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共 7024 条记录，以下是71-80 订阅

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Hand Gesture Recognition and an App for Deaf and Dumb People

Hand Gesture Recognition and an App for Deaf and Dumb People

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Harikrishnan, H. Johny, Joel Mathews, Marshel C Suresh, Meghana Joseph, Honey Amal Jyothi College of Engineering Dept. of Computer Science & Eng. Kottayam India

ISBN: (纸本)9798350333244

In this paper, a system for enhancing communication between sign language users and non-users is presented. The system recognizes and converts sign language into text and voice using picture data collected by a computer webcam. A masking approach is used to preprocess the picture data, then a Convolutional Neural Network (CNN) algorithm is used for feature extraction and classification. The system was trained using a dataset of masked images representing the 26 letters of the eng.ish alphabet, totaling 45500 images for training and 6500 images for testing. The system's goal is to make it simpler for those who use sign language and those who do not to communicate with one another. Keywords: Hand Gesture Recognition, Feature Extraction, Pre-Processing, Classification, CNN. © 2023 IEEE.

关键词： Feature extraction

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CMOS Mixer and VCO with Balun for ZigBee Health Monitoring and Communications

CMOS Mixer and VCO with Balun for ZigBee Health Monitoring a...

引用

2023 Cross Strait Radio science and Wireless Technology Conference, CSRSWTC 2023

作者： Islam, Tanvir Lai, Wen-Cheng University of Houston Dept. of Electrical & Computer Eng. HoustonTX77204 United States MingChi University of Technology Dept. of Electrical Eng. New Taipei City Taiwan

ISBN: (纸本)9798350358971

The proposed radio frequency integrated circuit development, intended for use in 2.4-GHz signal receiver has been product in tsmc 0.18-μm CMOS technology. The integrated 2.4-GHz frontend signal receiver consists of the 3rd order Gm-C passive bandpass loop filter with graph convolutional neural networks (GCNs) algorithm, double balance mixer with active balun and supercapacitors finetune voltage - controlled oscillator (VCO) as probe conductive experimental on wafer station. The proposed results meet 2.4 GHz wireless near field communication requirements for ZigBee and health monitoring. © 2023 IEEE.

关键词： active balun double-balance mixer Gm-C bandpass loop filter LC Tank VCO voltage-controlled oscillator

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BERT Goes to SQL School: Improving Automatic Grading of SQL Statements

BERT Goes to SQL School: Improving Automatic Grading of SQL ...

引用

2023 Congress in computer science, computer eng.neering, and Applied Computing, CSCE 2023

作者： Rivas, Pablo Schwartz, Donald R. Quevedo, Ernesto School of Eng. & Computer Science Baylor University Dept. of Computer Science United States School of Computer Science & Math. Marist College Dept. of Computing Technology United States

ISBN: (纸本)9798350327595

Automated grading of SQL queries is a challeng.ng task due to the complexity of the language and the variety of acceptable solutions for a given problem. In this paper, we propose a novel approach that leverages deep learning with a BERT model to understand the syntax and semantics of SQL statements. By training BERT on a dataset of SQL queries and their corresponding grades, we create a model that can automatically grade new questions accurately. Our experiments demonstrate that the proposed methodology achieves high accuracy and consistency in grading SQL queries, outperforming existing state-of-the-art models. Furthermore, we provide an analysis of the model's explainability, revealing a new capability that can be extremely beneficial for understanding the decision-making process. Overall, our work demonstrates the potential of deep learning with BERT for improving the efficiency and accuracy of SQL query grading. © 2023 IEEE.

关键词： bert deep learning machine learning natural language processing sql

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Joint Trajectory, Power Profile and Harvested Energy Management in Cooperative UAVs 10

Joint Trajectory, Power Profile and Harvested Energy Managem...

