing Palacios, Tomás Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Material Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9798350365429

This paper studies highly scaled, multichannel MoS2 transistors (MCTs) through both experiments and simulations. A low-temperature MoS2 synthesis enables a novel integration scheme for MCTs on 8" wafers with self-aligned source/drain contacts, multiple gate-dielectric-MoS2 channel-dielectric-gate layers stacked on 8" Si wafers, reduced lithography steps and no sacrificial layers. A 1-channel, double-gate MoS2 transistor features 386 μA/μm on current (ION) at VDS=1 V, subthreshold swing SS = 85 mV/dec, and record performance among 2D gate-all-around (GAA) transistors and MCTs. A low drain-induced-barrier-lowering DIBL = 28 mV/V is also demonstrated. We also report the first functional high performance 2-channel MoS2 transistor with 673 μA/μm ION and 88 mV/dec SS at 400 nm channel length (LCh). In parallel, local-back gate transistors with 1.5 nm physical oxide thickness and 4 nm physical channel length were fabricated to calibrate the device model at extreme scales for design-technology co-optimization (DTCO). Thanks to the DTCO analysis, a projection of multi-channel transistor’s scaling, including power, and performance for "1 nm" technology node and beyond is presented. © 2024 IEEE.

关键词： Silicon wafers

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Design of an RF-CMOS Switched-Capacitor Power Amplifier for NB-IoT RF Transceivers

Design of an RF-CMOS Switched-Capacitor Power Amplifier for ...

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2023 International Young Engineers Forum in electrical and computer engineering, YEF-ECE 2023

作者： Santos, Ana Isabel Pedro Oliveira, Joao NOVA School of Science and Technology Department of Electrical and Computer Engineering Caparica Portugal Centre of Technology and Systems CTS-UNINOVA Department of Electrical and Computer Engineering Caparica Portugal

ISBN: (纸本)9798350300420

The paper presents the implementation of a Switched Capacitor Power Amplifier (SCPA) to be integrated into a Narrowband Internet of Things (NB-IoT) Transceiver. The SCPA is designed to operate at a frequency of 0.9GHz and aiming the maximum output power allowed by the standard of 23dBm. All the blocks within the SCPA were developed using RF components of a standard 130nm CMOS process, with a 1.2V/2.4V supply voltage. Results show that the SCPA is able to produce a maximum output power of 15.61dBm with a maximum Power-Added Efficiency (PAE) of 26.52% and a Total Harmonic Distortion (THD) of 0.68%. The measured HD2 and HD3 are-70.23dBc and-43.41dBc, respectively. Additional Process, Voltage and Temperature (PVT) corners and Monte Carlo simulations indicate the SCPA operates properly when subject to different conditions and mismatches. Besides the SCPA, a 16 QAM modulation stage is designed in VerilogA in order to evaluate the performance of the RF PA when a sequence of symbols is transmitted. © 2023 IEEE.

关键词： Power amplifiers

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A Universal Virtual Reality Training Platform for Microscale Robotic Cell Injection in Biomedical Research and Practice

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SN computer science 2024年第8期5卷 1134页

作者： Hafeez, Rubab Amjad, Zohaib Waheed, Sadia Hasan, Osman Sharif, Muhammad Imran Ulhaq, Anwaar Department of Computer Science University of Wah Wah Cantt 47010 Pakistan School of Electrical Engineering and Computer Science NUST Islamabad 44320 Pakistan Department of Computer Science Kansas State University Manhattan 66506 United States Manna Institute School of Engineering and Technology Central Queensland University Brisbane QLD Australia

Micro-robotic cell injection is a widely used procedure in cell biology where a small quantity of biological material is inserted into a cell using an automated or semi-automated micro-robotic system. Given its micro-scale nature, cell injection using a semi-automated micro-robotic system requires extremely high precision, hand-eye coordination, and depth perception for maneuvering the micro-pipette. As the cell injection is a laborious work, there is a dire need of rigorous training and a great deal of experience using live specimens and high-end microscopes. To facilitate this training, we have presented a paper in the past about modular virtual reality (VR) with basic simulation modules, here, the present work deals with the modelling of advanced training modules and experimenting of VR simulator. The proposed simulator includes: (i) Advanced training modules to train users on complete injection/suction procedures;(ii) The performance evaluation of the users using three metrics, i.e., (i) time, (ii) trajectory, and (iii) positioning accuracy is also integrated within the simulator;(iii) An experimental set-up has been developed for the face, content, and construct validity of the VR simulator. Finally, the effectiveness of the proposed simulator is validated by experts from the relevant domains and can be used for various applications like drug delivery, injection procedures and in-vitro fertilization. The experimental results demonstrated that a Virtual cell injection training simulator is feasible for the training of cell injection procedures and depicted that the injection success rate is 100% after using VR simulator. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adherent cells Cell injection Collision modeling Micro-robotic Simulator validity VR simulation

