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/UCET51115.2020.9205486
T. Benseman, K. Kihlstrom, A. Koshelev, U. Welp, W. Kwok, K. Kadowaki
The high-temperature superconductor Bi2 Sr2 CaCu2 O8 contains stacked ‘intrinsic’ Josephson junctions, with unrivaled packing density and a high superconducting gap energy. Cuboid ‘mesa’ devices constructed from this material are consequently a promising technology for coherent, continuouswave radiation in the ‘terahertz gap’ range, spanning from approximately 0.3-1.5 THz. A key issue for practical applications of such devices is their cryocooling requirements, and it is therefore highly desirable to optimize their performance at temperatures that can be achieved by nitrogen cryogenics. Here we report generation of 0.13 milliwatts of coherent emission power at 0.461 THz, at a bath temperature of 77.4 Kelvin. This was achieved by exciting the (3, 0) cavity mode of a stack containing 579 junctions, and with Tc of 86.5 Kelvin. In order to minimize selfheating, the THz source was mounted on a copper substrate using PbSn solder. We will discuss the choice of mesa dimensions and cavity mode, and implications for the design of devices which are intended to operate close to the material’s superconducting critical temperature.
{"title":"Stacked Intrinsic Josephson Junction Bi2 Sr2 CaCu2 O8 Terahertz Sources: Design Issues for Achieving High Power Output Close to Tc","authors":"T. Benseman, K. Kihlstrom, A. Koshelev, U. Welp, W. Kwok, K. Kadowaki","doi":"10.1109/UCET51115.2020.9205486","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205486","url":null,"abstract":"The high-temperature superconductor Bi2 Sr2 CaCu2 O8 contains stacked ‘intrinsic’ Josephson junctions, with unrivaled packing density and a high superconducting gap energy. Cuboid ‘mesa’ devices constructed from this material are consequently a promising technology for coherent, continuouswave radiation in the ‘terahertz gap’ range, spanning from approximately 0.3-1.5 THz. A key issue for practical applications of such devices is their cryocooling requirements, and it is therefore highly desirable to optimize their performance at temperatures that can be achieved by nitrogen cryogenics. Here we report generation of 0.13 milliwatts of coherent emission power at 0.461 THz, at a bath temperature of 77.4 Kelvin. This was achieved by exciting the (3, 0) cavity mode of a stack containing 579 junctions, and with Tc of 86.5 Kelvin. In order to minimize selfheating, the THz source was mounted on a copper substrate using PbSn solder. We will discuss the choice of mesa dimensions and cavity mode, and implications for the design of devices which are intended to operate close to the material’s superconducting critical temperature.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"1 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":"129619416","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/UCET51115.2020.9205411
Moh Chuan Tan, Minghui Li, Q. Abbasi, M. Imran
Sensor fusion is a well-known technique to harvest the raw data from various type of sensors and generate a more accurate prediction on certain operation parameters that helps to improve the accuracy and efficiency of a big system. Many industries have been benefited from the sensor fusion such as robotic, agriculture, healthcare, autonomous vehicle, navigation and so on. In the smart antenna industry, the conventional beamforming is implemented in the costly field programmable grid array (FPGA) platform with the complex direction of arrival (DOA) algorithm. In this work, we are presenting a feasibility study on a lower cost alternative called sensor aided beamforming that make use of the raw data from the existing sensors in the vehicle, combined with some simple mathematically calculation to determine the beam angle of the mobile client and roadside infrastructure. We have presented a practical approach to study the sensor aided beamforming system in the real environment by simulating the beamforming parameters for a moving vehicle moves along the road that was pre-installed with roadside access points (AP). The result has proofed that the sensor aided method can be used to realize the beamforming in the smart antenna system, with the IoT sensors cost approximately less than U$20 compared with the FPGA price range of around U$200, the sensor aided beamforming will be a cheaper and affordable alternative to the conventional beamforming system that usually realized with the complex direction of arrival algorithm and higher cost.
