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Position-aware Human Activity Recognition with Smartphone sensors based on Deep Learning Approaches 46

Position-aware Human Activity Recognition with Smartphone Se...

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

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit Technology University of Phayao School of Information and Communication 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

Numerous research studies on human activity recognition (HAR) have utilized smartphone sensors, such as accelerometers, gyroscopes, and magnetometers. The accelerometer, in particular, has been considerably explored as the primary sensor in HAR research. Recently, researchers have integrated different smartphone sensors to enhance recognition interpretation. Nevertheless, it is still challenging to determine the optimal usage of these sensors, either individually or in combination, for better identification effectiveness in various motion situations. In this article, we explore how different motion sensors behave during the activity recognition process, using various deep learning (DL) approaches to classify physical actions based on smartphone sensor data. We evaluate the DL models using a publicly benchmarked dataset of data collected from ten participants performing eight movements while carrying smartphones in different situations. Our experimental results reveal that, except for the magnetometer, each sensor can play a leading role in HAR, depending on the recognized activity, body position, data features employed, and classification approach. Additionally, we find that the combination of sensors only improves overall recognition interpretation when their individual performances are low. © 2023 IEEE.

关键词： Smartphones

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Design of intelligent Image Recognition Algorithms Based on Optical sensors and Generative Adversarial networks

Design of Intelligent Image Recognition Algorithms Based on ...

引用

Power, Electrical Engineering, Electronics and Control (PEEEC), International conference on

作者： Cong Ye Department of big Data and Internet Suzhou College of Information Technology Jiangsu China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

This paper presents the design of an intelligent image recognition algorithm based on optical sensors and Generative Adversarial networks (GAN). In today’s digital era, the growth of image data is increasing exponentially, which puts higher requirements on image recognition algorithms. Optical sensors, as an important data acquisition tool, play an important role in the field of image processing. Generative Adversarial networks, on the other hand, is a powerful deep learning model that can learn and generate images with realistic features. In this paper, an intelligent image recognition algorithm is designed by combining optical sensors with GAN, which realizes efficient recognition and classification of images by taking full advantage of data acquisition and feature learning. This study first introduces the basic principles of optical sensor technology and its application in image recognition. With the development of technology, optical sensors have continuously improved the accuracy and speed of image acquisition, and have been widely used in industrial, medical and other fields. Then, this paper elaborates the working principle of Generative Adversarial networks (GAN) and its importance in image *** realizes the ability of learning from data and generating realistic images by means of competitive learning, which brings new breakthroughs in the field of image recognition. An intelligent image recognition algorithm design based on optical sensor and GAN is proposed. The algorithm realizes the effective extraction and learning of image features through the steps of data preprocessing, optical sensor feature extraction and GAN feature learning. In the process of algorithm design, the features and advantages of optical sensors and GAN in image processing are fully considered, which makes the algorithm have high recognition accuracy and robustness. Finally, this paper verifies the performance of the proposed algorithm in image classification and recognition

关键词： Representation learning Deep learning Image recognition Accuracy Data preprocessing Generative adversarial networks Feature extraction Robustness Classification algorithms Optical sensors

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intelligent Lighting and Shutters Control in Smart Homes using the Blynk Application 2

Intelligent Lighting and Shutters Control in Smart Homes usi...

引用

2nd International conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： Drouiche, I Saadi, H. Ferroukhi, M. Univ Sci & Technol Houari Boumediene USTHB Fac Elect Engn Lab Instrumentat POB 32 Algiers 16111 Algeria

ISBN: (纸本)9798350349740;9798350349757

The recent upsurge in home automation has significantly enhanced homeowners' ability to manage and enjoy their spaces. In this work, we specifically target the lighting and shutter control, essential for both energy conservation and creating the desired atmosphere at home. This study introduces a pioneering approach utilizing the Blynk application to facilitate seamless control based on user preferences, environmental conditions, and energy efficiency. Implementing a distributed architecture involving sensor nodes collecting data and a central controller processing information, the system underwent real-world testing in smart homes. Compared to traditional methods, the obtained results highlighted the Blynk application's superiority in energy efficiency, personalized control, and user satisfaction, thus validating its potential for effective and personalized management of lighting and shutters in smart homes.

