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Power Electronics, Computer applications (ICPECA), IEEE International conference on

作者： Lasitha Jayakody Chamathka Edirisinghe Tharush Senasinghe Shaminda Dissanayake Narmada Gamage Dinithi Pandithage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Malabe Sri Lanka

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires.

关键词： Temperature sensors Technological innovation Wildfires Prevention and mitigation Forestry sensor phenomena and characterization sensor systems Real-time systems Gas detectors Resilience

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Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

Gaussian Process Position-Dependent Feedforward: With Applic...

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17th IEEE International conference on advanced Motion Control (AMC)

作者： van Haren, Max Poot, Maurice Kostic, Dragan van Es, Robin Portegies, Jim Oomen, Tom Eindhoven Univ Technol Dept Mech Engn Control Syst Technol Eindhoven Netherlands ASM Pacific Technol Ctr Competency Beuningen Netherlands Eindhoven Univ Technol Dept Math & Comp Sci CASA Eindhoven Netherlands Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands

ISBN: (纸本)9781728177113

Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder.

关键词： Training Motion planning sensor placement Wires Training data Gaussian processes Feedforward systems

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A Paper-Based Keyboard Using ArUco Codes: ArUco Keyboard

A Paper-Based Keyboard Using ArUco Codes: ArUco Keyboard

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Human Computer Interaction Thematic Area conference Held as Part of the 24th International conference on Human-Computer Interaction (HCII)

作者： Toker, Onur Karaman, Bayazit Demirel, Doga Florida Polytech Univ Lakeland FL 33805 USA

ISBN: (纸本)9783031054099;9783031054082

Object tracking in computer vision can be done either by using a marker-less or marker-based approach. Computer vision systems have been using Fiducial markers for pose estimation in different applications such as augmented reality [5] and robot navigation [4]. With the advancements in Augmented Reality (AR), new tools such as AugmentedReality uco (ArUco) [6] markers have been introduced to the literature. ArUco markers, are used to tackle the localization problem in AR, allowing camera pose estimation to be carried out by a binary matrix. Using a binary matrix not just simplifies the process but also increases the efficiency. As a part of our initiative to create a cost-efficient, 24/7 accessible, Virtual Reality (VR) based chemistry lab for underprivileged students, we wanted to create an alternative way of interacting with the virtual scene. In this study, we used ArUco markers to create a low-cost keyboard only using a piece of paper and an off-the-shelf webcam. We believe this method of keyboard will be more beneficial to the user as they can see the keys before they are typing in the corner of the screen instead of an insufficient on the screen VR keyboard or a regular keyboard where the user can't see what they are typing with a VR headset. As potential extensions of the base system, we have also designed and evaluated a stereo camera and an IMU sensor based system with various sensor fusion techniques. In summary, the stereo camera reduces occlusion related problems, and the IMU sensor detects vibrations which in turn simplifies the KeyPress detection problem. It has been observed that use of any of these additional sensors improves the overall system performance.

关键词： ArUco codes IMU sensors sensor fusion

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Leveraging Multispectral Imagery for Fertigation Timing Recommendations Through the N- Time Automated Decision Support System 7

Leveraging Multispectral Imagery for Fertigation Timing Reco...

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conference on Autonomous Air and Ground Sensing systems for Agricultural Optimization and Phenotyping vii

作者： Stansell, Jackson S. Luck, Joe D. Smith, Tyler G. Yu, Hongfeng Rudnick, Daran R. Krienke, Brian T. Univ Nebraska Dept Biol Syst Engn Lincoln NE 68583 USA Univ Nebraska Sch Comp Lincoln NE 68588 USA Univ Nebraska Nebraska Extens Inst Agr & Nat Resources Lincoln NE 68583 USA

ISBN: (纸本)9781510651050;9781510651043

Multispectral imagery captured from aerial robots, aircraft, and satellites provides data that may be leveraged to quantify crop nitrogen (N) status. While multispectral imagery is commercially accessible, there are few systems designed to transform multispectral imagery into actionable recommendations and prescriptions for farmers. N-Time Fertigation Management System (N-Time) was developed as an autonomous software decision support system ( DSS) that analyzed multispectral imagery and applied a N recommendation algorithm to produce a binary action recommendation and accompanying prescription for an imminent fertilizer application via a center pivot irrigation system (fertigation). N-Time FMS automated the sensor-based fertigation management framework (SBFM) which utilized multispectral images of the crop canopy to inform the number and timing of fertigation applications made throughout the growing season. The framework involved a frequent (i.e. weekly) monitoring and recommendation cycle for which manual execution of computational processes for a single field required more than two hours. N-Time reduced the time requirement per field including user interaction to 7.4 minutes for 12 cm/pixel resolution multispectral imagery (i.e. UAV captured) and 3.1 min for 3 m/pixel multispectral imagery (i.e. satellite captured). N-Time also demonstrated greater than 99% agreement with computational outputs produced through manual execution with non-specialized software. During 2020 and 2021, N-Time was used to implement SBFM in research trials on approximately 320 ha of production-scale irrigated grain maize. These trials demonstrated that SBFM produced higher nitrogen use efficiency than the grower's best management in 94% of implementations and higher profitability than the grower's best management in 59% of implementations.

