Outdoor simultaneous localisation and mapping using RatSLAM

In this paper an existing method for indoor Simultaneous Localisation and Mapping (SLAM) is extended to operate in large outdoor environments using an omnidirectional camera as its principal external sensor. The method, RatSLAM, is based upon computational models of the area in the rat brain that maintains the rodent’s idea of its position in the world. The system uses the visual appearance of different locations to build hybrid spatial-topological maps of places it has experienced that facilitate relocalisation and path planning. A large dataset was acquired from a dynamic campus environment and used to verify the system’s ability to construct representations of the world and simultaneously use these representations to maintain localisation.

Applications of medical image processing in the diagnosis and treatment of spinal deformity

In Chapter 10, Adam and Dougherty describe the application of medical image processing to the assessment and treatment of spinal deformity, with a focus on the surgical treatment of idiopathic scoliosis. The natural history of spinal deformity and current approaches to surgical and non-surgical treatment are briefly described, followed by an overview of current clinically used imaging modalities. The key metrics currently used to assess the severity and progression of spinal deformities from medical images are presented, followed by a discussion of the errors and uncertainties involved in manual measurements. This provides the context for an analysis of automated and semi-automated image processing approaches to measure spinal curve shape and severity in two and three dimensions.

An exploration of feature detector performance in the thermal-infrared modality

Thermal-infrared images have superior statistical properties compared with visible-spectrum images in many low-light or no-light scenarios. However, a detailed understanding of feature detector performance in the thermal modality lags behind that of the visible modality. To address this, the first comprehensive study on feature detector performance on thermal-infrared images is conducted. A dataset is presented which explores a total of ten different environments with a range of statistical properties. An investigation is conducted into the effects of several digital and physical image transformations on detector repeatability in these environments. The effect of non-uniformity noise, unique to the thermal modality, is analyzed. The accumulation of sensor non-uniformities beyond the minimum possible level was found to have only a small negative effect. A limiting of feature counts was found to improve the repeatability performance of several detectors. Most other image transformations had predictable effects on feature stability. The best-performing detector varied considerably depending on the nature of the scene and the test.

Determination of stress-strain characteristics of railhead steel using image analysis

True stress-strain curve of railhead steel is required to investigate the behaviour of railhead under wheel loading through elasto-plastic Finite Element (FE) analysis. To reduce the rate of wear, the railhead material is hardened through annealing and quenching. The Australian standard rail sections are not fully hardened and hence suffer from non-uniform distribution of the material property; usage of average properties in the FE modelling can potentially induce error in the predicted plastic strains. Coupons obtained at varying depths of the railhead were, therefore, tested under axial tension and the strains were measured using strain gauges as well as an image analysis technique, known as the Particle Image Velocimetry (PIV). The head hardened steel exhibit existence of three distinct zones of yield strength; the yield strength as the ratio of the average yield strength provided in the standard (σyr=780MPa) and the corresponding depth as the ratio of the head hardened zone along the axis of symmetry are as follows: (1.17 σyr, 20%), (1.06 σyr, 20%- 80%) and (0.71 σyr, > 80%). The stress-strain curves exhibit limited plastic zone with fracture occurring at strain less than 0.1.

Learning informative features for indoor traversability

This paper presents a method for automatic terrain classification, using a cheap monocular camera in conjunction with a robot’s stall sensor. A first step is to have the robot generate a training set of labelled images. Several techniques are then evaluated for preprocessing the images, reducing their dimensionality, and building a classifier. Finally, the classifier is implemented and used online by an indoor robot. Results are presented, demonstrating an increased level of autonomy.

Effect of topic domain and task type on Web image searching

Many user studies in Web information searching have found the significant effect of task types on search strategies. However, little attention was given to Web image searching strategies, especially the query reformulation activity despite that this is a crucial part in Web image searching. In this study, we investigated the effects of topic domains and task types on user’s image searching behavior and query reformulation strategies. Some significant differences in user’s tasks specificity and initial concepts were identified among the task domains. Task types are also found to influence participant’s result reviewing behavior and query reformulation strategies.

On the robustness and security of digital image watermarking

In most of the digital image watermarking schemes, it becomes a common practice to address security in terms of robustness, which is basically a norm in cryptography. Such consideration in developing and evaluation of a watermarking scheme may severely affect the performance and render the scheme ultimately unusable. This paper provides an explicit theoretical analysis towards watermarking security and robustness in figuring out the exact problem status from the literature. With the necessary hypotheses and analyses from technical perspective, we demonstrate the fundamental realization of the problem. Finally, some necessary recommendations are made for complete assessment of watermarking security and robustness.

Probabilistic matching of image sets for video-based face recognition

We address the problem of face recognition on video by employing the recently proposed probabilistic linear discrimi-nant analysis (PLDA). The PLDA has been shown to be robust against pose and expression in image-based face recognition. In this research, the method is extended and applied to video where image set to image set matching is performed. We investigate two approaches of computing similarities between image sets using the PLDA: the closest pair approach and the holistic sets approach. To better model face appearances in video, we also propose the heteroscedastic version of the PLDA which learns the within-class covariance of each individual separately. Our experi-ments on the VidTIMIT and Honda datasets show that the combination of the heteroscedastic PLDA and the closest pair approach achieves the best performance.

