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Distributed systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： Muhammad Puzhakkalaveettil M. Praveena Department of Computer Science Dr.S.N.S Rajalakshmi College of Arts and Science Coimbatore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Wireless sensor networks (WSNs) are crucial for a wide range of applications. However, their open architecture and limited resources make them vulnerable to intrusion. Improve the security of WSNs by incorporating advanced deep learning methods, including RNNs, Autoencoders, and Convolutional Neural Networks (CNNs). When it comes to identifying intrusions, CNNs need to analyze spatial patterns in data, while RNNs are responsible for managing temporal relationships in sequential data. Autoencoders are commonly used in anomaly detection to identify typical data patterns. By training on preprocessed sensor data, these deep learning models ensure network availability and data integrity. They enable the immediate detection and handling of potential dangers. Evaluation metrics denote that in simulated assault scenarios, CNNs, RNNs, and autoencoders have achieved impressive accuracy rates of 95.2%, 97.8%, and 97.5%, respectively which showcases their effectiveness when compared to previous techniques. The proposed design emphasizes adaptability and resilience, offering WSNs robust protection against emerging cyber threats.

关键词： Deep learning Wireless sensor networks Adaptation models Accuracy Autoencoders Real-time systems Convolutional neural networks Protection Anomaly detection Resilience

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A High Range-to-Resolution Multiaxis μForce and Torque Sensing Platform

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2021年第4期26卷 1837-1845页

作者： Tiwari, Bhawnath Blot, Melissa Laurent, Guillaume J. Agnus, Joel Sandoz, Patrick Lutz, Philippe Clevy, Cedric Univ Bourgogne Franche Comte CNRS FEMTO ST Inst F-25000 Besancon France

This article presents a novel multiaxis mu Force sensor with a range-to-resolution ratio of 55 000, which makes the sensing device highly useful for wide range of applications. The presented device relies on a sensing strategy where a 2-D encoded micropattern is encrypted to the mobile part of the compliant platform. This encoded pattern allows multiaxis visual sensing of displacements over a long range, whereas Fourier computations ensure subpixel interpolation leading to the high resolution. The device is fabricated on a silicon wafer by means of clean room technology to comply with both precision encoding requirements and reliable mechanical behavior. This article presents the design, modeling, and fabrication as well as the experimental validation of the multiaxis mu Force-Torque sensor. A modeling strategy is proposed for an effective estimation of force/torque with its capability to adapt nonlinear stiffness evolution during the external loading. Experimentation demonstrates a sensing resolution of 2 mu N over a validated range of 110 mN. The proposed sensor measures the force along the two planar axes, and also the torque along the axis orthogonal to the plane, thus, validating its axial decoupling capabilities.

关键词： Force measurement force sensors instrumentation and measurement materials testing microsensors nanopositioning optoelectronic and photonic sensors periodic structures sensor systems and applications

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Neural Network Technology of Mine Recognition Based on Data From Magnetic Field sensors

Neural Network Technology of Mine Recognition Based on Data ...

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IEEE International Workshop on Intelligent Data Acquisition and advanced Computing systems: Technology and applications

作者： Roman Peleshchak Vasyl Lytvyn Ivan Peleshchak Serhii Voloshyn Department of Information Systems and Networks Lviv Polytechnic National University Lviv Ukraine

Mine detection is an ongoing and growing problem that affects many people around the world due to the enormous danger that mines pose to humans. Mine detection occurs using various methods that use active sensors, including ultra-wideband (UWB) radar. Although active landmine sensors are often used for performance reasons, they can cause unintended landmine detonations because they operate on transmitted and reflected electromagnetic signals. Another way to detect mines without detonating mechanisms is the use of passive detectors. In this article, an optimal neural network structure with Adam's optimization algorithm is developed for effective mine classification based on magnetic field sensor data. This approach made it possible to increase the accuracy of mine detection to 99.23 percent.

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Selective Surface Metallization of Single Crystal Silicon Nanowires via Stencil Lithography 18

Selective Surface Metallization of Single Crystal Silicon Na...

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18th IEEE Nanotechnology Materials and Devices conference, NMDC 2023

作者： Ali, B. Karimzadehkhouei, M. Esfahani, M.N. Leblebici, Y. Alaca, B.E. Koç University Department of Mechanical Engineering Sariyer Rumeli Feneri Yolu Istanbul34450 Turkey Koç University n2STAR-Koç University Nanofabrication and Nanocharacterization Center for Scientific and Technological Advanced Research Department of Mechanical Engineering Sariyer Rumeli Feneri Yolu Istanbul34450 Turkey University of York School of Physics Engineering and Technology YorkYO10 5DD United Kingdom Rectorate Sabanci University Istanbul Tuzla34956 Turkey Microelectronic Systems Laboratory LausanneCH-1015 Switzerland Koç University Department of Mechanical Engineering Istanbul Sariyer34450 Turkey