关键词： Fertigation N-Time multispectral imagery sensor-based fertigation management nitrogen use efficiency automation decision support

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A Visual-Tactile Coulping Mechanism sensor For Real-Time Force Calibration

A Visual-Tactile Coulping Mechanism Sensor For Real-Time For...

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IEEE International conference on Micro Electro Mechanical systems

作者： Xiaotong Hu Chunpeng Jiang Bin Yang Jingquan Liu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University China Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

This paper reports a visual-tactile coupling mechanism sensor for force calibration in real time through integrating high sensitivity piezoresistive thin film sensor and high resolution visual-tactile sensing. The elastic layer combined with dispensing-printed flexible electrodes forms piezoresistive sensing units. It employs the spatial weighted fusion algorithm to couple displacement of markers as weights with forces measured by the piezoresistive sensing units. High resolution and improved measurement accuracy at small forces are realized, and the coefficient of variation of measured force is reduced, which contributes to enhancing reliability. The sensor will be served as a reliable data source for future human-machine interaction applications.

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3rd Electric Power and Renewable Energy conference, EPREC 2022

3rd Electric Power and Renewable Energy Conference, EPREC 20...

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3rd Electric Power and Renewable Energy conference, EPREC 2022

ISBN: (纸本)9789811966040

The proceedings contain 19 papers. The special focus in this conference is on Electric Power and Renewable Energy. The topics include: An Off-MPPT Controller for Standalone PV systems;Designing and Simulation of Double Stage Grid-Linked PV System Using MATLAB/Simulink;a Review of sensor-Based Solar Trackers;Detection and Classification of Faults in VSC-HVDC System Based on Single End Ground and Pole Differential Current Components;investigation of Different Types of Bidirectional Charging Operations of Electric Vehicles;Intelligent Fault Location Scheme for HVDC System;Energy-Metrics Analysis of 2 kWp Rooftop PV System Located in Gwalior India;the Use of Biogas Energy for Electrical Power Generation in Zimbabwe—A Study;A Review on Recent advanced Three-Phase PLLs for Grid-Integrated Distributed Power Generation systems Under Adverse Grid Conditions;Performance Assessment and Evaluation of a 52-kW Solar PV Plant in India;green Energy in West Bengal, India: Status, Scope, and Future Challenges;Impact of Performance Parameters on the Efficiency of Cu2SnS3 (CTS)/Si Tandem Solar Cell by SCAPS-1D;expansion of the Burners Structure to Boost up the Ecological Properties of an Average Capacity Boiler "E-270-13.73-560";Rearrangement of the PV Array to Reduce the Effect of Partial Shading Using Meta-Heuristic Techniques;design of Lithium-Ion Battery Thermal Management System for Vehicular applications;wind and Tidal Power as a Dynamic Solution for Sri Lanka’s Dependency on Thermal Power Plants.

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Development of Machine Intelligence for Self-Driving Vehicles Through Video Capturing

Development of Machine Intelligence for Self-Driving Vehicle...

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IEEE/ASME International conference on Mechtronic and Embedded systems and applications, MESA

作者： Jordon Lowe Kaya Kuru School of Engineering and Computing University of Central Lancashire Preston UK

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

The automation of vehicles is progressing through several automation levels, with the goal of reaching full autonomy, i.e. level 5, which involves no steering wheel, brakes, pedals, or windshield. This is achieved by the vehicle taking on an increasing number of autonomous decision-making tasks under the guidance of smart control systems that are instilled with Artificial Intelligence (AI) and equipped with advanced sensor and actuator technologies. Major vehicle companies are competing to build the most talented driver, i.e. AI-agent. In this report, how the intelligence of Self-Driving Vehicles (SDVs) is being built by the automotive industry for the efficient deployment of handover wheels is analysed and applications of machine intelligence for SDVs are implemented through video capturing using Deep Learning (DL). The results show that i) the use of DL techniques as well as reinforcement learning (RL) – Deep RL approaches – can contribute to the intelligence of SDVs significantly and ii) SDVs, equipped with advanced mechatronics systems, can be fully autonomous with the level-5 automation as they are trained appropriately with proper datasets.

