Pub Date : 2021-09-13DOI: 10.1109/africon51333.2021.9570915
H. M. Hussien, K. Katzis, L. Mfupe, E. Bekele
Many rural and low-income areas around the globe have poor quality (or no) internet service with limited telephone coverage due to a lack of infrastructure, insufficient power supply, and a limited core telecommunications network. Because of limited mobile device propagation capabilities, sparse population density and poor purchasing power, it is not economically feasible for mobile operators to deploy traditional communication networks in these regions. Researchers recently discovered that a portion of the underutilized TV spectrum known as TV white space (TVWS) could be suitable for those areas in terms of bandwidth, improved reception features, good penetration of buildings, and broad reach. This paper presents a comprehensive evaluation of available TVWS channels in Ethiopia using a Geolocation Database front-end (GLSD) aided by the CSIR Calculation Engine hosted by the CSIR Meraka Institute. GLSD and the CSIR calculation engine together are able to quantify the amount of TVWSs through dynamically allocating the TVWS network radio services according to the geographic position of WSDs. The GLSD was developed using the ITU-R P.1546-5 propagation model. Using the GLSD and the CSIR calculation engine, the quantification of TVWS free channels are calculated. The results show that among the 58 VHF and UHF TV channels, 52 of the channels are free (underutilized) in Ethiopia. These free channels can be employed to provide wireless broadband solutions for connecting the rural areas as well as offloading the urban traffic.
{"title":"Calculation of TVWS Spectrum Availability Using Geo-location White Space Spectrum Database","authors":"H. M. Hussien, K. Katzis, L. Mfupe, E. Bekele","doi":"10.1109/africon51333.2021.9570915","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570915","url":null,"abstract":"Many rural and low-income areas around the globe have poor quality (or no) internet service with limited telephone coverage due to a lack of infrastructure, insufficient power supply, and a limited core telecommunications network. Because of limited mobile device propagation capabilities, sparse population density and poor purchasing power, it is not economically feasible for mobile operators to deploy traditional communication networks in these regions. Researchers recently discovered that a portion of the underutilized TV spectrum known as TV white space (TVWS) could be suitable for those areas in terms of bandwidth, improved reception features, good penetration of buildings, and broad reach. This paper presents a comprehensive evaluation of available TVWS channels in Ethiopia using a Geolocation Database front-end (GLSD) aided by the CSIR Calculation Engine hosted by the CSIR Meraka Institute. GLSD and the CSIR calculation engine together are able to quantify the amount of TVWSs through dynamically allocating the TVWS network radio services according to the geographic position of WSDs. The GLSD was developed using the ITU-R P.1546-5 propagation model. Using the GLSD and the CSIR calculation engine, the quantification of TVWS free channels are calculated. The results show that among the 58 VHF and UHF TV channels, 52 of the channels are free (underutilized) in Ethiopia. These free channels can be employed to provide wireless broadband solutions for connecting the rural areas as well as offloading the urban traffic.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123382227","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 : 2021-09-13DOI: 10.1109/africon51333.2021.9570960
L. Nagy, D. Arbet, M. Kovác, M. Potocný, R. Ondica, V. Stopjaková
The paper addresses a development and application of EKV MOS transistor compact model with focus on the ultra low-voltage / ultra low-power analog integrated circuit (IC) design employing bulk-driven (BD) technique. The presented contribution can be viewed as an extension of standard EKV model application and as a contribution to ultra low-voltage IC design techniques. The paper compares the measured and extracted small-signal parameters of standalone transistor samples fabricated in 130 nm CMOS technology and the simulation results obtained using the proposed bulk-driven EKV v2.63 model and foundry-provided BSIM model v3.3. The transistor samples were analyzed with power supply of VDD = 0.4 V The paper also discusses the implementation of 3D graphs as a result of introducing another degree of freedom into the essential MOS transistor characteristics, while maintaining the ease of using the design hand-calculation with the original gm/ID approach.
