Pub Date : 2021-12-21DOI: 10.1109/NICS54270.2021.9701505
Hong-Hai Thai, C. Pham, Duc Hung Le
This paper presents a high-speed 8-bit Flash ADC. The design, which is considered as a mixed-signal type, includes two main blocks – comparator and encoder. The comparator block contains a TIQ comparator, a control circuit, and a proposed architecture of a Double-Tail (DT) comparator. The advantage of using the DT comparator is to reduce the half number of comparators which helps reduce the design area. The comparator is implemented with custom analog design meanwhile, the encoder block is designed with digital design flow. This mixed-signal circuit is designed and simulated on 180nm CMOS technology. The 8-bit Flash ADC only employs 128 comparators. The applied input clock for testing is 50 MHz with the input voltage ranging from 0.6V to 1.8V. Comparator block outputs 127 bits of thermometer code and sends them to the encoder, which exports 7 LSB bits of the binary code. The MSB bit is decided by only one DT comparator.
{"title":"Design of a High-speed 8-bit Flash ADC using Double-Tail Comparator on 180nm CMOS Process","authors":"Hong-Hai Thai, C. Pham, Duc Hung Le","doi":"10.1109/NICS54270.2021.9701505","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701505","url":null,"abstract":"This paper presents a high-speed 8-bit Flash ADC. The design, which is considered as a mixed-signal type, includes two main blocks – comparator and encoder. The comparator block contains a TIQ comparator, a control circuit, and a proposed architecture of a Double-Tail (DT) comparator. The advantage of using the DT comparator is to reduce the half number of comparators which helps reduce the design area. The comparator is implemented with custom analog design meanwhile, the encoder block is designed with digital design flow. This mixed-signal circuit is designed and simulated on 180nm CMOS technology. The 8-bit Flash ADC only employs 128 comparators. The applied input clock for testing is 50 MHz with the input voltage ranging from 0.6V to 1.8V. Comparator block outputs 127 bits of thermometer code and sends them to the encoder, which exports 7 LSB bits of the binary code. The MSB bit is decided by only one DT comparator.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116408778","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 : 2021-12-21DOI: 10.1109/NICS54270.2021.9701541
H. Tran, Hanh Hong-Phuc Vo, Son T. Luu
Finding a suitable job and hunting for eligible candidates are important to job seeking and human resource agencies. With the vast information about job descriptions, employees and employers need assistance to automatically detect job titles based on job description texts. In this paper, we propose the multi-label classification approach for predicting relevant job titles from job description texts, and implement the Bi-GRULSTM-CNN with different pre-trained language models to apply for the job titles prediction problem. The BERT with multilingual pre-trained model obtains the highest result by Fl-scores on both development and test sets, which are 62.20% on the development set, and 47.44% on the test set.
{"title":"Predicting Job Titles from Job Descriptions with Multi-label Text Classification","authors":"H. Tran, Hanh Hong-Phuc Vo, Son T. Luu","doi":"10.1109/NICS54270.2021.9701541","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701541","url":null,"abstract":"Finding a suitable job and hunting for eligible candidates are important to job seeking and human resource agencies. With the vast information about job descriptions, employees and employers need assistance to automatically detect job titles based on job description texts. In this paper, we propose the multi-label classification approach for predicting relevant job titles from job description texts, and implement the Bi-GRULSTM-CNN with different pre-trained language models to apply for the job titles prediction problem. The BERT with multilingual pre-trained model obtains the highest result by Fl-scores on both development and test sets, which are 62.20% on the development set, and 47.44% on the test set.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"11 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120807328","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 : 2021-12-21DOI: 10.1109/NICS54270.2021.9701523
Tran Nguyen Thi Nhat Le, Hoang Dang Cuong, Nguyen Thi Thanh Tam, M. Le, N. Dinh
In this work, an X-band reflectarray antenna using two concentric rings and a cross is proposed. Three types of unit cells are designed and analyzed to improve the phase sensitivity of the unit cell. A center-feed reflectarray antenna with dimensions of 205.7 mm x 205.7 mm is then designed. It performs an excellent gain of 26 dBi at 10 GHz. It also achieves a good aperture efficiency of 67%, a sidelobe level of -15.6 dB, and a 1-dB bandwidth of 12%.
