This paper investigates architectural and capacity issues associated with analogue and digital radio over fibre fronthaul for MIMO in 5G cellular systems. The capacity of both systems is evaluated in terms of a static system deployment and also with varying traffic load. The results show that in a leased line scenario, the analogue systems offer an opportunity to reduce the cost of fibre infrastructure by more than 93% due to the more efficient use of bandwidth. but since the ARoF equipment is currently more expensive than the DRoF equivalent, there is a trade-off between CAPEX and OPEX for a given deployment.

We present an extension of our previous work in [1] by investigating the use of Local Septenary Patterns (LSP) for breast density classification in mammograms. The LSP operator is a variant of Local Binary Patterns (LBP) inspired by Local Ternary Patterns (LTP) and Local Quinary patterns (LQP). The main extensions in our work are i) we investigate the use of a multi-resolution technique when extracting micro texture information, ii) we investigate different neighbourhood topologies as different ways of extracting texture features, and iii) we use an additional dataset called InBreast as well as the most popular dataset in the literature, which is the Mammographic Image Analysis Society (MIAS) to further evaluate the performance of the LSP operator.

The paper presents quantitative results of a preliminary study undertaken as part of Decision Support and Information Management System for Breast Cancer (DESIREE). DESIREE is a European-funded project to improve the management of primary breast cancer through image-based, guideline-based, experience-based, and case-based information systems. In this study we explore the use of ensemble deep learning for breast mass classification in mammograms. The proposed method is based on AlexNet with some modifications in order to adapt it to our classification problem. Subsequently, model selection is performed to select the best three results based on the highest validation accuracies during the validation phase. Finally, the prediction is based on the average probability of the models. Experimental evaluation shows that accuracy from individual models ranges between 75% and 77%, but combining the best models (ensemble networks) results in over 80% classification accuracy and aura under the curve.

Computer-aided diagnosis (CAD) has great potential in providing real benefits to doctors and patients. Recent studies have, however, found lack of trust in CAD by radiologists in clinical diagnostic decision making. One of the main reasons is the lack of an appropriate confidence measure. This paper presents the first-ever study of classification confidence in the context of breast mass classification. We evaluated 11 state-of-the-art classification algorithms on breast mass image data using their confidence of classification metric, in addition to other standard evaluation metrics including accuracy and area under the curve (ROC). Experimental results show that although most classifiers produced very similar results with less than 2% difference in terms of accuracy and ROC, their performances are significantly different in terms of confidence levels. We suggest that the confidence measure should be used in conjunction with the existing performance metrics such as accuracy and ROC.

Most Information Technology (IT) services nowadays rely on one or multiple data centers. In this paper, we propose a Quality of Service (QoS) scheme for Intra/Inter data center communications systems. The proposed scheme is based on differentiation of traffic into different class categories. The differentiation is based on the source of traffic as well as the specific traffic's requirements. We conduct a comprehensive performance study for the proposed scheme. Our results demonstrate the benefits of deploying a QoS scheme in data centers especially if there is a considerable number of live virtual machine migrations among its servers. By completing such migration quickly, the Quality of Experience (QoE) will not degrade and the workload will be kept balanced among all the servers in the Data Center. Additionally, our proposed scheme considers the live virtual machine migrations among different Data Centers which is currently rapidly increasing.

Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in computer aided diagnosis (CAD) systems. Estimating the breast and pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast boundary and pectoral muscle segmentation method in mammograms is proposed. For breast boundary estimation, we determine the initial breast boundary via thresholding and employ Active Contour Models without edges to search for the actual boundary. A post-processing technique is proposed to correct the overestimated boundary caused by artifacts. The pectoral muscle boundary is estimated using Canny edge detection and a pre-processing technique is proposed to remove noisy edges. Subsequently, we identify five edge features to find the edge that has the highest probability of being the initial pectoral contour and search for the actual boundary via contour growing. The segmentation results for the proposed method are compared with manual segmentations using 322, 208 and 100mammograms from the Mammographic Image Analysis Society (MIAS), INBreast and Breast Cancer Digital Repository (BCDR) databases, respectively. Experimental results show that the breast boundary and pectoral muscle estimation methods achieved dice similarity coefficients of 98.8% and 97.8% (MIAS), 98.9% and 89.6% (INBreast) and 99.2% and 91.9% (BCDR), respectively.

Early and intensive physical therapy can improve upper arm and hand functionality for stroke survivors. Virtual reality systems that utilize state-of-art natural user interface tracking sensors and adaptive user profiling software has the potential to supplement traditional physiotherapy and engage patients to sustain beneficial quantity and quality of rehabilitation. In this paper we provide an overview of the problem area and present results from an initial experiment with healthy users. The potential for Fitts's law to model user motion effectively in reach and touch tasks within 3D virtual environments is investigated. Results indicate that Fitts's law may be effective though we propose that it would best be used as part of a more complex model of user motion.

We focus on feature extraction and selection to best represent texture and shape properties of plant diseases in an image-based leaf monitoring system implemented in a mobile-cloud environment. A number of textural and region-based features are aggregated from previous studies; also we introduce mean and peak indices of histogram-of-shape as disease property representations along with the proposed and enhanced shape features based on diseased regions. A total of 260 colour-based attributes and 163 shape attributes are searched to find the best potential features based on different aspects: probability of feature error, correlation, targeted-class relevancy and the separability quality of a feature. Experimental results show that the best selected feature set which combines colour-based and shape features yields high classification accuracy on wheat disease images captured by a smartphone camera and also provides insights into potential sets of features to be further implemented as a lightweight standalone mobile application.

