Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674152
Shuai Lingyu, Chen Huaixin, Wang Zhixi
Aiming at the defect detection problem in the complex detection picture of LCD, a defect detection method of complex display picture combining feature matching and color correction is proposed in this paper. Firstly, the image registration method of Speeded Up Robust Features (SURF) and projection transformation is used for high-precision geometric registration between the detected image and the standard image; Secondly, the average brightness of the RGB three channels of the image is calculated respectively, and the image color correction of adaptive histogram matching is proposed. The histogram of the low brightness channel is specified as the histogram of the high brightness channel, and the final registered image pair is obtained. Finally, support vector machine (SVM) is used to classify the residual image to obtain the binary image of defect detection. The experimental results show that the proposed method can detect complex picture display defects under illumination change and geometric distortion, the detection accuracy is 99.43%, and the recall rate is 86.19%; It has engineering application prospect.
{"title":"Defect Detection Method Of LCD Complex Display Screen Combining Feature Matching and Color Correction","authors":"Shuai Lingyu, Chen Huaixin, Wang Zhixi","doi":"10.1109/ICCWAMTIP53232.2021.9674152","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674152","url":null,"abstract":"Aiming at the defect detection problem in the complex detection picture of LCD, a defect detection method of complex display picture combining feature matching and color correction is proposed in this paper. Firstly, the image registration method of Speeded Up Robust Features (SURF) and projection transformation is used for high-precision geometric registration between the detected image and the standard image; Secondly, the average brightness of the RGB three channels of the image is calculated respectively, and the image color correction of adaptive histogram matching is proposed. The histogram of the low brightness channel is specified as the histogram of the high brightness channel, and the final registered image pair is obtained. Finally, support vector machine (SVM) is used to classify the residual image to obtain the binary image of defect detection. The experimental results show that the proposed method can detect complex picture display defects under illumination change and geometric distortion, the detection accuracy is 99.43%, and the recall rate is 86.19%; It has engineering application prospect.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117096953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674078
H. Monday, J. Li, G. Nneji, E. James, Y. B. Leta, Saifun Nahar, A. Haq
In recent times, researchers are showing more interest in the subject of biometric identification, which uses biological traits to confirm a user's identity. Traditional authentication techniques are prone to damage, fraud, and negligence. We investigate a unique biometric based on electrocardiogram (ECG) signals generated from the heart as a biometric security attribute for access control verification. In this research, we propose a shared weighted continuous wavelet capsule network for ECG biometric identification, in which a continuous wavelet transform (CWT) is utilized to convert one-dimensional time-domain ECG signals into scalograms of two-dimensional images to obtain good quality training data. Then, a siamese capsule network framework is utilized to predict the right match or mismatch of ECG query samples using the extracted specific attributes from the scalograms. The dataset utilized in this work is collected from the Physionet MIT-BIH Normal Sinus Rhythm database. Experimental result shows that the proposed approach properly predicted ECG query samples with 99.2% accuracy, which makes our model more robust.
{"title":"Shared Weighted Continuous Wavelet Capsule Network for Electrocardiogram Biometric Identification","authors":"H. Monday, J. Li, G. Nneji, E. James, Y. B. Leta, Saifun Nahar, A. Haq","doi":"10.1109/ICCWAMTIP53232.2021.9674078","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674078","url":null,"abstract":"In recent times, researchers are showing more interest in the subject of biometric identification, which uses biological traits to confirm a user's identity. Traditional authentication techniques are prone to damage, fraud, and negligence. We investigate a unique biometric based on electrocardiogram (ECG) signals generated from the heart as a biometric security attribute for access control verification. In this research, we propose a shared weighted continuous wavelet capsule network for ECG biometric identification, in which a continuous wavelet transform (CWT) is utilized to convert one-dimensional time-domain ECG signals into scalograms of two-dimensional images to obtain good quality training data. Then, a siamese capsule network framework is utilized to predict the right match or mismatch of ECG query samples using the extracted specific attributes from the scalograms. The dataset utilized in this work is collected from the Physionet MIT-BIH Normal Sinus Rhythm database. Experimental result shows that the proposed approach properly predicted ECG query samples with 99.2% accuracy, which makes our model more robust.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126840097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674117
Di Liu, Chuan Liu, Maosen Yuan
With the rapid development of Internet of Things technology, wireless communication become an essential part in every field, which also bring about many wireless communication security problems. Traditional solutions to wireless communication security problems are mostly at the software level and protocol level, ignoring the physical characteristics of the device itself. Radio frequency fingerprint (RFF) can distinguish different devices in the physical level. Most of the existing incremental learning based radio frequency fingerprint identification (RFFI) are need a large amount of old data. In this paper, we review lots of RFFI method based on ML, DL or IL, and summarize a generic framework for RFFI, and propose our method to efficiently reduce the needed amount of old data in IL based RFFI, which saves training time and storage space.
