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International Conference on computer and Information Technology (ICCIT)

作者： Niful Islam Debopom Sutradhar Swakkhar Shatabda Chowdhury Mofizur Rahman Department of Computer Science and Engineering United International University Centre for Artificial Intelligence and Robotics (CAIR) United International University Department of Computer Science and Engineering BRAC University

The textile industry is a prominent and influential sector worldwide, contributing significantly to the economy. This industry has long relied on manual inspections for determining the percentage of cotton in fabric samples, a critical factor for quality control and compliance. However, this conventional approach takes a significant amount of time and is subject to human error. A quicker and more accurate alternative is required due to the rising demand for objective quality assurance and speedier manufacturing cycles. In this article, we propose a computer vision based solution to this challenge. We leverage convolutional neural networks (CNNs), to analyze microscopic images of fabrics and precisely calculate the percentage of cotton. To prove our model's effectiveness, we also made a comparative analysis with several CNN models. The proposed solution achieves a mean absolute error of 3.56 and a mean squared error of 48.78 in detecting the percentage of cotton from microscopic images. The proposed automated method has the potential to affect the industry by removing the subjectivity of manual inspection and saving critical time and money leading to the goal of sustainability.

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Enhancement Three-Dimensional Localization of ArUco Marker Under Photon-Starved Conditions 23

Enhancement Three-Dimensional Localization of ArUco Marker U...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Ha, Jin-Ung Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose a method to improve the recognition rate of ArUco markers under photon-starved conditions by using preprocessing such as photon counting algorithm and histogram equalization to estimate the exact three-dimensional(3D) location of improved objects. In the conventional ArUco marker recognition technology, the recognition rate of ArUco markers decreases under photon-starved conditions, and the exact 3D location of the object cannot be estimated. In the conventional ArUco marker recognition technology, the photon counting algorithm can be used to estimate the 3D location of objects by recognizing ArUco markers under photon-starved conditions. However, the photon counting algorithm has the problem that the computation speed is slowed down when there are many photons to estimate, and the recognition rate of the ArUco marker is reduced when there are few photons to estimate. To solve these problems of the photon counting algorithm and improve the recognition rate of ArUco markers, we apply histogram equalization to ArUco markers with the photon counting algorithm. To verify the feasibility of the proposed method, we implement simulations using the Open Manipulator-X platform and show performance metrics such as Peak Signal-to-Noise Ratio (PSNR). © 2023 ICROS.

关键词： Photons

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Solution for Agri Disaster Using Climate Changes Adaptation by Ml 6

Solution for Agri Disaster Using Climate Changes Adaptation ...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ganesh, L. Venkatesh, R. Prasanna Kumar, Kaushal Kumari, Khushboo Borkakoty, Sangeeta Raju, A. Rajalakshmi Engineering College Robotics and Automation Dept. Tamilnadu Chennai India SACS MAVMM Engineering College Department of Mechanical Engineering Tamilnadu Madurai India School of Life Sciences Department of Botany Mahatma Gandhi Central University Bihar India University of Science & Technology Department of Computer Science & Electronic Meghalaya India KLEF Vaddeswaram Green Field Department of Commerce Vijayawada India

ISBN: (纸本)9798350304480

This paper proposes using good sized getting to know fashions to recognize lesions of cotton leaves in the context of pictures of the reaping within the subject. Cotton is among the maximum economically essential plant harvests, as it's far grown all over the international. Its spread in tropical areas has made it the goal of a wide variety of farming irritations and illnesses, necessitating succesful plans. moreover, the consequences of commonplace disturbances and disorders are difficult to isolate inside the early stages, and the author may additionally battle to locate the suitable visible verification of a scientific difficulty. To help in resolving the difficulty, it contemporary inquiry proposes a solution that takes into consideration substantial training within the filtering of cotton leaves, making it possible to reveal the cotton yield's health and make higher judgments approximately its management. The studying models GoogleNet like Resnet50, which use convolutional cerebrum associations, performed an accuracy of 86.6 percentage and 89.2 percentage, respectively. assist vector machines, Closest k-pals, faux creativeness associations, and neuro-tender are some of the extraordinary as well as everyday tactics again for treatment of photographs. The convolutional cerebellum networks ended up being 25% extra unique, implying that this method can upload to a more strong and short investigation of the flora looking to fill inside the field. © 2023 IEEE.