In today's world of internet connected devices and smart phones, it has become effortless to create and consume vast amounts of information. This is particularly the case with photographs, with vast amounts being created and shared online every day. Never-the-less, it still remains a challenge to discover the "right" information for the appropriate purpose. This paper describes and discusses the testing and evaluation of the MediaPlace system with the well-known dataset YFCC-100M, which contains 48 million geospatial geotagged photographs, from Flickr produced by Yahoo. MediaPlace is a system which we have developed to automatically enrich geotagged photographs with semantic geospatial information derived from several online geospatial datasets.

Given the growing complexity and energy consumption of customer premises equipment (CPE) in smart home networks and the consequent need for optimal energy usage, there is a need for better coordination of the power operational states of the CPE devices. In particular, this paper presents an energy model to synchronise the behaviour of a Home-Gateway (HG) with the CPE such as a set-top box with exemplar data flow such as IPTV. In addition, we explore the policy engine to monitor and control the energy consumption as a function of application QoS, traffic load, availability and link health across the networks, paying particular attention to the CPE device transitional power states. Our approach is, therefore, better suited for providing guaranteed quality and at the same time is more energy efficient.

As fixed and wireless access networks converge towards Internet Protocol based transport in next generation networks, the requirement for effective and scalable control and management solutions to address the complexity introduced by their heterogeneity becomes ever more critical. Over the years, policy-based management has emerged as a viable tool to address this challenge. Within the IU-ATC project, we are developing a policy-based framework, (Converged Networks QoS Framework, CNQF) for end-to-end QoS control and resource management in converged next generation networks. CNQF is designed to support QoS control and context-driven resource management policies across all networks (access, metro, core) on the end-to-end transport layer of converged networks. This paper describes CNQF and develops an exemplary use case scenario for policy-based resource management based on our CNQF architecture. The paper also presents the development of stochastic-analytic models to characterise and evaluate the impact of policies on operational performance. The model is used to analyse the performance of dynamic CNQF context-driven policies for admission control case study.

This paper presents a framework for context-driven policy-based QoS control and end-to-end resource management in converged next generation networks. The Converged Networks QoS Framework (CNQF) is being developed within the IU-ATC project, and comprises distributed functional entities whose instances co-ordinate the converged network infrastructure to facilitate scalable and efficient end-to-end QoS management. The CNQF design leverages aspects of TISPAN, IETF and 3GPP policy-based management architectures whilst also introducing important innovative extensions to support context-aware QoS control in converged networks. The framework architecture is presented and its functionalities and operation in specific application scenarios are described.

The identification and classification of landcover types from remotely sensed data is traditionally based on the assumption that pixels with similar spatial distribution patterns belong to the same spectral class. However, spectral data on its own has proven to be insufficient for classification. In addition, it is difficult to obtain enough accurate labelled samples from such data. Contextual data can be incorporated or fused' with spectral data to improve the estimation of class labels and therefore enhance the accuracy of the classification process as a whole when labelled data is not available. In this paper we use Dempster-Shafer theory of evidence to fuse the output of an unsupervised model-based clustering (MBC) technique and contextual data in the form of a digital elevation model. The final classification accuracy is shown to improve when using this approach.

Malignant melanoma is a skin tumour typified by high mortality rates when not diagnosed and excised in its earliest stages. Preoperative diagnostic accuracy may be improved through the development of computerised systems which accurately quantify features indicative of this cancer. One such feature is boundary contour asymmetry, which is typically measured across a skin lesion's major and minor axes of symmetry. In this paper techniques for detection of skin lesion asymmetry are discussed, and the viability of integrating Fourier descriptors into a shape asymmetry quantifier is investigated. It is concluded that Fourier descriptors facilitate accurate isolation and ranking of a lesion's symmetry axes and provide an approach which could easily be integrated into new or existing diagnostic procedures.

Accurate segmentation of the optic nerve head (or optic disk) is very useful during the analysis and assessment of glaucoma in the eye. The Heidelberg retinal tomograph (HRT) can acquire high quality images of the optic disk and also allows three dimensional topographic measurements to be made. However, there are significant problems in optic disk segmentation due to having to deal with issues such as distractors along blood vessel edges; the extent of pallor in the optic disk or the very variable appearance of the optic nerve head itself. We propose a multi-scale region and boundary hybrid snake method to extract the optic disk. This model takes account of the vessel edge gradient direction and tries to avoid its force influence when evolving the snake. Experimental results are assessed using an overlap ratio.

Image analysis is used to estimate fluid velocity everywhere in a complex, two-dimensional, physical model of fractured porous rock. Cross-correlation provides an indication of flow direction and particle streak analysis is used in the calculation of flow velocities. Preliminary results are consistent with the predictions of lattice Boltzmann computer simulations.

This paper describes a domain specific programming model for execution on parallel and distributed architectures. The model has initially been targeted at the application area of image processing, though the techniques developed may be more generally applicable to other domains where an algebraic or library-based approach is common. Efficiency is achieved by the concept of a self-optimising class library of primitive image processing operations, which allows programs to be written in a high level, algebraic notation and which is automatically parallelised (using an application-specific data parallel approach). The class library is extended automatically with optimised operations, generated by a transformation system, giving improved execution performance. The parallel implementation of the model described here is based on MPI and has been tested on a C40 processor network, a quad-processor Unix workstation, and a network of PCs running Linux. Timings are included to indicate the impact of the automatic optimisation facility (rather than the effect of parallelisation). Copyright © 1999 John Wiley & Sons, Ltd.

This paper describes the use of Prolog as an implementation language for a compiler for a parallel image processing language. The target machine for which code is generated is an abstract model for parallel image processing. The structure of the final compiler closely reflects the BNF syntax definition of the image processing language thus allowing ease of implementation and maintainability to be achieved with relatively little effort.