Browsing by Author "Mwikirize, Cosmas"

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    A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves
    (arXiv preprint arXiv, 2020) Katumba, Andrew; Bomera, Moses; Mwikirize, Cosmas; Namulondo, Gorret; Ajeroy, Mary Gorret; Ramathaniy, Idd; Nakayima, Olivia; Nakabonge, Grace; Okello, Dorothy; Serugunda, Jonathan
    Pests and diseases pose a key challenge to passion fruit farmers across Uganda and East Africa in general. They lead to loss of investment as yields reduce and losses increases. As the majority of the farmers including passion fruit farmers, in the country are smallholder farmers from low-income households, they do not have sufficient information and means to combat these challenges. While, passion fruits have the potential to improve the well-being of these farmers given their short maturity period and high market value [1], without the required knowledge about the health of their crops, farmers can not intervene promptly to turn the situation around. For this work, we partnered with the Uganda National Crop Research Institute (NaCRRI) to develop a dataset of expertly labeled passion fruit plant leaves and fruits, both diseased and healthy. We made use of their extension service to collect images from five districts in Uganda to create the dataset. Using the dataset, we are applying state-of-the-art techniques in machine learning, specifically deep learning at scale for object detection and classification for accurate plant health status prediction. While deep learning techniques have been applied to various disease diagnosis contexts with varying degrees of success([2], [3], [4], [5], [6]), there has not been any significant effort, to the best of our knowledge, to create a dataset or apply machine learning techniques to passion fruits despite their obvious financial benefits. With this work, we hope to fill this gap by generating and making publically available an image dataset focusing on passion fruit plant diseases and pest damage and training the first generation of machine learning-based models for passion fruit plant disease identification using this dataset. The initial focus is on the locally prevalent woodiness (viral) and brown spot (fungal) diseases.
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    Development of Online Laboratories for Modulation and Combinational Logic Circuit Analysis Using NI ELVIS IITM Platform
    (New Generations, 2010) Mwikirize, Cosmas; Tumusiime Asiimwe, Arthur; Musasizi, Lea; Namuswa, Victoria; Nakasozi, Mary Dawn; Mugga, Charles; Katumba, Andrew; Tickodri - Togboa, Sandy Stevens; Butime, Julius; Musasizi, Paul Isaac
    This paper describes the work carried out by the Makerere University iLabs Project Team, hereafter referred to as iLabs@MAK. The procedures followed to develop Online Laboratories using the National Instruments Educational Laboratory Virtual Instrumentation Suite (NI ELVIS II™) platform is discussed. The laboratories were developed based on the Massachusetts Institute of Technology (MIT) iLabs Shared Architecture (ISA), a model that provides highly reliable generic services independent of the experiment domains. Modifications were made to the ELVIS version 1.0 code to introduce desired functionalities. Experiments were selected from three fundamental courses offered in the Department of Electrical Engineering, Faculty of Technology, Makerere University. Starting with the rationale for development of iLabs in specific courses, the paper presents the methods employed and the results obtained from the various experiments. Experiences and perceptions from over 300 students who performed the experiments were captured as a core aspect of the Research.
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    An Online Digital Filters and Sound Effects Laboratory Utilizing NI SPEEDY 33 and LabVIEW DSP Module
    (New Generations, 2011) Kyesswa, Michael; Mbajja, Amru; Tumusiime Asiimwe, Arthur; Mwikirize, Cosmas; Musasizi, Paul Isaac; Tickodri-Togboa, Sandy Stevens; Katumba, Andrew; Butime, Julius
    There has been an increasing use and application of the internet due to the advances in technology. At Makerere University1, the internet has been used by students to remotely access and share scarce laboratory resources using the iLabs platform. This paper presents the design and implementation of an on-line laboratory that supports experimentation in Digital Filters and Sound Effects based on digital signal processing techniques. In the laboratory design, the National Instruments Signal Processing Educational Engineering Device for Youth (NI SPEEDY 33) is programmed to carry out the different processing on an applied signal at the input using the LabVIEW DSP Module. The input is an audio signal and the output is a modified audio signal whose wave form is be displayed at the client. The online laboratory is developed using the interactive iLabs Shared Architecture that allows more student interaction with the hard ware.
