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Design and Development of an Unmanned Ground Vehicle with Serial Manipulator and its control Mechanism 3

Design and Development of an Unmanned Ground Vehicle with Se...

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3rd international conference on Electrical, Communication and computer Engineering, ICECCE 2021

作者： Ali, Muhammad Hamza Kabir, Muhammad Salman Skolkovo Institute of Science and Technology Department of Energy System Moscow Russia Innopolis University Department of Robotics Innopolis Russia

ISBN: (纸本)9781665438971

The use of robotic vehicles for doing various jobs has been significantly increases in the last decade. Industries as well as militaries around the world, use them to perform their day to day operations. In this paper, a computationally efficient, cost-effective and user-friendly Arduino based, control methodology is presented to control an Unmanned Ground Vehicle (UGV) and its serial manipulator. A transmitter made up of a gamepad, WiFi module, and Arduino as a main controller is used to send commands. These commands reside on a cloud server from where the receiver picks them up and activates its various peripherals to fulfill them. The designed control mechanism is implemented on a fabricated UGV for real-time performance evaluation. Field tests indicate the effectiveness of the proposed methodology. © 2021 IEEE.

关键词： Cost effectiveness

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Latent 3D Scene Graph with Aligned Visual-Language Perception for Object-Goal Navigation 3

Latent 3D Scene Graph with Aligned Visual-Language Perceptio...

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3rd international conference on Mechatronics, control and Robotics, ICMCR 2025

作者： Zhang, Jianwei Zhou, Kang Yang, Junjie Li, Xiaojian College of Engineering Southern University of Science and Technology Shen Zhen China School of Computer Science City University of Hong Kong Hong Kong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shen Zhen China

ISBN: (纸本)9798331544546

Object-goal navigation requires agents to accurately locate and navigate to specified target objects in complex indoor environments. Existing methods primarily rely on single visual observations or simple semantic matching, making it difficult to effectively understand 3D scene relationships between target objects, which leads to inefficient path planning during navigation. Furthermore, the feature discrepancy between visual and language modalities poses challenges for accurate target identification. To address these issues, we propose Latent 3D Scene Graph with Aligned Visual-Language Perception (LSGAP), a novel approach that explicitly models 3D scene relationships between objects through depth perception and latent 3D scene graph construction. Meanwhile, we leverage multimodal pretraining strategies to achieve effective visual-language feature alignment, enhancing the model's target recognition capabilities. This end-to-end architectural design improves spatial cognition ability and enables more efficient navigation decision-making. We conduct extensive experimental evaluations in the AI2THOR interactive environment to validate the effectiveness of our navigator. © 2025 IEEE.

关键词： Depth perception

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TMFNet: Two-branch multilevel feature interaction compensation network for Infrared and visible image fusion

TMFNet: Two-branch multilevel feature interaction compensati...

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Big Data, Information and computer Network (BDICN), international conference on

作者： Lingna Gao Rencan Nie The School of Information Science and Engineering Yunnan University Kunming China The School of Automation Southeast University Nanjing China

ISBN: (数字)9798350363821

ISBN: (纸本)9798350374612

In recent years, the development of deep learning has also driven the process of multimodal image fusion, and unlike the traditional image fusion methods, deep learning-based image fusion stands out because of its powerful evidence. Therefore, a two-branch multilevel feature interaction compensation method for infrared and visible image fusion is proposed in the paper, in which a unique multilevel feature interaction compensation block is designed, and information guidance is introduced to make the feature information in the network adequately interact and compensate. The multimodal fusion task is converted to conditional weighted optimization learning to generate the final fusion maps under the training of the network constrained by the fidelity term loss and the designed luminance compensation loss. Comparison experiment and targeted detection tasks between this method and eight other fusion comparison methods were conducted under the FLIR dataset, and the results showed that TMFNet achieved better results in both objective metrics and subjective vision.

关键词： Training Measurement Deep learning Big Data Feature extraction computer networks Data mining

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Research on surface defect detection of disc harrow based on YOLOv5

Research on surface defect detection of disc harrow based on...

引用

Information Technology,Big Data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Xingxing Wang Guanghe Cheng Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences)

In view of the problems of low efficiency, missing detection and error detection, and inability to meet the requirements of intelligent manufacturing in the way of manual detection of the surface defects of agricultural machinery rake, in order to use computer vision technology based on depth learning to replace manual detection of the surface defects of agricultural machinery rake, this paper proposes a surface defect detection algorithm based on YOLOv5. Firstly, CA attention mechanism is introduced to make the network pay more attention to important channel information and enhance the ability of feature extraction network to extract effective features; The CIoU NMS designed based on the generalized intersection ratio CIoU is used to replace the traditional non-maximum value in the original algorithm to suppress NMS and reduce the problem of missing detection; In combination with SIoU Loss and FocalL1 Loss, Focal-SIoU Loss is proposed as a bounding box loss function to reduce the negative impact of low-quality samples, accelerate the convergence speed of the model, and improve the regression effect of the model. The experimental results show that the improved model proposed in this paper has better performance and can improve the detection efficiency compared with the baseline model.

