Pub Date : 2023-10-14DOI: 10.1080/24751839.2023.2267890
Tian-Bo Deng
This paper shows a 2-step procedure for obtaining a variable-bandwidth recursive digital filter whose structure contains cascaded second-order (2nd-order) sections. Such a cascade-form structure is insensitive to the round-off noises that come from filter-coefficient quantizations in hardware implementations. This paper also shows how to utilize a 2-step procedure to get a variable-bandwidth recursive filter that is absolutely stable. The first step (Step-1) of the 2-step procedure designs a series of constant-bandwidth filters for approximating a series of evenly discretized variable specifications, and the second step (Step-2) fits the coefficient values obtained from Step-1 by employing individual polynomials. To ensure the stability of the resultant constant-bandwidth filters in Step-1, coefficient transformations are first executed on the 2nd-order transfer function's denominator-coefficients, and then each coefficient of both numerator and transformed denominator is found as an individual polynomial. Once all the polynomials are obtained, the polynomials corresponding to the transformed denominator are further converted to composite functions for ensuring the stability. Hence, the 2-step procedure not only produces a cascade-form variable-bandwidth filter that has low quantization errors, but also ensures the stability. A lowpass example is included for verifying the achieved stability and showing the high approximation accuracy.
{"title":"Variable-bandwidth recursive-filter design employing cascaded stability-guaranteed 2nd-order sections using coefficient transformations","authors":"Tian-Bo Deng","doi":"10.1080/24751839.2023.2267890","DOIUrl":"https://doi.org/10.1080/24751839.2023.2267890","url":null,"abstract":"This paper shows a 2-step procedure for obtaining a variable-bandwidth recursive digital filter whose structure contains cascaded second-order (2nd-order) sections. Such a cascade-form structure is insensitive to the round-off noises that come from filter-coefficient quantizations in hardware implementations. This paper also shows how to utilize a 2-step procedure to get a variable-bandwidth recursive filter that is absolutely stable. The first step (Step-1) of the 2-step procedure designs a series of constant-bandwidth filters for approximating a series of evenly discretized variable specifications, and the second step (Step-2) fits the coefficient values obtained from Step-1 by employing individual polynomials. To ensure the stability of the resultant constant-bandwidth filters in Step-1, coefficient transformations are first executed on the 2nd-order transfer function's denominator-coefficients, and then each coefficient of both numerator and transformed denominator is found as an individual polynomial. Once all the polynomials are obtained, the polynomials corresponding to the transformed denominator are further converted to composite functions for ensuring the stability. Hence, the 2-step procedure not only produces a cascade-form variable-bandwidth filter that has low quantization errors, but also ensures the stability. A lowpass example is included for verifying the achieved stability and showing the high approximation accuracy.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766439","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 : 2023-10-07DOI: 10.1080/24751839.2023.2265190
Pavel Vácha, Michal Haindl
Visual scene recognition is predominantly based on visual textures representing an object's material properties. However, the single material texture varies in scale and illumination angles due to mapping an object's shape. We present a comparative study of the colour histogram, Gabor, opponent Gabor, Local Binary Pattern (LBP), and wide-sense Markovian textural features concerning their sensitivity to simultaneous scale and illumination variations. Due to their application dominance, these textural features are selected from more than 50 published textural features. Markovian features are information preserving, and we demonstrate their superior performance for scale and illumination variable observation conditions over the standard alternative textural features. We bound the scale variation by double size, and illumination variation includes illumination spectra, acquisition devices, and 35 illumination directions spanned above a sample hemisphere. Recognition accuracy is tested on textile patterns from the University of East Anglia and wood veneers from UTIA BTF databases.
