Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255822
S. Lahmiri
Numerous computer automated diagnosis (CAD) systems have been proposed to detect epilepsy in electroencephalogram (EEG) signals. The aim of this paper is to look at multi-scaling properties obtained by multi-scale analysis (MSA) as main distinctive features to simultaneously distinguish between all categories of EEG signals that compose the popular database hosted by the department of epileptology, University of Bonn, Germany. Particularly, multi-scale analysis is employed to capture long-range properties of the EEG signal at different scales used to represent its short and long variations. Then, the obtained multi-scale properties are used to train four different classifiers; namely, k-nearest neighbor (k-NN), linear discriminant analysis (LDA), naïve Bayes (NB), and the support vector machine (SVM). Experimental results based on ten-fold cross-validation method show that each single classifier achieves 100% accuracy. In this respect, multi-scale properties are found to be effective as they outperformed existing works on the same database by achieving perfect accuracy to distinguish between all five distinct EEG categories. Overall, the obtained results are promising.
{"title":"General Framework for Multi-Classification of EEG Signals Based on Multi-Scale Properties","authors":"S. Lahmiri","doi":"10.1109/CCECE47787.2020.9255822","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255822","url":null,"abstract":"Numerous computer automated diagnosis (CAD) systems have been proposed to detect epilepsy in electroencephalogram (EEG) signals. The aim of this paper is to look at multi-scaling properties obtained by multi-scale analysis (MSA) as main distinctive features to simultaneously distinguish between all categories of EEG signals that compose the popular database hosted by the department of epileptology, University of Bonn, Germany. Particularly, multi-scale analysis is employed to capture long-range properties of the EEG signal at different scales used to represent its short and long variations. Then, the obtained multi-scale properties are used to train four different classifiers; namely, k-nearest neighbor (k-NN), linear discriminant analysis (LDA), naïve Bayes (NB), and the support vector machine (SVM). Experimental results based on ten-fold cross-validation method show that each single classifier achieves 100% accuracy. In this respect, multi-scale properties are found to be effective as they outperformed existing works on the same database by achieving perfect accuracy to distinguish between all five distinct EEG categories. Overall, the obtained results are promising.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129289449","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255735
J. Khan, M. Armstrong, A. Moshref
Mobile disconnect switches allow electrical isolation on high voltage transmission lines where stationary switches are not available, or special switching is required. In particular, dropping/picking part of a line and loop switching are two key applications of mobile switches. These switches are used in live-line environment. Therefore, several worker safety considerations must be taken into account prior to their deployment. Electrical clearance, grounding design, switch duty calculation and switching sequence - all needs to be assessed. This article provides a set of simplified methods for initial calculations, and an example of real-world deployment where many of these issues are addressed.
{"title":"Worker Safety Considerations for Deployment of Mobile Disconnect Switches on Transmission Lines","authors":"J. Khan, M. Armstrong, A. Moshref","doi":"10.1109/CCECE47787.2020.9255735","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255735","url":null,"abstract":"Mobile disconnect switches allow electrical isolation on high voltage transmission lines where stationary switches are not available, or special switching is required. In particular, dropping/picking part of a line and loop switching are two key applications of mobile switches. These switches are used in live-line environment. Therefore, several worker safety considerations must be taken into account prior to their deployment. Electrical clearance, grounding design, switch duty calculation and switching sequence - all needs to be assessed. This article provides a set of simplified methods for initial calculations, and an example of real-world deployment where many of these issues are addressed.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123914357","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255823
Radwa A. Sultan, A. Refaey, W. Hamouda
There are high expectations for IoT devices and networks concerning reliability, performance, quality, and long-term availability. Indeed, wireless connectivity is the most critical success factor for the IoT era. Recently, the cellular technologies focused on introducing new releases, like LTE Cat-M1, to provide global coverage and mobility for the IoT applications. However, the cellular spectrum is already congested, and adding new services will defiant the existing ones. Herein, the network key performance indicator (KPI) should be considered to enhance the resource management for LTE and LTE CAT M1 users. Tackling the coexistence between the aforementioned in the 1.4 Mhz band, three coexistence optimization problems are formulated. The first and the second coexistence optimization problems are formulated assuming higher IoT-traffic priority, and higher LTE-traffic priority, respectively. On the other hand, the third problem is formulated assuming that both the IoT-traffic and the LTE-traffic have the same priority. Afterward, a scheduling optimization solution algorithm is proposed using the interior point method. Finally, the performance of the proposed scheduling algorithm is evaluated via numerical analysis.
