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High-efficient disturbance event recognition method of φ-OTDR utilizing region-segmentation differential phase signals

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APPLIED OPTICS 2022年第22期61卷 6609-6616页

作者： Huang, Yi Cheng, Shitong Li, Ying Chen, Xiaofeng Dai, Jingyi Hu, Chengyong Deng, Chuanlu Pang, Fufei Zhang, Xiaobei Wang, Tingyun Shanghai Univ Key Lab Specialty Fiber Opt & Opt Access Networks Joint Int Res Lab Specialty Fiber Opt & Adv Commu Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China

We demonstrate a disturbance event recognition method based on region segmentation, which utilizes differential phase signals of a phase-sensitive optical time-domain reflectometer (phi-OTDR) to recognize disturbance events efficiently. The long-haul sensing fiber is divided into subsensing regions;whereas the phase signals at the two end points of the subsensing regions are subtracted, unwrapped, and differenced to represent the disturbance information. Feature extraction and classification are performed separately on the subsensing regions datasets. The experimental results indicate that the average recognition accuracy of the region-segmentation-based event recognition method is up to 92.9%. Compared to the method without region segmentation, this proposed method improves the average recognition accuracy by 8%;whereas the recognition time of three disturbance events on a 14.8-km sensing system is only 0.39 s. The proposed method provides significant support for the development of disturbance event recognition of the phi-OTDR sensor system. (C) 2022 Optica Publishing Group

关键词： Feature extraction Fiber optic sensors Optical fibers Optical sensing Optical signals Signal processing

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ImageFed: Practical Privacy Preserving Intrusion Detection System for In-Vehicle CAN Bus Protocol 9

ImageFed: Practical Privacy Preserving Intrusion Detection S...

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9th IEEE International conference on Big Data Security on Cloud (BigDataSecurity) / 9th IEEE International conference on High Performance and Smart Computing (IEEE HPSC) / 9th IEEE International conference on intelligent Data and Security (IEEE IDS)

作者： Taslimasa, Hamideh Dadkhah, Sajjad Pinto Neto, Euclides Carlos Xiong, Pulei Iqbal, Shahrear Ray, Suprio Ghorbani, Ali A. UNB Fac Comp Sci Fredericton NB Canada Natl Res Council Canada Cybersecur Ottawa ON Canada Natl Res Council Fredericton New Brunswick NJ USA

ISBN: (纸本)9798350312935

In vehiclenetworks handle the communication among Electronic Control Units (ECUs) and sensors with a serial protocol called Controller Area Network (CAN), which takes advantage of a broadcast mechanism liar transferring data. However, the lack of authentication and encryption in CAN bus might lead to the vehicle being compromised by an untrusted agent. Meanwhile, AI -based Network Intrusion Detection Systems (NIDS) introduced in the literature require a centralized cloud server to undertake the resource -consuming task of processing network data, which also occupies the bandwidth while transferring data from vehicles to the remote server. Moreover, many car manufacturers avoid sharing information with the remote server to prevent further exposure of vehicles' private data. We utilize Federated Learning (FL) as a suitable solution to previous challenges, where data is trained locally on the vehicle side, and partially trained models are sent to the server. Nevertheless, private training inside the vehicle requires low memory utilization while achieving low latency and high performance compared to Centralized Learning (CL) approaches. In this study, we propose a practical privacy-preserving IDS (ImageFed) approach by employing federated Convolutional Neural Network (CNN). To evaluate the robustness of ImageFed in real -world applications, we investigate two scenarios that may cause performance degradation in FL, such as non-independent and identically distributed (non-iid) clients and scarcity of training data during the process. The resulting analysis proves the robustness of ImageFed while delivering an average 99.54% fl -score, 99.87% accuracy, and low detection latency.

关键词： Terms Internet of Vehicles (IoV) Security Intrusion Detection Systems (IDS) Federated Learning (FL) I)eep Learning (DL) Convolutional Neural networks (CNN)

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Application of Over-Sampling Techniques and Fuzzy ARTMAP to Condition Monitoring of a Power Generation System 3

Application of Over-Sampling Techniques and Fuzzy ARTMAP to ...

