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Enhanced Robotic Vision System Based on Deep Learning and Image Fusion

computers, Materials & Continua 2022年第10期73卷 1845-1861页

作者： E.A.Alabdulkreem Ahmed Sedik Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafrelsheikh33511Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt

Image fusion has become one of the interesting fields that attract researchers to integrate information from different image *** is involved in several *** of the recent applications is the robotic *** application necessitates image enhancement of both infrared(IR)and visible *** paper presents a Robot Human Interaction System(RHIS)based on image fusion and deep *** basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured ***,an enhancement model is carried out on the fused image to increase its *** image enhancement models such as fuzzy logic,Convolutional Neural Network(CNN)and residual network(ResNet)pre-trained model are utilized on the fusion results and they are compared with each other and with the state-of-the-art *** results prove that the fuzzy logic enhancement gives the best results from the image quality ***,the proposed system can be considered as an efficient solution for the robotic vision problem with multi-modality images.

关键词： Deep learning fuzzy logic image fusion IR images

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A region boundary-based geometric formation control scheme for multiple autonomous underwater vehicles

A region boundary-based geometric formation control scheme f...

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International Conference on Electrical, Control and computer engineering

作者： Ismail, Z.H. Dunnigan, M.W. Dept. of Mechtronics and Robotics Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia Malaysia Dept. of Electrical Electronics and Computer Engineering School of Engineering and Physical Sciences Heriot-Watt University United Kingdom

ISBN: (纸本)9781612842288

In this paper, a simple control approach based on a region boundary technique for geometric formation of multiple Autonomous Underwater Vehicles (AUVs) is presented. The control objective is to keep each underwater vehicle at each corner of a desired geometric shape, i.e. an equilateral triangle or a square. An edge-based segmentation approach is utilized rather than specifying the minimum distance between members to ensure that each vehicle is placed exactly at the desired position in their formation. This allows each vehicle that has its own function to carry out an effective individual task, thus improving the formation's performance. The model of the ODIN vehicle is used as an example to demonstrate the proposed controller. Simulation results show the effectiveness of the controllers. © 2011 IEEE.

关键词： Controllers

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Sudden Cardiac Arrest Detection Based on Temporal Features of ECG Using Support Vector Machine Classifier

Sudden Cardiac Arrest Detection Based on Temporal Features o...

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International Conference on Industrial Instrumentation and Control,ICI2C 2021

作者： Banerjee, Prakash Bhattacherjee, Saptak Dasgupta, Kousik Department of Electrical and Electronics Engineering University of Engineering and Management Kolkata India School of Mechatronics and Robotics Indian Institute of Engineering Science and Technology Howrah Shibpur India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani India

ISBN: (纸本)9789811670107

Sudden cardiac arrest (SCA) is caused due to malfunctioning in heart rhythm, and the patient needs to be hospitalized as early as possible just after the onset of SCA;otherwise, it can lead to the death of the patient. Therefore, detection of SCA with a very high accuracy and within a very short time after the onset of symptoms is very crucial for patient survival. Several approaches for detection of SCA have been made using temporal and morphological variations in the electrocardiogram (ECG). However, the existing methods suffer from low accuracy or involve long-term monitoring of ECG. Therefore, in the present work, an intelligent algorithm to detect SCA based on the extraction of temporal features like RR interval and the QT interval from ECG and subsequently classifying them using support vector machine (SVM) has been proposed. Experimental evaluation of the proposed method on five SCA and five normal subjects demonstrates a high classification accuracy of 100% which proves the superiority of the proposed algorithm as an effective method for detection of SCA. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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Framework for Ship Trajectory Forecasting Based on Linear Stationary Models Using Automatic Identification System

Framework for Ship Trajectory Forecasting Based on Linear St...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Srivastava, Smita Kumar, Lovneet Jeyanthi, R. Deepa, K. Aggrawal, Vinay Department of Computer Science and Engineering India Department of Electronics and Communication Engineering India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Centre for Artificial Intelligence and Robotics Defense Research and Development Organization Karnataka Bangalore India

Maritime surveillance is of utmost priority for a nation's security, and hence it's economy. For maritime awareness, coastal surveillance, and maritime activities in the Region of Interest (ROI) should be monitored. One of the ways to keep this in check is to restrain unwanted infiltration. Monitoring unwanted infiltration is feasible through vessel trajectory forecasting and anomaly detection in real time. Most solutions for trajectory predictions are available but require a huge amount of historical data, and high-power computing resources. Here, the requirement is developing a decision support framework consisting of both lite weight approaches for short-term predictions and deep learning-based techniques for long-term forecasting. This paper aims to find the suitability of Linear Stationary Models (LSM) like the Auto-Regressive Integrated Moving Average Model (ARIMA) for predicting and forecasting the Vessel Trajectory as means of lite weight short-term predictions. For this purpose, the Automatic Identification System (AIS) dataset of the U.S. West Coast is used. The significant effort was for data pre-processing to create a robust dataset for model training. An appropriate model after the model-selection process is used for trajectory forecasting. The model's accuracy is validated using Root Mean Square Error (RMSE) performance indices for residual and forecast errors. A window generator model is integrated with the best-fitted ARIMA model for recursive real-time predictions, with varied sizes and visualization. The proposed time-series model provided a very high accuracy as the RMSE value for prediction and 48 hours forecast are 0.023 and 0.017, respectively. © 2023 The Authors. Published by Elsevier B.V.