关键词： Artificial Intelligence Convolutional Neural Networks Image Processing Precision Agriculture

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Deep Neural Network in Dermatology: Pioneering Skin Lesion Classification

Deep Neural Network in Dermatology: Pioneering Skin Lesion C...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Nasif Fuad Abdur Rakib Abdullah Al Noman Md Rafiqul Islam Mahbubur Rahman Md Abu Rumman Refat Department of Computer Science and Engineering Green University of Bangladesh Dhaka Department of Internet of Things and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Dhaka

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Skin cancer is one of the most common cancer types in the world. Early detection of skin cancer is essential as it significantly improves the treatment outcomes. However, recognizing skin lesions is challenging due to the high degree of similarity in lesion's appearance, location, color, and size. In response to this challenge, we developed a deep learning based method for skin lesion classification by ensembling two pre-trained CNN models called ResNet50 and DenseNet201 using the HAM10000 dataset. With the imbalance in the dataset, we used the Synthetic Minority Over-sampling Technique(SMOTE) and class weighted approach along with the data Augmentation approach. As a result, our study demonstrates that the proposed approach attained a remarkable 96.0% accuracy on the HAM10000 dataset, surpassing other cutting-edge methods.

关键词： Deep learning Analytical models Accuracy Data augmentation Skin Lesions Convolutional neural networks Skin cancer Biomedical imaging Residual neural networks

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Balancing Worker Utility and Recruitment Cost in Spatial Crowdsensing: A Nash Game Approach 21

Balancing Worker Utility and Recruitment Cost in Spatial Cro...

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8th International Conference on Networking, Systems and Security, NSysS 2021

作者： Arafat, Md. Tanvir Emon, Mehedi Hasan Sarker, Sujan Razzaque, Md. Abdur Rahman, Md. Mustafizur Department of Robotics and Mechatronics Engineering University of Dhaka Bangladesh Department of Computer Science and Engineering University of Dhaka Bangladesh

ISBN: (纸本)9781450387378

Leveraging the power of mobile crowdsensing (MCS) to achieve unparalleled coverage of tasks by utilizing the crucial spatiooral co-relation between the workers and tasks results in the distributed sensing system named spatiooral crowdsensing system (STCS). An STCS outsources sensing tasks to ubiquitous mobile computing devices to exploit their multi-modal sensing capabilities, collects the sensing results and process those to provide meaningful services. While realizing sustainable crowdsensing services, an STCS struggles with two conflicting objectives- maximizing the assignment quality and minimizing the recruitment cost. The first one requires assigning highly reputed workers in the spatiooral task blocks which results in higher recruitment costs. Thus a tradeoff between the aformentioned objectives is required. Existing works bring this tradeoff either by formulating a single objective as the linear weighted sum of both of the objectives or incorporating one of these as a constraint to another, yielding to an unfair and skewed tradeoff. In this work, we formulate the worker recruitment problem in STCS as a Nash game to attain fairness between the objectives while bringing this tradeoff. The performance of the proposed system is evaluated using MATLAB and results show its effectiveness compared to the state-of-the-art works in terms of fairness and per cost utility gain. © 2021 ACM.

关键词： nash bargaining mobile crowdsensing cost and quality fair trade-off

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Human Multi-dimensional Stiffness Skills Transfer for Robot Teleoperation System

Human Multi-dimensional Stiffness Skills Transfer for Robot ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Situ, Liwen Lu, Zhenyu Si, Weiyong Yang, Chenguang College of Automation Science and Engineering South China University of Technology Guangzhou510640 China University of the West of England Bristol Robotics Laboratory BristolBS161QY United Kingdom University of Liverpool Department of Computer Science LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9781665410205

Neuroscience research has demonstrated the sig-nificance of modulating stiffness during human task performance. Similarly, endowing robots with such capability is expected. However, existing methods for robot teleoperation require operators to simultaneously control position and stiffness, resulting in high workload and task inefficiency. On the other hand, learning from demonstration (LfD) offers a feasible approach for autonomously generating stiffness. Therefore, this paper proposes a robot teleoperation system that combines the advantages of teleoperation and LfD. Teleoperation enables precise positioning guided by human operators, while LfD can transfer human stiffness skills to robots. A teleoperation-oriented stiffness-adaptive Gaussian Mixture Model/Gaussian Mixture Regression method is proposed to learn human multi-dimensional stiffness and reproduce robot stiffness on a Riemannian manifold. To enhance generalization and cooperate with teleoperation, reference points and position-driven output are introduced. Furthermore, a teleoperation strategy for both the single-leader-single-follower configuration and the single-leader-dual-follower configuration are designed, which allows operators to control either one or two robot arms with a single leader device. Finally, the effectiveness of our method is verified through a plugging-in task and a continuous flipping task, demonstrating that the proposed system is capable of performing tasks that demand high positioning accuracy and stiffness adjustment. A supplementary video for this paper is available in GitHub∗∗https://***/setowenGit/TOSA-GMM-GMR-Video. © 2024 IEEE.

