Pub Date : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660528
Yong Tang, Anqin Lu, Z. Liu, Y. Leng, Rongyan Wang, Chengli Sun, Jiande Sun, Chan Lin, Weiwei Zhao, Wenjing Li
CNN is a model which is currently widely used in acoustic scene classification. Sparse coding is a model which used to be very popular in acoustic classification field before deep learning technology is widely used. In this paper we combine these two models for acoustic scene classification. Specifically, the calibrated sparse representation based score is fused with the score obtained through CNN classification model for classification. Experimental results on TUT acoustic scenes 2017 dataset and LITIS Rouen dataset show that the proposed algorithm can make good use of the classification abilities of sparse coding and CNN.
{"title":"Acoustic Scene Classification Based on Sparse Coding and Convolutional Neural Networks","authors":"Yong Tang, Anqin Lu, Z. Liu, Y. Leng, Rongyan Wang, Chengli Sun, Jiande Sun, Chan Lin, Weiwei Zhao, Wenjing Li","doi":"10.1109/IC-NIDC54101.2021.9660528","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660528","url":null,"abstract":"CNN is a model which is currently widely used in acoustic scene classification. Sparse coding is a model which used to be very popular in acoustic classification field before deep learning technology is widely used. In this paper we combine these two models for acoustic scene classification. Specifically, the calibrated sparse representation based score is fused with the score obtained through CNN classification model for classification. Experimental results on TUT acoustic scenes 2017 dataset and LITIS Rouen dataset show that the proposed algorithm can make good use of the classification abilities of sparse coding and CNN.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124738831","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 : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660603
Lu Liu, Qifei Wu, Guang Chen
Frequently Asked Question (F AQ) retrieval is a valuable task which aims to find the most relevant question-answer pair from a FAQ dataset given a user query. Currently, most works implement F AQ retrieval considering the similarity between the query and the question as well as the relevance between the query and the answer. However, the query-answer relevance is difficult to model effectively due to the heterogeneity of query-answer pairs in terms of syntax and semantics. To alleviate this issue and improve retrieval performance, we propose a novel approach to consider answer information into F AQ retrieval by question generation, which provides high-quality synthetic positive training examples for dense retriever. Experiment results indicate that our method outperforms term-based BM25 and pretrained dense retriever significantly on two recently published COVID-19 F AQ datasets.
{"title":"Improving Dense FAQ Retrieval with Synthetic Training","authors":"Lu Liu, Qifei Wu, Guang Chen","doi":"10.1109/IC-NIDC54101.2021.9660603","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660603","url":null,"abstract":"Frequently Asked Question (F AQ) retrieval is a valuable task which aims to find the most relevant question-answer pair from a FAQ dataset given a user query. Currently, most works implement F AQ retrieval considering the similarity between the query and the question as well as the relevance between the query and the answer. However, the query-answer relevance is difficult to model effectively due to the heterogeneity of query-answer pairs in terms of syntax and semantics. To alleviate this issue and improve retrieval performance, we propose a novel approach to consider answer information into F AQ retrieval by question generation, which provides high-quality synthetic positive training examples for dense retriever. Experiment results indicate that our method outperforms term-based BM25 and pretrained dense retriever significantly on two recently published COVID-19 F AQ datasets.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114201337","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 : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660453
Tong Liu, K. Yu, Lu Wang, Xuanyu Zhang, Xiaofei Wu
In online social medias, there is a large amount of clickbait using various tricks such as curious words and well-designed sentence structures, to attract users to click on hyperlinks for unknown benefits. Clickbait detection aims to detect these hyperlinks through automated algorithms. Most of the previous clickbait datasets are based on English online social media corpus. Detection models based on these datasets usually cannot be well generalized to Chinese social media scenarios. In this paper, we construct a WeChat based Chinese clickbait dataset, i.e., WCD. Based on the WCD, we conduct a detailed analysis of the clickbait features from three aspects: behavior features, headline text features, and content text features. Finally, we use popular methods for clickbait detection on our dataset. We also respectively propose a machine learning detection model using feature fusion and a deep learning detection model combining headline semantic and POS tag information, both of which achieve excellent detection performance. The results of clickbait analysis and detection show that the dataset we constructed is of high quality.
