Non-stationary variogram models for geostatistical sampling optimisation: An empirical investigation using elevation data

A problem with use of the geostatistical Kriging error for optimal sampling design is that the design does not adapt locally to the character of spatial variation. This is because a stationary variogram or covariance function is a parameter of the geostatistical model. The objective of this paper was to investigate the utility of non-stationary geostatistics for optimal sampling design. First, a contour data set of Wiltshire was split into 25 equal sub-regions and a local variogram was predicted for each. These variograms were fitted with models and the coefficients used in Kriging to select optimal sample spacings for each sub-region. Large differences existed between the designs for the whole region (based on the global variogram) and for the sub-regions (based on the local variograms). Second, a segmentation approach was used to divide a digital terrain model into separate segments. Segment-based variograms were predicted and fitted with models. Optimal sample spacings were then determined for the whole region and for the sub-regions. It was demonstrated that the global design was inadequate, grossly over-sampling some segments while under-sampling others.

Towards hierarchical management of autonomic components: a case study

We address the issue of autonomic management in hierarchical component-based distributed systems. The long term aim is to provide a modelling framework for autonomic management in which QoS goals can be defined, plans for system adaptation described and proofs of achievement of goals by (sequences of) adaptations furnished. Here we present an early step on this path. We restrict our focus to skeleton-based systems in order to exploit their well-defined structure. The autonomic cycle is described using the Orc system orchestration language while the plans are presented as structural modifications together with associated costs and benefits. A case study is presented to illustrate the interaction of managers to maintain QoS goals for throughput under varying conditions of resource availability.

Hierarchical synthesis of complex DSP functions on FPGAs

SoC systems are now being increasingly constructed using a hierarchy of subsystems or silicon Intellectual Property (IP) cores. The key challenge is to use these cores in a highly efficient manner which can be difficult as the internal core structure may not be known. A design methodology based on synthesizing hierarchical circuit descriptions is presented. The paper employs the MARS synthesis scheduling algorithm within the existing IRIS synthesis flow and details how it can be enhanced to allow for design exploration of IP cores. It is shown that by accessing parameterised expressions for the datapath latencies in the cores, highly efficient FPGA solutions can be achieved. Hardware sharing at both the hierarchical and flattened levels is explored for a normalized lattice filter and results are presented.

Hierarchical Video Summarization in Reference Subspace

In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using K-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently.

Systematic random sampling of the comet assay

The comet assay is a technique used to quantify DNA damage and repair at a cellular level. In the assay, cells are embedded in agarose and the cellular content is stripped away leaving only the DNA trapped in an agarose cavity which can then be electrophoresed. The damaged DNA can enter the agarose and migrate while the undamaged DNA cannot and is retained. DNA damage is measured as the proportion of the migratory ‘tail’ DNA compared to the total DNA in the cell. The fundamental basis of these arbitrary values is obtained in the comet acquisition phase using fluorescence microscopy with a stoichiometric stain in tandem with image analysis software. Current methods deployed in such an acquisition are expected to be both objectively and randomly obtained. In this paper we examine the ‘randomness’ of the acquisition phase and suggest an alternative method that offers both objective and unbiased comet selection. In order to achieve this, we have adopted a survey sampling approach widely used in stereology, which offers a method of systematic random sampling (SRS). This is desirable as it offers an impartial and reproducible method of comet analysis that can be used both manually or automated. By making use of an unbiased sampling frame and using microscope verniers, we are able to increase the precision of estimates of DNA damage. Results obtained from a multiple-user pooled variation experiment showed that the SRS technique attained a lower variability than that of the traditional approach. The analysis of a single user with repetition experiment showed greater individual variances while not being detrimental to overall averages. This would suggest that the SRS method offers a better reflection of DNA damage for a given slide and also offers better user reproducibility.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.