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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Doungtap, Surasachai Petchhan, Jirayu Phanichraksaphong, Varinya Wang, Jenq-Haur International Graduate Program of Electrical Engineering and Computer Science Taiwan National Taiwan University of Science & Technology Dept. of Electrical Engineering Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle. © 2023 IEEE.

关键词： Virtual reality

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Cerebral cortex contributions to Parkinson's disease tremor:what exactly generates the tremor?

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Neural Regeneration Research 2025年第12期20卷 3535-3536页

作者： Manuel Bange Hao Ding Muthuraman Muthuraman Informatics for Medical Technology Institute of Computer ScienceUniversity AugsburgAugsburgGermany Department of Neurology Neural Engineering with Signal Analytics and Artificial Intelligence(NESA-AI)University Clinic WürzburgWürzburgGermany

Pathological tremor is one of the cardinal symptoms in Parkinson's disease (PD).Tremor is comprised of involuntary,rhythmic,a nd oscillating movements that can vary according to the circumstances under which they occur,the body parts that are involved,and the frequency at which they *** example,tremors can be mild to severe,are stress sensitive,and can affect arms,legs,or the head (Dirkx and Bologna,2022).

关键词： tremor cortex exactly

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Lightweight Human Posture Estimation Incorporating Hybrid Attention Mechanisms 5

Lightweight Human Posture Estimation Incorporating Hybrid At...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Cai, Yongheng Zou, Rongxin Liao, Wenbo Liang, Zhaohao Wang, Shu Zhuhai College of Science and Technology School of Computer Science Zhuhai China University of Minho School of Engineering Department of Industrial Electronics Guimaraes Portugal

ISBN: (纸本)9798350373820

Aiming at the problem that the existing human posture estimation models have high prediction accuracy and large computational complexity at the same time, this paper innovatively proposes LCBAM (Lightweight Convolutional Block Attention Module), which incorporates the hybrid attention mechanism, on the basis of the HRNet model. The new model adopts the Lightweight Design Sampling Module to reduce the running complexity of the model, and at the same time introduces the Hybrid Attention Mechanism Module (CBAM) to adaptively capture global features, so as to satisfy the lightweight design goal and improve the estimation accuracy of the model at the same time. The experimental results show that, compared with the benchmark model, the model in this paper exhibits obvious advantages in the detection of key points for posture estimation, especially in the detection of key points for the left wrist, right wrist, left knee, right knee, left ankle, and right ankle, with enhancements of 5.2%, 5%, 2.5%, 3.41%, 2.8%, and 4.04%, respectively. At the same time, the design of the fusion hybrid attention mechanism does not significantly increase the computational complexity of the lightweight network, but rather improves the accuracy of the key point detection, which solves the contradiction between the lightweight and prediction accuracy of the pose estimation model to a certain extent. © 2024 IEEE.

关键词： Computational complexity

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Projection Image Synthesis Using Adversarial Learning Based Spatial Transformer Network For Sparse Angle Sampling CT 46

Projection Image Synthesis Using Adversarial Learning Based ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Zhou, Huanyi Reeves, Stanley Liu, Jueting Auburn University Electrical and Computer Engineering Department AuburnAL36830 United States China University of Mining and Technology Computer Science and Technology 221000 China

ISBN: (纸本)9798350371499

In sparse-angle X-ray tomography, CT images possess significant noise and artifacts in reconstruction. An important image reconstruction research effort in this area aims to remove these artifacts while preserving image features. Approaches to the problem can be divided into two main categories, post-processing on reconstructed images and pre-processing on the sinogram. Following the rise of deep learning methods, many data-driven models have been proposed in both categories. However, those methods require a large amount of fully sampled data for training. In this paper, inspired by the development of the video frame synthesis technique, we propose an adversarial learning-based spatial transformer network for projection image synthesis, which aims to improve the quality of reconstructed images by reasonably increasing the number of projection images based on the existing projection data. Simulation and experiments have shown that this data-driven model can give rise to competitive results compared with conventional algorithms. © 2024 IEEE.

关键词： Spatio-temporal data

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A Comparison of the Performance of ResNet and DenseNet for Obstacle Avoidance and Path Following on a Mobile Robot Using Embedded GPUs

A Comparison of the Performance of ResNet and DenseNet for O...

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2024 IEEE International Conference on Agents, ICA 2024

作者： Ma, Shengjun Karkal, Pawan Sudhir Ma, Jing Alam, Fakhrul Auckland University of Technology Electrical and Electronic Engineering Department Auckland New Zealand Auckland University of Technology Computer Science and Software Engineering Department Auckland New Zealand

ISBN: (纸本)9798331539917

In this study, we evaluated the performance of deep learning-based navigation models on an novel platform that combines the Jetson Xavier NX with an RC four-wheel-drive car for autonomous driving. Focusing on data collection and model training using a single-camera setup, obstacle avoidance and path navigation were prioritised. The study compares the effectiveness of ResNet18, ResNet34, and DenseNet121 models by analysing training time, accuracy, obstacle avoidance efficiency, and path navigation capabilities. The results highlight the computational advantages of the Jetson Xavier NX over the Jetson Nano, demonstrating enhanced real-time processing and decision-making capabilities. This integration of the Jetson Xavier NX with an RC vehicle not only improves overall performance but also introduces a novel approach to autonomous vehicle development. The findings offer valuable insights into optimising deep learning models and hardware configurations for improved autonomous navigation. © 2024 IEEE.

