Some new techniques for pattern recognition research and lung sound signal analysis

This thesis describes the results of a collaborative research programme between the Department of Electronics & Electrical Engineering, University of Glasgow, and the Centre for Respiratory Investigation, Glasgow Royal Infirmary. The research was initially aimed at studying lung sound using signal processing and pattern recognition techniques. The use of pattern recogntion techniques was largely confined to exploratory data analysis which led to an interest in the methods themselves. A study was carried out to apply recent research in computational geometry to clustering Two geometric structures, the Gabriel graph and the relative neighbourhood graph, are both defined by a region of influence. A generalization of these graphs is used to find the conditions under which graphs defined by a region of influence are connected and planar. The Gabriel graph may be considered to be just planar and the relative neighbourhood graph to be just connected. From this two variable regions of influence were defined that were aimed at producing disconnected graphs and hence a partitioning of the data set, A hierarchic clustering based on relative distance may be generated by varying the size of the region of influence. The value of the clustering method is examined in terms of admissibility criteria and by a case study. An interactive display to complement the graph theoretical clustering was also developed. This display allows a partition in the clustering to be examined. The relationship between clusters in the partition may be studied by using the partition to define a contracted graph which is then displayed. Subgraphs of the original graph may be used to provide displays of individual clusterso This display should provide additional information about a partition and hence allow the user to understand the data better. The remainder of the work in this thesis concerns the application of pattern recogntition techniques to the analysis of lung sound signals. Breath sound was analysed using frequency domain methods since it is basically a continuous signal. Initially, a rather ad hoc method was used for feature extraction which was based on a piecewise constant approximation to the amplitude spectrum. While this method provided a useful set of features, it is clear that more systematic methods are required. These methods were used to study lung sound in four groups of patients: (1) normal patients, (2) patients with asbestosis, (3) patients with cryptogenic fibrosing alveolitis (CFA) and (4) patients with interstitial pulmonary oedema. The data sets were analysed using principal components analysis and the new graph theroretical clustering method (this data was used as a case study for the clustering method). Three groups of patients could be identified from the data;- (a) normal subjects, (b) patients with fibrosis of the lungs (asbestosis & CFA) and (c) patients with pulmonary oedema. These results suggest that lung sound may be able to make a useful contribution to non-invasive diagnosis. However more extensive studies are required before the real value of lung sound in diagnosis is established

Supply Chain Optimization and Economic Analysis of Using Industrial Spent Microbial Biomass (SMB) in Agriculture

This thesis uses a mixed integer program to minimize the transport and storage cost of delivering spent microbial biomass (SMB), a bio-coproduct resulting from the production of 1,3-propanediol, to farm fields as a soil amendment and fertilizer substitute. The case study examines focuses on a bioprocessing facility and corn production in East Tennessee. The results indicate on-farm storage of SMB minimizes transport and storage costs of the material. A one percent decrease in the moisture content of SMB results in less than five percent decrease in the total transport and storage costs. Future research should investigate farmers\u27 willingness to adopt the practice, cost sharing design, and to apply SMB to other crop production

Clustering in multivariate data: visualization, case and variable reduction

Cluster analysis is a very common problem for multivariate data. It is receiving intense attention due to the current boom in data warehousing and mining driven by the growth in information technology today. Technology is allowing us to collect massive data sets, both in cases and variables, and develop sophisticated interactive and dynamic graphics. There are three current issues for cluster analysis: visualizing cluster structure, reducing the number of cases, and reducing the number of variables in very large data sets. This thesis addresses each of these issues;The lower-dimensional projection of data found by projection pursuit which preserves the cluster structure helps clustering by eliminating the influence of nuisance variables. Initially partitioning data into a set of small classifications improves the efficiency of hierarchical agglomerative clustering by saving the time and memory for the beginning stage of clustering. Minimal spanning tree is used for this partitioning method

Small scale software engineering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In computing, the Software Crisis has arisen because software projects cannot meet their planned timescales, functional capabilities, reliability levels and budgets. This thesis reduces the general problem down to the Small Scale Software Engineering goal of improving the quality and tractability of the designs of individual programs. It is demonstrated that the application of eight abstractions (set, sequence, hierarchy, h-reduction, integration, induction, enumeration, generation) can lead to a reduction in the size and complexity of and an increase in the quality of software designs when expressed via Dimensional Design, a new representational technique which uses the three spatial dimensions to represent set, sequence and hierarchy, whilst special symbols and axioms encode the other abstractions. Dimensional Designs are trees of symbols whose edges perceptually encode the relationships between the nodal symbols. They are easy to draw and manipulate both manually and mechanically. Details are given of real software projects already undertaken using Dimensional Design. Its tool kit, DD/ROOTS, produces high quality, machine drawn, detailed design documentation plus novel quality control information. A run time monitor records and animates execution, measures CPU time and takes snapshots etc; all these results are represented according to Dimensional Design principles to maintain conceptual integrity with the design. These techniques are illustrated by the development of a non-trivial example program. Dimensional Design is axiomatised, compared to existing techniques and evaluated against the stated problem. It has advantages over existing techniques, mainly its clarity of expression and ease of manipulation of individual abstractions due to its graphical basis

APPLICATION OF IMAGE ANALYSIS TECHNIQUES TO SATELLITE CLOUD MOTION TRACKING

Cloud motion wind (CMW) determination requires tracking of individual cloud targets. This is achieved by first clustering and then tracking each cloud cluster. Ideally, different cloud clusters correspond to diiferent pressure levels. Two new clustering techniques have been developed for the identification of cloud types in multi-spectral satellite imagery. The first technique is the Global-Local clustering algorithm. It is a cascade of a histogram clustering algorithm and a dynamic clustering algorithm. The histogram clustering algorithm divides the multi-spectral histogram into'non-overlapped regions, and these regions are used to initialise the dynamic clustering algorithm. The dynamic clustering algorithm assumes clusters have a Gaussian distributed probability density function with diiferent population size and variance. The second technique uses graph theory to exploit the spatial information which is often ignored in per-pixel clustering. The algorithm is in two stages: spatial clustering and spectral clustering. The first stage extracts homogeneous objects in the image using a family of algorithms based on stepwise optimization. This family of algorithms can be further divided into two approaches: Top-down and Bottom-up. The second stage groups similar segments into clusters using a statistical hypothesis test on their similarities. The clusters generated are less noisy along class boundaries and are in hierarchical order. A criterion based on mutual information is derived to monitor the spatial clustering process and to suggest an optimal number of segments. An automated cloud motion tracking program has been developed. Three images (each separated by 30 minutes) are used to track cloud motion and the middle image is clustered using Global-Local clustering prior to tracking. Compared with traditional methods based on raw images, it is found that separation of cloud types before cloud tracking can reduce the ambiguity due to multi-layers of cloud moving at different speeds and direction. Three matching techniques are used and their reliability compared. Target sizes ranging from 4 x 4 to 32 x 32 are tested and their errors compared. The optimum target size for first generation METEOSAT images has also been found.Meteorological Office, Bracknel