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Research on Passive Optical Dental Implant Dynamic Navigation System Based on Binocular 15

Research on Passive Optical Dental Implant Dynamic Navigatio...

引用

15th IEEE International Conference on Advanced Infocomm technology, ICAIT 2023

作者： Chen, Hanlu Hu, Shejiao School of Computer Science and Information Engineering HeFei University of Technology Anhui China

ISBN: (纸本)9798350314120

In the digital era, the continuous advancement of computer technology has profoundly impacted the field of clinical medicine. Surgical navigation, as a key application of computer-assisted navigation, has played a pivotal role in surgical procedures. The integration of medical imaging and positioning technologies has driven surgery towards minimally invasive approaches and higher precision, offering safer and more effective treatment options for patients. Particularly in the field of dental implantology, precise surgical positioning and real-time monitoring are paramount. This paper introduces a dynamic navigation system for dental implants that utilizes passive optical positioning. The system relies on tracking the reflection of markers through dual-camera optical sources, establishing spatial relationships among the patient's oral cavity, the dual cameras, and the dental implant instrument. This technology not only enables precise registration of image data with the position of the drilling needle but also calculates and determines the spatial coordinates of the dental implant's tip in real time. These critical pieces of information are intuitively presented on a visual screen, integrating with the preoperative virtual model of the implant site. This allows surgeons to continuously monitor the angle and depth of implant placement in real-time, facilitating necessary adjustments during the surgical procedure. Consequently, this approach enhances the precision and safety of dental implant surgeries. © 2023 IEEE.

关键词： Binocular vision

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Advancements in Optimization Algorithms for Lung Nodule Detection and Classification: A Review 1

Advancements in Optimization Algorithms for Lung Nodule Dete...

引用

1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Kiran Kumar, M. Amalanathan, Anthoniraj Vellore Institute of Technology School of Computer Science & Engineering Tamilnadu Vellore India

ISBN: (纸本)9798350328042

Lung cancer remains a leading cause of cancer-related deaths globally. Timely identification and categorization of lung nodules are crucial for improving prognoses and survival rates. This review explores various optimization algorithms applied to the detection and classification of lung nodules, analyzing their effectiveness in computed tomography (CT) scans. The study delves into evolutionary, swarm-based, and gradient-based algorithms, evaluating their impact on sensitivity, specificity, accuracy, and computational efficiency. It also compares traditional machine learning, deep learning, and hybrid models while addressing associated challenges and proposing potential solutions. © 2023 IEEE.

关键词： Evolutionary algorithms

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Evaluation and Experimental Comparison of Overvoltage Suppression Methods for DC Circuit Breakers

Evaluation and Experimental Comparison of Overvoltage Suppre...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Zhang, Zhi Jin Justin Liu, Yang Graber, Lukas Saeedifard, Maryam Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States

ISBN: (纸本)9798350316445

Among the semiconductor-based dc circuit breaker (DCCB) topologies (e.g., solid-state circuit breakers, hybrid circuit breakers), a common element is the paralleled branches of power semiconductor switches and energy absorption components. To interrupt dc-side faults, the power switches turn off to redirect the fault current to the energy absorption elements, which absorb the fault energy. Metal-oxide varistors (MOVs) are currently the most widely used candidate for energy absorption. However, transient voltage overshoots can occur during the commutation of the fault current from the semiconductor switches to the MOVs. This is problematic as they can damage the paralleled semiconductors and potentially the mechanical switch in the case of hybrid circuit breakers. A number of DCCB overvoltage suppression methods have been proposed in the technical literature. However, there is a lack of systematic evaluation and comparison of these methodologies in terms of their suppression performance, design procedures, etc. Therefore, to inform designers of the advantages and drawbacks of each method and to guide the design procedures, the state-of-the-art DCCB overvoltage suppression techniques are surveyed and new analytical results are presented. Their performance is experimentally compared on a 1.2 kV DCCB hardware platform under two different DCCB tripping strategies. © 2023 IEEE.