关键词： Industries Automation Mechatronics Embedded systems Wheels Reinforcement learning Handover Artificial intelligence Machine intelligence Vehicles

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Flexible M-Tooth Hybrid Micro-Structure-Based Capacitive Pressure sensor With High Sensitivity and Wide Sensing Range

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IEEE sensorS JOURNAL 2021年第23期21卷 26261-26268页

作者： Palaniappan, Valliammai Panahi, Masoud Maddipatla, Dinesh Zhang, Xingzhe Masihi, Simin Emani, Himanaga Rama Krishna Manoj Narakathu, Binu B. Bazuin, Bradley J. Atashbar, Massood Z. Western Michigan Univ Elect & Comp Engn Dept Kalamazoo MI 49008 USA

A novel flexible capacitive pressure sensor based on hybrid micro-structured (HM) polydimethylsiloxane (PDMS) dielectric layer was developed. The HM-PDMS with M-tooth structured patterns was fabricated using a laser engraved acrylic master mold. The top and bottom electrodes were fabricated by depositing silver (Ag) on flexible polyethylene terephthalate PET) substrate using additive screen-printing process. The pressure sensor was assembled by sandwiching two HM-PDMS based dielectric layers between the top and bottom electrodes. The capability of the developed pressure sensor to detect a wide range of pressure was investigated by applying varying pressure from 0 to 1 kPa, 1 to 10 kPa, and 10 kPa to 40 kPa. A sensitivity of 51.36% kPa(-1), 8.37% kPa(-1), and 2.46% kPa(-1) was obtained for the pressure ranges of 0 to 1 kPa, 1 to 10 kPa, and 10 to 40 kPa, respectively. The capacitive pressure sensor response, hysteresis, and repeatability of the fabricated pressure sensor is investigated and presented in this paper. To demonstrate the wide sensing range of the pressure sensor, the sensor was attached to face mask for breath monitoring applications that require low pressure sensing of similar to 20 Pa, and to the hand glove for fingertip pressure monitoring applications that require relatively high pressure sensing of similar to 3 kPa.

关键词： Additive screen-printing hybrid micro-structures laser engrave laser etching M-tooth structured pattern pressure sensor polydimethylsiloxane wide pressure range

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A Hybrid Optimization Strategy for k-Coverage Maximization in Wireless sensor and IoT Networks

A Hybrid Optimization Strategy for k-Coverage Maximization i...

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International Symposium on advanced Computing and Communication (ISACC)

作者： Prithwish Manna Sudipta Majumder N. Hemarjit Singh Dept. of CSE DUIET Dibrugarh University Dibrugarh Assam India Dept. of ECE DUIET Dibrugarh University Dibrugarh Assam India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In this paper, we propose a combination of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to create a hybrid optimization technique which meets k-coverage criteria in Wireless sensor Network (WSN) and Internet of Things (IoT). The goal of this approach is to increase the coverage area alongside minimizing the deployment cost of the sensors. Another aim is to reduce the power consumption. This technique is helpful to determine the optimal position of the sensors. It helps to ensure that each target point in the network is getting the coverage of at least k sensors. In this way we can enhance the reliability and robustness of the network. From experimental results, we can conclude that with optimal sensor utilization and minimized redundancy, our proposed approach achieves 97.14% coverage ratio with k-coverage equals 3. According to performance metrics, it is clear that there is consistent energy efficiency through the cycles. The approach converges in less than 3 second. So, this approach is applicable for real time applications. It is a hybrid metaheuristic approach which is very efficient in managing various challenges associated with sensor deployment. From this approach we can get solutions of coverage enhancement, energy efficiency and improved sensor utilization.

关键词： Wireless communication Wireless sensor networks Turning Energy efficiency sensor systems Real-time systems Internet of Things Particle swarm optimization Intelligent sensors Genetic algorithms

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Electromagnetically Unclonable Function with Immunity to Machine Learning Attacks

Electromagnetically Unclonable Function with Immunity to Mac...

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International conference on Electromagnetics in advanced applications (ICEAA)

作者： Yichong Ren Hongyi Pan A. Enis Cetin Pai-Yen Chen Department of Electrical and Computer Engineering University of Illinois Chicago Chicago Illinois USA

ISBN: (数字)9798350360974

ISBN: (纸本)9798350360981

Over the past decade, non-Hermitian physical systems and their abnormal singularities, known as exceptional points (EPs), have deepen our understanding of how to engineer spectral and spatial properties of waves, leading to remarkable advances in signal synthesis, routing, and modulation across the spectrum. In particular, spectral sensitivity near the EP-singularity in non-Hermitian physical systems has recently been extensively explored for making the next-generation photonic, acoustic, and electronic sensors with unprecedented sensitivity and resolvability [1]–[5]. However, it is still debated whether this new class of sensors can surpass traditional sensors in terms of reliability and signal-to-noise ratio, since mesoscopic fluctuations and noises could also be amplified at these singular points [6–8].

关键词： Sensitivity Routing sensor systems Signal synthesis Optoelectronic and photonic sensors Spatial resolution Signal resolution Next generation networking Signal to noise ratio Photonics

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