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Assessing Parkinson's Disease Severity Using Channel Attention and Graph Attention Networks on Kinect Skeleton Images

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Assessing Parkinson's Disease Severity Using Channel Attenti...

Iranian Conference on Intelligent Systems (ICIS)

作者： Fatemeh Fadaie Hamed Aghapanah Alireza Sedighifard Hadi Niknam Shirvan Department of Computer Engineering AI and Robotics University of Isfahan Isfahan Iran Department of Bioelectrics and Biomedical Engineering School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Department of Artificial Intelligence Iran University of Science and Technology Isfahan Iran Faculty of New Sciences and Technologies University of Tehran Isfahan Iran

ISBN: (数字)9798331507565

ISBN: (纸本)9798331507572

Parkinson's Disease (PD), a prevalent global neurological disorder, presents with motor symptoms like rigidity and tremors. Accurate severity assessment is vital for intervention evaluation. This study uses computer vision, specifically Kinect cameras and deep learning, to develop a system for assessing the severity of Parkinson's disease. Leveraging Kinect cameras' Precision in tracking key motion patterns, the system utilizes convolutional neural networks, namely Spatial-Temporal Graph Convolutional Network (STGCN) and Two-Stream Adaptive Graph Convolutional Network (2SAGCN), to detect movement anomalies and provide quantitative severity measures. A novel model is proposed, representing an enhanced version of 2SAGCN featuring a channel and graph attention module. Parkinson's severity classification comprises 11 classes based on 9 motion categories, totaling 99 classes. Three-dimensional transfer learning and data augmentation improve model Accuracy. This research advances non-invasive, quantitative Parkinson's assessment using innovative technology and advanced neural network models. The program is hosted on GitHub for implementation and result observation. The 10-fold cross-validation results of the best model, incorporating 2SAGCN structure, Graph, and Channel Attention, yield Accuracy, Precision, Recall, and F1-Score of Confidence Intervals 94.12± 0.14, 99.6 ± 0.20, 98.9 ± 0.10,99.2 ± 0.80 respectively.

关键词： Adaptation models Solid modeling Attention mechanisms Accuracy Parkinson's disease Graph convolutional networks Tracking Transfer learning Cameras Data models

Using depth for improving referring expression comprehension in real-world environments

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arXiv 2021年

作者： Doǧan, Fethiye Irmak Leite, Iolanda Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

In a human-robot collaborative task where a robot helps its partner by finding described objects, the depth dimension plays a critical role in successful task completion. Existing studies have mostly focused on comprehending the object descriptions using RGB images. However, 3-dimensional space perception that includes depth information is fundamental in real-world environments. In this work, we propose a method to identify the described objects considering depth dimension data. Using depth features significantly improves performance in scenes where depth data is critical to disambiguate the objects and across our whole evaluation dataset that contains objects that can be specified with and without the depth dimension. © 2021, CC BY-NC-SA.

关键词： Robots

EFFICIENT UNIVERSAL SHUFFLE ATTACK FOR VISUAL OBJECT TRACKING

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arXiv 2022年

作者： Liu, Siao Chen, Zhaoyu Li, Wei Zhu, Jiwei Wang, Jiafeng Zhang, Wenqiang Gan, Zhongxue Academy for Engineering and Technology Fudan University Shanghai China School of Computer Science Fudan University Shanghai China The Department of Engineering Research Center for Intelligent Robotics Ji Hua Laboratory Foshan China

Recently, adversarial attacks have been applied in visual object tracking to deceive deep trackers by injecting imperceptible perturbations into video frames. However, previous work only generates the video-specific perturbations, which restricts its application scenarios. In addition, existing attacks are difficult to implement in reality due to the real-time of tracking and the re-initialization mechanism. To address these issues, we propose an offline universal adversarial attack called Efficient Universal Shuffle Attack. It takes only one perturbation to cause the tracker malfunction on all videos. To improve the computational efficiency and attack performance, we propose a greedy gradient strategy and a triple loss to efficiently capture and attack model-specific feature representations through the gradients. Experimental results show that EUSA can significantly reduce the performance of state-of-the-art trackers on OTB2015 and VOT2018. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational efficiency

NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5945-5957页

作者： Bingqian Lin Yunshuang Nie Ziming Wei Jiaqi Chen Shikui Ma Jianhua Han Hang Xu Xiaojun Chang Xiaodan Liang Shenzhen Campus Sun Yat-sen University Shenzhen China Shanghai Jiao Tong University Shanghai China University of Hong Kong Pok Fu Lam Hong Kong Dataa Robotics Company Beijing China Huawei Noah's Ark Lab Shanghai China School of Information Science and Technology University of Science and Technology of China Hefei China Department of Computer Vision Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural language instructions. Recent research has highlighted the promising capacity of large language models (LLMs) in VLN by improving navigational reasoning accuracy and interpretability. However, their predominant use in an offline manner usually suffers from substantial domain gap between the VLN task and the LLM training corpus. This paper proposes a novel strategy called Nav igational C hain- o f- T hought (NavCoT), where we fulfill parameter-efficient in-domain training to enable self-guided navigational decision, leading to a significant mitigation of the domain gap in a cost-effective manner. Specifically, at each timestep, the LLM is prompted to forecast the navigational chain-of-thought by: 1) acting as a world model to imagine the next observation according to the instruction, 2) selecting the candidate observation that best aligns with the imagination, and 3) determining the action based on the reasoning from the prior steps. In this way, the action prediction can be effectively simplified benefiting from the disentangled reasoning. Through constructing formalized labels for training, the LLM can learn to generate desired and reasonable chain-of-thought outputs for improving the action decision. Experimental results across various training settings and popular VLN benchmarks (e.g., Room-to-Room (R2R), Room-across-Room (RxR), Room-for-Room (R4R)) show the significant superiority of NavCoT over the direct action prediction variants. Through simple parameter-efficient finetuning, our NavCoT outperforms a recent GPT4-based approach with $\sim$ 7% relative improvement on the R2R dataset. We believe that NavCoT will help unlock more task-adaptive and scalable LLM-based embodied agents, which are helpful for developing real-world robotics applications.

关键词： Navigation Cognition Training Scalability Glass Planning Large language models History Electronic mail Visualization

Modular HfOx-RRAM for On-Demand Micromodular Electronics

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IEEE Electron Device Letters

IEEE Electron Device Letters 2025年

作者： Kurup, Siddharth Bhattacharya, Swagat Banner, Rebecca K. Yue, Kaifan Ayyappan, Praveen Raj Aziz, Daniel Elly, Hubert N. Barton, Kira L. Hasler, Jennifer Filler, Michael A. Vogel, Eric M. Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Georgia Institute of Technology School of Materials Science and Engineering AtlantaGA30332 United States University of Michigan Department of Robotics Ann ArborMI48109 United States Georgia Institute of Technology School of Chemical and Biomolecular Engineering AtlantaGA30332 United States University of Michigan Department of Mechanical Engineering Ann ArborMI48109 United States

Micromodular metal-HfOx-metal resistive random-access memories (RRAM) were fabricated, transferred to foreign substrates, and contacted using high-resolution electrohydrodynamic jet (e-jet) printing. The modular RRAM exhibited bipolar switching at low operating voltages and multi-level analog resistance states programmable using variable amplitude voltage pulses. These variable resistance RRAMs were also integrated with an amplifier and a bandpass filter to modulate their gain and corner frequencies. This work enables integration of high-quality RRAM devices in modular circuits and paves the way for microscale heterogeneous integration at the device level. © 1980-2012 IEEE.

关键词： RRAM

Analysis of Vegetation Indices in the Cotton Crop in South Asia region using UAV Imagery

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Analysis of Vegetation Indices in the Cotton Crop in South A...

International Conference on Emerging Technologies, ICET

作者： Noor us Sabah Muhammad Usama Zuhair Zafar Muhammad Shahzad Muhammad Moazam Fraz Karsten Berns School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Aerospace and Geodesy Technical University of Munich (TUM) Munich Germany Department of Computer Science Robotics Research Lab Technische Universität Kaiserslautern (TUK) Kaiserslautern Germany

