Pub Date : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357322
Ouafaa Raibi, A. Makrizi
Topology optimization has received recently a widespread fame in industry as well as in academia for its importance in determining the best distribution of materiel within a structure during its conceptual design stage. Several approaches have been proposed in this field but the resulted in programming models are computationally expensive and restrictive for large structures, domain decomposition methods are shown to be efficient to handle such issue. In this work a new formulation of topology optimization is presented. We reformulate the minimum compliance problem using domain decomposition method based Lagrange multipliers where both the objective function and the constraint are divided into several sub-problems then we prove the equivalence between the global problem and the decomposed one.
{"title":"Topology optimization problem using domain decomposition method based Lagrange multipliers","authors":"Ouafaa Raibi, A. Makrizi","doi":"10.1109/CiSt49399.2021.9357322","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357322","url":null,"abstract":"Topology optimization has received recently a widespread fame in industry as well as in academia for its importance in determining the best distribution of materiel within a structure during its conceptual design stage. Several approaches have been proposed in this field but the resulted in programming models are computationally expensive and restrictive for large structures, domain decomposition methods are shown to be efficient to handle such issue. In this work a new formulation of topology optimization is presented. We reformulate the minimum compliance problem using domain decomposition method based Lagrange multipliers where both the objective function and the constraint are divided into several sub-problems then we prove the equivalence between the global problem and the decomposed one.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115060702","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357304
E. Azeraf, E. Monfrini, Emmanuel Vignon, W. Pieczynski
Natural Language Processing (NLP) models' current trend consists of using increasingly more extra-data to build the best models as possible. It implies more expensive computational costs and training time, difficulties for deployment, and worries about these models' carbon footprint reveal a critical problem in the future. Against this trend, our goal is to develop NLP models requiring no extra-data and minimizing training time. To do so, in this paper, we explore Markov chain models, Hidden Markov Chain (HMC) and Pairwise Markov Chain (PMC), for NLP segmentation tasks. We apply these models for three classic applications: POS Tagging, Named-Entity-Recognition, and Chunking. We develop an original method to adapt these models for text segmentation's specific challenges to obtain relevant performances with very short training and execution times. PMC achieves equivalent results to those obtained by Conditional Random Fields (CRF), one of the most applied models for these tasks when no extra-data are used. Moreover, PMC has training times 30 times shorter than the CRF ones, which validates this model given our objectives.
{"title":"Highly Fast Text Segmentation With Pairwise Markov Chains","authors":"E. Azeraf, E. Monfrini, Emmanuel Vignon, W. Pieczynski","doi":"10.1109/CiSt49399.2021.9357304","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357304","url":null,"abstract":"Natural Language Processing (NLP) models' current trend consists of using increasingly more extra-data to build the best models as possible. It implies more expensive computational costs and training time, difficulties for deployment, and worries about these models' carbon footprint reveal a critical problem in the future. Against this trend, our goal is to develop NLP models requiring no extra-data and minimizing training time. To do so, in this paper, we explore Markov chain models, Hidden Markov Chain (HMC) and Pairwise Markov Chain (PMC), for NLP segmentation tasks. We apply these models for three classic applications: POS Tagging, Named-Entity-Recognition, and Chunking. We develop an original method to adapt these models for text segmentation's specific challenges to obtain relevant performances with very short training and execution times. PMC achieves equivalent results to those obtained by Conditional Random Fields (CRF), one of the most applied models for these tasks when no extra-data are used. Moreover, PMC has training times 30 times shorter than the CRF ones, which validates this model given our objectives.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115723761","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357209
Said Abenna, M. Nahid, A. Bajit
The topic of this article is for the development of the new high-performance BCI systems, and in this regard we have integrated optimization algorithms in basic BCI system at the level of the classification algorithms to improve the performance of the system recognition as illustrates the section of results. The optimizes were also programmed to work collectively in order to reach the maximum value of accuracy in a short period of time. This position can also be extended to include improving the system rest, algorithms of the selection features, algorithms of frequency and spatial filters, algorithms of the best electrodes selection.
