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6th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2022

作者： Kulkarni, Mukund Dusane, Atharva Daga, Shreya Bobby, Dyuti Durole, Krushna Dalve, Pranav Vishwakarma Institute of Technology Department of Multidisciplinary Engineering Pune India Vishwakarma Institute of Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9781665484510

This paper projects machine learning as a valuable tool for the restriction of the Covid-19 pandemic escalation in the global scenario. The proposed system involves detection of masked or unmasked people and a temperature sensing system for ensuring Covid-19 appropriate protocol is followed to allow only healthy person(s) in public/crowded places. The integration of Arduino Uno and MLX90614 non-contact temperature sensor, along with a MobileNetV2 machine learning model, is performed for complete execution. The system will classify a person as a masked or unmasked individual using ML techniques and detect their body temperature. If the individual meets the appropriate requirements, the system will enable them to access via the gate, which will be controlled by a servo motor in conjunction with a temperature sensor module. © 2022 IEEE.

关键词： Temperature sensors

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Plasmonic Point-of-Care Device for Sepsis Biomarker Detection

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IEEE sensors JOURNAL 2021年第17期21卷 18837-18846页

作者： Kundu, Souvik Tabassum, Shawana Kumar, Ratnesh Iowa State Univ ISU Dept Elect & Comp Engn ECE Ames IA 50011 USA Univ Texas Tyler Dept Elect Engn EE Tyler TX 75799 USA

The need for point-of-care (POC) devices for detecting the onset of sepsis has become critical since sepsis is one of the most prevalent causes of deaths worldwide in non-coronary intensive care units at the hospitals. Every one hour delay in exercising proper medication can lead to an exponential rise in mortality. Motivated by this, we propose a POC device for sepsis biomarker detection, which will complement traditional blood culture-based techniques for easy and quicker diagnosis and monitoring of sepsis state. The working principle of the device is based on amalgamation of surface plasmon resonance (SPR) technology with microfluidics. The sensing chip consists of a gold and graphene oxide coated patterned array of periodic nanoposts to detect target biomarker molecules in a limited sample volume. The nanoposts are functionalized with specific receptor molecules that serve as a nanostructured plasmonic crystal for SPR-based bio-sensing via the excitation of surface plasmon polaritons. The sensitivity of the device to one of the known sepsis biomarkers, Pro-calcitonin (PCT), was found to be 0.0643 a.u./ ***(-1) at lower concentration and 0.0224 a.u./ ***(-1) at higher concentration, and a LOD of 1.22 ***(-1). The sensor chip provides an opportunity to dynamically measure antigen-antibody bindings and the soft-lithography based sensor manufacturing technology provides high reproducibility of the sensor response to PCT molecules even at a picomolar level. The microfluidics-based platform provides potential for future integration with other microfluidic devices viz. plasma separator for separating the PCT-sized molecules to enable blood sample measurements

关键词： sensors Blood Proteins Gold Biosensors Surface waves Plasmas Bio-markers biosensor plasmonics point-of-care (PoC) sepsis surface plasmon resonance

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Research on nano-film composite lubricated triboelectric speed sensor for bearing skidding monitoring

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NANO ENERGY 2023年 113卷

作者： Li, Zhihao Wang, Xiaoli Fu, Tie Hu, Yanqiang Li, Lizhou Zhao, Zirui Wu, Liyan Wang, Chenfei Zhang, Shuaishuai Beijing Inst Technol Dept Mech Engn Beijing 100081 Peoples R China

Triboelectric speed sensors (TSS) have the advantage of easy integration to be used to monitor the bearing skidding. However, dry friction between materials can reduce the durability and stability of TSS and interfere with the operation of the cage. In this paper, BaTiO3/polyimide (BTO/PI) nanocomposite films and lubricants are introduced to triboelectric nanogenerator (TENG) and the effects of lubricant species and nanoparticles doping mass fraction on tribological and electrification performance are investigated. The results show that the TENG with squalane and BTO/PI nanocomposite film of 20 wt% has a lower friction coefficient and mass loss while higher output and stability. Then, the nano-film composite lubricated TSS (NFCL-TSS) integrated with bearing is constructed based on the rotary freestanding TENG (RF-TENG) with optimal nanocomposite film and squalene, where the signal processing circuit with wireless transmission is integrated on the back of RF-TENG stator to improve the integration of the sensing system. The results show that the NFCL-TSS has 84.81% lower mass loss, 95.26% and 97.75% higher electrical output and stability than that of the TSS with pure PI under dry conditions, respectively. Moreover, the cage speed before and after installation of NFCL-TSS remains mostly unchanged, and the error between the cage speed measured by NFCL-TSS and commercial sensor is below 0.26%. Finally, based on the bearing test platform, the monitoring of skidding is achieved by NFCL-TSS under different speeds and loads. This study provides a new strategy for the design of TSS with high durability, stability and integration.

