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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Dhumal, Yashomati R Gobinath, V.M. Jangirala, Srinivas Vidhya, M. Prakash, A. Shinde, Arundhati College of Engineering Electronics Department Pune India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Chengalpattu India O. P. Jindal Global University Jindal Global Business School Sonipat India Panimalar Institute of Technology Department of Computer Science and Engineering Varadarajapuram India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India College of Engineering Electronics Department Pune India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The Internet of Things (IoT) should be a new buzzword that emerged little by little (IoT). Organizations, governments, and the scientific community have already understood the relevance of IoT in the past few years, with a growing number of publications. A key component of this progress was IoT in health, but little research has been conducted on aspirations and potential developments. Only as the solution, they integrate bibliometric analysis and data mining to evaluate the longitudinal patterns of the IoT devices to Medical in this study. From 2000 to 2021, 778 articles were derived from the Web of science. Abstract textual data indicate that articles were divided into 30 groups, revealing eight IoT in health trends. The development of this field is worldwide, with distinctive patterns that should be noted to promote some areas. © 2022 IEEE.

关键词： Internet of things

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Sequential Transfer with Multi-Objective Genetic Algorithm for Feature Selection of Small, High-Dimensional Datasets

Sequential Transfer with Multi-Objective Genetic Algorithm f...

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Congress on Evolutionary Computation

作者： Parth C. Upadhyay G. N. DeSouza John A. Lory Department of Electrical Engineering and Computer Science (EECS) Vision-Guided and Intelligent Robotics (ViGIR) Laboratory Division of Plant Sciences University of Missouri Columbia MO

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Performing feature selection on a small number of instances with high-dimensional datasets poses a needed challenge in preventing over-fitting. To address this issue, this paper proposes a sequential transfer-learning approach combined with a multi-objective genetic algorithm (STMO-GA) for feature selection. Firstly, for the multi-objective component of our method, we employ a Non-dominated Sorting Genetic Algorithm (NSGA-II) to generate a Pareto front. Then, features are ranked based on their number of appearances in the same Pareto front. Next, during the sequential knowledge transfer process, the ranked features are iteratively selected until a predetermined $n$ number of features remains. This feature subspace is further refined by a k-fold cross-validation operation, starting from the rank-one feature, to determine the cut-off of the $n$ features that will remain. Comparative evaluations against both GA-based as well as traditional feature selection methods demonstrate that the proposed method achieves superior classification accuracy, while retaining the smallest number or a comparable number of features.

关键词： Accuracy Evolutionary computation Feature extraction Genetics Linear programming Robustness Optimization

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Towards Prediction of Motor Interference during synchronous Human-Robot Arm Movements Using Subjective Ratings of Anthropomorphism

Towards Prediction of Motor Interference during synchronous ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mertcan Kaya Kolja Kühnlenz Department of Electrical Engineering and Computer Science Robotics Research Lab Coburg University of Applied Sciences and Arts Coburg Germany

This paper reports a failed attempt to predict deviations of human arm movements from task elicited by perception of simultaneous robot arm movements (motor interference) using subjective ratings of anthropomorphism. This motor interference effect is known to result from unconscious imitation of movements of a human-like counterpart due to mirror-neuron activation. A study is conducted, with test participants and a robot conducting vertical and horizontal arm movements in front of each other in all combinations, and additionally with and without a robot head being present, in a between-subjects design. Perceived anthropomorphism is acquired using the evaluated tests HRIES and Godspeed. A correlation analysis on the dependency of motor interference on anthropomorphism did not reveal significant results of the constructs targeting human-like appearance, intelligence or movement behavior, which were expected to represent relevant predictor parameters. Further, scatter plots do not show a clear pattern. Only the likability Godspeed sub-scale is significant, which however may not be related to the targeted effect. Additionally, the ‘consciousness’ item within the ‘anthropomorphism sub-scale of Godspeed is significant, however, with unclear effect. So, no evidence could be found, that subjective assessments of anthropomorphism with existing tests may be suitable for prediction of motor interference and further research is required.

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Suturing Tasks Automation Based on Skills Learned from Demonstrations: A Simulation Study

Suturing Tasks Automation Based on Skills Learned from Demon...

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International Symposium on Medical robotics (ISMR)

作者： Haoying Zhou Yiwei Jiang Shang Gao Shiyue Wang Peter Kazanzides Gregory S. Fischer Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

In this work, we develop an open-source surgical simulation environment that includes a realistic model obtained by MRI-scanning a physical phantom, for the purpose of training and evaluating a Learning from Demonstration (LfD) algorithm for autonomous suturing. The LfD algorithm utilizes Dynamic Movement Primitives (DMP) and Locally Weighted Regression (LWR), but focuses on the needle trajectory, rather than the instruments, to obtain better generality with respect to needle grasps. We conduct a user study to collect multiple suturing demonstrations and perform a comprehensive analysis of the ability of the LfD algorithm to generalize from a demonstration at one location in one phantom to different locations in the same phantom and to a different phantom. Our results indicate good generalization, on the order of 91.5%, when learning from more experienced subjects, indicating the need to integrate skill assessment in the future.

