Pub Date : 2020-08-01DOI: 10.1109/UCET51115.2020.9205380
A. F. M. Fazilah, M. Jusoh, T. Sabapathy, Q. Abbasi, K. Hossain, H. Rahim, M. Yasin, M. N. Osman, M. Kamarudin, H. Majid, P. Soh
This paper presents a miniaturized UHF RFID tag antenna with increased gain using meander line techniques and metamaterial (MTM). The designed tag operates in the UHF RFID frequency band ranging from 860 to 960 MHz. It comprises of meandered lines with two hexagonal split ring resonators (H-SRRs) MTM cells. It is designed on a photo paper as its substrate which is 0.27 mm thick, with a dielectric constant of 3.2 and loss tangent of 0.05. Next, an RFID tag (NXP SL381213 UCODE G2iL chip) with an impedance of 23-j224 $Omega$ is integrated with the proposed antenna to assess its performance in terms of reflection coefficient, antenna gain and maximum reading range. The overall size of the tag is 92 mm x26 mm.
{"title":"A Flexible and Compact Metamaterial UHF RID Tag for Remote Sensing in Human Health","authors":"A. F. M. Fazilah, M. Jusoh, T. Sabapathy, Q. Abbasi, K. Hossain, H. Rahim, M. Yasin, M. N. Osman, M. Kamarudin, H. Majid, P. Soh","doi":"10.1109/UCET51115.2020.9205380","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205380","url":null,"abstract":"This paper presents a miniaturized UHF RFID tag antenna with increased gain using meander line techniques and metamaterial (MTM). The designed tag operates in the UHF RFID frequency band ranging from 860 to 960 MHz. It comprises of meandered lines with two hexagonal split ring resonators (H-SRRs) MTM cells. It is designed on a photo paper as its substrate which is 0.27 mm thick, with a dielectric constant of 3.2 and loss tangent of 0.05. Next, an RFID tag (NXP SL381213 UCODE G2iL chip) with an impedance of 23-j224 $Omega$ is integrated with the proposed antenna to assess its performance in terms of reflection coefficient, antenna gain and maximum reading range. The overall size of the tag is 92 mm x26 mm.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132423833","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}
Pub Date : 2020-08-01DOI: 10.1109/UCET51115.2020.9205446
A. T. Kiyani, A. Lasebae, Kamran Ali
A user authentication method consists of a username, password, or any other related credential. These methods are mostly used only once to validate the user’s identity at the start of session. However, one-time verification of user’s identity is not resilient enough to provide adequate security all over the session. Such authentication methods should be adopted which can continuously verify that only genuine user is using the system resources for entire session. This research work has implemented a true continuous authentication system, based on keystroke dynamics, which tends to validate the user on each action by using the proposed robust recurrent confidence model(R-RCM). Moreover, the recurrent neural network(RNN) has been used to exploit the sequential nature of keystroke data. System has been tested with two experimental approaches and results are reported in mean genuine actions (ANGA) and imposter actions (ANIA).
{"title":"Continuous User Authentication Based on Deep Neural Networks","authors":"A. T. Kiyani, A. Lasebae, Kamran Ali","doi":"10.1109/UCET51115.2020.9205446","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205446","url":null,"abstract":"A user authentication method consists of a username, password, or any other related credential. These methods are mostly used only once to validate the user’s identity at the start of session. However, one-time verification of user’s identity is not resilient enough to provide adequate security all over the session. Such authentication methods should be adopted which can continuously verify that only genuine user is using the system resources for entire session. This research work has implemented a true continuous authentication system, based on keystroke dynamics, which tends to validate the user on each action by using the proposed robust recurrent confidence model(R-RCM). Moreover, the recurrent neural network(RNN) has been used to exploit the sequential nature of keystroke data. System has been tested with two experimental approaches and results are reported in mean genuine actions (ANGA) and imposter actions (ANIA).","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128182852","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}
Pub Date : 2020-08-01DOI: 10.1109/UCET51115.2020.9205318
Guangjin Shen, Muhammad R. A. Khandaker, Faisal Tariq
In this paper, we propose a new deep neural network (DNN)-based channel estimation method for the Rayleigh fading channel model. While deep learning has been considered for estimating channels in many communication scenarios, direct estimation of the basic wireless single-input single-output (SISO) communication channel coefficients has not been considered. The proposed DNN-based method can efficiently estimate the channel in real time. Extensive simulation results demonstrate that the proposed channel estimator outperforms conventional least square (LS) estimators in terms of bit error rate (BER) and mean square error (MSE). In addition, the proposed channel does not need channel statistics information or complex matrix computation, thereby reducing the amount of calculation significantly.
