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3D Stackable Vertical Ferroelectric Tunneling Junction (V-FTJ) with on/off Ratio 1500x, Applicable Cell Current, Self-Rectifying Ratio 1000x, Robust Endurance of 10⁹ Cycles, Multilevel and Demonstrated Macro Operation Toward High-Density BEOL NVMs

3D Stackable Vertical Ferroelectric Tunneling Junction (V-FT...

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Symposium on VLSI technology

作者： J.-Y. Lee F.-S. Chang K.-Y. Hsiang P.-H. Chen Z.-F. Luo Z.-X. Li J.-H. Tsai C. W. Liu M. H. Lee Institute and Undergraduate Program of Electro-Optical Engineering National Taiwan Normal University Taiwan Graduate School of Advance Technology National Taiwan University Taiwan Department of Electronics Engineering Institute of Electronics National Yang Ming Chiao Tung University Taiwan Department of Electrical Engineering National Taiwan Normal University Taiwan

A 1500x I LRS /I HRS with a high cell current of ~100 nA/ cell (J = 83 A/cm 2 ) is achieved by antiferroelectric (AFE) vertical ferroelectric tunnel junctions (V-FTJs) that demonstrates multilevel, a self-rectifying rate > 1000x, and macro operation. The stackable 3 D architecture integrating multiple layers of AFE V-FTJs with contact optimization further increases memory density. Robust endurance > 10 9 cycles at |4V| and stable nonvolatile data retention > 10 4 sec with extrapolation to 10 years are achieved. The proposed cell contact V-FTJ by AFE is a promising pathway toward BEOL NVMs by a NAND/NOR-based framework.

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Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Predicting Human Activity with LSTM Face Detection on Server...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Alexander Nurenie Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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Case Study: Gain Scheduling Adaptive Control Applied on a Didactic Level System

Case Study: Gain Scheduling Adaptive Control Applied on a Di...

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International Carpathian Control Conference (ICCC)

作者： Vinícius Berno Da Silva Paulo César Barros Rodrigues Murillo Ferreira Dos Santos Paolo Mercorelli Milena Faria Pinto Wolmar Araújo Neto Department of Electroelectronics CEFET-MG Leopoldina Brasil Institute for Production Technology and Systems (IPTS) Leuphana University of Lüneburg Germany Graduate Program in Electrical Engineering CEFET-RJ Rio de Janeiro Brasil Federal University of Espírito Santo UFES Vitória Brazil

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Gain scheduling has been successfully applied in many different areas due to the positive results achieved when applied to nonlinear control. Its utilization enables the controller to respond rapidly to changes in operating conditions. This study demonstrates the application of gain scheduling to a SMAR didactic level system to implement adaptive control. The results exhibited a strong response with minor errors using a Proportional and Integral (PI) controller, which facilitated the development of two generic equations for determining the values of K p (proportional gain) and K i (integral gain) based on the desired set point.

关键词： Adaptive scheduling PI control Liquids System dynamics Integral equations Noise Gain measurement

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A Large Frequency Ratio Dual-band Microstrip Antenna with Consistent Radiation Pattern for Internet of Sea Applications

A Large Frequency Ratio Dual-band Microstrip Antenna with Co...

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European Conference on Antennas and Propagation, EuCAP

作者： Hanguang Liao Rana Muhammad Bilal Atif Shamim Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (纸本)9788831299022;9781728188454

Sensing and retrieving data from ocean to land are challenging tasks, while the Internet of Sea concept provides a realistic solution to that. A large frequency ratio dual-band microstrip antenna design working at GSM, LoRa, and BLE bands with consistent radiation pattern is proposed for Internet of Sea applications. The antenna is based on a modified Split Ring Antenna which provides two radiating modes, where the frequency ratio is close to 3. The proposed microstrip antenna is optimized for a better radiation efficiency and consistent radiation pattern at both bands. The method to control the H-plane HPBW of the proposed antenna is provided. The antenna prototype is fabricated and measured, which shows the radiation pattern at both bands are consistent and the H-plane HPBWs are 94°and 78°, at the lower and higher bands respectively.

关键词： Antenna measurements Dual band Sea measurements Prototypes Microstrip antennas Internet Sensors

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Boosting the Performance of Image Restration Models Through Training with Deep-Feature Auxiliary Guidance

SSRN

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SSRN 2024年

作者： Jang, Cheolhun Ji, Deahyun Cho, Nam Ik Interdisciplinary Program in Artificial Intelligence INMC Seoul National University Seoul08826 Korea Republic of Samsung Advanced Institute of Technology Suwon-si Gyenggi-do16678 Korea Republic of Department of Electrical and Computer Engineering INMC Seoul National University Seoul08826 Korea Republic of

Many neural network architectures have been suggested for image restoration models to enhance the accuracy of restored images while keeping the computational time reasonable. However, most previous studies have primarily focused on introducing new network architectures, with relatively less emphasis on efficient training techniques. This paper introduces a novel training strategy that improves the accuracy of image restoration models without increasing inference times by preserving their structures. To achieve this, we add auxiliary guidance at a feature map of each multi-scale level during the training process. All modules added to the model for the application of auxiliary guidance are only used during training time and can be removed at the inference phase, thereby eliminating the need for additional computation. Our proposed training method, named deep-feature auxiliary guidance (DFAG), can be easily applied to existing encoder-decoder architectures. By applying DFAG to train existing image restoration models, we have obtained enhanced performance in real-image denoising, Gaussian denoising, motion deblurring, JPEG artifact reduction, and super-resolution for all tested models. The source code and pre-train models will be released. © 2024, The Authors. All rights reserved.