关键词： Training Medical robotics Automation Heuristic algorithms Instruments Phantoms Needles

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De-Glared: Eyeglasses Glare and Reflection Removal Using Deep Neural Networks

De-Glared: Eyeglasses Glare and Reflection Removal Using Dee...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Sadik-E-Tawheed Tasnia Ahmed Masud Bhuiyan Abir Hossain Swakkhar Shatabda Department of Computer Science and Engineering United International University Centre for Artificial Intelligence and Robotics (CAIR) United International University

Eliminating reflections from eyeglasses in digital images presents a formidable challenge necessitating the deployment of deep learning techniques. When photographs featuring individuals wearing eyeglasses are captured, reflections and glare from various angles can obscure the eyes, rendering them indistinct. This impairment adversely affects image quality and poses significant hurdles for computer vision tasks such as eye identification, segmentation, and facial recognition. Our research has devised an effective neural network based on U-Net, proficiently designed to eradicate glare and reflections from digital eyeglass-clad images, thereby yielding crisp and unobstructed eye visuals. In constructing our dataset, we meticulously assembled 2541 images as we encountered a scarcity of extensive datasets containing human images both with and without eyeglass reflections. This study involved a comprehensive analysis of various algorithms and these algorithms underwent rigorous training using this dataset. Our comprehensive analysis unveiled that U-Net exhibited unparalleled performance in contrast to alternative algorithms, boasting an impressive PSNR value of 38.48 dB. Furthermore, it has demonstrated proficiency in effectively mitigating reflection glare in images.

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Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images

IEEE Transactions on Field Robotics

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IEEE Transactions on Field robotics 2024年 1卷 131-160页

作者： Philip Huang Tony Wang Florian Shkurti Timothy D. Barfoot Robotics Institute Carnegie Mellon University Pittsburgh PA USA Division of Engineering Science University of Toronto Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Institute for Aerospace Studies University of Toronto Toronto ON Canada

We introduce a multisensor navigation system for autonomous surface vessels (ASVs) intended for water-quality monitoring in freshwater lakes. Our mission planner uses satellite imagery as a prior map, formulating offline a mission-level policy for global navigation of the ASV and enabling autonomous online execution via local perception and local planning modules. A significant challenge is posed by the inconsistencies in traversability estimation between satellite images and real lakes, due to environmental effects such as wind, aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we specifically modeled these traversability uncertainties as stochastic edges in a graph and optimized for a mission-level policy that minimizes the expected total travel distance. To execute the policy, we propose a modern local planner architecture that processes sensor inputs and plans paths to execute the high-level policy under uncertain traversability conditions. Our system was tested on 3 km-scale missions on a Northern Ontario lake, demonstrating that our GPS-, vision-, and sonar-enabled ASV system can effectively execute the mission-level policy and disambiguate the traversability of stochastic edges. Finally, we provide insights gained from practical field experience and offer several future directions to enhance the overall reliability of ASV navigation systems.

关键词： Stochastic processes Planning Satellite images Robot sensing systems Satellite navigation systems Image edge detection Uncertainty

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Behavior Profiling of Role-Playing Game Players Based on Heuristic Event Log Mining 25th

Behavior Profiling of Role-Playing Game Players Based on Heu...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lin, Jason Tsai, Chia-Wei Yang, Chun-Wei Lafortune, Elie Department of Computer Science and Engineering National Chung Hsing University Taichung40227 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404 Taiwan Master Program for Digital Health Innovation College of Humanities and Sciences China Medical University Taichung406040 Taiwan

ISBN: (纸本)9789811995811

In this paper, we propose a novel player behavior model called the action priority model (APM) for representing player action behaviors. A play log is stored based on the game grammar under analysis, and heuristic filtering rules are applied to eliminate uninteresting events. The proposed model uses sequences and tendencies of event types as the main mining criteria to extract significant player action behaviors. Action sequences reflect the interdependencies of different actions and how a player may use their insights about the game rules to form a (hidden) action priority list (APL) with respect to action types. By mining for significant events from play logs, we aim to produce the APM and APL for different players. In experiments, the fuzzy miner tool was used to analyze a few event logs for the World of Warcraft game to obtain some noticeable discoveries about the structural and timing patterns of effective players. Experimental results show that two players in the same class with similar contributions to encounters will have a similar mined APM and may not have a minimal delay in the timing of actions. Players with a high contribution to an encounter will have an APM like a simulated optimal player along with large thickness edges in the APM to indicate a significant routine of actions. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Causal net Fuzzy miner Player behavior

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Implementing Genetic Algorithms for Optimization in Neuro-Cognitive Rehabilitation robotics 1

Implementing Genetic Algorithms for Optimization in Neuro-Co...

