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Real-time Heart Rate Estimation Using PVDF Flexible Piezoelectric sensor Based on Machine Learning 2

Real-time Heart Rate Estimation Using PVDF Flexible Piezoele...

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2nd International conference on advanced Manufacturing Technology and Manufacturing systems, ICAMTMS 2023

作者： Luo, Shijian Fang, Ting Dong, Chong Han, Jiaming School of Electrical and Information Engineering Anhui University of Technology Anhui Ma’anshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China

ISBN: (纸本)9781510667327

To achieve non-invasive, non-contact, and real-time heart rate monitoring, proposed a pulse signal acquisition system using PVDF (Polyvinylidene Fluoride) piezoelectric film. In order to address the issue of errors in heart rate extraction caused by differences in the morphology of pulse signals across individuals or in different states, the K-means clustering algorithm was innovatively used to locate the peak of pulse waveforms in different states and constructed a heart rate data set. Real-time heart rate monitoring by training a large number of pulse signal samples with the proposed CNN-LSTM network model. Experimental results demonstrated that the performance metrics of this model, including the MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and R2(Coefficient of Determination), are 0.2517, 0.3395, and 0.9863, respectively. the maximum error between the proposed system and the standard instrument within 3 minutes was only 1.55 beats/minute, indicating that the system exhibits high accuracy and reliability, and holds great potential for applications in heart rate detection. © 2023 SPIE.

关键词： Mean square error

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A Machine Learning (ML)-Inspired Method for Intrusion Detection in IoT Devices Networks 5th

A Machine Learning (ML)-Inspired Method for Intrusion Detect...

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5th International conference on Computing Science, Communication and Security

作者： Venkatesh, Veeramuthu Raj, Pethuru Nedium, Roshitha Edara, Jahnavi Reddy, Kalluru Amarnath Anushiadevi, R. SASTRA Deemed Univ Sch Comp Thanjavur 613401 Tamilnadu India Reliance Jio Cloud Serv JCS Bangalore 560025 Karnataka India

ISBN: (纸本)9783031751691;9783031751707

The Internet of Things (IoT) and Machine Learning (ML) represent dynamic research fields with significant growth. IoT applications have gained popularity among technology researchers and developers, and the increasing deployment of IoT devices in critical infrastructures improves efficiency and reliability and raises concerns about cyber-attacks. Enterprises have effectively implemented IoT services, enabling automated production through remote and intelligent control. However, this adoption has concurrently introduced novel security vulnerabilities. Addressing the security and privacy challenges in IoT, especially considering energy limitations and scalability issues, remains a critical focus in computer security. This paper aims to prevent multiple attacks targeting sensor nodes' data manipulation. It encompasses a range of threats, such as sensing layer attacks, malfunctions, tamper attacks, false data injection, base node and clone attacks. The proposed approach involves a threat model and a pairing algorithm that utilizes machine learning to associate each sensor node with its corresponding node. To accomplish the goal, the performance of the proposed solution is compared with various machine learning models, including Decision Tree, Linear Regression, k-nearest neighbors (KNN), Random Forest, and AdaBoost. The evaluation utilized two openly accessible real-world datasets, with metrics such as accuracy in attack detection, training time, and testing time being considered. By incorporating machine learning algorithms to improve attack detection in IoT environments, the proposed approach represents a significant advancement in enhancing security and privacy. The results highlight the importance of adopting advanced techniques to safeguard IoT systems from potential threats. The proposed method achieved an impressive 96.75% accuracy rate in detecting attacks, surpassing existing solutions with nearly twice the speed in training and testing times. As the IoT landsc

关键词： sensor nodes Security attacks Intrusion Detection System Critical infrastructure Machine learning Internet of Things (IoT)

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Comparison of Tests for Oscillations in Delay/advanced Difference Equations with Continuous Time 1

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26th International conference on Difference Equations and applications, ICDEA 2021

作者： Rožnjik, Andrea Péics, Hajnalka Chatzarakis, George E. Faculty of Civil Engineering University of Novi Sad Kozaračka 2/A Subotica Serbia Marousi Athens15122 Greece

