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Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

Microsystems & Nanoengineering 2024年第3期10卷 229-244页

作者： Emad Iranmanesh Zihao Liang Weiwei Li Congwei Liao Shunyu Jin Chuan Liu Kai Wang Shengdong Zhang Charalampos Doumanidis Gehan A.J.Amaratunga Hang Zhou Guangdong Provincial Key Laboratory of In-Memory Computing Chips School of Electronic and Computer EngineeringPeking University Shenzhen Graduate SchoolShenzhen 518055P.R.China School of Mechanical Engineering Guangdong Technion-Israel Institute of TechnologyShantou 515063P.R.China State Key Laboratory of Microelectronics Device and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029P.R.China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 23000PR China School of Electronics and Information Technology Sun Yat-sen UniversityNo.132 East Waihuan RoadGuangzhou 510006P.R.China Department of Mechanical Aerospaceand Biomedical EngineeringUniversity of South AlabamaShelby Hall3128MobileAL 36688USA Electrical Engineering Division Department of EngineeringUniversity of Cambridge9 JJ Thomson AvenueCambridge CB30FAUnited Kingdom Zhejiang University International CampusHainingChina

With the rapid development of the Internet of Things(IoTs),wearable sensors are playing an increasingly important role in daily monitoring of personal health and *** signal-to-noise-ratio has become the most critical performance factor to *** enhance it,on the one hand,good sensing materials/devices have been employed;on the other hand,signal amplification and noise reduction circuits have been ***,most of these devices and circuits work in an active sampling mode,requiring frequent data acquisition and hence,entailing high-power *** this scenario,a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great ***,we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor(PBJT)that acts as an autonomous and highly sensitive,current-and/or voltage-mediated pressure *** PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collectorbase *** force exertion on the emitter side,as a result of the piezoelectric effect,the emitter-base diode is forward biased while the collector-base diode is reverse *** to the inherent BJT amplification effect,the PBJT achieves record-high sensitivities of 139.7 kPa^(-1)(current-based)and 88.66 kPa^(-1)(voltage-based)in sensing *** PBJT also has a fast response time of<110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger ***,the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous,making it promising for pressure sensing and other IoT applications.

关键词： bipolar transistor IoT

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Novel Ultrasonic Image Reconstruction and Deep Learning Model-Based Categorization for Accurate Liver Disease Diagnosis

Novel Ultrasonic Image Reconstruction and Deep Learning Mode...

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2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Nijaguna, G.S. Sureddy, Sneha Bindumadhavi Mohapatra, Dayanidhi Parameshachari, B.D. S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Gitam University Bengaluru Campus Gst Dept. of Cse Bengaluru India Hyderabad Institute of Technology and Management Department of Ece Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350301595

The diagnosis of liver illnesses is critical for efficient treatment and management of the condition. By combining deep learning model-based classification with ultrasonic image reconstruction, the developed method aims to surpass standard deep learning techniques. This method's nine primary processes include pre-processing approaches, image reconstruction, liver area segmentation, feature extraction, deep learning model training, testing, and comparing model performance. The suggested approach was tested on patients with fatty liver disease by processing huge sets of ultrasound pictures and RF data frames. The InceptionResNetV2 CNN was used to detect fatty liver disease in these persons. The proposed approach has a sensitivity of 95.2 percent, an accuracy of 92.8 percent, and a specificity of 94.1 percent. By combining deep learning model-based classification with ultrasonic image reconstruction, the technology overcomes some of the limitations of classic deep learning methods. Medical ultrasound imaging is commonly used because it provides for the non-invasive, risk-free identification of liver disorders. Ultrasonic imaging is a sort of medical image diagnostic that assists doctors in diagnosing and treating a variety of ailments. This suggests that the suggested technique might considerably improve the precision and consistency with which liver disease is detected, resulting in better patient outcomes. © 2023 IEEE.

