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Integrated CWT-CNN for Epilepsy Detection Using Multiclass EEG Dataset

computers, Materials & Continua 2021年第10期69卷 471-486页

作者： Sidra Naseem Kashif Javed Muhammad Jawad Khan Saddaf Rubab Muhammad Attique Khan Yunyoung Nam Robotics and Machine Intelligence Engineering School of Mechanical and Manufacturing EngineeringNational University of Sciences and Technology(NUST)Islamabad44000Pakistan National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Electroencephalography is a common clinical procedure to record brain signals generated by human *** are useful in Brain controlled interfaces and other intelligent Neuroscience applications,but manual analysis of these brainwaves is complicated and time-consuming even for the experts of *** EEG analysis and classification techniques have been proposed to address this problem however,the conventional classification methods require identification and learning of specific EEG characteristics *** learning models can learn features from data without having in depth knowledge of data and prior feature *** of the great implementations of deep learning is Convolutional Neural Network(CNN)which has outperformed traditional neural networks in pattern recognition and image *** Wavelet Transform(CWT)is an efficient signal analysis technique that presents the magnitude of EEG signals as timerelated Frequency *** deep learning architectures suffer from poor performance when classifying EEG signals in the Time-frequency *** improve classification accuracy,we propose an integrated CWT and CNN technique which classifies five types of EEG signals *** compared the results of proposed integrated CWT and CNN method with existing deep learning models e.g.,GoogleNet,VGG16,***,the accuracy and loss of the proposed integrated CWT and CNN method have been cross validated using Kfold cross *** average accuracy and loss of Kfold cross-validation for proposed integrated CWT and CNN method are,76.12%and 56.02%*** model produces results on a publicly available dataset:Epilepsy dataset by UCI(Machine Learning Repository).

关键词： Deep learning electroencephalography epilepsy continuous wavelet transform

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Deep Learning Based FOD Detection For Runway Health Monitoring

Deep Learning Based FOD Detection For Runway Health Monitori...

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AIML-Applications for Engineering & technology (ICAET), International Conference on

作者： Amarjeet Kumar Aanya Mittal Abhishek Dixit Pooja Agrawal School of Robotics Defence Institute of Advanced Technology Pune India Department of Computer Science Manipal University Jaipur Rajasthan India Department of Aerospace Defence Institute of Advanced Technology Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This paper focuses on the safety of airport runways, which is crucial for aviation industries. Foreign Object Debris (FOD) i.e. objects like nails or bolts can cause significant harm to aircraft and potentially lead to catastrophic incidents and fatalities. This study delves into detecting objects on runways by using versions of the YOLO (You Only Look Once) model. Further, it also presents the differences in detecting objects and comparing the accuracy across a range of objects, paying attention to their ability to identify even the smallest things that have a potential danger. In order to identify the model variation and enable real-time runway surveillance, the proposed methodology considers the trade-off between detection speed and precision. The results illustrate the effectiveness of the latest YOLO model in object recognition over existing models, this can support the improvement of runway safety and the development of automated systems that monitor runway conditions.

关键词： YOLO Deep learning Accuracy Atmospheric modeling Surveillance Robustness Real-time systems Safety Object recognition Aircraft

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SELM: From Efficient Autonomous Exploration to Long-term Monitoring in Semantic Level

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Lang, Fang Qin, Yongsen Wang, Yinchuan Liu, Jin Wang, Chaoqun Song, Wei Zhu, Qiuguo Song, Rui Zhejiang University Polytechnic Institute Hangzhou310015 China Shandong University School of Control Science and Engineering Jinan China Zhejiang University Robotics Institute Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China

