A novel silicon membrane-based biosensing platform using distributive sensing strategy and artificial neural networks for feature analysis

A novel biosensing system based on a micromachined rectangular silicon membrane is proposed and investigated in this paper. A distributive sensing scheme is designed to monitor the dynamics of the sensing structure. An artificial neural network is used to process the measured data and to identify cell presence and density. Without specifying any particular bio-application, the investigation is mainly concentrated on the performance testing of this kind of biosensor as a general biosensing platform. The biosensing experiments on the microfabricated membranes involve seeding different cell densities onto the sensing surface of membrane, and measuring the corresponding dynamics information of each tested silicon membrane in the form of a series of frequency response functions (FRFs). All of those experiments are carried out in cell culture medium to simulate a practical working environment. The EA.hy 926 endothelial cell lines are chosen in this paper for the bio-experiments. The EA.hy 926 endothelial cell lines represent a particular class of biological particles that have irregular shapes, non-uniform density and uncertain growth behaviour, which are difficult to monitor using the traditional biosensors. The final predicted results reveal that the methodology of a neural-network based algorithm to perform the feature identification of cells from distributive sensory measurement has great potential in biosensing applications.

A smart sensing platform for the discrimination of human movement

This thesis documents the design, implementation and testing of a smart sensing platform that is able to discriminate between differences or small changes in a persons walking. The distributive tactile sensing method is used to monitor the deflection of the platform surface using just a small number of sensors and, through the use of neural networks, infer the characteristics of the object in contact with the surface. The thesis first describes the development of a mathematical model which uses a novel method to track the position of a moving load as it passes over the smart sensing surface. Experimental methods are then described for using the platform to track the position of swinging pendulum in three dimensions. It is demonstrated that the method can be extended to that of real-time measurement of balance and sway of a person during quiet standing. Current classification methods are then investigated for use in the classification of different gait patterns, in particular to identify individuals by their unique gait pattern. Based on these observations, a novel algorithm is developed that is able to discriminate between abnormal and affected gait. This algorithm, using the distributive tactile sensing method, was found to have greater accuracy than other methods investigated and was designed to be able to cope with any type of gait variation. The system developed in this thesis has applications in the area of medical diagnostics, either as an initial screening tool for detecting walking disorders or to be able to automatically detect changes in gait over time. The system could also be used as a discrete biometric identification method, for example identifying office workers as they pass over the surface.

A smart sensing platform for the classification of ambulatory patterns

This paper describes an innovative sensing approach allowing capture, discrimination, and classification of transients automatically in gait. A walking platform is described, which offers an alternative design to that of standard force plates with advantages that include mechanical simplicity and less restriction on dimensions. The scope of the work is to investigate as an experiment the sensitivity of the distributive tactile sensing method with the potential to address flexibility on gait assessment, including patient targeting and the extension to a variety of ambulatory applications. Using infrared sensors to measure plate deflection, gait patterns are compared with stored templates using a pattern recognition algorithm. This information is input into a neural network to classify normal and affected walking events, with a classification accuracy of just under 90 per cent achieved. The system developed has potential applications in gait analysis and rehabilitation, whereby it can be used as a tool for early diagnosis of walking disorders or to determine changes between pre- and post-operative gait.

Exploring the adoption of interactive digital television services as a retail shopping platform

The aim of this research is to consider the possible effect of an emerging technology platform on the uptake of online shopping: interactive (digital) Television (iTV), which enables viewers to select a variety of viewing options, publicity materials, games, entertainment and more recently shopping. An augmented version of the original TAM is applied to this study. Two new constructs are considered namely access and awareness together with perceived ease of use, perceived usefulness, perceived enjoyment and security. The results show that indeed the augmented TAM can be used as a predictive model for the adoption of iTV as an online shopping platform. It is concluded that access, perceived ease of use, perceived enjoyment and perceived usefulness are significant factors to determine the consumers’behavioural intentions towards the use of digital TV as a new shopping platform. However, awareness and security are considered to be insignificant with no effect on consumers’ behavioural intentions towards the new shopping medium.

Identifying the ICT challenges of the agri-food sector to define the architectural requirements for a future internet core platform

This paper discussed the specific challenges of the agri-food sector in the light of research carried out in the Smart-AgriFod project. Using questionnaires and focus groups, our research identified as number of business needs and drivers which enable the identification of suitable Future Internet technologies across the three sub-domains of Smart Farming, Smart Agri-logistics, and Smart Food Awareness. The universal need for information access and the importance of standards to enable this lead us to propose an integrated scenario for end to end information access from farm to fork. We conclude by discussing wider implications of such developments especially for climate change and urbanisation.

Tuning solid acid catalysts for glucose conversion to platform chemicals

Worldwide concern over dwindling fossil fuel reserves and impact of CO2 emissions on climate change means there is an urgent need to reduce our dependence on oil based sources of fuels and chemicals. The direct conversion of lignocellulosic derived glucose to 5-Hydroxymethylfurfural (5-HMF) is an attractive process for the production of chemicals and fuels but requires a bi-functional catalyst with acid-base or Lewis-Brönsted sites which can operate efficiently in the aqueous phase. While conventionally viewed as a superacid, the potential for tuning the acid strength in SO4/ZrO2 and potential for coupling bi-functional ZrO2-SO4/ZrO2 sites at low sulfate contents have been overlooked. Our previous work has shown effective tuning of the acid strength in SO4/ZrO2 can be used to direct selectivity in terpene isomerisation thus we rationalised control over HMF selectivity could achieved in a similar fashion. Here we report on a systematic study of the impact of acid properties of SO4/ZrO2 catalysts on the conversion of C6 sugars to 5-HMF in aqueous media and correlate the surface acid-base properties with glucose isomerisation and dehydration capabilities.