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共 48356 条记录，以下是181-190 订阅

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H4VR: One-handed Gesture-based Text Entry in Virtual Reality Using a Four-key Keyboard

H4VR: One-handed Gesture-based Text Entry in Virtual Reality...

引用

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Fallah, Saba MacKenzie, Scott Dept. of Electrical Engineering and Computer Science York University TorontoON Canada

ISBN: (纸本)9781450394222

H4VR is a text entry method offering two layouts - flat and cross - of a four-key keyboard in VR. Hand gestures select the keys with a different selection method for each layout. The entry speed and error rate were measured and analyzed in a five-session longitudinal user study. Five participants entered ten phrases on each keyboard in each session. They reached an average entry speed of 4.57 and 3.63 words per minute on the flat and cross keyboards, respectively. The average error rate was less than 2% on both keyboards. © 2023 Owner/Author.

关键词： Virtual reality

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A Bridgless Stacked Switches-Based AC-DC Converter with Reduced Storage Capacitances and Low-Frequency Voltage Ripple for HV EV Systems

A Bridgless Stacked Switches-Based AC-DC Converter with Redu...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Derakhshan, Siamak Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350316445

This paper presents a fully soft-switched single-stage stacked switches-based AC-DC converter with significantly reduced storage capacitances and low-frequency voltage ripple for High Voltage (HV) Electric Vehicle (EV) application. In the proposed converter, a front-end duty ratio closed-loop control system based on the input current equation provides a high-quality sinusoidal input current. Moreover, a dual feedback control loops in both the primary and secondary side of the converter are responsible to minimize the low-frequency DC-link and output voltage oscillations, eliminating the need for bulky DC-link and output storage capacitors and enhancing the reliability, lifetime, and cost-effectiveness of the converter. Moreover, the output voltage of the converter is regulated through a Variable Frequency (VF) control algorithm. The performance of the proposed converter is verified through a 1kW, 120Vrms/800Vdc, 150kHz proof-of-concept hardware prototype. © 2023 IEEE.

关键词： closed-loop control low capacitance size minimum low-frequency ripple Soft-switching

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New Interlinking PV Converter Modules with Individual MPPT and Integrated Power Balance Units

New Interlinking PV Converter Modules with Individual MPPT a...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Kanathipan, Kajanan Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350316445

A new interlinking photovoltaic (PV) step-up converter utilizing integrated power balancing units (PBU) featuring individual maximum power point tracking (MPPT) and high frequency active voltage quadruplers (AVQ) is presented in this paper. An integrated boost circuit with a CLL resonant circuit at the input achieves both MPPT and soft-switching operation for each module while interlinking inductors allows for power flow between modules, allowing the entire converter system to achieve simultaneous MPPT, soft-switching operation, and equal power distribution without the use of additional circuitry. The features of the proposed converter are validated on a 10kW, 10kV-output system simulation and a scaled down 250W, 500V-output hardware prototype. © 2023 IEEE.

关键词： DC/DC Converter Maximum Power Point Tracking (MPPT) Photovoltaic (PV) Power Balance Units (PBU)

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis 17

Quantum-Enhanced Machine Learning for Precision Breast Cance...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Banerjee, Agniva Indian Institute of Science Education and Research Bhopal Dept. Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798331531195

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes. © 2025 IEEE.

关键词： Diseases

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A Lightweight-CNN Model for Efficient Lung Cancer Detection and Grad-CAM Visualization 2

A Lightweight-CNN Model for Efficient Lung Cancer Detection ...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Tasmim Bakchy, Sagor Chandro Peyal, Hasibul Islam Islam, Md. Injamul Yeamin, Golam Kibria Miraz, Shafiun Abdal, Md. Nazmul Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Dept. of Electrical & Electronic Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350358667

