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17th Dallas Circuits and systems conference (DCAS)

作者： Grant, Spencer Harden, Alex Coggins, Kaleb Parker, David Tabei, Fatemehsadat Askarian, Behnam West Texas A&M Univ Coll Engn Canyon TX 79016 USA

ISBN: (纸本)9798350349535;9798350349542

Precision agriculture depends on precise and timely information to enhance resource utilization and increase crop production. This paper conducts an in-depth analysis of four different soil sensors, each employing unique sensing technologies: the Zigbee Soil sensor, True TDR-315H, ESP32 Capacitive Soil sensor, and ComWinTop Soil Moisture sensor. The objective is to assess and contrast their performance in delivering accurate data, consistency, and capabilities for immediate monitoring. By integrating accurate and immediate data, precision agriculture can make informed decisions to improve resource management and crop yields. The focus of this analysis is to offer a thorough review of these soil sensors, evaluating their effectiveness in real-time monitoring, precision of data, and dependability in agricultural settings. Upon examination of the specified sensors, the True TDR-315H emerged as the most accurate, with the ComWinTop sensor closely following. When using the TDR-315H as a standard for comparison in this research, the ComWinTop's accuracy was found to be around 90%. Additionally, the ComWinTop is significantly more cost-effective than the TDR-315H, being 60% cheaper, thereby offering a more viable solution for a wide range of soil moisture monitoring applications.

关键词： Precision agriculture soil sensor technology data accuracy real-time monitoring resource management

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Benchmarking of Machine Learning Algorithms for Temperature sensor Fault Time Series Classification 15

Benchmarking of Machine Learning Algorithms for Temperature ...

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15th International conference on Information, Intelligence, systems and applications, IISA 2024

作者： Prantikos, Konstantinos Pantopoulou, Maria Pantopoulou, Stella Hefeitz, Alexander Tsoukalas, Lefteri H. School of Nuclear Engineering Purdue University West LafayetteIN United States Nuclear Science and Engineering Division Argonne National Laboratory ArgonneIL United States

ISBN: (纸本)9798350368833

Reliable sensors functioning is important for safe and efficient operation of a nuclear system. Accurate detection and classification of sensor anomalies are crucial for efficient maintenance and prevention of reactor shutdowns. In this proof-of-principle study, we benchmark performance of several machine learning (ML) algorithms in temperature sensor fault detection. The database consists of four time series, with one representing normal sensor operation and the remaining three consisting of various sensor faults. The ML models in this study include Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Decision Tree (DT), and Extreme Gradient Boosting (xGBoost). Performance of the ML models is evaluated using Precision, Recall and F1-score metrics. The results of the benchmarking study show that xGBoost achieves the best performance. © 2024 IEEE.

关键词： Reactor shutdowns

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Development of Traffic Light and Road Sign Detection and Recognition Using Deep Learning

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INTERNATIONAL JOURNAL OF advanced COMPUTER SCIENCE AND applications 2024年第10期15卷 942-952页

作者： De Guia, Joseph M. Deveraj, Madhavi Mapua Univ Sch Informat Technol SOIT Manila Philippines Nanyang Technol Univ Energy Res Inst ERIN Singapore Singapore

Traffic light and road sign violations significantly contribute to traffic accidents, particularly at intersections in high-density urban areas. To address these challenges, this research focuses on enhancing the accuracy, robustness, and reliability of Autonomous Vehicle (AV) perception systems using advanced deep learning techniques. The novelty of this study lies in the comprehensive development and evaluation of real-time traffic light and road sign detection systems, comparing state-ofthe-art models including YOLOv3, YOLOv5, and YOLOv7. The models were rigorously tested in a controlled offline environment using the Nvidia Titan RTx, followed by extensive field testing on an AV test vehicle equipped with sensor suite and Nvidia RTx GPU. The testing was conducted across complex urban driving scenarios at the CETRAN proving test track, JTC Cleantech Park, and NTU Singapore campus. The traffic light detection and recognition (TLR) results demonstrate that YOLOv7 outperforms YOLOv5 and YOLOv3, achieving a mean Average Precision (mAP@0.5) of 93%, even under challenging conditions like poor lighting and occlusions. While the traffic road sign detection (TSD) mAP@0.5 of 96%. This superior performance highlights the potential of YOLOv7 in enhancing AV safety and reliability. The conclusions underscore the effectiveness of YOLOv7 for real-time detection in AV perception systems, offering crucial insights for future research. Potential implications include the development of more robust and accurate AV systems, capable of safely navigating complex urban environments.

