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Non-invasive flow measurement technology based on Taylor's frozen turbulence hypothesis

Non-invasive flow measurement technology based on Taylor's f...

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2021 International conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2021

作者： Krivonogov, Aleksey Laboratory for Self-Validating Sensors Systems and Advanced Instrumentation South Ural State University Chelyabinsk Russia

ISBN: (纸本)9781728145877

The article is devoted a new method of flow measurement using an original mathematical algorithm and piezo film sensor. We describe the first part of an investigative project to develop a new flow measurement system. The advantage of the presented method is that it supports noninvasive flow measurement through the pipe wall. We present different results of studying mathematical methods for signal processing and numerical simulations using LES turbulence models such as Stress-Blended Eddy Simulation (SBES). The physical effect based on Taylor's frozen turbulence hypothesis, which serves as the foundation of this measurement method, is described. The mathematical algorithm and signal processing method contain a two-dimensional Fourier transform to build the frequency-wavenumber spectrum and cross-correlation function. We provide a short description about the chosen sensor and justify its use for the proposed measured system. In conclusion, the findings of our investigation and a real prototype scheme are presented. © 2021 IEEE.

关键词： Flow measurement

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Intelligent Paving and Rolling Construction Technology of Asphalt Pavement

Intelligent Paving and Rolling Construction Technology of As...

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2021 International conference on advanced Technologies and applications of Modern Industry, ATAMI 2021

作者： Yuan, Taiping Wang, Zheng Hong, Qiaozhang Chen, Jing Lei, Jiechao Meng, Yongjun China Southern Power Grid Energy Development Research Institute China College of Civil Engineering and Architecture Guangxi University Nanning China Key Laboratory of Disaster Prevention and Engineering Safety Ministry of Education Nanning China National High- Performance Computing Center Nanning Branch Nanning China

The intelligent construction system of asphalt pavement consists of an intelligent paver, driverless roller, mobile base station, intelligent control program, etc. By installing GPS(BDS)+ GNS satellite receiver, 5G communication chip, microwave communication host, radio signal receiving antenna on the construction equipment and connecting with the processor, switch, program software, and other components set on the equipment, integrate the laser obstacle avoidance radar, infrared temperature detection device, laser ranging sensor and other terminal equipment set on the equipment to realize the intellectualization of equipment use function;By running the intelligent control system, mobile 5G communication base station system and construction area acquisition system, command the linkage operation of multiple intelligent types of equipment to realize the intelligent control of pavement construction and meet the requirements of asphalt pavement design specifications. © Published under licence by IOP Publishing Ltd.

关键词： 5G mobile communication systems

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Integrating In-Ear Wearable sensors with Deep Learning for Head and Facial Movement Analysis

Integrating In-Ear Wearable Sensors with Deep Learning for H...

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Big Data Analytics and Practices (IBDAP), International conference on

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

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

Accurate and inconspicuous identification of eating behavior has substantial implications for wellness surveillance, nutritional tracking, and technological assistance. Conventional approaches for detecting eating habits frequently rely on invasive or unfeasible techniques, which restrict their practicality in real-life situations. This research introduces an innovative eating recognition system that utilizes head and face motions recorded by in-ear wearing sensors and advanced deep learning algorithms. Our approach is a non-invasive and simple-to-use method that utilizes the unique patterns of head and face motions related to eating actions. We use a specially developed in-ear wearable sensor from the EarSet dataset to gather movement information while individuals are eating. The dataset comprises a range of cranial and facial motions recorded from 30 individuals. The unprocessed sensor data is subjected to pre-processing to eliminate interference and extract pertinent characteristics. We use advanced deep learning models, such as CNN, LSTM, BiL-STM, GRU, BiGRU, and our novel hybrid model CNN-LSTM, to acquire knowledge of distinctive patterns and identify eating occurrences. Our eating recognition system has been extensively tested and proven successful. The CNN-LSTM model achieved an accuracy of 95.12% and an F1-score of 95.09% during the 5-fold cross-validation studies. Our technique demonstrates superior efficiency and applicability compared to standard deep learning algorithms, as a comparative study shows. The suggested system can combine in-ear wearable sensors with deep learning to provide inconspicuous and accurate identification of eating activities. This study enhances the development of eating detection technology and introduces new opportunities for individualized wellness tracking and helpful applications. Subsequent research will prioritize the enlargement of the dataset, integration of supplementary modalities, enhancement of deep learning models for