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IEEE 3rd International conference on Power Engineering Applications (ICPEA) - Shaping Sustainability Through Power Engineering Innovation

作者： Chang, Timothy Zhi Wei Tan, Shing Chiang Sim, Kok Swee Lim, Chee Peng Goh, Pey Yun Multimedia Univ Fac Engn & Technol Melaka Malaysia Multimedia Univ Fac Informat Sci & Technol Melaka Malaysia Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic Australia

ISBN: (纸本)9781665475013

Condition monitoring is a process of assessing the health status of a system, process, or machine. Monitoring and identifying any potential fault can be conducted by leveraging measurements from the installed sensors that provide information on the state of the system. In this respect, machine learning models are useful for processing and analyzing the sensor data for fault detection. However, the imbalanced nature of these sensory data can cause misleading high accuracy scores. In this study, we employ an over-sampling method to tackle the imbalanced class problem. Specifically, both Synthetic Minority Over-sampling Technique (SMOTE) and Gaussian SMOTE are used to generate minority class samples. The balanced data set is used by the Fuzzy ARTMAP (FAM) model for fault classification. The effectiveness of the developed method is evaluated using a real-world circulating water system in a power generation plant. The results indicate that both SMOTE variants can improve the performance of FAM in detecting faults corresponding to operating conditions of the circulating water system for efficient power generation.

关键词： Imbalanced data classification power systems condition monitoring SMOTE Fuzzy ARTMAP

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Study of Architectural Designs for Underwater Wireless sensors Network in View of Underwater Applications 7th

Study of Architectural Designs for Underwater Wireless Senso...

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7th International conference on information and Communication Technology for intelligent Systems, ICTIS 2023

作者： Shelar, Pooja A. Mahalle, Parikshit N. Shinde, Gitanjali R. Barot, Janki Smt. Kashibai Navale College of Engineering Savitribai Phule Pune University Pune India Vishwakarma Institute of Information Technology Savitribai Phule Pune University Pune India Silver Oak University Ahmedabad India

ISBN: (纸本)9789819937578

In recent years, the development of underwater applications has received greater attention from researchers and marine industries. These applications are Underwater Territory Surveillance, Water Quality Monitoring, Tsunami Detection, Enemy Submarine Detection, Mine Detection, Underwater Internet Cable Maintenance, Oil Spill Detection, and many more. These applications are proven to be beneficial in different fields starting from official government projects to business industries. The building block for underwater applications is Underwater Wireless sensor Network (UWSN). The reason researchers are attracted to UWSN is due to a number of benefits achieved from different underwater applications. The development process of underwater applications using UWSN includes a way defined by researcher groups, in which the first step is designing UWSN architecture. To develop a successful underwater application, it’s very important to pre-design an appropriate underwater wired or wireless network architecture. In this paper, our main aim is to discuss three basic types of underwater architecture and also to discuss different application-specific UWSN architectures developed in state-of-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Marine applications

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An Investigative Approach of Context in Internet of Behaviours (IoB) 2nd

An Investigative Approach of Context in Internet of Behaviou...

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2nd International conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2023

作者： Chavhan, Pranali G. Ratil, Ritesh V. Mahalle, Parikshit N. Vishwakarma Institute of Information Technology Pune India Pune Dist. Education Assn. College Of Engineering Pune India