关键词： Artificial intelligence autonomous vehicle traffic light recognition road sign detection YOLO real-time object detection

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Metamaterial Based sensor Using Fractal Hilbert Structure for Liquid Characterization 25

Metamaterial Based Sensor Using Fractal Hilbert Structure fo...

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24th International conference on Electromagnetics in advanced applications (ICEAA) / 12th IEEE-APS Topical conference on Antennas and Propagation in Wireless Communications (IEEE APWC)

作者： AbdulSattar, Rusul Khalid Alibakhshikenari, Mohammad Eiwi, Taha A. Kouhalvandi, Lida Hassains, Zaid A. Abdul Virdee, Bal S. Soruri, Mohammad Tokan, Nurhan Turker Parchin, Naser Ojaroudi See, Chan Hwang Livreri, Patrizia Dayoub, Iyad Aissa, Sonia Limiti, Ernesto AI Hadi Univ Coll Med Instrumentat Tech Engn Dept Baghdad Iraq Univ Carlos III Madrid Dept Signal Theory & Commun Madrid 28911 Spain Int Appl & Theoret Res Ctr IATRC Baghdad Iraq Dogus Univ Dept Elect & Elect Engn TR-34775 Istanbul Turkiye Mustansiriyah Univ Dept Elect Engn Baghdad Iraq London Metropolitan Univ Ctr Commun Technol London N7 8DB England Univ Birjand Fac Ferdows Tech Birjand Iran Yildiz Tech Univ Dept Elect & Commun Engn TR-34220 Istanbul Turkiye Edinburgh Napier Univ Sch Engn & Built Environm 10 Colinton Rd Edinburgh EH10 5DT Midlothian Scotland Univ Palermo Dept Engn Viale Sci BLDG 9 IT-90128 Palermo Sicily Italy Univ Lille Univ Polytech Hauts De France Inst Elect Microelect & Nanotechnol IEMN CNRSUMR 8520Cent LilleISEN F-59313 Valenciennes France INSA Hauts De France F-59313 Valenciennes France Univ Quebec Inst Natl Rech Sci INRS Montreal PQ H5A 1K6 Canada Univ Roma Tor Vergata Dept Elect Engn Via Politecn 1 I-00133 Rome Italy

ISBN: (纸本)9798350320589

In this work, a simple and efficient approach is presented to design a metamaterial based sensitive sensor for liquid characterimtion. The proposed sensor based on the Hilbert structure has a compacted size of 40x60x1.6 mm(3). The Hilbert curve is used to enhance the sensitivity of the sensor by increasing the interaction area with the sample tested. The simulation studies are carried out by using the Computer Simulation Technology (CST) Microwave Studio. The resonant frequency of the proposed sensor is about 0.46 GHz. The resonant frequency has shifted approximately 30 MHz after the receptacle is printed on the sensor surface. The proposed sensor has successfully detected different samples of liquids. The variations of the resonant frequency, scattering parameters, bandwidth and quality factor of different samples are discussed.

关键词： Metamaterial Hilbert fractal receptacle liquid characterization

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Ultra-thin vertical tft photosensor and photosynapse based on au-doped-graphene under transition metal selenide reaction

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Journal of Materials Science & Technology 2024年第16期183卷 215-222页

作者： Ting Kang Adila Rani Wanqi Ren M Junaid Sultan Nae-Eung Lee Tae Geun Kim School of Electrical Engineering Korea UniversitySeoul 02841South Korea School of Advanced Materials Science&Engineering Sungkyunkwan UniversityGyeonggi-do 16419South Korea

The development of artificial photosensitive synapses with high sensitivity and biomimetic properties that combine innovative concepts and neuromorphic architectures is crucial to achieving highly integrated and flexible intelligent visual ***,graphene heterostructure-based photosensitive synaptic transistors have been extensively studied for this ***,compared to traditional transistors,vertical structure thin film transistors(VTFTs)with ultra-short channels and advantages,such as high integration,have yet to be investigated in photosensitive ***,we report an ultra-thin VTFT featuring a graphene/W_(x)Se_(x-1)van der Waals heterostructure that combines photonic and neuromorphic *** demonstrate a VTFT in which the channel layer is formed by covalently bonded W_(x)Se_(x-1)nanomaterials produced by introducing Se atoms on the surface of a tungsten metal thin film deposited via radio-frequency *** structure successfully simulated the main synaptic function,exhib-ited photosensitive synaptic responses to ultraviolet(λ=365 nm)light,and demonstrated highly reliable electrical ***,the incorporation of gold nanoparticles changed the photosensitive synaptic response properties of the graphene/W_(x)Se_(x-1)heterostructure from excitatory to inhibitory,show-ing a responsivity of about∼14 A W-1,which was attributed to the heterojunction interface resonant effects and efficient charge transfer induced by localized surface *** further enabled optical artificial synaptic applications while operating with low voltage spikes and low light *** work provides a novel strategy for integrating and developing biological and nano-electronic systems.