在这项工作中，提出了一种采用两个同心圆环和一个十字的x波段反射天线。为了提高单元电池的相灵敏度，设计并分析了三种类型的单元电池。设计了尺寸为205.7 mm × 205.7 mm的中心馈源反射天线。它在10ghz时具有26dbi的优异增益。孔径效率为67%，旁瓣电平为-15.6 dB, 1 dB带宽为12%。
{"title":"An X-Band Reflectarray Antenna Using Concentric Rings and a Cross","authors":"Tran Nguyen Thi Nhat Le, Hoang Dang Cuong, Nguyen Thi Thanh Tam, M. Le, N. Dinh","doi":"10.1109/NICS54270.2021.9701523","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701523","url":null,"abstract":"In this work, an X-band reflectarray antenna using two concentric rings and a cross is proposed. Three types of unit cells are designed and analyzed to improve the phase sensitivity of the unit cell. A center-feed reflectarray antenna with dimensions of 205.7 mm x 205.7 mm is then designed. It performs an excellent gain of 26 dBi at 10 GHz. It also achieves a good aperture efficiency of 67%, a sidelobe level of -15.6 dB, and a 1-dB bandwidth of 12%.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122319498","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 : 2021-12-21DOI: 10.1109/NICS54270.2021.9701566
Loi Nguyen-Khanh, Vy Nguyen-Ngoc-Yen, Hung Dinh-Quoc
Satellite images contain an enormous data warehouse and give us details to the general perspective of what is happening on the earth’s surface. These images are essential for agricultural development research, urban planning, surveying and, especially for evaluating the location design of broadcast stations, the input of coverage simulation and signal quality in telecommunications. The analysis of large amounts of complex satellite imagery is challenging while the evolving semantic segmentation approaches based on convolution neural network (CNN) can assist in analyzing this amount of data. In this paper, we introduce an approach for constructing digital maps with dataset provided by Google. We utilize the efficient U-Net architecture, which is an efficient combination of EfficientNet, namely EfficientNet-B0 as the encoder to extract the geographic features with U-Net as decoder to reconstruct the detailed features map. We evaluate our models using Google satellite images which demonstrate the efficiency in terms of Dice Loss and Categorical Cross-Entropy.
{"title":"U-Net Semantic Segmentation of Digital Maps Using Google Satellite Images","authors":"Loi Nguyen-Khanh, Vy Nguyen-Ngoc-Yen, Hung Dinh-Quoc","doi":"10.1109/NICS54270.2021.9701566","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701566","url":null,"abstract":"Satellite images contain an enormous data warehouse and give us details to the general perspective of what is happening on the earth’s surface. These images are essential for agricultural development research, urban planning, surveying and, especially for evaluating the location design of broadcast stations, the input of coverage simulation and signal quality in telecommunications. The analysis of large amounts of complex satellite imagery is challenging while the evolving semantic segmentation approaches based on convolution neural network (CNN) can assist in analyzing this amount of data. In this paper, we introduce an approach for constructing digital maps with dataset provided by Google. We utilize the efficient U-Net architecture, which is an efficient combination of EfficientNet, namely EfficientNet-B0 as the encoder to extract the geographic features with U-Net as decoder to reconstruct the detailed features map. We evaluate our models using Google satellite images which demonstrate the efficiency in terms of Dice Loss and Categorical Cross-Entropy.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131108501","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 : 2021-12-21DOI: 10.1109/NICS54270.2021.9701573
Q. Phan, N. H. Luong
In this paper, we adapt a method to enhance the efficiency of multi-objective evolutionary algorithms (MOEAs) when solving neural architecture search (NAS) problems by improving the initialization stage with minimal costs. Instead of sampling a small number of architectures from the search space, we sample a large number of architectures and estimate the performance of each one without invoking the computationally expensive training process but by using a zero-cost proxy. After ranking the architectures via their zero-cost proxy values and efficiency metrics, the best architectures are then chosen as the individuals of the initial population. To demonstrate the effectiveness of our method, we conduct experiments on the widely-used NAS-Bench-101 and NAS-Bench-201 benchmarks. Experimental results exhibit that the proposed method achieves not only considerable enhancements on the quality of initial populations but also on the overall performance of MOEAs in solving NAS problems. The source code of the paper is available at https://github.com/ELO-Lab/ENAS-TFI.
