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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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Optimized Real-Time Monitoring and Fault Diagnosis System for Industrial Robots with Integrated sensor Data

Optimized Real-Time Monitoring and Fault Diagnosis System fo...

引用

Image processing and Capsule networks (ICIPCN), international conference on

作者： Min Tan Peng Wang Wendong Luo Chongqing Aerospace Polytechnic Chongqing Chongqing China

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

this study presents an optimized real-time monitoring and fault diagnosis system tailored for industrial robots that integrates sensor data for improved performance. Industrial robots, as complex interdisciplinary products, are central to the field of intelligent manufacturing. Understanding their failure modes is essential for the development of robust fault diagnosis systems. Using Failure Mode and Effects Analysis (FMEA), this study systematically identifies and evaluates potential failure conditions. We then present a sensor-based fault diagnosis system and review recent advances in fault diagnosis techniques tailored for industrial robots. the proposed methodology includes real-time environmental monitoring using video sensors and a fault diagnosis system using integrated sensor data. Experimental analysis demonstrates the effectiveness of the proposed method in detecting anomalies, with accuracies ranging from 99.23% to 99.45%. the robustness and applicability of the system in the various industrial environments are highlighted, underscoring its potential to improve the reliability, safety, and efficiency of industrial robot operations.

关键词： Fault diagnosis Accuracy Service robots Streaming media Industrial robots Robot sensing systems Real-time systems

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Designing of controlled wearable robot for the treatment of ankle injuries using flex sensors 5

Designing of controlled wearable robot for the treatment of ...

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5th international conference on Multimedia, Signal processing and Communication Technologies (IMPACT)

作者： Krishnakumar, S. Janney, Bethanney J. Divakaran, Sindu Kumar, Aishwarya Mohan Robinson, Sheryl Poojahsri, S. Williams, Prakash G. Sathyabama Inst Sci & Technol Dept Biomed Engn Chennai 119 Tamil Nadu India Bishop Moore Coll Dept Bot & Biotechnol Mavelikara Kerala India

ISBN: (纸本)9781665476478

Rehabilitation after ankle injury requires considerable time and focus in order to restore the ankle's durability and range of motion. One such device used for rehabilitation of the ankle is ankle robots. the ankle robot has a bio inspired design, developed following a detailed examination of the areas where the current ankle robots may use improvement. the robot's design can accommodate users of all ages and physiological abilities. But the disadvantage is that patients have to visit the hospital daily for physiotherapy which is very difficult for the injured person as well as for the person accompanying them. For these reasons by using the proposed system, it would be very helpful for the patient as they can perform the ankle exercise at home itself without the help of others and the exercise pattern can be stored in the computer for reference by the therapist in future. Ankle robots utilize lightweight and the actuation of the skeletal muscles is mimicked by powerful pneumatic muscle actuators (PMA). the ankle injuries are treated via the movements of pneumatic muscle actuators. It is controlled by the flex sensor attached to the socks that is worn by the patient. this robot helps in improving the ankle mobility, flexibility, co-ordination, ankle strength and helps to return back to full fitness.

关键词： Flex sensor rehabilitation PIC16F877A microcontroller LabVIEW

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Restoration and enhancement optimization of blurred images based on SRGAN 5

Restoration and enhancement optimization of blurred images b...

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5th international conference on Computing and Data Science, CONF-CDS 2023

作者： Yuan, Ziqi International School Beijing University of Post and Telecommunications Beijing China

Blurred images pose a significant challenge in many applications, including medical imaging, remote sensing, and surveillance systems. these images suffer from low resolution, noise, and missing data, which can hinder their interpretation and analysis. Traditional methods for image restoration and enhancement have their limitations, such as low quality and slow processing times. To overcome these challenges, this paper proposes an innovative method using Super-resolution Generative Adversarial networks (SRGANs) to enhance image quality and fidelity. the proposed method employs adversarial training, perceptual loss, residual learning, and feature reconstruction to generate visually realistic and high-quality super-resolution (SR) images from low-resolution (LR) inputs. the SRGANs approach outperforms traditional methods, demonstrating its potential to advance image restoration and enhancement techniques. the paper also discusses possible improvements and future directions for this technique. © Published under licence by IOP Publishing Ltd.

关键词： Restoration

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Partical Swarm Optimization Based Adaptive Fault-Tolerant Control for Quadrotor Attitude Stabilization 5th

Partical Swarm Optimization Based Adaptive Fault-Tolerant Co...