The lungs' abnormal cell growth leads to the development of lung cancer. Early cancer identification could make treatment easier, potentially saving millions of lives annually. This study's main goal is to more rapidly and effectively classify various types of lung cancer by employing a lightweight, computationally efficient convolutional neural network (CNN) model to categorize three different types of lung cancer. With an outstanding validation accuracy of 99.48%, the suggested model surpasses the achievements of previous works. The 15,000 CT scan images in our dataset include three different forms of lung cancer. The proposed model performs exceptionally well, as evidenced by its astounding precision, recall, and F1-score, all above 99%, and by its flawless Area Under Curve (AUC) score of 100%. The proposed model has fewer parameters than the existing transfer learning models. Gradient Weighted Class Activation Mapping (Grad-CAM) was used to create class activation maps, which were then used to create a heatmap to display the classification zone. © 2023 IEEE.

关键词： Neural network models

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Voltage hopping induced by bias injection attack against Kalman filter of BLDC motor 2

Voltage hopping induced by bias injection attack against Kal...

引用

2nd IEEE International Conference on AI in Cybersecurity, ICAIC 2023

作者： Boiko, Yuri Kiringa, Iluju Yeap, Tet University of Ottawa Electrical Engineering and Computer Science Dept. OttawaON Canada

ISBN: (纸本)9798350396966

A new type of bias injection attack against Cyber Physical System with closed loop controlled Brushless DC (BLDC) motor and Kalman filter in the feedback loop is simulated herewith involving high frequency and high amplitude for modulation of the bias. The attack results in significant voltage jumps with no visible affects on state variables, which we call 'voltage hopping' effect. Implemented is operation of BLDC motor in position sensorless mode under Kalman filter drive. False data injection is conducted by substituting the angular velocity estimates from Kalman filter with distorted values. Conceptually, the attack is designed to mislead the controller and trigger the controller's effort to fix the distorted state of the system instead of the real one thus causing the controller to further distort the balance in the system. Herewith the introduction of high frequency component into the bias is tested in simulation experiment. It is shown that high frequency distortion strongly affects the operating voltage in the winding's circuits causing, as we named it, 'voltage hopping' effect when significant changes of applied voltage occur at a frequency higher than cut off frequency of operating RL circuits. Moreover, it is demonstrated that balancing the parameters of the attack allows to reach equilibrium state in which voltage jumps are not reflected in the changes of BLDC motor operation, such as currents in the motor's windings as well as rotor's angular speed. This offers to the attacker the route to increase stealthiness of the attack, while still maintaining its harmful potential. © 2023 IEEE.

关键词： Controllers

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Performance Analysis of Deep Learning Models in Driver Behavior Recognition

Performance Analysis of Deep Learning Models in Driver Behav...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kapoor, Rameshwari Kuliyal, Rashmi Bhanot, Nilesh Bagla, Piyush Rawat, Ruchira Graphic Era Hill University Dept. of Computer Science and Engineering Uttarakhand Dehradun India University Dept. of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798331542344

The World Health Organization report states that nearly 1.25 million people lose their lives due to an increasing number of road accidents every year. The majority of accidents are reported due to driver errors, which include distractions like the use of mobile phones and fatigue, which causes drowsiness. This study aims to address these issues by performing a comparative analysis between different deep learning models representing two types of learning mechanisms: Traditional deep learning and transfer learning. This study portrays the use of two datasets to accomplish this task. These two datasets are the Driver Drowsiness Dataset DDD and the State Farm Distracted Drivers Dataset. The purpose of this investigative research is to explore the execution and efficiency differences between three deep learning algorithms, the traditional convolutional neural network, ResNet50, and VGG16, in classifying driver behavior by training these models individually on both datasets. This study also targets developing an alarm system that sounds an alarm based on a threshold by using an ensemble of all the models trained for comparative study and averaging their predictions to classify driver behavior. VGG16 achieved an accuracy of 99.74% on the driver drowsiness dataset, whereas Custom CNN Architecture achieved an accuracy of 98.70% on the driver distraction dataset. © 2024 IEEE.