引用

10th International Conference on Social Networks Analysis, Management and Security, SNAMS 2023

作者： Ahmed, Ashfaq Naeem, Muhammad Ahmad, Akhlaque Ahmed, Sabeeh Khalifa University Dept. of Electrical Eng. & Cs Abu Dhabi United Arab Emirates Comsats University Islamabad Dept. of Electrical & Computer Eng. Wah Campus Pakistan Ted University Dept. of Software Eng. Ankara Turkey

ISBN: (纸本)9798350318906

Unmanned Aerial Vehicles (UAVs) have been a subject of research for cooperative communication for quite some time. To achieve the highest possible data rates, UAVs must be positioned optimally. For this cooperative communication to be most effective (maximizing data rates), the UAVs' positions and trajectories are critically important. Additionally, the limited onboard power supply, which depletes rapidly, becomes a decisive factor when burdened with transmission loads. In this research, UAV-assisted cooperative communication is explored, with a focus on moving UAVs rather than stationary ones. The objective is to maximize data rates through optimal trajectory planning, from the UAVs' initial positions to their final destinations, and through efficient utilization of onboard power resources. In this scenario, the UAVs harvest energy from the power received from the source and use it for further communication. A Mixed Integer Non-Linear Problem (MINLP) mathematical model is formulated to address these issues. Additionally, an efficient e-optimal algorithm is proposed, based on the mesh adaptive direct search method. This algorithm adaptively minimizes the search space of the problem using exploration and exploitation techniques to solve the non-convex MINLP model. The performance of the proposed algorithm is validated through Monte Carlo simulation. © 2023 IEEE.

关键词： Energy harvesting

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Fool 'Em All - Fool-X: A Powerful & Fast Method for Generating Effective Adversarial Images

Fool 'Em All - Fool-X: A Powerful & Fast Method for Generati...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Khamaiseh, Samer Y. Mancino, Mathew Jost, Deirdre Al-Alaj, Abdullah Bagagem, Derek Serra, Edoardo Miami University Dept. of Computer Science & Software Eng. OH United States Caci International NJ United States Miami University Dept. of Computer Science & Software Eng. Oxford United States Virginia Wesleyan University Department of Computer Science VA United States Boise State University Deptartment of Computer Science ID United States Boise State University Department of Computer Science Boise United States

ISBN: (纸本)9798350362480

The well-trained image classification neural networks are vulnerable to adversarial examples. An adversarial example is a malicious input carefully crafted by adding small perturbations to the original input, leading to misclassification. Despite advancements in generating adversarial examples, to the best of our knowledge, none of the well-known adversarial attacks can generate effective adversarial examples that work efficiently on large-scale datasets and very deep neural network architectures. In contrast to ordinary adversarial examples, effective adversarial examples have all the following four characteristics: (1) the ability to maximize the loss of DNNs, (2) the ability to cause a high misclassification rate for both undefended and defended DNN models using various defense methods, (3) minimal perturbations with low computational overhead on large-scale datasets, (4) the ability to be transferable across different DNN *** fill this void, we propose Fool-X, an algorithm to generate effective adversarial examples with the least perturbations that can fool state-of-the-art image classification neural networks. To evaluate the performance of Fool-X, we have conducted extensive experiments using 12 baseline adversarial training defense methods and six state-of-the-art adversarial attacks. The results reported on ImageNet-ILSVRC, CIFAR-100, and CIFAR-10 demonstrate that the proposed Fool-X algorithm can generate effective adversarial examples on large-scale datasets that can successfully fool the well-trained, defended image classification neural networks and significantly outperform the state-of-the-art adversarial attacks. The code is available: https://***/LAiSR-SK/fool-X-Attack © 2024 IEEE.

