A self-organising mixture network for density modelling

A completely unsupervised mixture distribution network, namely the self-organising mixture network, is proposed for learning arbitrary density functions. The algorithm minimises the Kullback-Leibler information by means of stochastic approximation methods. The density functions are modelled as mixtures of parametric distributions such as Gaussian and Cauchy. The first layer of the network is similar to the Kohonen's self-organising map (SOM), but with the parameters of the class conditional densities as the learning weights. The winning mechanism is based on maximum posterior probability, and the updating of weights can be limited to a small neighbourhood around the winner. The second layer accumulates the responses of these local nodes, weighted by the learning mixing parameters. The network possesses simple structure and computation, yet yields fast and robust convergence. Experimental results are also presente

Successful prediction of horse racing results using a neural network

Most application work within neural computing continues to employ multi-layer perceptrons (MLP). Though many variations of the fully interconnected feed-forward MLP, and even more variations of the back propagation learning rule, exist; the first section of the paper attempts to highlight several properties of these standard networks. The second section outlines an application-namely the prediction of horse racing result

Multitraining support vector machine for image retrieval

Relevance feedback (RF) schemes based on support vector machines (SVMs) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based RF approaches is often poor when the number of labeled feedback samples is small. This is mainly due to 1) the SVM classifier being unstable for small-size training sets because its optimal hyper plane is too sensitive to the training examples; and 2) the kernel method being ineffective because the feature dimension is much greater than the size of the training samples. In this paper, we develop a new machine learning technique, multitraining SVM (MTSVM), which combines the merits of the cotraining technique and a random sampling method in the feature space. Based on the proposed MTSVM algorithm, the above two problems can be mitigated. Experiments are carried out on a large image set of some 20 000 images, and the preliminary results demonstrate that the developed method consistently improves the performance over conventional SVM-based RFs in terms of precision and standard deviation, which are used to evaluate the effectiveness and robustness of a RF algorithm, respectively

Ground penetrating radar migration with uncertain parameters

The focusing principle of Kirchoff migration is described. This allows the introduction of optical focusing techniques that can be used to focus migration. Three focus measures are described that are useful for optimising migration. Simple optimisation routines are implemented that model uncertainties in the migration parameters. The focus measures are then used as cast functions to be maximised. Results show that these measures are useful in optimising migration when there are uncertainties in the parameter

Image processing applications using a novel parallel computing machine based on reconfigurable logic

Zelig is a 32 physical node fine-grained computer employing field-programmable gate arrays. Its application to the high speed implementation of various image pre-processing operations (in particular binary morphology) is described together with typical speed-up result

The UN Global Compacts and the Common European Asylum System: Coherence or Friction?

This paper examines the “protective potential” of the Global Compacts on Refugees and Migrants visà-vis existing commitments to fundamental rights within the European Union (EU). The relationship between the two normative frameworks is scrutinised to establish the extent to which the two might be mutually supportive or contradictory, since this determines the Compacts’ capacity to inform the interpretation of EU fundamental rights within the Common European Asylum System (CEAS). This paper explores this protective potential through three of the Compacts’ key guiding principles: respect for human rights and the rule of law, the principle of non-regression, and the principle of non-discrimination. The Compacts’ commitments to the first two are presented as sites of coherence where the Compacts concretely express pre-existing protections within EU law and provide a blueprint for implementation in the migration sphere. Yet, the Compacts’ principle of non-discrimination reveals an area of friction with EU primary law. It is argued that the implementation of this principle can address the inherently discriminatory system underpinning EU law. Within the EU, rather than undermining international and national human rights obligations, the Compacts present an opportunity to refine the implementation of existing EU fundamental rights obligations applicable to migrants and refugees

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS: The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION: The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management

Proton radiography and tomography with application to proton therapy

Proton radiography and tomography have long promised benefit for proton therapy. Their first suggestion was in the early 1960s and the first published proton radiographs and CT images appeared in the late 1960s and 1970s, respectively. More than just providing anatomical images, proton transmission imaging provides the potential for the more accurate estimation of stopping-power ratio (SPR) inside a patient and hence improved treatment planning and verification. With the recent explosion in growth of clinical proton therapy facilities, the time is perhaps ripe for the imaging modality to come to the fore. Yet many technical challenges remain to be solved before proton CT scanners become commonplace in the clinic. Research and development in this field is currently more active that at any time with several prototype designs emerging. This review introduces the principles of proton radiography and tomography, its historical developments, the raft of modern prototype systems and the primary design issues

Observations on metal concentrations in commercial landings of two species of tilapia (Oreochromis mossambicus and Oreochromis niloticus) from reservoirs in six river basins in Sri Lanka

Samples of the muscle of two species of tilapia (Oreochromis mossambicus and O. niloticus; 17-20 cm length) were obtained from at least one reservoir in each of the six river basins (Aruvi Aru, Kala Oya, Kirindi Oya, Ma Oya, Mahaweli, and Walawe Ganga catchments) in Sri Lanka. The metals Ca, Cu, Fe, K, Mg, Mn, Na, and Zn were consistently detected in the muscle tissue. Overall, there were few differences in the concentration of metals between the two species of fish, although there were also some statistically significant differences (p &lt; 0.05) in the concentrations of some metals in fish obtained from some of the reservoirs. Aruvi Aru stands out as a river basin in which the two fish species have significantly lower concentration of metals when compared to other river basins. The concentration of the metals studied were below WHO and FSANZ guideline values for fish, suggesting that the consumption of the metals found in tilapia from these reservoirs poses little risk to human health. <br /