Automatic detection of learner-style for adaptive eLearning

The advent of modern wireless technologies has seen a shift in focus towards the design and development of educational systems for deployment through mobile devices. The use of mobile phones, tablets and Personal Digital Assistants (PDAs) is steadily growing across the educational sector as a whole. Mobile learning (mLearning) systems developed for deployment on such devices hold great significance for the future of education. However, mLearning systems must be built around the particular learner’s needs based on both their motivation to learn and subsequent learning outcomes. This thesis investigates how biometric technologies, in particular accelerometer and eye-tracking technologies, could effectively be employed within the development of mobile learning systems to facilitate the needs of individual learners. The creation of personalised learning environments must enable the achievement of improved learning outcomes for users, particularly at an individual level. Therefore consideration is given to individual learning-style differences within the electronic learning (eLearning) space. The overall area of eLearning is considered and areas such as biometric technology and educational psychology are explored for the development of personalised educational systems. This thesis explains the basis of the author’s hypotheses and presents the results of several studies carried out throughout the PhD research period. These results show that both accelerometer and eye-tracking technologies can be employed as an Human Computer Interaction (HCI) method in the detection of student learning-styles to facilitate the provision of automatically adapted eLearning spaces. Finally the author provides recommendations for developers in the creation of adaptive mobile learning systems through the employment of biometric technology as a user interaction tool within mLearning applications. Further research paths are identified and a roadmap for future of research in this area is defined.

Electrochemical biosensor based on microfabricated electrode arrays for life sciences applications

In developing a biosensor, the utmost important aspects that need to be emphasized are the specificity and selectivity of the transducer. These two vital prerequisites are of paramount in ensuring a robust and reliable biosensor. Improvements in electrochemical sensors can be achieved by using microelectrodes and to modify the electrode surface (using chemical or biological recognition layers to improve the sensitivity and selectivity). The fabrication and characterisations of silicon-based and glass-based gold microelectrode arrays with various geometries (band and disc) and dimension (ranging from 10 μm-100 nm) were reported. It was found that silicon-based transducers of 10 μm gold microelectrode array exhibited the most stable and reproducible electrochemical measurements hence this dimension was selected for further study. Chemical electrodeposition on both 10 μm microband and microdisc were found viable by electro-assisted self-assembled sol-gel silica film and nanoporous-gold electrodeposition respectively. The fabrication and characterisations of on-chip electrochemical cell was also reported with a fixed diameter/width dimension and interspacing variation. With this regard, the 10 μm microelectrode array with interspacing distance of 100 μm exhibited the best electrochemical response. Surface functionalisations on single chip of planar gold macroelectrodes were also studied for the immobilisation of histidine-tagged protein and antibody. Imaging techniques such as atomic force microscopy, fluorescent microscopy or scanning electron microscope were employed to complement the electrochemical characterisations. The long-chain thiol of self-assembled monolayer with NTA-metal ligand coordination was selected for the histidine-tagged protein while silanisation technique was selected for the antibody immobilisation. The final part of the thesis described the development of a T-2 labelless immunosensor using impedimetric approach. Good antibody calibration curve was obtained for both 10 μm microband and 10 μm microdisc array. For the establishment of the T-2/HT-2 toxin calibration curve, it was found that larger microdisc array dimension was required to produce better calibration curve. The calibration curves established in buffer solution show that the microelectrode arrays were sensitive and able to detect levels of T-2/HT-2 toxin as low as 25 ppb (25 μg kg-1) with a limit of quantitation of 4.89 ppb for a 10 μm microband array and 1.53 ppb for the 40 μm microdisc array.

Highly sensitive surface enhanced Raman scattering (SERS) detection platforms formed by large area self-assembled Au nanorod arrays

This thesis explores a new method to fabricate SERS detection platforms formed by large area self-assembled Au nanorod arrays. For the fabrication of these new SERS platforms a new droplet deposition method for the self-assembly of Au nanorods was developed. The method, based in the controlled evaporation of organic suspensions of Au nanorods, was used for the fabrication of horizontal and vertical arrays of Au nanorods over large areas (100μm2). The fabricated nanorods arrays showed a high degree of order measured by SEM and optical microscopy over mm2 areas, but unfortunately they detached from the support when immersed in any analyte solutions. In order to improve adhesion of arrays to the support and clean off residual organic matter, we introduced an additional stamping process. The stamping process allows the immobilization of the arrays on different flexible and rigid substrates, whose feasibility as SERS platforms were tested satisfactory with the model molecule 4ABT. Following the feasibility study, the substrates were used for the detection of the food contaminant Crystal Violet and the drug analogue Benzocaine as examples of recognition of health menaces in real field applications.

SDC: Scalable description coding for adaptive streaming media

Video compression techniques enable adaptive media streaming over heterogeneous links to end-devices. Scalable Video Coding (SVC) and Multiple Description Coding (MDC) represent well-known techniques for video compression with distinct characteristics in terms of bandwidth efficiency and resiliency to packet loss. In this paper, we present Scalable Description Coding (SDC), a technique to compromise the tradeoff between bandwidth efficiency and error resiliency without sacrificing user-perceived quality. Additionally, we propose a scheme that combines network coding and SDC to further improve the error resiliency. SDC yields upwards of 25% bandwidth savings over MDC. Additionally, our scheme features higher quality for longer durations even at high packet loss rates.

ALD: adaptive layer distribution for scalable video

Recent years have witnessed a rapid growth in the demand for streaming video over the Internet and mobile networks, exposes challenges in coping with heterogeneous devices and varying network throughput. Adaptive schemes, such as scalable video coding, are an attractive solution but fare badly in the presence of packet losses. Techniques that use description-based streaming models, such as multiple description coding (MDC), are more suitable for lossy networks, and can mitigate the effects of packet loss by increasing the error resilience of the encoded stream, but with an increased transmission byte cost. In this paper, we present our adaptive scalable streaming technique adaptive layer distribution (ALD). ALD is a novel scalable media delivery technique that optimises the tradeoff between streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data are spread amongst all packets, thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the resiliency of the scalable video. The Subjective testing results illustrate that our techniques and models were able to provide levels of consistent high-quality viewing, with lower transmission cost, relative to MDC, irrespective of clip type. This highlights the benefits of selective packetisation in addition to intuitive encoding and transmission.

ALD: adaptive layer distribution for scalable video

Bandwidth constriction and datagram loss are prominent issues that affect the perceived quality of streaming video over lossy networks, such as wireless. The use of layered video coding seems attractive as a means to alleviate these issues, but its adoption has been held back in large part by the inherent priority assigned to the critical lower layers and the consequences for quality that result from their loss. The proposed use of forward error correction (FEC) as a solution only further burdens the bandwidth availability and can negate the perceived benefits of increased stream quality. In this paper, we propose Adaptive Layer Distribution (ALD) as a novel scalable media delivery technique that optimises the tradeoff between the streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data is spread amongst all datagrams thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the scalable video, while providing increased resilience to the highest quality layers. Our experimental results show that ALD improves the perceived quality and also reduces the bandwidth demand by up to 36% in comparison to the well-known Multiple Description Coding (MDC) technique.