{"title":"Resource Allocation in CAT-M and LTE-A Coexistence: A Joint Contention Bandwidth Optimization Scheme","authors":"Radwa A. Sultan, A. Refaey, W. Hamouda","doi":"10.1109/CCECE47787.2020.9255823","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255823","url":null,"abstract":"There are high expectations for IoT devices and networks concerning reliability, performance, quality, and long-term availability. Indeed, wireless connectivity is the most critical success factor for the IoT era. Recently, the cellular technologies focused on introducing new releases, like LTE Cat-M1, to provide global coverage and mobility for the IoT applications. However, the cellular spectrum is already congested, and adding new services will defiant the existing ones. Herein, the network key performance indicator (KPI) should be considered to enhance the resource management for LTE and LTE CAT M1 users. Tackling the coexistence between the aforementioned in the 1.4 Mhz band, three coexistence optimization problems are formulated. The first and the second coexistence optimization problems are formulated assuming higher IoT-traffic priority, and higher LTE-traffic priority, respectively. On the other hand, the third problem is formulated assuming that both the IoT-traffic and the LTE-traffic have the same priority. Afterward, a scheduling optimization solution algorithm is proposed using the interior point method. Finally, the performance of the proposed scheduling algorithm is evaluated via numerical analysis.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124210944","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255693
Daljit Josh, John-Anthony Elenis, Heman Muresan, P. Spachos, S. Gregori
This paper presents a low power audio front end for keyword spotting. A multi-stage approach is used to reduce the power consumption of the system by only using different stages when they are required. A working prototype was created and tested to verify its functionality. The effectiveness of the multistage approach is shown by comparing the power consumption of the system in its idle state to the systems active state. The prototype has a power consumption of 4.1 mW in the idle state that can be reduced below 3 mW with a keyword detection accuracy of 87 %.
{"title":"Low-Power Low-Cost Audio Front-End for Keyword Spotting","authors":"Daljit Josh, John-Anthony Elenis, Heman Muresan, P. Spachos, S. Gregori","doi":"10.1109/CCECE47787.2020.9255693","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255693","url":null,"abstract":"This paper presents a low power audio front end for keyword spotting. A multi-stage approach is used to reduce the power consumption of the system by only using different stages when they are required. A working prototype was created and tested to verify its functionality. The effectiveness of the multistage approach is shown by comparing the power consumption of the system in its idle state to the systems active state. The prototype has a power consumption of 4.1 mW in the idle state that can be reduced below 3 mW with a keyword detection accuracy of 87 %.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127945875","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255821
T. Ravichandran, Yuan Liu, Amar Kumar, A. Srivastava, Houman Hanachi, G. Heppler
The performance of a gas turbine engine (GTE) deteriorates with degradation and aging. The availability of the operating data from the GTE with the capability to perform data analysis provides an opportunity to identify short-term and longterm performance deterioration and relate to more difficult to detect components degradation. In this work, a data-driven and machine learning-based predictive modeling framework has been developed for performing combined input and model selection towards generating easily interpretable, parsimonious and accurate regression models intended for gas turbine engine performance analysis. The proposed multistage predictive modeling framework incorporates the orthogonal least squares (OLS) learning and multi-criteria decision-making approach for selecting inputs and model structures in a computationally efficient manner while optimizing multiple objectives. The regression models obtained from this framework for predicting power and exhaust gas temperature (EGT) outputs using GTE operational data collected over a period of three years have demonstrated short-term and long-term performance deterioration patterns for the GTE.
{"title":"Data-Driven Performance Prediction Using Gas Turbine Sensory Signals","authors":"T. Ravichandran, Yuan Liu, Amar Kumar, A. Srivastava, Houman Hanachi, G. Heppler","doi":"10.1109/CCECE47787.2020.9255821","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255821","url":null,"abstract":"The performance of a gas turbine engine (GTE) deteriorates with degradation and aging. The availability of the operating data from the GTE with the capability to perform data analysis provides an opportunity to identify short-term and longterm performance deterioration and relate to more difficult to detect components degradation. In this work, a data-driven and machine learning-based predictive modeling framework has been developed for performing combined input and model selection towards generating easily interpretable, parsimonious and accurate regression models intended for gas turbine engine performance analysis. The proposed multistage predictive modeling framework incorporates the orthogonal least squares (OLS) learning and multi-criteria decision-making approach for selecting inputs and model structures in a computationally efficient manner while optimizing multiple objectives. The regression models obtained from this framework for predicting power and exhaust gas temperature (EGT) outputs using GTE operational data collected over a period of three years have demonstrated short-term and long-term performance deterioration patterns for the GTE.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121626142","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255729
Victor Balogun, O. Sarumi
Cognitive Radio Network (CRN) was designed to lessen the shortage of radio resources. The Secondary Users (SUs) can opportunistically utilize any available spectrum when the Primary Users (PUs) are inactive. Some of the challenges of CRN include the service interruption loss, complexity of processing and exchange of large amount of data, limited available memory to SUs and the non-real-time exchange of spectrum sensing data. These challenges can lead to significant degradation in the performance of a CRN. Therefore, there is a need to seek solutions that will alleviate these problems. The Cloud system incorporated with Big Data Analytics algorithm can be a potential solution. In this paper, we propose a Cloud-based Cooperative Spectrum Sensing model for CRN that allows the SUs to aggregate their individual spectrum sensing data into a cloud environment, where it can be analyzed using a proposed expanded Apache Spark algorithm incorporated with the hybridization of three machine learning methods-ensemble classifier approach that can effectively and efficiently analyze the spectrum sensing data for easy access, real-time analysis, deep insight and on-demand decision support for the SUs. In addition, the two-layer Fusion Center design proposed introduces redundancy by using the cloud as a secondary Fusion Center while still maintaining a primary land-based Fusion Center.