关键词： Triboelectric nanogenerator Triboelectric speed sensor Nanocomposite films Lubricant Composite lubrication Bearing skidding

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Smart materials-integrated sensor technologies for COVID-19 diagnosis

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EMERGENT MATERIALS 2021年第1期4卷 169-185页

作者： Erdem, Ozgecan Derin, Esma Sagdic, Kutay Yilmaz, Eylul Gulsen Inci, Fatih Bilkent Univ UNAM Natl Nanotechnol Res Ctr TR-06800 Ankara Turkey Bilkent Univ Inst Mat Sci & Nanotechnol TR-06800 Ankara Turkey

After the first case has appeared in China, the COVID-19 pandemic continues to pose an omnipresent threat to global health, affecting more than 70 million patients and leading to around 1.6 million deaths. To implement rapid and effective clinical management, early diagnosis is the mainstay. Today, real-time reverse transcriptase (RT)-PCR test is the major diagnostic practice as a gold standard method for accurate diagnosis of this disease. On the other side, serological assays are easy to be implemented for the disease screening. Considering the limitations of today's tests including lengthy assay time, cost, the need for skilled personnel, and specialized infrastructure, both strategies, however, have impediments to be applied to the resource-scarce settings. Therefore, there is an urgent need to democratize all these practices to be applicable across the globe, specifically to the locations comprising of very limited infrastructure. In this regard, sensor systems have been utilized in clinical diagnostics largely, holding great potential to have pivotal roles as an alternative or complementary options to these current tests, providing crucial fashions such as being suitable for point-of-care settings, cost-effective, and having short turnover time. In particular, the integration of smart materials into sensor technologies leverages their analytical performances, including sensitivity, linear dynamic range, and specificity. Herein, we comprehensively review major smart materials such as nanomaterials, photosensitive materials, electrically sensitive materials, their integration with sensor platforms, and applications as wearable tools within the scope of the COVID-19 diagnosis.

关键词： Smart materials sensors Diagnostics COVID-19 Point-of-care

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Simulation of the atmospheric electric field and current structure for sensors with different geometry in relation to the electric current measured in the external circuit 36

Simulation of the atmospheric electric field and current str...

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36th International Conference on Lightning Protection (ICLP)

作者： Sobolewski, Konrad Kubicki, Marek Warsaw Univ Technol Fac Elect Engn Warsaw Poland Polish Acad Sci Inst Geophys Warsaw Poland

ISBN: (数字)9781665490245

ISBN: (纸本)9781665490245

Electric field measurements have many scientific applications. Their results can be used in atmospheric electricity, optimisation of electric safety, e.g. lightning protection and meteorology. However, a measurement should be performed equally reliably to draw reliable conclusions. In theory and practice, you can find various measurement methods, some used for many years and which can be verified in terms of their accuracy using the possibility of computer simulations. This work contains a field analysis of examples of electric field sensors, based on which a discussion was held and practical conclusions were drawn. Described measurement methodology is often used for measurements of lightning electric field distribution. The article concerns the theory of sensors for measuring electric field and current based on numerical simulations. It can be used both in the problems of the physics of atmospheric lightning discharges and fair-weather electricity. Electric field E and.E measurements are widely described in the literature and have large technical applications. Measurements of the Maxwell current density and its components for thunderstorm electricity and fair weather conditions are associated with many interpretations and technical difficulties. It may touch cases like currents under a thundercloud approaching or currents from distant discharges. For example, Krider and Musser [1] determined the Maxwell current in a situation where Ez(t)=0 (conduction current is that it always vanishes when E is zero). Current measurement methods based directly on Gauss or Ampere-Maxwell equations and selecting appropriate sensor surfaces to carry out the integration operation on these surfaces or the path that bounds that surface may prove difficult or impossible. It needs to choose a unique Gaussian surface to calculate the electric field and a particular Amperian loop to determine the magnetic field and current density.