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Smart Surveillance System Using Deep Learning

Smart Surveillance System Using Deep Learning

引用

Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Amira El-Shekhi Yusra Maatug Nizar Khemri Department of Computer Engineering University of Tripoli Tripoli Libya

With the increase in the prevalence of armed robberies and attacks in public places, an effective and reliable surveillance system has become indispensable to fulfill various security aspects and improve the quality of human life. The most common effective video surveillance system used is called closed-circuit television (CCTV), but it can be expensive, and it requires a large amount of memory. In addition to these problems, the need for manpower to detect unauthorized activities can lead to several security problems. An intelligent surveillance system (ISS) that may overcome most of the mentioned problems is proposed in this paper. This system is built with more affordable hardware components that provide automated security services more than what regular surveillance systems can offer to the user with better quality using the assistance of Artificial Intelligence (AI), Deep learning (DL), and Image Processing (IP). The obtained overall detection accuracy of 83.1% at 22.2ms helps to inform the user in a short time during the incident. Also, the classification Mean Average Precision (mAP) of 0.98, 0.95, and 0.94 for detecting Knives, Guns, and Pistols, respectively, can reduce the rate of disturbing the user with false alerts.

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Sign Language Recognition Using Hand Gestures

Sign Language Recognition Using Hand Gestures

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Advancement in Technology (ICONAT), international conference for

作者： Pravin Baban More Amolkumar N. Jadhav Irfan Khatik Sonam Singh Vishal Kisan Borate Yogesh Kisan Mali Department of CSE Annasaheb Dange Colleg of Engineering & Technology Ashta India Fergusson College Pune India Department of AIML Dr D Y Patil Institute of Technology Pune India Department of Computer Engineering Dr D Y Patil College of Engineering & Innovation Talegaon Pune India Department of AIML G.H Raisoni College of Engineering and Management Wagholi Pune India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Communication is an essential part of life, but it can be a significant challenge for those who cannot speak. That’s why we are working on a research project to develop a real-time American Sign Language (ASL) detector using computer vision and machine learning. Our goal is to create a solution that has the potential to transform the lives of people who rely on sign language. Imagine the frustration of wanting to express yourself or use voice-activated technology without the ability to speak. While sign language interpreters are a valuable resource, their cost can be a significant barrier to consistent access.

关键词： Training Sign language Technological innovation computer architecture Transforms Feature extraction Data models Real-time systems Communications technology Convolutional neural networks

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11th international conference on Intelligent Computing and Applications, ICRTC 2023

11th International Conference on Intelligent Computing and A...

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11th international conference on Intelligent Computing and Applications, ICRTC 2023

ISBN: (纸本)9789819717231

The proceedings contain 70 papers. The special focus in this conference is on Intelligent Computing and Applications. The topics include: RNN Learning for Dynamic Selection of Channel Access Scheme in FANETs;a Crime Knowledge Discovery Scheme Based on Entity Recognition, Relation Extraction, and Development of Criminal Profiling Using Modus Operandi;optimized Biometric Key Management System for Enhanced Security;human Posture Identification and Recognition Using Deep Learning Techniques;twitter Sentiment Analysis Using Different Machine Learning Techniques;exploring Empirical Mode Decomposition for Music Genre Classification Using Deep Learning;a Comparative Analysis of Fog Computing’s Problems, Challenges and Future Directions;explainable Artificial Intelligence Insight: An Orderly Survey;deep Learning-Based Sign Language Translator;Analysis of Arrhythmia from Electrocardiogram (ECG) Data Using ML Framework;brain Tumor Detection Using Quantum Neural Network;augmented Reality-Based Application for Indian Monuments;fog Obscurity Mitigation;a Load Balancing Using Multi-population Grasshopper Optimization Approach for Workflow Tasks in Clouds;parental control for Techie Child Using Keylogger;modified Box Filter Design and Noise Analysis on Two-Dimensional Images;Change Detection in Remote Sensing SAR Image Using a Ratio-Based Operator;automatic Question Generation: A Comparative Analysis of Rule-Based and Neural Network-Based Models;mango Leaf Images Quality Improvement Techniques Using Subjective Approach of Image Enhancement;real-Time 3D Texture and Motion Analysis for Face Anti-spoofing Using Deep Learning and computer vision;Hybrid Sentiment Polarity Prediction Scheme in Social Networks using Attention Mechanism and Improved CNN;survival Analysis of Heart Failure Patients with Advanced Machine Learning Models;a Differential Privacy Perturbation with Random Forest Classifier in Medical Database;smart Application for Early Detection of Rice Plant Disease Using Incept

