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Motion path planning method based on map reference points

Multimedia Tools and Applications 2024年 1-21页

作者： Yu, Wenbin Fan, Cheng Zhang, Chengjun Chen, Yadang Li, Yangsong Zhang, Yifan Yin, Na Cen, Xiaolin School of Software Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Wuxi Institute of Technology Nanjing University of Information Science and Technology Jiangsu Wuxi214000 China Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China School of Computer Science Northeastern University Boston02101 United States Computer Science and Engineering The University of New South Wales SydneyNSW02101 Australia

In recent years, significant progress has been made in the development of the Rapidly-exploring Random Trees (RRT) algorithm, a probabilistic sampling-based search approach. This algorithm effectively addresses complex challenges in path planning within high-dimensional spaces characterized by intricate constraints. However, the RRT algorithm faces certain limitations, including sluggish computational performance in handling intricate calculations, susceptibility to becoming trapped in confined path regions, and difficulties in generating randomized paths across a given map. To overcome these issues, this study introduces a motion path planning method based on map reference points. Initially, the algorithm selects strategic map reference points, followed by the utilization of an enhanced RRT algorithm for fundamental path selection. Real-world scenario data is collected to empirically evaluate the algorithm’s performance. The outcomes of the experiments reveal that, within the same experimental setting, the proposed method outperforms conventional motion path planning algorithms by effectively circumventing obstacles. Furthermore, it substantially reduces the resource wastage caused by global random sampling in the RRT algorithm, leading to expedited path planning and improved pathfinding success rates. Additionally, the proposed approach enhances the RRT algorithm’s adaptability to some extent, particularly in negotiating narrow passages and intricate environments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision trees

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

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computer Systems Science & engineering 2022年第5期41卷 479-492页

作者： A.S.Anakath R.Kannadasan Niju P.Joseph P.Boominathan G.R.Sreekanth School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science CHRIST(Deemed to be University)Bengaluru560029India Department of Information Security School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information *** use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing *** cloud is nowadays highly affected by internal threats of the *** applications such as banking,hospital,and business are more likely affected by real user *** intruder is presented as a user and set as a member of the *** becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or *** major issue in today's technological development is identifying the insider threat in the cloud *** data are lost,compromising cloud users is *** and security are not ensured,and then,the usage of the cloud is not *** solutions are available for the external security of the cloud ***,insider or internal threats need to be *** this research work,we focus on a solution for identifying an insider attack using the artificial intelligence *** insider attack is possible by using nodes of weak users’*** will log in using a weak user id,connect to a network,and pretend to be a trusted ***,they can easily attack and hack information as an insider,and identifying them is very *** types of attacks need intelligent solutions.A machine learning approach is widely used for security *** date,the existing lags can classify the attackers *** information hijacking process is very absurd,which motivates young researchers to provide a solution for internal *** our proposed work,we track the attackers using a user interaction behavior pattern and deep learning *** usage of mouse movements and clicks and keystrokes of the real user is stored in a *** deep belief neural

关键词： Cloud computing security insider attack network security privacy user interaction behavior deep belief neural network

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Real-Time Pill Identification for the Visually Impaired Using Deep Learning 6

Real-Time Pill Identification for the Visually Impaired Usin...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Dang, Bo Zhao, Wenchao Li, Yufeng Ma, Danqing Yu, Qixuan Zhu, Elly Yijun San Francisco Bay University Computer Science FremontCA United States School of Software Engineering University of Science and Technology of China Anhui Hefei China School of Electronics and Computer Science University of Southampton Southampton United Kingdom Stevens Institute of Technology Computer Science HobokenNJ United States College of Computing Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798350353174

The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. Utilizing the YOLO framework, the application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices. The system incorporates Text-to-Speech (TTS) to provide immediate auditory feedback, enhancing usability and independence for visually impaired users. Our study evaluates the application's effectiveness in terms of detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community. © 2024 IEEE.

关键词： Deep Learning Healthcare Mobile Application Pill Identification Visual Impairment YOLO Framework

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LOPE: A Low-Overhead Payment Verification Method for Blockchains

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Chinese Journal of Electronics 2021年第2期30卷 349-358页

作者： ZHANG Ying HE Zhengyuan JIA Linpeng DING Donghui ZHANG Hanwen SUN Yi School of Software Engineering Tongji University Blockchain Lab Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

With its characteristics of decentralization,security, data traceability, and tamper-resistance, the blockchain has been widely used in various *** the difference in the performance of the devices, the light client is proposed so that devices without the ability to store a full blockchain copy can also participate in the blockchain transactions. However,the light client has to communicate with full nodes and verify the authenticity of a transaction which brings in some extent of communication, computation, and storage overheads to the light client. These overheads cannot be ignored for some low-performance devices, such as embedded devices or Io T chips, and therefore the current light client scheme does not work in this situation. We propose LOPE(a Low-overhead payment v Erification method) for poor-capacity nodes in the blockchain *** LOPE, a grouping protocol is designed to partition full nodes into groups to serve the verification requests of the light client. In addition, Practical byzantine fault tolerance(PBFT) is used to ensure the light client to get a credible result in spite of a few dishonest nodes existing in the group. We conduct LOPE and evaluate it in a testbed. The experiment results show that LOPE reduces more than half of the communication overhead, degrades the computation overhead of the light client to a large extent, and avoids the storage overhead of the hash roots of block headers in the light client. We also conduct theoretical analysis to show the performance improvement and security issues of LOPE.

