"'That great little man": Dvorak and Wagner'

A study of Wagner's impact on Czech music in the later nineteenth century and Dvorak in particular

Merula

Merula is a thirty-minute work for bass flute and electronics, commissioned by Icelandic flautist Kolbeinn Bjarnason. The premiere took place in the Belfast Festival at Queen’s in November 2012. A recording will be made in 2014. Further performances in Iceland, Norway and Poland are anticipated in 2014-15. I have given a research seminar on this work at Queen’s and will deliver it again at the University of Oxford during 2013-14.

Research Goals
1) To develop an effective means of notating live electronics in a manner that would sustain the work's performance history beyond the current generation of software
2) To apply the techniques of transcription and spectralism used in my composition, Perseid, using birdsongs as source material
3) To explore the problem of sustaining large-scale form in music that is primarily fast
4) To facilitate the emergence of the solo bass flute as an important solo instrument through the completion of a new large-scale work

Methodology
• Methodologies employed in this project included sound recording, sound analysis and transcription, extensive precompositional sketching, electroacoustic techniques of sound manipulation, designing complex live processes of sound transformation and spatialisation
• A considerable part of this work was collaboration with the flautist, both in SARC and Iceland. Mr. Bjarnason was involved all stages of the work, frequently recording source materials and helping to ensure the idiomatic nature of the flute writing.
• Developing a means of notating the live electronics. Building on a model suggested by Pierre Boulez in Anthemes 2 (1998), the score of this work includes a technical manual that describes electronic processes in a manner that can be reprogrammed in subsequent generations of software. Combined with a system of notations employed in the full score, the technical manual will enable this composition to be performed by a wide range of performers and technical teams, with appropriately identical results.

Online Multiperson Tracking With Occlusion Reasoning and Unsupervised Track Motion Model

We address the problem of multi-target tracking in realistic crowded conditions by introducing a novel dual-stage online tracking algorithm. The problem of data-association between tracks and detections, based on appearance, is often complicated by partial occlusion. In the first stage, we address the issue of occlusion with a novel method of robust data-association, that can be used to compute the appearance similarity between tracks and detections without the need for explicit knowledge of the occluded regions. In the second stage, broken tracks are linked based on motion and appearance, using an online-learned linking model. The online-learned motion-model for track linking uses the confident tracks from the first stage tracker as training examples. The new approach has been tested on the town centre dataset and has performance comparable with the present state-of-the-art

Invariant Information Local Sub-map Filter (IILSF) for Efficient Simultaneous Localisation and Mapping of Large Environments

This paper presents an Invariant Information Local Sub-map Filter (IILSF) as a technique for consistent Simultaneous Localisation and Mapping (SLAM) in a large environment. It harnesses the benefits of sub-map technique to improve the consistency and efficiency of Extended Kalman Filter (EKF) based SLAM. The IILSF makes use of invariant information obtained from estimated locations of features in independent sub-maps, instead of incorporating every observation directly into the global map. Then the global map is updated at regular intervals. Applying this technique to the EKF based SLAM algorithm: (a) reduces the computational complexity of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. Simulation results show that the method was able to accurately fuse local map observations to generate an efficient and consistent global map, in addition to significantly reducing computational cost and data association ambiguities.

An intelligent configuration-sampling based local motion planner for robotic manipulators

Manipulator motion planning is a task which relies heavily on the construction of a configuration space prior to path planning. However when fast real-time motion is needed, the full construction of the manipulator's high-dimensional configu-ration space can be too slow and expensive. Alternative planning methods, which avoid this full construction of the manipulator's configuration space are needed to solve this problem. Here, one such existing local planning method for manipulators based on configuration-sampling and subgoal-selection has been extended. Using a modified Artificial Potential Fields (APF) function, goal-configuration sampling and a novel subgoal selection method, it provides faster, more optimal paths than the previously proposed work. Simulation results show a decrease in both runtime and path lengths, along with a decrease in unexpected local minimum and crashing issues.