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2nd IFAC Workshop on Aerospace Control Education (WACE)

作者： Bleier, Michael Julius Maximilians Univ Wurzburg Comp Sci Robot 17 D-97074 Wurzburg Germany

This paper describes the sensor Cube, an open-source hardware and software system for hands-on learning of sensor data processing. Various sensors, such as camera, GNSS, and inertial sensors, are included to teach sensor data analysis and processing. The sensor system is designed to work with popular operating systems without special drivers, and the data format is human-readable. Therefore, all sensor data is easily accessible through Python or C++ code without the need to learn an additional framework. In addition, the hardware design, firmware, and code examples for working with the sensor data are available as open-source. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： sensor systems sensor data processing Education Teaching hardware Lab experiments

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sensor Cube – A Tool for Hands-on Learning of sensor data processing

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IFAC-PapersOnLine 2024年第16期58卷 23-28页

作者： Michael Bleier Computer Science XVII: Robotics Julius-Maximilians-Universität Würzburg (JMU) D-97074 Würzburg Germany

关键词： sensor systems sensor data processing Education Teaching hardware Lab experiments

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NETREACT: Distributed Event Detection in sensor data Streams with Disaggregated Packet processing Pipelines 8

NETREACT: Distributed Event Detection in Sensor Data Streams...

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8th IEEE International Conference on Network Softwarization (NetSoft) - Network Softwarization Coming of Age - New Challenges and Opportunities

作者： Gyorgyi, Csaba Kecskemeti, Karoly Mallouhi, Hiba Voros, Peter Laki, Sandor Eotvos Lorand Univ Budapest Hungary

ISBN: (纸本)9781665406949

A new phenomenon called in-network computing has recently emerged with the aim of offloading calculations beyond the traditional task of packet forwarding to network switches. One of the most studied in-network computing applications is processing of sensor data streams. Existing works such as FastReact focus on solving this problem using flexible SmartNICs. In this paper, we propose NETREACT: an improved ASIC-oriented design for distributed event detection in sensor data streams to achieve a disaggregated processing pipeline. In contrast to existing approaches, NETREACT distributes the event detection task among a set of switches while leveraging the capabilities of the Intel Tofino platform in terms of boosting throughput and reducing latency. The proposed event-rule disaggregation method has the advantage of overcoming the hardware resource constraints and improving the overall network performance.

关键词： P4 in-network computing pipeline disaggregation event detection sensor data processing

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data Quality in IoT Temperature sensor Systems: Demonstrated on Time-Dependent Temperature Fluctuations

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IEEE sensorS JOURNAL 2024年第16期24卷 25960-25971页

作者： Ruhland, Tim Tobola, Andreas Scholl, Christoph Luebke, Maximilian Franchi, Norman Friedrich Alexander Univ Erlangen Nurnberg FAU Chair Elect Smart City Syst D-91058 Erlangen Germany Siemens AG Technol D-91058 Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Chair Elect D-91058 Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Chair Machine Learning & Data Analyt D-91058 Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Chair Elect Smart City Syst ESCS D-91058 Erlangen Germany

The growing integration of the Internet of Things (IoT) in Industry 4.0 is primarily attributed to its low cost and high adaptability. This article addresses the expansive application of IoT temperature sensor systems within industrial environments characterized by harsh conditions and rapid temperature fluctuations. Typically, these sensors lack awareness of the data quality they measure and provide. This article mitigates this limitation by introducing data quality models that enhance the accuracy and timeliness of temperature sensor data. These models interpret temperature readings affected by thermal lag and delayed response times. Timeliness is determined using three distinct methods that reflect data volatility, while accuracy is assessed by applying the Savitzky-Golay filter, which provides temperature transients. Both data quality models precede a knowledge-based approach, accumulating necessary information for the sensor system through calibration. These quality categories are combined into a unified quality of sensing (QoS) parameter, utilizing fuzzy logic to abstract the quality dimensions, thereby having profound implications for the IoT sensor system. This methodology has been encapsulated within a comprehensive framework and validated using an industrial-grade IoT temperature sensor in a real-world measurement setup. By transcending the conventional reliance on raw sensor outputs, our framework empowers the extraction of intrinsic quality information associated with each temperature reading, enhancing the resilience and reliability of IoT temperature sensor systems.