{"title":"Incremental Learning for Radio Frequency Fingerprint Identification","authors":"Di Liu, Chuan Liu, Maosen Yuan","doi":"10.1109/ICCWAMTIP53232.2021.9674117","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674117","url":null,"abstract":"With the rapid development of Internet of Things technology, wireless communication become an essential part in every field, which also bring about many wireless communication security problems. Traditional solutions to wireless communication security problems are mostly at the software level and protocol level, ignoring the physical characteristics of the device itself. Radio frequency fingerprint (RFF) can distinguish different devices in the physical level. Most of the existing incremental learning based radio frequency fingerprint identification (RFFI) are need a large amount of old data. In this paper, we review lots of RFFI method based on ML, DL or IL, and summarize a generic framework for RFFI, and propose our method to efficiently reduce the needed amount of old data in IL based RFFI, which saves training time and storage space.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128230451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674108
Zhang Yusen, Zhang Bozhou, Sun Ming
Deep learning is wildly used in remaining useful life estimation of mechanical equipment. However, existing methods couldn't avoid losing useful information during the process of extracting feature. In order to extract rich feature from limited data, we proposed a prognostic model using residual network and dilated convolution to aggregat complex contextual information during training. Furthermore, time-frequency analysis is also utilized in our method to combine useful information in frequency and time domain. Experimental results represented that our method makes better results on remaining useful life estimation over other methods using deep learning.
{"title":"Estimation for Remaining Useful Life Based on a Complex Context Aggregation Model","authors":"Zhang Yusen, Zhang Bozhou, Sun Ming","doi":"10.1109/ICCWAMTIP53232.2021.9674108","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674108","url":null,"abstract":"Deep learning is wildly used in remaining useful life estimation of mechanical equipment. However, existing methods couldn't avoid losing useful information during the process of extracting feature. In order to extract rich feature from limited data, we proposed a prognostic model using residual network and dilated convolution to aggregat complex contextual information during training. Furthermore, time-frequency analysis is also utilized in our method to combine useful information in frequency and time domain. Experimental results represented that our method makes better results on remaining useful life estimation over other methods using deep learning.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"460 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132971438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674158
Li Zongxun, Li Yujun, Zhang Haojie, Li Juan
With the ongoing usage of networks, the number of Advanced Persistent Threat (APT) attacks has grown in recent years. When compared to real-time APT attack detection, analyzing APT reports enables faster dissemination of cyber threat intelligence (CTI) and identification of APT attacks. Thus, this paper proposes a model for automatically extracting threat actions and generating Tactics, Techniques and Procedures (TTPs) from APT reports. The model analyzes the semantics of APT reports and extracts threat actions automatically based on BERT-BiLSTM-CRF that can accurately capture the semantics of sentences. A sentence containing a threat action is fed into the trained model, and the model marks the threat action contained in the sentence. Then, we leverage existing knowledge to build a cyber threat ontology, obtain complete attack information by mapping threat actions to the ontology, and generate high-level Indicators of Compromise (IOC) and generate TTPs. Threat actions are mapped to this ontology to construct TTPs. In comparison to traditional approaches, our method achieves an average of 96% precision on the test dataset.
