Martin Wright

This report details the development of a dual Long Short-Term Memory (LSTM) neural network model to optimize drone-based coastal surveys for marine species monitoring, which are frequently hampered by adverse weather. Trained on historical time-series data, the model forecasts conditions at two sites and uses a weighted policy to recommend the better location for drone flights, minimizing wasted efforts and improving the effectiveness of ecological data collection.

This paper introduces a novel hybrid framework for semantic video retrieval and localized caching in surveillance systems. This approach employs a two-tiered computer vision strategy using a real-time YOLO model deployed at the network edge combined with OpenAI's CLIP, where embeddings are generated to capture rich, high-dimensional zero-shot semantic content. These embeddings are then synchronized with a cloud-based vector database to establish a semantically indexed reservoir for query processing. Finally, we leverage a Large Language Model (LLM) augmented with Retrieval-Augmented Generation (RAG) to process natural language queries and index cached frames.