关键词： data fusion data quality fuzzy logic Internet of Things (IoT) sensor data processing temperature sensors data fusion data quality fuzzy logic Internet of Things (IoT) sensor data processing temperature sensors

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On sensor data Clustering for Machine Status Monitoring and Its Application to Predictive Maintenance

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IEEE sensorS JOURNAL 2023年第9期23卷 9620-9639页

作者： Oliosi, Eleonora Calzavara, Gabriele Ferrari, Gianluigi Univ Parma Dept Engn & Architecture Internet Things Lab IoT Lab I-43121 Parma Italy Ammagamma Srl I-41121 Modena Italy

Predictive maintenance is one of the main approaches on which Industry 4.0 is based since it aims at reducing unplanned downtime and maintenance costs of industrial machines. In this work, a time-aware clustering-based approach to the analysis of sensor data is presented for the purpose of monitoring the time evolution of the health status of an industrial machine. A possible application of the proposed framework to predictive maintenance is then proposed. As a relevant representative application scenario, the focus is on one of the key machines in a pharmaceutical plant: a freeze dryer. The illustrated procedure allows for carrying out a time segmentation of the properly sensed data. More precisely, the corresponding operational points (associated with features of the sensed data) are clustered using various algorithms, among which density-based spatial clustering of applications with noise (DBSCAN) turns out to be the best. The benefits of the proposed approach are: 1) its general nature and 2) the limited amount of needed features that have to be extracted from a single sensor signal. The proposed procedure is attractive when the collected data (e.g., from a single sensor) are not sufficient to build an accurate physical model of the monitored component.

关键词： sensors Electron tubes Monitoring Valves Predictive maintenance Feature extraction Cleaning Clustering density-based spatial clustering of applications with noise (DBSCAN) predictive maintenance sensor data processing

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Framework for Heterogeneous sensor data Stream Management with BIM and WSN

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2023年第3期36卷 1082-1094页

作者： Pinarer, Ozgun Galatasaray Univ Dept Comp Engn Istanbul Turkiye

By the 2010s, BIM is highly popular research area that gathers researchers from various domains. We tackle the major challenge of BIM and the main barrier for the application of BIM in smart environments: integration of BIM and wireless sensor networks. Existing studies in the literature have various dependencies and propose limited solutions. These solutions are dedicated to specific applications and domains. However, it is highly required to have a generic solution for real time indoor and outdoor environmental monitoring with full integration with the digital model of the building. To fulfill this need, in this study, we propose a novel approach to monitor and fulfill user requirements by a numerical model of the building, a novel framework and a BIMWSN integrated architecture based on the BIM principles for smart building environments. All the experiments are performed on a real testbed, physical sensor devices deployed on the classrooms of the university building. Such numerical model presents the digital model of the environment by gathering acquired data. Besides, data is gathered on a big data platform where it may be served to various other applications such as machine learning studies.

关键词： Smart environment automation systems Smart building sensor data processing Building information modeling Model transformation

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data-driven Modelling of an Indirect Photoacoustic Carbon dioxide sensor

Data-driven Modelling of an Indirect Photoacoustic Carbon di...

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25th IEEE Applied Sensing Conference (IEEE APSCON)

作者： Srivastava, Ananya Sharma, Pranav Sikora, Axel Bittner, Achim Dehe, Alfons Hahn Schickard Gesell Villingen Schwenningen Germany Albert Ludwigs Univ Freiburg Georg H Endress Chair Smart Syst Integrat IMTEK Freiburg Germany Hsch Offenburg Inst Reliable Embedded Syst & Commun Elect ivESK Offenburg Germany

ISBN: (纸本)9798350317275

This study aims to analyze a novel indirect photoacoustic sensor (PAS) using Machine Learning techniques. The studies focus on understanding the sensor's repeatability, the influence of temperature and humidity on microphone output voltage, and the applicability of Machine Learning models to accurately describe the sensor's behavior. To describe the sensor behavior, two studies are carried out in a controlled setting. With a R2 score of 0.964 between the microphone voltage and the gas concentration in ppm, the first study illustrates the sensor's repeatability for concentration measurements. The second study looks at how temperature and humidity affect microphone output voltage. For this study, R2 score of 0.948 is obtained. The studies underscore the necessity for further investigation of the sensor under diverse testing conditions. The findings demonstrate that the sensor exhibits consistent behavior and can be effectively modeled using Machine Learning techniques.

关键词： Machine Learning photoacoustic gas sensor sensor data processing parameter trend analysis Savitzky-Golay-Filter

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Proposal of Descriptive Approach for XR Device sensor data Handling and Compatibility 14

Proposal of Descriptive Approach for XR Device Sensor Data H...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Son, Yeseul Jeong, Heeyoon Yang, Yoonmo Choi, Kwang-Soon Korea Electronics Technology Institute VR/AR Research Center Seoul Korea Republic of

ISBN: (纸本)9798350313277

This paper presents an approach to address the challenges of handling sensor data and compatibility issues arising from the rapid advancement of Extended Reality (XR) technology and the diverse range of XR devices in the market. In order to overcome the diversity and development challenges of XR devices, the concept of device profiling is introduced. This aims to provide XR application developers with a standardized method to define and manage sensor data for each device, thus offering *** XR device profile consists of a device ID and sensor properties, where the latter includes sensor ID, type, data format, and characteristics. This information is expressed in JSON format, which offers various advantages, such as maintaining data structure consistency, hierarchical data representation, resolving language dependencies, readability, and ease of maintenance and customization. This approach of device profiling proposed in this paper offers a consistent and flexible way to handle the diverse sensor data of XR devices, thereby reducing developers' challenges and addressing compatibility issues. Furthermore, this profiling approach is expected to promote unity within the XR ecosystem and facilitate faster development and innovation in XR technology. We anticipate that future research and collaboration will further enhance sensor data management in XR devices and improve the developer experience. © 2023 IEEE.

