Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528725
Arslan Musaddiq, Tariq Rahim, Dong-Seong Kim
The Internet of Things (IoT) network consists of resource-constrained tiny devices. An efficient channel access mechanism for densely deployed IoT devices operating in a lossy environment is one of the major challenges for future IoT networks. The IoT nodes using IEEE 802.15.4 MAC protocol increase the backoff exponent (BE) during the channel sensing period. This blind increase of BE and contention window (CW) before frame transmission affects the network performance. Therefore, in this paper, we propose to use machine learning such as a reinforcement learning (RL) mechanism to handle channel access mechanisms efficiently. The proposed mechanism is evaluated using Contiki 3.0 Cooja simulations. The simulation results indicate that the proposed RL-based mechanism enhances the network performance.
{"title":"Enhancing IEEE 802.15.4 Access Mechanism with Machine Learning","authors":"Arslan Musaddiq, Tariq Rahim, Dong-Seong Kim","doi":"10.1109/ICUFN49451.2021.9528725","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528725","url":null,"abstract":"The Internet of Things (IoT) network consists of resource-constrained tiny devices. An efficient channel access mechanism for densely deployed IoT devices operating in a lossy environment is one of the major challenges for future IoT networks. The IoT nodes using IEEE 802.15.4 MAC protocol increase the backoff exponent (BE) during the channel sensing period. This blind increase of BE and contention window (CW) before frame transmission affects the network performance. Therefore, in this paper, we propose to use machine learning such as a reinforcement learning (RL) mechanism to handle channel access mechanisms efficiently. The proposed mechanism is evaluated using Contiki 3.0 Cooja simulations. The simulation results indicate that the proposed RL-based mechanism enhances the network performance.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128821322","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528796
Tae-yun Kim, Hua Lee, Suk-seung Hwang
Since most studies for estimating an angle-of-arrival (AOA) based on the antenna array have considered the antenna array with a single configuration, they are not proper to simultaneously estimate AOAs of multiple signals with various frequencies. In order to enhance this problem, in this paper, we propose a cascade AOA estimation technique based on a Combined Array Antenna (CAA) with Uniform Rectangular Frame Array (URFA) and Uniform Circular Array (UCA). It consists of Capon for roughly finding AOA groups including multiple signal AOAs, followed by Beamspace Multiple Signal Classification (MUSIC) for detailedly estimating signal AOAs in the calculated AOA groups. The proposed algorithm does not only have low computational complexity compared to the conventional AOA estimation technique like MUSIC, but also it has both characteristics of URFA and UCA.
{"title":"Cascade AOA Estimation Based on Combined Array Antenna with URFA and UCA","authors":"Tae-yun Kim, Hua Lee, Suk-seung Hwang","doi":"10.1109/ICUFN49451.2021.9528796","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528796","url":null,"abstract":"Since most studies for estimating an angle-of-arrival (AOA) based on the antenna array have considered the antenna array with a single configuration, they are not proper to simultaneously estimate AOAs of multiple signals with various frequencies. In order to enhance this problem, in this paper, we propose a cascade AOA estimation technique based on a Combined Array Antenna (CAA) with Uniform Rectangular Frame Array (URFA) and Uniform Circular Array (UCA). It consists of Capon for roughly finding AOA groups including multiple signal AOAs, followed by Beamspace Multiple Signal Classification (MUSIC) for detailedly estimating signal AOAs in the calculated AOA groups. The proposed algorithm does not only have low computational complexity compared to the conventional AOA estimation technique like MUSIC, but also it has both characteristics of URFA and UCA.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115534019","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528400
H. Jeong, Kangyoon Lee
In this Paper, a high efficiency and low area dc-dc buck converter with the digital feedback loop is proposed for wireless device. The digital feedback loop is consisted of two-step digital pulse width modulation (DPWM) and low power self-tracking zero current detector (ST-ZCD). To implement a high-efficiency dc-dc converter, a hybrid DPWM core is proposed with high linearity and low power consumption. To reduce the output voltage ripple within 20mV, an adaptive window analog-to-digital converter is proposed. To minimize the reverse current, a dead time generator is implemented with the proposed ST-ZCD. The circuit is designed with a Samsung 28nm CMOS process that produces an output voltage of 1.8V using a standard supply voltage of 3.3V.