关键词： Image reconstruction

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Federated Machine Learning for Cardiac Disease Detection Using Internet of Medical Things

Federated Machine Learning for Cardiac Disease Detection Usi...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Aggarwal, Kapil Vimal, V.R. Patil, Upasana Senthilkumar, C. Ingle, Anup Kumar, T. Sampath Department of Computer Science & Engineering Guntur Andhra Pradesh Vaddeswaram India Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Thandalam India Lingaraj Appa Engineering College Karnataka Bidar India Sri Krishna College of Technology Department of Ece Tamilnadu Coimbatore India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunications Maharashtra Pune India School of ComputerScience and Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350300857

The abundant availability of medical data from healthcare organizations increases the application of Machine learning (ML) techniques in medical field. Reliable and trustworthy ML models needs to be developed in order to maintain the efficacy of the healthcare providers. ML models are used extensively to make reliable disease predictions while also safeguarding the data collected through Internet of Medical Things (IoMT) devices. Federated Learning (FL) techniques are suitable for preserving IoMT data since it retains only the trained models and progresses with information from dispersed consumers. FL techniques have the caliber to drastically transform medical industry by making prompt disease diagnosis to enhance efficacy of therapy. The accuracy of these FL techniques decreases due to the massive data being exchanged between local and remote sites. In order to mitigate this issue, a novel approach FedEDFA, that combines Federated Machine learning with meta-heuristic optimization algorithm is proposed. Enhanced Dragonfly Optimization algorithm is utilized to optimally choose the relevant characteristics and employ it further for disease prediction. This approach improves the resilience of the system in insecure networking environments. The performance of the proposed FedEDFA is assessed by applying it to UCI Cleveland dataset for predicting cardiovascular disease in a secure and privacy preserving way. The accuracy of the proposed approach is 98.3% which is superior to the other existing methods taken for comparison. © 2023 IEEE.

关键词： Machine learning

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A Novel Approach to Social Sustainability with Machine Learning and the Best Worst Method in Manufacturing 1

A Novel Approach to Social Sustainability with Machine Learn...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Yogi, Kottala Sri Franklin Jino, R.E. Dankan Gowda, V. Murthy, C. Ravindra Prasad, Veduri Veera Srinivas, Ved Hyderabad Symbiosis International University Symbiosis Institute of Business Management Department of Operations Pune India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Mohan Babu University School of Engineering Department of Ece Andhra Pradesh Tirupati India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Thiagarajar School of Management Department of Human Resources Tamil Nadu Madurai India

ISBN: (纸本)9798350367720

This paper is aimed at presenting a novel approach for enhancing the measurement of social sustainability in manufacturing domain with the help of ML with BWM. Social sustainability when dealing with manufacturing is part of the four sustainability pillars that seeks to address aspects such as;welfare of workers, social organization, and practice within the firm. This is why the conventional tools may not sufficiently inform on the right combination of the said aspects. The information from the big data is then processed by the means of ML to disclose the patterns illuminating the stance influences and BWM is integrated to analyze specialists' insight the power of evolving influential aspects of stance. They are useful in minimizing the time as well as the energy required in the formulation of the decisions because the details and correlations needed are already provided. It is illustrated with the help of an example in the manufacturing setting and significant improvements in the values of the social sustainability indices compared to the classical approach. From these conclusions, it is possible to indicate the prospects for the integration of ML and BWM as the authors' contribution to tackling the problem of social sustainability in the manufacturing sector. © 2024 IEEE.

关键词： Smart manufacturing

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Deep Learning Techniques for Autonomous Navigation of Underwater Robots 10

Deep Learning Techniques for Autonomous Navigation of Underw...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, electronics and computer engineering, UPCON 2023

作者： Sasi, J.Priscilla Nidhi Pandagre, Karuna Royappa, Angelina Walke, Suchita Pavithra, G. Natrayan, L. Vet Institute of Arts and Science CO-Education College Computer Science Tamil Nadu Erode India Bansal Institute of Science and Technology Department of Mca Bhopal India S.A. Engineering College Department of ECE Tamil Nadu Chennai India Pillai Collage of Engg and Technology Mumbai University Computer Engineering India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore560111 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350382471