{"title":"BCI: Classifiers Optimization for EEG Signals Acquiring in Real-Time","authors":"Said Abenna, M. Nahid, A. Bajit","doi":"10.1109/CiSt49399.2021.9357209","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357209","url":null,"abstract":"The topic of this article is for the development of the new high-performance BCI systems, and in this regard we have integrated optimization algorithms in basic BCI system at the level of the classification algorithms to improve the performance of the system recognition as illustrates the section of results. The optimizes were also programmed to work collectively in order to reach the maximum value of accuracy in a short period of time. This position can also be extended to include improving the system rest, algorithms of the selection features, algorithms of frequency and spatial filters, algorithms of the best electrodes selection.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126425047","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357203
Y. Verbelen, S. Kaluvan, Ulrike Haller, Morgan J. Boardman, Tom B. Scott
The outbreak of the COVID-19 pandemic early 2020 has presented humanity with the unprecedented challenge to tackle a global health emergency that spreads through close social contact on an overpopulated planet. After easing nationwide lockdown measures, and with socio-economic activities starting to normalise, there is an increased awareness for the importance of social distancing to prevent further spreading of the virus. The implementation of a contact tracing system to identify infected individuals and isolate them from society through rapid quarantining requires technological assistance on a scale that does not yet exist. In an effort to solve the problem, a novel design of a social distancing and contact tracing wearable is presented. The wearable uses RF communication both to exchange information with other devices, and to estimate proximity to each other. The demonstrated prototype shows the successful implementation of a highly autonomous, user friendly, robust, and technically capable wearable that can be deployed in critical environments where social distancing is difficult or impossible to enforce.
{"title":"Design and Implementation of a Social Distancing and Contact Tracing Wearable","authors":"Y. Verbelen, S. Kaluvan, Ulrike Haller, Morgan J. Boardman, Tom B. Scott","doi":"10.1109/CiSt49399.2021.9357203","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357203","url":null,"abstract":"The outbreak of the COVID-19 pandemic early 2020 has presented humanity with the unprecedented challenge to tackle a global health emergency that spreads through close social contact on an overpopulated planet. After easing nationwide lockdown measures, and with socio-economic activities starting to normalise, there is an increased awareness for the importance of social distancing to prevent further spreading of the virus. The implementation of a contact tracing system to identify infected individuals and isolate them from society through rapid quarantining requires technological assistance on a scale that does not yet exist. In an effort to solve the problem, a novel design of a social distancing and contact tracing wearable is presented. The wearable uses RF communication both to exchange information with other devices, and to estimate proximity to each other. The demonstrated prototype shows the successful implementation of a highly autonomous, user friendly, robust, and technically capable wearable that can be deployed in critical environments where social distancing is difficult or impossible to enforce.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131965006","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357311
Abderrahman Jaize, A. Hajami, Abdelhak Annajar, Aissam Jadli
The improvement in assessment is an obligation when relating to any technological lesson. This study has only one goal: to get more information from applying a formative assessment system using an e-learning tool called Qwiklabs in our University Hassan I. Having in plain sight the necessity of being very active in the classroom and based on a combination of a self-learning process and a social cognitive approach, these students get the time to give thought to the feedback and their execution (metacognition). The Google Developers Ecosystem wants to train students on some of its products. As part of that ecosystem, we proposed to implement a series of monthly hands-on labs while using texts, description, pictures, videos, hints, rewards, and quizzes through the Qwiklabs platform, which is the main Google Developers contribution, this platform will provide sufficient feedback as a follow-up to the student's attempts. While giving the students access to the platform, they receive at each lesson a new identification that offers the system the possibility to monitor the students' performance and achievements through every experience, while having a scoring system that allows measuring the users understanding and verifying the proper execution of the lesson's steps.
{"title":"Evaluating the Effectiveness of an Online Learning Platform - A Study Of A Google Cloud Learning System","authors":"Abderrahman Jaize, A. Hajami, Abdelhak Annajar, Aissam Jadli","doi":"10.1109/CiSt49399.2021.9357311","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357311","url":null,"abstract":"The improvement in assessment is an obligation when relating to any technological lesson. This study has only one goal: to get more information from applying a formative assessment system using an e-learning tool called Qwiklabs in our University Hassan I. Having in plain sight the necessity of being very active in the classroom and based on a combination of a self-learning process and a social cognitive approach, these students get the time to give thought to the feedback and their execution (metacognition). The Google Developers Ecosystem wants to train students on some of its products. As part of that ecosystem, we proposed to implement a series of monthly hands-on labs while using texts, description, pictures, videos, hints, rewards, and quizzes through the Qwiklabs platform, which is the main Google Developers contribution, this platform will provide sufficient feedback as a follow-up to the student's attempts. While giving the students access to the platform, they receive at each lesson a new identification that offers the system the possibility to monitor the students' performance and achievements through every experience, while having a scoring system that allows measuring the users understanding and verifying the proper execution of the lesson's steps.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"478 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133604717","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357183
Bamba Kane, Fabio Rossi, Ophélie Guinaudeau, V. Chiesa, Ilhem Quénel, S. Chau
Intent detection and slot filling are two main tasks in the domain of Spoken Language Understanding (SLU). The methods employed may treat the intent detection and slot filling as two independent tasks or use a joint model. Using a joint model takes into account the cross impact between the two tasks. In this article, we introduce CoBiC a new model combining CNN (Convolutional Neural Network), Bidirectional LSTM (Long Short-Term Memory) and CRF (Conditional Random Field) to extract the intents and the related slots. The same architecture of CoBiC can either be used as an independent model or joint model for intent detection and slot filling. Our method improves the state-of-the-art results on ATIS (Airline Travel Information Systems) benchmark. We also apply our model on a private dataset consisting of clients requests to a vocal assistant. The results demonstrate that CoBiC has strong generalization capability.