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    Online Laboratories: Enhancing the Quality of Higher Education in Africa
    (Conference of Rectors, 2011) Akinwale, O.B.; Ayodele, K.P.; Jubril, A.M.; Kehinde, L.O.; Osasona, O.; Akinwunmi, O.; Tumusiime Asiimwe, Arthur; Mwikirize, Cosmas; Musasizi, Paul Isaac; Tickodri Togboa, Sandy-Stevens; Katumba, Andrew; Butime, Julius; Nombo, Josiah P.; Baraka, Maiseli M.; Teyana, Sapula; Alfred, Mwambela J.; Musa, Kissaka M.
    Online laboratories have been adopted by the Obafemi Awolowo University (OAU) - Nigeria, Makerere University (MAK) - Uganda and the University of Dar-es-Salaam (UDSM) -Tanzania to enhance the delivery and quality of higher education. Utilizing the Massachusetts Institute of Technology (MIT) iLabs Shared Architecture (ISA), grant-related teams at each of the three universities partook research into the development of online laboratories (iLabs) to support Science and Technology curricula. This has provided students with a low-cost, flexible, convenient and reliable experimentation platform. The iLab-Africa project has also opened up specific beneficial areas viz.: collaboration between universities, staff-student exchanges between these universities and MIT, funding opportunities, the possibility of collaboration between universities in different countries on the use of scarce laboratory equipment, and the collaboration of students between universities in different countries on specific laboratories. This paper discusses the application of iLabs in improving the teaching and learning processes at each of the partner Universities and highlights the offshoot advantages arising from the same. It thus lays a case for adoption of the technology throughout Africa to support curricula and promote inter-institutional collaboration.
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    Time‑aware deep neural networks for needle tip localization in 2D ultrasound
    (International Journal of Computer Assisted Radiology and Surgery, 2021) Mwikirize, Cosmas; Kimbowa, Alvin B.; Imanirakiza, Sylvia; Katumba, Andrew; Nosher, John L.; Hacihaliloglu, Ilker
    Accurate placement of the needle is critical in interventions like biopsies and regional anesthesia, during which incorrect needle insertion can lead to procedure failure and complications. Therefore, ultrasound guidance is widely used to improve needle placement accuracy. However, at steep and deep insertions, the visibility of the needle is lost. Computational methods for automatic needle tip localization could improve the clinical success rate in these scenarios. Methods We propose a novel algorithm for needle tip localization during challenging ultrasound-guided insertions when the shaft may be invisible, and the tip has a low intensity. There are two key steps in our approach. First, we enhance the needle tip features in consecutive ultrasound frames using a detection scheme which recognizes subtle intensity variations caused by needle tip movement. We then employ a hybrid deep neural network comprising a convolutional neural network and long short-term memory recurrent units. The input to the network is a consecutive plurality of fused enhanced frames and the corresponding original B-mode frames, and this spatiotemporal information is used to predict the needle tip location. Results We evaluate our approach on an ex vivo dataset collected with in-plane and out-of-plane insertion of 17G and 22G needles in bovine, porcine, and chicken tissue, acquired using two different ultrasound systems. We train the model with 5000 frames from 42 video sequences. Evaluation on 600 frames from 30 sequences yields a tip localization error of 0.52 ± 0.06 mm and an overall inference time of 0.064 s (15 fps). Comparison against prior art on challenging datasets reveals a 30% improvement in tip localization accuracy. Conclusion The proposed method automatically models temporal dynamics associated with needle tip motion and is more accurate than state-of-the-art methods. Therefore, it has the potential for improving needle tip localization in challenging ultrasound-guided interventions.