An integrated multi-sensor data fusion algorithm and autopilot iImplementation in an uninhabited surface craft

Unmanned surface vehicles (USVs) are able to accomplish difficult and challenging tasks both in civilian and defence sectors without endangering human lives. Their ability to work round the clock makes them well-suited for matters that demand immediate attention. These issues include but not limited to mines countermeasures, measuring the extent of an oil spill and locating the source of a chemical discharge. A number of USV programmes have emerged in the last decade for a variety of aforementioned purposes. Springer USV is one such research project highlighted in this paper. The intention herein is to report results emanating from data acquired from experiments on the Springer vessel whilst testing its advanced navigation, guidance and control (NGC) subsystems. The algorithms developed for these systems are based on soft-computing methodologies. A novel form of data fusion navigation algorithm has been developed and integrated with a modified optimal controller. Experimental results are presented and analysed for various scenarios including single and multiple waypoints tracking and fixed and time-varying reference bearings. It is demonstrated that the proposed NGC system provides promising results despite the presence of modelling uncertainty and external disturbances.

Turning the categories inside-out: Complex identifications and multiple interactions in religious ethnography

This paper challenges the fixed boundaries that ethnographers have often constructed between religious insiders and outsiders. Drawing on Neitz's observations, it argues that the main task of reflexive fieldwork is locating the self in relation to ambiguous and shifting boundaries. We offer a comparative analysis of the experiences of two differently socially located researchers to illustrate how religious identity emerges as a continuum, on which one's place is negotiated with one's research participants. We also examine the importance of intersecting multiple identities. Finally, the paper questions whether social identity categories are the primary way that we relate with our respondents. It explores the spiritual and emotional dimensions of research relationships and argues that these may transform, reinforce and generally interact with social identities. Comparing our experiences, we outline the consequences of these reflections for data gathering and analysis.

Managing Cultural Diversity in Healthcare Partnerships: The Case of LIFT

Purpose: The National Health Service (NHS) Local Improvement Finance Trust (LIFT) programme was launched in 2001 as an innovative public-private partnership to address the historical under-investment in local primary care facilities in England. The organisations from the public and private sector that comprise a local LIFT partnership each have their own distinctive norms of behaviour and acceptable working practices - ultimately different organisational cultures. The purpose of this article is to assess the role of organisational culture in facilitating (or impeding) LIFT partnerships and to contribute to an understanding of how cultural diversity in public-private partnerships is managed at the local level. Design/methodology/approach: The approach taken was qualitative case studies, with data gathering comprising interviews and a review of background documentation in three LIFT companies purposefully sampled to represent a range of background factors. Elite interviews were also conducted with senior policy makers responsible for implementing LIFT policy at the national level. Findings: Interpreting the data against a conceptual framework designed to assess approaches to managing strategic alliances, the authors identified a number of key differences in the values, working practices and cultures in public and private organisations that influenced the quality of joint working. On the whole, however, partners in the three LIFT companies appeared to be working well together, with neither side dominating the development of strategy. Differences in culture were being managed and accommodated as partnerships matured. Research limitations/implications: As LIFT develops and becomes the primary source of investment for managing, developing and channelling funding into regenerating the primary care infrastructure, further longitudinal work might examine how ongoing partnerships are working, and how changes in the cultures of public and private partners impact upon wider relationships within local health economies and shape the delivery of patient care. Originality/value: To the authors' knowledge this is the first study of the role of culture in mediating LIFT partnerships and the findings add to the evidence on public-private partnerships in the NHS

Encouraging lifestyle behaviour change in mild cognitive impairment patients:development of appropriate educational material