认知无线电网络(Cognitive Radio Network, CRN)是为了缓解无线电资源的短缺而设计的。当主用户(pu)处于非活动状态时，从用户(su)可以利用任何可用的频谱。CRN面临的一些挑战包括业务中断损失、处理和交换大量数据的复杂性、单元可用内存有限以及频谱感知数据的非实时交换。这些挑战会导致CRN的性能显著下降。因此，有必要寻求缓解这些问题的解决办法。结合大数据分析算法的云系统可能是一个潜在的解决方案。在本文中，我们提出了一种基于云的CRN协同频谱感知模型，该模型允许su将其单独的频谱感知数据聚合到云环境中，在云环境中可以使用所提出的扩展Apache Spark算法进行分析，该算法结合了三种机器学习方法的杂交-集成分类器方法，可以有效和高效地分析频谱感知数据，以便于访问，实时分析;为SUs提供深入的洞察力和按需决策支持。此外，提出的双层融合中心设计通过使用云作为二级融合中心，同时仍然保持主要的陆基融合中心，引入了冗余。
{"title":"A Cooperative Spectrum Sensing Architecture and Algorithm for Cloud- and Big Data-based Cognitive Radio Networks","authors":"Victor Balogun, O. Sarumi","doi":"10.1109/CCECE47787.2020.9255729","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255729","url":null,"abstract":"Cognitive Radio Network (CRN) was designed to lessen the shortage of radio resources. The Secondary Users (SUs) can opportunistically utilize any available spectrum when the Primary Users (PUs) are inactive. Some of the challenges of CRN include the service interruption loss, complexity of processing and exchange of large amount of data, limited available memory to SUs and the non-real-time exchange of spectrum sensing data. These challenges can lead to significant degradation in the performance of a CRN. Therefore, there is a need to seek solutions that will alleviate these problems. The Cloud system incorporated with Big Data Analytics algorithm can be a potential solution. In this paper, we propose a Cloud-based Cooperative Spectrum Sensing model for CRN that allows the SUs to aggregate their individual spectrum sensing data into a cloud environment, where it can be analyzed using a proposed expanded Apache Spark algorithm incorporated with the hybridization of three machine learning methods-ensemble classifier approach that can effectively and efficiently analyze the spectrum sensing data for easy access, real-time analysis, deep insight and on-demand decision support for the SUs. In addition, the two-layer Fusion Center design proposed introduces redundancy by using the cloud as a secondary Fusion Center while still maintaining a primary land-based Fusion Center.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133886119","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255761
Ali Yazhari Kermani, R. Fadaeinedjad, A. Maheri, E. Mohammadi, G. Moschopoulos
This paper presents the modeling and analysis of a wind energy conversion system with a stand-alone small-scale induction-generator based wind turbine. The wind turbine is connected to a buck converter to achieve maximum power point tracking under variable wind speed conditions and to charge a battery and feed a DC load. Also, this converter is responsible for stalling the turbine when wind speed exceeds the nominal value for the turbine. The paper explains how the modeling and analysis have been done and presents the results of tests that have been carried out under different wind conditions, with battery charging and discharging.
{"title":"Stall Control and MPPT for a Wind Turbine, Using a Buck Converter in a Battery Storage System","authors":"Ali Yazhari Kermani, R. Fadaeinedjad, A. Maheri, E. Mohammadi, G. Moschopoulos","doi":"10.1109/CCECE47787.2020.9255761","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255761","url":null,"abstract":"This paper presents the modeling and analysis of a wind energy conversion system with a stand-alone small-scale induction-generator based wind turbine. The wind turbine is connected to a buck converter to achieve maximum power point tracking under variable wind speed conditions and to charge a battery and feed a DC load. Also, this converter is responsible for stalling the turbine when wind speed exceeds the nominal value for the turbine. The paper explains how the modeling and analysis have been done and presents the results of tests that have been carried out under different wind conditions, with battery charging and discharging.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134293324","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255757
Mohamed Elsayed, A. Badawy, A. Shafie, Amr M. Mohamed, T. Khattab
One of the highly promising radio access strategies for enhancing performance in the next generation cellular communications is non-orthogonal multiple access (NOMA). NOMA offers a number of advantages including better spectrum efficiency. This paper focuses primarily on proposing an energy efficient system for transmitting medical data, such as electroencephalogram (EEG), collected from patients for the sake of continuous monitoring. The framework proposes the use of deep reinforcement learning (DRL) to provide smart data compression in uplink-NOMA protocol. DRL enforces the data compression ratios for the nodes in order to avoid outage constraints at any sensor node. Jointly, it optimizes the power consumption of these sensor nodes. The data compression for such sensor network is vital in order to minimize the power every sensor consumes to maximize its service lifetime. We minimize the expected distortion under practical channel realization and outage probability constraints using NOMA-uplink protocol. Meanwhile, we optimize the power efficiency of the user node in order to increase the battery lifetime.