{"title":"Learning the Wireless Channel: A Deep Neural Network Approach","authors":"Guangjin Shen, Muhammad R. A. Khandaker, Faisal Tariq","doi":"10.1109/UCET51115.2020.9205318","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205318","url":null,"abstract":"In this paper, we propose a new deep neural network (DNN)-based channel estimation method for the Rayleigh fading channel model. While deep learning has been considered for estimating channels in many communication scenarios, direct estimation of the basic wireless single-input single-output (SISO) communication channel coefficients has not been considered. The proposed DNN-based method can efficiently estimate the channel in real time. Extensive simulation results demonstrate that the proposed channel estimator outperforms conventional least square (LS) estimators in terms of bit error rate (BER) and mean square error (MSE). In addition, the proposed channel does not need channel statistics information or complex matrix computation, thereby reducing the amount of calculation significantly.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128322997","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}
Pub Date : 2020-08-01DOI: 10.1109/UCET51115.2020.9205356
Muhammad Umaid Bukhari, M. Sajid, M. Q. Mehmood, M. Zubair, K. Riaz
Triboelectric nanogenerators (TENGs) have been introduced as a rational and effective way of powering the low consuming electronic devices and sensors. Mostly these nanogenerators are manufactured using costly materials and complex fabrication processes that restrict their usage especially in developing countries. In this paper, we have presented an inexpensive and facile TENG for powering the environmental sensor. Readily available and cost-effective materials were used for manufacturing TENG like paper, thread sealant PTFE tape, and pencil. The manufactured paper based TENG produced short circuit current and open circuit voltage of $110 mu mathrm{A}$ and 234 V respectively. The maximum power density of $96.22 mu mathrm{W} /$ cm2 was achieved at 5 M $Omega$. The rectified signal from TENG was used to charge a capacitor array of $300 mu mathrm{F}$ which was successively used to power the environmental sensor. Temperature and humidity can be measured by powering the environmental sensor with the help of the proposed TENG. This facile and cost-effective TENG can be easily manufactured and deployed in resource limited environment for self-powered sensing systems.
摩擦纳米发电机(TENGs)作为一种合理而有效的低功耗电子器件和传感器供电方式被引入。这些纳米发电机大多是使用昂贵的材料和复杂的制造工艺制造的，这限制了它们的使用，特别是在发展中国家。在本文中，我们提出了一种廉价和方便的TENG为环境传感器供电。制造TENG所用的材料包括纸张、螺纹密封剂、聚四氟乙烯胶带和铅笔。纸制TENG的短路电流和开路电压分别为$110 mu mathrm{A}$和234v。在5 M处达到$96.22 mu mathrm{W} /$ cm2的最大功率密度$Omega$。来自TENG的整流信号用于给$300 mu mathrm{F}$电容器阵列充电，该电容器阵列依次用于为环境传感器供电。温度和湿度可以通过在提出的TENG的帮助下为环境传感器供电来测量。这种简易和经济高效的TENG可以很容易地制造和部署在资源有限的环境中用于自供电传感系统。
{"title":"Facile and Cost Effective Paper Based Triboelectric Nanogenerators for Self Powered Environmental Sensing System","authors":"Muhammad Umaid Bukhari, M. Sajid, M. Q. Mehmood, M. Zubair, K. Riaz","doi":"10.1109/UCET51115.2020.9205356","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205356","url":null,"abstract":"Triboelectric nanogenerators (TENGs) have been introduced as a rational and effective way of powering the low consuming electronic devices and sensors. Mostly these nanogenerators are manufactured using costly materials and complex fabrication processes that restrict their usage especially in developing countries. In this paper, we have presented an inexpensive and facile TENG for powering the environmental sensor. Readily available and cost-effective materials were used for manufacturing TENG like paper, thread sealant PTFE tape, and pencil. The manufactured paper based TENG produced short circuit current and open circuit voltage of $110 mu mathrm{A}$ and 234 V respectively. The maximum power density of $96.22 mu mathrm{W} /$ cm2 was achieved at 5 M $Omega$. The rectified signal from TENG was used to charge a capacitor array of $300 mu mathrm{F}$ which was successively used to power the environmental sensor. Temperature and humidity can be measured by powering the environmental sensor with the help of the proposed TENG. This facile and cost-effective TENG can be easily manufactured and deployed in resource limited environment for self-powered sensing systems.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131631012","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}