Maintaining up-to-date environmental models from initial deployment through long-term autonomy in service is critical for applications like navigation and task planning. To address the challenges of persistent monitoring in unknown environments, we introduce a Two-Stage monitoring strategy, termed the Semantic-level autonomous Exploration and Long-term environment Monitoring (SELM) framework. In the first stage, we introduce a novel semantic exploration method to adapt to new environments quickly. Leveraging the semantic information within the incrementally constructed 3D Scene Graph (3DSG), we combine the Next-Best-View (NBV) selection with room semantics, introducing a more efficient and comprehensive approach for multi-room indoor environment exploration. In addition, the exploration provides patrol routes, the room distance-connectivity graph, and complete environment initial states for subsequential monitoring. The monitoring stage aims to persistently patrol to update the world model in the presence of dynamic changes, including changes in objects' positions. We formulate the long-term monitoring problem as the Partially Observable Markov Decision Process (POMDP) to cope with the environmental uncertainty. To solve the POMDP, we propose the Graph Attention Bidirectional long short-term memory Proximal Policy Optimization (GABPPO) algorithm for the optimal patrol strategy. The feasibility and effectiveness of the proposed SELM framework are verified through extensive experiments. © 2016 IEEE.

关键词： Environmental monitoring

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DLPO : Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective

arXiv

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arXiv 2025年

作者： Peng, Dengyun Zhou, Yuhang Chen, Qiguang Liu, Jinhao Chen, Jingjing Qin, Libo Research Center for Social Computing and Interactive Robotics Harbin Institute of Technology China School of Computer Science and Engineering Central South University China Fudan University China

Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requires considerable human effort, significantly limiting its scalability. To mitigate this, recent studies have explored automated prompt optimization as a promising solution. Despite these efforts, existing methods still face critical challenges in robustness, efficiency, and generalization. To systematically address these challenges, we first conduct an empirical analysis to identify the limitations of current reflection-based prompt optimization paradigm. Building on these insights, we propose 7 innovative approaches inspired by traditional deep learning paradigms for prompt optimization (DLPO), seamlessly integrating these concepts into text-based gradient optimization. Through these advancements, we progressively tackle the aforementioned challenges and validate our methods through extensive experimentation. We hope our study not only provides valuable guidance for future research but also offers a comprehensive understanding of the challenges and potential solutions in prompt optimization. Our code is available at https://***/sfasfaffa/DLPO. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Complexity based investigation in collaborative assembly scenarios via non intrusive techniques

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Procedia computer science 2023年 217卷 478-485页

作者： Sotirios Panagou Monica Sileo Konstantinos Papoutsakis Fabio Fruggiero Ammar Qammaz Antonis Argyros School of Engineering University of Basilicata 85100 Potenza Italy Computational Vision and Robotics Laboratory Institute of Computer Science Foundation for Research and Technology – Hellas 70013 Heraklion Greece

Human and robot collaboration in assembly tasks is an integral part in modern manufactories. Robots provide advantages in both process and productivity with their repeatability and usability in different tasks, while human operators provide flexibility and can act as safeguards. However, process complexity increases which can lower the overall quality. Increased complexity can negatively influence decision making due to cognitive load on human operators, which can lead to lower quality, be it product, process or human work. Moreover, it can lead to safety risks, human-system error and accidents. In this work, we present the preliminary results on an experiment performed with student-participants, based on an assembly task. The experiment was set up to emulate an industrial assembly, and data collection was performed through qualitative and non-intrusive quantitative methods. Questionnaires were used to assess perceptual task complexity and cognitive load, while a stereo camera provided recordings for after-task analysis on process errors and human work quality based on a 3D skeleton-based human pose estimation and tracking method. The aim of the study is to investigate causes of errors and implications on quality. Future direction of the work is discussed.

关键词： collaboration assembly task complexity cognitive load human pose estimation tracking

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Restoration of Dunhuang Murals on Large-scale pretraining 23

Restoration of Dunhuang Murals on Large-scale pretraining

引用

6th International Conference on Signal Processing and Machine Learning, SPML 2023

作者： Xu, Zishan Yao, Minda Chen, Wei Zhu, Min Tian, Zijian Zhang, Fan Zhang, Xiaofeng Zhang, Chuanlei China University of Mining and Technology China School of Computer Science & Technology China University of Mining and Technology Xuzhou221116 China Beijing100083 China Key Laboratory of Intelligent Mining and Robotics Ministry of Emergency Management China Department of Intensive Care Unit Xuzhou First People's Hospital Jiangsu221000 China China Shanghai Jiaotong University China Tianjin University of Science and Technology China