{"title":"WCD: A New Chinese Online Social Media Dataset for Clickbait Analysis and Detection","authors":"Tong Liu, K. Yu, Lu Wang, Xuanyu Zhang, Xiaofei Wu","doi":"10.1109/IC-NIDC54101.2021.9660453","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660453","url":null,"abstract":"In online social medias, there is a large amount of clickbait using various tricks such as curious words and well-designed sentence structures, to attract users to click on hyperlinks for unknown benefits. Clickbait detection aims to detect these hyperlinks through automated algorithms. Most of the previous clickbait datasets are based on English online social media corpus. Detection models based on these datasets usually cannot be well generalized to Chinese social media scenarios. In this paper, we construct a WeChat based Chinese clickbait dataset, i.e., WCD. Based on the WCD, we conduct a detailed analysis of the clickbait features from three aspects: behavior features, headline text features, and content text features. Finally, we use popular methods for clickbait detection on our dataset. We also respectively propose a machine learning detection model using feature fusion and a deep learning detection model combining headline semantic and POS tag information, both of which achieve excellent detection performance. The results of clickbait analysis and detection show that the dataset we constructed is of high quality.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114417224","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 : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660588
Xinxin Liu, Zhan Xu, Lu Tian, Xiaolong Yang
Orthogonal frequency division multiplexing (OFDM) is very sensitive to frequency offset. Although the existence of integer frequency offset in OFDM will not destroy the orthogonality between subcarriers, it will cause a cyclic shift of the frequency domain data after FFT transformation at the receiving end, which will affect the demodulation of data. In this paper, we propose a novel integer frequency offset estimation method based on the similarity property of adjacent subcarriers of the preamble. This method not only guarantees the original accuracy and performance of the conventional method but also achieves less resource consumption and lower computational complexity.
{"title":"A Novel Integer Frequency Offset Estimation Method for OFDM Based on Preamble","authors":"Xinxin Liu, Zhan Xu, Lu Tian, Xiaolong Yang","doi":"10.1109/IC-NIDC54101.2021.9660588","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660588","url":null,"abstract":"Orthogonal frequency division multiplexing (OFDM) is very sensitive to frequency offset. Although the existence of integer frequency offset in OFDM will not destroy the orthogonality between subcarriers, it will cause a cyclic shift of the frequency domain data after FFT transformation at the receiving end, which will affect the demodulation of data. In this paper, we propose a novel integer frequency offset estimation method based on the similarity property of adjacent subcarriers of the preamble. This method not only guarantees the original accuracy and performance of the conventional method but also achieves less resource consumption and lower computational complexity.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114660649","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 : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660412
Yuehong Gao, Xiaoqi Wang, Jiamo Jiang, Yuming Jiang, Juan Deng
In order to better guarantee different quality of service requirements for different types of traffic, characteristics of each traffic type should be studied and modeled. In this paper, a measurement-based study is reported, which includes traffic data collection and processing. For traffic modeling, the concept of cumulative arrival process is adopted. In particular, the arrival curve model in network calculus is used for traffic characterization. Four types of traffic data are collected and analyzed as examples. The results for an online video watching application under three resolutions are discussed in detail. As a novel aspect of the study, the traffic of the application on both directions, i.e., the two-way traffic, is considered. For basic traffic characteristics, the traffic rates and the probability density functions of packet length and packet interval are analyzed. To characterize the traffic, the corresponding arrival curves are derived and discussed. The method adopted in this paper may also be applied to other traffic cases.
{"title":"Basic Characteristics and Arrival Curve Characterization for Two-way Traffic of Online Video Watching","authors":"Yuehong Gao, Xiaoqi Wang, Jiamo Jiang, Yuming Jiang, Juan Deng","doi":"10.1109/IC-NIDC54101.2021.9660412","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660412","url":null,"abstract":"In order to better guarantee different quality of service requirements for different types of traffic, characteristics of each traffic type should be studied and modeled. In this paper, a measurement-based study is reported, which includes traffic data collection and processing. For traffic modeling, the concept of cumulative arrival process is adopted. In particular, the arrival curve model in network calculus is used for traffic characterization. Four types of traffic data are collected and analyzed as examples. The results for an online video watching application under three resolutions are discussed in detail. As a novel aspect of the study, the traffic of the application on both directions, i.e., the two-way traffic, is considered. For basic traffic characteristics, the traffic rates and the probability density functions of packet length and packet interval are analyzed. To characterize the traffic, the corresponding arrival curves are derived and discussed. The method adopted in this paper may also be applied to other traffic cases.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128284765","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}
Human fingerprints is of critical importance for law enforcement agencies to identify suspects. At present, the number of fingerprint matching feature points is generally used to determine whether two fingerprints are from the same person, and the criteria vary from country to country. This paper establishes a probabilistic expression system from a statistical point of view to give the probability of similarity between two fingerprints, which can be used to characterize the degree of similarity between the two fingerprints. Our method is now being tried for latent fingerprint and tenprints identification conclusion expression in public safety.
