star identification is one of the most important stages in attitude determination with star trackers. this can be performed using matching algorithms between observed stars and a master star catalogue. the main challenge in this approach is to provide a fast and reliable identification algorithm that is sufficiently robust in different pointing views of the star tracker optical system in the sp...

In the age of information and globalisation, secure communication as well as the protection of sensitive data against unauthorised access are of utmost importance. Quantum cryptography currently provides the only way to exchange a cryptographic key between two parties in an unconditionally secure fashion. Owing to losses and noise of today’s optical fibre and detector technology, at present qua...

Space interferometers consisting of several free flying telescopes, such as the planned Darwin mission, require a complex metrology system to make all the components operate as a single instrument. Our research focuses on one of its sub-systems that measures the absolute distance between two satellites with high accuracy. For Darwin the required accuracy would be in the order of 10 μm over 250 ...

A good distance function is an essential tool in applications which involve querying large databases, such as image retrieval and bioinformatics. We describe a non-parametric algorithm for distance function learning which is based on the boosting of low grade weak learners in a product space. The algorithm learns a function defined over pairs of points, using supervision in the form of equivale...

A nonlinear discriminant analysis based approach to feature space dimensionality reduction in noise robust automatic speech recognition (ASR) is proposed. It utilizes a correlation based distance measure instead of the conventional Euclidean distance. The use of this ‘correlation preserving discriminant analysis’ (CPDA) procedure is motivated by evidence suggesting that correlation based cepstr...

Probabilistic distance measures are important quantities in many research areas. For example, the Chernoff distance (or the Bhattacharyya distance as its special example) is often used to bound the Bayes error in a pattern classification task and the Kullback-Leibler (KL) distance is a key quantity in the information theory literature. However, computing these distances is a difficult task and ...

We present an integrated experimental and computational approach for de novo protein structure determination in which short-distance cross-linking data are incorporated into rapid discrete molecular dynamics (DMD) simulations as constraints, reducing the conformational space and achieving the correct protein folding on practical time scales. We tested our approach on myoglobin and FK506 binding...

This paper proposes the video similarity measurement approach for sports video classification by dimensionality reduction with random projection (RP) and distance space. Most video data are huge files, which vary in terms of length and amount of data, resulting in time-consuming data processing; therefore, reducing the dimensionality of the data becomes a necessity. All frames of training video...

Clustering geographic data using traditional methods often result in clusters that look dispersed over the geographic space and poorly reflect any underlying spatial structure. We propose a polygon-based spatial clustering approach, which models a spatial object as a polygon with three groups of attributes: general attributes, boundary attributes, and spatial events. We have developed a general...

in this paper an approach based on evolutionary algorithms to find pareto optimal pair of state and control for multi-objective optimal control problems (moocp)'s is introduced‎. ‎in this approach‎, ‎first a discretized form of the time-control space is considered and then‎, ‎a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using ...