Pub Date : 2022-10-20DOI: 10.1109/ATC55345.2022.9942980
Nguyen Van Binh, Trinh Phu Duy, Thi Thuy Nga Le, Nguyen Van Son
Along with socio-economic development, the number of cars is increasing, which means more and more potential hazards on the road. These dangers come not only from the subjective behavior of the driver but also from objective incidents. Building an automated system to detect incidents in time plays an important role in reducing traffic accidents. This study proposes a fast image processing method to detect driver distractions to give timely warnings. In addition, the objects on the road that are at risk of causing an accident are also recognized and the system warns the driver in real time. Selected study results are also provided to verify the effectiveness of the method.
{"title":"Fast Warning System for Driver of Distraction with Traffic Object Recognition by Image Processing","authors":"Nguyen Van Binh, Trinh Phu Duy, Thi Thuy Nga Le, Nguyen Van Son","doi":"10.1109/ATC55345.2022.9942980","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9942980","url":null,"abstract":"Along with socio-economic development, the number of cars is increasing, which means more and more potential hazards on the road. These dangers come not only from the subjective behavior of the driver but also from objective incidents. Building an automated system to detect incidents in time plays an important role in reducing traffic accidents. This study proposes a fast image processing method to detect driver distractions to give timely warnings. In addition, the objects on the road that are at risk of causing an accident are also recognized and the system warns the driver in real time. Selected study results are also provided to verify the effectiveness of the method.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127503829","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}
With the continuous growth of the Internet of Things applications, increasingly sophisticated and malicious network security attacks have been posing new security requirements. One of the first protection solutions to ensure security is to use an intrusion detection system (IDS) for detecting cyberattacks. Another hand, edge computing technology has been bringing many benefits to communication network infrastructure and IoT applications in terms of performance and privacy. However, the implementation of IDS systems on edge devices encounters many obstacles stemming from the resource constraints of edge devices. Hence, machine learning-based IDS systems have emerged to address such challenges. In this paper, we propose a lightweight deep neuron network-based IDS suitable for deployment at edge devices while still ensuring high attack detection accuracy. The evaluation results on the IoT23 dataset with various cases show that our proposed model has overcome previous proposals and reached an attack detection rate of 99%.
{"title":"A Lightweight DNN-based IDS for Detecting IoT Cyberattacks in Edge Computing","authors":"Trong-Minh Hoang, Tuan-Anh Pham, Van-Viet Do, Van-Nhan Nguyen, Manh-Hung Nguyen","doi":"10.1109/ATC55345.2022.9943049","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943049","url":null,"abstract":"With the continuous growth of the Internet of Things applications, increasingly sophisticated and malicious network security attacks have been posing new security requirements. One of the first protection solutions to ensure security is to use an intrusion detection system (IDS) for detecting cyberattacks. Another hand, edge computing technology has been bringing many benefits to communication network infrastructure and IoT applications in terms of performance and privacy. However, the implementation of IDS systems on edge devices encounters many obstacles stemming from the resource constraints of edge devices. Hence, machine learning-based IDS systems have emerged to address such challenges. In this paper, we propose a lightweight deep neuron network-based IDS suitable for deployment at edge devices while still ensuring high attack detection accuracy. The evaluation results on the IoT23 dataset with various cases show that our proposed model has overcome previous proposals and reached an attack detection rate of 99%.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121902802","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 : 2022-10-20DOI: 10.1109/ATC55345.2022.9943032
T. N. Tien, Khanh-Van Nguyen
Automated Guided Vehicle (AGV) is the key factor to improve favorable logistics solutions for human supply chains. One of the most difficult problems of controlling AGV in a large-scale human-aware environment is the uncertainty of transportation. This uncertainty boosts demand for a graph-based model that reflects not only transportation layout but also traffic situations. Given this graph, our algorithmic solution updates weights of the graph over time as well as predicts any congestion ahead of AGVs. We validate our approach by a simulation model in Omnet++/Veins/SUMO and the results show that dynamic path planning allows AGVs to bypass both the ongoing and forthcoming traffic jams.
