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Low Frequency Oscillator Circuit Using Si-Gated Field Emitter Arrays 23

Low Frequency Oscillator Circuit Using Si-Gated Field Emitte...

引用

23rd International Vacuum Electronics Conference, IVEC 2022

作者： Bhattacharya, Ranajoy Hay, Robert Cannon, Mason Rughoobur, Girish Karaulac, Nedeljko Akinwande, Akintunde I. Browning, Jim Boise State University Dept. of Electrical & Computer Engineering BoiseID United States Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science CambridgeMA United States

ISBN: (纸本)9781665443258

Si-gated field emitter arrays (Si-GFEAs) are strong candidates for nano vacuum channel transistors (NVCTs). In this work, Si-GFEA die with 1000 X1000 arrays were used to create a Colpitts oscillator circuit. First, transfer and output characteristics sweeps were carried and based on those data, a LTSpice vacuum transistor (VT) model was developed. Then the model was used to develop a Colpitts oscillator circuit. The circuit was experimentally tested where the collector voltage was kept at 200 V DC and the gate voltage was kept at 50V DC. From the experiment, an oscillation frequency of 152 kHz was observed with a maximum amplitude of of Vpp=4.38 V. © 2022 IEEE.

关键词： Timing circuits

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Digital Solutions for Crime Control: A Comprehensive Criminal Identification and Reporting Framework

Digital Solutions for Crime Control: A Comprehensive Crimina...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Harinee S M. Vijay Anand Dept. of Computer Science and Engineering Saveetha Engineering College (Autonomous) Chennai India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

This paper presents the design and implementation of a comprehensive Criminal Identification and Reporting System (CIRS) aimed at enhancing law enforcement capabilities and improving public safety. The system enables users, including victims and law enforcement officers, to submit and manage complaints seamlessly through an intuitive online platform. Key features include the storage of user data in a secure database, real-time monitoring of criminal activity using CCTV footage, and advanced facial recognition technology for swift identification of potential offenders. The CIRS facilitates efficient case management by allowing users to track the status of their complaints and providing law enforcement agencies with valuable information to aid in criminal investigations. Moreover, the system assists local government agencies in maintaining records of known criminals, enabling proactive measures to prevent illegal activities. By leveraging cutting-edge technology and providing seamless integration with existing surveillance infrastructure, the CIRS promises to revolutionize criminal identification and reporting processes, ultimately contributing to improved public safety and law enforcement efficiency.

关键词： Law enforcement Databases Face recognition Surveillance Market research Public security Real-time systems

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Exploring the Efficacy of Modified Transfer Learning in Identifying Parkinson's Disease Through Drawn Image Patterns

Exploring the Efficacy of Modified Transfer Learning in Iden...

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Information and Communication Technology (ICICT), International Conference on

作者： Nabil Daiyan Md Rakibul Haque Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

Parkinson's disease (PD) is a progressive neurode-generative condition characterized by the death of dopaminergic neurons, leading to various movement disorder symptoms. Early diagnosis of PD is crucial to prevent adverse effects, yet traditional diagnostic methods are often cumbersome and costly. In this study, a machine learning-based approach is proposed using hand-drawn spiral and wave images as potential biomarkers for PD detection. Our methodology leverages convolutional neural networks (CNNs), transfer learning, and attention mechanisms to improve model performance and resilience against overfitting. To enhance the diversity and richness of both spiral and wave categories, the training dataset undergoes augmentation to increase the number of images. The proposed architecture comprises three phases: utilizing pre-trained CNNs, incorporating custom convolutional layers, and ensemble voting. Employing hard voting further enhances performance by aggregating predictions from multiple models. Experimental results show promising accuracy rates. For spiral images, weighted average precision, recall, and F1-score are 90%, and for wave images, they are 96.67%. After combining the predictions through ensemble hard voting, the overall accuracy is 93.3%. These findings underscore the potential of machine learning in early PD diagnosis, offering a non-invasive and cost-effective solution to improve patient outcomes.