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Design of Microcontroller Based Automated Diesel Level and Pump Control System for Efficient Pit Dewatering in Botswana Mines 4

Design of Microcontroller Based Automated Diesel Level and P...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kokoro, Abotle Tito Yahya, Abid Samikannu, Ravi Begam, K.Maruliya Meeradevi, T. Kaliappan, Vishnu Kumar Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Kongu Engineering College Department of Electronics and Communications Engineering Tamil Nadu Perundurai India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331529635

This paper proposes a novel strategy to address the difficulties encountered in Botswana's open-pit mine dewatering procedures. The conventional approach, which relies on labor-intensive manual labor and mechanical switching, is expensive, time-consuming, and present significant safety hazards. To overcome these limitations, this project proposes a multi-sensor network system that automates and optimizes the dewatering process and improve operational efficiency. The system integrates real-time sensor data to monitor fuel consumption and water levels in mining pits, enabling precise pump operation control. The system has two sets of sensors for data collection on fuel levels in the reserve and dewatering pump tank. Water level sensors are also used to monitor the water levels in the tank and turn the pump on or off based on predetermined thresholds. After receiving the sensor data, a microcontroller controls the entire process and shows the fuel levels and consumption on an LCD screen. The system's circuitry was designed and simulated using Proteus software, and the LCD successfully displayed measurements obtained from the sensors. When the sump level surpassed the predetermined threshold, a relay was energized to initiate the motor, thereby pumping out the water. Similarly, the tank level sensor (LV800) detected water levels exceeding 80% capacity, sending a signal to the microcontroller, which, in turn, energized the relay to activate the stage motor. The LCD also displayed readings from the temperature and current sensors. Simulation results indicate that the proposed sensor-based approach effectively monitors the condition of the pumps. At the same time, the microcontroller, in conjunction with the sensors, can adequately control the pump's operation during the dewatering process in the mining pit. This research demonstrates the feasibility and effectiveness of employing sensors and a microcontroller in managing and controlling dewatering pumps. By automating the

关键词： Open pit mining

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Wearable and interactive multicolored photochromic fiber display

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Light(science & Applications) 2024年第3期13卷 454-465页

作者： Pan Li Yuwei Wang Xiaoxian He Yuyang Cui Jingyu Ouyang Ju Ouyang Zicheng He Jiayu Hu Xiaojuan Liu Hang Wei Yu Wang Xiaoling Lu Qian Ji Xinyuan Cai Li Liu Chong Hou Ning Zhou Shaowu Pan Xiangru Wang Huamin Zhou Cheng-Wei Qiu Yan-Qing Lu Guangming Tao State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and Engineering and Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Vascular Aging(HUST) Ministry of EducationWuhan 430030China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and ManipulationCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China School of Performing Arts Wuhan Conservatory of MusicWuhan 430060China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Architecture and Urban Planning Huazhong University of Science and TechnologyWuhan 430074China School of Fashion Beijing Institute of Fashion TechnologyBeijing 100029China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Tongji Medical College Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai 201620China

Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent,wearable interactive ***,significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight ***,we introduce a mass-produced,wearable,and interactive photochromic fiber that provides uniform multicolored light *** designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the *** impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission *** addition,we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single *** prepared fibers on mass-produced kilometer-long using the thermal drawing *** fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as *** work provides a new perspective and inspiration to the existing field of fiber display interaction,paving the way for future human–machine integration.

