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Design of motion pattern recognition system based on MPU6050 and pressure sensor 5

Design of motion pattern recognition system based on MPU6050...

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5th international conference on Mechatronics and Computer Technology Engineering, MCTE 2022

作者： Li, Haoyuan Hu, Xufeng Huang, Jingxu Wang, Yang School of Automation Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781510661073

Aimed at the defects of low accuracy of motion pattern recognition and large volume of measuring equipment, a motion pattern recognition system based on MPU6050 and pressure sensor is designed, and the overall structure of the system is given. the system takes nRF2401 wireless transmission module as the node to transmit the pressure and acceleration data collected by the data acquisition system to the data processing system. After preprocessing the data, the system extracts the eigenvalues, passes them into the double classification model based on SVM, corrects the model parameters, and achieve the task of motion pattern recognition. Experiments show that the system has small volume, high accuracy, and the accuracy is as high as 92%, which is obviously superior to the model using pressure data and classification model based on SVM. © 2022 SPIE.

关键词： Wavelet decomposition

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A Lightweight Signcryption Protocol for Multihop Data Aggregation in Wireless sensor networks

A Lightweight Signcryption Protocol for Multihop Data Aggreg...

引用

Innovative Trends in Information Technology (ICITIIT), international conference on

作者： Radhakrishnan Maivizhi Palanichamy Yogesh Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamilnadu India Information Science and Technology College of Engineering Guindy Anna University Chennai India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

Data aggregation is a fundamental and inherent technique for increasing the lifespan of wireless sensor networks. By combining multiple data into a single one, it conserves the bandwidth and energy of sensor nodes. To preserve the privacy of aggregated data, security features are added during the aggregation process. Nevertheless, the addition of security features increases the computational cost of data aggregation algorithms. To overcome this issue, this paper proposes a lightweight Signcryption protocol for Multihop Data Aggregation (SMDA) in wireless sensor networks. the proposed protocol, due to its homomorphic feature, preserves privacy and significantly reduces the computational overhead of sensor nodes. Also, the proposed SMDA protocol is oracle free. Without executing signing algorithms, it can generate signatures on aggregated ciphertext during transmissions. In addition, the proposed scheme validates the signatures in batches and the signature verification cost does not depend on the number of messages. Extensive security analysis demonstrates that the proposed protocol meets the security requirements of WSNs. Furthermore, the results of performance evaluation proves that the computational efficiency of the proposed protocol outperforms other existing secure aggregation schemes.

关键词： Performance evaluation Wireless sensor networks Data privacy Privacy Protocols Spread spectrum communication Data aggregation

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PoSBert: Log Classification via Modified Bert Based on Part-of-Speech Weight 5

PoSBert: Log Classification via Modified Bert Based on Part-...

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5th international conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Zhang, Jing Li, Zezhou Zhang, Xianbo Lin, Feng Wang, Chao Cai, Xingye Jd Tech Beijing China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Logs that record the information of events conducted by clusters are of great importance to the system maintenance. the log classification is also of great value to help engineers monitor the system running status, predict the log anomaly, and take corresponding measures. In order to comprehensively consider the various significance of each token in the template and build a comprehensive representation of the log template, we propose a natural language processing-based model, PoSBert, which is a modified Bert model based on the part-of-speech (PoS) analysis of the tokens of log template. the PoS analysis is firstly carried out and the PoS-based weight embedding is obtained, which realizes the proper weight allocation and helps modify the representation of log templates. the segmented embedding of the vanilla Bert is replaced by this PoS-based weight embedding, which is of more practical significance compared to the segment embeddings in the domain of log classification. the PoS analysis and PoS-based weight embedding provide the Bert with more informative inputs which promotes the model to make better results. Four public datasets and one dataset collected from a practical warning platform are employed for evaluation. the results prove that our model can achieve a better classification result in the log classification domain. © 2022 IEEE.

关键词： Deep neural networks

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An Enhanced DFFNN for Location-Based Services of Indoor Device-Free Submissive Localization 5

An Enhanced DFFNN for Location-Based Services of Indoor Devi...