{"title":"EKV Model for Bulk-Driven Circuit Design Using gmb/ID Method","authors":"L. Nagy, D. Arbet, M. Kovác, M. Potocný, R. Ondica, V. Stopjaková","doi":"10.1109/africon51333.2021.9570960","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570960","url":null,"abstract":"The paper addresses a development and application of EKV MOS transistor compact model with focus on the ultra low-voltage / ultra low-power analog integrated circuit (IC) design employing bulk-driven (BD) technique. The presented contribution can be viewed as an extension of standard EKV model application and as a contribution to ultra low-voltage IC design techniques. The paper compares the measured and extracted small-signal parameters of standalone transistor samples fabricated in 130 nm CMOS technology and the simulation results obtained using the proposed bulk-driven EKV v2.63 model and foundry-provided BSIM model v3.3. The transistor samples were analyzed with power supply of VDD = 0.4 V The paper also discusses the implementation of 3D graphs as a result of introducing another degree of freedom into the essential MOS transistor characteristics, while maintaining the ease of using the design hand-calculation with the original gm/ID approach.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123836869","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 : 2021-09-13DOI: 10.1109/africon51333.2021.9570870
G. Andrews, U. Hilleringmann, T. Joubert
An analysis on the viability of implementing non-flow based capacitive biosensing for bulk counting of whole cancer cells is presented. A mathematical model is implemented based on established research to test the validity of the hypothesis that cells are considered to act like parallel electric connections. A finite element analysis model is also presented to investigate the effect of electrode geometry and cell position on the measured capacitance. The electrical interaction between cells is found not to be strictly parallel and in fact dependent on a host of uncontrollable parameters. Bulk sensing of multiple cells is found to be impractical for a single electrode setup due to the unpredictability of cell location and cell interactions. An array-based implementation is proposed, which allows for the use of current models and data of single-cell analyses to be used.
{"title":"The Viability of a Non-Flow Capacitive Biosensing Microsystem for Whole Cell Counting","authors":"G. Andrews, U. Hilleringmann, T. Joubert","doi":"10.1109/africon51333.2021.9570870","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570870","url":null,"abstract":"An analysis on the viability of implementing non-flow based capacitive biosensing for bulk counting of whole cancer cells is presented. A mathematical model is implemented based on established research to test the validity of the hypothesis that cells are considered to act like parallel electric connections. A finite element analysis model is also presented to investigate the effect of electrode geometry and cell position on the measured capacitance. The electrical interaction between cells is found not to be strictly parallel and in fact dependent on a host of uncontrollable parameters. Bulk sensing of multiple cells is found to be impractical for a single electrode setup due to the unpredictability of cell location and cell interactions. An array-based implementation is proposed, which allows for the use of current models and data of single-cell analyses to be used.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"4 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124614643","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 : 2021-09-13DOI: 10.1109/africon51333.2021.9570847
Mlanjeni Tikiso, E. N. Mambou, T. Swart
Many countries in the world, in particular the least developed ones, face the challenge of unhealthy drinking water. In this paper, the design and implementation of a trackable and notification-enabled remote water quality monitoring system prototype is proposed to mitigate this problem. The system is aimed at remotely monitoring the quality of water in fixed or erected and motorized or mobile storage water tanks. The system can be adopted for use by countries to enhance their rural villages’ water supply schemes and to help remote villages have access to clean and healthy drinking water. Besides quality monitoring, the system also features location tracking in the case of motorized tanks, which get transported to different destinations as part of the rural water supply schemes.
{"title":"Tracking and Notification Enabled Remote Water Quality Monitoring System","authors":"Mlanjeni Tikiso, E. N. Mambou, T. Swart","doi":"10.1109/africon51333.2021.9570847","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570847","url":null,"abstract":"Many countries in the world, in particular the least developed ones, face the challenge of unhealthy drinking water. In this paper, the design and implementation of a trackable and notification-enabled remote water quality monitoring system prototype is proposed to mitigate this problem. The system is aimed at remotely monitoring the quality of water in fixed or erected and motorized or mobile storage water tanks. The system can be adopted for use by countries to enhance their rural villages’ water supply schemes and to help remote villages have access to clean and healthy drinking water. Besides quality monitoring, the system also features location tracking in the case of motorized tanks, which get transported to different destinations as part of the rural water supply schemes.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127473196","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 : 2021-09-13DOI: 10.1109/africon51333.2021.9570973
Ayodeji Olalekan Salau, Sodessa Soma Shonkora, V. A. Owoeye
Solar photovoltaics are in recent times being used to replace fuel-based lighting and off-grid electrical needs. This study presents the evaluation of solar photovoltaic power systems in Dirashe Woreda of Ethiopia. The study was performed to assess and analysis the data which were acquired from the selected site. A descriptive survey was used to assess the information gathered. Empirical models were used to forecast the radiation based on sunshine hours. The linear monthly average solar radiation of the study site was calculated using data from the National Aeronautics and Space Administration (NASA). The study also includes an estimate of the study area’s solar energy resource based on primary data collected between January and December 2019. Finally, an equation using Angstrom-Prescott Model was derived for the study area.