{"title":"Updated Weight Graph for dynamic path planning of multi-AGVs in healthcare environments","authors":"T. N. Tien, Khanh-Van Nguyen","doi":"10.1109/ATC55345.2022.9943032","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943032","url":null,"abstract":"Automated Guided Vehicle (AGV) is the key factor to improve favorable logistics solutions for human supply chains. One of the most difficult problems of controlling AGV in a large-scale human-aware environment is the uncertainty of transportation. This uncertainty boosts demand for a graph-based model that reflects not only transportation layout but also traffic situations. Given this graph, our algorithmic solution updates weights of the graph over time as well as predicts any congestion ahead of AGVs. We validate our approach by a simulation model in Omnet++/Veins/SUMO and the results show that dynamic path planning allows AGVs to bypass both the ongoing and forthcoming traffic jams.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128700952","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 : 2022-10-20DOI: 10.1109/ATC55345.2022.9943039
Khuat Dinh Chinh, Phi Van Lam, Tran Thi Lan
This paper proposes a wideband high gain circularly polarized (CP) antenna based on a novel metasurface (MS) at 5.8 GHz. The struture of the antenna consists of two parts are stacked on top of each other. The bottom part is a square patch antenna with a diagonal slot at the center and four parasitic elements added to the sides of the driven patch to improve reflection coefficient and axial ratio of the proposed antenna. The top part is a novel MS, formed by $4times 4$ nut-shape unit cells for improvement gain, matching impandance and CP bandwidth expansion. The antenna has a high gain of 9.35 $boldsymbol{text{dBi}}$ and compact size of $boldsymbol{31times 31times 6.7text{mm}^{3}}$. Wide bandwidth is another advantage of the proposed antenna: impedance bandwidth (S11 $boldsymbol{ < -10text{dB}}$ BW) of 1.8 GHz (29.66%) and axial ratio bandwidth (AR BW) of 1.34 GHz (21.42%). The anenna has left hand circularly polarized (LHCP) is suitable for electronic toll collection (ETC) application.
{"title":"A Wideband High Gain Circularly Polarized Antenna Based on Nut-Shape Metasurface","authors":"Khuat Dinh Chinh, Phi Van Lam, Tran Thi Lan","doi":"10.1109/ATC55345.2022.9943039","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943039","url":null,"abstract":"This paper proposes a wideband high gain circularly polarized (CP) antenna based on a novel metasurface (MS) at 5.8 GHz. The struture of the antenna consists of two parts are stacked on top of each other. The bottom part is a square patch antenna with a diagonal slot at the center and four parasitic elements added to the sides of the driven patch to improve reflection coefficient and axial ratio of the proposed antenna. The top part is a novel MS, formed by $4times 4$ nut-shape unit cells for improvement gain, matching impandance and CP bandwidth expansion. The antenna has a high gain of 9.35 $boldsymbol{text{dBi}}$ and compact size of $boldsymbol{31times 31times 6.7text{mm}^{3}}$. Wide bandwidth is another advantage of the proposed antenna: impedance bandwidth (S11 $boldsymbol{ < -10text{dB}}$ BW) of 1.8 GHz (29.66%) and axial ratio bandwidth (AR BW) of 1.34 GHz (21.42%). The anenna has left hand circularly polarized (LHCP) is suitable for electronic toll collection (ETC) application.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114847030","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 : 2022-10-20DOI: 10.1109/ATC55345.2022.9942990
Ngo-Doanh Nguyen, Duy-Hieu Bui, Xuan-Tu Tran
The idea of Artificial Intelligence of Things (AIoT), a combination of Artificial Intelligence, especially Deep Learning, with edge devices in IoT networks, has recently emerged to reduce the communication cost, and server workloads and improve user experiences. This work presents our current research on a tiny neural network accelerator in a RISC-V System-on-Chip (SoC) to accelerate AI in IoT applications. This accelerator implements a variable-bit-precision MAC or a stochastic MAC to reduce hardware area and power consumption. The tiny AI accelerator has been successfully integrated into a low-power IoT SoC. The design has been implemented on FPGA technology using Arty A7 100T development kit with the operating frequency of 50MHz and the hardware resource of 12K slices. For the MNIST dataset, the accelerator with 8-bit precision can perform Convolutional Neural Network with an accuracy of 98.55%.