{"title":"Joint Intent Detection and Slot Filling via CNN-LSTM-CRF","authors":"Bamba Kane, Fabio Rossi, Ophélie Guinaudeau, V. Chiesa, Ilhem Quénel, S. Chau","doi":"10.1109/CiSt49399.2021.9357183","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357183","url":null,"abstract":"Intent detection and slot filling are two main tasks in the domain of Spoken Language Understanding (SLU). The methods employed may treat the intent detection and slot filling as two independent tasks or use a joint model. Using a joint model takes into account the cross impact between the two tasks. In this article, we introduce CoBiC a new model combining CNN (Convolutional Neural Network), Bidirectional LSTM (Long Short-Term Memory) and CRF (Conditional Random Field) to extract the intents and the related slots. The same architecture of CoBiC can either be used as an independent model or joint model for intent detection and slot filling. Our method improves the state-of-the-art results on ATIS (Airline Travel Information Systems) benchmark. We also apply our model on a private dataset consisting of clients requests to a vocal assistant. The results demonstrate that CoBiC has strong generalization capability.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132308474","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357241
Badiâa Dellal-Hedjazi, Z. Alimazighi
Faced with the ever-increasing complexity, volume and dynamism of online information, recommendation systems are among the solutions that anticipate the needs of users and offer them items (articles, products, web pages, etc.) that they are likely to appreciate. Unlike traditional recommendation models, deep learning offers a better understanding of user requests, characteristics of objects, historical interactions between them and it can process massive amounts of data. In this work we realize a recommendation system based on MLP deep learning adapted to data already defined by their characteristics. In addition to the use of deep learning, we offer a new hybrid recommendation system solution between the demographic approach and the content-based approach in order to eliminate the limits of each and to combine their strengths, through a deep neural network that harnesses the mass of data.” Experimentation with our approach has produced good results in terms of accuracy and speed, whether through the use of deep learning or the hybridization of content-based and demographic filtering, which is a particular case of collaborative filtering.
{"title":"Deep learning for recommendation systems","authors":"Badiâa Dellal-Hedjazi, Z. Alimazighi","doi":"10.1109/CiSt49399.2021.9357241","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357241","url":null,"abstract":"Faced with the ever-increasing complexity, volume and dynamism of online information, recommendation systems are among the solutions that anticipate the needs of users and offer them items (articles, products, web pages, etc.) that they are likely to appreciate. Unlike traditional recommendation models, deep learning offers a better understanding of user requests, characteristics of objects, historical interactions between them and it can process massive amounts of data. In this work we realize a recommendation system based on MLP deep learning adapted to data already defined by their characteristics. In addition to the use of deep learning, we offer a new hybrid recommendation system solution between the demographic approach and the content-based approach in order to eliminate the limits of each and to combine their strengths, through a deep neural network that harnesses the mass of data.” Experimentation with our approach has produced good results in terms of accuracy and speed, whether through the use of deep learning or the hybridization of content-based and demographic filtering, which is a particular case of collaborative filtering.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131057172","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357248
Hanane Bouhmoud, D. Loudyi
Considering the significant operational and economic potential of Building Information Modeling (BIM), many countries have already set fundamental strategies toward its implementation including developing common BIM standards and guidelines leading to enhancing the productivity of Architecture, Engineering and Construction (AEC) industry and driving noticeable optimizations. However, despite the AEC industry development worldwide, several recent studies revealed that the level of BIM adoption in Africa is the lowest comparing to other continents. This paper aims to identify, classify and prioritize the barriers hindering BIM adoption and implementation in African countries through a literature review of existing challenges in African countries compared to countries in other continents. As result, this study reveals that Africa is facing further challenges that confine BIM technology to early stages of adoption.