{"title":"Analysis of Solar Energy Potential Using Sunshine Based Model","authors":"Ayodeji Olalekan Salau, Sodessa Soma Shonkora, V. A. Owoeye","doi":"10.1109/africon51333.2021.9570973","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570973","url":null,"abstract":"Solar photovoltaics are in recent times being used to replace fuel-based lighting and off-grid electrical needs. This study presents the evaluation of solar photovoltaic power systems in Dirashe Woreda of Ethiopia. The study was performed to assess and analysis the data which were acquired from the selected site. A descriptive survey was used to assess the information gathered. Empirical models were used to forecast the radiation based on sunshine hours. The linear monthly average solar radiation of the study site was calculated using data from the National Aeronautics and Space Administration (NASA). The study also includes an estimate of the study area’s solar energy resource based on primary data collected between January and December 2019. Finally, an equation using Angstrom-Prescott Model was derived for the study area.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"100 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131187875","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 : 2021-09-13DOI: 10.1109/africon51333.2021.9570911
D. Kirli, Johannes Hampp, Koen van Greevenbroek, Rebecca Grant, Matin Mahmood, Maximilian Parzen, A. Kiprakis
Electricity network modelling and grid simulations form a key enabling element for the integration of newer and cleaner technologies such as renewable energy generation and electric vehicles into the existing grid and energy system infrastructure. This paper reviews the models of the African electricity systems and highlights the gaps in the open model landscape. Using PyPSA (an open Power System Analysis package), the paper outlines the pathway to a fully open model and data to increase the transparency in the African electricity system planning. Optimisation and modelling can reveal viable pathways to a sustainable energy system, aiding strategic planning for upgrades and policy-making for accelerated integration of renewable energy generation and smart grid technologies such as battery storage in Africa.
{"title":"PyPSA meets Africa: Developing an open source electricity network model of the African continent","authors":"D. Kirli, Johannes Hampp, Koen van Greevenbroek, Rebecca Grant, Matin Mahmood, Maximilian Parzen, A. Kiprakis","doi":"10.1109/africon51333.2021.9570911","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570911","url":null,"abstract":"Electricity network modelling and grid simulations form a key enabling element for the integration of newer and cleaner technologies such as renewable energy generation and electric vehicles into the existing grid and energy system infrastructure. This paper reviews the models of the African electricity systems and highlights the gaps in the open model landscape. Using PyPSA (an open Power System Analysis package), the paper outlines the pathway to a fully open model and data to increase the transparency in the African electricity system planning. Optimisation and modelling can reveal viable pathways to a sustainable energy system, aiding strategic planning for upgrades and policy-making for accelerated integration of renewable energy generation and smart grid technologies such as battery storage in Africa.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130728414","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 : 2021-09-13DOI: 10.1109/africon51333.2021.9571014
P. N. Mwaro, Kennedy Ogada, W. Cheruiyot
Talent management is the process of identifying the vacant position, recruiting the suitable person, developing the skills and expertise of the person to make the person more suitable for the position and retaining him to achieve long term business objectives of the institution. The purpose of this research was to develop a machine learning model for talent recruitment and management for use in human resource department. The research therefore proposes a human resource Ensemble neural network model for use in talent recruitment and management for employee development and retention. The model developed attained a predictive accuracy of 95.313% and this showed that machine learning models can be used successively for talent recruitment in human resource department. The study explores literature review on neural network and how it has been used which gives the basis of this study.
{"title":"Neural Network Model for Talent Recruitment and Management for Employee Development and Retention","authors":"P. N. Mwaro, Kennedy Ogada, W. Cheruiyot","doi":"10.1109/africon51333.2021.9571014","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9571014","url":null,"abstract":"Talent management is the process of identifying the vacant position, recruiting the suitable person, developing the skills and expertise of the person to make the person more suitable for the position and retaining him to achieve long term business objectives of the institution. The purpose of this research was to develop a machine learning model for talent recruitment and management for use in human resource department. The research therefore proposes a human resource Ensemble neural network model for use in talent recruitment and management for employee development and retention. The model developed attained a predictive accuracy of 95.313% and this showed that machine learning models can be used successively for talent recruitment in human resource department. The study explores literature review on neural network and how it has been used which gives the basis of this study.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127072969","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 : 2021-09-13DOI: 10.1109/africon51333.2021.9570901
Bonny Mgawe, Twahir Kazema, Hoang Nam Dao, M. Krairiksh
This work reports the investigation results on how dielectric properties of soil change with fertilizer concentration for catena in Mwanza of Tanzania at 945 MHz frequency band using reflection measurement. Composite soil samples were collected from three catenas: upper, middle, and lower catena sites in Misungwi district located in Mwanza, Tanzania. Two chemical fertilizers UREA and CAN were used since they are widely used in these areas. With the low-cost equipment, reliable results are obtained that show relationship of soil fertility and dielectric properties. The results provide essential information for preparing on-farm measurement to get soil fertility contour that supports land management.