Underwater exploration, environmental monitoring, and infrastructure inspection are just few of the many applications where autonomous navigation of underwater robots is a tough and crucial task. The undersea world is complicated and ever-changing, making conventional navigation systems ineffective. When it comes to autonomous and effective underwater robot navigation, deep learning, and more especially Convolutional Neural Networks (CNNs), has emerged as a potent technique. This study reviews the use of convolutional neural networks (CNNs) in the context of underwater robot navigation in great detail. The major difficulties of underwater navigation are covered, including vision issues, changing illumination, and the existence of objects. The article goes on to investigate how convolutional neural networks (CNNs) might help solve these problems and boost navigational accuracy. CNNs' capacity to extract useful characteristics from unprocessed sensor data like sonar, LiDAR, and camera pictures is a major benefit. These capabilities improve underwater robots' perception, allowing them to avoid obstacles, identify objects, and map their environments. Because of their flexibility in responding to novel situations, CNN s are well-suited for use in the real world. Additionally, this research explores the methods of instruction and transfer learning that are most applicable to the field of underwater navigation. Reduce the burden of collecting massive amounts of data in harsh underwater settings by applying what has been learned in simulations to real-world *** also investigate how to combine CNNs with other sensor fusion methods like Simultaneous Localization and Mapping (SLAM) algorithms. These synergies boost underwater robots' overall navigation ability, allowing them to safely and independently traverse unfamiliar and sometimes dangerous terrain. This study also describes recent developments and case studies in which CNN s played a critical role in accompl

关键词： Autonomous Navigation Convolutional Neural Networks (CNNs) Deep Learning etc Underwater Exploration Underwater Robots

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Unleashing Innovation: Experimental Evaluation of Blockchain Assisted Electronic Voting System Using Secured Authentication Scheme

Unleashing Innovation: Experimental Evaluation of Blockchain...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Kalaiselvi, K. Saravanan, K. Shalini, M. Venkatesan, T. Ravi, S. Karthi, J. Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India R. M. D. Engineering College Department of Mathematics Tamil Nadu Kavaraipettai India Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

The abstract presents a concise overview of the methodology and key findings of the experimental evaluation conducted for a Blockchain Assisted Electronic Voting System (BAEVS) employing a Secured Authentication Scheme. This study focuses on the design, implementation, and assessment of the BAEVS, aiming to enhance the transparency, security, and reliability of electronic voting processes. The methodology encompasses three main stages: Prototype Development, Blockchain Platform Selection, and Smart Contract Implementation. The prototype is developed using modern web development frameworks to ensure usability and accessibility for diverse users. A suitable blockchain platform, such as Ethereum or Hyperledger Fabric, is chosen to implement the distributed ledger component, considering factors such as scalability and security. Smart contracts, developed in platform-specific languages, govern the voting process and undergo rigorous testing to ensure correctness and reliability. The experimental evaluation reveals promising results, demonstrating the effectiveness of the BAEVS in providing secure and transparent electronic voting. Overall, this study contributes to the advancement of electronic voting systems by offering insights into the design and implementation of blockchain-assisted solutions with secured authentication mechanisms. © 2024 IEEE.

关键词： Blockchain

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Design of liquid cooling cold plate for high performance electric traction module on two-wheeler EV

Design of liquid cooling cold plate for high performance ele...

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electronics Packaging Technology Conference (EPTC)

作者： He Bin Jaydeep Saha Tang Gongyue Sanjib Kumar Panda Agency for Science Technology & Research (A*STAR) Institute of Microelectronics (IME) Singapore Department of Electrical & Computer Engineering (ECE) National University of Singapore (NUS)

Recent progress in the design of drive-train inverters for Electric Vehicles (EVs) predominantly emphasizes the minimization of component sizes while concurrently enhancing power densities. This evolution, though significant in advancing automotive technology, introduces complex challenges concerning thermal management. The presented research in this paper delineates a novel liquid cooling system combining jet impingement and mini-channel methodologies to tackle the specific thermal management issues associated with a SiC MOSFET module. Subsequently, the study employs two distinct optimization techniques to perform design optimization of thermal management system, focusing on critical parameters influencing the manufacturability characteristics of the cooling mechanism. After implementing the optimization process, the "JF" performance indicator is improved to a value of 1.92, proving the method’s efficacy. Furthermore, the outcomes of the optimization analysis indicate that Bayesian Optimization method embodies substantial promise for future design implementations, characterized by its expeditious and reliable optimization process.

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Multiple-Input and Multiple-Output (MIMO) Channel Measurement in Heterogeneous Networks 2

Multiple-Input and Multiple-Output (MIMO) Channel Measuremen...

