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Understanding Properties of DriveBay Electromagnetic Tracker for Use in Soft Robot Experiments 28th

Understanding Properties of DriveBay Electromagnetic Tracker...

引用

2024 International conference Automation

作者： Smaz, Tomasz Zubrycki, Igor Lodz Univ Technol Inst Automat Control Stefanowskiego 18 PL-90537 Lodz Poland

ISBN: (纸本)9783031782657;9783031782664

Soft Robotics experiments necessitate advanced measurement techniques to track robot pose and deformations accurately. Electromagnetic tracking systems provide substantial benefits over vision-based systems, especially in capturing both the position and orientation of a robot's surface without occlusion issues. However, these systems are typically designed for applications in controlled environments, with multiple guidelines stated by the producers, necessitating an evaluation of their effectiveness in different contexts. This paper presents a series of foundational experiments to assess the capabilities and limitations of such a system in Soft Robotics. We investigate the impact of distance from the base station, resistance to environmental interference, effects of metallic elements, and the accuracy of rotational measurements. The results demonstrate a significant influence of positional accuracy relative to the distance from the transmitter, with a tenfold decrease in accuracy observed. Additionally, the presence of metallic objects in close proximity (less than 20mm) to the probes adversely affects accuracy, as do large metallic elements such as construction materials. This effect is particularly pronounced when the sampling frequency of the system aligns with multiples of the electrical network frequency, resulting in more than a tenfold reduction in accuracy.

关键词： Soft Robotics Electromagnetic Tracking sensor Accuracy Robotics Measurement systems Environmental Interference in Robotics

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TimeGAE: A Multivariate Time-Series Generation Method via Graph Auto Encoder 29th

TimeGAE: A Multivariate Time-Series Generation Method via Gr...

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29th International conference on Database systems for advanced applications (DASFAA)

作者： Bai, Zhao Guo, Fangda xi, Yuxin Zhu, Zhuoming Guo, Yu Bie, Rongfang Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Comp Technol CAS Key Lab AI Safety Beijing Peoples R China

ISBN: (纸本)9789819755516;9789819755523

In numerous practical scenarios, the scarcity of data presents a challenge for the implementation of large and complex deep learning models. To address this issue, time series generation methods have emerged as a promising approach to alleviate data scarcity. However, most existing methods do not explicitly consider multivariate time series, thereby failing to fully exploit the potential spatial dependencies among different variables. The ability to capture temporal dynamics also plays a critical role in generating diverse and feasible synthetic data. In this paper, we propose TimeGAE, a novel multivariate time series generation model based on graph auto encoder. Our approach explicitly models multivariate time series by leveraging graph learning layers to learn the internal relationships among variables. This enables the generation of realistic time series based on the learned internal relationships. We also incorporate transformer-encoder or RNN modules to enhance the ability to retain temporal dynamics for time series feature extraction. Several comparisons and ablation experiments on three multivariate time series datasets have been conducted. Our results demonstrate that TimeGAE-generated synthetic data is significantly more realistic and that TimeGAE outperforms other state-of-the-art time series generation methods on multivariate time series datasets.

关键词： Time-series generation Graph neural networks Data augmentation Multivariate time series

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Smart Fish Feeding in IoT-based Aquarium: A Comprehensive Approach Integrating Computer Vision, Machine Learning and sensor Technology 4

Smart Fish Feeding in IoT-based Aquarium: A Comprehensive Ap...

引用

4th International conference on Next Generation Computing applications, NextComp 2024

作者： Cassy, Ryan Bekaroo, Girish Middlesex University Mauritius Flic-en-Flac Mauritius

ISBN: (纸本)9798331517403

With the increasingly busy lifestyles of people, advanced maintenance systems such as automated feeding has become of utmost importance to aquarium owners. Despite existing technologies, there remains a gap in smart feeding solutions tailored to the specific dietary requirements of different fish species and population sizes. This paper investigates smart fish feeding through automated detection, counting, classification and feeding of fishes within aquariums. As part of this paper, a smart population-driven aquarium maintenance system, named AquaTrack, is proposed that integrates Internet of Things (IoT), computer vision, machine learning, and sensor technologies. The system detects, classifies, and counts fish, adjusting feeding amounts based on species and population to mitigate the risks of overfeeding and underfeeding, which can significantly affect water quality. The system was evaluated through four research questions, where a general accuracy of 76.9% in fish species identification and 77.1% in fish counting were found. While promising outcomes were observed under specific conditions, variability and limitations in performance across different scenarios were also noted. Environmental factors such as lighting, reflections, shadows, and particulate matter affected the accuracies, particularly in scenarios involving smaller fish quantities. Based on these findings, the study recommends further refinement of the system to enhance its capabilities. © 2024 IEEE.

关键词： Fish

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Incorporating LoRa Based Wireless sensor Network and Machine Learning Technologies to Improve Precision Agriculture 3rd

Incorporating LoRa Based Wireless Sensor Network and Machine...

