EEG Group Research Archives

Not-quite-so-broken TLS in OCaml 2014

Researcher:  Hannes Mehnert, David Kaloper-Mersinjak

Supervisor: Anil Madhavapeddy, Peter Sewell

Summary:

Ideally, a forest monitoring system should be cost-effective, time-efficient, and accurate. However, existing tools fall short, raising scalability concerns. Traditional forest inventory and terrestrial laser scanning are slow and costly. Airborne laser scanning covers larger areas but remains expensive. Spaceborne missions are scalable but lack accuracy. The rapid development of UAVs and 3D vision algorithms offers a promising alternative. Yet, scalability depends on both learning and data — creating comprehensive, annotated forest datasets is challenging.

As extreme weather events, in particular heatwaves, increasingly threaten global infrastructure, a precise understanding of their impact on electricity grids is crucial for enhancing societal resilience and security. This study delves into the effects of heatwaves on the European electricity grid using a comprehensive real-world dataset.

This project expedites geospatial data processing by applying modern lightweight compression techniques to GeoTIFF-stored data, addressing the challenge of CPU bandwidth surpassing RAM bandwidths. We mitigate the impact of poor cache locality and the resulting memory bottlenecks by leveraging CPUs' superscalar capabilities and SIMD (Single Instruction, Multiple Data) instructions. By implementing SIMD-optimised compression, data remains compressed in RAM and closer to the CPU caches, facilitating faster access and alleviating memory constraints.

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This research introduces an AI-based alert system to reduce human-wildlife conflicts in the Romanian Carpathian Mountains. Globally, conflicts between people and wildlife are rising due to population growth, shifting land use patterns and climate change. In Romania, mountain communities are impacted by bears and wild boars, which damage livestock, crops and property. These conflicts can undermine conservation efforts and may result in the killing of problematic animals.

We address a problem that arises at the confluence of three recent trends: the popularity of storage-coupled photovoltaic (PV) systems amongst homeowners, the rapid proliferation of electric vehicles (EVs) with potential for bidirectional energy storage within PV-enabled single-family homes, and third, the surge in remote working accelerated by the Covid-19 pandemic.

LIFE uses state-of-the-art remote sensing products and data on species habitat preferences to quantify changes in Area of Habitat (AoH). Estimated contemporary AoH is compared with potential AoH in the absence of humans to estimate a species’s probability of persistence and to test the effects of different land-use