关键词： Deep learning Accuracy Tracking Face recognition Surveillance sensor phenomena and characterization sensor systems

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Enhancing Safety in Autonomous Vehicles through advanced AI-Driven Perception and Decision-Making systems

Enhancing Safety in Autonomous Vehicles through Advanced AI-...

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Intelligent Data Science Technologies and applications (IDSTA), International conference on

作者： Yazan Alahmed Reema Abadla Mohammed Jassim Al Ansari Faculty of Engineering Al Ain University Abu Dhabi U.A.E. School of Business University of Central Lancashire preston UK

ISBN: (数字)9798350354751

ISBN: (纸本)9798350354768

Accidents, congested roads, consumption of energy, and emissions may all decrease significantly with the rise of self-driving cars, providing a promising response to social and environmental issues. In this study, researchers investigate the way the integration of innovative AI-driven perception and decision-making systems into AVs impacts safety. AVs have the potential to change transportation by reducing accidents caused mainly by human error. They may operate on their own or in conjunction with human drivers. The primary goal is to investigate and enhance AV safety by creating highly sophisticated perception and decision-making technologies driven by machine learning. Pragmatism research philosophy, experimental research design, and inductive approach serve as the selected methods of the study. In addition, secondary qualitative data analysis methods help evaluate the entire study. The outcomes of the study demonstrated that the advancement of autonomous vehicles depends significantly on the creation of AI-driven sensors. “Vehicle-to-vehicle (V2X) networks” for communication and safety features increase security, while integrated camera systems with acoustic and thermal sensors improve sensing capacities. Deep learning techniques, especially “convolutional neural networks (CNN)” and “fully convolutional networks (FCN)”, facilitate accurate object recognition and segmentation. Vehicles’ ability to handle difficult road conditions is further improved by a real-time risk evaluation and trajectory planning based on human-like behavioral modeling.

关键词： Decision making Vehicular ad hoc networks Thermal sensors Real-time systems sensor systems Safety Autonomous automobiles Convolutional neural networks Vehicle-to-everything Vehicles

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Gesture to Speech: A Wearable Solution for Sign Language Translation

Gesture to Speech: A Wearable Solution for Sign Language Tra...

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International conference on advanced Computing and Communication systems (ICACCS)

作者： Balamanikandan A R Bindu Madhavi Pudi Abhiram Reddy Pinjari Ameer Sohel Pamala Vijay Kumar Ashokkumar N Electronics and Communication Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This paper introduces a groundbreaking wearable device designed to address communication challenges faced by the global mute community. The device utilizes embedded flex sensors strategically placed on a glove to capture intricate sign language gestures. An embedded system, featuring a microcontroller (Arduino), processes these gestures through a sophisticated translation algorithm, converting them into both textual and speech output. The seamless integration of Bluetooth technology enables real-time wireless communication with external devices, such as smartphones or tablets. The device's versatile design ensures comfort and unobtrusiveness, making it suitable for various social, educational, and professional settings. Additionally, a rechargeable battery and efficient power management contribute to prolonged and sustainable usage. The embedded system's role is pivotal, acting as the cognitive hub that interprets and translates sign language gestures, fostering inclusivity and breaking down communication barriers. The translation algorithm, backed by machine learning techniques, enhances accuracy, making the device a reliable and practical solution. The optional inclusion of Text-to-Speech software further enriches the user experience, offering a comprehensive communication tool for the mute community. This paper discusses the device's technological architecture, design considerations, and real-world applications, emphasizing its transformative impact on global inclusivity. The research explores the potential for widespread adoption and its contribution to creating a more accessible and cohesive world.