{"title":"Texture recognition under scale and illumination variations","authors":"Pavel Vácha, Michal Haindl","doi":"10.1080/24751839.2023.2265190","DOIUrl":"https://doi.org/10.1080/24751839.2023.2265190","url":null,"abstract":"Visual scene recognition is predominantly based on visual textures representing an object's material properties. However, the single material texture varies in scale and illumination angles due to mapping an object's shape. We present a comparative study of the colour histogram, Gabor, opponent Gabor, Local Binary Pattern (LBP), and wide-sense Markovian textural features concerning their sensitivity to simultaneous scale and illumination variations. Due to their application dominance, these textural features are selected from more than 50 published textural features. Markovian features are information preserving, and we demonstrate their superior performance for scale and illumination variable observation conditions over the standard alternative textural features. We bound the scale variation by double size, and illumination variation includes illumination spectra, acquisition devices, and 35 illumination directions spanned above a sample hemisphere. Recognition accuracy is tested on textile patterns from the University of East Anglia and wood veneers from UTIA BTF databases.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135253276","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 : 2023-09-28DOI: 10.1080/24751839.2023.2260230
Duong Hien Thuan, Nhat-Tien Nguyen, Xuan Tien Nguyen, Nguyen Thi Hau, Bui Vu Minh, Tan N. Nguyen
In this paper, we consider an uplink non-orthogonal multiple access (NOMA) for energy harvesting at the base station or access point in the wireless system. By exploiting energy enough, a base station or access point can serve many users at the downlink. The fixed power allocation factors are adopted, and the power splitting energy harvesting protocol brings many benefits to both the uplink and downlink of a wireless system. The closed-form expressions of outage probability are investigated and examined in a group of two users. Moreover, the optimal outage probability for two users is shown numerically. Finally, Monte Carlo simulations are presented to further support the validity of our framework and findings.
{"title":"Uplink and downlink of energy harvesting NOMA system: performance analysis","authors":"Duong Hien Thuan, Nhat-Tien Nguyen, Xuan Tien Nguyen, Nguyen Thi Hau, Bui Vu Minh, Tan N. Nguyen","doi":"10.1080/24751839.2023.2260230","DOIUrl":"https://doi.org/10.1080/24751839.2023.2260230","url":null,"abstract":"In this paper, we consider an uplink non-orthogonal multiple access (NOMA) for energy harvesting at the base station or access point in the wireless system. By exploiting energy enough, a base station or access point can serve many users at the downlink. The fixed power allocation factors are adopted, and the power splitting energy harvesting protocol brings many benefits to both the uplink and downlink of a wireless system. The closed-form expressions of outage probability are investigated and examined in a group of two users. Moreover, the optimal outage probability for two users is shown numerically. Finally, Monte Carlo simulations are presented to further support the validity of our framework and findings.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135385418","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}
While social media can serve as public discussion forums of great benefit to democratic debate, discourse propagated through them can also stoke political polarization and partisanship. A particularly dramatic example is the January 6, 2021 incident in Washington D.C., when a group of protesters besieged the US Capitol, resulting in several deaths. The public reacted by posting messages on social media, discussing the actions of the participants. Aiming to understand their perspectives under the broad concept of unhealthy online conversation (i.e. bad faith argumentation, overly hostile or destructive discourse, or other behaviours that discourage engagement), we sample 1,300,000 Twitter posts taken from the #Election2020 dataset dating from January 2021. Using a fine-tuned XLNet model trained on the Unhealthy Comment Corpus (UCC) dataset, we label these texts as healthy or unhealthy, furthermore using a taxonomy of 7 unhealthy attributes. Using the NRCLex sentiment analysis lexicon, we also detect the emotional patterns associated with each attribute. We observe that these conversations contain accusatory language aimed at the ‘other side’, limiting engagement by defining others in terms they do not themselves use or identify with. We find evidence of three attribute clusters, in addition to sarcasm, a divergent attribute that we argue should be researched separately. We find that emotions identified from the text do not correlate with the attributes, the two approaches revealing complementary characteristics of online discourse. Using latent Dirichlet allocation (LDA), we identify topics discussed within the attribute-sentiment pairs, linking them to each other using similarity measures. The results we present aim to help social media stakeholders, government regulators, and the general public better understand the contents and the emotional profile of the debates arising on social media platforms, especially as they relate to the political realm.