ISBN: (数字)9783031252259

ISBN: (纸本)9783031252242

In this paper we compare sufficient conditions for the oscillation of all solutions of the delay (advanced) difference equation with continuous time inspired by our results published in [Filomat 34(8), 2693–2704 (2020)] to relevant results in the literature. The oscillatory conditions appear for various functional equations, but their comparison is possible for some special forms. We provide various examples with constant delays (advances) and with variable or constant coefficients, on which we have shown the independency of our conditions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Continuous time systems

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Multi-sensor Ensemble-guided Attention Network for Aerial Vehicle Perception Beyond Visible Spectrum

Multi-sensor Ensemble-guided Attention Network for Aerial Ve...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kwasniewska, Alicja MacAllister, Anastacia Nicolas, Rey Garza, Javier SiMa Technol 226 Airport Pkwy San Jose CA 95110 USA Gen Atom 14200 Kirkham Way Poway CA 92064 USA Lockheed Martin 1 Lockheed Blvd Ft Worth TX 76108 USA

ISBN: (纸本)9798350302493

Researchers from different market domains have made significant developments in Artificial Intelligence (AI) enabling more advanced automated sensing systems and, thus, eliminating the need for the time-consuming manual analysis of data, which is prone to human errors. However, successful deployment of such systems in real world applications requires careful design and analysis of the proposed models. This work focuses on perception done on Unmanned Aerial Vehicles (UAV) using multi-task learning. There are multiple challenges when considering such platforms. First of all, they often operate in difficult and dynamic conditions affected by various factors, such as background noises, ego-noise of the motors and occluded views. At the same time, they require high performance local compute, co-designed with optimized software solutions that meet small size, weight, and power (SWaP) requirements. Therefore, the AI models designed for such systems should not introduce computational and memory overheads to allow for real time processing at the embedded edge. Taking this into account, this work proposes a novel neural network-based system that utilizes ensemble-guided modulations of audio path fused with the infrared (IR) visual embedding using the attention mechanism. The ensemble mechanism doesn't require spawning new ensemble members, but instead operates on FiLM (Feature-wise Linear Modulation) activation, making it suitable for resource-constraints embedded edge platforms. The performed experiments show that the proposed network outperforms a single FiLM network by 15% and is more robust to noise.

关键词： Real time systems

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Optimization of Energy Demand Management in Cold Storage systems: Advances through the Integration of IoT, Machine Learning, and AI

Optimization of Energy Demand Management in Cold Storage Sys...

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2024 IEEE Latin American conference on Computational Intelligence, LA-CCI 2024

作者： Salgado, Robinson Alvarado, Gaddiel Loo, Luis Universidad Tecnológica de Honduras Maestría en Automatización Industrial Tegucigalpa Honduras Universidad Tecnológica de Honduras Escuela de Postgrado San Pedro Sula Honduras

ISBN: (纸本)9798350374575

Cold storage facilities play a crucial role in preserving perishable products across various industries, including food, pharmaceuticals, and agriculture. The high energy consumption of refrigeration systems in these facilities necessitates efficient energy management. This article uses Long Short-Term Memory (LSTM) models in a novel way to predict temperature and power demand variations in cold storage systems, leading to more accurate forecasts. The research improves demand response applications and achieves significant energy savings through real-world deployment by combining real-Time IoT sensor data and deep learning. The study provides scalable framework that can be applied to a variety of industrial settings, helping to promote more sustainable energy management practices by optimizing power usage and lowering operational costs. Various methodologies and models were evaluated to predict and manage temperature variations and energy demand efficiently. The results demonstrate significant enhancements in energy efficiency, reduced energy consumption, and optimized resource usage. Among the evaluated machine learning techniques, deep neural networks, specifically Long Short-Term Memory (LSTM) models, showed superior accuracy and responsiveness. Mini-ROCKET and DrCIF, along with their precision and lower mean absolute error (MAE), make reliable predictions possible with a MAPE below 0.2 having overall accuracy of this model is 96%. Additionally, the adaptability of these models to abrupt operational changes was analyzed, highlighting their advantages and limitations. The paper concludes with recommendations for future research and development, promoting the adoption of advanced and sustainable technologies in energy management for cold storage systems. © 2024 IEEE.

关键词： Demand response

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Routing Protocols in Wireless sensor Networks: A Comprehensive Review and Future Perspectives 7

Routing Protocols in Wireless Sensor Networks: A Comprehensi...

