Deep learning in computational microscopy

We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.Published versio

Transforming pre-service teacher curriculum: observation through a TPACK lens

This paper will discuss an international online collaborative learning experience through the lens of the Technological Pedagogical Content Knowledge (TPACK) framework. The teacher knowledge required to effectively provide transformative learning experiences for 21st century learners in a digital world is complex, situated and changing. The discussion looks beyond the opportunity for knowledge development of content, pedagogy and technology as components of TPACK towards the interaction between those three components. Implications for practice are also discussed. In today’s technology infused classrooms it is within the realms of teacher educators, practising teaching and pre-service teachers explore and address effective practices using technology to enhance learning

Teaching and learning in virtual worlds: is it worth the effort?

Educators have been quick to spot the enormous potential afforded by virtual worlds for situated and authentic learning, practising tasks with potentially serious consequences in the real world and for bringing geographically dispersed faculty and students together in the same space (Gee, 2007; Johnson and Levine, 2008). Though this potential has largely been realised, it generally isn’t without cost in terms of lack of institutional buy-in, steep learning curves for all participants, and lack of a sound theoretical framework to support learning activities (Campbell, 2009; Cheal, 2007; Kluge & Riley, 2008). This symposium will explore the affordances and issues associated with teaching and learning in virtual worlds, all the time considering the question: is it worth the effort

Vision-based Detection of Mobile Device Use While Driving

The aim of this study was to explore the feasibility of an automatic vision-based solution to detect drivers using mobile devices while operating their vehicles. The proposed system comprises of modules for vehicle license plate localisation, driver’s face detection and mobile phone interaction. The system were then implemented and systematically evaluated using suitable image datasets. The strengths and weaknesses of individual modules were analysed and further recommendations made to improve the overall system’s performance