{"title":"January 6th on Twitter: measuring social media attitudes towards the Capitol riot through unhealthy online conversation and sentiment analysis","authors":"Kovacs Erik-Robert, Cotfas Liviu-Adrian, Delcea Camelia","doi":"10.1080/24751839.2023.2262067","DOIUrl":"https://doi.org/10.1080/24751839.2023.2262067","url":null,"abstract":"While social media can serve as public discussion forums of great benefit to democratic debate, discourse propagated through them can also stoke political polarization and partisanship. A particularly dramatic example is the January 6, 2021 incident in Washington D.C., when a group of protesters besieged the US Capitol, resulting in several deaths. The public reacted by posting messages on social media, discussing the actions of the participants. Aiming to understand their perspectives under the broad concept of unhealthy online conversation (i.e. bad faith argumentation, overly hostile or destructive discourse, or other behaviours that discourage engagement), we sample 1,300,000 Twitter posts taken from the #Election2020 dataset dating from January 2021. Using a fine-tuned XLNet model trained on the Unhealthy Comment Corpus (UCC) dataset, we label these texts as healthy or unhealthy, furthermore using a taxonomy of 7 unhealthy attributes. Using the NRCLex sentiment analysis lexicon, we also detect the emotional patterns associated with each attribute. We observe that these conversations contain accusatory language aimed at the ‘other side’, limiting engagement by defining others in terms they do not themselves use or identify with. We find evidence of three attribute clusters, in addition to sarcasm, a divergent attribute that we argue should be researched separately. We find that emotions identified from the text do not correlate with the attributes, the two approaches revealing complementary characteristics of online discourse. Using latent Dirichlet allocation (LDA), we identify topics discussed within the attribute-sentiment pairs, linking them to each other using similarity measures. The results we present aim to help social media stakeholders, government regulators, and the general public better understand the contents and the emotional profile of the debates arising on social media platforms, especially as they relate to the political realm.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134960479","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 : 2023-09-10DOI: 10.1080/24751839.2023.2254956
Amina Affes, Ismaïl Biskri, Adel Jebali
The interlanguage of second and foreign language French learners presents certain patterns that offer a formal framework within the context of the Applicative Combinatory Categorical Grammar. These patterns manifest in the acquisition of object clitics which presents considerable difficulties for French second language learners. To address this, we thoroughly analysed the object clitic pronouns that appeared in a corpus of texts by examining the different facets of these pronouns. A formal analysis was then proposed. The objective of this research is to establish an automated system for processing learners’ interlanguage in terms of the ACCG.
{"title":"The formalization of interlanguage: the example of object clitic pronouns acquisition in French L2*","authors":"Amina Affes, Ismaïl Biskri, Adel Jebali","doi":"10.1080/24751839.2023.2254956","DOIUrl":"https://doi.org/10.1080/24751839.2023.2254956","url":null,"abstract":"The interlanguage of second and foreign language French learners presents certain patterns that offer a formal framework within the context of the Applicative Combinatory Categorical Grammar. These patterns manifest in the acquisition of object clitics which presents considerable difficulties for French second language learners. To address this, we thoroughly analysed the object clitic pronouns that appeared in a corpus of texts by examining the different facets of these pronouns. A formal analysis was then proposed. The objective of this research is to establish an automated system for processing learners’ interlanguage in terms of the ACCG.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136071235","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 : 2023-09-06DOI: 10.1080/24751839.2023.2250113
Andras Ferenczi, C. Bǎdicǎ
{"title":"Prediction of Ethereum gas prices using DeepAR and probabilistic forecasting","authors":"Andras Ferenczi, C. Bǎdicǎ","doi":"10.1080/24751839.2023.2250113","DOIUrl":"https://doi.org/10.1080/24751839.2023.2250113","url":null,"abstract":"","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47534826","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 : 2023-08-31DOI: 10.1080/24751839.2023.2252186