ISBN: (纸本)9789819919451

In the context of language and communication, context refers to the circumstances or background information that surrounds a situation or event and helps to give it meaning. Context can include the time and place of an event, the people involved, the cultural and social norms, and the larger issues and events that may be related. Understanding the context of a situation or event can help to provide deeper insight and understanding. In the context of computing and technology, context can refer to the information and settings that are relevant to a specific task or application. In this context, context may include data, preferences, and other factors that help to shape the way a task or application operates. So for the interaction between humans to machines, we need to explore the concept of Context. So, Context means any information of a person, place, object, things, activity or entity. It has been used in almost all domains like Internet of Things, Internet of Behaviours, pervasive computing, intelligent environment, ubiquitous computing, etc. In such a domain to sense data according to the Behaviours of an entity and then adapting Behaviours or context information. There are various techniques for gathering data and then processing information. In embedded system, we can collect data using sensors and sensed data can be place, person, pattern, and activity of humans, so we need the type of context. So, in this paper, we explored the taxonomy of context in different scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ubiquitous computing

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Design and Research of Pilot Physiological Signal intelligent Analysis System Based on Deep Learning

Design and Research of Pilot Physiological Signal Intelligen...

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sensors, Electronics and Computer Engineering (ICSECE), IEEE International conference on

作者： Tao Liu Qian Wang Zuosong Zhao Yongjuan Li College of Aviation Foundation Naval Aviation University Shandong China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

A computer aided data processing system for pilot flight physiological index measurement device is developed. The idea of modularization is used to design the architecture of the software system. This paper uses the modern digital information processing theory and method. QRS wave detection, respiration rate detection, heart rate heterogeneity analysis, heart rate variation analysis and multi-parameter synchronization analysis were performed on the data collected by the pilot's flight physiological parameter acquisition device. The data processing module realizes the functions of reading, retrieving, output and input of original data. The data section mainly includes the identification of QRS wave group, arrhythmia, ST segment, heart rate and other indicators. The system includes waveform diagram and trend diagram. Report module generates and prints reports. The maintenance department is mainly responsible for the maintenance of equipment. The actual use of several departments shows that the system is simple, stable and functional. The system provides a reliable basis for aviation military doctors to know the physical condition of pilots.

关键词： Heart rate sensor fusion Data processing Software systems Physiology sensor systems Maintenance sensors Personnel Synchronization

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Research on GelSight materials and reconstruction algorithm based on visual touch

Research on GelSight materials and reconstruction algorithm ...

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Chinese Control and Decision conference, CCDC

作者： Peixin Niu Jingjin Shen School of Automation and Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Robot sensors play a crucial role in robotics. Through sensors, robots can sense and acquire environmental information to make intelligent decisions and act accordingly. Tactile sensors are particularly important because they enable robots to simulate human tactile perception and achieve high precision perception of surface details. As an innovative tactile sensor, GelSight sensor has the ability to capture and analyze the microstructure and deformation information of the surface of objects in real time, and has broad prospects for development. This high-resolution tactile perception provides the robot with a more accurate and comprehensive understanding of the environment, allowing the robot to interact with the environment more intelligently. However, the cost of GelSight sensor research is still relatively high, which limits its popularization in research and applications. To solve this problem, in this paper, we propose a low-cost version design scheme, and the use of carbonized particles to make the reflector. At the same time, the traditional photometric stereo algorithm is combined with the designed GelSight sensor hardware to explore the development prospect of this idea. The experimental results show that the low cost GelSight sensor version design and the new height reconstruction method are feasible and effective. These research results provide a new idea and method for further exploration and application of GelSight sensing technology, and have important theoretical significance and practical value for promoting the development of mechanical science and technology.

关键词： Mechanical sensors Visualization Costs Accuracy Silicon carbide Stereo image processing Tactile sensors

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Internet-of-Things (IoT)-Based Parking Prototype System

Internet-of-Things (IoT)-Based Parking Prototype System

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Advancement in Electronics & Communication Engineering (AECE), International conference on

作者： Lovely Mae Dagsa Christopher J. Agosila Jeffrey T. Dellosa Department of Electronics Engineering Caraga State University Butuan City Philippines Electronics Engineering Dep’t CRAFT Caraga State University Butuan City Philippines MREC Ateneo de Davao University Davao City Philippines

