Research activities arising from the University of Kent

In this paper I describe research activities in the field of optical fiber sensing undertaken by me after leaving the Applied Optics Group at the University of Kent. The main topics covered are long period gratings, neural network based signal processing, plasmonic sensors, and polymer fiber gratings. I also give a summary of my two periods of research at the University of Kent, covering 1985–1988 and 1991–2001.

Knowledge, technological catch-up and economic growth

‘The literature on economic growth has needed for a long time a simple, but rigorous, textbook exposition of the role of knowledge in the growth process, suitable for undergraduates and policymakers. Mark Rogers’s new book provides an excellent introduction, combining clear and succinct theory with up-to-date empirical evidence on this important topic.’ – A.P. Thirlwall, University of Kent, UK Knowledge, Technological Catch-up and Economic Growth investigates the relationship between knowledge diffusion and economic growth. Using a broad definition of knowledge – encompassing technology, production skills, know-how and firm capabilities – the central argument of the book is that the extent of knowledge diffusion is an important determinant of economic growth.

Implementation of supply chain management theory in practice:a questionnaire survey in the United Kingdom

Purpose: The purpose of the research described in this paper is to disentangle the rhetoric from the reality in relation to supply chain management (SCM) adoption in practice. There is significant evidence of a divergence between theory and practice in the field of SCM. Research Approach: The authors’ review of the extant SCM literature highlighted a lack of replication studies in SCM, leading to the concept of refined replication being developed. The authors conducted a refined replication of the work of Sweeney et al. (2015) where a new SCM definitional construct – the Four Fundamentals – was proposed. The work presented in this article refines the previous study but adopts the same three-phase approach: focussed interviews, a questionnaire survey, and focus groups. This article covers the second phase of the refined replication study and describes an integrated research design of a questionnaire research to be undertaken in Britain. Findings and Originality: The article presents an integrated research design of a questionnaire research with emphases on the refined replication of previous work of Sweeney et al. (2015) carried out in Ireland and adapting it to the British context. Research Impact: The authors introduce the concept of refined replication in SCM research. This allows previous research to be built upon in order to test understanding of SCM theory and its practical implementation - based on the Four Fundamentals construct - among SCM professionals in Britain. Practical Impact: The article presents the integrated research design of a questionnaire research that may be used in similar studies.

Benchmarking the n-tuple classifier with statlog dataset

The n-tuple recognition method was tested on 11 large real-world data sets and its performance compared to 23 other classification algorithms. On 7 of these, the results show no systematic performance gap between the n-tuple method and the others. Evidence was found to support a possible explanation for why the n-tuple method yields poor results for certain datasets. Preliminary empirical results of a study of the confidence interval (the difference between the two highest scores) are also reported. These suggest a counter-intuitive correlation between the confidence interval distribution and the overall classification performance of the system.

Good-turing estimation for the frequentist n-tuple classifier

We present results concerning the application of the Good-Turing (GT) estimation method to the frequentist n-tuple system. We show that the Good-Turing method can, to a certain extent rectify the Zero Frequency Problem by providing, within a formal framework, improved estimates of small tallies. We also show that it leads to better tuple system performance than Maximum Likelihood estimation (MLE). However, preliminary experimental results suggest that replacing zero tallies with an arbitrary constant close to zero before MLE yields better performance than that of GT system.