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2nd International Conference on Innovative and Creative Information technology, ICITech 2021

作者： Siagian, Pandapotan Fernando, Erick Computer Engineering Study Program Del Institute of Technology Faculty of Informatics and Electrical Engineering North Sumatera22381 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781728197463

Good air quality is very important in human health. However, air pollution has been polluted everywhere, whether in the house, in cities. This is caused by a growing industry that produces air pollution. Room air pollution and various cities will contribute to air pollution, which will cause problems for human health. This work measures the carbon monoxide level using the MQ-7 sensor and measures the air's carbon dioxide level with the CCS811 sensor and MH-Z19 sensor. All three sensors are connected to the NodeMCU ESPS266, and we call it a NodeMCU module. Tests are carried out in three rooms in the house, and then three NodeMCU modules are used so that the three modules will send data alternately. Raspberry Pi3 (RPi3) functions as a server that collects data and acquires information data from sensors. Data from the CCS811 (TCOV) sensor, MH-Z19 (co2), will be processed first with fuzzy logic rules to make decisions about CO2, which are right, moderate, nasty conditions. The process is carried out on the Raspberry Pi 3 server with MQTT using publication, customer, fuzzy logic, and broker services. The value of the sensor's measurement results can be accessed via a smartphone and provides good air control values. © 2021 IEEE.

关键词： Air quality

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Active Battery Management System for Electric Longboard

Active Battery Management System for Electric Longboard

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： David Bailey Kaleb Hutto Peter Wallace Adel El-Shahat Jason Qian Hala M. Abdel Mageed Dept. of Electrical and Computer Engineering Southern University Statesboro Georgia USA Energy Program School of Engineering Technology Purdue University West Lafayette USA High Voltage Department National Institute of Standards (NIS) Giza Egypt

Emphasizing the focus on recycling and sustainability is one of the hot topics all over the world. In this work, a long, non-electrical board was transformed into an electrical board. Hence, the battery management system was created to effectively balance 18,650 cells during charge and discharge cycles as well as a wireless remote control to control the electrical long panel. To do that, the skateboard has been fitted with a brushless DC motor to drive a single wheel located on the rear truck to allow the user to use controllers for sending analog signals to drive the motor with variable speed outputs. A better battery management system has been designed to make the power system more affordable, and ecological. Moreover, hot swap battery system using 18650 interchanged Lithium-Ion battery cells has been built. Besides, it is proven that this system is more robust and reliable than the ordinary two-piece AA battery system. The new proposed system has the capability of having a large battery grid that can be rearranged to satisfy the user's needs. It allows the user to change between the speed function and the torque function.

关键词： Wireless communication Brushless DC motors Capacitors Battery management systems Wheels Transportation Discharges (electric)

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Long Short Term Memory Networks for Stroke Activity Recognition base on Smartphone 5

Long Short Term Memory Networks for Stroke Activity Recognit...

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5th IEEE International Conference on Information technology, Information Systems and Electrical engineering, ICITISEE 2021

作者： Siagian, Pandapotan Fernando, Erick Del Institute Of Technology Faculty Of Informatics And Electrical Engineering Computer Engineering Study Program North Sumatera 22381 Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

Aims this study to analyze rehabilitation exercises using sensor data embedded in smartphones is widely used to recognize human activities regularly to gain a better understanding of human behavior. However, it is rarely used to measure accelerators of patient movement in hospital wards. This paper proposes an optimized neural network-based feature extraction approach based on the Long Short Term Memory technique to recognize human activity using data from a tri-axial accelerometer. The activity to be measured is the Range of Motion for patient rehabilitation, and rehabilitation exercises are one of the most important steps for recovery. The results of the softmax regression trained on the dataset set have 1, 076, 105 records and 12 class attributes (class distribution). The experimental results on the SAR dataset show that our Long Short Term Memory-based approach is practical and successful, with an accuracy of 89.74%. © 2021 IEEE.

关键词： Smartphones

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Performance Investigation of Tri-Layer Double-Gate Ingazno Thin-Film Transistor in Synchronous Bias for Active-Matrix Lcds

SSRN

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SSRN 2023年

作者： Mishra, Richa Dargar, Shashi Kant Dargar, Abha Birla, Shilpi Lai, Wen-Cheng Department of Electronics Engineering Kalasalingam Academy of Research and Education TN India Department of Electronics Engineering Manipal University Jaipur India Department of Computer Science Engineering Parul University Vadodara India Bachelor Program in Industrial Projects National Yunlin University of Science and Technology Taiwan Department of Electronic Engineering National Yunlin University of Science and Technology Taiwan

In the current display electronics, a high-resolution pixel density and refresh rate ranging 7680 × 4320 pixels, 300 ppi, and 240 Hz are in high demand. The pixel-driving Thin–Film Transistors (TFTs) are primarily responsible for the switch in such displays. The amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) TFTs are popular in realizing better high–speed properties in line to achieve those needs. In previous years, amorphous-IGZO channel TFTs in one-gate and dual-gate structures have exhibited improved gate control. In this paper, the authors present the analytical performance of double–gate tri-layer channel structure design of a-IGZO TFT with three stacked channel layers (IGZO/ITO/ITO1) to model and evaluate the device performance at different gate biasing. The proposed device simulation results were encouraging, in terms of high capacitance in dual gate bias (CDG) 26.2 nF/cm2, high mobility (µFE) 16.93 cm2/V.s, and steeper subthreshold swing (SS) 95 mV/decade. © 2023, The Authors. All rights reserved.