{"title":"A Probabilistic Expression System for Fingerprint Identification Findings Based on Stability","authors":"Jing-Yi Wang, Yixiao Zheng, Weiyu Xiong, Junhan Chen, Zhanyu Ma, Rongliang Ma","doi":"10.1109/IC-NIDC54101.2021.9660455","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660455","url":null,"abstract":"Human fingerprints is of critical importance for law enforcement agencies to identify suspects. At present, the number of fingerprint matching feature points is generally used to determine whether two fingerprints are from the same person, and the criteria vary from country to country. This paper establishes a probabilistic expression system from a statistical point of view to give the probability of similarity between two fingerprints, which can be used to characterize the degree of similarity between the two fingerprints. Our method is now being tried for latent fingerprint and tenprints identification conclusion expression in public safety.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131505271","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 : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660551
Luhua Zhang, Yuhang Liu, Lei Wang, Wenping Zhang, Xiaoning He, Siyi Guo, Lingshan Li
Time series model is an important method for forecasting data series in the time dimension, which is widely used in many fields such as finance, economy, climate, etc. However, traditional time series forecasting methods are often too complicated and have limited effects. In order to effectively predict the flow of people in campus bathrooms and optimize public resources and management models, this paper develops a campus bathroom prediction system based on Facebook's open-source Prophet time series prediction model, and it's composed of growth trend model, seasonal trend model and holiday model. It can accurately fit the non-linear periodic trend and forecast the campus bathroom flow in a simpler and more flexible way, which greatly improves the availability and accuracy of the traditional model. In addition, this paper designs and elaborates on the system functions, database construction and interactive pages of campus bathroom prediction from the perspective of system development. Experiments show that the campus bathroom prediction method based on the Prophet algorithm has the advantages of simplicity, flexibility, high accuracy and good practicability, which can scientifically improve the utilization of bathroom equipment and optimize student experience.
{"title":"Design and Implementation of Campus Bathroom Prediction System Based on Prophet Algorithm","authors":"Luhua Zhang, Yuhang Liu, Lei Wang, Wenping Zhang, Xiaoning He, Siyi Guo, Lingshan Li","doi":"10.1109/IC-NIDC54101.2021.9660551","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660551","url":null,"abstract":"Time series model is an important method for forecasting data series in the time dimension, which is widely used in many fields such as finance, economy, climate, etc. However, traditional time series forecasting methods are often too complicated and have limited effects. In order to effectively predict the flow of people in campus bathrooms and optimize public resources and management models, this paper develops a campus bathroom prediction system based on Facebook's open-source Prophet time series prediction model, and it's composed of growth trend model, seasonal trend model and holiday model. It can accurately fit the non-linear periodic trend and forecast the campus bathroom flow in a simpler and more flexible way, which greatly improves the availability and accuracy of the traditional model. In addition, this paper designs and elaborates on the system functions, database construction and interactive pages of campus bathroom prediction from the perspective of system development. Experiments show that the campus bathroom prediction method based on the Prophet algorithm has the advantages of simplicity, flexibility, high accuracy and good practicability, which can scientifically improve the utilization of bathroom equipment and optimize student experience.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131904380","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 : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660484
Jimeng Sun, Si Li
Semantic textual similarity is a common task to determine whether two sentences in a pair own the same meaning. In the medical domain, the annotated data is limited and sparse, which brings great difficulty to obtain accurate semantic information from it. In this paper, we propose a two-stream model to adapt knowledge learned from other domains to the medical domain. To optimize and reduce the computation, we further compress the proposed model by knowledge distillation. Experimental results show that our proposed method achieves better performance than the baseline methods.