{"title":"Construction of TTPS From APT Reports Using Bert","authors":"Li Zongxun, Li Yujun, Zhang Haojie, Li Juan","doi":"10.1109/ICCWAMTIP53232.2021.9674158","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674158","url":null,"abstract":"With the ongoing usage of networks, the number of Advanced Persistent Threat (APT) attacks has grown in recent years. When compared to real-time APT attack detection, analyzing APT reports enables faster dissemination of cyber threat intelligence (CTI) and identification of APT attacks. Thus, this paper proposes a model for automatically extracting threat actions and generating Tactics, Techniques and Procedures (TTPs) from APT reports. The model analyzes the semantics of APT reports and extracts threat actions automatically based on BERT-BiLSTM-CRF that can accurately capture the semantics of sentences. A sentence containing a threat action is fed into the trained model, and the model marks the threat action contained in the sentence. Then, we leverage existing knowledge to build a cyber threat ontology, obtain complete attack information by mapping threat actions to the ontology, and generate high-level Indicators of Compromise (IOC) and generate TTPs. Threat actions are mapped to this ontology to construct TTPs. In comparison to traditional approaches, our method achieves an average of 96% precision on the test dataset.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124438836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674171
Peter Atandoh, Z. Feng, D. Adu-Gyamfi, H. Leka, Paul H. Atandoh
Reviewing products online has become an increasingly popular way for consumers to voice their opinions and feelings about a product or service. Analyzing this Big data of online reviews would help to discern and extract useful facts and information that could provide a competitive and economic advantage to merchants and other organizations that are interested. Text classification organizes documents according to a variety of predefined categories. In other to solve the aforementioned problems, we employed Glove embeddings for our review sentiment analysis. We further integrate this embedding layer into a deep convolutional neural network (CNN)-bidirectional LSTM model. We further train our model on the IMDB and movie review dataset to extract the polarity as positive or negative and subsequently compare our model with other state-of- the-art models. The aforementioned experiments validate the efficacy and superiority of our proposed approach.
{"title":"A Glove CNN-Bilstm Sentiment Classification","authors":"Peter Atandoh, Z. Feng, D. Adu-Gyamfi, H. Leka, Paul H. Atandoh","doi":"10.1109/ICCWAMTIP53232.2021.9674171","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674171","url":null,"abstract":"Reviewing products online has become an increasingly popular way for consumers to voice their opinions and feelings about a product or service. Analyzing this Big data of online reviews would help to discern and extract useful facts and information that could provide a competitive and economic advantage to merchants and other organizations that are interested. Text classification organizes documents according to a variety of predefined categories. In other to solve the aforementioned problems, we employed Glove embeddings for our review sentiment analysis. We further integrate this embedding layer into a deep convolutional neural network (CNN)-bidirectional LSTM model. We further train our model on the IMDB and movie review dataset to extract the polarity as positive or negative and subsequently compare our model with other state-of- the-art models. The aforementioned experiments validate the efficacy and superiority of our proposed approach.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123659619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674162
Muhammad Hassaan Farooq Butt, J. Li, Tehreem Saboor, M. Arslan, Muhammad Adnan Farooq Butt
On the Internet, every different day, kinds of attacks are deployed on innocent users. Among all, phishing is the most severe attack in which users lose their credentials or private information and their financial status quickly. The attacker uses their credibility or sensitive information to harm the target or victim. The attacker is clever and uses different strategies to fetch user-sensitive information. The existing techniques fail to overcome these issues to some extent. This work focuses on discovering the essential features that help to differentiate the legitimate and illegitimate URLs. We applied a deep learning technique on the benchmark datasets to identify the pattern of phishing URLs. We used gradient boosted decision trees algorithm to train our model and applied the regular deeply connected neural network layers in various sequences and Adam optimizer. The most found patterns will help the system to detect phishing URLs and avoid phishing. We consider the accuracy, Ff-score, and Root Mean Square Error (RMSE) as our evaluation metrics for model evaluation. The results show that the trained model can achieve an approximately 92% accuracy and 94% f-score.
{"title":"Intelligent Phishing Url Detection: A Solution Based On Deep Learning Framework","authors":"Muhammad Hassaan Farooq Butt, J. Li, Tehreem Saboor, M. Arslan, Muhammad Adnan Farooq Butt","doi":"10.1109/ICCWAMTIP53232.2021.9674162","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674162","url":null,"abstract":"On the Internet, every different day, kinds of attacks are deployed on innocent users. Among all, phishing is the most severe attack in which users lose their credentials or private information and their financial status quickly. The attacker uses their credibility or sensitive information to harm the target or victim. The attacker is clever and uses different strategies to fetch user-sensitive information. The existing techniques fail to overcome these issues to some extent. This work focuses on discovering the essential features that help to differentiate the legitimate and illegitimate URLs. We applied a deep learning technique on the benchmark datasets to identify the pattern of phishing URLs. We used gradient boosted decision trees algorithm to train our model and applied the regular deeply connected neural network layers in various sequences and Adam optimizer. The most found patterns will help the system to detect phishing URLs and avoid phishing. We consider the accuracy, Ff-score, and Root Mean Square Error (RMSE) as our evaluation metrics for model evaluation. The results show that the trained model can achieve an approximately 92% accuracy and 94% f-score.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127191361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674157
Abebe Tegene, Qiao Liu, S. Muhammed, H. Leka
Collaborative Filtering based on matrix factorization (MF) has shown tremendous success in the field recommender system. However, MF has difficulty in handling sparsity and scalability. These resulted in low quality of recommendations. In this regard, deep learning has shown immense success in different application areas including recommender systems. To address the limitations, we incorporate deep learning architecture to matrix factorization and develop a novel mode. The core idea of the method is to map users and items input vector to two well-structured deep neural network architectures separately for factorization. Then, we incorporate inner product to the output layers of the network to predict the rating scores. The use of this structure significantly improve the quality of recommendation. The experimental result on real data sets shows that our proposed model outperformed state of the art methods.
