Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

Towards an analysis framework of technology habituation by older users

Smart everyday objects could support the wellbeing, independent living and social connectedness of ageing people, but their successful adoption depends upon them fitting with their skills, values and goals. Many technologies fail in this respect. Our work is aimed at designs that engage older people by building on their individual affective attachment to habituated objects and leveraging, from a participatory design perspective, the creative process through which people continuously adapt their homes and tools to their own lifestyle. We contribute a novel analytic framework based on an analysis of related research on appropriation and habituated objects. It identifies steps in appropriation from inspection to performance and habituation. We test this framework with the preliminary testing of an augmented habituated object, a messaging kettle. While only used in one home so far, its daily use has provoked many thoughts, scenarios and projections about use by friends, both practical, utopian and dystopian.

Detailed analysis of a conservative difference approximation for the time fractional diffusion equation

Diffusion equations that use time fractional derivatives are attractive because they describe a wealth of problems involving non-Markovian Random walks. The time fractional diffusion equation (TFDE) is obtained from the standard diffusion equation by replacing the first-order time derivative with a fractional derivative of order α ∈ (0, 1). Developing numerical methods for solving fractional partial differential equations is a new research field and the theoretical analysis of the numerical methods associated with them is not fully developed. In this paper an explicit conservative difference approximation (ECDA) for TFDE is proposed. We give a detailed analysis for this ECDA and generate discrete models of random walk suitable for simulating random variables whose spatial probability density evolves in time according to this fractional diffusion equation. The stability and convergence of the ECDA for TFDE in a bounded domain are discussed. Finally, some numerical examples are presented to show the application of the present technique.

Hydrazine-hydrate intercalated halloysite under controlled-rate thermal analysis conditions

The thermal behaviour of halloysite fully expanded with hydrazine-hydrate has been investigated in nitrogen atmosphere under dynamic heating and at a constant, pre-set decomposition rate of 0.15 mg min-1. Under controlled-rate thermal analysis (CRTA) conditions it was possible to resolve the closely overlapping decomposition stages and to distinguish between adsorbed and bonded reagent. Three types of bonded reagent could be identified. The loosely bonded reagent amounting to 0.20 mol hydrazine-hydrate per mol inner surface hydroxyl is connected to the internal and external surfaces of the expanded mineral and is present as a space filler between the sheets of the delaminated mineral. The strongly bonded (intercalated) hydrazine-hydrate is connected to the kaolinite inner surface OH groups by the formation of hydrogen bonds. Based on the thermoanalytical results two different types of bonded reagent could be distinguished in the complex. Type 1 reagent (approx. 0.06 mol hydrazine-hydrate/mol inner surface OH) is liberated between 77 and 103°C. Type 2 reagent is lost between 103 and 227°C, corresponding to a quantity of 0.36 mol hydrazine/mol inner surface OH. When heating the complex to 77°C under CRTA conditions a new reflection appears in the XRD pattern with a d-value of 9.6 Å, in addition to the 10.2 Ĺ reflection. This new reflection disappears in contact with moist air and the complex re-expands to the original d-value of 10.2 Å in a few h. The appearance of the 9.6 Å reflection is interpreted as the expansion of kaolinite with hydrazine alone, while the 10.2 Å one is due to expansion with hydrazine-hydrate. FTIR (DRIFT) spectroscopic results showed that the treated mineral after intercalation/deintercalation and heat treatment to 300°C is slightly more ordered than the original (untreated) clay.

Romano-British wall-painting fragments: a spectroscopic analysis

Raman spectroscopic analyses of fragmented wall-painting specimens from a Romano-British villa dating from ca. 200 AD are reported. The predominant pigment is red haematite, to which carbon, chalk and sand have been added to produce colour variations, applied to a typical Roman limewash putty composition. Other pigment colours are identified as white chalk, yellow (goethite), grey (soot/chalk mixture) and violet. The latter pigment is ascribed to caput mortuum, a rare form of haematite, to which kaolinite (possibly from Cornwall) has been added, presumably in an effort to increase the adhesive properties of the pigment to the substratum. This is the first time that kaolinite has been reported in this context and could indicate the successful application of an ancient technology discovered by the Romano-British artists. Supporting evidence for the Raman data is provided by X-ray diffraction and SEM-EDAX analyses of the purple pigment.