{"title":"Efficiency Enhancement of Evolutionary Neural Architecture Search via Training-Free Initialization","authors":"Q. Phan, N. H. Luong","doi":"10.1109/NICS54270.2021.9701573","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701573","url":null,"abstract":"In this paper, we adapt a method to enhance the efficiency of multi-objective evolutionary algorithms (MOEAs) when solving neural architecture search (NAS) problems by improving the initialization stage with minimal costs. Instead of sampling a small number of architectures from the search space, we sample a large number of architectures and estimate the performance of each one without invoking the computationally expensive training process but by using a zero-cost proxy. After ranking the architectures via their zero-cost proxy values and efficiency metrics, the best architectures are then chosen as the individuals of the initial population. To demonstrate the effectiveness of our method, we conduct experiments on the widely-used NAS-Bench-101 and NAS-Bench-201 benchmarks. Experimental results exhibit that the proposed method achieves not only considerable enhancements on the quality of initial populations but also on the overall performance of MOEAs in solving NAS problems. The source code of the paper is available at https://github.com/ELO-Lab/ENAS-TFI.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132670937","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 : 2021-12-21DOI: 10.1109/NICS54270.2021.9701542
Phan Ninh Thai, H. N. Viet, Nathan Shone, M. Babenko
Patch testing is a core component of patch management and is used to verify that modified software modules (i.e. an update or patch) work as expected (functional testing) and do not contain any known vulnerabilities (security testing). Security patch testing requires a lot of time and a professional security knowledge from the tester. In recent years, chopped symbolic execution has been successfully applied in automatic or semiautomatic program testing, to reduce the amount of testing work. Chopped symbolic execution (Chopper) allows users to specify “uninteresting” functions to ignore during analysis, therefore allowing the testing of software modules without running all functions of the program. It is an effective solution for path explosion (one of the main problems of symbolic execution). The effectiveness of the chopped symbolic execution method in patch testing depends on how well the ignored functions are initially chosen. In this paper, we propose a novel method to automatically exclude functions for chopped symbolic execution in patch testing, using a control flow graph. Moreover, we use cyclomatic complexity to optimize the speed of the testing process. Experimental results show that our method can automatically choose the ignored functions and reduce the required testing time, in comparison to typical Chopper techniques.
{"title":"Function exclusion in automated security patch testing using chopped symbolic execution","authors":"Phan Ninh Thai, H. N. Viet, Nathan Shone, M. Babenko","doi":"10.1109/NICS54270.2021.9701542","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701542","url":null,"abstract":"Patch testing is a core component of patch management and is used to verify that modified software modules (i.e. an update or patch) work as expected (functional testing) and do not contain any known vulnerabilities (security testing). Security patch testing requires a lot of time and a professional security knowledge from the tester. In recent years, chopped symbolic execution has been successfully applied in automatic or semiautomatic program testing, to reduce the amount of testing work. Chopped symbolic execution (Chopper) allows users to specify “uninteresting” functions to ignore during analysis, therefore allowing the testing of software modules without running all functions of the program. It is an effective solution for path explosion (one of the main problems of symbolic execution). The effectiveness of the chopped symbolic execution method in patch testing depends on how well the ignored functions are initially chosen. In this paper, we propose a novel method to automatically exclude functions for chopped symbolic execution in patch testing, using a control flow graph. Moreover, we use cyclomatic complexity to optimize the speed of the testing process. Experimental results show that our method can automatically choose the ignored functions and reduce the required testing time, in comparison to typical Chopper techniques.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132824510","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 : 2021-12-21DOI: 10.1109/NICS54270.2021.9700992
Vinh Tran-Quang, Duc Nguyen-Dinh
In this paper, we propose to design and develop a device called V2X-Tag with compact dimensions suitable for mounting on vehicles such as cars, allowing these cars to communicate with each other through a mobile cellular network. We also develop firmware for the V2X-Tag and propose a communication protocol between the V2X-Tag and Server. We also built prototypes of the V2X-Tag and tested these devices in a smart street parking management system. The test results show that, with low power consumption, the V2X-Tag proposed in this paper is suitable for use in IoT systems such as street parking management systems.
{"title":"Design and Implementation of a V2X-Tag for IoT-Based Smart On-Street Parking System","authors":"Vinh Tran-Quang, Duc Nguyen-Dinh","doi":"10.1109/NICS54270.2021.9700992","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9700992","url":null,"abstract":"In this paper, we propose to design and develop a device called V2X-Tag with compact dimensions suitable for mounting on vehicles such as cars, allowing these cars to communicate with each other through a mobile cellular network. We also develop firmware for the V2X-Tag and propose a communication protocol between the V2X-Tag and Server. We also built prototypes of the V2X-Tag and tested these devices in a smart street parking management system. The test results show that, with low power consumption, the V2X-Tag proposed in this paper is suitable for use in IoT systems such as street parking management systems.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134146721","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 : 2021-12-21DOI: 10.1109/NICS54270.2021.9701451
Masayuki Fujita
Recent progress in terahertz devices and systems based on terahertz silicon photonics with resonant tunneling diodes for next-generation information communication technology, 6G and beyond, is reviewed.