ISBN: (纸本)9798400707575

Dunhuang murals are a precious cultural heritage and their restoration is of vital importance. Traditional image restoration methods and methods based on generative adversarial networks (GANs) have limitations in the mural restoration task. In this paper, we propose a diffusion model-based method for restoring Dunhuang murals using the RePaint model for image restoration. We first collected and produced 5135 Dunhuang mural image data that had undergone data augmentation methods such as cropping and panning, and then pre-trained the RePaint model on a large public dataset and fine-tunedthe model on the Dunhuang mural data. The experimental results show that the fine-tuned RePaint model has significantly improved the evaluation metrics such as PSNR and SSIM in the Dunhuang mural restoration task compared to other recent excellent image restoration models. This suggests that the RePaint model has superior performance in mural restoration tasks, especially in the areas of texture generation and structure retention. This study provides a novel and effective method for the field of Dunhuang mural restoration, which is expected to provide more support for heritage conservation. © 2023 ACM.

关键词： Image reconstruction

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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An Image Acquisition Scheme for Visual Odometry based on Image Bracketing and Online Attribute Control

An Image Acquisition Scheme for Visual Odometry based on Ima...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shuyang Zhang Jinhao He Bohuan Xue Jin Wu Pengyu Yin Jianhao Jiao Ming Liu Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong SAR China Thrust of Robotics and Autonomous Systems the Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Center for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science University College London London UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Visual odometry (VO) system is challenged by complex illumination environments. Image quality and its consistency in the time domain directly determine feature detection and tracking performance, which further affect the robustness and accuracy of the entire system. In this paper, an image acquisition scheme with image bracketing patterns is proposed. Images with different exposure levels are continuously captured to sufficiently explore the scene under varying illumination. An attribute control method is designed to adjust image exposures within the brackets online. Gaussian process regression fits the relationship between image quality metric and exposure via image synthesis technique. The optimal exposures for the next bracket are obtained directly without attempts to ensure a quick response. Experiments show our acquisition system’s effectiveness and performance improvement for VO tasks in complex illumination scenes.

关键词： Image quality Measurement Image synthesis Lighting Gaussian processes Robustness Time-domain analysis

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Informative Sensor Planning for a Single-Axis Gimbaled Camera on a Fixed-Wing UAV

arXiv

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arXiv 2024年

作者： Parandekar, Aditya Moon, Brady Suvarna, Nayana Scherer, Sebastian Birla Institute of Technology and Science Pilani – Goa Campus Goa Sancoale403726 India The Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

Uncrewed Aerial Vehicles (UAVs) are a leading choice of platforms for a variety of information-gathering applications. Sensor planning can enhance the efficiency and success of these types of missions when coupled with a higher-level informative path-planning algorithm. This paper aims to address these data acquisition challenges by developing an informative non-myopic sensor planning framework for a single-axis gimbal coupled with an informative path planner to maximize information gain over a prior information map. This is done by finding reduced sensor sweep bounds over a planning horizon such that regions of higher confidence are prioritized. This novel sensor planning framework is evaluated against a predefined sensor sweep and no sensor planning baselines as well as validated in two simulation environments. In our results, we observe an improvement in the performance by 21.88% and 13.34% for the no sensor planning and predefined sensor sweep baselines respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Fixed wings

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Robust STL Control Synthesis under Maximal Disturbance Sets

Robust STL Control Synthesis under Maximal Disturbance Sets

引用

IEEE Conference on Decision and Control

作者： Joris Verhagen Lars Lindemann Jana Tumova Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This work addresses maximally robust control synthesis under unknown disturbances. We consider a nonlinear system, subject to a Signal Temporal Logic (STL) specification and jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds. Many works have considered STL satisfaction under given bounded disturbances yet, to the authors’ best knowledge, this is the first work that aims to maximize the permissible disturbance set and find corresponding maximally robust controllers. We, therefore, introduce disturbance robustness as a model-based robustness metric for STL planning and control synthesis. We extend the notion of disturbance-robust semantics for STL, which is a property of a specification, dynamical system, and controller, and provide an algorithm for maximally robust controllers satisfying an STL specification using Hamilton-Jacobi reachability. We show its soundness and provide a simulation example with an Autonomous Underwater Vehicle (AUV).

关键词： Measurement Robust control Heuristic algorithms Semantics Control systems Robustness Planning Logic Dynamical systems Nonlinear systems

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