关键词： Wireless communication Sign language Wireless sensor networks Bluetooth Embedded systems Real-time systems User experience Text to speech Wearable devices Speech processing

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Determining Various Gait Metrics using IMU and the Geographical Location of the Patient Using GPS

Determining Various Gait Metrics using IMU and the Geographi...

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International conference on advanced Computing and Communication systems (ICACCS)

作者： K. Rajasekaran Kishor. S Alan Winston. M. A Naveen. V Department of Biomedical Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India

Walking is an integral part of our daily routine and requires a high level of coordination between our limbs and trunk. Gait analysis, which assesses the way we stand and move while walking, is a crucial evaluation tool in determining our health. This analysis can be performed using wearable device-based gait analysis, which utilizes sensors to provide data relevant to various health applications. The proposed novel system, with the integration of IoT technology, patients are able to perform gait analysis using wearable technology at the comfort of their own homes, eliminating the need to visit a hospital or use expensive equipment. The data collected through wearable devices, such as foot angle, height, location, oxygen saturation levels, and step count, can be easily accessed by doctors through a mobile app. This information is then used to assess the patient's health and determine the next course of action. Hence, this innovation has the potential to revolutionize the way we monitor our health and manage illnesses. Its practicality, cost-effectiveness, and accuracy make it a valuable tool for both patients and healthcare professionals and has shown promising results in over 30 people evaluated in real-time.

关键词： Wireless communication Performance evaluation Legged locomotion Wireless sensor networks Wearable computers Stroke (medical condition) Real-time systems

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An Iot Based Framework For Prediction Of Enviornment Quality Using Artificial Intelligence

An Iot Based Framework For Prediction Of Enviornment Quality...

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International conference on advanced Technologies for Signal and Image Processing (ATSIP)

作者： Omar Saad Alqahtani Pravin Ramdas Kshirsagar Department of Computer Science King Khalid University Abha Saudi Arabia Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur Maharashtra India

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Environmental phenomena affect our everyday life greatly. This included flexible structures the resources of life, including the pure water and air of our planet. The word climate is characterized as a platform that provides numerous facilities for several atmospheric applications in the areas of water allocation, poor air quality, meteorological conditions, radioactive detection, wastewater treatment, catastrophic event as well as many other predisposing factors. Monitoring system, modeling and management provide a better knowledge of key development and environmental changing management methods. Over time, the problem of emissions has increased owing to a number of reasons, such as increasing population and usage of cars, urban development, which has a significant impact on workers’ health. This article proposes AI and IoT for the predictions and modeling of the environmental quality assessment system. The Artificial Intelligence method can evaluate data extremely efficiently and take accurate choices on services in many kinds. The IoT gateway includes the entire application software from sensor level to environmental information systems. The test data suggest that the new approach offers an efficient means of analyzing environmental information over a lengthy period of time.

关键词： Accuracy systems modeling Air quality Internet of Things Resource management Reliability Artificial intelligence

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Automation of User Pace-Adjusted Treadmill: Comparison of Control Methods based on LRF sensors

Automation of User Pace-Adjusted Treadmill: Comparison of Co...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Brayan S. Moreno Ricardo C. Mello Anselmo Frizera Graduate Program in Electrical Engineering Federal University of Espírito Santo Vitória ES Brazil

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Neurological disorders affect gait and posture, leading to significant difficulties in daily life. Traditional gait assessment lacks objective and quantifiable data, hindering comprehensive evaluation. To address these limitations, interactive and objective systems, such as smart walkers and specialized treadmills, have been developed utilizing advanced technologies to gather detailed gait data. This study focuses on evaluating two control strategies (A controller based on adaptive estimation of gait parameters and a PI controller-based systems) to automate a conventional treadmill, utilizing LRF sensors and the user’s walking speed for personalized system control. The results demonstrate high adaptability of the control models to gait variations, surpassing reported walking speeds in the literature using these algorithms. These findings are relevant for enhancing user interaction and experience in rehabilitation applications and serious games, which can enable improvements in rehabilitation and human-machine interaction. Our results show that the strategy employing adaptive filters exhibits superior responsiveness, enabling adaptation to different treadmill speeds with greater ease.

