Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images. PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

Developmental, functional brain imaging and electrophysiological evidence of visual and auditory working memory

Intact function of working memory (WM) is essential for children and adults to cope with every day life. Children with deficits in WM mechanisms have learning difficulties that are often accompanied by behavioral problems. The neural processes subserving WM, and brain structures underlying this system, continue to develop during childhood till adolescence and young adulthood. With functional magnetic resonance imaging (fMRI) it is possible to investigate the organization and development of WM. The present thesis aimed to investigate, using behavioral and neuroimaging methods, whether mnemonic processing of spatial and nonspatial visual information is segregated in the developing and mature human brain. A further aim in this research was to investigate the organization and development of audiospatial and visuospatial information processing in WM. The behavioral results showed that spatial and nonspatial visual WM processing is segregated in the adult brain. The fMRI result in children suggested that memory load related processing of spatial and nonspatial visual information engages common cortical networks, whereas selective attention to either type of stimuli recruits partially segregated areas in the frontal, parietal and occipital cortices. Deactivation mechanisms that are important in the performance of WM tasks in adults are already operational in healthy school-aged children. Electrophysiological evidence suggested segregated mnemonic processing of visual and auditory location information. The results of the development of audiospatial and visuospatial WM demonstrate that WM performance improves with age, suggesting functional maturation of underlying cognitive processes and brain areas. The development of the performance of spatial WM tasks follows a different time course in boys and girls indicating a larger degree of immaturity in the male than female WM systems. Furthermore, the differences in mastering auditory and visual WM tasks may indicate that visual WM reaches functional maturity earlier than the corresponding auditory system. Spatial WM deficits may underlie some learning difficulties and behavioral problems related to impulsivity, difficulties in concentration, and hyperactivity. Alternatively, anxiety or depressive symptoms may affect WM function and the ability to concentrate, being thus the primary cause of poor academic achievement in children.

Multispectral imaging and genomic mapping

Identifying QTLs linked to malting and protein levels in barley using multispectral image data.

Fisheries Queensland Long Term Monitoring Program Underwater Visual Census of twenty-four reefs on the Great Barrier Reef between 1999 and 2004.

In 1999, the Department of Employment, Economic Development and Innovation (DEEDI), Fisheries Queensland undertook a new initiative to collect long term monitoring data of various important stocks including reef fish. This data and monitoring manual for the reef fish component of that program which was based on Underwater Visual Census methodology of 24 reefs on the Great Barrier Reef between 1999 and 2004. Data was collected using six 50m x 5m transects at 4 sites on 24 reefs. Benthic cover type was also recorded for 10m of each transect. The attached Access Database contains 5 tables being: SITE DETAILS TABLE Survey year Data entry complete REF survey site ID Site # (1-4) Location (reef name) Site Date (date surveyed) Observer 1 (3 initials to identify who estimated fish lengths and recorded benthic cover) TRANSECT DETAILS Survey ID Transect Number (1-6) Time (the transect was surveyed) Visibility (in metres) Minimum Depth surveyed (m) Maximum Depth surveyed (m) Percent of survey completed (%) Comments SUBSTRATE Survey ID Transect Number (1-6) then % cover of each of eth following categories of benthic cover types Dead Coral Live Coral Soft Coral Rubble Sand Sponge Algae Sea Grass Other COORDINATES (over survey sites) from -14 38.792 to -19 44.233 and from 145 21.507 to 149 55.515 SIGHTINGS ID Survey ID Transect Number (1-6) CAAB Code Scientific Name Reef Fish Length (estimated Fork Length of fish; -1 = unknown or not recorded) Outside Transect (if a fish was observed outside a transect -1 was recorded) Morph Code (F = footballer morph for Plectropomus laevis, S = Spawning colour morph displayed)

Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision

There is an increased interest on the use of Unmanned Aerial Vehicles (UAVs) for wildlife and feral animal monitoring around the world. This paper describes a novel system which uses a predictive dynamic application that places the UAV ahead of a user, with a low cost thermal camera, a small onboard computer that identifies heat signatures of a target animal from a predetermined altitude and transmits that target’s GPS coordinates. A map is generated and various data sets and graphs are displayed using a GUI designed for easy use. The paper describes the hardware and software architecture and the probabilistic model for downward facing camera for the detection of an animal. Behavioral dynamics of target movement for the design of a Kalman filter and Markov model based prediction algorithm are used to place the UAV ahead of the user. Geometrical concepts and Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of the user, thus delivering a new way point for autonomous navigation. Results show that the system is capable of autonomously locating animals from a predetermined height and generate a map showing the location of the animals ahead of the user.

