Pub Date : 2021-08-17DOI: 10.1109/ICUFN49451.2021.9528561
Y. Hwang, E. Kim
In this paper, In this paper, We propose augmented reality(AR) HoloGlass Digital Signage Display for AI Holo-Avatar, which is holographic diffusing projection display established by use of photopolymer based full-color holographic diffusing diffraction film which have high optical qualities such as high transparency and high diffraction efficiency. To form diffusing diffraction pattern keeping high transparency, we fabricated the unique scattering holographic plate with wide viewing zone including the effective removal of color dispersion.
{"title":"Full-color High Transparent VHOE HoloGlass Digital Signage Display for AI Holo-Avatar","authors":"Y. Hwang, E. Kim","doi":"10.1109/ICUFN49451.2021.9528561","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528561","url":null,"abstract":"In this paper, In this paper, We propose augmented reality(AR) HoloGlass Digital Signage Display for AI Holo-Avatar, which is holographic diffusing projection display established by use of photopolymer based full-color holographic diffusing diffraction film which have high optical qualities such as high transparency and high diffraction efficiency. To form diffusing diffraction pattern keeping high transparency, we fabricated the unique scattering holographic plate with wide viewing zone including the effective removal of color dispersion.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"31 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":"121216471","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/ICUFN49451.2021.9528706
Alwin Poulose, Chinthala Sreya Reddy, Jung Hwan Kim, Dong Seog Han
The facial emotion recognition (FER) system has a very significant role in the autonomous driving system (ADS). In ADS, the FER system identifies the driver's emotions and provides the current driver's mental status for safe driving. The driver's mental status determines the safety of the vehicle and prevents the chances of road accidents. In FER, the system identifies the driver's emotions such as happy, sad, angry, surprise, disgust, fear, and neutral. To identify these emotions, the FER system needs to train with large FER datasets and the system's performance completely depends on the type of the FER dataset used in the model training. The recent FER system uses publicly available datasets such as FER 2013, extended Cohn-Kanade (CK+), AffectNet, JAFFE, etc. for model training. However, the model trained with these datasets has some major flaws when the system tries to extract the FER features from the datasets. To address the feature extraction problem in the FER system, in this paper, we propose a foreground extraction technique to identify the user emotions. The proposed foreground extraction-based FER approach accurately extracts the FER features and the deep learning model used in the system effectively utilizes these features for model training. The model training with our FER approach shows accurate classification results than the conventional FER approach. To validate our proposed FER approach, we collected user emotions from 9 people and used the Xception architecture as the deep learning model. From the FER experiment and result analysis, the proposed foreground extraction-based approach reduces the classification error that exists in the conventional FER approach. The FER results from the proposed approach show a 3.33% model accuracy improvement than the conventional FER approach.
{"title":"Foreground Extraction Based Facial Emotion Recognition Using Deep Learning Xception Model","authors":"Alwin Poulose, Chinthala Sreya Reddy, Jung Hwan Kim, Dong Seog Han","doi":"10.1109/ICUFN49451.2021.9528706","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528706","url":null,"abstract":"The facial emotion recognition (FER) system has a very significant role in the autonomous driving system (ADS). In ADS, the FER system identifies the driver's emotions and provides the current driver's mental status for safe driving. The driver's mental status determines the safety of the vehicle and prevents the chances of road accidents. In FER, the system identifies the driver's emotions such as happy, sad, angry, surprise, disgust, fear, and neutral. To identify these emotions, the FER system needs to train with large FER datasets and the system's performance completely depends on the type of the FER dataset used in the model training. The recent FER system uses publicly available datasets such as FER 2013, extended Cohn-Kanade (CK+), AffectNet, JAFFE, etc. for model training. However, the model trained with these datasets has some major flaws when the system tries to extract the FER features from the datasets. To address the feature extraction problem in the FER system, in this paper, we propose a foreground extraction technique to identify the user emotions. The proposed foreground extraction-based FER approach accurately extracts the FER features and the deep learning model used in the system effectively utilizes these features for model training. The model training with our FER approach shows accurate classification results than the conventional FER approach. To validate our proposed FER approach, we collected user emotions from 9 people and used the Xception architecture as the deep learning model. From the FER experiment and result analysis, the proposed foreground extraction-based approach reduces the classification error that exists in the conventional FER approach. The FER results from the proposed approach show a 3.33% model accuracy improvement than the conventional FER approach.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"55 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":"114939433","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/ICUFN49451.2021.9528743
Seung-hee Oh, Woo-Sug Jung, Kyung-Seok Kim
In the event of a disaster, obtaining information about the disaster quickly and accurately is an important matter that not only reduces the associated economic damage, but also leads to survival. We suggest the method to more efficiently utilize emergency messages transmitted to smartphones through telecommunication networks. The method we propose is to process the emergency message received by the smartphone according to the emergency level and send it back to the smart watch and earphone connected via Bluetooth. This enables the disaster vulnerable people, such as the elderly, children, foreigners, and visually impaired people, to quickly receive disaster information.