关键词： Robotic arms

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WildGaussians: 3D Gaussian Splatting in the Wild 38

WildGaussians: 3D Gaussian Splatting in the Wild

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kulhanek, Jonas Peng, Songyou Kukelova, Zuzana Pollefeys, Marc Sattler, Torsten Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Science ETH Zurich Switzerland Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework. © 2024 Neural information processing systems foundation. All rights reserved.

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Integrated CWT-CNN for Epilepsy Detection Using Multiclass EEG Dataset

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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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VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model 41

VoroNav: Voronoi-based Zero-shot Object Navigation with Larg...

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41st International Conference on Machine Learning, ICML 2024

作者： Wu, Pengying Mu, Yao Wu, Bingxian Hou, Yi Ma, Ji Zhang, Shanghang Liu, Chang Department of Advancded Manufacturing and Robotics College of Engineering Peking University Beijing China The University of Hong Kong Hong Kong OpenGVLab Shanghai AI Laboratory China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China

In the realm of household robotics, the Zero-Shot Object Navigation (ZSON) task empowers agents to adeptly traverse unfamiliar environments and locate objects from novel categories without prior explicit training. This paper introduces VoroNav, a novel semantic exploration framework that proposes the Reduced Voronoi Graph to extract exploratory paths and planning nodes from a semantic map constructed in real time. By harnessing topological and semantic information, VoroNav designs text-based descriptions of paths and images that are readily interpretable by a large language model (LLM). In particular, our approach presents a synergy of path and farsight descriptions to represent the environmental context, enabling LLM to apply commonsense reasoning to ascertain waypoints for navigation. Extensive evaluation on HM3D and HSSD validates VoroNav surpasses existing benchmarks in both success rate and exploration efficiency (absolute improvement: +2.8% Success and +3.7% SPL on HM3D, +2.6% Success and +3.8% SPL on HSSD). Additionally introduced metrics that evaluate obstacle avoidance proficiency and perceptual efficiency further corroborate the enhancements achieved by our method in ZSON planning. Copyright 2024 by the author(s)

关键词： Semantics

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Visual-Kinematics Graph Learning for Procedure-Agnostic Instrument Tip Segmentation in Robotic Surgeries

Visual-Kinematics Graph Learning for Procedure-Agnostic Inst...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jiaqi Liu Yonghao Long Kai Chen Cheuk Hei Leung Zerui Wang Qi Dou Department of Computer Science and Engineering The Chinese University of Hong Kong Cornerstone Robotics Ltd.

Accurate segmentation of surgical instrument tip is an important task for enabling downstream applications in robotic surgery, such as surgical skill assessment, tool-tissue interaction and deformation modeling, as well as surgical autonomy. However, this task is very challenging due to the small sizes of surgical instrument tips, and significant variance of surgical scenes across different procedures. Although much effort has been made on visual-based methods, existing segmentation models still suffer from low robustness thus not usable in practice. Fortunately, kinematics data from the robotic system can provide reliable prior for instrument location, which is consistent regardless of different surgery types. To make use of such multi-modal information, we propose a novel visual-kinematics graph learning framework to accurately segment the instrument tip given various surgical procedures. Specifically, a graph learning framework is proposed to encode relational features of instrument parts from both image and kinematics. Next, a cross-modal contrastive loss is designed to incorporate robust geometric prior from kinematics to image for tip segmentation. We have conducted experiments on a private paired visual-kinematics dataset including multiple procedures, i.e., prostatectomy, total mesorectal excision, fundoplication and distal gastrectomy on cadaver, and distal gastrectomy on porcine. The leave-one-procedure-out cross validation demon-strated that our proposed multi-modal segmentation method significantly outperformed current image-based state-of-the-art approaches, exceeding averagely 11.2% on Dice.

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