关键词： Contrastive Learning

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Real-Time Performance Verification of a Commercial Transformer Differential Relay under GIC Conditions 4

Real-Time Performance Verification of a Commercial Transform...

引用

4th Annual IEEE Kansas Power and Energy Conference, KPEC 2023

作者： Ahmadzadeh-Shooshtari, Babak Rezaei-Zare, Afshin York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350335408

Transformer differential protection employs a harmonic blocking (HB) function that blocks the relay operation when harmonic levels, specifically second harmonics, in transformer differential currents are high. Such a condition can occur when a transformer experiences part-cycle core saturation caused by geomagnetically induced current (GIC). Offline studies on transformer differential relays in the literature show that these relays are likely to remain blocked by the HB function for in-zone faults during the GIC. In this paper, hardware-in-the-loop (HIL) tests are conducted to analyze the response of a commercial differential relay to the faults during the GIC. The experimental results show that the relay fails to operate for in-zone faults during the GIC, regardless of the configuration of the employed HB function. This study concludes that the performance of the existing commercial relays should be experimentally verified and proposes a solution to immunize the transformer protection schemes against GIC consequences. © 2023 IEEE.

关键词： Relay protection

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qCrop: An IoT Based Framework to Enhance Crop Productivity in Smart Agriculture 32

qCrop: An IoT Based Framework to Enhance Crop Productivity i...

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32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Shamsa, Mahdi Rachakonda, Laavanya Mohanty, Saraju P. Kougianos, Elias University of North Texas Dept. of Computer Science and Engineering Denton United States University of North Carolina Wilmington Dept. of Computer Science Wilmington United States University of North Texas Dept. of Electrical Engineering Denton United States

ISBN: (纸本)9798331539672

Food is crucial in our life. Despite food manufacturers' efforts to meet consumers' needs with manufactured food, it cannot attain the identical level of quality and flavor as natural food. Some fruits and vegetables are classified as food that cannot be manufactured. Also, they play an essential role worldwide. This paper focuses on how farmers can protect their farms from an object that might threaten the crops and cause damage to them. It proposes a framework that farmers may utilize to safeguard the crops against any species of birds. Considering the adverse impact of bird attacks on crop production, this system effectively addresses this problem and consistently enhances the quality and quantity of them. It utilizes computer vision technology to establish a secure environment for the crop. This system is called Quality of Crop Device (qCrop), and it works with a You Only Look Once (Yolov8m) model to detect birds with high accuracy to protect farms. © 2024 IEEE.

关键词： Agri-food

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Intelligent Resource Allocation for RIS-Assisted Cell-Free Massive MIMO Systems 22

Intelligent Resource Allocation for RIS-Assisted Cell-Free M...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Mollah, Gloria Haider, Majumder Rawat, Danda B. Ahmed, Imtiaz Howard University Dept. of Electrical Engineering & Computer Science WashingtonDC United States

ISBN: (纸本)9798331508050

Cell-free massive multiple-input multiple-output (mMIMO) systems distribute a large number of access points (APs) over a wide geographic region, improving coverage, capacity, and user experiences notably. Similarly, reconfigurable intelligent surface (RIS) allows a significant leap forward in signal quality and energy efficiency by adding additional flexibility and intelligent control over the propagation environment of wireless systems. However, optimal resource allocation in RIS-integrated cell-free mMIMO systems faces substantial challenges due to the presence of a large number of APs and passive reflecting elements (PREs) at RIS while simultaneously providing network coverage to aerial and ground users. To efficiently navigate the high-dimensional optimization space, this paper proposes a novel framework for optimal resource allocation in RIS-assisted cell-free mMIMO systems employing deep learning (DL) techniques. In particular, we train a deep neural network offline to optimize the transmit powers at APs and the phase shift of PREs at RIS. The trained model is tested online to perform real-time optimization with low computational complexity. Simulation results demonstrate the effectiveness of the proposed data-driven resource allocation scheme while comparing it with the state-of-the-art baseline scheme. © 2025 IEEE.

关键词： Deep neural networks

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