关键词： Time series analysis

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Drone-Based Predictive Structure Analysis: A Review

Drone-Based Predictive Structure Analysis: A Review

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Patil, Trushant Nandan, Durgesh Sharma, Kanhaiya Parashar, Deepak Department of Robotics and Automation Pune India Department of Electronics and Telecommunication Engg. Pune India Department of Computer Science & Engineering Pune India Faculty of Engineering Pune India

ISBN: (纸本)9798350329186

Drone-based predictive structure analysis is a rapidly growing field that has the potential to revolutionize infrastructure and building inspection. In this review of the research, we give a general overview of how drones could increase the effectiveness and precision of structural analysis, particularly for keeping an eye on difficult-to-reach locations. The usage of drones for 3D mapping, inspection, and monitoring of diverse constructions, such as concrete buildings, bridges, tunnels, and dams. The investigations show that drone-based inspection can produce 3D models and high-resolution photos, allowing for remote monitoring and the early identification of possible problems. The findings show that drones may greatly boost infrastructure management's safety and effectiveness, making them an important tool for predictive structural research. © 2023 IEEE.

关键词： Drones

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Efficient Deep Learning Modalities for Object Detection from Infrared Images

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computers, Materials & Continua 2022年第8期72卷 2545-2563页

作者： Naglaa F.Soliman E.A.Alabdulkreem Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Ahmed Sedik Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafr el-sheikhEgypt

For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video *** vision is a visual search trend that is used to identify objects in images or video *** military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime ***,there is a need for such a system,which provides a continuous surveillance task to support the drone *** a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous *** addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious *** paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term Memory(ConvLSTM).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video *** accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented *** addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.

关键词： Deep learning object detection military applications OFDM SPIHT IoT

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Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

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computer Systems Science & engineering 2023年第2期44卷 961-977页

作者： E.Dhiravidachelvi M.Suresh Kumar L.D.Vijay Anand D.Pritima Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Electronics and Communication Engineering Mohamed Sathak A.J.College of EngineeringChennai603103India Department of Information Technology Sri Sairam Engineering CollegeChennai602109India Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatore641114India Department of Mechatronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artiﬁcial Intelligence(AI)*** techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and *** wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor ***,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,***-developed Artiﬁcial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR *** this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare *** proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare *** addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the ***-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of ***,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN ***,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different *** experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.

关键词： Artiﬁcial intelligence human activity recognition deep learning deep belief network hyperparameter tuning healthcare

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A Multi-Scale Control Framework for Electric and Automated Buses in Intercity Lines 63

A Multi-Scale Control Framework for Electric and Automated B...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Graffione, S. Bracco, S. Pasquale, C. Sacone, S. Siri, S. Ferrara, A. University of Genova Department of Informatics Bioengineering Robotics and Systems Engineering Italy University of Genova Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798350316339

This paper proposes a multi-scale control scheme aimed at controlling speed, dwell time, and charging time of electric and automated buses performing the transport service on lines where there are no reserved lanes. The proposed control scheme has two layers characterized by different control objectives, level of detail and time scales. The high-level controller periodically solves a multi-objective optimal control problem formalized to ensure an adequate transport service, while the low-level controller, being based on a more accurate description of the bus dynamics and energy consumption, seeks to follow the high-level control actions while taking into account the effective traffic conditions encountered on the line and the effective dynamic behaviour of the bus. The paper concludes with the application to a case study in order to demonstrate the effectiveness of the proposed control scheme. © 2024 IEEE.

关键词： Bus transportation

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Customized Touch UI Development for E-Cockpit in Automobile 3

Customized Touch UI Development for E-Cockpit in Automobile

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3rd International Conference on Smart Technologies, Communication and robotics, STCR 2023

作者： Sivaraj, D. Rathika, P.D. Jayasree, D. Dwarkesh Psg College of Technology Department of Electronics and Communication Engineering Coimbatore India Psg College of Technology Department of Robotics and Automation Engineering Coimbatore India Psg Polytechnic College Department of Electronics and Communication Engineering Coimbatore India Psg College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370867

In recent times, the automotive industry has made significant steps towards digitization by focusing on electronic cockpits (E-Cockpits) to enhance the driving experience, comfort and safety. The incorporation of Touch-Based User Interface (UI) technologies has played a crucial role in revolutionizing Driver-Vehicle interaction [10]. The project of E-Cockpit represents a concept that consolidates various internal car systems onto one unified digital platform, enabling seamless control and access to essential infotainment, safety features, and connectivity services [14]. For the realization of the complete potential of an E-Cockpit, it is important to develop an innovative and User-Friendly touch UI as this directly influences both user experience and safety concerns. © 2023 IEEE.

关键词： User interfaces

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Rlsnake: A hybrid reinforcement learning approach for road detection 24

Rlsnake: A hybrid reinforcement learning approach for road d...

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2021 24th ISPRS Congress Commission III: Imaging Today, Foreseeing Tomorrow

作者： Botteghi, N. Sirmacek, B. Ünsalan, C. Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Smart Cities School of Creative Technology Saxion University of Applied Sciences Netherlands Electrical and Electronics Engineering Faculty of Engineering Marmara University Turkey

Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study, we picked an earlier proposed road detection method which works based on traditional computer vision and probability theory algorithms. We improved it by further steps using reinforcement learning theory. With the help of the novel hybrid technique (traditional computer vision method combined with reinforcement learning based artificial intelligence), we achieved a solution that we call RLSnake. This new method can learn new image scenes and resolutions rapidly and can work reliably. We believe that the proposed RLSnake will be a significant step in the remote sensing field in order to develop solutions which might increase performance by combining the power of traditional and new techniques. © 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. All rights reserved.

关键词： Reinforcement learning

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