关键词： Smart Home Lightening sensors IoT ESP 32 Blynk

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Minimizing Age of Processed information in Wireless networks

Minimizing Age of Processed Information in Wireless Networks

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IEEE International conference on Communications (IEEE ICC)

作者： Nikunram, Chanikarn Meesena, Wasin Turner, Stephen John Supittayapornpong, Sucha Vidyasirimedhi Inst Sci & Technol Rayong Thailand Carleton Coll Northfield MN USA

ISBN: (纸本)9781538674628

The freshness of real-time status processing of time-sensitive information is crucial for several applications, including patient monitoring and autonomous driving. This freshness is considered in this paper for the system where unprocessed information is sent from sensors to a base station over a shared wireless network. The base station has a dedicated non-preemptive processor with a constant processing time to process information from each sensor. The age of processed information is the time elapsed since the generation of the packet that was most recently processed by a processor. Our objective is to minimize the average age of processed information over an infinite time-horizon. We first show that a drop-free policy simplifies the system without sacrificing optimality. From this simplification, we propose three transmission-scheduling policies with 2-optimal guarantees for different requirements. A distributed Power-2 policy can be implemented without a central scheduler. With a central scheduler, both Back-Off and Max-Weight policies are near optimal with different advantages. The Back-Off policy guarantees a bound on the maximum age of processed information, while the Max-Weight policy achieves the lowest average age in simulation without the guarantee of bound. Simulation results confirm our theoretical findings.

关键词： Age of information Transmission scheduling Max-weight Scheduling policy Wireless network Optimization

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Deep Learning networks for Complex Activity Recognition Based on Wrist-Worn sensor 38

Deep Learning Networks for Complex Activity Recognition Base...

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38th IEEE Region 10 conference, TENCON 2023

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

ISBN: (纸本)9798350302196

Wearable smart devices, such as smartphones and smartwatches, offer great potential as platforms for automated human action identification. However, accurately monitoring complex human actions on these devices poses a challenge due to the presence of similarities in patterns across different actions. This occurs when distinct human actions exhibit comparable signal patterns or characteristics. The placement of motion sensors on the body plays a crucial role in detecting human behavior. Typically, wearable sensors placed at the trouser pocket or a similar location are used for this purpose. However, this positioning is not suitable for identifying actions involving manual gestures. To address this, wrist-worn motion sensors are employed to detect these specific behaviors. This study aims to investigate the effectiveness of deep learning models in accurately categorizing complex human actions using sensor data from wrist-worn devices. Nine deep learning models utilizing convolutional neural networks and recurrent neural networks were examined for their identification capabilities. The models were evaluated using the WHARF dataset, a publicly available benchmark dataset for human activity recognition. The investigation revealed that the proposed CNN-BiGRU model outperformed other deep learning models, achieving an accuracy rate of 87.20% and an Fl-score of 84.46%. © 2023 IEEE.

关键词： Wearable sensors

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Optimizing Power Consumption in Wireless sensor networks for Prolonged Sustainability 2

Optimizing Power Consumption in Wireless Sensor Networks for...

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2nd IEEE International conference on Advances in information Technology, ICAIT 2024

作者： Sivasankar, C. Subramanian, E.K. Sarala, V. Moorthy, A. Purushothaman, N. Chennai India Department of Computer Science and Engineering Saveetha school of Engineering Simats Chennai India Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Thandalam Chennai India Department of CSE SKP Engineering College Tiruvannamalai India

ISBN: (纸本)9798350383867

As Wireless sensor networks (WSNs) grow rapidly, power optimization for sustainability and efficiency is needed. This study offers a complete energy-saving and WSN-extending method. Hardware optimization, software algorithms, network management, adaptive network design, AI-based optimization, security, and reliability are advised. Hardware optimization creates energy-efficient low-power sensor nodes. Battery power and solar cells and vibration energy harvesters form a hybrid energy system. It conserves node energy and prolongs network life. The method uses algorithms and software. To save energy, intelligent duty cycle and power management algorithms optimize sensor activity and sleep. Aggregating and compressing data saves transmission and processing energy. These technologies reduce data handling power to improve network efficiency. Optimizing energy use involves network management. Energy-aware routing techniques and multi-hop routing optimize data transmission paths and reduce network layer energy use. To save energy, cross-layer optimization merges programs across network protocol stack levels. Scalable, adaptive network architecture efficiently handles more sensor nodes. Network configuration adapts to energy and environment. Network infrastructures must be scalable and adaptable for energy efficiency. Advanced machine learning and AI-based optimization are used. Machine learning algorithms forecast energy use and needs in predictive energy management. AI optimises energy use in self-optimizing networks, enhancing efficiency. Security and reliability impact energy efficiency. Energy-efficient secure connection protects data. Fault-tolerant networks handle node failures and communication disruptions without affecting energy utilization. It ensures energy economy and network reliability. This method could work. We anticipate 40% reduced node energy use and 75% longer network longevity. Data transmission efficiency should rise by 28.6% and system dependability by