{"title":"Tiny Neuron Network System based on RISC-V Processor: A Decentralized Approach for IoT Applications","authors":"Ngo-Doanh Nguyen, Duy-Hieu Bui, Xuan-Tu Tran","doi":"10.1109/ATC55345.2022.9942990","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9942990","url":null,"abstract":"The idea of Artificial Intelligence of Things (AIoT), a combination of Artificial Intelligence, especially Deep Learning, with edge devices in IoT networks, has recently emerged to reduce the communication cost, and server workloads and improve user experiences. This work presents our current research on a tiny neural network accelerator in a RISC-V System-on-Chip (SoC) to accelerate AI in IoT applications. This accelerator implements a variable-bit-precision MAC or a stochastic MAC to reduce hardware area and power consumption. The tiny AI accelerator has been successfully integrated into a low-power IoT SoC. The design has been implemented on FPGA technology using Arty A7 100T development kit with the operating frequency of 50MHz and the hardware resource of 12K slices. For the MNIST dataset, the accelerator with 8-bit precision can perform Convolutional Neural Network with an accuracy of 98.55%.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125692219","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 : 2022-10-20DOI: 10.1109/ATC55345.2022.9943042
Duc Ngoc Minh Dang, Van Thau Tran, Hoang Lam Nguyen, Nhat Truong Pham, Anh Khoa Tran, Ngoc-Hanh Dang
IEEE 802.11ah is a sub-GHz communication technology to offer longer range and low power connectivity for the Internet of Things (IoT) applications. A Restricted Access Window (RAW) is specified to decrease the collision probability. Stations are divided into groups and stations from each group attempt to access the channel by employing the Distributed Coordination Function during their assigned RAW slots. However, the network throughput is limited by a single channel MAC protocol. In this paper, Space-Frequency Diversity-based MAC protocol for the IEEE 802.11ah network (SF-MAC protocol) is proposed to allow stations of different sectors to transmit packets on different channels with the help of Forwarders. The proposed SF-MAC protocol improves the packet delivery ratio and aggregate throughput of the network.
{"title":"Space-Frequency Diversity based MAC protocol for IEEE 802.11 ah networks","authors":"Duc Ngoc Minh Dang, Van Thau Tran, Hoang Lam Nguyen, Nhat Truong Pham, Anh Khoa Tran, Ngoc-Hanh Dang","doi":"10.1109/ATC55345.2022.9943042","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943042","url":null,"abstract":"IEEE 802.11ah is a sub-GHz communication technology to offer longer range and low power connectivity for the Internet of Things (IoT) applications. A Restricted Access Window (RAW) is specified to decrease the collision probability. Stations are divided into groups and stations from each group attempt to access the channel by employing the Distributed Coordination Function during their assigned RAW slots. However, the network throughput is limited by a single channel MAC protocol. In this paper, Space-Frequency Diversity-based MAC protocol for the IEEE 802.11ah network (SF-MAC protocol) is proposed to allow stations of different sectors to transmit packets on different channels with the help of Forwarders. The proposed SF-MAC protocol improves the packet delivery ratio and aggregate throughput of the network.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134057398","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 : 2022-10-20DOI: 10.1109/ATC55345.2022.9943007
Bui Hai Phong, N. Trọng, Manh- Thang Hoang, Thi-Lan Le
The detection of license plates (LPs) is a crucial step to develop the intelligent traffic management systems. Several challenges exist for the detection of LPs such as the high variation of the geometry of LPs or the frequent variation in the conditions of LP image acquisition. The paper presents an end-to-end framework for the detection of LPs. The framework consists of two steps. The first one is the application and optimization of YOLOv4 network to detect LPs accurately. The second one is the strategy of the deployment and testing of the neural network on the NPU VIM3 tool kit. We have performed the evaluation on the large public dataset (Vietnamese license plate detection dataset). The performance comparison (the detection accuracy and execution time) with existed methods on various hardware platforms shows the effectiveness of the proposed method.
{"title":"Performance evaluation of license plate detection using deep neural networks on NPU VIM3 hardware platform","authors":"Bui Hai Phong, N. Trọng, Manh- Thang Hoang, Thi-Lan Le","doi":"10.1109/ATC55345.2022.9943007","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943007","url":null,"abstract":"The detection of license plates (LPs) is a crucial step to develop the intelligent traffic management systems. Several challenges exist for the detection of LPs such as the high variation of the geometry of LPs or the frequent variation in the conditions of LP image acquisition. The paper presents an end-to-end framework for the detection of LPs. The framework consists of two steps. The first one is the application and optimization of YOLOv4 network to detect LPs accurately. The second one is the strategy of the deployment and testing of the neural network on the NPU VIM3 tool kit. We have performed the evaluation on the large public dataset (Vietnamese license plate detection dataset). The performance comparison (the detection accuracy and execution time) with existed methods on various hardware platforms shows the effectiveness of the proposed method.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131217115","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 : 2022-10-20DOI: 10.1109/ATC55345.2022.9942994
D. Vu, Thiem V. Pham, D. Nguyen
This paper proposes a decentralized path-following guidance algorithm for the tracking formation of multiple fixed-wing UAVs. In particular, our proposal adopts the decoupled control technique to separate the lateral and longitudinal channels. Using the well-known vector-field law, the former is in charge of making UAV track a defined path with wind disturbances. The latter guarantees that a group of fixed-wing UAVs reaches the desired formation by modifying the commanded airspeed of UAVs. The decentralized communication network handles the shared information between a ground control station (GCS) and UAVs. Finally, numerical simulation results for a five-UAV group verify the feasibility and benefits of our proposed approach.