{"title":"The Method of Emergency Message Retransmission for the Disaster Vulnerable People","authors":"Seung-hee Oh, Woo-Sug Jung, Kyung-Seok Kim","doi":"10.1109/ICUFN49451.2021.9528743","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528743","url":null,"abstract":"In the event of a disaster, obtaining information about the disaster quickly and accurately is an important matter that not only reduces the associated economic damage, but also leads to survival. We suggest the method to more efficiently utilize emergency messages transmitted to smartphones through telecommunication networks. The method we propose is to process the emergency message received by the smartphone according to the emergency level and send it back to the smart watch and earphone connected via Bluetooth. This enables the disaster vulnerable people, such as the elderly, children, foreigners, and visually impaired people, to quickly receive disaster information.","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":"122389786","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/ICUFN49451.2021.9528735
Ung-Gyo Lee, Kyung-Jea Choi, Soon-Yong Park
In this paper, we present the implementation of autonomous mobile pallet robot system using by ROS (Robot Operating System) and it shows that the packages provided by ROS are well loaded in our custom robot system. In session II, III, we will briefly introduce the robot's hardware and software system and then explain the process that how to implement the custom robot using by ROS and describe each require packages step by step. In session IV, we will experiment the autonomous navigation system with our handcraft pallet robot. In order to experiment, we built the map using by Google's Cartographer SLAM and the pallet robot successfully navigating on the grid map.
{"title":"The Design and Implementation of Autonomous Driving Pallet Robot System using ROS","authors":"Ung-Gyo Lee, Kyung-Jea Choi, Soon-Yong Park","doi":"10.1109/ICUFN49451.2021.9528735","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528735","url":null,"abstract":"In this paper, we present the implementation of autonomous mobile pallet robot system using by ROS (Robot Operating System) and it shows that the packages provided by ROS are well loaded in our custom robot system. In session II, III, we will briefly introduce the robot's hardware and software system and then explain the process that how to implement the custom robot using by ROS and describe each require packages step by step. In session IV, we will experiment the autonomous navigation system with our handcraft pallet robot. In order to experiment, we built the map using by Google's Cartographer SLAM and the pallet robot successfully navigating on the grid map.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"7 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":"116601260","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/ICUFN49451.2021.9528599
Hyoungsoo Lim, Sanguk Lee, J. Ryu
According to increasing needs for advanced services in satellite navigation system, an exhaustive search method of chip pulse design for an additional service signal is proposed. The candidate waveforms considered include BPSK, BOC, and BOCcos. In this paper, the design is performed for three candidate chip rates of 1.023, 2.046, and 10.23Mcps. The preliminary design results presented in this paper are chosen to minimize the worst interference to the existing legacy satellite navigation system with practical implementation complexity of the satellite signal generator and the corresponding receivers as well.
