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IEEE Conference on Nanotechnology

作者： Sekhar Reddy Kola Min-Hui Chuang Yiming Li Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering Institute of Communications Engineering Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan

Three-dimensional (3D) bulk fin-typed field effect transistors (FinFETs) have emerged as key devices that can scale down the technology node beyond 22-nm. However, the scaled devices have created new sources of fluctuation inherent in 3D geometry. The interface trap is one such fluctuation that is caused by the trapping and de-trapping of charge carriers and has an adverse effect on device characteristics and variability. In this work, we study impacts of random interface traps (RITs) on electrical characteristic of bulk FinFETs by using a 3D quantum-mechanically corrected device simulation. RIT position effects on short channel effects (SCEs) are examined with physical governed influence to show the major fluctuations. More than 50% reductions of the RITs-induced characteristic fluctuation of the germanium (Ge) devices are observed, compared with Si devices. The Ge ones can reduce SCE variations and exhibit high immunity to RITs.

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An adaptable mobile robot platform with vision-based perception for precision agriculture

An adaptable mobile robot platform with vision-based percept...

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IEEE Latin American Robotics Symposium, LARS

作者： Hugo Oliveira Arthur Vangasse Lívia Soares Andressa Oliveira Bruno Ferreira Glauber Leite Ícaro Araújo Davi Brito Institute of Computing Universidade Federal de Alagoas Maceió Brazil Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil EDGE Innovation Center Universidade Federal de Alagoas Maceió Brazil

This paper presents the development of a versatile mobile robot platform designed for precision agriculture. The robot’s proposed overview architecture and its manufacturing are presented while it further discusses the incorporated vision-based perception modules for road segmentation in farm environments and maize stem detection. For road segmentation, the Segment Anything Model (SAM) based on Zero-Shot Segmentation was utilized. The SAM algorithm effectively extracted navigable spaces in challenging scenarios, demonstrating its robustness and adaptability. Furthermore, considerations were made for computational efficiency, motivating future implementation on low-power devices. In the maize stem detection module, a comprehensive dataset of maize stem images obtained from a local agricultural field was created. The images were processed using the YOLOv5 model, resulting in a highly accurate and efficient maize stem detection module. The validation of both perception modules highlights the successful integration of vision-based technologies into the platform. The platform’s adaptability and robustness make it a valuable tool for precision agriculture applications. By leveraging these technologies, the proposed vehicle contributes to improving crop monitoring and management, enhancing overall agricultural practices.

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An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

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Computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

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IoT-based Wireless Sensor Networks for Monitoring Drinking Water Treatment Plants

IoT-based Wireless Sensor Networks for Monitoring Drinking W...

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International Workshop on Secure Internet of Things (SIoT)

作者： Luiz Octávio Martini Lima Lucas Carvalho Goncalves Gustavo Campos Menezes Lucas Silva de Oliveira Gleb Wataghin Institute of Physics UNICAMP Campinas SP Brazil Master Degree Program in Automation and Systems CEFET-MG Leopoldina MG Brazil Department of Electronics and Computing CEFET-MG Contagem MG Brazil Department of Mechatronics Engineering CEFET-MG Divinópolis MG Brazil

ISBN: (数字)9798331507466

ISBN: (纸本)9798331507473

The increasing need for efficient monitoring of parameters in challenging industrial environments, such as basic sanitation, has led to the search for integrated and user-friendly solutions that can address specific challenges of this setting. This project aims to develop a solution to meet monitoring requirements in water treatment plants. The proposed solution involves the development of both hardware and software for collecting data from the water treatment process using sensors and transmitting this information to a server structure, database, and communication interface with the cloud-based user. The effectiveness of the proposed device is being evaluated in six water treatment units located in the states of Alagoas, Mato Grosso, Paraná, Rio de Janeiro, and São Paulo. Preliminary results indicate that the proposed solution is capable of monitoring the variables involved in the water flocculation stage and generating a comprehensive set of information about the process over time.

关键词： Cloud computing Chemical sensors Wireless sensor networks User interfaces Hardware Software Sensors Social Internet of Things Servers Monitoring

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Is Attention All You Need for EEG to Predict Neurological Outcomes in Cardiac Arrest?