{"title":"A path-following guidance algorithm for fixed-wing UAV swarms on a decentralized network","authors":"D. Vu, Thiem V. Pham, D. Nguyen","doi":"10.1109/ATC55345.2022.9942994","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9942994","url":null,"abstract":"This paper proposes a decentralized path-following guidance algorithm for the tracking formation of multiple fixed-wing UAVs. In particular, our proposal adopts the decoupled control technique to separate the lateral and longitudinal channels. Using the well-known vector-field law, the former is in charge of making UAV track a defined path with wind disturbances. The latter guarantees that a group of fixed-wing UAVs reaches the desired formation by modifying the commanded airspeed of UAVs. The decentralized communication network handles the shared information between a ground control station (GCS) and UAVs. Finally, numerical simulation results for a five-UAV group verify the feasibility and benefits of our proposed approach.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133041579","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 : 2022-10-20DOI: 10.1109/ATC55345.2022.9942988
Viet-Manh Do, Tran Quang-Huy, Nguyen Van Son, P. Van Thanh, Nguyen Canh Minh, Duc-Tan Tran, D. Tran
The behavioral recognition system based on accelerometers can support the assessment of cow health. Machine learning algorithms can efficiently classify accelerometer data collected from cow-mounted sensors. However, with cow activities, the sensor may deviate from its original position, which may affect the accelerometer data collected, thereby affecting the performance of behavior classification. From the collected data, we generate deviated collar sensor simulation data to evaluate the classification performance of the model under different circumstances. In the case of using synchronized acceleration data from the leg and neck of cow, applying the Random Forest algorithm with mean and RMS features, the results showed that the behavioral classification performance did not change significantly when the collar-mounted sensor deviated.
{"title":"The effect of sensor position deflection on behavior classification performance","authors":"Viet-Manh Do, Tran Quang-Huy, Nguyen Van Son, P. Van Thanh, Nguyen Canh Minh, Duc-Tan Tran, D. Tran","doi":"10.1109/ATC55345.2022.9942988","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9942988","url":null,"abstract":"The behavioral recognition system based on accelerometers can support the assessment of cow health. Machine learning algorithms can efficiently classify accelerometer data collected from cow-mounted sensors. However, with cow activities, the sensor may deviate from its original position, which may affect the accelerometer data collected, thereby affecting the performance of behavior classification. From the collected data, we generate deviated collar sensor simulation data to evaluate the classification performance of the model under different circumstances. In the case of using synchronized acceleration data from the leg and neck of cow, applying the Random Forest algorithm with mean and RMS features, the results showed that the behavioral classification performance did not change significantly when the collar-mounted sensor deviated.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128822945","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 : 2022-10-20DOI: 10.1109/ATC55345.2022.9942961
T. Kojima, Rikiya Azuma
The periodic blockage of the transmission channel is the most important issue in helicopter satellite communications. Time diversity is a practical solution to overcome it, requiring channel estimation to implement maximal ratio combining (MRC). This paper proposes a channel estimation method for helicopter satellite communications employing time diversity. The proposed method estimates the channel gain by removing modulation from the received signal using the provisional demodulated data. The estimation is rapid and accurate. In addition, signal processing is simple. Computer simulation shows that the degradation of bit error rate performance of MRC due to channel estimation error is under 0.1 dB.
{"title":"Data-Aided Channel Estimation for Time Diversity in Helicopter Satellite Communications","authors":"T. Kojima, Rikiya Azuma","doi":"10.1109/ATC55345.2022.9942961","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9942961","url":null,"abstract":"The periodic blockage of the transmission channel is the most important issue in helicopter satellite communications. Time diversity is a practical solution to overcome it, requiring channel estimation to implement maximal ratio combining (MRC). This paper proposes a channel estimation method for helicopter satellite communications employing time diversity. The proposed method estimates the channel gain by removing modulation from the received signal using the provisional demodulated data. The estimation is rapid and accurate. In addition, signal processing is simple. Computer simulation shows that the degradation of bit error rate performance of MRC due to channel estimation error is under 0.1 dB.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124817597","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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