Van-Nho Do, Quang-Vu Nguyen, Thanh-Binh Nguyen
{"title":"Predicting higher order mutation score based on machine learning","authors":"Van-Nho Do, Quang-Vu Nguyen, Thanh-Binh Nguyen","doi":"10.1080/24751839.2023.2252186","DOIUrl":"https://doi.org/10.1080/24751839.2023.2252186","url":null,"abstract":"","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46978345","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 : 2023-08-25DOI: 10.1080/24751839.2023.2250123
Hamdi Eltaief, Ali El kamel, H. Youssef
{"title":"MSA-SDMN: multicast source authentication scheme for multi-domain software defined mobile networks","authors":"Hamdi Eltaief, Ali El kamel, H. Youssef","doi":"10.1080/24751839.2023.2250123","DOIUrl":"https://doi.org/10.1080/24751839.2023.2250123","url":null,"abstract":"","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43599465","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 : 2023-08-05DOI: 10.1080/24751839.2023.2235114
Hoang Hai Son, Vo Phuc Tinh, Duc Ngoc Minh Dang, Bui Thi Duyen, Duy-Dong Le, Thai-Thinh Dang, Q. Nguyen, Thanh-Qui Pham, Van-Luong Nguyen, Tran Anh Khoa, Nguyen Hoang Nam
{"title":"A novel solution for energy-saving and lifetime-maximizing of LoRa wireless mesh networks","authors":"Hoang Hai Son, Vo Phuc Tinh, Duc Ngoc Minh Dang, Bui Thi Duyen, Duy-Dong Le, Thai-Thinh Dang, Q. Nguyen, Thanh-Qui Pham, Van-Luong Nguyen, Tran Anh Khoa, Nguyen Hoang Nam","doi":"10.1080/24751839.2023.2235114","DOIUrl":"https://doi.org/10.1080/24751839.2023.2235114","url":null,"abstract":"","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47367923","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 : 2023-07-25DOI: 10.1080/24751839.2023.2239617
Ahlem Abid, F. Jemili, O. Korbaa
ABSTRACT Industry 4.0 refers to a new generation of connected and intelligent factories that is driven by the emergence of new technologies such as artificial intelligence, Cloud computing, Big Data and industrial control systems (ICS) in order to automate all phases of industrial operations. The presence of connected systems in industrial environments poses a considerable security challenge, moreover with the huge amount of data generated daily, there are complex attacks that occur in seconds and target production lines and their integrity. But, until now, factories do not have all the necessary tools to protect themselves, they mainly use traditional protection. In order to improve industrial control systems in terms of efficiency and response time, the present paper propose a new distributed intrusion detection approach using artificial intelligence methods, Big Data techniques and deployed in a cloud environment. A variety of Machine Learning and Deep Learning algorithms, basically convolutional neural networks (CNN), have been tested to compare performance and choose the most suitable model for the classification. We test the performance of our model by using the industrial dataset SWat.
{"title":"Distributed deep learning approach for intrusion detection system in industrial control systems based on big data technique and transfer learning","authors":"Ahlem Abid, F. Jemili, O. Korbaa","doi":"10.1080/24751839.2023.2239617","DOIUrl":"https://doi.org/10.1080/24751839.2023.2239617","url":null,"abstract":"ABSTRACT Industry 4.0 refers to a new generation of connected and intelligent factories that is driven by the emergence of new technologies such as artificial intelligence, Cloud computing, Big Data and industrial control systems (ICS) in order to automate all phases of industrial operations. The presence of connected systems in industrial environments poses a considerable security challenge, moreover with the huge amount of data generated daily, there are complex attacks that occur in seconds and target production lines and their integrity. But, until now, factories do not have all the necessary tools to protect themselves, they mainly use traditional protection. In order to improve industrial control systems in terms of efficiency and response time, the present paper propose a new distributed intrusion detection approach using artificial intelligence methods, Big Data techniques and deployed in a cloud environment. A variety of Machine Learning and Deep Learning algorithms, basically convolutional neural networks (CNN), have been tested to compare performance and choose the most suitable model for the classification. We test the performance of our model by using the industrial dataset SWat.","PeriodicalId":32180,"journal":{"name":"Journal of Information and Telecommunication","volume":"1 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41982654","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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