Infrared thermal imaging for automated detection of diabetic foot complications

Background Although thermal imaging can be a valuable technology in the prevention and management of diabetic foot disease, it is not yet widely used in clinical practice. Technological advancement in infrared imaging increases its application range. The aim was to explore the first steps in the applicability of high-resolution infrared thermal imaging for noninvasive automated detection of signs of diabetic foot disease. Methods The plantar foot surfaces of 15 diabetes patients were imaged with an infrared camera (resolution, 1.2 mm/pixel): 5 patients had no visible signs of foot complications, 5 patients had local complications (e.g., abundant callus or neuropathic ulcer), and 5 patients had difuse complications (e.g., Charcot foot, infected ulcer, or critical ischemia). Foot temperature was calculated as mean temperature across pixels for the whole foot and for specified regions of interest (ROIs). Results No diferences in mean temperature >1.5 °C between the ipsilateral and the contralateral foot were found in patients without complications. In patients with local complications, mean temperatures of the ipsilateral and the contralateral foot were similar, but temperature at the ROI was >2 °C higher compared with the corresponding region in the contralateral foot and to the mean of the whole ipsilateral foot. In patients with difuse complications, mean temperature diferences of >3 °C between ipsilateral and contralateral foot were found. Conclusions With an algorithm based on parameters that can be captured and analyzed with a high-resolution infrared camera and a computer, it is possible to detect signs of diabetic foot disease and to discriminate between no, local, or difuse diabetic foot complications. As such, an intelligent telemedicine monitoring system for noninvasive automated detection of signs of diabetic foot disease is one step closer. Future studies are essential to confirm and extend these promising early findings.

Assessment of signs of foot infection in diabetes patients using photographic foot imaging and infrared thermography

Background Patients with diabetic foot disease require frequent screening to prevent complications and may be helped through telemedical home monitoring. Within this context, the goal was to determine the validity and reliability of assessing diabetic foot infection using photographic foot imaging and infrared thermography. Subjects and Methods For 38 patients with diabetes who presented with a foot infection or were admitted to the hospital with a foot-related complication, photographs of the plantar foot surface using a photographic imaging device and temperature data from six plantar regions using an infrared thermometer were obtained. A temperature difference between feet of > 2.2 °C defined a ''hotspot.'' Two independent observers assessed each foot for presence of foot infection, both live (using the Perfusion-Extent-Depth- Infection-Sensation classification) and from photographs 2 and 4 weeks later (for presence of erythema and ulcers). Agreement in diagnosis between live assessment and (the combination of ) photographic assessment and temperature recordings was calculated. Results Diagnosis of infection from photographs was specific (> 85%) but not very sensitive (< 60%). Diagnosis based on hotspots present was sensitive (> 90%) but not very specific (<25%). Diagnosis based on the combination of photographic and temperature assessments was both sensitive (> 60%) and specific (> 79%). Intra-observer agreement between photographic assessments was good (Cohen's j = 0.77 and 0.52 for both observers). Conclusions Diagnosis of foot infection in patients with diabetes seems valid and reliable using photographic imaging in combination with infrared thermography. This supports the intended use of these modalities for the home monitoring of high-risk patients with diabetes to facilitate early diagnosis of signs of foot infection.