Is Attention All You Need for EEG to Predict Neurological Ou...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Dionisio, Jefferson Rizwan, Muhammad Suzuki, Hidetaka Hassan, Kazi Mahmudul Chanpornpakdi, Ingon Lin, Lian-Yu Tanaka, Toshihisa Lin, Che Tokyo University of Agriculture and Technology Tokyo Japan Japanese Red Cross Musashino Hospital Department of Emergency and Critical Care Tokyo Japan National Taiwan University College of Medicine and Hospital Division of Cardiology Department of Internal Medicine Taipei Taiwan Artificial Intelligence of Things Taiwan International Graduate Program Academia Sinica Taipei Taiwan National Taiwan University Department of Electrical Engineering Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan National Taiwan University Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan Department of Smart Medicine and Health Informatics National Taiwan University Taipei Taiwan

ISBN: (纸本)9798350351552

Sudden cardiac arrest (SCA) poses a significant health challenge, necessitating accurate predictions of neurological outcomes in comatose patients, where good outcomes are defined as the recovery of most cognitive functions. Electroencephalogram (EEG) serves as a valuable biomarker for monitoring neurological states due to its rich, time-dependent information. This study aims to predict neurological outcomes using early EEG data by employing the Transformer model, which leverages multi-headed attention to identify patterns in lengthy sequences such as hour-long EEG recordings. Unlike traditional methods that use subsampled EEG epochs, we utilize the entire EEG sequences, subdivided into time steps, allowing our model to capture detailed temporal patterns via the attention mechanism. Moreover, we trained our proposed model using each EEG recording as an individual data sample but evaluated our model through aggregated patient-wise predictions. This approach allows us to boost the data sample size. Our results demonstrate promising predictive performance, achieving an AUROC of 0.82 and AUPRC of 0.90 on the holdout test set and an AUROC of 0.73 and AUPRC of 0.93 on an external test set with patient-wise predictions. This study highlights the potential of utilizing attention mechanisms to capture important time series progressions across EEG sequences for improving SCA prognosis. © 2024 IEEE.

关键词： Electroencephalography

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Effect of Ag2O Doping on the H2S Gas Sensing Properties of WO3Nanosensor 4

Effect of Ag2O Doping on the H2S Gas Sensing Properties of W...

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4th International Conference on Circuits, Systems and Simulation, ICCSS 2021

作者： Al-Sarraj, Ahmad Saoud, Khaled M. Bermak, Amine Hamad Bin Khalifa University Qatar Foundation College of Science and Engineering Doha Qatar Virginia Commonwealth University in Qatar Liberal Arts and Science Program Doha Qatar Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

ISBN: (纸本)9781728167527

In this paper we report the synthesis and characterization of tungsten oxide doped silver oxide nanostructures. The effect of silver doping on the optical and sensing properties of tungsten doped silver oxide is studied and characterized for low-temperature H2S gas detection. Furthermore, we investigated the sensor response, electrical characteristics, response time of tungsten oxide (WO3) and tungsten oxide doped with silver oxide (Ag2O/ WO3) at different concentrations at 25°C. The study also presents the influence of the silver oxide doping of WO3 on the H2S based sensors performance in terms of sensitivity and response time. The obtained results proved the possibility of synthesizing tungsten oxide-based gas sensors for future H2S sensor devices. UV-VIS results reveal that doping WO3 with Ag2O enhanced the absorption of WO3 in the visible region which is indicated in the red shift. Furthermore, the sensing properties of WO3 is increased compared to bare WO3 and Ag2O which shoed no response to H2S in this temperature range. The electrical properties of WO3 also becomes ohmic as a result of Ag2O doping. The study illustrates a good way to improve the gas sensors performance through Ag2O doping. © 2021 IEEE.

关键词： Tungsten compounds

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Natural features, wettability, and optical diffraction patterns on an elastomeric replica of a superhydrophobic leaf derived via soft lithography

Natural features, wettability, and optical diffraction patte...

