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A Study on Underwater Wireless sensor networks-Void Area 5

A Study on Underwater Wireless Sensor Networks-Void Area

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5th international conference on Computing Methodologies and Communication, ICCMC 2021

作者： Battula, Ashok Emaldaroslin, S. Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (纸本)9781665403603

Underwater Wireless sensor networks (UWSNs) consists of the set of wireless sensors in the seabed and it sends information to the nodes in the surface of water. Routing protocols play a vital role in transferring information from the source node to sensor node. there are several challenges in developing routing protocols for UWSNs. the most challenging field is a void communication area. Many researchers proposed different void handling techniques. An enhanced survey of different routing protocols used in the literature for efficient UWSNs have been discussed in this paper. Further, various future implantations to enhance the lifetime, throughput, among others has been identified. © 2021 IEEE.

关键词： Routing protocols

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AI-Powered Soil Stability Assessment and Landslide Forecasting with Multi-sensor networks, Edge Computing, and Jetson Nano Integration

AI-Powered Soil Stability Assessment and Landslide Forecasti...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： N. Muthukumaran A. Vignesh S. Aruna M. Jenish T. Vinoth Kumar Shanthi Madasamy Department of ECE Sri Eshwar College of Engineering Coimbatore India Department of EEE Dr. N.G.P Institute of Technology Coimbatore India Department of EEE RVS College of Engineering and Technology Coimbatore India Department of EEE JCT College Of Engineering And Technology Coimbatore India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Landslides pose a severe threat to mountainous regions, often resulting in catastrophic damage and loss of life. Existing prediction methods rely on limited data sources and infrequent monitoring, leading to delays in early warnings. Previous technologies have explored individual sensor applications, such as Ground-Penetrating Radar (GPR) and seismic sensors, but lack a comprehensive multi-sensor approach with real-time processing. Notable cases, like the Wayanad landslides in Kerala, India, highlight the urgent need for advanced predictive systems. this study proposes an AI-driven landslide prediction system using a network of 40 sensor-equipped slave kits. Each kit integrates GPR, seismic, Electromagnetic Induction (EMI), ultrasonic, LiDAR, capacitance probes, gravimeters, Electrical Resistivity Tomography (ERT), and magnetometers onto a Teensy 4.1 microcontroller with an ESP8266 for wireless data transmission. Data is processed at a central master unit featuring a Jetson Nano module, employing the ALEXNET+BILSTM model for real-time analysis of soil stability and landslide risk. By leveraging edge computing, this system enables rapid decision-making and early warnings, addressing the limitations of existing methods. Its scalable, cost-effective architecture enhances environmental monitoring, offering a robust solution for landslide-prone communities and significantly improving disaster preparedness.

关键词： Wireless communication Landslides Wireless sensor networks Soft sensors Soil Tomography Stability analysis Real-time systems Terrain factors Edge computing

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Recent Gas/Odor sensor Array Signal processing Trend for Model Nose: A Mini Review

Recent Gas/Odor Sensor Array Signal Processing Trend for Mod...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Shiv Nath Chaudhri Department of Computer Science and Design Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

the study of merging artificial intelligence and the processing of gas and odor sensors' data is known as artificial olfaction or model nose. Gas/odor sensor data is formed by sensor response modalities that produce signature patterns that represent a particular response of a gas/odor sensor. It can be difficult to analyze the particular objective by using drift-affected gas and odor sensor data and trying to gain hidden insight from the model nose. the classification and quantification of gas and odor sensor signals is significantly challenging in model nose tasks and plays a vital role in careful sensor signal processing. the application of data-driven tactics for utilizing the outcomes of the model nose for higher performance is quite challenging. the gas/odor sensor signal processing aims to provide salient features as per the application area, in addition to achieving high accuracy. the recent sensor signal processing methods for the model nose were reviewed. the dataset, response modalities employed, trade-offs associated with each technique, and classification and quantification techniques were all covered in the article. At the end, it is demonstrated that there is a significant challenge and an open research area regarding the improvement of sensor signal processing techniques, such as raising accuracy and sensitivity values and being practical in using model noses efficiently.

关键词： Accuracy Nose Signal processing sensor phenomena and characterization Data models sensor systems sensors Intelligent sensors Gas detectors sensor arrays

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Bed-Leaving Action Recognition Based on YOLOv3 and AlphaPose 2022

Bed-Leaving Action Recognition Based on YOLOv3 and AlphaPose

引用

5th international conference on Image and Graphics processing, ICIGP 2022

作者： Zhang, Caixia Yang, Xiaoyu College of Science North China University of Technology Beijing China

ISBN: (纸本)9781450395465

Considering a very few of research on bed-leaving action recognition, especially which can recognize the human actions with occlusion, we propose a bed-leaving action recognition algorithm based on YOLOv3 and AlphaPose. Six kinds of specific human actions are classified in the process of leaving the bed, which particularly include the normal bed-exit action(BEA) and the abnormal bed-fall action(BFA). First, YOLOv3 is used to extract the bed region and human region. then, combined five skeleton key points extracted by AlphaPose with the angle and length measurements, the multi-layer neural network is constructed and trained for classification and recognition. the experiments on our datasets show that the accuracy of BEA and BFA could reach 98.82% and 95.27%, so our method can assist the medical staff to monitor the bed-leaving action. © 2022 ACM.