ISBN: (纸本)9798350335460

Surface metallization of Silicon Nanowires (Si NWs) is a critical preparation technology for surface functionalization in biochemical sensing applications. Conventional metallization approaches rely on resist lift-off or non-selective protocols, which can lead to contamination and device damage. This work demonstrates a selective and resist-free surface metallization of suspended Si NW in a 3D device architecture via gold stencil lithography (SL). The approach involves control of the stencil-device gap via a thickness-controlled spacer, fabricated onto the stencil, to characterize and minimize geometrical blurring. The approach also prevents potential stencil-device contact-induced damage to suspended Si NW during SL. A significant improvement in spatial resolution for selective metallization onto NW is achieved compared to the state-of-the-art. Additionally, using the proposed approach with spacer thickness control, miniaturization of the stencil aperture dimensions to nanoscale raises the potential for selective metallization of Si NW down to sub-1 μ m in-plane spatial resolution for highly sensitive biochemical sensor applications. © 2023 IEEE.

关键词： Nanowires

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Enhancing Search and Rescue Efforts During CSSR Operations Using Drone Mounted Rover System

Enhancing Search and Rescue Efforts During CSSR Operations U...

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Smart systems for applications in Electrical Sciences (ICSSES), International conference on

作者： Sayed Mohamed Saqib Nupoor Kumari Pratibha Pankaj Mishra Deepak Ch School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh India School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

In Collapsed Structures Search and Rescue (CSSR) operations, rapid and efficient survivor detection and safe navigation through hazardous environments are crucial. Traditional methods, such as manual probing and canine searches, are slow, limited, and risky. This is addressed through a comprehensive integrated drone and rover system where the rover is mounted on a drone. Equipped with advanced sensors and AI-driven capabilities, flying drones and deployable rovers utilize Light Detection and Ranging (LiDAR) for 3-Dimensional (3D) terrain mapping, Ground Penetrating Radar (GPR) for locating bodies beneath debris, thermal imaging to distinguish living people from dead, optical cameras for live video stream, and microphone arrays for voice detection. A dashboard with an integrated AI system processes real-time sensor data, offering precise situational awareness, hazard identification, survivor classification, and optimized rescue paths. Drones provide aerial surveys and path mapping, while rovers navigate confined areas to relay detailed environmental and human presence data. The AI generates hazard maps, suggests extraction routes, and prioritizes survivors, all the data collected is relayed in real time to ground rescue teams. Two-way communication with victims further enhances response efficacy. The proposed system revolutionizes CSSR by enhancing speed, precision, and safety, minimizing human risk while maximizing successful victim extractions.

关键词： Image sensors Laser radar Navigation Thermal sensors Streaming media sensor systems Real-time systems Optical sensors Drones sensor arrays

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Review and application of Edge AI solutions for mobile collaborative robotic platforms 55

Review and application of Edge AI solutions for mobile colla...

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55th CIRP conference on Manufacturing systems, CIRP CMS 2022

作者： Ramasubramanian, Aswin K. Mathew, Robins Preet, Inder Papakostas, Nikolaos Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical and Materials Engineering University College Dublin Ireland CeADAR-Ireland's Center for Applied AI University College Dublin Ireland

Edge AI technologies have emerged as a powerful solution for gathering and processing data in real-time, supporting the development of advanced applications for process monitoring, planning and control. Edge AI devices coupled with different sensory systems can be used for facilitating the synergetic human-robot collaboration at the shop floor level. This paper reviews edge AI devices and solutions that may potentially support the operation of cells or workstations comprising human operators and mobile robotic platforms. The use of edge AI-capable devices for operator tracking in real-world applications and the potential of edge AI technologies towards developing and deploying process digital twins are discussed. The main goal is to investigate how edge AI solutions may improve the performance of processes where a collaborative mobile manipulator works alongside a human operator in a safe and reliable manner. Lastly, the use of advanced communication frameworks, such as the Robot Operating System (ROS), is discussed in the context of supporting the seamless interaction between a collaborative mobile platform and Edge AI devices. © 2022 The Authors. Published by Elsevier B.V.

关键词： Collaborative Manipulators Control Robot sensor

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advanced GPS Tracking System for Pets

Advanced GPS Tracking System for Pets

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Smart systems for applications in Electrical Sciences (ICSSES), International conference on

作者： Sayujaya Sharma Amit Kumar Aryal Aanchal Singh Sweata Kumari Rauniyar Babasaheb Satpute Department of Computer Science and Engineering Jain (Deemed-to-be University) Bengaluru India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

In today’s perceptive, pets have become a valuable member of a family. However, the prevalence of pet lost poses a significant risk, often attributed to carelessness of parents/owners. To address this, a proposed system utilizing a microcontroller (Arduino), a GPS (Geo Positioning System) Module, a GSM Module and a Pulse (Heart beat) sensor aims to augment safety for both pets and parents/owners. The system’s core relies on the GPS module’s capability to detect the location of pets when it is lost or escaped. If the pet escapes beyond the geo-fencing area, it collects the location (in the form of latitude and longitude) and pulse (heart beat) rate with the help of GPS module and Pulse sensor respectively and sends those details to the owner with help of GSM module, later on the owner map those co-ordinates into real time map. This innovative approach seeks to proactively address the problem of losing pets, mitigating pet losing/dying risks and fostering a secure and healthy pet. Its implementation holds promise in significantly reducing pet losing problem, ensuring safer and healthier pet keeping and adoption.