关键词： DC circuit breaker gate resistor MOV overvoltage snubber solid-state circuit breaker voltage overshoot

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Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study 24

Self-supervised Activity Representation Learning with Increm...

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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Liu, Jason Deldari, Shohreh Xue, Hao Nguyen, Van Salim, Flora D. School of Computer Science and Engineering Australia Defence Science and Technology Group Australia

ISBN: (纸本)9798350341010

In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data. Analyzing this ever-increasing data presents several challenges, including limited access to annotated data and a constantly changing environment. Recent advancements in self-supervised learning have been utilized as a pre-training step to enhance the performance of conventional supervised models to address the absence of labelled datasets. This research examines the impact of using a self-supervised representation learning model for time series classification tasks in which data is incrementally available. We proposed and evaluated a workflow in which a model learns to extract informative features using a corpus of unlabeled time series data and then conducts classification on labelled data using features extracted by the model. We analyzed the effect of varying the size, distribution, and source of the unlabeled data on the final classification performance across four public datasets, including various types of sensors in diverse applications. © 2023 IEEE.

关键词： Time series

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Accessible e-News Summary Browser Extension for Visually Challenged Users 3

Accessible e-News Summary Browser Extension for Visually Cha...

引用

3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Rajyalakshmi, Tamma Kuppusamy, K.S. School of Engineering and Technology Pondicherry University Department of Computer Science Puducherry India

ISBN: (纸本)9798350303360

The news media is a vital global information source, evolving from physical newspapers to digital platforms. technology has revolutionized dissemination, ensuring broader accessibility. The main goal of news is to provide significant information and raise public awareness about a wide range of issues, notably jobs, healthcare, governmental initiatives, and alerts about natural disasters. This study delves into the challenges faced by individuals with visual impairments when accessing e-news online. The paramount goal is to ensure unbridled access to news for everyone, despite barriers. In this paper, We undertake a web accessibility evaluation of the homepages of 12 Indian news websites under three conformance levels of the WCAG 2.0 recommendation using AChecker and the WAVE Tool. Our evaluation is grounded in quantifying accessibility barriers. In particular, We address issues related to web accessibility and highlight the adverse effects of pop-up advertisements and automatic video play on news websites. We propose a browser extension for ad-free summaries, enhancing the news experience for the visually impaired. Evaluation insights guide ad format decisions, optimizing characteristics and placements on websites. © 2023 IEEE.

关键词： Websites

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Time-Graph Adjustive Graph Convolutional Recurrent Network for Traffic Forecasting

Time-Graph Adjustive Graph Convolutional Recurrent Network f...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Qingyuan, Shao Hui, Yan Yuxin, Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350337020

Traffic forecasting is a crucial undertaking in the transportation domain. Current practices rely heavily on Recurrent Neural Networks (RNNs) and Temporal Convolutional Networks (TCNs) to model temporal dependencies in traffic forecasting. However, these approaches tend to overlook the interdependence of multi-hop time steps, impeding their ability to capture long-term dependencies and ultimately limiting their effectiveness in long-term forecasting. To address this issue, we present a novel method, the Time-Graph Adjustive Graph Convolutional Recurrent Network (TAGRN), for traffic forecasting. Our approach employs a Time-Graph model on the temporal domain, treating each time step of the traffic series as a graph node. We incorporate the similarity between time steps as weighted edges, enabling the Time-Graph to capture correlations between multi-hop time steps and model long-term dependencies through graph convolution. We utilize the Simple Graph Convolution (SGC) technique for information propagation due to its simplicity, linearity, and efficiency. Additionally, we introduce an adaptive semantic graph to enhance the capture of spatial information on the spatial domain. Experimental results on various real traffic datasets demonstrate the effectiveness of our proposed method. Compared to existing approaches, TAGRN achieves superior performance in long-term traffic forecasting, highlighting its potential for practical applications. The code is available at https://***/sqy123qwer/TAGRN. © 2023 IEEE.