关键词： electric field electric field sensor simulations Ansys lightning atmospheric measurements

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Request integration and data prediction based energy efficient cloud integrated wireless sensor network 1

Request integration and data prediction based energy efficie...

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1st IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021

作者： Das, Kalyan Das, Satyabrata Darji, Rabi Kumar Mohanta, Jyoti Prakash Burla India Burla India Burla India Department of Computer Science Fakir Mohan Autonomous College Balasore India

ISBN: (纸本)9781728157900

An energy-efficient cloud-integrated wireless sensor network (WSN) model is proposed based on request integration and data prediction method. Many users want to access the same sensors in the sensor cloud environment through the cloud at the same time. So, to optimize the requests, the Hybrid Request integration (HRI) algorithm is used where redundant requests are merged and executed once by the cloud to reduce the power consumption within the cloud system. To minimize data communication, the Artificial Neural Network (ANN) based prediction method is used in the cloud, which provides future sensor data for one day in advance with 94% accuracy. The users' requests are generated every second, and in the traditional model, all users' queries need to be redirected to the WSN, which requires more energy. Rather than one second, the cloud transmits and receives data with the sensors every 24 hours in our method. Maximum users' requests are answered by the prediction method in the cloud system, which results in less communication and more battery life for the sensor. In case of alert condition, the sensor sends data to the cloud, and our model can also predict the event and alerts the users. © 2021 IEEE

关键词： Wireless sensor networks

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Smart sensor-Based Rear Seat Monitoring System to Prevent Pediatric Vehicular Heatstroke

Smart Sensor-Based Rear Seat Monitoring System to Prevent Pe...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Leila Sharara Jonas Gillner Roy Taylor Klaus Thelen Lubna Alazzawi Mohammed Ismail Electrical and Computer Engineering Wayne State University Detroit USA Measurement and Sensor Technology Ruhr West University of Applied Sciences Muelheim Germany

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

This research presents an innovative solution aimed at preventing in-vehicle pediatric heatstroke incidents through the integration of advanced sensors, connectivity, and intelligent algorithms. The proposed system continuously monitors crucial parameters including occupancy status, temperature, and the child's Respiratory Rate (RR). A hybrid detection approach, combining Force Sensing Resistor (FSR) sensors and motion sensors, is employed to ensure enhanced accuracy. To optimize the system efficiency, the solution utilizes Simplified Frequency Analysis (SFA), offering processing speeds up to 80 times faster than conventional methods, making it ideal for real-time applications. The processed data is seamlessly transmitted via a mobile application, enabling immediate caregiver notification and intervention. Initial testing confirms the solution's high accuracy in occupancy detection and breath monitoring, satisfying automotive standards for temperature resilience and power efficiency. Engineered to withstand extreme environmental conditions ranging from -40° to +85°C, while maintaining a minimal standby current of 104 µA, this technology holds immense potential in not only saving lives but also establishing itself as a standard safety feature.

关键词： Temperature sensors Temperature measurement Pediatrics Tracking Force Safety Monitoring

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AI-Assisted Multimodal Breath Sensing System with Semiconductive Polymers for Accurate Monitoring of Ammonia Biomarkers

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ADVANCED MATERIALS TECHNOLOGIES 2024年第9期9卷

作者： Weisbecker, Hannah Shanahan, Jordan Liu, Yihan Zhang, Lin Xie, Wanrong McDow, Samuel Lambert, Noah Huang, Amber Sopp, Stephen You, Wei Bai, Wubin Univ N Carolina Dept Appl Phys Sci Chapel Hill NC 27514 USA Univ N Carolina Dept Biol Chapel Hill NC 27514 USA Univ N Carolina Dept Chem Chapel Hill NC 27514 USA