关键词： Training Spirals Attention mechanisms Accuracy Parkinson's disease Computational modeling Transfer learning Transforms Predictive models Resilience

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Smart Gadgets Cockpit Using IoT

Smart Gadgets Cockpit Using IoT

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Computing and Data science (ICCDS), International Conference on

作者： Maya Eapen H. Mercy Beulah Jayakumari R Lavanya Mala A Indumathi S Pooja S Dept. of Computer Science and Engineering Jerusalem College oif Engineering Chennai India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

The Smart Device Cockpit collects data from IoT enabled household appliances like Air conditioner, refrigerators, microwave ovens, and washing machines. Once transmitted to the Smart Device Cockpit portal, the data undergoes analysis and is then presented in the form of both usage data and error data. This platform facilitates a thorough comprehension of the utilization patterns of these household devices and offers insights into any possible errors or malfunctions that may occur. In essence, the Smart Device Cockpit serves as a powerful tool for homeowners to not only monitor and manage their smart appliances but also to make informed decisions about usage optimization and timely maintenance.

关键词： Industries Organizations User experience Real-time systems Maintenance Continuous improvement Smart devices

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Churn Buster: Uncovering Patterns and Predicting Churn in OTT Platforms

Churn Buster: Uncovering Patterns and Predicting Churn in OT...

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engineering and Technology (PICET), Parul International Conference on

作者： Akshat Anand Sarika D Harshit Malviya Dipti Vatsa Sunita Adarsh Yadwad Shreya M. Dholariya Dept. Computer Science Engineering Parul Institute of Engineering and Technology Vadodara India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Churn, a common concern among business leaders, poses a significant challenge in today's fast-paced world where consumer attention spans are short. This research focuses on churn analysis within the OTT streaming platform sector, essential for retaining subscribers and ensuring business longevity. Utilising data preprocessing methods and machine learning algorithms, this study aims to identify factors influencing churn and provide insights for targeted retention strategies. The dataset includes 16 variables, with churn serving as the binary-dependent variable. Data preprocessing involved addressing missing values and converting categorical data into numerical format. Four machine learning models-Random Forest, XGBoost, K-Nearest Neighbors, and Support Vector Machine-were applied, with Random Forest achieving the highest accuracy of 89%. Evaluation metrics like confusion matrices, ROC curves, and Precision-Recall curves were used to evaluate model performance, revealing Random Forest's effectiveness in churn prediction. Principal Component Analysis (PCA) was employed to handle the complexity of user data, aiding in the identification of influential churn factors. This study emphasises the importance of understanding customer behaviour and utilising machine learning to forecast subscription choices. The insights gained provide valuable guidance to OTT companies for enhancing customer satisfaction and retention strategies, ensuring competitiveness in a dynamic industry landscape.

关键词： Over-the-top media services Data preprocessing Forestry Predictive models Prediction algorithms Vectors Numerical models Random forests Principal component analysis Business

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Hand Gesture Recognition using OpenCV 10

Hand Gesture Recognition using OpenCV

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Gupta, Rohan Singh, Akashdeep University Institute of Engineering Chandigarh University Deptt. of Electronics and Communication Engineering Punjab Mohali India University Institute of Engineering Chandigarh University Dept. of Computer Science Engineering Punjab Mohali India

ISBN: (纸本)9789380544472

For regulating the gestures the module which is mainly employed is Opencv. To regulate the structure the hand gesture will act as input. A hand gesture recognition technology allows us to communicate nonverbally in a natural, groundbreaking, and ultramodern way. The goal of the program is to spread a revolutionary gesture reputation strategy based on the discovery of a few form grounded traits. One digital digicam is included in the arrangement to capture the stoner's gesture and use it as an entry point into the device. One of the most important aspects of this program is to provide a device that can recognize particular physical motions and send information for tools operation. For example, a user can control a computer by performing a particular action next to a videotape digital digicam connected to a computer. Using the OpenCV module, we can extend the range of a hand gesture device in this arrangement. The device can then be controlled with hand gestures rather than using a physical component. The system utilizes high definition cameras for identifying the gestures. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Palmprint recognition

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A Comprehensive Review of Deep Learning Methods for Hyperspectral Image Compression

A Comprehensive Review of Deep Learning Methods for Hyperspe...

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International Conference on Advancement in Electrical and Electronic engineering (ICAEEE)

作者： Afsana Afrin Md. Al Mamun Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Rapid advancement in the development of hyperspectral image (HSI) sensors has significantly enhanced the capabilities of capturing detailed spectral information across a wide range of wavelengths, enabling more precise analysis and interpretation of the observed scenes. Managing the storage of these extensive images poses a crucial challenge, requiring the application of compression techniques. Deep Learning (DL) algorithms have seen a massive rise in popularity over the past few years for HSI compression as well as HSI analysis. This review paper provides a comprehensive summary of the current literature on deep learning methods for HSI compression. Additionally, it explores the challenges, opportunities, and potential directions for future research in HSI compression.