Objectives: A healthy lifestyle may help maintain cognitive function and reduce the risk of developing dementia. This study employed a focus group approach in order to gain insight into opinions of mild cognitive impairment (MCI) patients, caregivers (CG) and health professionals (HP) regarding lifestyle and its relationship with cognition. The qualitative data were used to design, develop and pilot test educational material (EM) to help encourage lifestyle behaviour change. Method: Data gathering phase: structured interviews were conducted with HP (n = 10), and focus groups with MCI patients (n = 24) and CG (n = 12). EM was developed and pilot tested with a new group of MCI patients (n = 21) and CG (n = 6). Results: HP alluded to the lack of clinical trial evidence for a lifestyle and MCI risk link. Although they felt that lifestyle modifications should be recommended to MCI patients, they appeared hesitant in communicating this information and discussions were often patient-driven. MCI patients lacked awareness of the lifestyle cognition link. Participants preferred EM to be concise, eye-catching and in written format, with personal delivery of information favoured. Most pilot testers approved of the EM but were heterogeneous in terms of lifestyle, willingness to change and support needed to change. Conclusion: MCI patients need to be made more aware of the importance of lifestyle for cognition. EM such as those developed here, which are specifically tailored for this population would be valuable for HP who, currently, appear reticent in initiating lifestyle-related discussions. Following further evaluation, the EM could be used in health promotion activities targeting MCI patients.

Content and discontent: a qualitative exploration of obstacles to elearning engagement in medical students

BACKGROUND: Elearning is ubiquitous in healthcare professions education. Its equivalence to 'traditional' educational delivery methods is well established. There is a research imperative to clarify when and how to use elearning most effectively to mitigate the potential of it becoming merely a 'disruptive technology.' Research has begun to broadly identify challenges encountered by elearning users. In this study, we explore in depth the perceived obstacles to elearning engagement amongst medical students. Sensitising concepts of achievement emotions and the cognitive demands of multi-tasking highlight why students' deeply emotional responses to elearning may be so important in their learning.

METHODS: This study used focus groups as a data collection tool. A purposeful sample of 31 participated. Iterative data gathering and analysis phases employed a constant comparative approach to generate themes firmly grounded in participant experience.

RESULTS: Key themes that emerged from the data included a sense of injustice, passivity and a feeling of being 'lost at sea'. The actual content of the elearning resource provided important context.

CONCLUSIONS: The identified themes have strong emotional foundations. These responses, interpreted through the lens of achievement emotions, have not previously been described. Appreciation of their importance is of benefit to educators involved in curriculum development or delivery.

Cost function adaptation: a stochastic gradient algorithm for data echo cancellation

A family of stochastic gradient algorithms and their behaviour in the data echo cancellation work platform are presented. The cost function adaptation algorithms use an error exponent update strategy based on an absolute error mapping, which is updated at every iteration. The quadratic and nonquadratic cost functions are special cases of the new family. Several possible realisations are introduced using these approaches. The noisy error problem is discussed and the digital recursive filter estimator is proposed. The simulation outcomes confirm the effectiveness of the proposed family of algorithms.

A novel forward gene selection algorithm for microarray data

This paper investigates the gene selection problem for microarray data with small samples and variant correlation. Most existing algorithms usually require expensive computational effort, especially under thousands of gene conditions. The main objective of this paper is to effectively select the most informative genes from microarray data, while making the computational expenses affordable. This is achieved by proposing a novel forward gene selection algorithm (FGSA). To overcome the small samples' problem, the augmented data technique is firstly employed to produce an augmented data set. Taking inspiration from other gene selection methods, the L2-norm penalty is then introduced into the recently proposed fast regression algorithm to achieve the group selection ability. Finally, by defining a proper regression context, the proposed method can be fast implemented in the software, which significantly reduces computational burden. Both computational complexity analysis and simulation results confirm the effectiveness of the proposed algorithm in comparison with other approaches

On Variable Selection of a Nonlinear Non-parametric System with a Limited Data Set: A Stepwise Algorithm

his paper considers a problem of identification for a high dimensional nonlinear non-parametric system when only a limited data set is available. The algorithms are proposed for this purpose which exploit the relationship between the input variables and the output and further the inter-dependence of input variables so that the importance of the input variables can be established. A key to these algorithms is the non-parametric two stage input selection algorithm.