关键词： Image denoising

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A 3D Dual-band electrically Small Monopole Antenna for Internet of Sea Applications

A 3D Dual-band Electrically Small Monopole Antenna for Inter...

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IEEE International Symposium on Antennas and Propagation

作者： Hanguang Liao Rana Muhammad Bilal Atif Shamim Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (纸本)9781728146713

Sensing and retrieving data from ocean to land are challenging and expensive tasks, while the Internet of Sea (IoS) concept can help to monitor ocean environment in a low-cost way. In this paper, an electrically Small (ES) dual-band monopole antenna is proposed for the IoS application. Dual-band operation (GSM-900, LoRa, and BLE bands) and vertical polarization are maintained in a very compact size. The proposed antenna operates as a folded monopole antenna at 900MHz, and as a simple monopole antenna at 2.4 GHz, by combining the top-loaded folded monopole antenna and the 2-layer monopole feeding structure. The proposed antenna has ka of 0.45 at the LB, with a decent polarization purity and radiation efficiency of 54%. The proposed antenna is designed with a low-cost additive manufacturing material system.

关键词： Three-dimensional displays Oceans Dual band Antenna feeds Three-dimensional printing Internet Sensors

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Sampled-Data Observer Design With Exponential Time-Varying Gains for Semilinear Parabolic PDE Systems

Sampled-Data Observer Design With Exponential Time-Varying G...

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2021 China Automation Congress, CAC 2021

作者： Wang, Zi-Peng Wu, Huai-Ning Li, Han-Xiong Huang, Tingwen Shen, Tao The School of Electrical Engineering University of Jinan Jinan250022 China The Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Department of Systems Engineering and Engineering Management City University of Hong Kong Hong Kong The Science Program Texas A&M University at Qatar Doha Qatar The HIT Robot Group Co. Ltd China

ISBN: (纸本)9781665426473

For semilinear parabolic PDE systems, the sampled-data observer (SDO) design under spatially point measurements (SPMs) with exponential time-varying gains is concerned. On basis of a Lyapunov functional, a SDO with exponential time-varying gains under SPMs via linear matrix inequalities is presented to guarantee the exponential stability of estimation error PDE system. Lastly, two numerical examples are provided to support the proposed SDO design approach. © 2021 IEEE

关键词： Linear matrix inequalities

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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International Conference on engineering technology and their Applications (IICETA)

作者： Mohammad Alslatie Hiam Alquran Wan Azani Mustafa Isam Abu-Qasmieh Ali Mohammad Alqudah Ahmed Alkhayyat King Hussein Medical Center Royal Jordanian Medical Service The Institute of Biomedical Technology Amman Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Department of Electrical and Computer Engineering Biomedical Engineering Program University of Manitoba Canada Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images.

关键词： Image segmentation Visualization Transfer learning Feature extraction Convolutional neural networks Artificial intelligence X-ray imaging

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Machine Learning Techniques for Improving Multiclass Anomaly Detection on Conveyor Belts

Machine Learning Techniques for Improving Multiclass Anomaly...

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IEEE Instrumentation and Measurement technology Conference

作者： Saulo N. Matos Otávio F. Coletti Rafael Zimmer Fernando U. Filho Ricardo C.C. L. de Carvalho Victor R. da Silva Jorge L. Franco Thomas V. B. Pinto Luiz G. D. de Barros Caetano M. Ranieri Bruno E. Lopes Diego F. Silva Jó Ueyama Gustavo Pessin Institute of Mathematics and Computer Science University of Sã o Paulo Brazil São Carlos School of Engineering University of São Paulo Brazil Vale Institute of Technology Ouro Preto Brazil Graduate Program in Electrical Engineering Universidade Federal of Minas Gerais Belo Horizonte MG Brazil Vale SA Vitória Brazil

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Industrial conveyor belt systems are an efficient means of transport due to their adaptability and extension. Nonetheless, such systems are prone to various failures, including but not limited to: idler anomalies; belt tears; and pin misalignment which can cause significant disruptions in the production process. Preemptive maintenance and health monitoring of these conveyor belts is a common practice for avoiding these failures, but a challenging task due to the rarity of comprehensive anomaly detection datasets in the area, with current works aimed at evaluating the belt's immediate condition at fixed points. This study addresses this research gap by comparing multiple machine learning techniques, such as a proposed Hybrid Neural Network (HNN) tailored for classification of multiple anomaly classes, as well as machine learning approaches for time series based on feature extraction, Catch22, Minirocket Arsenal, and Time Series Forest. Our proposed HNN architecture performed better in classifying and distinguishing between different types of anomalies, with an accuracy of 95.55%. The obtained results suggest a promising approach for the area of predictive maintenance for industrial conveyor systems, as well as gives insights on possible improvements on the model and future research.

关键词： Machine learning algorithms Accuracy Time series analysis Machine learning Belts Transformers Feature extraction

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Nu-Ri Park Min-Joong Kim Jongsu Lee Minah Seo Yong-Sang Ryu Myung-Ki Kim KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA School of Biomedical Engineering Korea University Seoul Republic of Korea Interdisciplinary Program in Precision Public Health Korea University Seoul Republic of Korea

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen (<3%... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Electric potential Accuracy Hydrogen Lasers and electrooptics Optical sensors Electro-optical waveguides

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