{"title":"Deep Reinforcement Learning Algorithm for Smart Data Compression under NOMA-Uplink Protocol","authors":"Mohamed Elsayed, A. Badawy, A. Shafie, Amr M. Mohamed, T. Khattab","doi":"10.1109/CCECE47787.2020.9255757","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255757","url":null,"abstract":"One of the highly promising radio access strategies for enhancing performance in the next generation cellular communications is non-orthogonal multiple access (NOMA). NOMA offers a number of advantages including better spectrum efficiency. This paper focuses primarily on proposing an energy efficient system for transmitting medical data, such as electroencephalogram (EEG), collected from patients for the sake of continuous monitoring. The framework proposes the use of deep reinforcement learning (DRL) to provide smart data compression in uplink-NOMA protocol. DRL enforces the data compression ratios for the nodes in order to avoid outage constraints at any sensor node. Jointly, it optimizes the power consumption of these sensor nodes. The data compression for such sensor network is vital in order to minimize the power every sensor consumes to maximize its service lifetime. We minimize the expected distortion under practical channel realization and outage probability constraints using NOMA-uplink protocol. Meanwhile, we optimize the power efficiency of the user node in order to increase the battery lifetime.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128924435","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255730
M. R. Hamouda, M. Marei, M. Nassar, M. Salama
Distributed generators based on Micro-turbine Generators (MTGs) are used widely for their proven advantages e.g. flexibility, Compatibility, low emissions…etc. This paper presents a novel interface system based on an artificial neural network (ANN) for the MTGs. The proposed interface system can identify and adapt itself to the operation mode of the system i.e. grid-connected, islanded, or fault modes. The ANN system is integrated with a back-to-back voltage source converter (VSC) interface to control MTGs in different operation modes.
{"title":"ANN-supervised Interface System for Microturbine Distributed Generator","authors":"M. R. Hamouda, M. Marei, M. Nassar, M. Salama","doi":"10.1109/CCECE47787.2020.9255730","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255730","url":null,"abstract":"Distributed generators based on Micro-turbine Generators (MTGs) are used widely for their proven advantages e.g. flexibility, Compatibility, low emissions…etc. This paper presents a novel interface system based on an artificial neural network (ANN) for the MTGs. The proposed interface system can identify and adapt itself to the operation mode of the system i.e. grid-connected, islanded, or fault modes. The ANN system is integrated with a back-to-back voltage source converter (VSC) interface to control MTGs in different operation modes.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131140913","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 : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255779
J. Castillo-Velazquez, Luis-Carlos Revilla-Melo
AFRICACONNECT is composed of three advanced networks UBUNTUNET, WACREN and ASREN, which connect the national research and education networks in 29 countries in Africa. Each backbone infrastructure has evolved over time and has been updated, with bandwidth and backbone router capability being added. IPv6 connectivity and management assessment emulation were developed using the backbone topology of AFRICACONNECT from 2019. The results demonstrate the capabilities of the GNS3 emulator when using high-performance backbone networks and offer a top-down view that can support strategic decisions on the evolution of this kind of network, which can be useful to Internet Service Provider companies.
{"title":"Management Emulation of Advanced Network Backbones in Africa: 2019 Topology","authors":"J. Castillo-Velazquez, Luis-Carlos Revilla-Melo","doi":"10.1109/CCECE47787.2020.9255779","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255779","url":null,"abstract":"AFRICACONNECT is composed of three advanced networks UBUNTUNET, WACREN and ASREN, which connect the national research and education networks in 29 countries in Africa. Each backbone infrastructure has evolved over time and has been updated, with bandwidth and backbone router capability being added. IPv6 connectivity and management assessment emulation were developed using the backbone topology of AFRICACONNECT from 2019. The results demonstrate the capabilities of the GNS3 emulator when using high-performance backbone networks and offer a top-down view that can support strategic decisions on the evolution of this kind of network, which can be useful to Internet Service Provider companies.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131234641","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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