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3rd International conference on Intelligent systems and applications, ICISA 2024

作者： Shaikh, Aman Ranjan, Nihar M. Rai, Satayush Rao, Pranil Ashok Shinde, Ganesh Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Pimpri-Chinchwad India

ISBN: (纸本)9789819798384

In the contemporary landscape, the agricultural sector has undergone transformational changes, embracing progressive dynamics through the integration of cutting-edge technologies and methodologies. The global adoption of IoT, AI/ML, and similar innovations reflects their capacity to furnish precise, reliable, and consistent outcomes, enabling data-driven decision-making. The amalgamation of IoT and ML models emerges as a strategic choice to enhance data analytics, aiming at a substantial improvement in both the quantity and quality of agricultural yields. This paper seeks to elucidate the synergistic interaction of various agrarian elements, orchestrating the computational prowess facilitated by ML models and data derived from IoT sensors transmitted through LoRa-based Wireless sensor Network (WSN) infrastructure. The proposed system advocates a comprehensive three-tier architectural framework, wherein ML and IoT systems collaborate seamlessly to process data for field management. The envisioned system extends its utility by translating these predictions into visually comprehensible representations. The integrated approach signifies a departure from conventional methods, offering a novel pathway to ensure the sustainability of yield, encompassing both production and grading standards. This paper thus contributes to the discourse on leveraging advanced technologies for agricultural optimization in response to evolving global demands. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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Modelling and analysis of QPD based track sensor developed for inter-satellite optical communication 18

Modelling and analysis of QPD based track sensor developed f...

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18th IEEE International conference on advanced Networks and Telecommunications systems, ANTS 2024

作者： Singh, Nirlesh Keshavappa, Ravikumar Senapati, Priyabrata Raha, Bijoy Praveen, K.C. Kalyani, K. Mittal, Sanjay Umesh, S.B. Karantha, S.P. Laboratory for Electro Optics Systems LEOS-ISRO Bangalore India

ISBN: (纸本)9798350391725

Free Space Optical Communication (FSO) is an emerging and promising technology that utilizes optical signals to transmit data through free space. It offers several advantages, including high data rates, low latency, security, and immunity to electromagnetic interference. FSO systems find applications in various fields, including telecommunications, satellite communication, disaster recovery, and military operations. Utilizing lasers for free space optical communication presents an appealing alternative to conventional radio frequency (RF) communication methods, particularly in managing substantial data volumes while simultaneously reducing size, weight, and power (SWaP) requirements. Among the formidable challenges in the realm of satellite-based optical communication systems lies the necessity for highly precise acquisition and tracking of target satellites. Quad Photodetector (QPD) tracking sensors stand out as the preferred choice for discerning angular positions due to their rapid responsiveness and excellent accuracy. In this paper, we expound upon the operational principles and mathematical modelling of QPD-based tracking sensors, aiming to attain optimal sensitivity and meet predefined field of view (FOV) specifications. Additionally, we elucidate the focusing technique, detail the testing setup, and present the achieved outcomes of the tracking sensor. © 2024 IEEE.

关键词： Photodetectors

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Hydrogen sensor based on Planar Polymer Bragg Grating coated with Pd/Ni composite film 12

Hydrogen sensor based on Planar Polymer Bragg Grating coated...

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conference on advanced sensor systems and applications xiI

作者： Zhou, Yong Hu, Wenbin Ye, Yongxin Wang, Jingjing Dai, Jixiang Yang, Minghong Hellmann, Ralf Kefer, Stefan Wuhan Univ Technol Natl Engn Res Ctr Fiber Opt Sensing Technol & Net Wuhan 430070 Hubei Peoples R China Aschaffenburg Univ Appl Sci Appl Laser & Photon Grp Aschaffenburg Germany

ISBN: (纸本)9781510657090;9781510657083

As one of the cleanest energy sources in the 21st century, the development of hydrogen energy has attracted the attention of all countries in the world, so the monitoring of hydrogen leakage has become a current research focus. This study demonstrates a novel hydrogen sensor that combines a planar polymer grating with a Pd/Ni hydrogen-sensitive material that takes advantage of the hydrogen-absorbing expansion properties of Pd to cause the central wavelength drift of Planar Polymer Bragg Grating (PPBG) by strain transformation. The experimental results show when the hydrogen concentration is 0.3%, 0.6% and 1%, the wavelength shift of the sensor is 50 pm, 85 pm and 110 pm respectively, and the response time is approximately 30 seconds. This hydrogen sensor has the advantages of high sensitivity, low cost and compact structure, leading to great potential in industrial applications.

关键词： PPBG Hydrogen sensor Pd/Ni composite film

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Fiber-optic airflow sensor for air quantity estimation in air conduit 11

Fiber-optic airflow sensor for air quantity estimation in ai...