Breath ammonia is an essential biomarker for patients with many chronic illnesses, such as chronic kidney disease (CKD), chronic liver disease (CLD), urea cycle disorders (UCD), and hepatic encephalopathy. However, existing breath ammonia sensors fail to compensate for the impact of breath humidity and complex breathing motions associated with a human breath sample. Here, a multimodal breath sensing system is presented that integrates an ammonia sensor based on a thermally cleaved conjugated polymer, a humidity sensor based on reduced graphene oxide (rGO), and a breath dynamics sensor based on a 3D folded strain-responsive mesostructure. The miniaturized construction and module-based configuration offer flexible integration with a broad range of masks. Experimental results present the capabilities of the system in continuously detecting diagnostic ranges of breath ammonia under real, humid breath conditions with sufficient sensing accuracy and selectivity over 3 weeks. A machine-learning algorithm based on K-means clustering decodes multimodal signals collected from the breath sensor to differentiate between healthy and diseased breath concentrations of ammonia. The on-body test highlights the operational simplicity and practicality of the system for noninvasively tracing ammonia biomarkers. A wearable multimodal breath sensing system, incorporating an ammonia sensor, humidity sensor, and a 3D folded strain-responsive mesostructure, employs a K-means clustering algorithm to differentiate between concentrations of ammonia in healthy and diseased breath. This holds significant potential for noninvasively tracking breath biomarkers. image

关键词： breath sensor multi-modality sensing responsive polymer semiconducting polymer wearable sensor

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Application of Embedded Capacitive Pressure sensors in Pressure Drop Measurement of Microchannels

Application of Embedded Capacitive Pressure Sensors in Press...

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IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS)

作者： Xiaoda Cao Tiantong Xu Zhi Tao Haiwang Li Yanxin Zhai School of Energy and Power Engineering Beihang University Beijing China Research Institute of Aero-Engine Beihang University Beijing China

ISBN: (数字)9798350359831

ISBN: (纸本)9798350359848

The measurement of pressure drops within microchannels is pivotal for exploring microfluidic flow dynamics and advancing microfluidic device technology. Traditionally, assessing liquid pressure drops in silicon microchannels has necessitated alterations to the original design of the microchannels to accommodate the integration of small pressure sensors. This adaptation, however, introduces a limitation: the small size of the sensor compromises its sensitivity, thereby impairing the reliability of pressure drop measurements. This study explored an approach to the application of capacitive pressure sensors, designed to be seamlessly embedded into microchannels, to accurately measure pressure drops in silicon microchannels. By carefully regulating the outlet pressure, the overall absolute pressure in the microchannel was increased without affecting the pressure drop values. This approach enhanced the capacitive pressure sensor’s sensitivity across a broader operational range. The findings from pressure drop tests conducted on a straight microchannel, possessing a hydraulic diameter of 524 μm and examined across a Reynolds number range of 383.8 to 1644.9, align with established macroscopic theories.

关键词： Pressure sensors Sensitivity Nanoelectromechanical systems Length measurement Size measurement Silicon Pressure measurement

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Cosmic-Swamp: Iot Processing of Cosmic Ray Soil Moisture sensors

SSRN

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SSRN 2024年

作者： Kamienski, Carlos A. Stowell, Patrick Heideker, Alexandre Silva, Dener Kleinschmidt, João Prati, Ronaldo Chadwick, Paula Power, Daniel Rosolem, Rafael Köhli, Markus Rocha, Humberto Heinemann, Alexandre Bryan Madari, Beata Soler, Melissa Neto, André Torre University of Sheffield Sheffield United Kingdom Federal University of ABC Santo André Brazil Durham University Durham United Kingdom University of Bristol Bristol United Kingdom University of Heidelberg Heidelberg Germany University of São Paulo São Paulo Brazil São Carlos Goiania Brazil

COSMIC-SWAMP is an Internet of Things solution designed to support the integration of cosmic ray soil moisture sensors into data-driven irrigation management systems. By automatically accounting for the sensitive footprint of different in-situ probes, the platform can aggregate soil properties measured by diverse networks of sensors whilst dynamically responding to changes in sensor locations. Results from first trials using the COSMIC-SWAMP platform with a small array of boron-nitride-based scintillating cosmic ray sensors demonstrate the ability to automatically extract soil moisture in almost real-time when cosmic ray data is sent to a centralised server. Furthermore, tests using simulated sensor data demonstrate that the COSMIC-SWAMP platform can estimate soil moisture across collections of distributed management zones and forecast future soil moisture based on detailed crop simulations. © 2024, The Authors. All rights reserved.

关键词： Internet of things

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