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7th International conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kaur, Puneet Kaur, Kiranbir Verma, Deepak Kumar Singh, Kuldeep Kaushik, Keshav Singh, Vineeta Guru Nanak Dev University Department of Computer Engineering & Technology Punjab Amritsar India Department of Computer Science and Engineering Uttar Pradesh Kalyanpur India Guru Nanak Dev University Department of Electronics Technology Punjab Amritsar India Amity University Amity School of Engineering and Technology Punjab Mohali India

ISBN: (纸本)9798350350067

Wireless sensor Networks (WSNs) have significantly transformed diverse domains such as industrial automation, healthcare, disaster management, and environmental monitoring. The complex interaction of routing protocols, essential for enabling smooth data transport despite the specific difficulties in WSN environments, is the core of their success. The objective of this study is to get a accurate understanding of routing algorithms specifically projected for wireless sensor networks. Expanding upon the essential features and significant disputes faced in WSN systems, admitting limited energy resources, bandwidth constraints, and node processing capabilities, the discussion accentuates the significance of effective routing protocols. Furthermore, it thoroughly analyzes many forms of routing protocols, including basic hierarchical and flat schemes, as well as advanced location-based and data-centric protocols. The protocol category is thoroughly examined, uncovering its unique benefits, limitations, and suitability for various WSN applications. Furthermore, the review reveals the latest developments and upcoming trends in WSN routing protocols, brooding areas such as energy efficiency, dependability, scalability, and adaptive resilience to changing network considerations. In conclusion, the evaluation furnishes a forward-looking view by outlining potential research areas and possible pathways for meliorating the designs of WSN routing protocols. The main goal is to heighten the efficacy and practicality of WSNs. In this work, we examined a number of routing algorithms according to important criteria including mobility, scalability, energy efficiency, and Quality of Service (QoS). We looked at how each method handles node mobility, performs as the network expands, guarantees dependable data transmission, and optimizes energy use. We also looked at ways for choosing the cluster head (CH), energy-saving data aggregation strategies, redundancy-reduction mechanisms, and the

关键词： Routing algorithms

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22nd International conference on Next Generation Teletraffic and Wired/Wireless advanced Networks and systems, NEW2AN 2022

22nd International Conference on Next Generation Teletraffic...

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22nd International conference on Next Generation Teletraffic and Wired/Wireless advanced Networks and systems, NEW2AN 2022

ISBN: (纸本)9783031302572

The proceedings contain 58 papers. The special focus in this conference is on Next Generation Teletraffic and Wired/Wireless advanced Networks and systems. The topics include: TEDCTSSA: Trust Enabled Data Collection Technique Based Sparrow Search Algorithm for WSN-Based applications;ISTOA: An Improved Sooty Tern Optimization Algorithm for Multilevel Threshold Image Segmentation;implementing Digital Transformation in the Logistics System of Uzbekistan;Numerical Modeling of Vertical Axis Wind Turbines Using ANSYS Fluent Software;"i’ll wait 4 ur answr!" A Study on Modern Style of Cyber-Writing and User Reactions;improvement of Information Support in Intelligent Information Energy systems;the Assessment of the Effectiveness of the Development of Digital Technologies in Commercial Banks of Uzbekistan;the Study of the Impact of the Digital Economy on the Growth of E-Government Services in Uzbekistan;deep Learning Algorithm for Classifying Dilated Cardiomyopathy and Hypertrophic Cardiomyopathy in Transport Workers;comparison of Finite Difference Schemes of Different Orders of Accuracy for the Burgers Wave Equation Problem;eCommerce Benchmarking: Theoretical Background, Variety of Types, and Application of Competitive-Integration Benchmarking;cryptocurrencies as the Money of the Future;a Data Security Technique Combining Asymmetric Cryptography and Compressive Sensing for IoT Enabled Wireless sensor Networks;energy Efficient and Secure Scheme Based Compressive Sensing Method for Internet of Vehicles;impact of Digital Technologies on Women’s Employment;the Impact of Digitalisation on the Safe Development of Individuals in Society;econometric Evaluation of the Efficiency of the Management of the Enterprise Through the Supply of Raw Materials in Oil Enterprises in the Conditions of the Digital Economy;on Digital Twin Software and Cyber Threats.

