Deep transfer learning for improving single-EEG arousal detection

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model (F1=0.682 and F1=0.694, respectively), and was significantly better than a comparable single-channel model. Our results are promising for researchers working with small databases who wish to use deep learning models pre-trained on larger databases.Comment: Accepted for presentation at EMBC202

Experimental Approaches to the Composition of Interactive Video Game Music

This project explores experimental approaches and strategies to the composition of interactive music for the medium of video games. Whilst music in video games has not enjoyed the technological progress that other aspects of the software have received, budgets expand and incomes from releases grow. Music is now arguably less interactive than it was in the 1990’s, and whilst graphics occupy large amounts of resources and development time, audio does not garner the same attention. This portfolio develops strategies and audio engines, creating music using the techniques of aleatoric composition, real-time remixing of existing work, and generative synthesisers. The project created music for three ‘open-form’ games : an example of the racing genre (Kart Racing Pro); an arena-based first-person shooter (Counter-Strike : Source); and a real-time strategy title (0 A.D.). These games represent a cross-section of ‘sandbox’- type games on the market, as well as all being examples of games with open-ended or open-source code

Combining hardware and software instrumentation to classify program executions

Several research efforts have studied ways to infer properties of software systems from program spectra gathered from the running systems, usually with software-level instrumentation. While these efforts appear to produce accurate classifications, detailed understanding of their costs and potential cost-benefit tradeoffs is lacking. In this work we present a hybrid instrumentation approach which uses hardware performance counters to gather program spectra at very low cost. This underlying data is further augmented with data captured by minimal amounts of software-level instrumentation. We also evaluate this hybrid approach by comparing it to other existing approaches. We conclude that these hybrid spectra can reliably distinguish failed executions from successful executions at a fraction of the runtime overhead cost of using software-based execution data

Rapid Development and Flexible Deployment of Adaptive Wireless Sensor Network Applications

Wireless sensor networks (WSNs) are difficult to pro-gram and usually run statically-installed software limiting its flexibility. To address this, we developed Agilla, a new middleware that increases network flexibility while simplifying application development. An Agilla network is deployed with no pre-installed application. Instead, users inject mobile agents that spread across nodes performing application-specific tasks. Each agent is autonomous, allowing multiple applications to share a network. Programming is simplified by allowing programmers to create agents using a high-level language. Linda-like tuple spaces are used for inter-agent communication and context discovery. This preserves each agent’s autonomy while providing a rich infrastructure for building complex applications, and marks the first time mobile agents and tuple spaces are used in a unified framework for WSNs. Our efforts resulted in an implementation for MICA2 motes and the development of several applications. The implementation consumes a mere 41.6KB of code and 3.59KB of data memory. An agent can migrate 5 hops in less than 1.1 seconds with 92% reliability. In this paper, we present Agilla and provide a detailed evaluation of its implementation, an empirical study of its overhead, and a case study demonstrating its use