关键词： Digital storage

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Integrating In-Ear Wearable sensors with Deep Learning for Head and Facial Movement Analysis 5

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

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5th International conference on Big Data Analytics and Practices (IBDAP)

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanaku, Anuchit Univ Phayao Dept Comp Engn Sch Informat & Commun Technol Phayao Thailand King Mongkuts Univ Technol North Bangkok Dept Math Intelligent & Nonlinear Dynam Innovat Res Ctr Fac Appl Sci Bangkok Thailand

ISBN: (纸本)9798350391749

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： eating detection in-ear wearable sensors deep learning head movements facial movements health monitoring

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The intelligent Control System of Economic Entities Based on the Combined Use of IoT and Unmanned Technologies 1st

The Intelligent Control System of Economic Entities Based on...

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1st International conference on Smart Environment and Green Technologies, ICSEGT 2024

作者： Abbasov, Ali Aliyev, Elchin Rzayev, Ramin Institute of Control Systems Baku Azerbaijan

ISBN: (纸本)9783031815638

The development of an intelligent control system implies the creation of a hardware/software package designed for the maximally accurate recreation (emulation) of human decision-making methods. Unlike various forms of computer modeling, which simulate the behavior of some abstract models, the emulation proposed in the paper involves the processes of adaptation, learning and planning in conditions of uncertainty, as well as the processing of significantly large amounts of data. The hardware/software package is based on the combined use of IoT and unmanned technologies. Convergence of these technologies on the basis of a single information IoT platform allows significantly expanding the range of problems to be solved related to adaptive (intelligent) control of economic entities in conditions of uncertainty. In particular, the data (including multispectral data) of remote monitoring conducted by multicopters, combined with real-time sensor readings of IoT devices, can provide the necessary information base for extracting contextual knowledge and, thus, ensure prompt management decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Digital storage

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Design and Implementation of Fuzzy-Based Natural Routing System for Mobile Internet of Things 3

Design and Implementation of Fuzzy-Based Natural Routing Sys...

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3rd International conference on High Performance Computing and Communication Engineering, HPCCE 2023

作者： Qi, Cai National Grid Henan Provincial Electric Power Company Information Communication Branch Zhengzhou China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

Mobile Internet of Things is an Internet application based on satellite positioning technology, wireless sensor networks and communication networks. With the popularity of mobile terminal devices, people's demand for mobile Internet of Things is also growing. In response to this trend, this article proposes an optimization design method based on fuzzy algorithm, which can be used for natural node evaluation and route selection, thereby realizing data interactive processing functions in the two processes of intelligent path identification and real-time dynamic route query. Moreover, this article designs a natural router system for the mobile Internet of Things, and conducts relevant test simulations on the basic performance of the system. The test results are as follows: the average delay of the system is between 137ms-175ms, and the response time is between 121ms-185ms;the overall throughput is between 2314k-3235k;the overall network coverage is basically above 90%. © 2024 SPIE.

关键词： Internet of things

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Water Leak Classification Using Acoustic Data and Machine Learning

Water Leak Classification Using Acoustic Data and Machine Le...

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2024 International conference on Telecommunications and intelligent Systems, ICTIS 2024

作者： Tobbal, Abdelhafid Kara, Reda Bengherbia, Billel Wira, Patrice Medea Algeria Mulhouse France

ISBN: (纸本)9798331527396

Water Distribution networks (WDNs) are critical infrastructures for delivering potable water from its source to consumers. Unfortunately, these systems often suffer significant water loss from leaks which typically remain undetected until they result in extensive damage. To effectively address these issues there is a pressing need for innovative maintenance strategies that enable early fault detection and rapid response. This paper presents a diagnostic approach that uses acoustic monitoring and Edge-AI techniques to detect failures in urban water pipelines through hydrophone sensor recordings. The methodology employed in this study involves extracting both time-domain and frequency-domain features to classify various pipeline faults using three distinct Machine Learning (ML) models: Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM). Furthermore, we implemented the processing method on an embedded platform, demonstrating its effectiveness in real-world scenarios. The results of this study reveal an average fault detection accuracy of 96.83% with a fast response time of 1.055 seconds. © 2024 IEEE.

关键词： information leakage

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