引用

conference on advanced sensor systems and applications xi

作者： Zhang, Peng Wang, Shuang Jiang, Junfeng Li, Zhiyuan Yang, Haokun Liu, Tiegen Tianjin Univ Sch Precis Instrument & Optoelect Engn Tianjin Peoples R China Minist Educ Key Lab Optoelect Informat Technol Tianjin Peoples R China Tianjin Univ Tianjin Opt Fiber Sensing Engn Ctr Inst Opt Fiber Sensing Tianjin Peoples R China

ISBN: (数字)9781510646520

ISBN: (纸本)9781510646520;9781510646513

A metal diaphragm-based airflow sensor based on fiber-optic Fabry-Perot (F-P) interference has been proposed and experimentally demonstrated. The sensor is composed of glass sleeving, ceramic ferrule and metal diaphragm. Through data calibration, a practical airflow sensor has been fabricated. As a result of the stainless steel diaphragm and open F-P cavity, the durability of the sensor is ensured, and it can be used in poor air quality environments. Experimental results in the airflow field show that the sensor has the potential to estimate the air quantity of high-speed airflow in various air conduit.

关键词： Airflow Fabry-Perot interference Fiber-optic sensing

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Learning Topological Representation of sensor Network with Persistent Homology in HCI systems

Learning Topological Representation of Sensor Network with P...

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2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yan, Yan Li, Chengdong xiong, Jing Wang, Lei Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China Wenzhou Institute of Technology Wenzhou China

ISBN: (纸本)9798350337488

Hand gesture and movement analysis is a crucial learning task in Human-computer interaction (HCI) applications. sensor-based HCI systems simultaneously capture the information with multiple locations to track the coordination of different regions of muscles. Based on the fact that there exists a temporal correlation between the regions, the connectivity analysis of sensor signals builds a network. The graph-based approach for analyzing the sensor network has provided novel insight into the learning in HCI, which has not been broadly investigated in hand gesture recognition tasks. This work proposes a topological representation learning scheme as a graph-based approach for sensor network analysis. Through investigation of the topological properties with persistent homology, the spatial-temporal characteristics are well described to build recognition models. Experiments on the NinaPro DB-2, DB-4, DB-5, and DB-7 datasets with sensor networks built with sEMG signal and IMU signal demonstrate exceptional performance of the proposed topological approach. The topological features are effective in graph representation learning with sensor networks used in hand gesture recognition. The proposed work provides a novel learning scheme in HCI systems and human-in-the-loop studies. © 2023 IEEE.

关键词： body sensor networks gesture recognition human-computer interfaces topological data analysis topological machine learning

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Real-Time Monitoring and Control systems Using IoT-Integrated Wireless sensor Networks-Leveraging Machine Learning Algorithms for Enhanced Performance and Efficiency

Real-Time Monitoring and Control Systems Using IoT-Integrate...

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2024 International conference on Cybernation and Computation, CYBERCOM 2024

作者： Swaminathan, Subbiah Poongavanam, N. Arun, M. Pushparani, S. Muthukrishnan, A. Johri, Prashant Rajalakshmi Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai602105 India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600 078 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Avadi Chennai India School of Computer Application and Technology Galgotias University India

ISBN: (纸本)9798331542344

This paper introduces a developed real-Time monitoring and control system using Internet of Things (IoT) incorporated Wireless sensor Networks (WSNs) supported by machines learning algorithms. The system focuses at meeting core issues and questions in terms of productivity, reliability and timeliness within evolving settings. The current paper contributes to the ongoing research and development in the area of IoT and WSN to enable, always-on real-Time data gathering from a variety of discrete sensors which is invaluable for various applications including industrial, environmental and smart city applications. The heart of the system is based on the idea of utilising machine learning methods to acquire and elaborate sensor signals. More specifically, the deep learning models that are used include Long Short-Term Memory (LSTM) networks as well as Reinforcement Learning (RL) algorithms for predictive analytics, as well as anomaly detection. The work also entails a comparison of the system performance based on energy consumption and other factors such as latency, packet delivery ratio and even the accuracy of the predictions made by the system. The findings suggest improved utilisation within substantial ranges;decreased energy use by 30 per cent or more. 7%, latency decrease by 33. Although achieving this was accompanied by an increased data packet loss rate to 3%, the overall packet delivery ratio was boosted to 98%. Additionally, the system established overall accuracy rate of 95% with negligible error percentage. In conclusion, this research proves the applicability of incorporating the IoT and the advanced machine learning methods in the real-Time monitoring and control systems development to overcome cases of low efficiency and reduced performance in numerous real-world applications. © 2024 IEEE.

关键词： Predictive analytics

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Deep Learning for Spectrum Sensing of Sub-Nyquist Sampled Signals 20

Deep Learning for Spectrum Sensing of Sub-Nyquist Sampled Si...

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20th International conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Li, Shiyao Ji, Hongbing Li, Lin Lu, Jian School of Electronic Engineering Xidian University Xi'an China College of Mathematics and Statistics Shenzhen University Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen China

ISBN: (纸本)9798350356328

Traditional Nyquist rate sampling faces significant challenges as communication technology progresses towards 6G. Multi-coset sampling emerges as a viable solution by reducing the sampling rate. However, this method necessitates signal restoration before processing. In response, an intelligent spectrum management system has been developed that utilizes deep learning to directly extract semantic information from multi-coset sampled signals, eliminating the need for complex data preprocessing. The system integrates multi-band spectrum sensing and modulation recognition, achieving an impressive 99% accuracy in both modulation recognition of single signals and sub-band positioning of multiple signals. © 2024 IEEE.

关键词： Signal modulation

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