关键词： fiber interactive colored

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Short-term Wind Power Ramp Forecasting Using Sequential Approach

Short-term Wind Power Ramp Forecasting Using Sequential Appr...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Gopalakrishnan, Leechita Barrera, Julian L Cardenas Fatima, N. Sabiyath B.S. Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India University of New Brunswick Dept. of Electrical and Computer Engineering Fredericton Canada

ISBN: (纸本)9798331533311

As wind energy becomes a key player in the global transition to renewable energy, its variability presents significant challenges. Accurate wind ramp forecasting is crucial for managing such rapid fluctuations in wind power output, ensuring grid stability, and enhancing the reliability of renewable energy integration. This paper presents a short-term wind ramp forecasting model using a sequential approach: initially forecasting wind power through the integration of Variational Mode Decomposition (VMD) and the XGBoost algorithm, and subsequently detecting wind ramps using the Definition Based Sign Indicator (DSI) method. The historical wind speed and power data for Turkey was obtained from Kaggle. The dataset comprises readings from a wind turbine's SCADA system in Turkey, with measurements recorded at 10minute intervals. Variational Mode Decomposition (VMD) was applied to deconstruct and restore the historical wind power of the wind field, resulting in the formation of three training datasets. Wind power projections for the subsequent 8 hours, at 10 -minute intervals, are generated using historical wind power, wind speed, and wind direction as inputs. The DSI algorithm is employed to identify ramps. To assess the predictive capability of the proposed model, two algorithms, SVR and XGBoost, were employed for forecasting and the resulting error is minimal. The proposed model, VMD + XGBOOST is also compared without VMD to evaluate the performance. Taking mean square error (MSE), root mean square error (RMSE) mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) as evaluation indicators, the results show that the forecasted accuracy of wind power is significantly improved after VMD processing. The performance of XGBOOST is better than SVR in the two evaluation indicators. Precision, Recall, and F1-score are calculated to evaluate the accuracy of the predicted wind ramps. © 2024 IEEE.

关键词： Wind forecasting

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Machine Learning and Deep Learning Approach for Predicting Neonatal Mortality in Indonesia 10

Machine Learning and Deep Learning Approach for Predicting N...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Hamid, Nur Rumiati, Fidya Prayogo, Rizal Dwi Nambo, Hidetaka Kanazawa University Division of Electrical Engineering and Computer Science Japan Universitas Indonesia Faculty of Public Health Department of Epidemiology Indonesia

ISBN: (纸本)9798350394924

This research explores machine learning approaches to determine the most significant features related to neonatal mortality in Indonesia. We create prediction tasks with deep learning models including MLP, LSTM, and CNN. We found that low birth weight and early breastfeeding becomes the most significant features related to neonatal mortality in Indonesia. For prediction task, implementing feature importance task as feature selection can improve prediction performance and reduce algorithm complexity. LSTM and CNN achieved the best prediction model with 90.91% accuracy. © 2024 IEEE.

关键词： Forecasting

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A novel facial emotion recognition model using segmentation VGG-19 architecture

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International Journal of Information technology (Singapore) 2023年第4期15卷 1777-1787页

作者： Vignesh, S. Savithadevi, M. Sridevi, M. Sridhar, Rajeswari Department of Electrical and Electronics Engineering National Insistute of Technology Tiruchirappalli India Department of Computer Science and Engineering National Insistute of Technology Tiruchirappalli India

Facial Emotion Recognition (FER) has gained popularity in recent years due to its many applications, including biometrics, detection of mental illness, understanding of human behavior, and psychological profiling. However, developing an accurate and robust FER pipeline is still challenging because multiple factors make it difficult to generalize across different emotions. The factors that challenge a promising FER pipeline include pose variation, heterogeneity of the facial structure, illumination, occlusion, low resolution, and aging factors. Many approaches were developed to overcome the above problems, such as the Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) histogram. However, these methods require manual feature selection. Convolutional Neural Networks (CNN) overcame this manual feature selection problem. CNN has shown great potential in FER tasks due to its unique feature extraction strategy compared to regular FER models. In this paper, we propose a novel CNN architecture by interfacing U-Net segmentation layers in-between Visual Geometry Group (VGG) layers to allow the network to emphasize more critical features from the feature map, which also controls the flow of redundant information through the VGG layers. Our model achieves state-of-the-art (SOTA) single network accuracy compared with other well-known FER models on the FER-2013 dataset. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Classification Emotion recognition FER dataset VGG

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