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

The growing number of automobiles has resulted in significant congestion in urban parking areas. This study tackles the problem by developing a car park monitoring system prototype using Internet of Things (IoT) technology to help drivers locate available parking spaces in real-time. The prototype utilizes HMC1021Z Magneto Resistive sensors and US-100 Ultrasonic Sonar sensors for vehicle detection. These sensors, along with UHF-Ex data transceivers and Gizduino microcontrollers, create a wireless network that monitors parking spaces. Real-time parking information is displayed via a Raspberry Pi with an IR-based touchscreen reservation system at the parking lot entrance. Testing demonstrated effective communication between sensor nodes, the sink node, and the gateway. To improve sensor range and power efficiency, the study recommends using magnetic sensors with greater distance sensing capabilities and incorporating solar-powered sensor nodes.

关键词： Wireless sensor networks Microcontrollers Magnetic sensors Prototypes Real-time systems Acoustics Internet of Things Automobiles intelligent sensors Monitoring

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Service Placement Problem for IoT Applications on Fog Computing 23th

Service Placement Problem for IoT Applications on Fog Comp...

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23rd International conference on intelligent Systems Design and Applications, ISDA 2023

作者： Ben Rjeb, Hanene Zorgati, Hela Ben Djemaa, Raoudha Sliman, Layth University of Sousse Higher Institute of Computer Science and Communication Techniques of H. Sousse Sousse Tunisia University of Sfax Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia Engineering School of Information and Digital Technologies EFREI Paris Paris France

ISBN: (纸本)9783031647758

Every day, there is a significant rise in the number of connected objects, such as smart devices, sensors, and other IoT-enabled devices. Furthermore, many IoT applications heavily rely on real-time data processing, facing substantial latency when communicating with centralized Cloud servers, mainly attributed to the substantial geographical separation between IoT devices and Cloud servers. Therefore, Fog Computing (FC) can be a suitable paradigm to overcome the limitations of Cloud-based solutions and address the challenges posed by the massive growth of IoT devices. In this context, the service placement problem (SPP) pertains to selecting suitable Fog resources and allocating services to these resources. However, providing an efficient solution to this problem is challenging due to difficulties such as the system’s dynamism. This work presents a survey on the SPP for IoT applications on FC. To this end, we first introduce some basic concepts related to the SPP in Fog, emphasizing the importance of this paradigm. Next, we propose a classification for the solutions that have been proposed to solve the SPP for IoT applications over fog nodes and compare these recent works based on performance parameters such as latency, cost, etc. Finally, we conclude this paper with a discussion of future issues and challenges and a suggestion for improvements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fog computing

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CM-ASAP: Cross-Modality Adaptive Sensing and Perception for Efficient Hand Gesture Recognition

CM-ASAP: Cross-Modality Adaptive Sensing and Perception for ...

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IEEE conference on Multimedia information processing and Retrieval (MIPR)

作者： Soheil Hor Mostafa El-Khamy Yanlin Zhou Amin Arbabian SukHwan Lim Electrical Engineering Department Stanford University Stanford CA Device Solutions Research America Samsung Semiconductor Inc. San Diego CA

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

Hand Gesture Recognition (HGR) is an important component of multimedia solutions and human-computer interaction systems. Utilizing multimodal information, e.g. from both the camera and depth sensors, to improve the recognition accuracy, usually comes at the cost of additional power consumption. In this paper, we introduce a novel framework for cross-modality adaptive sensing and perception (CM-ASAP) using multimodal Deep Neural networks (DNNs). Inspired by human cognition and multimodal perception, our proposed framework dynamically allocates computation and sensing resources between different modalities to optimize the trade-off between efficiency and accuracy of multimodal HGR DNNs. CM-ASAP accounts for the dominance of sensor power consumption over computational costs, as well as for the variance in the effectiveness of modality fusion. Furthermore, our proposed CM-ASAP takes advantage of potential early gesture classification based on initial frames.

关键词： Human computer interaction Adaptive systems Accuracy Power demand Multimedia systems Gesture recognition information processing sensor fusion sensors Computational efficiency

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