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5th international conference on Electrical Engineering and Control Applications, ICEECA 2022

作者： Abdelhamid, Bounemeur Mohamed, Chemachema Abdelmalek, Zahaf Salah, Bouzina Sofiane, Bououden Signla Processing Laboratory Department of Electronics Faculty of Engineering University of Brothers Mentouri Constantine 1 Constantine Algeria Polytech Electrical Engineering Laboratory LGEPC National Polytechnic School of Constantine Constantine Algeria University Abdel Hamid Mehri Constantine 2 Constantine Algeria University Abbès Laghrour Khenchela Algeria

ISBN: (纸本)9789819700448

this paper introduces a novel approach for achieving optimal attitude control of a quadrotor in the presence of uncertainty, external disturbances, time-varying sensor faults, and nonaffine nonlinear actuator faults. the proposed method utilizes the Particle Swarm Optimization (PSO) technique to approximate adaptive parameters and fuzzy initial values, resulting in a reliable controller that can quickly compensate for changes in the starting point. Fuzzy systems are employed to estimate unknown nonlinearities, nonaffine nonlinear actuator faults, and time-varying sensor faults. To address approximation errors and external disturbances, a robust control term is incorporated. the issue of an algebraic loop is resolved using a Butterworth low-pass filter. Additionally, the proposed robust scheme effectively handles external disturbances without relying on approximations, and the controller is dynamically updated through online reconfiguration. the stability of the entire closed system is analyzed based on Lyapunov theory. Simulation scenarios are presented to demonstrate the efficacy and benefits of the proposed approach. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Low pass filters

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Dragonfly algorithm for hierarchical clustering in wireless sensor networks 5

Dragonfly algorithm for hierarchical clustering in wireless ...

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5th international conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Pitchaimanickam, B. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu626126 India

ISBN: (纸本)9781665412728

Wireless sensor networks have worked with the help of battery sources. Every sensor node monitors the physical or environmental conditions. the sensor nodes are utilized to perform data processing and transmitted to BS. this task requires extra energy for data processing which also affects the lifetime. the clustering technique gives a solution for energy-efficient data processing processes. Simulated annealing in LEACH-C algorithm is not suitable for enhancing the lifetime. In this paper, the dragonfly algorithm is proposed in the LEACH-C methodology for locating the cluster head. this technique exploits the swarming behavior of the dragonflies to locate the cluster head. the proposed methodology is measured with the help of alive nodes, and network lifetime. the proposed methodology minimizes energy utilization and improves the network lifetime. © 2021 IEEE.

关键词： Energy utilization

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Hair Loss Stage Prediction Using Deep Learning 5

Hair Loss Stage Prediction Using Deep Learning

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Behal, Rupashi Priya, Pallavi Yuvraj Kapoor, Anjali Mishra, Anju Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350383522

Hair losses diseases are common and pose challenges in diagnosing accurately and promptly. Traditional diagnostic methods involve visual and medical tests by dermatologists, leading to delays that worsen conditions. To address this, a deep learning solution using a 2D convolutional neural network (CNN) was implemented, effectively predicting hair loss categories: alopecia, psoriasis, and folliculitis. Challenges included limited dataset access and diverse online images affecting model precision. Besides the model's success, a dataset was created using factors like SSB, Ageing, hierarchical characteristics, and Stress, valuable for future research. this study's significance lies in aiding timely hair loss identification via neural network applications, benefiting both medical professionals and individuals. the project's approach hinges on leveraging the power of deep learning models to discern intricate patterns within frontal facial images. the utilization of Convolutional Neural networks (CNNs) will enable the automatic extraction and learning of relevant information from the images, hence enabling the capture of minute variations in hair density, coverage, and distribution. these learned features will subsequently serve as the foundation for an accurate and comprehensive classification system that aligns with the Hamilton-Norwood scale's progressive stages of hair loss. To sum up, the goal of this research study is to show that deep learning techniques can automatically identify different stages of hair loss from frontal facial photos. through the integration of neural networks and image processing skills, this research aims to further the development of hair loss diagnosis and treatment approaches, ultimately improving the lives of those who suffer from this illness. © 2024 IEEE.

关键词： Convolution neural networks hair loss stages Hamilton Norwood scale

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Security Analysis of Two Authentication and Key Agreement Protocols Based on Wireless sensor networks 5th

Security Analysis of Two Authentication and Key Agreement ...

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5th international conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2022

作者： Wang, Liyang Kumari, Saru Wu, Tsu-Yang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India

ISBN: (纸本)9789819908479

In wireless sensor network (WSN), various data is collected via sensors and transmitted over a public channel. However, during the transmission, an attacker can intercept and eavesdrop on data and use it to launch some attacks. therefore, it is necessary to design secure authentication and key agreement (AKA) protocols in WSN environments, which ensures secure communication between entities. Recently, Yu et al. designed an authentication protocol in WSN and claimed their proposed protocol can resist well-known attacks. In addition, Wang et al. proposed another three-party mutual authentication protocol in IoT-enabled wireless sensor networks. In this paper, we find some security vulnerabilities in both protocols, including sensor node capture attacks, known session-specific temporary information attacks, and violating perfect forward secrecy. Finally, we introduce several suggestions for the improvement of both protocols. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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5G Channel Estimation Kernels on RISC-V Vector Digital Signal Processors

5G Channel Estimation Kernels on RISC-V Vector Digital Signa...