Pub Date : 2020-08-01DOI: 10.1109/UCET51115.2020.9205420
Salah Berra, M. Albreem, M. Abed
In massive multiple-input multiple-output (MIMO), the base-station (BS) is equipped with tens or hundreds of antennas to serve many users. The demand for a low complexity precoding algorithm is significantly increased. Linear precoding schemes, such as zero-forcing (ZF), are capable to obtain a quasi-optimum performance when a favourable channel propagation occurs in downlink massive (MIMO) systems. Particularly, conventional linear precoding techniques require to calculate a matrix inverse which increases the expenses of computational complexity. In this paper, a low complexity linear precoder based on a hybrid acceleration overrelaxation (AOR) method is proposed. The optimal relaxation, acceleration parameters, and the initial solution are selected to achieve an attractive balance between the performance and the computational complexity. Numerical results show that the proposed algorithm has greatly reduced the complexity from $mathcal{O}(K^{3})$ to $mathcal{O}(K^{2})$ (where K is the number of users). It also outperforms the up-to-date algorithms.
{"title":"A Low Complexity Linear Precoding Method for Massive MIMO","authors":"Salah Berra, M. Albreem, M. Abed","doi":"10.1109/UCET51115.2020.9205420","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205420","url":null,"abstract":"In massive multiple-input multiple-output (MIMO), the base-station (BS) is equipped with tens or hundreds of antennas to serve many users. The demand for a low complexity precoding algorithm is significantly increased. Linear precoding schemes, such as zero-forcing (ZF), are capable to obtain a quasi-optimum performance when a favourable channel propagation occurs in downlink massive (MIMO) systems. Particularly, conventional linear precoding techniques require to calculate a matrix inverse which increases the expenses of computational complexity. In this paper, a low complexity linear precoder based on a hybrid acceleration overrelaxation (AOR) method is proposed. The optimal relaxation, acceleration parameters, and the initial solution are selected to achieve an attractive balance between the performance and the computational complexity. Numerical results show that the proposed algorithm has greatly reduced the complexity from $mathcal{O}(K^{3})$ to $mathcal{O}(K^{2})$ (where K is the number of users). It also outperforms the up-to-date algorithms.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125607151","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}
Pub Date : 2020-08-01DOI: 10.1109/UCET51115.2020.9205423
Jasmine Zidan, Osama Alluhaibi, E. I. Adegoke, E. Kampert, M. Higgins, Col R. Ford
In urban canyons, the positioning accuracy obtainable from global navigation satellite systems (GNSS) is mainly impaired by signal interference due to multipath and non-lineof-sight (NLOS) effects. GNSS is one of the sensors used in connected autonomous vehicles (CAVs) for positioning, navigation and timing (PNT). Hence, it is essential that GNSS receivers in CAVs are robust and resilient. In this paper, a method consisting of two layers of GNSS observation checks is suggested to exclude these effects in order to improve the positioning accuracy. The first layer excludes all non-consistent measurements identified by a chi-square test threshold. The second layer uses a decision tree for the exclusion of any remaining multipath/NLOS affected measurements, based on a data set obtained from a ray tracer for a 3D mapped model environment. The simulation results show an enhancement in positioning accuracy greater than 95%.
