Wireless data encoding and decoding using OAM modes

A means of encoding and decoding data using wireless orbital angular momentum (OAM) modes is proposed and analysed. Source data symbols are used to select an OAM mode, which is generated using an 8-element circular array. A 2-element array is used to detect the mode by estimating the phase gradient of the received signal, and hence identifying the transmitted data symbol. The results are presented in terms of mode estimation error.

Address inaccuracy in health registration data: determinants and impacts

Access to demographic data that are complete, accurate and up-to-date is fundamental to many aspects of public health, government and academic work and for accurate interpretation of other databases. Health registration data are the prime source of demographic information for health and social care systems; for example, as an indicator of need, as a source of denominators to convert number of events into rates, or in the case of the residential address information as the basis for generating the call-recall invitation letters that are used for most screening programs (e.g. breast, colo-rectal and AAA screening). However, list inflation (ghosts, duplicates or emigrants) and a degree of address inaccuracy are recognised caveats with the health registration data and a recent NILS-related study on breast screening suggests that improved address accuracy might be a fast and efficient means of increasing screening uptake rates in cities and amongst deprived populations. In NI these data are collated by the BSO who uniquely in the UK also have access to data relating to prescribing, dental registrations and use of A&E services. These can be used to supplement the standard demographic and address information by (i) indicating patients who are alive and resident in NI and (ii) providing an independent source of probably improved address information. This study will use the NI Unique Property Reference Number (UPRN), rather than the addresses per se which are difficult to work with, to compare the addresses registered in the BSO with those addresses in the enumerated 2011 census. Assuming that the census is a more accurate source of address information for individuals, a comparison of the health registration addresses with those recorded at the census, the aim of the proposed study will be to (i) characterise the amount and distributions of these differences, (ii) to see what proportion of those who do not attend for screening did not actually receive an invitation letter because the addresses were incorrect, (iii) to determine how much of the social gradient (and urban/rural differences) in screening uptake are due to address inaccuracies, (iv) a comparison of timing of address changes at the BSO will provide information on the delays in updating of addresses.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.

Phototextual Journeys: Nicolas Bouvier in Asia

Travel literature's inherent intergenericity extends into the realm of the interaesthetic in Nicolas Bouvier's textual and photographic representations of Asia. Although produced as distinct narratives, successive editorial decisions and the layering of these two media in the mind of the reader have transformed Bouvier's already palimpsestic texts into fluid, phototextual constructs. This article will offer ‘contrapuntal’ readings of a selection of Bouvier's texts in relation to the photographs charting his intercultural encounters in China and Japan. Countering the relegation of these photographs to the conventional status of aide-mémoire, the article will consider the shifting relationships of complementarity, tension, or disjuncture between image and text. These relationships are characterised by slippage, subversion and paradox. Text does not ‘load’ image, and images do not illustrate text. Indeed, Bouvier's photographs frequently contest, modify, or debunk the textual narratives. Ultimately, the article will argue that Bouvier's representations of Asia, both textual and visual, offer a challenge to cultural essentialism, to self-other binaries, and to monolithic discourses of otherness.

Image, Video and 3D Data Registration: Medical, Satellite and Video Processing Applications with Quality Metrics

Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods.

This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state-of-the-art registration methodologies used in a variety of targeted applications.

Key features:
- Provides a state-of-the-art review of image and video registration techniques, allowing readers to develop an understanding of how well the techniques perform by using specific quality assessment criteria
- Addresses a range of applications from familiar image and video processing domains to satellite and medical imaging among others, enabling readers to discover novel methodologies with utility in their own research
- Discusses quality evaluation metrics for each application domain with an interdisciplinary approach from different research perspectives

Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.

Data mining approach identifies research priorities and data requirements for resolving the red algal tree of life

Background. The assembly of the tree of life has seen significant progress in recent years but algae and protists have been largely overlooked in this effort. Many groups of algae and protists have ancient roots and it is unclear how much data will be required to resolve their phylogenetic relationships for incorporation in the tree of life. The red algae, a group of primary photosynthetic eukaryotes of more than a billion years old, provide the earliest fossil evidence for eukaryotic multicellularity and sexual reproduction. Despite this evolutionary significance, their phylogenetic relationships are understudied. This study aims to infer a comprehensive red algal tree of life at the family level from a supermatrix containing data mined from GenBank. We aim to locate remaining regions of low support in the topology, evaluate their causes and estimate the amount of data required to resolve them. Results. Phylogenetic analysis of a supermatrix of 14 loci and 98 red algal families yielded the most complete red algal tree of life to date. Visualization of statistical support showed the presence of five poorly supported regions. Causes for low support were identified with statistics about the age of the region, data availability and node density, showing that poor support has different origins in different parts of the tree. Parametric simulation experiments yielded optimistic estimates of how much data will be needed to resolve the poorly supported regions (ca. 103 to ca. 104 nucleotides for the different regions). Nonparametric simulations gave a markedly more pessimistic image, some regions requiring more than 2.8 105 nucleotides or not achieving the desired level of support at all. The discrepancies between parametric and nonparametric simulations are discussed in light of our dataset and known attributes of both approaches. Conclusions. Our study takes the red algae one step closer to meaningful inclusion in the tree of life. In addition to the recovery of stable relationships, the recognition of five regions in need of further study is a significant outcome of this work. Based on our analyses of current availability and future requirements of data, we make clear recommendations for forthcoming research.

Robust bimodal person identification using face and speech with limited training data and corruption of both modalities

This paper presents a novel method of audio-visual fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new representation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited training data as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal data set created from the SPIDRE and AR databases with variable noise corruption of speech and occlusion in the face images. The new method has demonstrated improved recognition accuracy.

Streaming Elements for FPGA Signal and Image Processing Accelerators

Field programmable gate array devices boast abundant resources with which custom accelerator components for signal, image and data processing may be realised; however, realising high performance, low cost accelerators currently demands manual register transfer level design. Software-programmable ’soft’ processors have been proposed as a way to reduce this design burden but they are unable to support performance and cost comparable to custom circuits. This paper proposes a new soft processing approach for FPGA which promises to overcome this barrier. A high performance, fine-grained streaming processor, known as a Streaming Accelerator Element, is proposed which realises accelerators as large scale custom multicore networks. By adopting a streaming execution approach with advanced program control and memory addressing capabilities, typical program inefficiencies can be almost completely eliminated to enable performance and cost which are unprecedented amongst software-programmable solutions. When used to realise accelerators for fast fourier transform, motion estimation, matrix multiplication and sobel edge detection it is shown how the proposed architecture enables real-time performance and with performance and cost comparable with hand-crafted custom circuit accelerators and up to two orders of magnitude beyond existing soft processors.