{"title":"Sensor Aided Beamforming in Vehicular Environment","authors":"Moh Chuan Tan, Minghui Li, Q. Abbasi, M. Imran","doi":"10.1109/UCET51115.2020.9205411","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205411","url":null,"abstract":"Sensor fusion is a well-known technique to harvest the raw data from various type of sensors and generate a more accurate prediction on certain operation parameters that helps to improve the accuracy and efficiency of a big system. Many industries have been benefited from the sensor fusion such as robotic, agriculture, healthcare, autonomous vehicle, navigation and so on. In the smart antenna industry, the conventional beamforming is implemented in the costly field programmable grid array (FPGA) platform with the complex direction of arrival (DOA) algorithm. In this work, we are presenting a feasibility study on a lower cost alternative called sensor aided beamforming that make use of the raw data from the existing sensors in the vehicle, combined with some simple mathematically calculation to determine the beam angle of the mobile client and roadside infrastructure. We have presented a practical approach to study the sensor aided beamforming system in the real environment by simulating the beamforming parameters for a moving vehicle moves along the road that was pre-installed with roadside access points (AP). The result has proofed that the sensor aided method can be used to realize the beamforming in the smart antenna system, with the IoT sensors cost approximately less than U$20 compared with the FPGA price range of around U$200, the sensor aided beamforming will be a cheaper and affordable alternative to the conventional beamforming system that usually realized with the complex direction of arrival algorithm and higher cost.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"16 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":"129831458","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/UCET51115.2020.9205414
Syed Yaseen Shah, H. Larijani, Ryan M. Gibson, D. Liarokapis
The past few decades have witnessed a sharp increase in life expectancy. As a result, the proportion of elderly people is increasing worldwide. Consequently, Dementia and Parkinson’s disease are expected to rise, thereby increasing the risk of critical events such as falls for elderly people. This has prompted many researchers to develop a wide range of solutions for fall detection and prevention. However, these solutions are either inaccurate or impractical due to hardware complexity. In this paper, we have proposed a novel Random Neural Network (RNN) based fall detection scheme. Results obtained from the proposed RNN-based scheme are compared with traditional machine learning methods such as Support Vector Machine (SVM) and traditional Artificial Neural Network (ANN) etc. From the results, it is evident that the proposed scheme has a higher accuracy of 98%. Additionally, several other parameters such as precision, recall, specificity, and F-measure show that the proposed algorithm has better generalisation capabilities when compared with other traditional machine learning schemes. Furthermore, the proposed RNN is also compared with a recent scheme and the obtained results demonstrate the superiority of the proposed scheme.
{"title":"A Novel Random Neural Network-based Fall Activity Recognition","authors":"Syed Yaseen Shah, H. Larijani, Ryan M. Gibson, D. Liarokapis","doi":"10.1109/UCET51115.2020.9205414","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205414","url":null,"abstract":"The past few decades have witnessed a sharp increase in life expectancy. As a result, the proportion of elderly people is increasing worldwide. Consequently, Dementia and Parkinson’s disease are expected to rise, thereby increasing the risk of critical events such as falls for elderly people. This has prompted many researchers to develop a wide range of solutions for fall detection and prevention. However, these solutions are either inaccurate or impractical due to hardware complexity. In this paper, we have proposed a novel Random Neural Network (RNN) based fall detection scheme. Results obtained from the proposed RNN-based scheme are compared with traditional machine learning methods such as Support Vector Machine (SVM) and traditional Artificial Neural Network (ANN) etc. From the results, it is evident that the proposed scheme has a higher accuracy of 98%. Additionally, several other parameters such as precision, recall, specificity, and F-measure show that the proposed algorithm has better generalisation capabilities when compared with other traditional machine learning schemes. Furthermore, the proposed RNN is also compared with a recent scheme and the obtained results demonstrate the superiority of the proposed scheme.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"6 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":"130557555","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/UCET51115.2020.9205366
Yixin Li, Bin Cao, Liang Liang, Lei Zhang, M. Peng, M. Imran
Blockchain, a distributed ledger technology, has attracted many attentions to enable a decentralized and safe wireless networks for various applications. Considering the high density of nodes and the massive service requests in next-generation wireless network will result in a surge of blockchain forking, this paper proposes a Block Access Control (BAC) approach to address forking problem and transmit block effectively while improving transaction throughput and saving computational power. Then, using a Markov chain model, we analyse the performance of a wireless blockchain network by involving the effect of BAC approach. The results show that the BAC approach can help the network to achieve a high transaction throughput while addressing forking problem.
{"title":"A Block Access Control in Wireless Blockchain Networks","authors":"Yixin Li, Bin Cao, Liang Liang, Lei Zhang, M. Peng, M. Imran","doi":"10.1109/UCET51115.2020.9205366","DOIUrl":"https://doi.org/10.1109/UCET51115.2020.9205366","url":null,"abstract":"Blockchain, a distributed ledger technology, has attracted many attentions to enable a decentralized and safe wireless networks for various applications. Considering the high density of nodes and the massive service requests in next-generation wireless network will result in a surge of blockchain forking, this paper proposes a Block Access Control (BAC) approach to address forking problem and transmit block effectively while improving transaction throughput and saving computational power. Then, using a Markov chain model, we analyse the performance of a wireless blockchain network by involving the effect of BAC approach. The results show that the BAC approach can help the network to achieve a high transaction throughput while addressing forking problem.","PeriodicalId":163493,"journal":{"name":"2020 International Conference on UK-China Emerging Technologies (UCET)","volume":"281 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":"125861896","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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