{"title":"Domain Adaptation for Medical Semantic Textual Similarity","authors":"Jimeng Sun, Si Li","doi":"10.1109/IC-NIDC54101.2021.9660484","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660484","url":null,"abstract":"Semantic textual similarity is a common task to determine whether two sentences in a pair own the same meaning. In the medical domain, the annotated data is limited and sparse, which brings great difficulty to obtain accurate semantic information from it. In this paper, we propose a two-stream model to adapt knowledge learned from other domains to the medical domain. To optimize and reduce the computation, we further compress the proposed model by knowledge distillation. Experimental results show that our proposed method achieves better performance than the baseline methods.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130117380","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 : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660450
Jiaqi Zhang
In the past 50 years, cancer has become one of the major causes of death in the world. Researchers are currently implementing various strategies to identify and prevent this disease, including treatment methods, molecular imaging technology and ultrasensitive monitoring of cancer biomarkers using innovative biosensor tools. Monitoring biomarkers will prove very useful for preventive measures. What is used to identify biomarkers is called a biological receptor. This review discusses the latest developments in the field of cancer biomarkers and bioreceptors. It introduces the biomarkers widely used in recent years, including proteins, nucleic acids, circulating tumor cells, and bioreceptors used for recognition, such as antibodies and aptamer. Moreover, this review discusses the advantages and disadvantages of various biomarkers and bioreceptors.
{"title":"Recent Advances on Biomarkers and Bioreceptor Used for Cancer Detection","authors":"Jiaqi Zhang","doi":"10.1109/IC-NIDC54101.2021.9660450","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660450","url":null,"abstract":"In the past 50 years, cancer has become one of the major causes of death in the world. Researchers are currently implementing various strategies to identify and prevent this disease, including treatment methods, molecular imaging technology and ultrasensitive monitoring of cancer biomarkers using innovative biosensor tools. Monitoring biomarkers will prove very useful for preventive measures. What is used to identify biomarkers is called a biological receptor. This review discusses the latest developments in the field of cancer biomarkers and bioreceptors. It introduces the biomarkers widely used in recent years, including proteins, nucleic acids, circulating tumor cells, and bioreceptors used for recognition, such as antibodies and aptamer. Moreover, this review discusses the advantages and disadvantages of various biomarkers and bioreceptors.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129509632","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 : 2021-11-17DOI: 10.1109/IC-NIDC54101.2021.9660496
Jupeng Ding, I. Chih-Lin, Jintao Wang, Hui Yang, Lili Wang
Optical wireless communication (OWC) has emerged as one promising candidate to mitigate the frequency spectrum crisis. Due to the broadcast nature of OWC channel, physical layer security (PLS) techniques have been considered and explored to improve the transmission confidentiality of OWC links. However, almost all current schemes focus on scenarios including multiple transmitters and fail to match the scenarios with limited transmitters, even single transmitter. For addressing this issue, in this work, the dynamic optical beam based PLS enhancement scheme is proposed. Unlike conventional Lambertian beam based technique paradigm, the above scheme tentatively utilize the commercially available non-Lambertian beam to configure the secure OWC links. Numerical results show that, compared with about 1.10 bps/Hz average secrecy capacity (SC) of the conventional configuration, up to 1.93 bps/Hz average SC gain could be provided by the proposed dynamic beam scheme. Moreover, this potential gain will be increased to about 2.39 bps/Hz when the diversity combing is available for the legitimate user receiver.
{"title":"Secure Optical Wireless Links with Dynamic Beam and Diversity Receiver","authors":"Jupeng Ding, I. Chih-Lin, Jintao Wang, Hui Yang, Lili Wang","doi":"10.1109/IC-NIDC54101.2021.9660496","DOIUrl":"https://doi.org/10.1109/IC-NIDC54101.2021.9660496","url":null,"abstract":"Optical wireless communication (OWC) has emerged as one promising candidate to mitigate the frequency spectrum crisis. Due to the broadcast nature of OWC channel, physical layer security (PLS) techniques have been considered and explored to improve the transmission confidentiality of OWC links. However, almost all current schemes focus on scenarios including multiple transmitters and fail to match the scenarios with limited transmitters, even single transmitter. For addressing this issue, in this work, the dynamic optical beam based PLS enhancement scheme is proposed. Unlike conventional Lambertian beam based technique paradigm, the above scheme tentatively utilize the commercially available non-Lambertian beam to configure the secure OWC links. Numerical results show that, compared with about 1.10 bps/Hz average secrecy capacity (SC) of the conventional configuration, up to 1.93 bps/Hz average SC gain could be provided by the proposed dynamic beam scheme. Moreover, this potential gain will be increased to about 2.39 bps/Hz when the diversity combing is available for the legitimate user receiver.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133788497","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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