关键词： graph convolutional network spatial-temporal data traffic forecasting

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Enhancing Deep Learning Performance Through Parallel Processing: A Comprehensive Research Study 6

Enhancing Deep Learning Performance Through Parallel Process...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Thakur, Rudraditya Goel, Naman Bhavesh Vijaykumar, Vaidehi Vellore Institute of Technology Chennai School of Computer Science Engineering Chennai India

ISBN: (纸本)9798350394702

The computational demands of training complex deep learning models have hindered the widespread adoption of this transformative technology. Parallel processing techniques have emerged as a promising approach to address this challenge by distributing the computational workload across multiple processors. This research delves into the multifaceted dimensions of enhancing deep learning performance through parallel processing. It explores the theoretical underpinnings, parallelization strategies, hardware and software infrastructures, and application-specific impact of parallel processing in deep learning. © 2023 IEEE.

关键词： Application programs

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Vulnerability Analysis for Captchas Using Deep Learning 3

Vulnerability Analysis for Captchas Using Deep Learning

引用

3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Walia, Jaskaran Singh Odugoudar, Aryan Vellore Institute of Technology School of Computer Science and Engineering India Canberra Australia

ISBN: (纸本)9798350343274

Several websites improve their security and avoid dangerous Internet attacks by implementing CAPTCHAs (Completely Automated Public Turing test to tell computers and Humans Apart), a type of verification to identify whether the end-user is human or a robot. The most prevalent type of CAPTCHA is text-based, designed to be easily recognized by humans while being unsolvable towards machines or robots. However, as deep learning technology progresses, development of convolutional neural network (CNN) models that predict text based CAPTCHAs becomes easier. The purpose of this research is to investigate the flaws and vulnerabilities in the CAPTCHA generating systems in order to design more resilient CAPTCHAs. To achieve this, we created CapNet, a Convolutional Neural Network. The proposed platform can evaluate both numerical and alphanumerical CAPTCHAs. © 2023 IEEE.

关键词： Deep learning

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Research on small target garbage detection method based on improved YOLOv7 8

Research on small target garbage detection method based on i...

引用

8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Xiao, Lizhong Hu, Fan Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350369564

A DFST-YOLO (Detection for small targets-YOLO) garbage detection model with improved YOLO v7 model is proposed to address the problems of small target size, susceptibility to background interference, weak feature information, and easy to produce missed and false detection in garbage classification detection tasks to be detected. By introducing Omni-Dimensional Dynamic Convolution and Parameter-Free Attention Module in the backbone network, the computing power of the convolutional neural network is improved and the ability to capture key information is enhanced. On the self-built garbage dataset Trash_dataset, mAP0.5 and mAP0.5:0.95 reach 95.15% and 70.71%, respectively. The experimental results show that the detection for small targets possesses better detection results compared to some popular algorithms. © 2023 IEEE.

关键词： Convolution

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DTSAC: Smart Contract-based Access Control with Delegation and Trust Management 29

DTSAC: Smart Contract-based Access Control with Delegation a...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liao, Junguo Wu, Qiong School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350330717

With the proliferation of the Internet of Things (IoT), access control has become particularly important in protecting data security. However, traditional access control models have limited capabilities in addressing challenges such as single points of failure, coarse-grained access control, and insufficient security in distributed environments. To tackle these challenges, we propose DTSAC, a smart contract-based access control with distributed delegation and dynamic trust management. This model utilizes blockchain for the decentralized delegation of capabilities and distributes delegation relationships across multiple delegation trees to enable fast revocation of capabilities. In addition, we design a dynamic trust assessment mechanism between the delegator and the delegatee to more comprehensively evaluate the credibility of the subject. Finally, a functional prototype of the DTSAC is successfully deployed on the public Sepolia Ethereum testnet. The experimental evaluation is conducted to verify the feasibility and effectiveness of the proposed model. © 2023 IEEE.

关键词： access control blockchain delegation trust management

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