{"title":"Building Information Modeling (BIM) barriers in Africa versus global challenges","authors":"Hanane Bouhmoud, D. Loudyi","doi":"10.1109/CiSt49399.2021.9357248","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357248","url":null,"abstract":"Considering the significant operational and economic potential of Building Information Modeling (BIM), many countries have already set fundamental strategies toward its implementation including developing common BIM standards and guidelines leading to enhancing the productivity of Architecture, Engineering and Construction (AEC) industry and driving noticeable optimizations. However, despite the AEC industry development worldwide, several recent studies revealed that the level of BIM adoption in Africa is the lowest comparing to other continents. This paper aims to identify, classify and prioritize the barriers hindering BIM adoption and implementation in African countries through a literature review of existing challenges in African countries compared to countries in other continents. As result, this study reveals that Africa is facing further challenges that confine BIM technology to early stages of adoption.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120977734","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357191
Sara Sbai, Oussama Chabih, Mohammed Reda Chbihi Louhdi, Hicham Behja, E. Zemmouri, B. Trousse
Relational databases are widely used; they are at the backend of the majority of information systems. However, these databases are semantically poor. To solve this issue, it is necessary to build ontologies. In this paper, we propose an automatic approach to learn ontologies from relational databases by using classification techniques, more specifically decision trees. Finally, we evaluate our approach by conducting tests and comparing our results with results from previous works. The results were satisfactory in terms of extracting taxonomies from relational databases.
{"title":"Using decision trees to learn ontology taxonomies from relational databases","authors":"Sara Sbai, Oussama Chabih, Mohammed Reda Chbihi Louhdi, Hicham Behja, E. Zemmouri, B. Trousse","doi":"10.1109/CiSt49399.2021.9357191","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357191","url":null,"abstract":"Relational databases are widely used; they are at the backend of the majority of information systems. However, these databases are semantically poor. To solve this issue, it is necessary to build ontologies. In this paper, we propose an automatic approach to learn ontologies from relational databases by using classification techniques, more specifically decision trees. Finally, we evaluate our approach by conducting tests and comparing our results with results from previous works. The results were satisfactory in terms of extracting taxonomies from relational databases.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122969341","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 : 2020-06-05DOI: 10.1109/CiSt49399.2021.9357176
Anne-Carole Honfoga, M. Dossou, Péniel Dassi, V. Moeyaert
The Digital Video Broadcasting-Terrestrial, second generation (DVB-T2) system has been the object of many research during the last decade and is now mature. This paper focuses on the identification of the maximum obtainable performance improvement of DVB-T2 CP-OFDM with NUCs in typical TU6 fading environment. It first concentrates on the ultimate improvement achievable using joint UFMC and NUCs in standardized DVB-T2 system. In these conditions, a 0.5 dB SNR improvement (for $text{BER}=3.10^{-3}$) is reported in TU6 using CP-OFDM NUC 32K 256-QAM and $text{CR}=1/2$ and 3/5 in place of sole CP-OFDM. Also, using in conjunction both studied technologies, namely UFMC NUC $32K$ 256-QAM $text{CR}=1/2$ and 3/5, an SNR improvement of 1.2 dB (for $text{BER}=3.10^{-3}$) is achievable which provides a good SNR margin e.g. to increase the emitter's coverage.
{"title":"Joint use of 5G waveform UFMC and Non Uniform Constellations in DVB-T2","authors":"Anne-Carole Honfoga, M. Dossou, Péniel Dassi, V. Moeyaert","doi":"10.1109/CiSt49399.2021.9357176","DOIUrl":"https://doi.org/10.1109/CiSt49399.2021.9357176","url":null,"abstract":"The Digital Video Broadcasting-Terrestrial, second generation (DVB-T2) system has been the object of many research during the last decade and is now mature. This paper focuses on the identification of the maximum obtainable performance improvement of DVB-T2 CP-OFDM with NUCs in typical TU6 fading environment. It first concentrates on the ultimate improvement achievable using joint UFMC and NUCs in standardized DVB-T2 system. In these conditions, a 0.5 dB SNR improvement (for $text{BER}=3.10^{-3}$) is reported in TU6 using CP-OFDM NUC 32K 256-QAM and $text{CR}=1/2$ and 3/5 in place of sole CP-OFDM. Also, using in conjunction both studied technologies, namely UFMC NUC $32K$ 256-QAM $text{CR}=1/2$ and 3/5, an SNR improvement of 1.2 dB (for $text{BER}=3.10^{-3}$) is achievable which provides a good SNR margin e.g. to increase the emitter's coverage.","PeriodicalId":253233,"journal":{"name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125022709","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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