Underwater Sustainability of the ``Cassie'' State of Wetting

A rough hydrophobic surface when immersed in water can result in a ``Cassie'' state of wetting in which the water is in contact with both the solid surface and the entrapped air. The sustainability of the entrapped air on such surfaces is important for underwater applications such as reduction of flow resistance in microchannels and drag reduction of submerged bodies such as hydrofoils. We utilize an optical technique based oil total internal reflection of light at the water-air interface to quantify the spatial distribution of trapped air oil such a surface and its variation with immersion time. With this technique, we evaluate the sustainability of the Cassie state on hydrophobic surfaces with four different kinds of textures. The textures studied are regular arrays of pillars, ridges, and holes that were created in silicon by a wet etching technique, and also a texture of random craters that was obtained through electrodischarge machining of aluminum. These surfaces were rendered hydrophobic with a self-assembled layer Of fluorooctyl trichlorosilane. Depending on the texture, the size and shape of the trapped air pockets were found to vary. However, irrespective of the texture, both the size and the number of air pockets were found to decrease with time gradually and eventually disappear, suggesting that the sustainability of the ``Cassie'' state is finite for all the microstructures Studied. This is possibly due to diffusion of air from the trapped air pockets into the water. The time scale for disappearance of air pockets was found to depend on the kind of microstructure and the hydrostatic pressure at the water-air interface. For the surface with a regular array of pillars, the air pockets were found to be in the form of a thin layer perched on top of the pillars with a large lateral extent compared to the spacing between pillars. For other surfaces studied, the air pockets are smaller and are of the same order as the characteristic length scale of the texture. Measurements for the surface with holes indicate that the time for air-pocket disappearance reduces as the hydrostatic pressure is increased.

Pseudo-time marching schemes for inverse problems in structural health assessment and medical imaging

We propose a self-regularized pseudo-time marching strategy for ill-posed, nonlinear inverse problems involving recovery of system parameters given partial and noisy measurements of system response. While various regularized Newton methods are popularly employed to solve these problems, resulting solutions are known to sensitively depend upon the noise intensity in the data and on regularization parameters, an optimal choice for which remains a tricky issue. Through limited numerical experiments on a couple of parameter re-construction problems, one involving the identification of a truss bridge and the other related to imaging soft-tissue organs for early detection of cancer, we demonstrate the superior features of the pseudo-time marching schemes.

Blended learning in medical imaging pre-clinical skills training – does it work?

Undergraduate Medical Imaging (MI)students at QUT attend their first clinical placement towards the end of semester two. Students undertake two (pre)clinical skills development units – one theory and one practical. Students gain good contextual and theoretical knowledge during these units via a blended learning model with multiple learning methods employed. Students attend theory lectures, practical sessions, tutorial sessions in both a simulated and virtual environment and also attend pre-clinical scenario based tutorial sessions. The aim of this project is to evaluate the use of blended learning in the context of 1st year Medical Imaging Radiographic Technique and its effectiveness in preparing students for their first clinical experience. It is hoped that the multiple teaching methods employed within the pre-clinical training unit at QUT builds students clinical skills prior to the real situation. A quantitative approach will be taken, evaluating via pre and post clinical placement surveys. This data will be correlated with data gained in the previous year on the effectiveness of this training approach prior to clinical placement. In 2014 59 students were surveyed prior to their clinical placement demonstrated positive benefits of using a variety of learning tools to enhance their learning. 98.31%(n=58)of students agreed or strongly agreed that the theory lectures were a useful tool to enhance their learning. This was followed closely by 97% (n=57) of the students realising the value of performing role-play simulation prior to clinical placement. Tutorial engagement was considered useful for 93.22% (n=55) whilst 88.14% (n=52) reasoned that the x-raying of phantoms in the simulated radiographic laboratory was beneficial. Self-directed learning yielded 86.44% (n=51). The virtual reality simulation software was valuable for 72.41% (n=42) of the students. Of the 4 students that disagreed or strongly disagreed with the usefulness of any tool they strongly agreed to the usefulness of a minimum of one other learning tool. The impact of the blended learning model to meet diverse student needs continues to be positive with students engaging in most offerings. Students largely prefer pre -clinical scenario based practical and tutorial sessions where 'real-world’ situations are discussed.