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sensor-Agnostic Graph-Aware Kalman Filter for Multi-Modal Multi-Object Tracking 27th

Sensor-Agnostic Graph-Aware Kalman Filter for Multi-Modal Mu...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Sani, Depanshu Iyer, Anirudh Rai, Prakhar Anand, Saket Srivastava, Anuj Kalyanaraman, Kaushik IIIT-Delhi New Delhi India Florida State University Tallahassee United States

ISBN: (纸本)9783031784439

Recent progress in open-source object detection techniques has significantly advanced Multi-Object Tracking (MOT) methodologies, primarily under the tracking-by-detection paradigm. To enhance the robustness and reliability of MOT systems, recent research has proposed integrating information gathered from diverse sensors. However, many Kalman filter-based MOT approaches assume the independence of object trajectories, overlooking potential inter-object relationships. While some efforts have been made to incorporate these relationships, they often concentrate on learning feature representations to facilitate better association. Moreover, the existing filter-based method for estimating graphs from noisy data is unsuitable for online MOT applications. To alleviate these problems, we introduce a sensor Agnostic Graph-Aware (SAGA) Kalman filter, which is the first online state estimation technique designed to fuse multi-modal graphs derived from noisy multi-sensor data. We validate the effectiveness of our proposed framework through extensive experiments conducted on both synthetic and real-world driving dataset (nuScenes). Our results showcase an improvement in MOTA and a reduction in estimated position errors (MOTP) and identity switches (IDS) for tracked objects using the SAGA-KF. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Kalman filters

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Experimental Study of a LoRa Based Wireless sensor Network for Low Power Localization in an Outdoor Environment 2

Experimental Study of a LoRa Based Wireless Sensor Network f...

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2nd International conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Tobbal, Abdelhafid Bengherbia, Billel Bouziani, Anis Cherifi, Youcef University Yahia Fares of Medea Research Laboratory in Advanced Electronics Systems Lsea Medea Algeria

ISBN: (纸本)9798350314243

The Internet of Things (IoT) and Wireless sensor Network (WSN) have transformed industries by highlighting their crucial roles in sensing, communication, and data-driven decisions. One paramount feature in WSN is localization, enabling precise positioning of devices in real-world applications. Global Positioning systems (GPS) have long been the mainstay of high-precision localization, but their high energy consumption is a significant obstacle to their seamless integration with low-power devices. In the quest for suitable solutions, Low Power Wide Area Networks (LPWAN) have emerged as key players, providing energy-efficient connectivity. However, these advances raise questions about the localization accuracy within these networks. In this paper, we present the implementation of WSN based on Long Range (LoRa) technology to estimate the position of an object in an outdoor environment. We took advantage of LoRa technology to collect Received Signal Strength Indicator (RSSI) measurements over a long distance. An Artificial Neural Network (ANN) is then trained on a collected dataset to estimate the 2-dimensional position of the object. The results show an estimate of the position with an average absolute error of 40.5 meters in an area of 360 meters by 360 meters. A web interface is made with Node-Red to monitor the position of the mobile object. © 2023 IEEE.

关键词： Internet of things

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Estimation Network Design framework for efficient distributed optimization 63

Estimation Network Design framework for efficient distribute...

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63rd IEEE conference on Decision and Control, CDC 2024

作者： Bianchi, Mattia Grammatico, Sergio Automatic Control Laboratory Eth Zürich Switzerland Tu Delft Delft Center for Systems and Control Netherlands

ISBN: (纸本)9798350316339

Distributed decision problems feature a group of agents that can only communicate over a peer-to-peer network, without a central memory. In applications such as network control and data ranking, each agent is only affected by a small portion of the decision vector: this sparsity is typically ignored in distributed algorithms, while it could be leveraged to improve efficiency and scalability. To address this issue, our recent paper [1] introduces Estimation Network Design (END), a graph theoretical language for analysis and design of distributed iterations. END methods can be tuned to exploit the sparsity of specific problem instances, reducing communication overhead and minimizing redundancy, yet without requiring case-by-case convergence analysis. In this paper, we showcase the flexibility of END in the context of distributed optimization. In particular, we study the sparsity-aware version of many established algorithms, including ADMM, AugDGM and PushSum DGD. Simulations on an estimation problem in sensor networks demonstrate that END algorithms can boost convergence speed and greatly reduce the communication cost. © 2024 IEEE.

关键词： Peer to peer networks

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