关键词： Ultra-thin Vertical TFT Au nanoparticles Photo sensor Photo responsivity Photo synapse

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CONFORMAL DESIGN AND FABRICATION OF WEARABLE PIEZOELECTRIC sensor ARRAY FOR SPATIOTEMPORAL DISTRIBUTION OF ARTERIAL PULSE WAVES 37

CONFORMAL DESIGN AND FABRICATION OF WEARABLE PIEZOELECTRIC S...

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37th IEEE International conference on Micro Electro Mechanical systems (MEMS)

作者： Zhen, Liyun Zhai, Yueqi Zhu, xiantao xu, Mengfei Li, Yizhi Liu, Jingquan Yang, Bin Shanghai Jiao Tong Univ Natl Key Lab Adv Micro & Nano Mfg Technol Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Micro Nano Elect Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9798350357929

This paper reports a wearable piezoelectric pressure MEMS sensor array for multi-site conformal sensing of pulse waves. Thinned bulk lead zirconate titanate (PZT) piezoceramic films with high performance are embedded into soft materials, allowing optimal deformability and conformality. Specifically, a spin-on polymeric adhesive is adopted to transfer uniform PZT film during bonding and releasing processes. As a result, the device of single element exhibits high sensitivity with 47.45 mV/N. Furthermore, it can capture a weak pulse with the spatial and temporal distribution without cross talk among channels, which has great potential applications in the diagnosis of cardiovascular disease.

关键词： Conformal Design Piezoelectric sensor Array Pulse Perception Spatiotemporal Distribution

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Soft robotic fingers with sensorized pre-charged finger pulp

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SMART MATERIALS AND STRUCTURES 2024年第8期33卷 085001-085001页

作者： Zhu, Zihui Yang, Yang Yan, Shaoyang Wang, Songyan xie, Yuan Fu, Yili Li, Yunquan Jiang, Pei Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Jiangsu Peoples R China State Key Lab Robot & Syst HIT Harbin 150001 Peoples R China South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guang Zhou 510640 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Adv Equipment Chongqing Peoples R China

Pneumatically driven soft actuators with sensors have been developing rapidly these years. They can perceive external stimulus and be applied to different scenarios. In this study, we present a novel soft robotic finger with sensorized finger pulp based on sealing a flexible fabric piezoresistive film called Velostat into a pre-charged air bag, which can perceive the contact force with an object based on changes in resistance value of the sensor. The soft sensor mimics human finger pulp and deforms passively according to the shape of objects during grasping, so that it can firmly contact with objects and as such improves the gripper's grasping stability. Moreover, based on force feedback, the actuator can reduce or increase the input pressure to hold the object and control the contact force precisely. The sensor exhibits a sensitivity of up to 0.328 kPa-1 and can measure pressures ranging from 0 to over 10 kPa. The sensor's measurement range and sensitivity can be pre-adjusted by regulating the pre-charged pressure during fabrication for different grasping tasks. The response/recovery time of the sensor is 80/60 ms on average. Experiments show that the finger with sensorized pulp can be applied for object softness and size detection, object transport minitoring as well as force control grasping. The proposed soft robotic finger has potential for applications in scenarios that require safe contact and closed-loop control.

关键词： soft sensor pneumatical actuator soft robotics passive deformation robotic gripper

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FINGERTIP PLETHYSMOGRAPHY USING A SENSITIVE WAFER-LEVEL-PACKAGED CAPACITIVE MEMS STRAIN sensor 37

FINGERTIP PLETHYSMOGRAPHY USING A SENSITIVE WAFER-LEVEL-PACK...