{"title":"Advanced Terahertz Devices and Systems Toward 6G and Beyond","authors":"Masayuki Fujita","doi":"10.1109/NICS54270.2021.9701451","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701451","url":null,"abstract":"Recent progress in terahertz devices and systems based on terahertz silicon photonics with resonant tunneling diodes for next-generation information communication technology, 6G and beyond, is reviewed.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133813574","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 : 2021-12-21DOI: 10.1109/NICS54270.2021.9701555
C. D. Nguyen, Phong Nguyen, Anh Tuan Nguyen, N. Pham, Khoa Dang Nguyen
In this study, we evaluate the performance of neural network-based channel detection under the support of spares coding for spin-torque transfer magnetic random access memory (STT-MRAM). Due to its unique features, such as high density, high endurance, and high-speed input/output, the STT-MRAM is considered to have a significant opportunity in the consumer electronics market for the Internet of Things (IoT) field and artificial intelligence (AI) applications. Yet, the reliability of STT-MRAM is significantly degraded due to the influence of both write and read errors. A proposed scheme that the user signal is encoded by sparse codes and detected by the RNN-based detector is evaluated in this paper. Improvements over the conventional detection are shown through simulation results.
{"title":"Performance Evaluation Of Neural Network-Based Channel Detection For STT-MRAM","authors":"C. D. Nguyen, Phong Nguyen, Anh Tuan Nguyen, N. Pham, Khoa Dang Nguyen","doi":"10.1109/NICS54270.2021.9701555","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701555","url":null,"abstract":"In this study, we evaluate the performance of neural network-based channel detection under the support of spares coding for spin-torque transfer magnetic random access memory (STT-MRAM). Due to its unique features, such as high density, high endurance, and high-speed input/output, the STT-MRAM is considered to have a significant opportunity in the consumer electronics market for the Internet of Things (IoT) field and artificial intelligence (AI) applications. Yet, the reliability of STT-MRAM is significantly degraded due to the influence of both write and read errors. A proposed scheme that the user signal is encoded by sparse codes and detected by the RNN-based detector is evaluated in this paper. Improvements over the conventional detection are shown through simulation results.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131537622","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 : 2021-12-21DOI: 10.1109/NICS54270.2021.9701528
Nguyen Canh Minh, T. Dao, D. Tran, Nguyen Quang Huy, Nguyen Thi Thu, Duc-Tan Tran
In recent years, the need to monitor health using sensors integrated on popular smart devices is receiving attention. The development of the human activity classification (HAR) system allowed the monitoring and assessing human health status. Most research in this area has been done on smartphones with the limitation of a fixed position on the body to collect raw data and combine it with other machine learning algorithms to improve activity classification performance. However, the phone’s location on the body in many studies was not the same, leading to different data collection. Smartwatches solved this problem because they were worn on the human hand and had stability and sensitivity to the body’s activities. This research would evaluate the accuracy using data from accelerometers on smartphones and smartwatches, combining with some machine learning algorithms to classify four activities: sitting, standing, walking, and jogging. The classification performance was evaluated through accuracy, sensitivity, and specificity. The overall results showed that the data from the smartwatches accelerometer had higher accuracy than data from smartwatches.
{"title":"Evaluation of Smartphone and Smartwatch Accelerometer Data in Activity Classification","authors":"Nguyen Canh Minh, T. Dao, D. Tran, Nguyen Quang Huy, Nguyen Thi Thu, Duc-Tan Tran","doi":"10.1109/NICS54270.2021.9701528","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701528","url":null,"abstract":"In recent years, the need to monitor health using sensors integrated on popular smart devices is receiving attention. The development of the human activity classification (HAR) system allowed the monitoring and assessing human health status. Most research in this area has been done on smartphones with the limitation of a fixed position on the body to collect raw data and combine it with other machine learning algorithms to improve activity classification performance. However, the phone’s location on the body in many studies was not the same, leading to different data collection. Smartwatches solved this problem because they were worn on the human hand and had stability and sensitivity to the body’s activities. This research would evaluate the accuracy using data from accelerometers on smartphones and smartwatches, combining with some machine learning algorithms to classify four activities: sitting, standing, walking, and jogging. The classification performance was evaluated through accuracy, sensitivity, and specificity. The overall results showed that the data from the smartwatches accelerometer had higher accuracy than data from smartwatches.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134118663","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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