关键词： Deep neural networks

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Adaptive Traffic Signal Timing: Leveraging YOLOv10 and computer Vision for Real-Time Optimization 8

Adaptive Traffic Signal Timing: Leveraging YOLOv10 and Compu...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Papanashi, Suhas Chadaga, Manya Kshithi, R. Huddar, Santosh S. Sreelakshmi, K. Ramakanth Kumar, P. Rv College of Engineering Dept. of Electronics and Telecomm Bengaluru India Rv College of Engineering Dept. of Computer Science & Eng. Bengaluru India

ISBN: (纸本)9798331505462

Traffic congestion poses a significant challeng. in modern urban environments, particularly in densely populated cities. Traditional traffic signals, which operate on fixed timings, often require manual adjustments to respond to varying traffic densities, leading to inefficient traffic flow. This issue arises because fixed intervals do not account for the dynamic nature of traffic patterns throughout the day. While developing intelligent systems that adjust signal durations dynamically offers a potential solution, such systems involve substantial financial investment and extended implementation timelines. This paper proposes a method to optimize green light durations by analyzing footage collected from a traffic junction in Beng.luru. This analysis can be performed using existing CCTV footage or manually gathered data. Even limited sample sizes can provide valuable insights for preliminary adjustments. The approach utilizes YOLOv10, a state-of-the-art computer vision algorithm, to extract relevant traffic data. The data is processed by an algorithm designed to reallocate green light durations, which are then compared with the original timings. An experimental setup involved recording one-minute videos over three days at the selected junction and applying the proposed algorithm to assess and refine signal durations. The results demonstrate the feasibility of using such algorithms to improve traffic signal management effectively. © 2024 IEEE.

关键词： Investments

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Building Energy Consumption Prediction: A Machine Learning Approach with Feature Selection 10

Building Energy Consumption Prediction: A Machine Learning A...

引用

10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Liu, Linfeng.Juwono, Filbert H. Wong, W.K. Liu, Huanyu Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China Xi'an Jiaotong-Liverpool University Dept. of Electrical and Electronic Eng. Suzhou China Curtin University Malaysia Dept. of Electrical and Computer Eng. Miri Malaysia

ISBN: (纸本)9798350363104

Building energy management system is required to achieve sustainable energy in the context of global environmental and climate change challeng.s. Energy consumption prediction is a typical procedure used to manage the energy consumption in buildings. By accurately predicting energy consumption, the effectiveness of various energy-saving measures can be assessed, and corresponding optimization strategies can be developed. This paper presents a machine learning-based energy consumption prediction in buildings. We evaluate nine machine learning models to predict energy consumption. There are 52 features characterizing the dataset, such as the building's energy rating, environmental impact, number of rooms, and lighting descriptions. These features are selected based on their potential relevance to energy consumption, and cover the physical properties of the building, energy usage characteristics and equipment parameters, thus providing the basis for a comprehensive analysis. By performing feature selection using mutual information, we can reduce the number of features into eight, thereby reducing the complexity of the models. The model evaluation is performed using the coefficient of determination, R2 and the Root Mean Square Error (RMSE) metrics. Simulation results show that selecting the features can reduce the complexity of the model while resulting in relatively acceptable performance. © 2024 IEEE.

关键词： Green buildings

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An Enhancement Algorithm for Mobility over Fog Computing 3

An Enhancement Algorithm for Mobility over Fog Computing

引用

3rd IEEE International Conference on Electronic eng.neering, ICEEM 2023

作者： Hussien, Doaa Abo El-Sisi, Ashraf El-Bahnasawy, Nirmeen A. Menoufia University Faculty of Electronic Eng. Computer Science & Eng. Dept. El-Menoufia Egypt Menoufia University Faculty of Computers and Information Computer Science Department El-Menoufia Egypt

ISBN: (纸本)9798350323511

Due to the mobility of users and end devices, providing continuous service in Fog Computing (FC) is a difficult challeng.. With fog-assisted IoT healthcare frameworks, dealing with user mobility becomes even more complex because low latency in mobility support and fog node locating is a critical requirement. In this work, we offer a fog computing architecture with hand-over strategy that is tailored to user mobility and that reduces latency for location-sensitive operations and service stability with great data broadcast quality. In our design, it is no longer necessary to either complete a work or send it to the cloud;instead, it is necessary to connect with other FNs to continue with a request for a new job. This reduces average network delay by 30%-40% and increases end-to-end communication by 35%-45%. © 2023 IEEE.