{"title":"Chip Pulse Design for an Additional Satellite Navigation Signal in L6 Band","authors":"Hyoungsoo Lim, Sanguk Lee, J. Ryu","doi":"10.1109/ICUFN49451.2021.9528599","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528599","url":null,"abstract":"According to increasing needs for advanced services in satellite navigation system, an exhaustive search method of chip pulse design for an additional service signal is proposed. The candidate waveforms considered include BPSK, BOC, and BOCcos. In this paper, the design is performed for three candidate chip rates of 1.023, 2.046, and 10.23Mcps. The preliminary design results presented in this paper are chosen to minimize the worst interference to the existing legacy satellite navigation system with practical implementation complexity of the satellite signal generator and the corresponding receivers as well.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"60 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":"117039236","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/ICUFN49451.2021.9528826
Daesung Yu, Hoon Lee, Seung‐Eun Hong, Seok-Hwan Park
Cell-free massive multiple-input multiple-output (MIMO) systems are envisioned to achieve the improved spectral efficiency by supporting users via nearby access points (APs). This work addresses the optimization of beamforming weights for cell-free massive MIMO systems. The connectivity level constraints are taken into account to accommodate finite-rate fronthaul links. The minimum rate maximization problem is tackled by the weighted minimum mean squared error (WMMSE) algorithm. Numerical results show that the proposed scheme achieves significantly improved performance than a baseline scheme in overall signal-to-noise ratio (SNR) regime.
{"title":"Weighted MMSE Optimization of Conjugate Beamforming for Cell-Free Massive MIMO","authors":"Daesung Yu, Hoon Lee, Seung‐Eun Hong, Seok-Hwan Park","doi":"10.1109/ICUFN49451.2021.9528826","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528826","url":null,"abstract":"Cell-free massive multiple-input multiple-output (MIMO) systems are envisioned to achieve the improved spectral efficiency by supporting users via nearby access points (APs). This work addresses the optimization of beamforming weights for cell-free massive MIMO systems. The connectivity level constraints are taken into account to accommodate finite-rate fronthaul links. The minimum rate maximization problem is tackled by the weighted minimum mean squared error (WMMSE) algorithm. Numerical results show that the proposed scheme achieves significantly improved performance than a baseline scheme in overall signal-to-noise ratio (SNR) regime.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"94 8 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":"128694257","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/ICUFN49451.2021.9528396
Saeed Ahmed, Z. Khan, N. Gul, Junsu Kim, S. Kim
The data collected from advanced metering infrastructure enables the electric utilities to develop a deep insight about the energy consumption behavior of the consumer. However, the load signature and consumption pattern varies due to addition of multiple types of new loads, such as electric vehicles (EVs). Therefore, it becomes imminent to further dig down these variations. To this end, this paper investigates the impacts of insertion of EV profiles in the household level smart meter data. The Irish CER dataset and EV data from the NREL residential PEV are utilized in this study to classify the users with and without EVs' loads. The results show that change in the cluster membership can help to separate the consumers with the EV load from the stand-alone consumers without the EV load.
{"title":"Machine Learning-Based Clustering of Load Profiling to Study the Impact of Electric Vehicles on Smart Meter Applications","authors":"Saeed Ahmed, Z. Khan, N. Gul, Junsu Kim, S. Kim","doi":"10.1109/ICUFN49451.2021.9528396","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528396","url":null,"abstract":"The data collected from advanced metering infrastructure enables the electric utilities to develop a deep insight about the energy consumption behavior of the consumer. However, the load signature and consumption pattern varies due to addition of multiple types of new loads, such as electric vehicles (EVs). Therefore, it becomes imminent to further dig down these variations. To this end, this paper investigates the impacts of insertion of EV profiles in the household level smart meter data. The Irish CER dataset and EV data from the NREL residential PEV are utilized in this study to classify the users with and without EVs' loads. The results show that change in the cluster membership can help to separate the consumers with the EV load from the stand-alone consumers without the EV load.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"25 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":"128235159","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/ICUFN49451.2021.9528820
Hassan Mistareehi
Vehicular Ad hoc NETworks (VANETs) are likely to play an important role in Intelligent Transport Systems (ITS). Information collected by On Board Units (OBUs) located in vehicles can help in avoiding congestion, provide useful information to drivers, etc. However, not all drivers on roads can benefit from OBU implementation because OBU is currently not available in all car models. Therefore, in this paper, we designed and built a hardware implementation for OBU which allows to disseminate messages in rural areas. This OBU implementation is simple, efficient, and at low cost. Evaluation results show that our proposed model can transmit and receive plaintext and encrypted messages (e.g., safety messages) to nearby vehicles, Access Point (AP), and destination with acceptable time.