关键词： Compatibility Issues Developer Experience Device Profiling Extended Reality (XR) sensor data processing

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Interactive Feature Extraction Network for Target Intention Recognition

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IEEE sensorS JOURNAL 2025年第3期25卷 4850-4868页

作者： Zuo, Zhen Wu, Peng Sun, Xiaoyong Tong, Xiaozhong Guo, Runze Huang, Honghe Su, Shaojing Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

Conventional intention recognition for a group of sea-surface targets mainly focuses on the short-term state of motion without considering the long-term series of actions and collaborative relationships, which ignores the dynamic and temporal variations in targets. In this article, we propose a cell-averaging constant false alarm rate (CA-CFAR) processor with parameter optimization based on the particle swarm optimization (PSO) algorithm to suppress sea clutter and obtain a target position. Then, we propose an interactive feature extraction network based on a graph neural network (GNN) to effectively improve the tactical intention recognition of sea-surface targets, which can be used with unmanned surface vehicles (USVs). First, the motion state sequences of the targets and USVs are encoded by a transformer using relative position information to obtain a high-dimensional feature. Second, an action difference feature extraction module (ADFEM) is designed to extract the salient motion features between targets and USVs, which reflects how the targets react to the USV movements. Third, the intent recipient feature extraction module (IRFEM) is proposed to extract the assigned subtask features of the targets to reflect which USV is the intended recipient of each target's actions. Then, the collaborative feature extraction module (CFEM), based on a GNN, is designed to dynamically extract the cooperative features of targets using dynamic attention from a long-term sequence of actions, which reflects the way that targets cooperate to accomplish a certain intention. We present the first public benchmark dataset for testing the performance of methods to recognize the intention of a group of sea-surface targets. Experimental results indicate that the accuracy (Acc), precision (Pre), recall (Rec), the F1-score (F 1) of the proposed method are 0.9666, 0.9675, 0.9666, and 0.9668, respectively, which demonstrates the high robustness of the proposed method. The proposed method and d

关键词： Target recognition Feature extraction sensors Vectors Collaboration Pattern recognition Accuracy Graph neural networks Long short term memory Vehicle dynamics Action difference feature collaborative feature graph neural network (GNN) group target intention recognition intent recipient feature marine radar sensor sensor data processing

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Advanced self-sensing road stud: integrating deep learning-based speed detection and energy harvesting

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SMART MATERIALS AND STRUCTURES 2025年第1期34卷 015025-015025页

作者： Aslam, Touqeer Azam, Ali Ahmed, Ammar Mugheri, Shoukat Ali Zhang, Zutao Abdelrahman, Mansour Ali, Asif Tairab, Alaeldin M. Zhou, Qiqi Xiong, Bendong Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Yibin Res Inst Yibin 64000 Peoples R China Chengdu Technol Univ Chengdu 611730 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

This study introduces a self-powered and self-sensing vehicle speed detection sensor, representing a significant advancement in transportation. The system employs mechanical components like a slider crank, bevel gears, and one-way bearings for unidirectional rotation, converting translational motion into electrical energy upon the impact of vehicle tyres on road studs. The electrical power generation module, including a DC generator, rectifier, and battery circuit, captures and stores this energy. In addition to energy harvesting, the system integrates a deep learning model using long short-term memory (LSTM) networks to precisely calculate vehicle speed from the displacement signals of the road studs. Displacement data from an ultrasonic distance sensor (SR-04) is processed and fed into the LSTM network, achieving a classification accuracy of 98.90% for vehicle speed categories of low, medium, high, and overspeed. A mathematical model and MATLAB Simscape simulations were developed, followed by experimental validation using a mechanical testing and sensing system under laboratory conditions. Lab-scale testing, a maximum output power of 3.72 W and an efficiency of 62.7% were recorded at 8 Hz. Field tests were performed at various vehicle speeds. A peak voltage output of 10 V was recorded for a single phase of a three-phase DC generator at 15 km h-1. The displacement sensor beneath the road stud was used to record the relative time signal between adjacent peaks to calculate vehicle speed. The sensor is sustainable in energy and easily installable without infrastructure changes, enhances transportation efficiency, and is useful for traffic management, road safety, and smart transportation networks.

关键词： smart transportation deep learning sensor data processing self-powered self-sensing mechanical energy harvester

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