2021 IEEE AFRICON最新文献_第3页

Quality of Service Enabled Network Slicing Model in 5G and the Next Generation Virtualized Networks 5G和下一代虚拟化网络中服务质量支持的网络切片模型

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9571005

Poovendren Govender, K. Ogudo, Chabalala S. Chabalala

引用次数: 0

Assessment of the Used Mobile Applications by Small-Scale Farmers at Miono and User Requirements in the Present and Future Mobile Apps 对Miono小农使用的移动应用程序的评估以及当前和未来移动应用程序的用户需求

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570945

Ezra Misaki

引用次数: 1

A Performance Evaluation of 3D Deep Learning Algorithms for Crime Classification 三维深度学习犯罪分类算法的性能评价

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570983

Tawanda Matereke, Clement N. Nyirenda, Mehrdad Ghaziasgar

引用次数: 0

Academic challenges as part of capstone project resilience training 作为顶点项目弹性训练的一部分的学术挑战

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570882

Jacob J. Greeff, A. Nel, Reolyn Heymann, Johnson Carroll

引用次数: 1

Focusing Benford’s Law on the Daily Covid-19 Cases Numbers and Daily Covid-19 Deaths Numbers 将本福德定律重点放在每日新冠肺炎病例数和每日新冠肺炎死亡人数上

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570989

L. Butgereit

引用次数: 1

Mammalian Species Detection Using a Cascade of Unet and SqueezeNet 使用Unet和SqueezeNet级联的哺乳动物物种检测

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570950

Michael Njeru, C. Maina, K. Langat

{"title":"Mammalian Species Detection Using a Cascade of Unet and SqueezeNet","authors":"Michael Njeru, C. Maina, K. Langat","doi":"10.1109/africon51333.2021.9570950","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570950","url":null,"abstract":"Monitoring of wild animals has taken different approaches with an aim to provide vital information used in animal protection in their natural habitats. To recognize animal species without human trackers requires machine learning models that extract specie's features from an image. This project proposes a method of counting animals in an image and specifying the species of each animal using Unet and a variant of the SqueezeNet model. To train the Unet model, images and corresponding masks are used as the training data. Different optimizers are applied to each model. During inference, Unet outputs a binary mask with ones where an animal is detected and zeros elsewhere. SqueezeNet model is trained with images corresponding to six classes: bushbuck, impala, llama, warthog, waterbuck, and zebra. Three variants of the SqueezeNet model have been trained. The first contains the original backbone while the other two have the original backbone with an additional fire module. In one model the Fire module is similar to the Fire modules of the original backbone while in the other model, the extra fire module contains batch normalization layers. The trained models show that Unet trained with Nadam optimizer achieves the highest dice coefficient while the SqueezeNet with an extra Fire module containing batch norm layers and RMSprop optimizer achieves the highest training accuracy. The combined system containing the two models takes an image and outputs the image with bounding boxes around each animal and the corresponding animal species. The system achieves both counting and recognition of the species for each image placed at the input.","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":"134304194","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}

引用次数: 0

A Simulation Approach to Analyse the Impacts of Battery Swap Stations for e-Motorcycles in Africa 非洲电动摩托车电池交换站影响分析的仿真方法

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570895

Cameron S Sheehan, T. Green, N. Daina

{"title":"A Simulation Approach to Analyse the Impacts of Battery Swap Stations for e-Motorcycles in Africa","authors":"Cameron S Sheehan, T. Green, N. Daina","doi":"10.1109/africon51333.2021.9570895","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570895","url":null,"abstract":"Electric motorcycles are being introduced in some African countries to combat the negative environmental impacts from the rapid growth in the use of traditional internal combustion engine motorcycle taxis. However, the electricity systems in many of these countries are strained, with generation and/or distribution capacity at their limits, leading to regular power outages that could impact the charging of these e-motorcycles. These fragile grids may be put under further strain by additional e-motorcycle charging. Commercial motorcycle taxi drivers may not be willing to wait for extended periods to charge during their shift. The use of battery swapping stations could mitigate these issues. However, modelling of their system impacts is required to fully understand their potential. This paper presents a hybrid model to simulate the key operational processes of battery swapping stations and their energy systems, allowing various configurations and scenarios to be investigated for the specific context of e-motorcycles in Africa. The configuration parameters include the numbers of batteries and charging slots, the charging power, and the addition of solar PV and static battery energy storage capacity. Power outages can be modelled for various scenarios. A test case of a battery swap station in Nairobi, Kenya, was used to showcase and validate the model. The results demonstrated how the various sub-models performed and interacted with each other, and clearly showed what impact the chosen BSS configuration would have on the grid.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"41 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":"133273015","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}

