Andrew D. Hellicar

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We present a method for estimating the point spread function of a terahertz imaging system designed to operate in reflection mode. The method is based on imaging phantoms with known geometry, which have patterns with sharp edges at all orientations. The point spread functions are obtained by a deconvolution technique in the Fourier domain. We validate our(More)
The combination of low density SNP arrays and DNA pooling is a fast and cost effective approach to genotyping that opens up basic genomics to a range of new applications and studies. However we have identified significant limitations in the existing approach to calculating allele frequencies with DNA pooling. These limitations include a reduced ability to(More)
Recurrent Neural Networks (RNNs) have been successfully used in many applications. However, the problem of learning long-term dependencies in sequences using these networks is still a major challenge. Recent methods have been suggested to solve this problem by constraining the transition matrix to be unitary during training, which ensures that its norm is(More)
A cross-correlating 186-GHz passive millimeter-wave imager has been built. The key components in the signal processing hardware are two 186-GHz receivers and a broadband complex correlator. To evaluate the performance of this imager, its point-spread function, beam pattern, baseline vector, and their variations with the scanning direction have been(More)
New sensor streams are being generated at a rapidly increasing rate. The sources of these streams are a diverse set of networked sensors, diverse both in sensing hardware and sensing modality. Machine learning algorithms are ideally placed to develop generalized methods for stream analysis. One exemplar problem is the detection and analysis of periodic(More)
A distributed finite element method (FEM) solver using Sun’s JavaSpaces technology was implemented and tested on a heterogeneous mix of computers including laptops, desktops and SMPs running MS Windows, Linux and Unix operating systems. Test problems in 2 and 3 dimensions were solved using a distributed iterative conjugate gradient method.
An experiment is introduced which demonstrates the application of supervised feature learning using a Convolutional Neural Network for cattle behaviour classification. The data set used contains observations from sensors attached to the cattle. Previously this problem was addressed by classifying features learned by a stacked autoencoder. Here we explore an(More)