Supporting University ICT Developments: The Makerere University Experience

No Abstract Available Africa Development Vol. XXX (1&2) 2005: 86-9

Low level multiplexing in celluar radio systems

Low level multiplexing is proposed as an alternative to high level multiplexing, especially for the smaller cells in cellular radio. The main problem to be overcome is shown to be the generation of intermodulation products in pre-combining and post-combining amplifiers. It is demonstrated that the required intermodulation product margins can be maintained at the combining stage through the use of cheap planar microstrip combiners plus a modest output backoff in the precombining amplifiers. To reduce intermodulation products to acceptable levels while keeping costs down, the post-combining amplifier must be linearized. Linearization techniques are reviewed with the aim of identifying a method which can be adapted to improve post-combining amplifier linearity at 900 MHz. An experimental investigation is used to show that feedforward, amplitude predistortion as well as combined amplitude and phase predistortion can all provide the necessary bandwidth and dynamic range. It is however argued that amplitude predistortion offers the best compromise between cost and performance. (DX84426)</p

Consolidating research and education networking in Africa, phase 2 : implementation; final technical report, 1st August 2009 – 14th February 2012

UbuntuNet Alliance is a driving force in the development of national research and education networks (NRENs), leading the formation of NRENs in its membership region and also playing a major role in the formation of the West and Central African Research and Education Network. UbuntuNet Alliance posits that digital isolation is a major contributing factor limiting intellectual output from the African continent, due largely to the excessively high cost of internet connectivity. Consolidating research and education networking in Africa (CORENA) focuses on the provision of intra-African and global connectivity at bandwidths and costs that are comparable to the rest of African NRENS

A web-based intelligence platform for diagnosis of malaria in thick blood smear images : A case for a developing country

Malaria is a public health problem which affects developing countries world-wide. Inadequate skilled lab technicians in remote areas of developing countries result in untimely diagnosis of malaria parasites making it hard for effective control of the disease in highly endemic areas. The development of remote systems that can provide fast, accurate and timely diagnosis is thus a necessary innovation. With availability of internet, mobile phones and computers, rapid dissemination and timely reporting of medical image analytics is possible. This study aimed at developing and implementing an automated web-based Malaria diagnostic system for thick blood smear images under light microscopy to identify parasites. We implement an image processing algorithm based on a pre-trained model of Faster Convolutional Neural Network (Faster R-CNN) and then integrate it with web-based technology to allow easy and convenient online identification of parasites by medical practitioners. Experiments carried out on the online system with test images showed that the system could identify pathogens with a mean average precision of 0.9306. The system holds the potential to improve the efficiency and accuracy in malaria diagnosis, especially in remote areas of developing countries that lack adequate skilled labor

Coffee and cashew nut dataset: A dataset for detection, classification, and yield estimation for machine learning applications

Conventional methods of crop yield estimation are costly, inefficient, and prone to error resulting in poor yield estimates. This affects the ability of farmers to appropriately plan and manage their crop production pipelines and market processes. There is therefore a need to develop automated methods of crop yield estimation. However, the development of accurate machine-learning methods for crop yield estimation depends on the availability of appropriate datasets. There is a lack of such datasets, especially in sub-Saharan Africa. We present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons. The datasets were collected over nine months, from September 2022 to May 2023. The data was collected using a high-resolution camera mounted on an Unmanned Aerial Vehicle . The datasets contain 3000 coffee and 3086 cashew nut images, constituting 6086 images. Annotated objects of interest in the coffee dataset consist of five classes namely: unripe, ripening, ripe, spoilt, and coffee_tree. Annotated objects of interest in the cashew nut dataset consist of six classes namely: tree, flower, premature, unripe, ripe, and spoilt. The datasets may be used for various machine-learning tasks including flowering intensity estimation, fruit maturity stage analysis, disease diagnosis, crop variety identification, and yield estimation

Investigating the association between wood and charcoal domestic cooking, respiratory symptoms and acute respiratory infections among children aged under 5 years in Uganda: A cross-sectional analysis of the 2015/16 Demographic and Health Survey

Background: Household air pollution associated with biomass (wood, dung, charcoal, and crop residue) burning for cooking is estimated to contribute to approximately 4 million deaths each year worldwide, with the greatest burden seen in low and middle-income countries. We investigated the relationship between solid fuel type and respiratory symptoms in Uganda, where 96% of households use biomass as the primary domestic fuel. Materials and Methods: Cross-sectional study of 15,405 pre-school aged children living in charcoal or wood-burning households in Uganda, using data from the 2016 Demographic and Health Survey. Multivariable logistic regression analysis was used to identify the associations between occurrence of a cough, shortness of breath, fever, acute respiratory infection (ARI) and severe ARI with cooking fuel type (wood, charcoal); with additional sub-analyses by contextual status (urban, rural). Results: After adjustment for household and individual level confounding factors, wood fuel use was associated with increased risk of shortness of breath (AOR: 1.33 [1.10–1.60]), fever (AOR: 1.26 [1.08–1.48]), cough (AOR: 1.15 [1.00–1.33]), ARI (AOR: 1.36 [1.11–1.66] and severe ARI (AOR: 1.41 [1.09–1.85]), compared to charcoal fuel. In urban areas, Shortness of breath (AOR: 1.84 [1.20–2.83]), ARI (AOR: 1.77 [1.10–2.79]) and in rural areas ARI (AOR: 1.23 [1.03–1.47]) and risk of fever (AOR: 1.23 [1.03–1.47]) were associated with wood fuel usage. Conclusions: Risk of respiratory symptoms was higher among children living in wood compared to charcoal fuel-burning households, with policy implications for mitigation of associated harmful health impacts