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5th international conference on Information Technology for Education and Development, ITED 2022

作者： Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Bhoi, Akash Kumar Jimoh, Rasheed Gbenga Ojo, Stephen Panigrahi, Ranjit Faruk, Nasir University of Ilorin Faculty of Communication and Information Sciences Department of Computer Science Ilorin Nigeria University of Lagos Department of Electrical and Electronics Engineering Nigeria Institute of Digital Communication Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany Sikkim Manipal University Directorate of Research Sikkim Gangtok737102 India Sambalpur Burla768018 India Anderson University Department of Electrical and Computer Engineering AndersonSC29621 United States Sikkim Manipal Institute of Technology Department of Computer Applications Sikkim Manipal University Sikkim Majitar737136 India Sule Lamido University Department of Physics Nigeria SLU Directorate of ICT Nigeria

ISBN: (纸本)9781509064229

With no associated devices, device-free localization (DFL) uses wireless sensor networks to find a target. DFL has created comprehensive applications, smart cities and the Internet of things (IoT), among other things. this technique has attracted significant attention from various fields, increasing the demand for tracking indoor location-based services. the critical challenge in DFL is a way to retrieve essential characteristics to illustrate raw signals with various locations linked with diverse patterns. the complexity of an indoor environment with limited space has created low indoor positioning reliability and effectiveness problems. therefore, this study proposes and formulated an image classification problem for the DFL problem by initially transforming the receiving signal strength (RSS) inputs into picture frames. the feature extraction from raw signals was performed using Deep Feed-Forward Neural Network (DFFNN) and deep auto-encoder (DAE) to fine-turning for classification. the DAE combined DFFNN were used for signal reconstruction and feature learning to present the DFL better. the findings revealed an accuracy of 100% using real-world data collected, and a signal-to-noise ratio over -5dB, 0dB, and 5dB was used to measure the react to noisy data. Moreover, in IoT applications, its time cost is very fast in single activity by 5ms for classification. the proposed method is better in noiseless and noisy situations, localization accuracy, and other related techniques. © 2022 IEEE.

关键词： Wireless sensor networks

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

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

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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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A Comparative Study of Deep Learning Robustness for sensor-based Human Activity Recognition 46

A Comparative Study of Deep Learning Robustness for Sensor-b...

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46th international conference on Telecommunications and Signal processing, TSP 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350303964

Recognition of human activities has become a highly significant area of research in the current era of the Internet of things and A.I. technologies. Human activity recognition (HAR) plays a crucial role in monitoring and analyzing human behaviors using various types of sensors. Such sensor-based HAR systems are being used in various industry domains, including healthcare, professional sports, and worker assessment in intelligent factories. the integration of micro electronics mechanical systems sensor technology in smart wearable devices has significantly improved the capabilities of HAR using machine learning and deep learning techniques. However, processing longtime dependent sequence samples with noisy data is still a significant challenge, affecting the classification time and accuracy. this study aimed to analyze noise-tolerant deep learning models for recognizing physical activities from noisy smartphone data. the study analyzed the recognition performances of the five most popular deep learning networks for HAR under different levels of Gaussian noise. the results indicated that the BiGRU network demonstrated outstanding robustness to Gaussian noise of different levels. © 2023 IEEE.

关键词： Smartphones

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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...

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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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Advanced Deep Learning Framework for MRI Brain Tumor Detection: ResNet50 and GAN-Driven Data Augmentation for Rare Tumor Analysis 5

Advanced Deep Learning Framework for MRI Brain Tumor Detecti...

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5th international conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Agarwal, Mansheel Abhisikta, Anouska Mallick, Pradeep Kumar Jagadev, Alok Kumar Sahoo, Biswajit Graduate Group of Computer Science University of California Davis Davis United States Institute of Computer Science Brandenburg University of Technology Cottbus Germany Kiit Deemed to be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

this comprehensive paper combines advanced image processing and deep learning techniques for brain tumor detection and classification in MRI images. Beginning with robust preprocessing methods to extract relevant brain regions, the project employs a ResNet50-based Convolutional Neural Network (CNN) for initial classification. Furthermore, it introduces a Generative Adversarial Network (GAN) for effective data augmentation, creating synthetic images to enhance model performance. the approach extends to the identification of rare tumor cases, including irregular tumors, enhanced patterns, calcifications, cystic components, and those with hemorrhage or necrosis. the proposed CNN model achieves a test accuracy of approximately 90% after training with augmented data. the project provides a holistic framework for accurate brain tumor detection, leveraging a diverse set of image processing and machine learning methodologies. © 2025 IEEE.

关键词： Convolutional neural networks

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