关键词： current light client scheme storage overhead blockchains Low-overhead payment vErification method peer-to-peer computing software fault tolerance electronic money data integrity LOPE low-performance devices cryptographic protocols fault tolerance blockchain transactions

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MaGIC: Multi-modality Guided Image Completion 12

MaGIC: Multi-modality Guided Image Completion

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Hao Yu, Yongsheng Luo, Tiejian Fan, Heng Zhang, Libo School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Software Chinese Academy of Sciences China Department of Computer Science University of Rochester United States Department of Computer Science and Engineering University of North Texas United States

Vanilla image completion approaches exhibit sensitivity to large missing regions, attributed to the limited availability of reference information for plausible generation. To mitigate this, existing methods incorporate the extra cue as a guidance for image completion. Despite improvements, these approaches are often restricted to employing a single modality (e.g., segmentation or sketch maps), which lacks scalability in leveraging multi-modality for more plausible completion. In this paper, we propose a novel, simple yet effective method for Multi-modal Guided Image Completion, dubbed MaGIC, which not only supports a wide range of single modality as the guidance (e.g., text, canny edge, sketch, segmentation, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i.e., arbitrary multi-modality) for image completion. For building MaGIC, we first introduce a modality-specific conditional U-Net (MCU-Net) that injects single-modal signals into a U-Net denoiser for single-modal guided image completion. Then, we devise a consistent modality blending (CMB) method to leverage modality signals encoded in multiple learned MCU-Nets through gradient guidance in latent space. Our CMB is training-free, thereby avoiding the cumbersome joint re-training of different modalities, which is the secret of MaGIC to achieve exceptional flexibility in accommodating new modalities for completion. Experiments show the superiority of MaGIC over state-of-the-art methods and its generalization to various completion tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Image segmentation

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A Smart Assistive Device for the Blind People 7th

A Smart Assistive Device for the Blind People

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7th International Conference on Information and Communication technology for Competitive Strategies , ICTCS 2022

作者： Valarmathi, B. Gupta, N. Srinivasa Chellatamilan, T. Santhi, K. Reddy, R. Hemadri Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789811996375

Mobility is the biggest issue for blind individuals. In order to move around, they must rely on others. Therefore, we are creating a navigational aid for blind people, to help the blind people in the world from various difficulties they face in day-to-day life. The World Health Organization (WHO) reported that a survey was conducted in 2014 and that there are an estimated 285 million visually impaired persons worldwide, of which 37 million are blind, with over 15 million of them being Indian. The majority of blind persons depend on other people for their activities since INDIA accounts for 21% of the global population of blind people (Khanam et al. in Int J Adv Res Sci Eng 7:195–199, 2018). Smart shoes play an important role. Obstacle detection is performed using ultrasonic sensor which is also done by many people in the previous projects or researches. The new concept that is included in this paper is that it can also detect any flame and potholes around the person using flame sensor and ultrasonic sensor, respectively. And we use shock sensor to detect any shock that occurs. If the smart assistive device or smart shoes senses an object, a pothole, a fire (from about three feet away), or a shock in front of the user, a buzzer will sound. These sensors are integrated using Arduino Uno. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Obstacle detectors

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A Novel Redundant Service Caching and Task Offloading Method in Mobile Edge Computing 31st

A Novel Redundant Service Caching and Task Offloading Metho...

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31st International Conference on Web Services, ICWS 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Ouyang, Zhaobin Xia, Yunni Li, Jingpeng Feng, Jiafeng Yu, Yang Zhang, Ke Xu, Xifeng Ma, Yong Chen, Peng Li, Xiaobo School of Computer Chongqing University Chongqing400030 China China Huadian Corporation Guangdong Branch Guangdong China Nanjing Nanzi Huadun Digital Technology Co. Ltd. Nanjing China School of Computer Science and Technology Beijing Institute of Technology Beijing100083 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330000 China School of Computer and Software Engineering Xihua University Chengdu China Chongqing Animal Husbandry Techniques Extension Center Chongqing China

ISBN: (纸本)9783031770715

Edge caching is a promising technologies, which enhances content delivery and reduces service latency by caching potentially interested content and services on edge nodes near users. Due to constraints such as limited edge server capacities and unstable communications in a highly dynamic MEC environment, caching and content delivery are prone to failures and inconsistencies. Consequently, redundant caching mechanisms are in high need as counter measures in this paper, we proposes a novel redundant service caching and task offloading Decision (FT-STD) method. Specifically, we utilize the primary backup (PB) approach for offloading fault tolerance and employ a learning-based algorithm for yielding redundant service caching schedules in MEC. Simulation results demonstrate thatclearly outperforms its peers across multiple performance metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mobile edge computing

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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