关键词： Thin film transistors

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Interferometry Among Strongly Driven Quasiparticles with Distinct Dynamical and Geometrical Phases

Interferometry Among Strongly Driven Quasiparticles with Dis...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： O'Hara, Seamus Costello, Joe Tian, Qi Wu, Qile West, Ken Pfeiffer, Loren Sherwin, Mark Wu, Liang Department of Physics Institute of Terahertz Science and Technology University of California Santa Barbara Santa BarbaraCA93106 United States Intelligence Community Post-Doctoral Research Fellowship Program PhiladelphiaPA19104 United States Department of Physics University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08542 United States

ISBN: (纸本)9781957171395

In this proceeding, we present polarization data of sidebands emitted from strongly driven quasiparticles, demonstrate the dependence of these data on various parameters of the external driving field, and describe the data using an analytical expression, dependent on driving field parameters and the effective Hamiltonian of the system. We propose using this method on spin-momentum locked systems, such as altermagnets, to observe and reconstruct sidebands which are influenced by the topology of individual bands. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Topology

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Smart Home Security System Using Face Recognition Based on IoT- CNN

Smart Home Security System Using Face Recognition Based on I...

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Information technology Research and Innovation (ICITRI), International Conference on

作者： M. Aldiki Febriantono Alvin Zuhair Khaeruddin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Electrical Engineering Department University of Islam Blitar Blitar Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Malang Indonesia

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners.

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Interferometry Among Strongly Driven Quasiparticles with Distinct Dynamical and Geometrical Phases

Interferometry Among Strongly Driven Quasiparticles with Dis...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Hamiltonians

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Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic Systems using Deep GRU-based Bayesian optimization

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IFAC-PapersOnLine 2024年第4期58卷 449-454页

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine 1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Tunisia

The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.

关键词： Bayesian optimization fault detection fault diagnosis gated recurrent units grid-connected PV systems interval-data representation uncertainties

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Quasi-Nondestructive Read Out of Ferroelectric Capacitor Polarization by Exploiting a 2TnC Cell to Relax the Endurance Requirement

TechRxiv

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TechRxiv 2023年

作者： Xiao, Yi Deng, Shan Zhao, Zijian Faris, Zubair Xu, Yixin Huang, Tzu-Jung Narayanan, Vijaykrishnan Ni, Kai Microsystems Engineering Ph.D. program Rochester Institute of Technology RochesterNY14623 United States Department of Computer Science and Engineering Penn State University University ParkPA16802 United States Department of Electrical and Microelectronic Engineering Rochester Institute of Technology RochesterNY14623 United States

In this work, we propose a 2TnC ferroelectric random access memory (FeRAM) cell design to realize the quasi-nondestructive readout (QNRO) of ferroelectric polarization (PFE) in a capacitor, which can relax the endurance requirement of the ferroelectric thin film and exploits the benefits of both FeRAM and ferroelectric FET (FeFET). We demonstrate that: i) QNRO sensing of PFE is conducted successfully in experiment with a ON/OFF ratio (ION/IOFF)>103, ION>10 µA, and read endurance>106 cycles, which can relax the FeRAM endurance requirement by 106x;ii) optimization of the cell performance can be realized by tuning the metal-ferroelectric-metal capacitor (MFM) capacitor to read transistor area ratio and read transistor threshold voltage (VTH);iii) the proposed cell structure is 3D-compatible, enabling integration of highly dense memory solution;iv) the proposed cell structure also enables compute-in-memory (CIM) applications of FeRAM, which has not been widely explored. With this technology, storage and memory-centric computing can be enabled. © 2023, CC BY.

关键词： Cytology

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Ambient Cardiovascular Monitoring with Metamaterial Textile Sensors

Ambient Cardiovascular Monitoring with Metamaterial Textile ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Dat T. Nguyen Qihang Zeng Xi Tian John S. Ho Integrative Sciences and Engineering Program National University of Singapore Graduate School Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore SIA-NUS Digital Aviation Corporate Laboratory and the Institute for Health Innovation and Technology National University of Singapore Singapore Department of Electrical and Computer Engineering Institute for Health Innovation and Technology N.1 Institute for Health at the National University of Singapore Singapore

Contactless sensors embedded in the ambient environment have broad applications in unobtrusive, long-term health monitoring for preventative and personalized healthcare. Microwave radar sensors are an attractive candidate for ambient sensing due to their high sensitivity to physiological motions, ability to penetrate through obstacles and privacy-preserving properties, but practical applications in complex real-world environments have been limited because of challenges associated with background clutter and interference. In this work, we propose a thin and soft textile sensor based on microwave metamaterials that can be easily integrated into ordinary furniture for contactless ambient monitoring of multiple cardiovascular signals in a localized manner. Evaluations of our sensor’s performance in human subjects show high accuracy of heartbeat and arterial pulse detection, with ≥ 96.5% sensitivity and < 5% mean absolute relative error (MARE) across all subjects. We demonstrate our sensor’s utility for cuffless blood pressure monitoring on a human subject over a continuous 10-minute period. Our results highlight the potential of metamaterial textile sensors in ambient health and wellness monitoring *** relevance—The contactless metamaterial textile sensors demonstrated in this paper provide unobtrusive, convenient and long-term monitoring of multiple cardiovascular health metrics, including heart rate, pulse rate and cuffless blood pressure, which can facilitate preventative and personalized healthcare.

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