High level data fusion

We address the question of how to obtain effective fusion of identification information such that it is robust to the quality of this information. As well as technical issues data fusion is encumbered with a collection of (potentially confusing) practical considerations. These considerations are described during the early chapters in which a framework for data fusion is developed. Following this process of diversification it becomes clear that the original question is not well posed and requires more precise specification. We use the framework to focus on some of the technical issues relevant to the question being addressed. We show that fusion of hard decisions through use of an adaptive version of the maximum a posteriori decision rule yields acceptable performance. Better performance is possible using probability level fusion as long as the probabilities are accurate. Of particular interest is the prevalence of overconfidence and the effect it has on fused performance. The production of accurate probabilities from poor quality data forms the latter part of the thesis. Two approaches are taken. Firstly the probabilities may be moderated at source (either analytically or numerically). Secondly, the probabilities may be transformed at the fusion centre. In each case an improvement in fused performance is demonstrated. We therefore conclude that in order to obtain robust fusion care should be taken to model the probabilities accurately; either at the source or centrally.

Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

Practical methods of tracking of nonstationary time series applied to real-world data

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Two real world data sets, containing electricity load demands and foreign exchange market prices, are used to test several different methods, ranging from linear models with fixed parameters, to non-linear models which adapt both parameters and model order on-line. Training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. The results of our experiments show that there are advantages to be gained in tracking real world non-stationary data through the use of more complex adaptive models.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

Investigating microphone efficacy for facilitation of mobile speech-based data entry

Despite being nominated as a key potential interaction technique for supporting today's mobile technology user, the widespread commercialisation of speech-based input is currently being impeded by unacceptable recognition error rates. Developing effective speech-based solutions for use in mobile contexts, given the varying extent of background noise, is challenging. The research presented in this paper is part of an ongoing investigation into how best to incorporate speechbased input within mobile data collection applications. Specifically, this paper reports on a comparison of three different commercially available microphones in terms of their efficacy to facilitate mobile, speech-based data entry. We describe, in detail, our novel evaluation design as well as the results we obtained.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

Monitoring of transcriptional dynamics of HIF and NFκB activities

Genetic experiments over the last few decades have identified many regulatory proteins critical for DNA transcription. The dynamics of their transcriptional activities shape the differential expression of the genes they control. Here we describe a simple method, based on the secreted luciferase, to measure the activities of two transcription factors NF?B and HIF. This technique can effectively monitor dynamics of transcriptional events in a population of cells and be up-scaled for high-throughput screening and promoter analysis, making it ideal for data-demanding applications such as mathematical modelling.

Data driven graphical applications:a fluid approach

The inclusion of high-level scripting functionality in state-of-the-art rendering APIs indicates a movement toward data-driven methodologies for structuring next generation rendering pipelines. A similar theme can be seen in the use of composition languages to deploy component software using selection and configuration of collaborating component implementations. In this paper we introduce the Fluid framework, which places particular emphasis on the use of high-level data manipulations in order to develop component based software that is flexible, extensible, and expressive. We introduce a data-driven, object oriented programming methodology to component based software development, and demonstrate how a rendering system with a similar focus on abstract manipulations can be incorporated, in order to develop a visualization application for geospatial data. In particular we describe a novel SAS script integration layer that provides access to vertex and fragment programs, producing a very controllable, responsive rendering system. The proposed system is very similar to developments speculatively planned for DirectX 10, but uses open standards and has cross platform applicability. © The Eurographics Association 2007.

Applications of fractals to image data compression

Digital image processing is exploited in many diverse applications but the size of digital images places excessive demands on current storage and transmission technology. Image data compression is required to permit further use of digital image processing. Conventional image compression techniques based on statistical analysis have reached a saturation level so it is necessary to explore more radical methods. This thesis is concerned with novel methods, based on the use of fractals, for achieving significant compression of image data within reasonable processing time without introducing excessive distortion. Images are modelled as fractal data and this model is exploited directly by compression schemes. The validity of this is demonstrated by showing that the fractal complexity measure of fractal dimension is an excellent predictor of image compressibility. A method of fractal waveform coding is developed which has low computational demands and performs better than conventional waveform coding methods such as PCM and DPCM. Fractal techniques based on the use of space-filling curves are developed as a mechanism for hierarchical application of conventional techniques. Two particular applications are highlighted: the re-ordering of data during image scanning and the mapping of multi-dimensional data to one dimension. It is shown that there are many possible space-filling curves which may be used to scan images and that selection of an optimum curve leads to significantly improved data compression. The multi-dimensional mapping property of space-filling curves is used to speed up substantially the lookup process in vector quantisation. Iterated function systems are compared with vector quantisers and the computational complexity or iterated function system encoding is also reduced by using the efficient matching algcnithms identified for vector quantisers.