{"title":"3D Mapping Methods and Consistency Checks to Exclude GNSS Multipath/NLOS Effects","authors":"Jasmine Zidan, Osama Alluhaibi, E. I. Adegoke, E. Kampert, M. Higgins, Col R. Ford","doi":"10.1109/UCET51115.2020.9205423","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205423","url":null,"abstract":"In urban canyons, the positioning accuracy obtainable from global navigation satellite systems (GNSS) is mainly impaired by signal interference due to multipath and non-lineof-sight (NLOS) effects. GNSS is one of the sensors used in connected autonomous vehicles (CAVs) for positioning, navigation and timing (PNT). Hence, it is essential that GNSS receivers in CAVs are robust and resilient. In this paper, a method consisting of two layers of GNSS observation checks is suggested to exclude these effects in order to improve the positioning accuracy. The first layer excludes all non-consistent measurements identified by a chi-square test threshold. The second layer uses a decision tree for the exclusion of any remaining multipath/NLOS affected measurements, based on a data set obtained from a ray tracer for a 3D mapped model environment. The simulation results show an enhancement in positioning accuracy greater than 95%.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133948116","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}
Pub Date : 2020-08-01DOI: 10.1109/UCET51115.2020.9205350
N. Malik, T. Ajmal, P. Sant, M. Rehman
Implantable antennas play a vital role in implantable sensors and medical devices. In this paper, we present the design of a compact size implantable antenna for biomedical applications. The antenna is designed to operate in ISM band at 915 MHz and the overall size of the antenna is $4 times 4 times 0.3 mm ^{3}$. A shorting pin is used to lower the operating frequency of the antenna. For excitation purpose a 50-ohm coaxial probe feed is used in the design. A superstrate layer is placed on the patch to prevent the direct contact between the radiating patch and body tissues. The antenna is simulated in skin layer model. The designed antenna demonstrates a gain of 3.22 dBi while having a -10 dB bandwidth of 240 MHz with good radiation characteristics at 915 MHz. The simulated results show that this antenna is an excellent candidate for implantable applications.
{"title":"A Compact Size Implantable Antenna for Bio-medical Applications","authors":"N. Malik, T. Ajmal, P. Sant, M. Rehman","doi":"10.1109/UCET51115.2020.9205350","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205350","url":null,"abstract":"Implantable antennas play a vital role in implantable sensors and medical devices. In this paper, we present the design of a compact size implantable antenna for biomedical applications. The antenna is designed to operate in ISM band at 915 MHz and the overall size of the antenna is $4 times 4 times 0.3 mm ^{3}$. A shorting pin is used to lower the operating frequency of the antenna. For excitation purpose a 50-ohm coaxial probe feed is used in the design. A superstrate layer is placed on the patch to prevent the direct contact between the radiating patch and body tissues. The antenna is simulated in skin layer model. The designed antenna demonstrates a gain of 3.22 dBi while having a -10 dB bandwidth of 240 MHz with good radiation characteristics at 915 MHz. The simulated results show that this antenna is an excellent candidate for implantable applications.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124102375","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}
Pub Date : 2020-08-01DOI: 10.1109/UCET51115.2020.9205452
Lemayian Joel Poncha, Jehad M. Hamamreh
Massive MIMO (mMIMO) has been classified as one of the high potential future wireless communication technologies due to its unique abilities such as high user capacity, increased spectral density, and diversity. Due to the exponential increase of connected devices, these properties are critical for the current 5G-IoT era and future telecommunication networks. However, outdated channel state information (CSI) causes major performance degradation in mMIMO systems. Nevertheless, channel prediction using neural networks (NN) has gained tremendous attention as a way of mitigating outdated CSI. Hence, combined mMIMO and NN-based channel prediction is a revolutionary technology of future wireless communications. In this work, we review the current recurrent neural network-based (RNN-based) mMIMO channel prediction schemes and propose a low complexity, low cost channel prediction scheme.