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2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Sheela, M. Sahaya Rekha, M. Samuel, Roxanna Divya, G. Rajendran, T. Umarani, B. Pavai College of Technology Department of Electronics and Communication Engineering Pachal India Department of Ece Perambalur India Rajalakshmi Engineering College Department of Computer Science Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Chennai India Tamilnadu Coimbatore India Kogunadu College of Engineering and Technology Department of Electronics and Communication Engineering Thottiam Trichirappalli India

ISBN: (纸本)9781665436052

The recent technologies that try to offer huge improvement in spectral efficiency, data rate, transmission efficiency;throughput and improves scalability for next generation is huge Multiple Input Multiple Output (MIMO) system. This may show advantages in enormous MIMO based on propagation environment. Moreover, owing to stationary properties of MIMO, channel characterization is not more appropriate for spatial domain. Dynamic adoption is not attained in 2D channels with more possibilities and considerable 5G network capacity enhancements. Here, channel characterization of MIMO is considered and simulated. © 2022 IEEE.

关键词： Spectrum efficiency

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Design and Simulation of Dual-Band MIMO Antenna for Radar and Sub-6-GHz 5G Applications

Design and Simulation of Dual-Band MIMO Antenna for Radar an...

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International Conference on Futuristic Communication and Network Technologies, CFCNT 2020

作者： Vasu Babu, K. Kokkirigadda, Suneetha Das, Sudipta Departmentof ECE Vasireddy Venkatadri Institute of Technology Nambur Andhra Pradesh India Department of Electrical and Computer Engineering Bule Hora University Oromia Ethiopia Department of Electronics & Communication Engineering IMPS College of Engineering & Technology Malda West Bengal India

ISBN: (纸本)9789811646249

The MIMO antenna proposed in this article presents dual-band operations for radar in C-band and sub-6-GHz 5G applications. The simulated results show that the suggested dual-band antenna offers impedance bandwidths of 1300 MHz (2.8–4.1 GHz) for the lower band of operation and 500 MHz (7–7.5 GHz) for the higher band. The minimum isolation achieved for the first band is more than 40 dB and its noted more than 45 dB for the second band of operation. The structure is designed with Minkowski shape structure and dumb-bell shape is inserted between the two patches with a separation distance of 3 mm to reduce the interference between the identical patches. The designed antenna offers good radiation characteristics in terms of better gain, better efficiency, and stable radiation patterns. The proposed MIMO structure shows good diversity performance offering desired values of envelope correlation coefficient (ECC) and diversity gain (DG). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Microwave antennas

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A Robust Methodology to Detect Glaucoma Disease using Modified Deep Learning Principles with Image Processing Logics

A Robust Methodology to Detect Glaucoma Disease using Modifi...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Charulatha. R.T G. Nirmaladevi Vasumathi G S. Hemajothi K. Suresh Karthikeyan. C Department of ECE Rajalakshmi Institute of Technology Chennai Dept of Computer Science and Engineering Sri Sairam Institute of Technology Chennai Department of ECE S.A. Engineering College Chennai Department of ECE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electrical and Electronics Engineering QIS College of Engineering and Technology Ongole AndhraPradesh Department of ECE R.M.K. Engineering College Kavaraipettai Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Glaucoma is a leading cause of irreversible blindness, requiring early and accurate diagnosis to prevent vision loss. This study proposes a robust methodology for detecting glaucoma using a hybrid approach that integrates deep learning (Autoencoder) with a machine learning classifier (Multi-Layer Perceptron). The methodology utilizes image processing techniques, including noise reduction and data augmentation, to enhance the quality of retinal fundus images. Feature extraction was performed using Convolutional Neural Networks (CNNs) and Gabor filters, followed by dimensionality reduction through Principal Component Analysis (PCA) and wavelet decomposition. The model was tested on multiple publicly available datasets, including RIGA, HRF, ACRIMA, and Kaggle. The hybrid model achieved an accuracy of 97.1% on the RIGA dataset, 95.6% on the HRF dataset, and 96.3% on the ACRIMA dataset. The model outperformed traditional classifiers such as Support Vector Machine (SVM) and Random Forest, which achieved accuracies of 92.3% and 94.5% on the RIGA dataset, respectively. The study demonstrates that combining deep learning with machine learning classifiers, along with effective preprocessing, significantly improves glaucoma detection accuracy. Furthermore, explainable AI techniques were employed to enhance model interpretability, ensuring that clinical practitioners can trust and understand the model's decisions.

关键词： Glaucoma Deep learning Support vector machines Accuracy Explainable AI Computational modeling Autoencoders Wavelet analysis Convolutional neural networks Principal component analysis

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