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Optical Manipulation and Structured Materials Conference 2023

作者： Budlayan, Marco Laurence M. Palangyos, Dina C. Patricio, Jonathan N. Arco, Susan D. Guerrero, Raphael A. Department of Physics Ateneo de Manila University Loyola Heights Quezon City1108 Philippines Mathematics and Natural Sciences Department College of Computing Pangasinan State University Pangasinan Urdaneta Philippines Materials Science and Engineering Program College of Science University of the Philippines Diliman Diliman Quezon City1101 Philippines Synthetic Organic Chemistry Laboratory Institute of Chemistry College of Science University of the Philippines Diliman Diliman Quezon City1101 Philippines

ISBN: (纸本)9781510663398

Surface features of natural materials such as cuticles, shells, and leaves continue to be used as models for creating sophisticated infrastructure, landscape, and microdevice designs. Optical microscopy is the easiest and most accessible tool for examining such surface features and micropatterns. This method, however, fails to probe opaque surfaces like thick skin and leaves since it needs enough light to penetrate the sample. In this work, we demonstrate the feasibility of probing the natural structures of a superhydrophobic leaf using simple optical microscopy by replicating the leaf surface on an elastomer via soft lithography. Optical images of the replica revealed intricate details of features present on the leaf, including the stoma, cuticle boundary, and trichome. Contact angle measurements were also conducted to investigate the wettability of the real leaves and their replicas. Finally, we utilized the fabricated replicas as optical gratings that were observed to generate various diffraction patterns with fluctuating intensities. Our work offers a new perspective on alternative optical microscopy strategies and optical beam tuning using biomimetic, low-cost materials. © 2023 SPIE.

关键词： Contact angle

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Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting

Quantum Kernel-Based Long Short-term Memory for Climate Time...

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Quantum Communications, Networking, and computing (QCNC), International Conference on

作者： Yu-Chao Hsu Nan-Yow Chen Tai-Yu Li Po-Heng Henry Lee Kuan-Cheng Chen NARlabs National Center for High-Performance Computing Hsinchu Taiwan Cross College Elite Program National Cheng Kung University Tainan Taiwan National Synchrotron Radiation Research Center Hsinchu Taiwan Department of Electrical and Electronic Engineering Imperial College London London UK Department of Civil Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

We present the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy and computational efficiency in climate time-series forecasting tasks, such as Air Quality Index (AQI) prediction. By embedding classical inputs into high-dimensional quantum feature spaces, QK-LSTM captures intricate nonlinear dependencies and temporal dynamics with fewer trainable parameters. Leveraging quantum kernel methods allows for efficient computation of inner products in quantum spaces, addressing the computational challenges faced by classical models and variational quantum circuit-based models. Designed for the Noisy Intermediate-Scale Quantum (NISQ) era, QK-LSTM supports scalable hybrid quantum-classical implementations. Experimental results demonstrate that QK-LSTM outperforms classical LSTM networks in AQI forecasting, showcasing its potential for environmental monitoring and resource-constrained scenarios, while highlighting the broader applicability of quantum-enhanced machine learning frameworks in tackling large-scale, high-dimensional climate datasets.

关键词： Quantum computing Accuracy Computational modeling Time series analysis Predictive models Computational efficiency Forecasting Kernel Long short term memory Meteorology

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Design and exploration of vertically stacked complementary tunneling field-effect transistors

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Applied Physics Express 2024年第1期17卷 014001-014001页

作者： Thoti, Narasimhulu Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

The purpose of this letter is to study the design and explore vertically stacked complementary tunneling field-effect transistors (CTFETs) using CFET technology for emerging technology nodes. As a prior work, the CTFET’s device-level simulations are implemented and deliberated in strict compliance with the experimental settings. This work comprises the study of physical and DC analyses by scaling the p- to n-CTFET separation (D pn), being a significant factor in CFET/CTFET design for its process difficulty. By utilizing the 50% benefit in footprint, the work is further extended to CTFET static random access memory implementation and characterization with hold/read noise margin analysis. © 2023 The Author(s). Published on behalf of The Japan Society of Applied Physics by IOP Publishing Ltd

关键词： Si-Ge alloys

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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