Towards a process framework for assessing the potential value of technologies

Current technology valuation literature predominantly focuses on explaining the merits and implications of specific tools, but little research is available that takes a contextual process perspective. The aim of this paper is to further develop an integrative process framework that supports the structuring of the valuation process and the more systematic choice of valuation techniques for new technologies. The paper starts by reviewing key concepts and issues that surround the assessment of technology investments and the evidence of what companies use. Many factors need to be brought into the appraisal process, reflecting technological and market conditions. While there is usually a desire to reduce the assessment to a financial value, it is also widely appreciated that there is long term strategic value in securing a technological lead, which is difficult, or even inappropriate, to assess in purely financial terms. The multiple factors involved in the evaluation activity are identified with respect to the changing nature of the appraisal process as the technology matures and the implications for associated tools. The result of the literature review is a process framework which provides a conceptual basis for integrating valuation techniques. This framework is then populated with the results of industrial case studies on technology valuation to allow conclusions on its applicability to be drawn. © 2011 IEEE.

Understanding and navigating industrial emergence

Industrial emergence is a broad and complex domain, with relevant perspectives ranging in scale from the individual entrepreneur and firm with the business decisions and actions they make to the policies of nations and global patterns of industrialisation. The research described in this article has adopted a holistic approach, based on structured mapping methods, in an attempt to depict and understand the dynamics and patterns of industrial emergence across a broad spectrum from early scientific discovery to large-scale industrialisation. The breadth of scope and application has enabled a framework and set of four tools to be developed that have wide applicability. The utility of the approaches has been demonstrated through case studies and trials in a diverse range of industrial contexts. The adoption of such a broad scope also presents substantial challenges and limitations, with these providing an opportunity for further research. © IMechE 2013.

Simulation-based optimal sensor scheduling with application to observer trajectory planning

The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. The aim is to minimise the variance of the estimation error of the hidden state w.r.t. the action sequence. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal actions. © 2007 Elsevier Ltd. All rights reserved.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

Spatio-temporal clustering of probabilistic region trajectories

We propose a novel model for the spatio-temporal clustering of trajectories based on motion, which applies to challenging street-view video sequences of pedestrians captured by a mobile camera. A key contribution of our work is the introduction of novel probabilistic region trajectories, motivated by the non-repeatability of segmentation of frames in a video sequence. Hierarchical image segments are obtained by using a state-of-the-art hierarchical segmentation algorithm, and connected from adjacent frames in a directed acyclic graph. The region trajectories and measures of confidence are extracted from this graph using a dynamic programming-based optimisation. Our second main contribution is a Bayesian framework with a twofold goal: to learn the optimal, in a maximum likelihood sense, Random Forests classifier of motion patterns based on video features, and construct a unique graph from region trajectories of different frames, lengths and hierarchical levels. Finally, we demonstrate the use of Isomap for effective spatio-temporal clustering of the region trajectories of pedestrians. We support our claims with experimental results on new and existing challenging video sequences. © 2011 IEEE.

Self-similar organization of arrays of individual carbon nanotubes and carbon nanotube micropillars

It is well-known that carbon nanotube (CNT) growth from a dense arrangement of catalyst nanoparticles creates a vertically aligned CNT forest. CNT forests offer attractive anisotropic mechanical, thermal, and electrical properties, and their anisotropic structure is enabled by the self-organization of a large number of CNTs. This process is governed by individual CNT diameter, spacing, and the CNT-to-CNT interaction. However, little information is known about the self-organization of CNTs within a forest. Insight into the self-organization is, however, essential for tailoring the properties of the CNT forests for applications such as electrical interconnects, thermal interfaces, dry adhesives and energy storage. We demonstrate that arrays of CNT micropillars having micron-scale diameters organize in a similar manner as individual CNTs within a forest. For example, as previously demonstrated for individual CNTs within a forest, entanglement of small-diameter CNT micropillars during the initial stage of growth creates a film of entwined pillars. This layer enables coordinated subsequent growth of the pillars in the vertical direction, in a case where isolated pillars would not grow in a self-supporting fashion. Finally, we provide a detailed overview of the self-organization as a function of the diameter, length and spacing of the CNT pillars. This study, which is applicable to many one-dimensional nanostructured films, demonstrates guidelines for tailoring the self-organization which can enable control of the collective mechanical, electrical and interfacial properties of the films. © 2009 Elsevier B.V. All rights reserved.

Free-space locomotion with thread formation

The paper presents a new concept of locomotion for wheeled or legged robots through an object-free space. The concept is inspired by the behaviour of spiders forming silk threads to move in 3D space. The approach provides the possibility of variation in thread diameter by deforming source material, therefore it is useful for a wider coverage of payload by mobile robots. As a case study, we propose a technology for descending locomotion through a free space with inverted formation of threads in variable diameters. Inverted thread formation is enabled with source material thermoplastic adhesive (TPA) through thermally-induced phase transition. To demonstrate the feasibility of the technology, we have designed and prototyped a 300-gram wheeled robot that can supply and deform TPA into a thread and descend with the thread from an existing hanging structure. Experiment results suggest repeatable inverted thread formation with a diameter range of 1.1-4.5 mm, and a locomotion speed of 0.73 cm per minute with a power consumption of 2.5 W. © 2013 IEEE.

A framework for analysing business performance, firm innovation and related contextual factors: Perceptions of managers and policy makers in two European regions

The aim of this paper is to propose a novel reference framework that can be used to study how different kinds of innovation can result in better business performance and how external factors can influence both the firm's capacity to innovate and innovation itself. The value of the framework is demonstrated as it is applied in an exploratory study of the perceptions of public policy makers and managers from two European regions - the Veneto Region in Italy and the East of England in the UK. Amongst other things, the data gathered suggest that managers are generally less convinced than public policy makers, that the innovativeness of a firm is affected by factors over which policy makers have some control. This finding poses the question "what, if any, role can public policy makers play in enhancing a company's competitiveness by enabling it to become more innovative?".