关键词： GSM Tracking Heart beat Animals Real-time systems Safety Biomedical monitoring

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TSV-MAC: Time Slot Variable MAC Protocol Based on Deep Reinforcement Learning for UASNs 17th

TSV-MAC: Time Slot Variable MAC Protocol Based on Deep Reinf...

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17th International conference on Wireless Algorithms, systems, and applications (WASA)

作者： Wu, Yuchen Liu, Yao Zhao, Zhao Liu, Chunfeng Qu, Wenyu Tianjin Univ Tianjin Key Lab Adv Networking Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9783031192104;9783031192111

With the increasing variety and number of ocean applications, the underwater transmission of heterogeneous ocean data has become a hot spot in the research field of underwater acoustic sensor networks (UASNs). However, due to lack of flexibility in time slot allocation, the existing multiple access control (MAC) protocols for UASNs cannot be effectively applied to the transmission of heterogeneous ocean data. In order to solve the above problem in UASNs with heterogeneous ocean data, we propose a time slot variable MAC protocol (TSV-MAC) based on deep reinforcement learning. In TSV-MAC, the long short term memory (LSTM) deep learning model is constructed and is trained by considering the usage efficiency of time slots and the data collection condition of underwater nodes. Then, the trained LSTM model is applied to predict the generation and transmission of data from each underwater node and a Q-learning model is adopted to allocate a suitable number of time slots to underwater nodes. The TSV-MAC protocol periodically updates the time slot allocation table, to enable UASNs to adapt the different data packets which are dynamically generated. Finally, the effectiveness of the protocol is verified by extensive simulation results.

关键词： Underwater acoustic sensor networks Multiple access control protocol Deep reinforcement learning Time slot

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Two stage lock in amplification for improving magnetic data acquisition systems using Quantum Well Hall Effect sensors 60

Two stage lock in amplification for improving magnetic data ...

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60th Annual conference of the British Institute of Non-Destructive Testing, NDT 2023

作者： Murshudov, Ruslan Sexton, James Lindley, Alexander Missous, Mohamed Advanced Hall Sensors Ltd Manchester Greater ManchesterM17 1RW United Kingdom School of Engineering University of Manchester United Kingdom

ISBN: (纸本)9781713874836

Quantum Well Hall Effect (QWHE) sensors are a type of magnetic field sensor with previously demonstrated applications in NDT imaging systems. These sensors are typically biased in the 10-100kHz frequency range to avoid both 1/f noise and magnetic pickup. The signal is then further amplitude modulated by the measured magnetic field. Traditionally, this magnetic signal's amplitude is then extracted through Fast Fourier Transforms (FFT). With this traditional method however, most of the measured signal consists of unwanted pick up, and large unchanging offset values resulting from both the circuit and any measurements, with the actual measured signal making up less than 1% of the ADC dynamic range. By designing a two-stage lock in amplifier, the signal is demodulated with respect to both the biasing signal and the AC magnetic signal, leaving only the DC component corresponding to the amplitude of the magnetic signal. The offset from this can then be subtracted, and further gain can be applied, allowing the full dynamic range of the measurement ADC to be utilised, while only measuring the changes in magnetic field strength resulting from the presence of any flaws in the measurement sample. This theoretically allows for smaller signals to be measured than is possible with the FFT method, provided that the signals are not lower than the noise floor of the measurement circuitry. An example of both FFT and early prototype lock in amplifier demodulation is demonstrated in this work. This method of signal demodulation also has the added benefit of decoupling the measurement parameters from the frequencies used in the biasing and magnetic signals, allowing for arbitrarily slow sample rates to be used in measurement. © 60th Annual conference of the British Institute of Non-Destructive Testing, NDT 2023.

关键词： Semiconductor quantum wells

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Bibliometric review of smart sensor for health monitoring

Bibliometric review of smart sensor for health monitoring

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International conference on Green Energy, Computing and Intelligent Technology 2024 (GEn-CITy 2024)

作者： Christian V. Halim Murti Marinah Dinda A. I. Shafira Muhammad Y. Dwiyantara Imam Abadi Department of Physics Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9781837242863

Smart sensors and medical health technologies are becoming increasingly popular due to their potential for real-time, non-invasive health monitoring and diagnostics. With the integration of Artificial Intelligence (AI), these devices can track vital signs and important biomarkers, supporting the management of chronic diseases. This study employs bibliometric analysis using tools like VOSviewer to map research trends in this domain, identifying collaborations, emerging keywords, and contributions from institutions and countries. Findings indicate a significant increase in publications and citations, especially after 2010, highlighting influential journals such as IEEE sensors Journal. The primary focus is on health monitoring applications and advancements in sensor technology, with an increasing use of terms like "machine learning" and "data analytics." These findings provide insights into future research directions and technological innovations that impact medical health applications. It is recommended that future development integrate advanced data processing techniques and apply sensors in infrastructure and smart city environments, aiming to create systems that are more responsive, efficient, and sustainable.

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