{"title":"High Efficiency & Low Area DC-DC Buck Converter with the Digital Feedback Loop for the Wireless Applications","authors":"H. Jeong, Kangyoon Lee","doi":"10.1109/ICUFN49451.2021.9528400","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528400","url":null,"abstract":"In this Paper, a high efficiency and low area dc-dc buck converter with the digital feedback loop is proposed for wireless device. The digital feedback loop is consisted of two-step digital pulse width modulation (DPWM) and low power self-tracking zero current detector (ST-ZCD). To implement a high-efficiency dc-dc converter, a hybrid DPWM core is proposed with high linearity and low power consumption. To reduce the output voltage ripple within 20mV, an adaptive window analog-to-digital converter is proposed. To minimize the reverse current, a dead time generator is implemented with the proposed ST-ZCD. The circuit is designed with a Samsung 28nm CMOS process that produces an output voltage of 1.8V using a standard supply voltage of 3.3V.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114364134","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528715
Youngboo Kim, Seungmin Oh, Gayoung Kim, Junho Jeong
The Quiet Time Period (QTP) is a new feature introduced in IEEE 802.11ax standard to support the coexistence of the Station-to-Station (S2S) and uplink/downlink in a WLAN. However, according to the standard, an AP has a restriction that it should accept QTP procedure only if the introduction of QTP would benefit the network performance. Therefore, prior to the designing a practical QTP control scheme, it is necessary to analyze the effect of QTP on network performance. For this purpose, this paper evaluates the performance of QTP, Uplink OFDMA Random Access (UORA), and MU DL (Multi-User Down Link) in terms of throughput and transmission delay, respectively, by simulation.
QTP (Quiet Time Period)是IEEE 802.11ax标准中为支持无线局域网中S2S (Station-to-Station)和上行/下行链路共存而引入的新特性。但是，根据该标准，AP有一个限制，即只有在引入QTP有利于网络性能的情况下，AP才应该接受QTP过程。因此，在设计实用的QTP控制方案之前，有必要分析QTP对网络性能的影响。为此，本文通过仿真分别对QTP、上行OFDMA随机接入(UORA)和多用户下行链路(MU DL)的吞吐量和传输延迟进行了性能评估。
{"title":"Performance Analysis of QTP-based S2S Transmission in IEEE 802.11axWLANs","authors":"Youngboo Kim, Seungmin Oh, Gayoung Kim, Junho Jeong","doi":"10.1109/ICUFN49451.2021.9528715","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528715","url":null,"abstract":"The Quiet Time Period (QTP) is a new feature introduced in IEEE 802.11ax standard to support the coexistence of the Station-to-Station (S2S) and uplink/downlink in a WLAN. However, according to the standard, an AP has a restriction that it should accept QTP procedure only if the introduction of QTP would benefit the network performance. Therefore, prior to the designing a practical QTP control scheme, it is necessary to analyze the effect of QTP on network performance. For this purpose, this paper evaluates the performance of QTP, Uplink OFDMA Random Access (UORA), and MU DL (Multi-User Down Link) in terms of throughput and transmission delay, respectively, by simulation.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114779974","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528730
Ishan Khatri, Toya Acharya, A. Annamalai, M. Chouikha
The frequency scarcity imposed by the fast-growing need for mobile data service requires promising spectrum aggregation systems. The so-called higher-order statistics (HOS) of the channel capacity (CC) is a suitable metric on the system performance. While prior relevant works have improved our knowledge of HOS characterization on the spectrum aggregation systems, an analytical framework encompassing generalized fading models of interest is not yet available. However, the expressions of HOS are not correct in several previous research works. In this paper, we present novel method by expressing the closed-form expression of CC as the sum of weighted exponential terms and then invoke multinomial expansion to obtain the required coefficients and utilize MGF (Moment Generating Function) based maximum ratio combining (MRC) diversity receivers technique over κ-µ fading distribution to compute higher order moments. Also, we provide correct, simplified and efficient HOS expressions for the asymptotically low and high signal-to-noise regimes and provide a detailed HOS analysis of κ-µ fading channel by obtaining vital statistical measures, such as the amount of dispersion, skewness, and kurtosis by the HOS results. Finally, all derived expressions are validated via the Semi-infinite Gauss Hermite quadrature method.