关键词： Fog computing

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Enhancing Facial Recognition in Visual Prostheses using Region of Interest Magnification and Caricaturing 5

Enhancing Facial Recognition in Visual Prostheses using Regi...

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5th International Conference on computer and Applications, ICCA 2023

作者： Moneer, Hesham M. Elnabawy, Reham H. Eldawlatly, Seif German University in Cairo Digital Media Eng. and Technology Dept. Cairo Egypt Ain Shams University Computer and Systems Eng. Dept. Cairo Egypt The American University in Cairo Computer Science and Eng. Depr. Cairo Egypt

ISBN: (纸本)9798350303254

Blindness is a prevalent disability with significant personal and societal consequences. While medical advancements offer treatment options, severe damage to the retina, optic nerve, or brain may remain untreated. Visual prostheses are implantable medical devices that aim at providing limited vision to such individuals. However, such prostheses offer low spatial resolution making activities like reading, facial recognition, and navigation challeng.ng. This work aims to enhance implantees' ability to recognize faces through real-time scene preprocessing, machine learning and computer vision techniques. Virtual-reality visual models simulating prosthetic vision were tested on normally/corrected sighted subjects, investigating the use of histogram equalization for contrast enhancement, facial region magnification, and caricaturing of facial features. Results revealed that histogram equalization with magnification increases facial recognition accuracy by 60%, distinguishability accuracy by 50%, and accuracy of seeing facial details by 90%. In contrast, adding facial caricaturing improved the accuracies by 66.66%, 25%, and 75% for recognizability, distinguishability, and seeing facial details, respectively. Consequently, the combination of visual field histogram equalization, face magnification, and optional caricaturing can be considered as a promising enhancement approach that could enhance the quality of vision perceived through visual prostheses. © 2023 IEEE.

关键词： Prosthetics

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An Enhancement Multi-Objective Resource Allocation Algorithm in Fog Environment 3

An Enhancement Multi-Objective Resource Allocation Algorithm...

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3rd IEEE International Conference on Electronic eng.neering, ICEEM 2023

作者： El-Bahnasawy, Nirmeen A. El-Nattat, Amal El-Sayed, Ayman Elkazzaz, Sahar Menoufia University Faculty of Electronic Eng. Computer Science & Eng. Dept. El-Menoufia Menouf32952 Egypt Sadat City University Faculty of Computing and Artificial Intelligence Computer Science Dept. El-Menoufia Egypt

ISBN: (纸本)9798350323511

For high transmission latency from users, cloud computing (CC) is not the best choice for processing latency sensitive applications that require real time response with minimal delay. To tolerate this issue, a new computing paradigm named fog computing (FC) was introduced to make cloud services closer to the user. It has been proposed to support real-time applications. Resource allocation and Task assignment are important issues for fog environment. Inefficient resource allocation causes poor performance for the system. Some tasks are constrained with a time limit called deadline. In case of real time applications, tasks need to be scheduled in the fog environment without violating its deadline. A new algorithm called Modified Deadline Aware Resource Allocation (MDARA) was proposed in this paper. Its aim is minimizing the completion time of applications, minimizing average response time, augment usage of resources under a constraint of deadline for enhancing the performance of fog environment. The proposed algorithm is compared with one of effective load balancing algorithms as Modified Round Robin algorithm (MRR) and compared with one of the recent algorithms like DRAM algorithm, which focuses on load balancing and resource utilization without taking into consideration the overall completion time and user-defined deadline of tasks. According to the results, the proposed algorithm outperformed both MRR and DRAM algorithms. © 2023 IEEE.

关键词： Resource allocation

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