关键词： Multilayer neural networks

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State and Parameter Estimation of Nonlinear Systems Using Extended Kalman Filter Based sensor Fusion Approach

State and Parameter Estimation of Nonlinear Systems Using Ex...

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Emerging Technology (INCET), international conference for

作者： Tushar Dhiman Bharat Bhushan Sharma Department of Electrical Engineering NIT Hamirpur Hamirpur India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

the primary objective of this paper is to devise and execute sensor fusion employing the Extended Kalman Filter (EKF) for parameter estimation in nonlinear systems. sensor fusion tackles a data processing challenge where individual measurements from diverse sensors are conducted independently, yet the measured values exhibit correlation due to the system dynamics. Furthermore, these measurements are affected by various types of noise. Leveraging the Extended Kalman Filter (EKF) for sensor fusion enables the extraction of accurate information from imprecise measurements. the study employs the Extended Kalman Filter (EKF) algorithm for sensor fusion in the context of the Lorenz system and the Wilberforce Pendulum. Numerical simulations are conducted to validate the efficacy of the proposed methodology.

关键词： Parameter estimation System dynamics Service robots sensor fusion Robot sensing systems Numerical simulation Robustness

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Research on Detection Method of Foxing on Paper Cultural Relics Based on CNN and Hyperspectral 5

Research on Detection Method of Foxing on Paper Cultural Rel...

引用

5th international conference on Computer Information Science and Application Technology, CISAT 2022

作者： Hang, Liu Bin, Tang Zourong, Long Jianxu, Wang Qing, Chen Junfeng, Miao Jinfu, Zhang Mingfu, Zhao Nianbing, Zhong Huan, Tang Chongqing Key Laboratory of Fiber Optical Sensor and Photodetector Chongqing University of Technology Chongqing400054 China Key Scientific Research Base of Pest and Mold Control of Museum Collections of National Cultural Heritage Administration Chongqing China Three Gorges Museum Chongqing400054 China

ISBN: (纸本)9781510660076

Paper cultural relics are one of the most important cultural carriers of ancient human civilization and precious material materials. Due to age and human factors, a large amount of paper cultural heritage is being corroded by foxing, which seriously affects the safety of paper cultural heritage. At present, the detection and control of insect and mould diseases on paper cultural relics is still a recognized problem in the world. In order to solve the practical problem of foxing detection on paper cultural relics, this paper proposes a paper cultural relic foxing based on one-dimensional convolutional neural network based on the characteristics of nondestructive detection of hyperspectral imaging, aiming at the spectral characteristics difference between the foxing part and the healthy part. the spot detection method realizes the non-destructive detection of foxing on paper cultural relics. the experimental results show that the classification accuracy of the model in this paper is 99.17% for the foxing area and healthy area of paper cultural relics. Compared with traditional methods such as K nearest neighbor method and support vector machine, the convolutional neural network model can fully extract the deep spectral features of the foxing region, and can effectively avoid overfitting. the method of combining deep learning theory with hyperspectral provides a methodological basis for the protection of paper cultural relics. © 2022 SPIE.

关键词： Convolutional neural networks

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Gearbox Fault Diagnosis Using Stacked Multisensor Image Fusion and Self-Normalizing Convolutional Neural networks

Gearbox Fault Diagnosis Using Stacked Multisensor Image Fusi...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Boubker Najdi Mohammed Benbrahim Mohammed Nabil Kabbaj Systems Engineering Modeling and Analysis Laboratory Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

the accurate diagnosis of gearbox faults in industrial machinery is essential for ensuring operational efficiency and preventing costly downtime. this paper introduces a novel diagnostic framework that employs advanced signal processing and deep learning techniques to elevate fault detection accuracy and reliability in gearbox systems. By leveraging stacked Synchrosqueezing Wavelet Transforms (SSWT) for multi-sensor image fusion, our approach begins with transforming raw vibration signals from multiple sensors into high-resolution Time-Frequency (TF) representations, essential for detecting subtle mechanical anomalies. these images are then processed using Self-Normalizing Convolutional Neural networks (SCNNs), which exploit their inherent normalization capabilities to maintain consistency across network layers and mitigate the vanishing gradient problem. the spatial features extracted are further analyzed through Gated Recurrent Units (GRUs), adept at modeling long-term dependencies and capturing fault progression dynamics. Enhancement of this process with a Multihead Attention layer allows selective focusing on salient features, significantly improving diagnostic precision. Validated on two public gearbox datasets, our method achieved 100% accuracy on both, advancing gearbox fault diagnostics and setting a new benchmark for industrial fault detection methods.