A fast hybrid algorithm for exoplanetary transit searches

We present a fast and efficient hybrid algorithm for selecting exoplanetary candidates from wide-field transit surveys. Our method is based on the widely used SysRem and Box Least-Squares (BLS) algorithms. Patterns of systematic error that are common to all stars on the frame are mapped and eliminated using the SysRem algorithm. The remaining systematic errors caused by spatially localized flat-fielding and other errors are quantified using a boxcar-smoothing method. We show that the dimensions of the search-parameter space can be reduced greatly by carrying out an initial BLS search on a coarse grid of reduced dimensions, followed by Newton-Raphson refinement of the transit parameters in the vicinity of the most significant solutions. We illustrate the method's operation by applying it to data from one field of the SuperWASP survey, comprising 2300 observations of 7840 stars brighter than V = 13.0. We identify 11 likely transit candidates. We reject stars that exhibit significant ellipsoidal variations caused indicative of a stellar-mass companion. We use colours and proper motions from the Two Micron All Sky Survey and USNO-B1.0 surveys to estimate the stellar parameters and the companion radius. We find that two stars showing unambiguous transit signals pass all these tests, and so qualify for detailed high-resolution spectroscopic follow-up.

Neural network based control for synchronous generators

In this paper, a Radial Basis Function neural network based AVR is proposed. A control strategy which generates local linear models from a global neural model on-line is used to derive controller feedback gains based on the Generalised Minimum Variance technique. Testing is carried out on a micromachine system which enables evaluation of practical implementation of the scheme. Constraints imposed by gathering training data, computational load, and memory requirements for the training algorithm are addressed.

Adaptive data echo cancellation using cost function adaptation

For a digital echo canceller it is desirable to reduce the adaptation time, during which the transmission of useful data is not possible. LMS is a non-optimal algorithm in this case as the signals involved are statistically non-Gaussian. Walach and Widrow (IEEE Trans. Inform. Theory 30 (2) (March 1984) 275-283) investigated the use of a power of 4, while other research established algorithms with arbitrary integer (Pei and Tseng, IEEE J. Selected Areas Commun. 12(9)(December 1994) 1540-1547) or non-quadratic power (Shah and Cowan, IEE.Proc.-Vis. Image Signal Process. 142 (3) (June 1995) 187-191). This paper suggests that continuous and automatic, adaptation of the error exponent gives a more satisfactory result. The family of cost function adaptation (CFA) stochastic gradient algorithm proposed allows an increase in convergence rate and, an improvement of residual error. As special case the staircase CFA algorithm is first presented, then the smooth CFA is developed. Details of implementations are also discussed. Results of simulation are provided to show the properties of the proposed family of algorithms. (C) 2000 Elsevier Science B.V. All rights reserved.

Genetic Algorithm Search for Predictive Patterns in Multidimensional Time Series

Based on an algorithm for pattern matching in character strings, we implement a pattern matching machine that searches for occurrences of patterns in multidimensional time series. Before the search process takes place, time series are encoded in user-designed alphabets. The patterns, on the other hand, are formulated as regular expressions that are composed of letters from these alphabets and operators. Furthermore, we develop a genetic algorithm to breed patterns that maximize a user-defined fitness function. In an application to financial data, we show that patterns bred to predict high exchange rates volatility in training samples retain statistically significant predictive power in validation samples.

Data-driven optimal filtering for phase and frequency of noisy oscillations: Application to vortex flow metering

A method for measuring the phase of oscillations from noisy time series is proposed. To obtain the phase, the signal is filtered in such a way that the filter output has minimal relative variation in the amplitude over all filters with complex-valued impulse response. The argument of the filter output yields the phase. Implementation of the algorithm and interpretation of the result are discussed. We argue that the phase obtained by the proposed method has a low susceptibility to measurement noise and a low rate of artificial phase slips. The method is applied for the detection and classification of mode locking in vortex flow meters. A measure for the strength of mode locking is proposed.

Dynamic pricing model and algorithm for perishable products with fuzzy demand (ABS 2* Journal)

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, a-optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real-world example is presented to highlight the effectiveness of the developed model and algorithm.