Combining data driven programming with component based software development:with applications in geovisualisation and dynamic data driven application systems

Software development methodologies are becoming increasingly abstract, progressing from low level assembly and implementation languages such as C and Ada, to component based approaches that can be used to assemble applications using technologies such as JavaBeans and the .NET framework. Meanwhile, model driven approaches emphasise the role of higher level models and notations, and embody a process of automatically deriving lower level representations and concrete software implementations. The relationship between data and software is also evolving. Modern data formats are becoming increasingly standardised, open and empowered in order to support a growing need to share data in both academia and industry. Many contemporary data formats, most notably those based on XML, are self-describing, able to specify valid data structure and content, and can also describe data manipulations and transformations. Furthermore, while applications of the past have made extensive use of data, the runtime behaviour of future applications may be driven by data, as demonstrated by the field of dynamic data driven application systems. The combination of empowered data formats and high level software development methodologies forms the basis of modern game development technologies, which drive software capabilities and runtime behaviour using empowered data formats describing game content. While low level libraries provide optimised runtime execution, content data is used to drive a wide variety of interactive and immersive experiences. This thesis describes the Fluid project, which combines component based software development and game development technologies in order to define novel component technologies for the description of data driven component based applications. The thesis makes explicit contributions to the fields of component based software development and visualisation of spatiotemporal scenes, and also describes potential implications for game development technologies. The thesis also proposes a number of developments in dynamic data driven application systems in order to further empower the role of data in this field.

An adaptive IEEE 802.15.4 data transmission scheme for smart grid applications

IEEE 802.15.4 networks (also known as ZigBee networks) has the features of low data rate and low power consumption. In this paper we propose an adaptive data transmission scheme which is based on CSMA/CA access control scheme, for applications which may have heavy traffic loads such as smart grids. In the proposed scheme, the personal area network (PAN) coordinator will adaptively broadcast a frame length threshold, which is used by the sensors to make decision whether a data frame should be transmitted directly to the target destinations, or follow a short data request frame. If the data frame is long and prone to collision, use of a short data request frame can efficiently reduce the costs of the potential collision on the energy and bandwidth. Simulation results demonstrate the effectiveness of the proposed scheme with largely improve bandwidth and power efficiency. © 2011 Springer-Verlag.

Solvability and asymptotics of the heat equation with mixed variable lateral conditions and applications in the opening of the exocytotic fusion pore in cells

We investigate a mixed problem with variable lateral conditions for the heat equation that arises in modelling exocytosis, i.e. the opening of a cell boundary in specific biological species for the release of certain molecules to the exterior of the cell. The Dirichlet condition is imposed on a surface patch of the boundary and this patch is occupying a larger part of the boundary as time increases modelling where the cell is opening (the fusion pore), and on the remaining part, a zero Neumann condition is imposed (no molecules can cross this boundary). Uniform concentration is assumed at the initial time. We introduce a weak formulation of this problem and show that there is a unique weak solution. Moreover, we give an asymptotic expansion for the behaviour of the solution near the opening point and for small values in time. We also give an integral equation for the numerical construction of the leading term in this expansion.

Scaling up question-answering to linked data

Linked Data semantic sources, in particular DBpedia, can be used to answer many user queries. PowerAqua is an open multi-ontology Question Answering (QA) system for the Semantic Web (SW). However, the emergence of Linked Data, characterized by its openness, heterogeneity and scale, introduces a new dimension to the Semantic Web scenario, in which exploiting the relevant information to extract answers for Natural Language (NL) user queries is a major challenge. In this paper we discuss the issues and lessons learned from our experience of integrating PowerAqua as a front-end for DBpedia and a subset of Linked Data sources. As such, we go one step beyond the state of the art on end-users interfaces for Linked Data by introducing mapping and fusion techniques needed to translate a user query by means of multiple sources. Our first informal experiments probe whether, in fact, it is feasible to obtain answers to user queries by composing information across semantic sources and Linked Data, even in its current form, where the strength of Linked Data is more a by-product of its size than its quality. We believe our experiences can be extrapolated to a variety of end-user applications that wish to scale, open up, exploit and re-use what possibly is the greatest wealth of data about everything in the history of Artificial Intelligence. © 2010 Springer-Verlag.