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In a century where technology is rapidly shaping the way we communicate, travel, work, and live, the numbers of students studying the natural sciences (which are often perceived as more difficult) in both the high school and the university is on the decline. Many universities and schools have been addressing this lack of interest using a wide variety of engagement programs to encourage and retain students in science, technology, engineering and mathematics (STEM) disciplines. This paper describes a hands-on activity, LaserTag , that has been developed by the Department of Engineering at La Trobe University and has had thousands of high school participants over the last few years. During the activity, students solder together (and keep) electronic LaserTag devices, which they can use to shoot infrared light packets at each other to have their own skirmish activities. The effectiveness of the activity was measured based on anonymous student surveys evaluating students prior and post interest in engineering and the STEM disciplines. The survey results were very positive indicating 97% of the participants found the activity ‘highly enjoyable’ or ‘enjoyable’ and that 55% of students who were previously unsure about engineering as a career ‘strongly agreed’ or ‘agreed’ they were more interested in studying engineering as a result.

An important problem in image smoothing is to reduce noise while preserving sharp edges. In this paper we extend the bilateral filter for better impulse noise suppression and near edge artifacts avoidance. The proposed method weights each pixel prior to bilateral filtering by an extra coefficient that is dependent on a noisiness measurement. Consequently, the influence of noisy pixels can be significantly reduced in the filtering process. Experimental results have demonstrated that the performance of the proposed filter is competitive with bilateral filter in removing impulse noise while preserving sharp edges. Additionally, the proposed method can well avoid near edge artifacts in comparison with other state-of-the-art edge-aware filters.

This paper describes and evaluates a novel algorithm for adaptive local stereo vision, named Successive Disparity Difference Feedback (SDDF). Traditionally, local stereo vision algorithms compute matching cost based on intensity values within a fixed-size window for left and right images. Fixed-size window algorithms encounter problems related to correspondence accuracy over featureless regions. The SDDF algorithm overcomes these problems by adaptively adjusting the window size based on feedback from the disparity map. The SDDF algorithm is shown to significantly enhance correspondence matching in featureless regions, having only a marginal increase in computational complexity. The proposed algorithm was validated on standard stereo data sets, showing significantly improvement to the accuracy of disparity maps compared to traditional local algorithms.

This paper describes and evaluates a novel 3D inspection system to detect anomalies in sewer pipes using stereo vision coupled with novel image processing algorithms. Currently, most commercial pipe inspection systems are designed with one or more Closed-circuit Television (CCTV) cameras. These systems are slow, costly and have limited accuracy (caused by human and environmental factors). More sophisticated systems (Laser-based, Infrared Thermography, Ultrasonic-based and Ground Penetrating Radar) suffer from: low resolution, high noise, high operational costs and an inability to detect water infiltration. The main objective of this research is to apply stereo vision and robust image processing to generate 3D images of anomalies in sewer pipes in order to achieve high efficiency and accuracy for pipe inspection. The results show that various types of defects are successfully detectable. In addition, the correspondence time can be reduced by up to 45% and the accuracy of disparity maps is maintained compared to traditional local correspondence algorithms. Each component of the proposed system was tested individually with real and simulated data sets.

This paper describes and evaluates a novel computer vision algorithm for automatic thin crack detection in pipelines using dou-edge evaluation (DEE). Inspection for pipes is crucial and it is performed periodically to ensure that the structured integrity of the pipe systems is maintained. Thin cracks and fractures are among the defects which can cause critical damage to pipe systems. Numerous techniques have been used to detect cracks in pipes including machine vision, mostly based on edge-detection algorithms (i.e. Sobel, Laplace). However, these algorithms encounter difficulties in extracting cracks from complicated and noisy environments (i.e. sewer pipes). The DEE algorithm overcomes this problem by evaluating the size and shape of each object in the inspection environment. The results show that thin cracks were automatically extracted by the proposed algorithm.

This paper describes and evaluates a new sensing methodology for anomaly inspection in sewer pipes using a machine vision approach. Currently, most commercial pipe inspection systems include a mobile remote platform and one or more Closed Circuit Television (CCTV) cameras. These inspection systems are slow, costly and have limited accuracy (caused by humans or environmental reasons). More sophisticated approaches (Laser-based, Infra-red Thermography, Ultrasonic-based and Ground Penetrating Radar) suffer from, lack of resolution and an inability to detect water inflow. The main objective of this research is to apply stereo vision technique to generate 3D images of anomalies in sewer pipes in order to achieve high efficiency and accuracy for pipe inspection. The results showed that various types of defects were successfully reconstructed for later advanced processing.

Nowadays, VLC-based system has been widely and developed for usage of localization. However, some of them are impractical and inefficient. In this paper, we report a novel design of VLC-based system that not only deals with localization problem but also tries to improve the system practicality and efficiency. We focus on utilizing the capacity of handset devices to provide high precision indoor position. The system makes use of image sensor (IS) and acceleration sensor (AS), which are going to capture and demodulate the digital information transmitting from predetermined LED mounted on the ceiling. We describe the algorithm and get verified by the simulations.

This paper proposes and evaluates a novel algorithm for local correspondence matching in stereo vision, named Constrained Sliding Window (CSW). Conventional local algorithms compute the disparity map based on intensity values of pixels within a window for left and right images. Local algorithms are considered to be faster than global methods and capable of implementing applications which require prompt responses. Nevertheless, local algorithms exhibit the critical disadvantage of having a fixed search space, resulting in repetitive scanning. The main objective of this paper is to dynamically constrain the search space to reduce unnecessary scanning and hence reduce the processing time. The proposed CSW algorithm was proven to significantly reduce processing time by up to 45% compared to unconstrained algorithms. The proposed algorithm was evaluated experimentally using Tsukuba image pair and stereo data set from the Middlebury database and was compared against traditional algorithms.

One of the key components of dye-sensitized solar cell (DSSC) is electrolyte that strongly affects its stability and overall conversion efficiency. In order to improve charge transfer of the electrolyte, lab-made platinum nanoparticles (nano Pt) were employed in this present work as catalysts in both conventional and flexible DSSC. The presence of nano Pt in a commercial electrolyte (R50, Solaronix) was found to significantly improve the photovoltaic performance of the resulting DSSC at AM 1.5: VOC of 0.74 V, JSC of 19.7 mA/cm2, FF of 43.2% and η of 6.33 %, which were much better than those of a corresponding DSSC fabricated with the R50 electrolyte alone (η of 5.0%). Addition of nano Pt as catalysts for charge transfer in gel electrolyte was also found to substantially enhance the efficiency of the derived flexible-DSSC at AM 1.5: VOC of 0.69 V, JSC of 5.22 mA/cm2, FF of 51.0% and η of 1.84 %, compared with η of 1.20 % in the case of using the gel electrolyte without nano Pt. The effect of anti-electron-hole-recombination layer on the overall conversion efficiency of the obtained DSSC was also studied and discussed.

We have demonstrated a modified doctor-blade method using a dense TiO 2 paste in combination with moderate compression for preparation of TiO 2 thin film. Dye-sensitized solar cell (DSSC) with the working area of 0.25 cm2 using that novel TiO 2 thin film was observed to show its superior photovoltaic performance at AM 1.5: V OC of 0.77 V, J SC of 18.2 mA/cm2, FF of 0.50 and η of 7.0 %. This overall conversion efficiency was approximately 2 times as much as that of a corresponding DSSC fabricated with a conventional TiO 2 thin film (η of 3.7%). The modified doctor-blade method was also found to significantly improve the efficiencies of DSSCs with the working areas of 0.5, 1.0 and 5.0 cm2.