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Oakleigh Weekes's Projects

bacteria-in-blood icon bacteria-in-blood

My submission for my image processing module assignment. The task: Use classical image processing techniques to segment bacteria and blood cells in microscopic images of blood.

deliverytrackingdatabase icon deliverytrackingdatabase

Delivery Tracking database normalised to 3NF, for a Database Module at University. Queries are written in a more complex nesting format due to assessment requirements. Documentation included in the attached PDF file.

fish_fg_detect_scraps icon fish_fg_detect_scraps

Code scraps showing how traditional foreground detection with MOG/KNN is too noisy for tracking fish in streams and various reflective atmospheres.

gclouddeployment icon gclouddeployment

Yaml, Jinja & Schema files for a GCloud Deployment for a theoretical online shopping company.

signal_imaging_assignment icon signal_imaging_assignment

Work from a MSc Intelligent Vision Module (Applied signal/imaging). Imaging work was improved in the Computer Vision module, so please see that repository also.

trafficanalyser icon trafficanalyser

BSc Computer Science Level 1 C# Assignment which reads from a text file and uses various sort and search algorithms

tseg33colourisationproject icon tseg33colourisationproject

This is the repository holding the stages of development for the Team Software Module Solution. Documentation as pdf.

upliftingpoleai_bsc_dissertation icon upliftingpoleai_bsc_dissertation

Contains dissertation code and report for my level 3 BSc Computer Science 'Project' module. I achieved 84% in this assessment, and I am very proud of this work which covers domains in AI, Machine learning, data science, Ux and ethics. It also applies technology to a niche which has made a greatly positive impact on my life; Pole Sport.

usabilityheuristics icon usabilityheuristics

Iterative concept design of a Student Companion Robot for the Ux Design Level 2 Module at University

yolo_dataset_cleaning icon yolo_dataset_cleaning

Contains scripts for cleaning YOLO datasets, pooling frames with certain classes, and creating a test set with uniform empty frames (to assess for false positives).

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