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Artificial Intelligence Contribution to Art-Therapy using Drawings of the House-Person-Tree Test

Artificial Intelligence Contribution to Art-Therapy using Dr...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Ahmet Atakan Salar Hajar Faiyad Elena Battini Sönmez Sibel Hafton Dept. of Computer Engineering Istanbul Bilgi University Istanbul Türkiye Dept. of Psychology Istanbul Bilgi University Istanbul Türkiye

This paper applies computer vision and artificial intelligence algorithms to the HTP (House-Tree-Person) test, a projective test intended to measure different aspects of personality using drawings. The drawn pictures are assumed to represent the subject’s attitudes and feelings regarding themselves, other and their family. The House-Tree-Person evaluation uses "Qualitative Scoring," which is a subjective analysis influenced by the therapists that can be used to infer aggressive, depressive or anxious characteristics in the drawings. This paper is part of a larger project that aims to use artificial intelligence and image-processing techniques to support this process, hence reducing the bias factor from the equation. With the collaboration of the Department of Psychology at Istanbul Bilgi University, the project investigates on possible approaches to extract discriminative features out of HTP sketch images and it searches for a meaningful combination, which will support therapists in their diagnostic assessments. After data pre-processing, image classification of clinical HTP data was conducted using the ResNet152 model and achieving a test accuracy of 66%. Furthermore, the experiment of the detection of the "pen pressure" feature was performed using Skeletonization and morphological image processing; however, due to a lack of ground truth, the performance of the proposed algorithm is not determined yet.

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Analysis and Rendering of Deauthentication Attack Using IoT Technology 1

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3rd international conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2022

作者： Aggrawal, Abhay Arora, Isha Giri, Animesh Department of Computer Science PES University Electronic City Karnataka Bengaluru560100 India

ISBN: (数字)9789811960888

ISBN: (纸本)9789811960871

The Internet has developed exponentially over the years and has become an identity. Along with development, different kinds of networks such as LAN, WLAN, and WAN came into picture. Wireless local area network (WLAN) is the most used communication network in relatively smaller areas such as houses, schools, and offices. WLAN follows security such as WEP and encryption, but there are still vulnerabilities in the lower layer of the protocol stack. The vulnerabilities in the IEEE 802.11 have proved to be a medium for denial-of-service attack. Due to susceptibility of management frames, WLAN is vulnerable to medium access control-based (MAC) denial-of-service attacks. The results of these DOS attacks can range from man-in-the-middle attack to network congestion. By spoofing the MAC address of any legitimate user, deauthentication can be rendered to break the connection between the user and the Internet access point. There have been various amendments in the protocols to avoid the attacks on the MAC layer. But, much success hasn’t been registered till date. Various algorithms such as medium access control spoof detection and prevention (MAC SDP DoS) perform spoof detection and prevention to render MAC layer attacks affectless. This paper talks about the technique for rendering the DoS deauthentication attack and also present the analysis of the packet that is sent to attack on MAC layer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medium access control

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Visual Servo control Based Method for Hooking up Drain Wire Clip

Visual Servo Control Based Method for Hooking up Drain Wire ...

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Electronic Information Engineering and computer Science (EIECS), 2021 international conference on

作者： Shuzhou Xiao Qiuyan Zhang Ruyun Huang Ruifeng Zhang Peng Zeng Electric Power Research Institute Guizhou Power Grid Co. Ltd. Guiyang City China

During the operation and maintenance of power distribution grid, it is necessary to use the drain wire to ensure the stability and continuity of the power supply. The accurate identification, positioning and wire-hanging clip operation are directly crucial to the drain wire connection. In this paper, a method of hooking up drain wire clip based on visual servo control is proposed, which is applied to the live working robot to fulfil the operation of connecting drain wire. Firstly, a cable is identified by the improved YOLOX-S object detection algorithm. Then, a binocular camera is used to obtain the accurate three-dimensional coordinate information of the cable, and the coordinate system is converted to obtain the cable position in the coordinate system where the clip is viewed as the origin. Finally, based on the clip size model, the optimal initial hanging height and appropriate hanging path are analyzed, and a fuzzy controller is used to precisely control the robot’s operation of hooking up drain wire clip. Experiments show that the method proposed in this paper can quickly and accurately identify and locate power distribution grid cable, and precisely control the robot’s operation of connecting the drain wire clip.

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