{"title":"Dielectric properties of fertilized soil in a Catena: A case of Mwanza, Tanzania","authors":"Bonny Mgawe, Twahir Kazema, Hoang Nam Dao, M. Krairiksh","doi":"10.1109/africon51333.2021.9570901","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570901","url":null,"abstract":"This work reports the investigation results on how dielectric properties of soil change with fertilizer concentration for catena in Mwanza of Tanzania at 945 MHz frequency band using reflection measurement. Composite soil samples were collected from three catenas: upper, middle, and lower catena sites in Misungwi district located in Mwanza, Tanzania. Two chemical fertilizers UREA and CAN were used since they are widely used in these areas. With the low-cost equipment, reliable results are obtained that show relationship of soil fertility and dielectric properties. The results provide essential information for preparing on-farm measurement to get soil fertility contour that supports land management.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"20 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129088918","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 : 2021-09-13DOI: 10.1109/africon51333.2021.9570875
Matthew Dicks, Josiah Chavula
Community networks are infrastructures that are run by the citizens for the citizens. These networks are often run with limited resources compared to traditional Internet Service Providers. For such networks, careful traffic classification can play an important role in improving quality of service. Deep learning techniques have been shown to be effective for this classification task, especially since classical approaches struggle to deal with encrypted traffic. However, deep learning models often tend to be computationally expensive, which limits their suitability for low-resource community networks. This paper explores the computational efficiency and accuracy of Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) deep learning models for packet-based classification of traffic in a community network. We find that LSTM models attain higher out-of-sample accuracy than traditional support vector machines classifiers and the simpler multi-layer perceptron neural networks, given the same computational resource constraints. The improvement in accuracy offered by the LSTM has a tradeoff of slower prediction speed, which weakens their relative suitability for use in real-time applications. However, we observe that by reducing the size of the input supplied to the LSTMs, we can improve their prediction speed whilst maintaining higher accuracy than other simpler models.
{"title":"Deep Learning Traffic Classification in Resource-Constrained Community Networks","authors":"Matthew Dicks, Josiah Chavula","doi":"10.1109/africon51333.2021.9570875","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570875","url":null,"abstract":"Community networks are infrastructures that are run by the citizens for the citizens. These networks are often run with limited resources compared to traditional Internet Service Providers. For such networks, careful traffic classification can play an important role in improving quality of service. Deep learning techniques have been shown to be effective for this classification task, especially since classical approaches struggle to deal with encrypted traffic. However, deep learning models often tend to be computationally expensive, which limits their suitability for low-resource community networks. This paper explores the computational efficiency and accuracy of Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) deep learning models for packet-based classification of traffic in a community network. We find that LSTM models attain higher out-of-sample accuracy than traditional support vector machines classifiers and the simpler multi-layer perceptron neural networks, given the same computational resource constraints. The improvement in accuracy offered by the LSTM has a tradeoff of slower prediction speed, which weakens their relative suitability for use in real-time applications. However, we observe that by reducing the size of the input supplied to the LSTMs, we can improve their prediction speed whilst maintaining higher accuracy than other simpler models.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"2002 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129572056","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 : 2021-09-13DOI: 10.1109/africon51333.2021.9570855
Gabriela Da Silva Sousa, E. N. Mambou, T. Swart
In this paper the design and implementation of a real-time face recognition security system is presented. The security system demonstrates how surveillance can be adapted to offer a greater level of security with facial recognition. The security system was implemented in a four-person household. Results show that the designed facial recognition security system achieved a 100% training and 96% validation accuracy with a 100% accuracy on the test images dataset consisting of unseen images of the four enrolled users.
{"title":"Facial Recognition Security Alert System","authors":"Gabriela Da Silva Sousa, E. N. Mambou, T. Swart","doi":"10.1109/africon51333.2021.9570855","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570855","url":null,"abstract":"In this paper the design and implementation of a real-time face recognition security system is presented. The security system demonstrates how surveillance can be adapted to offer a greater level of security with facial recognition. The security system was implemented in a four-person household. Results show that the designed facial recognition security system achieved a 100% training and 96% validation accuracy with a 100% accuracy on the test images dataset consisting of unseen images of the four enrolled users.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130905253","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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