引用次数: 2

Development of Optimal Algorithm to Interconnect Multiple Microgrids in an Agricultural-based Remote Community 基于农业的偏远社区多微电网互联优化算法的发展

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9571000

F. Mthethwa, C. Gomes, D. Dorrell

引用次数: 0

Arbitrary Alloy Semiconductor Material Based DG MOSFET for High-Frequency Industrial and Hybrid Consumer Applications 用于高频工业和混合消费应用的基于任意合金半导体材料的DG MOSFET

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570845

Naveenbalaji Gowthaman, V. Srivastava

{"title":"Arbitrary Alloy Semiconductor Material Based DG MOSFET for High-Frequency Industrial and Hybrid Consumer Applications","authors":"Naveenbalaji Gowthaman, V. Srivastava","doi":"10.1109/africon51333.2021.9570845","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570845","url":null,"abstract":"The performance of the transistors is a measure for the system to perform desired output. The research in the development of transistors is continuously growing and defines an efficient system and device modeling. This work shows a comparative analysis of the various materials in the double-gate (DG) MOSFET. Here, the DG MOSFETs have been constructed using high-ƙ dielectrics to reduce the short channel effects and improve the performance of the transistors, which is the unit cell of the industrial and consumer electronics devices. This MOSFET can further be optimized by enhancing the analog characteristics in the nanometer regime. This work focuses on the performance of the DG MOSFETs by considering channels like Indium Arsenide (InAs) and Indium Phosphide (InP) in the Aluminium Gallium Arsenide (AlxGa1-xAs) and Gallium Arsenide Antimonide (GaAs1-xSbx) substrates. From the simulation and tabulations, it is evident that the properties of the DG MOSFETs with these novel materials change drastically, thereby inferring the optimum materials in MOSFETs for the high speed and long haul RF applications. In this work, three different models have been proposed, namely AlxGa1-xAs/HfO2, AlxGa1-xAs/La2O3, and GaAs1-xSbx/La2O3; which shows improvement in threshold voltage with 1.16%, 1.20%, and 1.64% respectively when compared to the conventional Silicon-based DG MOSFETs.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"8 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":"115466898","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}

引用次数: 4

Conceptual Development of an Autonomous Underwater Robot Design for Monitoring and Harvesting Invasive Weeds 自主水下杂草监测与收获机器人的概念发展

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570971

D. Modungwa, F. Mekuria, Mzuziwezulu Kekana

{"title":"Conceptual Development of an Autonomous Underwater Robot Design for Monitoring and Harvesting Invasive Weeds","authors":"D. Modungwa, F. Mekuria, Mzuziwezulu Kekana","doi":"10.1109/africon51333.2021.9570971","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570971","url":null,"abstract":"The design of a biomimicry autonomous underwater robot for monitoring and harvesting invasive weeds in lakes is presented in this paper. The systematic design of the robot focuses on integrating 5G-AI-IoT as effective technological tools to autonomously monitor and harvest invasive weeds in order to replace traditional weed control approaches. The robustness and versatility of the robotic platform to structural topology and autonomous navigation that uses convolutional neural network methods and unsupervised learning techniques will be demonstrated. The robotic concept design will investigate real time sensing, mapping and visualization of the invasive weeds. The system based on real-time mapping information obtained from the swarm of drones will also manage the control of the underwater robots equipped with smart networked sensors using State of the Art IoT technologies. The mechanical dislodging machine will be guided to the mapped areas and accurately controlled and guided through smart sensors via the 5G Ultra-reliable Low-Latency Communication Control (URLLC) and tactile control system to dislodge the invasive weed with no impact on other organisms and the biodiversity of the lake.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"203 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":"116179515","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}

引用次数: 2