关键词： Fault diagnosis Deep learning Wavelet transforms Vibrations Accuracy Fault detection Signal processing Feature extraction Convolutional neural networks Image fusion

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Control Method of Dynamic Clustering Network in Wireless sensor Network

Control Method of Dynamic Clustering Network in Wireless Sen...

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IEEE international conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Hailin Liu Chengguang Zhang Bingfu Hu Department of Information Engineering Weifang Engineering Vocational College Qingzhou Shandong Province China

To solve the routing holes in wireless sensor networks caused by high energy consumption of cluster head nodes and shortage of spectrum, a dynamic clustering based method for wireless sensor network networking control is presented. In the early stage of cluster networking, the communication efficiency index is constructed by collecting the signal-to-noise ratio and energy consumption of wireless sensors (nodes), and the cluster head node and working frequency are selected. To avoid the routing holes caused by cluster head node death, periodically check the communication efficiency index of the nodes to dynamically adjust the cluster head node and working frequency. A smooth transition between old and new cluster head nodes is achieved by substituting nodes. this control method ensures that a wireless sensor with the best performance always has a stable communication with the external terminal as the cluster head node in the wireless sensor network, and improves the performance of the entire wireless sensor network.

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Predictive Maintenance and Real Time Monitoring using IoT and Cloud Computing

Predictive Maintenance and Real Time Monitoring using IoT an...

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

作者： Aaryan Suthar Kishor Kolhe Vitthal Gutte Dhanashri Patil Department of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University Pune India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Industrial Revolution 4.0 describes the rapid transformation that industry is going through due to Artificial Intelligence (AI), Machine Learning (ML) and IoT (Internet of things). Industrial IoT (IIOT) combines traditional industrial processes with modern technology such as sensors, data analytics, and machine learning to optimize and automate processes. Like the Industrial Revolution, IIoT is transforming the way that goods are produced, but with some significant differences. IoT enables machines and devices to communicate with one another in real-time, providing valuable insights into the performance of industrial processes. this allows for predictive maintenance, where machines can be repaired or replaced before they fail, reducing downtime and maintenance costs. OEE (Overall Equipment Efficiency), an industry standard for gauging production efficiency, is raised with the use of predictive maintenance. By employing IoT, cloud computing, machine learning and real-time monitoring to identify abnormalities, malfunctions, and errors in manufacturing equipment, this paper seeks to boost OEEas a result of predictive maintenance. sensors are employed in equipment to identify motion and other crucial data, which is subsequently transmitted to the cloud for analysis. As a result of this work, equipment dependability and efficiency are increased while downtime and maintenance costs are decreased.

关键词： Cloud computing Data analysis Data visualization Machine learning Real-time systems sensor systems sensors

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Mask R-CNN with Multi-Backbones - A Comparative Analysis 5

Mask R-CNN with Multi-Backbones - A Comparative Analysis

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5th international conference on Signal processing and Information Security, ICSPIS 2022

作者： Rubin, Rufus Jacob, Chinnu Anzar, S.M. Panthakkan, Alavikunhu TKM College of Engineering Centre for Artificial Intelligence Kollam691005 India TKM College of Engineering Department of Electronics and Communication Kollam691005 India University of Dubai College of Engineering and IT United Arab Emirates

ISBN: (纸本)9781665492652

Instance segmentation is a computer vision task for detecting and localizing an object in an image. It involves identifying, segmenting, and categorizing each object in an image. the idea behind instance segmentation is to categorize objects that belong to the same class into different instances. With instance segmentation, one can locate the bounding boxes of each instance as well as the object segmentation maps for each instance, allowing one to determine the number of instances in the image. Mask Region-based convolutional neural network is the cutting-edge method for instance segmentation. the backbone architecture of the mask R-CNN consists of a feature pyramid network, a region proposal network, and a region of interest alignment network. In this paper, three CNN models such as ResNet101, ResNet50, and MobileNetV1 are used as backbone network structures to compare the mask R-CNN architecture. the performance of these models is compared with the Penn- Fudan dataset using performance metrics such as mean Average Precision, mean Average Recall, and F1-score. the ResNet-101 output achieves a mean Average Precision of 98.3% over the other two models. the performance scores and losses of the three models were also discussed. Experimental studies show that ResNet-101 outperforms the other two models. © 2022 IEEE.

关键词： Convolutional neural networks

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