Browsing by Author "Lyimo, Martine"

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An Integrated Deep Learning-based Lane Departure Warning and Blind Spot Detection System: A Case Study for the Kayoola Buses
(IEEE, 2023-11-16) Ziryawulawo, Ali; Mduma, Neema; Lyimo, Martine; Mbarebaki, Adonia; Madanda, Richard; Sam, Anael
Deep learning-based driver assistance systems (ADAS) have attracted interest from researchers due to their impact on improving vehicle safety and reducing road traffic accidents. In Uganda, road accidents have continued to soar with an increase of up to 42% in 2021 due to the growing road traffic density. To curb the high rates of road accidents, especially for heavy-duty vehicles, Kiira Motors Corporation a state-owned mobility solutions enterprise needs advanced driver assistance systems for improved safety of their market entry products- the Kayoola buses. This research presents an approach to vehicular safety enhancement through the integration of Lane Departure Warning (LDW) and Blind Spot Detection systems (BSD) using advanced deep learning algorithms. The resultant LDW and BSD system is realized on the Raspberry Pi platform, incorporating diverse sensors. By combining these advanced features, the study not only bridges an essential research void but also offers a practical resolution to pressing road safety concerns in the East African context. The integration of LDW and BSD systems through deep learning techniques marks a pivotal advancement in vehicular safety. The lane detection model was tested on DET and TuSimple datasets. Our model attained a mean F1 Score of 77.59% and a mean IoU of 65.26% on the Dataset for Lane Extraction (DET) and an overall accuracy of 97.96% on the TuSimple dataset. Our work presents an integrated lane departure warning and blind spot detection system that will be able to alert the driver using the graphical user interface, and auditory feedback. The anticipated real-world implementation is poised to substantiate the system’s effectiveness, thereby contributing to safer roads regionally and inspiring innovation in automotive engineering by leveraging artificial intelligence.
Mobile-based business-to-business platform for the pharmaceutical industry in Tanzania
(NM-AIST, 2021-07) Lyimo, Martine
The right to the uppermost attainable standard of health is a fundamental human right. In everyday life, mobile phones have become essential devices for most people in both developed and developing countries. Paper-based ordering of medicines in the pharmaceutical industry is time consuming and can enhance the spread of diseases such as COVID-19. Electronic ordering and stock management can solve these challenges, but while it has been widely adopted in developed countries, it remains underused in Tanzania. This study aimed to develop a mobile application (DawaFasta) and web application which links wholesale and retail pharmacies in Tanzania, to support electronic ordering and stock management. System and user requirements were collected through questionnaires, interviews and observations from 105 wholesale and retail pharmacies in Arusha, Dar es salaam and Kilimanjaro regions of Tanzania. The developed applications were evaluated for acceptability and usability by 9 wholesale and 15 retail pharmacy personnel to assess serviceability and usefulness. The results show that 96% found it very useful. The main reason is to bridge the gap between wholesale and retail pharmacies and provide easy access to medications. The 4% were not sure because they were unable to distinguish between legitimate and illegitimate online pharmacies. The application was assessed as having good usability for online pharmacy business purposes by wholesale and retail pharmacies. This application would need further development in order to raise awareness and add more features
Optimizing LoRaWAN Throughput in Maritime Environments Through Adaptive Coding and Modulation in Rayleigh Fading Channels
(Zenodo, 2025) Lyimo, Martine; Mgawe, Bonny; Leo, Judith; Dida, Mussa; Michael, Kisangiri
This dataset supports the article “Optimizing LoRaWAN Throughput in Maritime Environments through Adaptive Coding and Modulation under Rayleigh Fading”. It includes simulation outputs and MATLAB source code for reproducing all figures and results in the study. Files include throughput, PER, energy efficiency, spectral efficiency, and an ACM algorithm function.