This paper presents a new algorithm for synthesising image texture. Texture synthesis is an important process in image post-production. The best previous approaches have used non-parametric methods for synthesising texture. Unfortunately, these methods generally suffer from high computational cost and difficulty in handling scale in the synthesis process. This paper introduces a new idea of usi...

We report on an asymmetric high energy dual optical parametric amplifier (OPA) which is capable of having either the idlers, signals, or depleted pumps, relatively phase locked at commensurate incommensurate wavelengths. Idlers and signals can be order 200 mrad rms better, corresponding to a 212 as jitter λ =2 µ m. The arm OPA outputs combined 3.5 mJ signal idler, while low 1.5 mJ, with entire ...

Substructure synthesis from a controls perspective is considered. An eÆcient and computationally robust method for synthesis of substructures is developed. The method considers the interface forces/moments as control inputs and rede nes the synthesis problem in terms of obtaining a constant gain compensator which would ensure the connectivity requirements of the combined structure. Orthogonal s...

We describe a synthesis system that takes operating range constraints and inter and intracircuit parametric manufacturing variations into account while designing a sized and biased analog circuit. Previous approaches to computer-aided design for analog circuit synthesis have concentrated on nominal analog circuit design, and subsequent optimization of these circuits for statistical fluctuations...

This paper presents a high-level parametric approach for synthesis of novel human animation sequences from short clips data. The parametric approach generates motions according to user-defined high-level motion parameters, such as speed, slope and direction, while maintaining the quality and realism of captured motions, and preserving the motion constraints. The approach allows simultaneous con...

In this paper, we investigate a combination of several feedforward deep neural networks (DNNs) for a high-quality statistical parametric speech synthesis system. Recently, DNNs have significantly improved the performance of essential components in the statistical parametric speech synthesis, e.g. spectral feature extraction, acoustic modeling and spectral post-filter. In this paper our proposed...

In expressive speech synthesis, a key challenge is the generation of flexibly varying expressive tone while maintaining the high quality achieved with unit selection speech synthesis methods. Existing approaches have either concentrated on achieving high synthesis quality with no flexibility, or they have aimed at parametric models, requiring the use of parametric synthesis technologies such as...

This paper introduces a novel form of parametric synthesis that uses context embeddings produced by the bottleneck layer of a deep neural network to guide the selection of models in a rich-context HMM-based synthesiser. Rich-context synthesis – in which Gaussian distributions estimated from single linguistic contexts seen in the training data are used for synthesis, rather than more conventiona...

This paper documents a method of controlling complex sound synthesis processes such as granular synthesis, additive synthesis, timbre morphology, swarm-based spatialisation, spectral spatialisation, and timbre spatialisation via a multi-parametric 2D interface. This paper evaluates the use of audio-rate control signals for sound synthesis, and discussing approaches to de-interleaving, synchroni...