关键词： Legged locomotion Neurological diseases Human computer interaction Automation Games Control systems sensor systems Biosensors Engineering in medicine and biology Intelligent sensors

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Development of a ML-Control Strategy for a Wrist Exoskeleton Based on EMG and Force Measurements with sensor Strategy Optimisation

Development of a ML-Control Strategy for a Wrist Exoskeleton...

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IEEE/RAS-EMBS International conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Roberto F. Pitzalis Nicholas Cartocci Christian Di Natali Darwin G. Caldwell Giovanni Berselli Jesús Ortiz Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Department of Mechanical Energy and Transportation Engineering (DIME) University of Genoa Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genoa Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

The aim of this paper is to infer the development of a control and sensor strategy for an industrial wearable wrist exoskeleton through preliminary classification and prediction of workers' actions gathered in a real working environment. The effort intensity, correlated with the exerted force, and sensor strategy optimization are considered and evaluated for design purposes. In this paper, an EMG-based wrist motion classification and a force prediction models are presented based on measurements taken on six healthy subjects in a manufacturing plant. A wrist motion pattern recognition algorithm has been developed on top of surface EMG data measured through an 8-channel commercially available sensor (Myo Armband). Moreover, a force regression model has been built based upon wrist force measurements detected by using a commercial hand-held dynamometer (Vernier GoDirect Hand Dynamometer). This control strategy lays the basis for a more complex architecture which will be designed to control an active wrist exoskeleton for industrial applications, simplifying its design, costs and data process by adopting as few sensors and electronic components as possible while maintaining sufficient reliability and accuracy to provide adequate assistance.

关键词： Wrist Maximum likelihood estimation Accuracy Force Exoskeletons Prediction algorithms Feature extraction Reliability engineering Electromyography Classification algorithms

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Optimized IRS Positioning for Phase-Tuned Wireless Physiological Motion Detection

Optimized IRS Positioning for Phase-Tuned Wireless Physiolog...

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IEEE MTT-S International Microwave Bio conference ((IMBIOC)

作者： Denny Vishnu Puri Landika Mohammad Shadman Ishrak Haofan Cai Alvin Yang Yao Zheng Khaldoon Ishmael Olga Borić-Lubecke Victor Lubecke University of Hawai'i Mānoa USA

ISBN: (数字)9798331510473

ISBN: (纸本)9798331510480

This paper explores the impact of physical positioning of Intelligent Reflective Surfaces (IRS) on enhancing physiological motion detection in non-line-of-sight (NLOS) millimeter-wave (mmWave) integrated sensing and communication (ISAC) systems. Operating at 28 GHz with an Orthogonal Frequency Division Multiplexing (OFDM) scheme, the proposed system incorporates a static IRS whose physical placement is systematically adjusted to optimize signal propagation and phase alignment. Experimental results reveal that precise IRS positioning significantly improves detection accuracy, with peak-to-peak (P2P) phase shifts increasing by 57 % in the perpendicular configuration and 56 % in the parallel configuration, corresponding to optimal signal reflections. These findings demonstrate the potential of physically repositioning IRS to achieve superior sensitivity and robustness in wireless physiological sensing, paving the way for advanced positioning algorithms and applications in complex multipath environments.

关键词： Wireless communication Wireless sensor networks Sensitivity OFDM Integrated sensing and communication Physiology Reflection Motion detection Millimeter wave communication Tuning

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