{"title":"Higher Order Statistics of channel capacity in κ- µ fading channel","authors":"Ishan Khatri, Toya Acharya, A. Annamalai, M. Chouikha","doi":"10.1109/ICUFN49451.2021.9528730","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528730","url":null,"abstract":"The frequency scarcity imposed by the fast-growing need for mobile data service requires promising spectrum aggregation systems. The so-called higher-order statistics (HOS) of the channel capacity (CC) is a suitable metric on the system performance. While prior relevant works have improved our knowledge of HOS characterization on the spectrum aggregation systems, an analytical framework encompassing generalized fading models of interest is not yet available. However, the expressions of HOS are not correct in several previous research works. In this paper, we present novel method by expressing the closed-form expression of CC as the sum of weighted exponential terms and then invoke multinomial expansion to obtain the required coefficients and utilize MGF (Moment Generating Function) based maximum ratio combining (MRC) diversity receivers technique over κ-µ fading distribution to compute higher order moments. Also, we provide correct, simplified and efficient HOS expressions for the asymptotically low and high signal-to-noise regimes and provide a detailed HOS analysis of κ-µ fading channel by obtaining vital statistical measures, such as the amount of dispersion, skewness, and kurtosis by the HOS results. Finally, all derived expressions are validated via the Semi-infinite Gauss Hermite quadrature method.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116355273","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 : 2021-08-17DOI: 10.1109/icufn49451.2021.9528665
Myeong Gwan Kim, Kangyoon Lee
This paper presents a 5.8GHz Ultra-Low-power based Wake-up Receiver for ETCS (Electronic Toll Collection System) using DSRC (Dedicated Short Range Communication) Transceiver Application. The suggested Wake-up receiver is modulated 5.8 GHz signal and 14 kHz to On-Off Keying (OOK) signal. The Wake-up controller receives a14 kHz OOK signal and generates WUR INT signal which goes to MODEM. The suggested Wake-up Receiver (WuRx) is manufactured in 0.13-um CMOS bulk of 0.26 mn2 technology. When operating at 5.8 GHz frequency, WuRx consumes 3.3uW at 0.9V supply and achieves - 62dBm sensitivity.
介绍了一种基于专用短距离通信(DSRC)收发器的5.8GHz超低功耗电子收费系统唤醒接收机。建议的唤醒接收器是调制5.8 GHz信号和14 kHz的开-关键控(OOK)信号。唤醒控制器接收到14khz的OOK信号并产生WUR INT信号，该信号送到MODEM。建议的唤醒接收器(WuRx)采用0.26 mn2技术的0.13 um CMOS体制造。当工作在5.8 GHz频率时，WuRx在0.9V电源下消耗3.3uW，灵敏度达到- 62dBm。
{"title":"5.8GHz Ultra-Low-Power Based Wake-up Receiver for DSRC Application","authors":"Myeong Gwan Kim, Kangyoon Lee","doi":"10.1109/icufn49451.2021.9528665","DOIUrl":"https://doi.org/10.1109/icufn49451.2021.9528665","url":null,"abstract":"This paper presents a 5.8GHz Ultra-Low-power based Wake-up Receiver for ETCS (Electronic Toll Collection System) using DSRC (Dedicated Short Range Communication) Transceiver Application. The suggested Wake-up receiver is modulated 5.8 GHz signal and 14 kHz to On-Off Keying (OOK) signal. The Wake-up controller receives a14 kHz OOK signal and generates WUR INT signal which goes to MODEM. The suggested Wake-up Receiver (WuRx) is manufactured in 0.13-um CMOS bulk of 0.26 mn2 technology. When operating at 5.8 GHz frequency, WuRx consumes 3.3uW at 0.9V supply and achieves - 62dBm sensitivity.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128394644","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528565
R. Perdana, Toan-Van Nguyen, Beongku An
In this paper, we design a deep learning framework for the power allocation problems in massive MIMO networks. In particular, we formulate the max-min and max-product power allocation problems by using signal-to-interference-plus-noise ratio (SINR) and signal-to-leak-plus-noise ratio (SLNR) criteria for linear precoder design. Multiple base stations are deployed to serve multiple user equipments, the power allocation process to each user equipment takes long processing time to converge, which is inefficient approach. We tackle this problem by designing a framework based on deep neural network, where the user equipment position is used to train the deep model, and then it is used to predict the optimal power allocation according to the user's locations. The resulting deep learning helps to reduce the processing time of the system in determining the optimal power allocation for the user equipment. Compared to the standard optimization approach, the deep learning design helps to obtain the optimal solution of the power allocation problem within a short time via a quick-inference process. Simulation results show that the SINR criterion outperforms the SLNR one. Meanwhile, deep learning performance in predicting power allocation gets excellent results with an accuracy of 85% for the max-min strategy and 99% for the max-product strategy.
