2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO)最新文献_第2页

Synthesis of Carbon Nanotubes with Unintentional Secondary Growth of Carbon Nanofibers on Graphene 碳纳米管在石墨烯上非故意二次生长的合成

2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO) Pub Date : 2021-09-22 DOI: 10.1109/sennano51750.2021.9642549

Mohamad Nur Zharif Mohamad, Syarifah Norfaezah Sabki, N. Othman, Norzilah Abdul Halif, Mohd Shafiq Hafly Shamsuri, N. Sabani

引用次数: 0

Understanding the role of the density of traps in quantum dots integrated into light-emitting devices 了解集成到发光器件中的量子点陷阱密度的作用

2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO) Pub Date : 2021-09-22 DOI: 10.1109/sennano51750.2021.9642616

S. Rani, Jitendra Kumar

引用次数: 1

Study of the Impact of Design Factors on Micro-Fluxgate Sensing Element 设计因素对微磁通门传感元件的影响研究

2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO) Pub Date : 2021-09-22 DOI: 10.1109/sennano51750.2021.9642679

M. Elkattan, H. Mostafa, A. Khalil

{"title":"Study of the Impact of Design Factors on Micro-Fluxgate Sensing Element","authors":"M. Elkattan, H. Mostafa, A. Khalil","doi":"10.1109/sennano51750.2021.9642679","DOIUrl":"https://doi.org/10.1109/sennano51750.2021.9642679","url":null,"abstract":"One of the most important magnetic field sensors is the fluxgate magnetometer. Fluxgates are vectorial magnetic field sensors, used at low frequencies when a nano tesla accuracy and pico tesla resolution measurements are required. Fluxgate sensor operation depends on driving a nonlinear magnetic material into periodical saturation in order to detect weak magnetic fields at a certain measuring range. The main component of a fluxgate sensor is the core that constitutes its sensing element. Thus, an accurate prediction of the core’s behavior is mandatory in order to ensure a proper design of the fluxgate sensor. In this paper, a detailed study on the fluxgate core’s performance, and its dependence of operational and geometrical design factors, is done through numerical approach. Furthermore, the conducted study involves an appropriate model of the magnetic material part of the core, to get a better representative behavior of the fluxgate sensing element against different operating conditions. Results of the proposed numerical approach give a more reliable insight about the response of the fluxgate core against the investigated design factors.","PeriodicalId":325031,"journal":{"name":"2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131889318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and simulation of AIN-Based FBAR Resonator for Hydrogen sulfide Gas detection. 用于硫化氢气体检测的基于ai的FBAR谐振器设计与仿真。

2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO) Pub Date : 2021-09-22 DOI: 10.1109/sennano51750.2021.9642668

Saeed S. Ba Hashwan, M. H. M. Md Khir, Y. Al-Douri, A. Algamili, S. S. Alabsi

{"title":"Design and simulation of AIN-Based FBAR Resonator for Hydrogen sulfide Gas detection.","authors":"Saeed S. Ba Hashwan, M. H. M. Md Khir, Y. Al-Douri, A. Algamili, S. S. Alabsi","doi":"10.1109/sennano51750.2021.9642668","DOIUrl":"https://doi.org/10.1109/sennano51750.2021.9642668","url":null,"abstract":"The mathematical modeling, design, and simulation of the film bulk acoustic resonator (FBAR) sensor based on Aluminum nitride (AIN) thin film for hydrogen sulfide gas detection are presented in this paper. The FBAR sensor is designed according PiezoMUMPs fabrication technology. The mathematical modeling and finite element simulation were performed using MATLAB and CoventorWare software to investigate the FBAR sensor resonant frequency. The resonant frequency monitoring is considered the core principle of the FBAR gas sensor detection mechanism. The fundamental of the FBAR sensor is based on the resonant frequency reduction due to the mass changes of the nanomaterial sensitive layer that coated on the surface of the top electrode induced by hydrogen sulfide gas species absorption. The development of the graphene oxide-copper oxide hybrid thin film as sensitive layer is illustrated and their mass loaded was evaluated in the theoretical calculation. The theoretical calculation of the resonant frequency for the thickness extensional mode of the FBAR sensor was found to be 9.4524 GHz and it was verified using the CoventorWare simulation software which shown an excellent agreement between both calculated and simulated frequencies which found to be 9.4524 and 8.955 GHz, respectively.","PeriodicalId":325031,"journal":{"name":"2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129219311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Machine Control via Real Time Eye Detector 机器控制通过实时眼睛探测器

2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO) Pub Date : 2021-09-22 DOI: 10.1109/SENNANO51750.2021.9642685

Pei Yan Wong, R. Hussin, M. N. Md Isa

{"title":"Machine Control via Real Time Eye Detector","authors":"Pei Yan Wong, R. Hussin, M. N. Md Isa","doi":"10.1109/SENNANO51750.2021.9642685","DOIUrl":"https://doi.org/10.1109/SENNANO51750.2021.9642685","url":null,"abstract":"As the population ages, the number of people dependent on others who are paralyzed or losing their self-movement is increasing. This paper is focusing on the development of a smart robot based on wireless vision control which is designed for physically challenged individuals. The research employs a human pupil movement hands-free control machine for the moderate or severe physically disabled individuals by applying Convolution Neural Networks (CNNs) method to pre-train the model. The dataset contains the annotation information. By using the coordinates of the eye to identify the Iris’ location. This feature is to ensure the alignment of the eyes to identify the dominant eyes for Strabismus users. The result of this paper works as per expected with a preferable accuracy which fulfilled the objectives of doing this project. The delay time of the transmission data is negligible. Therefore, the synchronizing between the prototype and the eyeball movement of the user’s intention is nearly perfect in which the eyeball moves upward, the robot will go forward, and so on. In short, this project is to have a contribution to society in a small way by presenting an idea for a system that can truly improve the lives of physically disabled people around the world.","PeriodicalId":325031,"journal":{"name":"2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124877916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sensitivity analysis of a label-free detection using Opto-plasmonic nano-structured antenna 光等离子体纳米结构天线无标记检测的灵敏度分析

