In this paper, we examine a coding scheme for quantising feature vectors in a distributed speech recognition environment that is more robust to noise. It consists of a vector quantiser that operates on the logarithmic filterbank energies (LFBEs). Through the use of a perceptually-weighted Euclidean distance measure, which emphasises the LFBEs that represent the spectral peaks, the vector quanti...

Most colour image printing and display devices do not have the capacity to deal with true-colours. To display the image anyway, they need to apply a quantisation method to reduce the number of colours present into the image. The research of this article is directed towards the study of a special kind of quantisation methods known as dithering techniques. Several quantisation techniques already ...

A discussion is given of the quantisation of a physical system with finite degrees of freedom subject to a Hamiltonian constraint by treating time as a constrained classical variable interacting with an unconstrained quantum state. This leads to a quantisation scheme that yields a Schrödinger-type equation which is in general nonlinear in evolution. Nevertheless it is compatible with the probab...

Learning Vector Quantisation (LVQ) is a method of applying the Vector Quantisation (VQ) to generate references for Nearest Neighbour (NN) classification. Though successful in many occasions, LVQ suffers from several shortcomings, especially the reference vectors are prone to diverge. In this paper, we propose a Classified Vector Quantisation (CVQ) to establish VQ for classification. By CVQ, eac...

A discussion is given of the quantisation of a physical system with finite degrees of freedom subject to a Hamiltonian constraint by treating time as a constrained classical variable interacting with an unconstrained quantum state. This leads to a quantisation scheme that yields a Schrödinger-type equation which is in general nonlinear in evolution. Nevertheless it is compatible with the probab...

Although widely studied for many years, colour quantisation remains a practical problem in image processing. Unlike previous works where the image can only be quantised after the whole set of image data is acquired, we propose to use an evolving localised learning model for on-line colour quantisation. This approach is compared with some conventional algorithms.

This is a pedagogical and (almost) self-contained introduction into the Theorem of Groenewold and van Howe, which states that a naive transcription of Dirac’s quantisation rules cannot work. Some related issues in quantisation theory are also discussed. First-class constrained systems a briefly described in a slightly more ‘global’ fashion.1