关键词： Deep learning Image sensors Technological innovation Image coding Recurrent neural networks Satellites Reviews

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Like Adding a Small Weight to a Scale about to Tip: Personalizing Micro-Financial Incentives for Digital Wellbeing 25

Like Adding a Small Weight to a Scale about to Tip: Personal...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Jang, Sueun Seo, Youngseok Choi, Woohyeok Lee, Uichin School of Computing KAIST Daejeon Korea Republic of Dept. of Computer Science and Engineering Kangwon National University Chuncheon-si Korea Republic of

ISBN: (纸本)9798400713941

Personalized behavior change interventions can be effective as they dynamically adapt to an individual's context. Financial incentives, a commonly used intervention in commercial applications and policy-making, offer a mechanism for creating personalized micro-interventions that are both quantifiable and amenable to systematic evaluation. However, the effectiveness of such personalized micro-financial incentives in real-world settings remains largely unexplored. In this study, we propose a personalization strategy that dynamically adjusts the amount of micro-financial incentives to promote smartphone use regulation and explore its efficacy and user experience through a four-week, in-the-wild user study. The results demonstrate that the proposed method is highly cost-effective without compromising intervention effectiveness. Based on these findings, we discuss the role of micro-financial incentives in enhancing awareness, design considerations for personalized micro-financial incentive systems, and their potential benefits and limitations concerning motivation change. © 2025 Copyright held by the owner/author(s).

关键词： digital wellbeing

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A Graph Convolutional Attention Network with Q-net on Multi-agent Systems 2

A Graph Convolutional Attention Network with Q-net on Multi-...

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2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2022

作者： Li, Yupei Shen, Tianqi Liu, Zhiyuan Dept. of Computer Science University of Oxford Oxford United Kingdom Department of AeroSpace Information Engineering Beihang University Beijing China Dept. of Computer Science University of Toronto TorontoON Canada

ISBN: (纸本)9781510657717

In recent years, GNNs have becoming one of the hottest topics of deep learning due to their powerful ability in modeling relational data and their wide applications in real world. Besides, reinforcement learning is widely applied in many fields. However, the tradition way to train the reinforcement learning is not that accurate enough. It can be assumed that it is wise to combine GNN and reinforcement learning, making good use of the modeling capability of GNNs, to enhance the reinforcement learning performance. In this paper, the most ordinary GNN is compared within the existing works to see whether it is possible to combine GNN and reinforcement learning. PettingZoo is chosen on behalf of the multi-agent system, and a Graph Convolutional Attention Network with Q-net (GCANQ) is designed and validated, which shows the efficient computational capability of GNN. Experiments show that GNN performs strikingly well on a common multiagent system and in a small scale with some overfit as well. Moreover, the speed of the training of GCANQ is acceptable for the complex calculation needed. © 2022 SPIE. All rights reserved.

关键词： Graph neural networks

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Effective Pest Detection by YOLOv5 with BottleneckCSP

Effective Pest Detection by YOLOv5 with BottleneckCSP

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Power, Electrical, and Electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： Ishrat Zahan Tani Tasmia Jannat Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

Pest damage poses a significant risk to crop quality and productivity, underscoring the necessity for accurate and efficient detection methods for farmers. Artificial intelligence (AI) facilitates targeted pest management with reduced reliance on broad-spectrum insecticides and enables early detection of insect infestations. Object detection, a computer vision approach, is revolutionizing pest identification in agriculture by locating pests in digital crop photographs. However, the detection of tiny objects within these datasets presents challenges due to sparse spatial information. In response to this challenge, we have replaced the C3 block of YOLOv5 with the BottleneckCSP module, which is more effective for feature mapping of tiny objects. Our enhanced model demonstrates superior performance on the IP102 dataset, which is characterized by the presence of tiny objects. Comparative analysis reveals that our BottleneckCSP-integrated model significantly improves feature mapping, resulting in a notable increase in mean average precision (mAP). Specifically, our modified model achieves an mAP of 63.9%, outperforming many state-of-the-art methods on this dataset. This advancement in the YOLOv5 architecture sets a new benchmark for AI-powered precision agriculture, providing farmers with a highly efficient tool for crop protection and pest management. The significant performance gains highlight the potential of our approach to transform current agricultural practices, paving the way for more sustainable and effective pest control strategies.

关键词： YOLO Accuracy Crops computer architecture Transforms Feature extraction Object recognition Artificial intelligence Protection Resilience

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