{"title":"Message Dissemination Scheme for Rural Areas Using VANET (Hardware Implementation)","authors":"Hassan Mistareehi","doi":"10.1109/ICUFN49451.2021.9528820","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528820","url":null,"abstract":"Vehicular Ad hoc NETworks (VANETs) are likely to play an important role in Intelligent Transport Systems (ITS). Information collected by On Board Units (OBUs) located in vehicles can help in avoiding congestion, provide useful information to drivers, etc. However, not all drivers on roads can benefit from OBU implementation because OBU is currently not available in all car models. Therefore, in this paper, we designed and built a hardware implementation for OBU which allows to disseminate messages in rural areas. This OBU implementation is simple, efficient, and at low cost. Evaluation results show that our proposed model can transmit and receive plaintext and encrypted messages (e.g., safety messages) to nearby vehicles, Access Point (AP), and destination with acceptable time.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"191 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":"123385714","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/ICUFN49451.2021.9528658
Kang-in Choi, Keunho Park, Sung-Gyun Jeong
Recently, the analysis research of crop's growth condition is done with the use of hyperspectral image. However, there are many factors such as physical factors and complexity of data make the hyperspectral image analysis difficult. This study presents the classification method of crop's leaf growth condition using hyperspectral image(HSI) and Deep Neural Network(DNN). Major information of plants is acquired through hyperspectral image, and the preprocessing is followed for the information to be used for DNN learning. The preprocessing is used by cutting the data in small patch size and rotating it for the models to be operated effectively. In the experiment, paprika leaves are divided into four types of leaves and backgrounds such as normal and damaged by harmful insects, and the result of the experiment showed 90.9% of accuracy. The presented method has advantages that the data generation method does not affect DNN and can classify various growth conditions that are difficult in the existing RGB image.
{"title":"Classification of Growth Conditions in Paprika Leaf Using Deep Neural Network and Hyperspectral Images","authors":"Kang-in Choi, Keunho Park, Sung-Gyun Jeong","doi":"10.1109/ICUFN49451.2021.9528658","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528658","url":null,"abstract":"Recently, the analysis research of crop's growth condition is done with the use of hyperspectral image. However, there are many factors such as physical factors and complexity of data make the hyperspectral image analysis difficult. This study presents the classification method of crop's leaf growth condition using hyperspectral image(HSI) and Deep Neural Network(DNN). Major information of plants is acquired through hyperspectral image, and the preprocessing is followed for the information to be used for DNN learning. The preprocessing is used by cutting the data in small patch size and rotating it for the models to be operated effectively. In the experiment, paprika leaves are divided into four types of leaves and backgrounds such as normal and damaged by harmful insects, and the result of the experiment showed 90.9% of accuracy. The presented method has advantages that the data generation method does not affect DNN and can classify various growth conditions that are difficult in the existing RGB image.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"69 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":"114738194","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/ICUFN49451.2021.9528756
Dongwon Lee, Joohyung Lee
Wireless communication contains many fluctuations than wired networks. In this paper, we present several machine learning and deep learning models to predict future network throughput, which is crucial for reducing latency in online streaming services. This paper explains the main components of the throughput prediction system. The throughput prediction model includes data input, data training, and prediction computation parts. This model accepts network throughput for the training data of the model and forecasts future data. We also present the advantages and limitations of utilizing AI models for throughput prediction. Finally, we believe that this study highlights the impact of deep learning techniques for throughput prediction.
{"title":"Machine Learning and Deep Learning for Throughput Prediction","authors":"Dongwon Lee, Joohyung Lee","doi":"10.1109/ICUFN49451.2021.9528756","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528756","url":null,"abstract":"Wireless communication contains many fluctuations than wired networks. In this paper, we present several machine learning and deep learning models to predict future network throughput, which is crucial for reducing latency in online streaming services. This paper explains the main components of the throughput prediction system. The throughput prediction model includes data input, data training, and prediction computation parts. This model accepts network throughput for the training data of the model and forecasts future data. We also present the advantages and limitations of utilizing AI models for throughput prediction. Finally, we believe that this study highlights the impact of deep learning techniques for throughput prediction.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"4 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":"133552905","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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