关键词： Mobile robots

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Understanding Social Networks Using Machine Learning Sentimental Analysis Algorithms

Understanding Social Networks Using Machine Learning Sentime...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Goswami, Garima Varun Somanna, M.N. Inzimam College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Social networks are dynamic and complex structures that depend closely on human interplay. Machine learning and nostalgic evaluation algorithms can be used to gain insights into the shape and dynamics of these networks. Through those techniques, patterns of connections between people may be exposed, in addition to sentiment associated with subjects or entities. This know-how can help understand higher social networks over more than a few subjects, including fitness, politics, and the stock market. Moreover, such techniques may be implemented to leverage community effects to optimize the overall performance of advertising and marketing techniques. © 2024 IEEE.

关键词： advertising associated evaluation nostalgic techniques

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Edge Caching Based on Deep Reinforcement Learning

Edge Caching Based on Deep Reinforcement Learning

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Niknia, Farnaz Wang, Ping Agarwal, Aakash Wang, Zixu York University Department of Electrical Engineering and Computer Science Toronto Canada IIIT-Naya Raipur Electronic and Communication Engineering Naya Raipur India South China University of Technology School of Computer Science and Engineering Guangdong China

ISBN: (纸本)9798350345384

The use of mobile apps has caused a lot of data to be transmitted repeatedly, which has put a strain on the networks. One solution to this problem is caching, which stores data closer to the user to reduce data transfer and delay. Typically, when designing a caching policy, only a limited set of file features, such as freshness and popularity, are taken into account in the majority of prior studies. Nevertheless, it is crucial to incorporate factors like importance and size since highly valuable files may hold more significance compared to freely accessible ones. In this paper, we modeled caching problem using Semi-Markov Decision Process that considers file features such as popularity, lifetime, size, and importance. Then, we used a Reinforcement Learning algorithm called Double Deep Q-Learning to solve the Semi-Markov Decision Process. Simulation results show that the proposed method outperforms an existing caching method in terms of cache hit rate and total utility in various settings. This method is the first to comprehensively consider all file features, making it more practical for real-world scenarios. © 2023 IEEE.

关键词： Reinforcement learning

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A Conditional GAN and Dual-Channel Hybrid Deep Feature Framework for Robust Sensor Fault Detection in WSNs

A Conditional GAN and Dual-Channel Hybrid Deep Feature Frame...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Khan, Rehan Saeed, Umer Koo, Insoo University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of School of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9798331510756

Sensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. Wireless Sensor Networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial Network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection. © 2025 IEEE.

关键词： Gallium nitride

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Analysis of Resource Utilization in Lightweight Cryptographic Algorithms 1

Analysis of Resource Utilization in Lightweight Cryptographi...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Rahul, Ch Kousarr, Noushheen Yadav, T Abhishek Keerthi, P. Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350372748

In the dynamic landscape of the Internet of Things (IoT), the demand for efficient lightweight cryptographic (LWC) algorithms transcends traditional security considerations. This research addresses the compelling need to explore and enhance LWC algorithms to accommodate the distinctive challenges posed by resource-constrained devices in the expansive IoT ecosystem. This study analyzes key LWC algorithms - Ascon, Gift, Elephant, Tiny Jambu, Isap, Sparkle, PHOTON-Beetle, Romulus and Grain - evaluating the performance through various metrics such as context switches, page faults, CPU utilization, and memory usage. The existing discourse on IoT security often overlooks a comprehensive evaluation of LWC algorithms, emphasizing the critical importance of this research in bridging that gap. Beyond mere security applications, the study broadens its focus to address the multifaceted challenges of optimizing cryptographic algorithms for efficiency and resource utilization within IoT environments. By examining context switches and page faults, the research sheds light on the adaptability of these algorithms to dynamic IoT contexts. Furthermore, the study introduces innovative ideas for performance enhancements, extending the scope beyond security to contribute to the overall efficiency of IoT ecosystems. This work strives to meet the evolving demands of the IoT landscape, offering insights and solutions that transcend conventional security paradigms. © 2024 IEEE.

关键词： Network security

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A Multimodal Approach Utilizing the IOMT to Address both the Pandemic and its Aftermath

A Multimodal Approach Utilizing the IOMT to Address both the...

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2023 International Conference on Recent Advances in science and engineering technology, ICRASET 2023

作者： Rao, Manjula Gururaj Divakarala, Usha Kumar Reddy, K Hemant Priyanka, H. Kumar, Ch Sree Nmam Institute of Technology Karkala Nitte India Vit Ap University Department of Computer Science and Engineering India Computer Science & Engineering Pesu Bangaluru India Nist Department of Computer Science Berhampur India

ISBN: (纸本)9798350306927

Many IOT in the healthcare industry offers improved medical facilities for patients, benefiting doctors' offices and hospitals as well. The Internet of Medical Things (IOMT) plays a crucial role in enhancing the accuracy, reliability, and efficiency of electronic devices within the healthcare sector. Researchers are advancing a digital healthcare system by connecting existing medical resources and healthcare services. While IOT is impacting various industries, our focus is on its research contributions to the healthcare sector. The proposed system integrates multiple medical equipment, such as sensors and web or mobile-based applications, enabling communication across a network. This system facilitates the monitoring and storage of patient health data and medical information during the pandemic situation and after math. Additionally, using machine learning and data analysis, the proposed system aids in predicting the severity of the pandemic in different zones, offering potential solutions. The system seeks to improve outcomes for post-COVID-19 patients by using a multimodal approach to track their health. © 2023 IEEE.

关键词： Digital storage

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