{"title":"Deep Learning-based Power Allocation in Massive MIMO Systems with SLNR and SINR Criterions","authors":"R. Perdana, Toan-Van Nguyen, Beongku An","doi":"10.1109/ICUFN49451.2021.9528565","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528565","url":null,"abstract":"In this paper, we design a deep learning framework for the power allocation problems in massive MIMO networks. In particular, we formulate the max-min and max-product power allocation problems by using signal-to-interference-plus-noise ratio (SINR) and signal-to-leak-plus-noise ratio (SLNR) criteria for linear precoder design. Multiple base stations are deployed to serve multiple user equipments, the power allocation process to each user equipment takes long processing time to converge, which is inefficient approach. We tackle this problem by designing a framework based on deep neural network, where the user equipment position is used to train the deep model, and then it is used to predict the optimal power allocation according to the user's locations. The resulting deep learning helps to reduce the processing time of the system in determining the optimal power allocation for the user equipment. Compared to the standard optimization approach, the deep learning design helps to obtain the optimal solution of the power allocation problem within a short time via a quick-inference process. Simulation results show that the SINR criterion outperforms the SLNR one. Meanwhile, deep learning performance in predicting power allocation gets excellent results with an accuracy of 85% for the max-min strategy and 99% for the max-product strategy.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"110 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128733573","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528583
Dae-Geun Cho, Kangyoon Lee
This paper proposes Selectable Voltage Genrator. In order to supply a stable power supply voltage, a linear regulator LDO was used. The entire system in which this circuit is used is divided into Charging Mode and Discharging Mode. Charging Mode is the mode to charge the battery with USB voltage (VBUS), and the Discharging Mode is the mode to charge the USB voltage with the battery voltage (VBAT). The input voltage of VBUS is 2.7 ~ 20 V, The input voltage of VBAT supports up to 4 battery cells, The voltage of 1 cell is 4.2 V. According to the suggested input voltage, the voltage supplied into the chip is divided into 5V LDO and 3.3V LDO.
{"title":"Design of Voltage Selectable Circuit based on Power Mux for Charger IC","authors":"Dae-Geun Cho, Kangyoon Lee","doi":"10.1109/ICUFN49451.2021.9528583","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528583","url":null,"abstract":"This paper proposes Selectable Voltage Genrator. In order to supply a stable power supply voltage, a linear regulator LDO was used. The entire system in which this circuit is used is divided into Charging Mode and Discharging Mode. Charging Mode is the mode to charge the battery with USB voltage (VBUS), and the Discharging Mode is the mode to charge the USB voltage with the battery voltage (VBAT). The input voltage of VBUS is 2.7 ~ 20 V, The input voltage of VBAT supports up to 4 battery cells, The voltage of 1 cell is 4.2 V. According to the suggested input voltage, the voltage supplied into the chip is divided into 5V LDO and 3.3V LDO.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128998063","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 : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528670
Seungmin Lee, Dongkyu Lee, Daejin Park
Abnormal beat detection in electrocardiogram (ECG) signal is an important research subject. Abnormal beat detection can be used effectively for adaptive signal compression according to normal/abnormal beat, and it enable to save time and cost of arrhythmia diagnosis by providing the detected abnormal beats to cardiologist. However, the fiducial point detection for feature value extraction has low reliability and is difficult to implement in embedded edge devices due to the auxiliary signal acquisition and complex algorithm for detection. In this study, we propose a method that expresses a signal as a small number of vertices using linear approximation and detects an abnormal beat quickly and reliably using the feature value of vertices. The proposed method is based on the similar distribution of feature values of the approximate vertices for the same type of beat. As a result of an experiment on a record containing premature ventricular contraction (PVC) whose shape was deformed from a normal beat, we confirmed that the proposed algorithm enable to detect whole abnormal beat correctly.
{"title":"Binary Classification for Linear Approximated ECG Signal in IoT Embedded Edge Device","authors":"Seungmin Lee, Dongkyu Lee, Daejin Park","doi":"10.1109/ICUFN49451.2021.9528670","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528670","url":null,"abstract":"Abnormal beat detection in electrocardiogram (ECG) signal is an important research subject. Abnormal beat detection can be used effectively for adaptive signal compression according to normal/abnormal beat, and it enable to save time and cost of arrhythmia diagnosis by providing the detected abnormal beats to cardiologist. However, the fiducial point detection for feature value extraction has low reliability and is difficult to implement in embedded edge devices due to the auxiliary signal acquisition and complex algorithm for detection. In this study, we propose a method that expresses a signal as a small number of vertices using linear approximation and detects an abnormal beat quickly and reliably using the feature value of vertices. The proposed method is based on the similar distribution of feature values of the approximate vertices for the same type of beat. As a result of an experiment on a record containing premature ventricular contraction (PVC) whose shape was deformed from a normal beat, we confirmed that the proposed algorithm enable to detect whole abnormal beat correctly.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129293496","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 : 2021-08-17DOI: 10.1109/icufn49451.2021.9528620
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icufn49451.2021.9528620","DOIUrl":"https://doi.org/10.1109/icufn49451.2021.9528620","url":null,"abstract":"","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123343387","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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