{"title":"Recurrent Neural Network-based Channel Prediction in mMIMO for Enhanced Performance in Future Wireless Communication","authors":"Lemayian Joel Poncha, Jehad M. Hamamreh","doi":"10.1109/UCET51115.2020.9205452","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205452","url":null,"abstract":"Massive MIMO (mMIMO) has been classified as one of the high potential future wireless communication technologies due to its unique abilities such as high user capacity, increased spectral density, and diversity. Due to the exponential increase of connected devices, these properties are critical for the current 5G-IoT era and future telecommunication networks. However, outdated channel state information (CSI) causes major performance degradation in mMIMO systems. Nevertheless, channel prediction using neural networks (NN) has gained tremendous attention as a way of mitigating outdated CSI. Hence, combined mMIMO and NN-based channel prediction is a revolutionary technology of future wireless communications. In this work, we review the current recurrent neural network-based (RNN-based) mMIMO channel prediction schemes and propose a low complexity, low cost channel prediction scheme.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124525391","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}
Pub Date : 2020-08-01DOI: 10.1109/UCET51115.2020.9205378
K. Driss, W. Boulila, Amreen Batool, Jawad Ahmad
Since the last decade, many research studies has been conducted on machine learning-based diabetes disease prediction using diagnostic measurement. However, the main challenge in machine learning-based diabetes disease prediction is the preprocessing of data, which contains, in most cases missing values and outliers. For data analytics and accurate prediction, data cleansing is highly desired and recommended. The goal of this study is to predict diabetic patients using realworld datasets. The proposed approach is based on three main steps: cleansing, modelling, and storytelling. In the first step, an imputation process is conducted to remove missing values. Then, k-nearest neighbor’s algorithm is applied to classify patients. To evaluate the performance of the proposed approach, two criteria, namely the F1 score and the Receiver Operating Characteristic (ROC) has been used. F1 score and ROC curve show a clear distinction between diabetic and nondiabetic patients.
{"title":"A Novel Approach for Classifying Diabetes’ Patients Based on Imputation and Machine Learning","authors":"K. Driss, W. Boulila, Amreen Batool, Jawad Ahmad","doi":"10.1109/UCET51115.2020.9205378","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205378","url":null,"abstract":"Since the last decade, many research studies has been conducted on machine learning-based diabetes disease prediction using diagnostic measurement. However, the main challenge in machine learning-based diabetes disease prediction is the preprocessing of data, which contains, in most cases missing values and outliers. For data analytics and accurate prediction, data cleansing is highly desired and recommended. The goal of this study is to predict diabetic patients using realworld datasets. The proposed approach is based on three main steps: cleansing, modelling, and storytelling. In the first step, an imputation process is conducted to remove missing values. Then, k-nearest neighbor’s algorithm is applied to classify patients. To evaluate the performance of the proposed approach, two criteria, namely the F1 score and the Receiver Operating Characteristic (ROC) has been used. F1 score and ROC curve show a clear distinction between diabetic and nondiabetic patients.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115285663","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}
Pub Date : 2020-08-01DOI: 10.1109/UCET51115.2020.9205377
Zongkang Li, J. Marsh, L. Hou
A miniature portable laser ranging system based on an STM32 microcontroller and VL53L0X time-of-flight laser ranging sensor was developed. An LCD display indicates the real time measured range value. Two working modes with different accuracy and detectable range were designed. These comprise high accuracy and long-distance modes which make the equipment very versatile for real applications. Following code optimization and verification by calipers, the relative measurement error is around 1% – 2% for both modes.
{"title":"High Precision Laser Ranging Based on STM32 Microcontroller","authors":"Zongkang Li, J. Marsh, L. Hou","doi":"10.1109/UCET51115.2020.9205377","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205377","url":null,"abstract":"A miniature portable laser ranging system based on an STM32 microcontroller and VL53L0X time-of-flight laser ranging sensor was developed. An LCD display indicates the real time measured range value. Two working modes with different accuracy and detectable range were designed. These comprise high accuracy and long-distance modes which make the equipment very versatile for real applications. Following code optimization and verification by calipers, the relative measurement error is around 1% – 2% for both modes.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126294997","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}
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