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

作者： Abhang, L.B. Changala, Ravindra Ghosh, Anudeb Manage, Prabhakar S Rao, Vuda Sreenivasa Kiran Bala, B. Loni Under. SPPU University Pravara Rural Engineering College Automation and Robotics Dept Pune India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Siksha - Bhavana Department of Environmental Studies Visva-Bharati Santiniketan India Kle Technological University's Msscet Belagavi Campus Department of Electronics and Communication Karnataka Belagavi India Education Foundation Department of Computer Science and Engineering Koneru Lakshmaiah A.P Vaddeswaram522302 India K.Ramakrishnan College of Engineering The Department Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350372748

The study explores the intersection of robotics and neuro-cognitive rehabilitation to offer personalized assistance for individuals with neurological disorders, emphasizing autism spectrum disorder (ASD). The proposed methodology integrates Genetic Algorithms (GAs) to optimize the performance of robotic interventions. The data collection phase involves compiling a comprehensive image dataset capturing facial expressions, gestures, and relevant visual cues associated with ASD. Leveraging robotic platforms designed for therapeutic interventions, feature extraction techniques identify intricate patterns within the data. Advanced algorithms, including GAs, classify the dataset into positive (ASD) and negative (non-ASD) categories. The framework introduces a Diagnosis Matrix for enhanced diagnostic precision, correlating observed robotic interactions with clinical assessments. An Ontology Knowledge base adapts responses based on evolving patient needs. The proposed method surpasses all others, achieving an accuracy of 95.08% and demonstrating superior precision, recall, and F1-score metrics. This indicates the efficacy of the proposed approach in achieving a well-balanced performance with high accuracy and robustness in correctly identifying positive instances. The results underscore the potential of the proposed method for classification tasks, showcasing its superiority in comparison to traditional SVM, CNN, and even a well-established deep learning architecture like VGG-16. The Receiver Operating Characteristic curve validates the model's discriminatory power. © 2024 IEEE.

关键词： Genetic algorithms

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GS-LIVM: Real-Time Photo-Realistic LiDAR-Inertial-Visual Mapping with Gaussian Splatting

arXiv

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arXiv 2024年

作者： Xie, Yusen Huang, Zhenmin Wu, Jin Ma, Jun Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In this paper, we introduce GS-LIVM, a real-time photorealistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering in large-scale unbounded outdoor environments. In this work, Gaussian Process Regression (GPR) is employed to mitigate the issues resulting from sparse and unevenly distributed LiDAR observations. The voxel-based 3D Gaussians map representation facilitates real-time dense mapping in large outdoor environments with acceleration governed by custom CUDA kernels. Moreover, the overall framework is designed in a covariance-centered manner, where the estimated covariance is used to initialize the scale and rotation of 3D Gaussians, as well as update the parameters of the GPR. We evaluate our algorithm on several outdoor datasets, and the results demonstrate that our method achieves state-of-the-art performance in terms of mapping efficiency and rendering quality. The source code is available on GitHub . Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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CLEAR-Net: A Cascaded Local and External Attention Network for Enhanced Dehazing of Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Ali, Anas M. El-Rabaie, El-Sayed M. Ramadan, Khalil F. El-Samie, Fathi E. Abd El-Shafai, Walid Prince Sultan University Robotics and Internet-of-Things Laboratory Riyadh12435 Saudi Arabia Faculty of Electronic Engineering Menoufia University Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Computer Science Department Riyadh11586 Saudi Arabia

Atmospheric haze degrades remote sensing images by obscuring critical details, diminishing data interpretation accuracy. This paper presents CLEAR-Net: Cascaded Local and External Attention Resource Network, a novel dehazing framework designed to enhance clarity and detail in remote sensing images. CLEAR-Net employs a two-block cascaded architecture to address both global and local haze effects. The first block utilizes an Outer Global Attention (OGA) mechanism, processing global features via a U-Net Inner Attention (UIA) module to mitigate widespread haze. The second block focuses on Outer Local Attention (OLA), refining local image features through reintegration with the UIA module. An ensemble method selects the most effective model weights based on perceptual quality metrics, and a composite loss function emphasizes the preservation of high-frequency details to maintain textural and edge fidelity. CLEAR-Net's efficacy is evaluated on the SateHaze1k and RICE datasets, outperforming existing state-of-theart dehazing techniques. On SateHaze1k, CLEAR-Net achieves an average PSNR of 27.876 dB, surpassing the next best model by approximately 4.81%;under moderate fog, it attains a PSNR of 30.824 dB, about 9.8% higher than existing methods. On the RICE dataset, which includes challenging cloud cover, CLEARNet achieves PSNRs of 36.873 dB on RICE1 and 34.624 dB on RICE2, improving over next-best models by approximately 18.2% and 12%, respectively, and attains the lowest LPIPS values, indicating superior perceptual quality. © 2008-2012 IEEE.

关键词： Demulsification

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