{"title":"Deep Learning Based Matrix Factorization For Collaborative Filtering","authors":"Abebe Tegene, Qiao Liu, S. Muhammed, H. Leka","doi":"10.1109/ICCWAMTIP53232.2021.9674157","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674157","url":null,"abstract":"Collaborative Filtering based on matrix factorization (MF) has shown tremendous success in the field recommender system. However, MF has difficulty in handling sparsity and scalability. These resulted in low quality of recommendations. In this regard, deep learning has shown immense success in different application areas including recommender systems. To address the limitations, we incorporate deep learning architecture to matrix factorization and develop a novel mode. The core idea of the method is to map users and items input vector to two well-structured deep neural network architectures separately for factorization. Then, we incorporate inner product to the output layers of the network to predict the rating scores. The use of this structure significantly improve the quality of recommendation. The experimental result on real data sets shows that our proposed model outperformed state of the art methods.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127207732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674164
L. Wei, Guo Xue, Yu Lin, L. Yi
The natural language processing of Bronze inscriptions is the pivotal step to the study of the historical use of Bronze inscriptions, and the recognition of Bronze inscription words relics is the most important part. Due to its long history and complex font, the difficulty of recognition is increased a lot. At present, convolutional neural network has been widely used in the field of photo recognition, but it has few application in the field of Bronze inscriptions recognition. This paper proposes a method of Bronze inscriptions' extract and recognition based on improved k-means and convolutional neural network, using the improved k-means algorithm to extract characters, which makes preparations for the recognition of convolutional neural network. Experiments has shown that the improved method has significantly improved the accuracy and speed of Bronze inscriptions recognition, and it is also considerably helpful to the Bronze inscriptions research.
{"title":"Research and Analysis Of Extraction and Recognition INS Criptions of Bronzebased On Improved K-Means and Convo Lutional Neural Network","authors":"L. Wei, Guo Xue, Yu Lin, L. Yi","doi":"10.1109/ICCWAMTIP53232.2021.9674164","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674164","url":null,"abstract":"The natural language processing of Bronze inscriptions is the pivotal step to the study of the historical use of Bronze inscriptions, and the recognition of Bronze inscription words relics is the most important part. Due to its long history and complex font, the difficulty of recognition is increased a lot. At present, convolutional neural network has been widely used in the field of photo recognition, but it has few application in the field of Bronze inscriptions recognition. This paper proposes a method of Bronze inscriptions' extract and recognition based on improved k-means and convolutional neural network, using the improved k-means algorithm to extract characters, which makes preparations for the recognition of convolutional neural network. Experiments has shown that the improved method has significantly improved the accuracy and speed of Bronze inscriptions recognition, and it is also considerably helpful to the Bronze inscriptions research.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114858365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674090
Chang Zeyu
While 5G is being deployed commercially worldwide, scientists have carried out research for 6G as well as WIFI 6G bands, and LIFI are also tested. Both advantages and disadvantages of these three wireless communication methods are focused and the respective application scenarios are described as well as the difficulties and challenges to be overcome. An integrated network system of space and earth is proposed to provide users with ubiquitous wireless network connection. Technologies and challenges required by three communication methods are sorted out and the way they can be combined and applied are analyzed through extensive research and analysis.
{"title":"6G, LIFI and WIFI Wireless Systems: Challenges, Development and Prospects","authors":"Chang Zeyu","doi":"10.1109/ICCWAMTIP53232.2021.9674090","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674090","url":null,"abstract":"While 5G is being deployed commercially worldwide, scientists have carried out research for 6G as well as WIFI 6G bands, and LIFI are also tested. Both advantages and disadvantages of these three wireless communication methods are focused and the respective application scenarios are described as well as the difficulties and challenges to be overcome. An integrated network system of space and earth is proposed to provide users with ubiquitous wireless network connection. Technologies and challenges required by three communication methods are sorted out and the way they can be combined and applied are analyzed through extensive research and analysis.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130183822","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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