2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO) Pub Date : 2021-09-22 DOI: 10.1109/sennano51750.2021.9642511

S. Verma, Souvik Ghosh, B. M. A. Rahman

引用次数: 1

Project Kappa: An augmented-reality based sensor data crowdsourcing platform for environmental monitoring 卡帕项目：基于增强现实技术的环境监测传感器数据众包平台

2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO) Pub Date : 2021-09-22 DOI: 10.1109/sennano51750.2021.9642575

Ting Yang Ling, Yee Bryan Teh, Gerald Khing Kiat Ting, Wei Zhen Wong, N. Mahmood

{"title":"Project Kappa: An augmented-reality based sensor data crowdsourcing platform for environmental monitoring","authors":"Ting Yang Ling, Yee Bryan Teh, Gerald Khing Kiat Ting, Wei Zhen Wong, N. Mahmood","doi":"10.1109/sennano51750.2021.9642575","DOIUrl":"https://doi.org/10.1109/sennano51750.2021.9642575","url":null,"abstract":"Recent advancement in sensor technologies and internet of things (IoT) paved ways for the deployment of sensor networks for environmental monitoring. However, current sensor network requires the use of networking modules, such as WiFi, LoRaWan, NB-IoT or SigFox (LP-WAN), which requires higher power demand. The main feature of Project Kappa the synergy between the use of augmented reality (AR), gamification, Bluetooth and cloud-based IoT servers to crowdsource environmental sensor data acquisition. The application incentivizes users to connect their smartphones to low-powered environmental sensors via an augmented reality game. Once connected, data transmission between the sensor node and the cloud server will be made via the phone’s internet connection, reducing the need for power hungry networking modules. As a proof-of-concept, this paper describes the development of an air-quality monitoring sensor node, the augmented reality app, and its subsequent integration the cloud database. The prototype was tested with a group of 80 volunteers, demonstrating the viability of deploying the system for real world application.","PeriodicalId":325031,"journal":{"name":"2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129479191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Stability of guanine-quadruplexes as sensing elements investigated using solvation free energy 用溶剂自由能研究鸟嘌呤四聚物作为传感元件的稳定性

2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO) Pub Date : 2021-09-22 DOI: 10.1109/SENNANO51750.2021.9642634

Nadia Norhakim, Gohvinath Sandrasagran, H. F. Hawari, Z. Arif Burhanudin

引用次数: 0

SENNANO 2021 Table of Contents SENNANO 2021目录

2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO) Pub Date : 2021-09-22 DOI: 10.1109/sennano51750.2021.9642501

引用次数: 0

Artificial Neural Network Based Forecasting of Power Under Real Time Monitoring Environment 实时监测环境下基于人工神经网络的电力预测

2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO) Pub Date : 2021-09-22 DOI: 10.1109/sennano51750.2021.9642611

Muhammad Zilal Bin Ab Hamid Pahmi, A. Ayob, Shaheer Ansari, M. Saad, A. Hussain

{"title":"Artificial Neural Network Based Forecasting of Power Under Real Time Monitoring Environment","authors":"Muhammad Zilal Bin Ab Hamid Pahmi, A. Ayob, Shaheer Ansari, M. Saad, A. Hussain","doi":"10.1109/sennano51750.2021.9642611","DOIUrl":"https://doi.org/10.1109/sennano51750.2021.9642611","url":null,"abstract":"While photovoltaic (PV) has grown in popularity as a viable alternative to traditional energy sources in recent decades, it still has to improve in some areas to become the preferred energy source. The degradation of output power owing to soiling is one of the areas that require attention as it results in inefficient operation. The performance of clean and soiled solar panels is investigated in this work. The prediction algorithm is designed to forecast the output power of a solar panel based on the input parameters. Forecasting PV power is essential as it reduces uncertainty and assists in developing an effective PV technology. This work utilized three phases of development and validation to achieve these goals. Firstly, a data acquisition system to collect solar panel parameter data is designed, built, data stored on the ThingsSentral™ cloud platform. Secondly, a prediction algorithm based on machine learning ANN is created to estimate the output power of a solar panel, and thirdly the algorithm is simulated and tested. Measurements from two solar panels were used to test and analyze the performance of the proposed technique. Clean solar panels have an RMSE of 1.328, while dusty solar panels have an RMSE of 1.272, indicating this system can reliably forecast based on the training and test datasets provided. Both solar panel conditions have an R2 of 0.999, indicating that the solar panel dataset used in this project precisely matches the ANN model and accuracy. In conclusion, the data acquisition system and prediction algorithm employed in this work successfully met the project objectives based on the results.","PeriodicalId":325031,"journal":{"name":"2021 IEEE International Conference on Sensors and Nanotechnology (SENNANO)","volume":"388 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123509894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0