Human Detection for Flood Rescue: Application of YOLOv5 Algorithm and DeepSort Object Tracking

This thesis proposes a method of human detection using high-resolution surveillance cameras to monitor sections of the Chattahoochee River that require frequent search and rescue efforts due to flooding. The areas of interest are located in the city of Columbus, Georgia. The goals of this study are to evaluate the effectiveness of the YOLO (You Only Look Once) algorithm for human detection on the river as well as to propose future improvements to the city’s existing alert methods in the event of a flood.M.S

Cloud-Based Machine Learning Service for Astronomical Sub-Object Classification: Case Study On the First Byurakan Survey Spectra

The classification of astronomical objects in the Digitized First Byurakan Survey (DFBS), comprising low-dispersion spectra for approximately twenty million objects, presents challenges regarding performance and computational resources. However, considering the distinct spectral characteristics within subgroups, sub-object classification becomes crucial for a more detailed understanding of the dataset. The article addresses these challenges by proposing a comprehensive cloud-based service for classifying objects into spectral classes and subtypes, with a focus on carbon stars, white dwarfs / subdwarfs, and Markarian (UV-excess) galaxies, which are the primary objects in DFBS. By leveraging the power of cloud computing, it effectively handles the computational requirements associated with analyzing the extensive DFBS dataset. The service employs advanced machine learning algorithms trained on labeled data to classify objects into their respective spectral types and subtypes. The service can be accessed and utilized through a user-friendly interface, making it accessible to a wide range of users in the astronomical community

Hunting for the electro-magnetic counterpart to gravitational waves

The detection of GW170817, the first known binary neutron star merger, ushered in the era of multi-messenger astronomy. The Gravitational-Wave Optical Transient Observer (GOTO) is a new multi-camera instrument designed to cover large gravitational-wave localisation regions quickly, with the aim of identifying gravitational-wave counterparts quickly. This thesis covers the deployment of GOTO and the technical development of an automatic focus script, a real-time image subtraction pipeline, and a machine learner all with the aims of finding and announcing transients in real-time. The methods developed here can be used in other highcadence optical surveys. The thesis is motivated in the introduction, summarising the history of gravitational-wave astronomy and the importance of finding counterparts. GOTO is introduced properly in the methodology section. Here, I explain how GOTO is built and optimised for rapid transient discovery. From there, I show the development of an automatic focus script that exploits source geometry to quickly achieve focus. The following two chapters detail the the development of a new image-subtraction pipeline, which proves to be faster and better quality than the techniques currently used in the literature. Finally, I conclude this work using GOTO's first Gravitational-Wave follow-up campaign in compliment with the techniques developed in this thesis to find transients coincident with Gravitational- Wave detections. Showing GOTO is indeed capable and primed to find transients associated with gravitational-waves quickly