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2024 international conference on Microelectronics, ICM 2024

作者： Acevedo, Javier Fitzek, Frank H. P. Seeling, Patrick Deutsche Telekom Chair of Communication Networks TU Dresden Germany Germany Central Michigan University Department of Computer Science United States

ISBN: (纸本)9798350379396

Each generation of the wireless communication networks has contiguous demands for higher computing capabilities and lower the power consumption in the integrated circuitry. thus, the optimization of the base-band signal processing kernels and the hardware architectures plays a pivotal role in the performance, latency, and energy efficiency of the Physical Layer (PHY). Channel Estimation (CE), a computationally intensive task with hard real-time requirements, involves significant amounts of matrix multiplications and inversions, when data flows between transmitter (TX) and receiver (RX). In this work, we present a software implementation of CE for the 5th Generation Cellular networks (5G) New Radio (NR) in the Reduced Instruction Set Computer (RISC)-V architecture, showcasing how its vector processing capabilities can satisfy the throughput and delay requirements of 5G use cases. Specifically, we ported two CE kernels, the Least-Squares (LSE) and Minimum Mean Square Error (MMSE), onto a state-of-the-art single core vector processor. By exploiting data vectorization, the ARA core computes matrix arithmetic operations on multiple data sets simultaneously. Our single vector core serial optimization demonstrates that with a 16× 16 matrix window size and 16 vector lanes, the application's performance achieves a speedup of 78.27 when computing the LSE algorithm. When the number of operations per cycle are compared, the vector processor outperforms its scalar counterpart by 181.91 for the MMSE kernel. Our implementation orders the nested loops in a latency-reducing fashion to compute matrix arithmetic operations, resulting in LSE and MMSE estimator computing times of 8.44 μiota s and 12.31 μs, respectively. © 2024 IEEE.

关键词： Mean square error

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5th IEEE international Image processing, Applications and Systems conference, IPAS 2022

5th IEEE International Image Processing, Applications and Sy...

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5th IEEE international Image processing, Applications and Systems conference, IPAS 2022

ISBN: (纸本)9781665462198

the proceedings contain 59 papers. the topics discussed include: comparative studies on similarity distances for remote sensing image classification;combination of object tracking and object detection for animal recognition;a combined acute and chronic risk assessment rolling window for type 1 diabetes;evaluating attention in convolutional neural networks for blended images;computer vision-based Bengali sign language to text generation;experimental validation of photogrammetry based 3D reconstruction software;vehicle color identification framework using pixel-level color estimation from segmentation masks of car parts;image processing and control of tracking intelligent vehicle based on grayscale camera;a light weight approach for real-time background subtraction in camera surveillance systems;real-time powered wheelchair assistive navigation system based on intelligent semantic segmentation for visually impaired users;innovative tools for investigation on flame dynamics by means of fast imaging;and improving 3D point cloud reconstruction with dynamic tree-structured capsules.

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PILOT: Synergy between Text processing and Neural networks to Detect Self-Admitted Technical Debt 5

PILOT: Synergy between Text Processing and Neural Networks t...

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5th international conference on Technical Debt (TechDebt)

作者： Di Salle, Amleto Rota, Alessandra Nguyen, Phuong T. Di Ruscio, Davide Fontana, Francesca Arcelli Sala, Irene Univ LAquila Laquila Italy Univ Milano Bicocca Milan Italy

ISBN: (纸本)9781665452113

During the development phase, software programmers usually introduce code that contains issues intentionally left for additional treatment. To allow for future fixing, they mark such code using textual comments, resulting in Self-Admitted Technical Debt (SATD). Detecting SATD contained in source code has become crucial in the development cycle since it helps programmers locate issues that need to be solved, thus improving code quality. We introduce PILOT, a technical debt detector built on top of a combination of different natural language processing (NLP) and machine learning (ML) techniques. First, the semantic among SATD comments is captured using feature extraction steps. then, neural network algorithms are applied to classify comments, represented as vectors. We built a PILOT prototype with a feed-forward neural network and evaluated it using real-world datasets as proof of concept. the empirical evaluation shows that PILOT obtains an encouraging performance and outperforms a well-established baseline. We anticipate that our tool will come in handy, as once being embedded in the IDE, it can help developers recognize SATD manifested in their code, allowing them to conveniently identify and fix issues.

关键词： Self-Admitted Technical Debt NLP techniques Neural networks

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