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37th IEEE International conference on Micro Electro Mechanical systems (MEMS)

作者： Jiang, xinyu Shokouhmand, Arash Ebadi, Negar Ayazi, Farrokh Georgia Inst Technol Atlanta GA 30332 USA Stevens Inst Technol Hoboken NJ USA

ISBN: (纸本)9798350357929

We introduce a highly sensitive out-of-plane capacitive silicon strain sensor designed for wearable health monitoring applications, specifically targeting finger plethysmography. Featuring a compact wafer-level-packaged die size measuring 2.5 mm x 2.2 mm x 0.85 mm, the sensor exhibits a strain sensitivity of 1.1 fF/mu epsilon in a linear range of 700 mu epsilon. We demonstrate that fingertip strain plethysmography (SPG), facilitated by this novel device, effectively tracks photo-plethysmography (PPG) recordings that are conventionally used for pulse oximetry. SPG delivers accurate monitoring of heart rate (HR), heart rate variability (HRV), and respiratory rate (RR). It also holds promise for non-invasive continuous measurement of blood pressure (BP) through the application of the pulse transit time (PTT) principle. The high sensitivity of the device enables detection of small physiological strains even after being mounted onto a small PCB board, showcasing great potential for use in challenging wearable health-monitoring platforms.

关键词： MEMS Strain sensor Wearable sensor Strain Plethysmography PPG

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Laser-Induced, Cost-effective Manufacturing of Stretchable Electrochemical sensor for Glucose Monitoring 15

Laser-Induced, Cost-effective Manufacturing of Stretchable E...

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15th International conference on Signal Processing systems, ICSPS 2023

作者： Zhang, Chunxiao Luo, Zebang Wang, Wenqiang xiang, Li Research Institute of Hunan University in Chongqing Hunan University Chongqing401120 China College of Material Science and Engineering Hunan University Changsha410082 China

ISBN: (纸本)9781510675056

Chemical indicators in biofluids could provide tremendous amount of information, leading to great application potential of stretchable chemical sensors in the field of advanced clinical therapy and health monitoring. Graphene possesses excellent optical, electrical, and mechanical properties, making it highly promising for many applications in electrochemical sensing and biosensing. However, traditional methods of graphene preparation have notable limitations, such as low production efficiency or complex manufacturing processes. In this study, using laser induced graphene (LIG) produced on polyimide (PI) substrate, we propose a method to transfer LIG onto a flexible substrate using the semi-solidification state of PDMS(Polydimethylsiloxane) and explore effects of temperature and time on transfer printing. As a result, the LIG/PDMS could serve as a flexible electrode and the electrode’s resistance only changes 14% after 70 cycles of 10% stretching. The practicality of the LIG/PDMS flexible electrode is effectively demonstrated through the electrodeposition of Prussian blue and detection of 50μM glucose, which could be used as stretchable glucose sensor in the future integrated chemical health monitoring systems. © 2024 SPIE.

关键词： Graphene

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A 22-nm All-Digital Time-Domain Neural Network Accelerator for Precision In-sensor Processing

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) systems 2024年第12期32卷 2220-2231页

作者： Mohey, Ahmed M. Leslin, Jelin Singh, Gaurav Kosunen, Marko Ryynanen, Jussi Andraud, Martin Aalto Univ Dept Elect & Nanoengn Espoo 02150 Finland UCLouvain ICTEAM B-1348 Leuven Belgium

Deep neural network (DNN) accelerators are increasingly integrated into sensing applications, such as wearables and sensor networks, to provide advanced in-sensor processing capabilities. Given wearables' strict size and power requirements, minimizing the area and energy consumption of DNN accelerators is a critical concern. In that regard, computing DNN models in the time domain is a promising architecture, taking advantage of both technology scaling friendliness and efficiency. Yet, time-domain accelerators are typically not fully digital, limiting the full benefits of time-domain computation. In this work, we propose an all-digital time-domain accelerator with a small size and low energy consumption to target precision in-sensor processing like human activity recognition (HAR). The proposed accelerator features a simple and efficient architecture without dependencies on analog nonidealities such as leakage and charge errors. An eight-neuron layer (core computation layer) is implemented in 22-nm FD-SOI technology. The layer occupies 70x70 mu m while supporting multibit inputs (8-bit) and weights (8-bit) with signed accumulation up to 18 bits. The power dissipation of the computation layer is 576 mu W at 0.72-V supply and 500-MHz clock frequency achieving an average area efficiency of 24.74 GOPS/mm(2) (up to 544.22 GOPS/mm(2)), an average energy efficiency of 0.21 TOPS/W (up to 4.63 TOPS/W), and a normalized energy efficiency of 13.46 1b-TOPS/W (up to 296.30 1b-TOPS/W).

关键词： Human activity recognition Neurons Accuracy Time-domain analysis Computational modeling Artificial neural networks Computer architecture Wearable devices Energy efficiency sensors Edge computing human activity recognition (HAR) inertial measurement unit (IMU) in-sensor processing multiply-and-accumulate multiply and accumulate (MAC) neural network accelerator smart sensor interface time-domain signal processing

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