ISBN: (纸本)9781665459938

Cotton is one of the most economically significant crops in the South Asia region. Following the decline in cotton production in recent years, it is necessary to study and analyse cotton growth patterns by using modern precision agriculture techniques to enhance crop yield and quality. Multispectral imagery plays a vital role in estimating crop quantification by computing vegetation indices at different phenological stages of the crop. It facilitates in estimating plant health and impact on yield before the visible symptoms appear. In this study, four different vegetation indices maps are computed using two-band multispectral imagery acquired through Unmanned Aerial Vehicle (UAV). The UAV multispectral data is collected from two fields with different sowing dates using Sentera NDVI single sensor. The variation in the vegetation indices map generated is analysed by vegetation index thresholding and k-means clustering. By using Sentinel-2A imagery, an average value of each vegetation index for every phenological stage is validated. The correlation coefficient of each vegetation index conveys a positive progression trend and validates the accuracy of UAV data computed results. In conclusion, vegetation indices computed using multispectral UAV imagery conveys vital information regarding the progression of crop phenology and growth pattern.

关键词： Image resolution Asia Vegetation mapping Crops Autonomous aerial vehicles Market research Cotton

UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition

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arXiv 2023年

作者： Li, Qiufu Jia, Xi Zhou, Jiancan Shen, Linlin Duan, Jinming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Computer Vision Institute Shenzhen University China School of Computer Science University of Birmingham United Kingdom Aqara Lumi United Technology Co. Ltd China Alan Turing Institute United Kingdom SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https://***/CVI-SZU/UniTSFace. © 2023, CC BY.

关键词： Face recognition

Residual Dynamics Learning for Trajectory Tracking for Multi-rotor Aerial Vehicles

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arXiv 2023年

作者： Kulathunga, Geesara Hamed, Hany Klimchik, Alexandr Centre for Robotics and Mechatronics Components Innopolis University 420500 Russia Lincoln Institute for Agri-Food Technology University of Lincoln LN1 United Kingdom Daejeon28210 Korea Republic of School of Computer Science University of Lincoln LN6 United Kingdom

This paper presents a technique to model the residual dynamics between a high-level planner and a low-level controller by considering reference trajectory tracking in a cluttered environment as an example scenario. We focus on minimising residual dynamics that arise due to only the kinematical modelling of high-level planning. The kinematical modelling is sufficient for such scenarios due to safety constraints and aggressive manoeuvres that are difficult to perform when the environment is cluttered and dynamic. We used a simplified motion model to represent the motion of the quadrotor when formulating the high-level planner. The Sparse Gaussian Process Regression-based technique is proposed to model the residual dynamics. Recently proposed Data-Driven MPC is targeting aggressive manoeuvres without considering obstacle constraints. The proposed technique is compared with Data-Driven MPC to estimate the residual dynamics error without considering obstacle constraints. The comparison results yield that the proposed technique helps to reduce the nominal model error by a factor of 2 on average. Further, the proposed complete framework was compared with four other trajectory-tracking approaches in terms of tracking the reference trajectory without colliding with obstacles. The proposed approach outperformed the others with less flight time without losing computational efficiency. © 2023, CC BY.

关键词： Computational efficiency

Integrating a Digital Twin Concept in the Zero Emission Sea Transporter (ZEST) Project for Sustainable Maritime Transport using Stonefish Simulator

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arXiv 2024年

作者： Grimaldi, Michele Cernicchiaro, Carlo Rossides, George Ktoris, Angelos Yfantis, Elias Kyriakides, Ioannis Computer Vision and Robotics Institute University of Girona Girona Spain School of Engineering & Physical Sciences Heriot-Watt University United Kingdom Marine Robotics Innovation Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Marine & Offshore Science Technology & Engineering Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Marine Technology Division Cyprus Marine and Maritime Institute Larnaca Cyprus Computational Cognition Lab Open University of Cyprus Nicosia Cyprus Department of Engineering University of Nicosia Nicosia Cyprus

In response to stringent emission reduction targets imposed by theInternational Maritime Organization (IMO) and the European Green Deal\'s Fit for55 legislation package, the maritime industry has shifted its focus towardsdecarbonization. While significant attention has been placed on vesselsexceeding 5,000 gross tons (GT), emissions from coastal and short sea shipping,amounting to approximately 13% of global shipping transportation and 15% withinthe European Union (EU), have not received adequate consideration. Thisabstract introduces the Zero Emission Sea Transporter (ZEST) project, designedto address this issue by developing a zero-emissions multi-purpose catamaranfor short sea route © 2024, CC BY.

关键词： Emission control