Sensor fault-tolerant observer applied in satellite attitude control

The observing failure and feedback instability might happen when the partial sensors of a satellite attitude control system (SACS) go wrong. A fault diagnosis and isolation (FDI) method based on a fault observer is introduced to detect and isolate the fault sensor at first. Based on the FDI result, the object system state-space equation is transformed and divided into a corresponsive triangular canonical form to decouple the normal subsystem from the fault subsystem. And then the KX fault-tolerant observers of the system in different modes are designed and embedded into online monitoring. The outputs of all KX fault-tolerant observers are selected by the control switch process. That can make sense that the SACS is part-observed and in stable when the partial sensors break down. Simulation results demonstrate the effectiveness and superiority of the proposed method.

Statistical modelling of wind effects on signal propagation for wireless sensor networks

A wireless sensor network system must have the ability to tolerate harsh environmental conditions and reduce communication failures. In a typical outdoor situation, the presence of wind can introduce movement in the foliage. This motion of vegetation structures causes large and rapid signal fading in the communication link and must be accounted for when deploying a wireless sensor network system in such conditions. This thesis examines the fading characteristics experienced by wireless sensor nodes due to the effect of varying wind speed in a foliage obstructed transmission path. It presents extensive measurement campaigns at two locations with the approach of a typical wireless sensor networks configuration. The significance of this research lies in the varied approaches of its different experiments, involving a variety of vegetation types, scenarios and the use of different polarisations (vertical and horizontal). Non–line of sight (NLoS) scenario conditions investigate the wind effect based on different vegetation densities including that of the Acacia tree, Dogbane tree and tall grass. Whereas the line of sight (LoS) scenario investigates the effect of wind when the grass is swaying and affecting the ground-reflected component of the signal. Vegetation type and scenarios are envisaged to simulate real life working conditions of wireless sensor network systems in outdoor foliated environments. The results from the measurements are presented in statistical models involving first and second order statistics. We found that in most of the cases, the fading amplitude could be approximated by both Lognormal and Nakagami distribution, whose m parameter was found to depend on received power fluctuations. Lognormal distribution is known as the result of slow fading characteristics due to shadowing. This study concludes that fading caused by variations in received power due to wind in wireless sensor networks systems are found to be insignificant. There is no notable difference in Nakagami m values for low, calm, and windy wind speed categories. It is also shown in the second order analysis, the duration of the deep fades are very short, 0.1 second for 10 dB attenuation below RMS level for vertical polarization and 0.01 second for 10 dB attenuation below RMS level for horizontal polarization. Another key finding is that the received signal strength for horizontal polarisation demonstrates more than 3 dB better performances than the vertical polarisation for LoS and near LoS (thin vegetation) conditions and up to 10 dB better for denser vegetation conditions.

Static and dynamic strain sensing using a polymer : carbon nanotube film strain sensor

The search for new multipoint, multidirectional strain sensing devices has received a new impetus since the discovery of carbon nanotubes. The excellent electrical, mechanical, and electromechanical properties of carbon nanotubes make them ideal candidates as primary materials in the design of this new generation of sensing devices. Carbon nanotube based strain sensors proposed so far include those based on individual carbon nanotubes for integration in nano or micro elecromechanical systems (NEMS/MEMS) [1], or carbon nanotube films consisting of spatially connected carbon nanotubes [2], carbon nanotube - polymer composites [3,4] for macroscale strain sensing. Carbon nanotube films have good strain sensing response and offer the possibility of multidirectional and multipoint strain sensing, but have poor performance due to weak interaction between carbon nanotubes. In addition, the carbon nanotube film sensor is extremely fragile and difficult to handle and install. We report here the static and dynamic strain sensing characteristics as well as temperature effects of a sandwich carbon nanotube - polymer sensor fabricated by infiltrating carbon nanotube films with polymer.

Field portable time resolved SORS sensor for the identification of concealed hazards

Raman spectroscopy, when used in spatially offset mode, has become a potential tool for the identification of explosives and other hazardous substances concealed in opaque containers. The molecular fingerprinting capability of Raman spectroscopy makes it an attractive tool for the unambiguous identification of hazardous substances in the field. Additionally, minimal sample preparation is required compared with other techniques. We report a field portable time resolved Raman sensor for the detection of concealed chemical hazards in opaque containers. The new sensor uses a pulsed nanosecond laser source in conjunction with an intensified CCD detector. The new sensor employs a combination of time and space resolved Raman spectroscopy to enhance the detection capability. The new sensor can identify concealed hazards by a single measurement without any chemometric data treatments.

Functionalised zinc oxide nanowire gas sensors : enhanced NO2 gas sensor response by chemical modification of nanowire surfaces

Surface coating with an organic self-assembled monolayer (SAM) can enhance surface reactions or the absorption of specific gases and hence improve the response of a metal oxide (MOx) sensor toward particular target gases in the environment. In this study the effect of an adsorbed organic layer on the dynamic response of zinc oxide nanowire gas sensors was investigated. The effect of ZnO surface functionalisation by two different organic molecules, tris(hydroxymethyl)aminomethane (THMA) and dodecanethiol (DT), was studied. The response towards ammonia, nitrous oxide and nitrogen dioxide was investigated for three sensor configurations, namely pure ZnO nanowires, organic-coated ZnO nanowires and ZnO nanowires covered with a sparse layer of organic-coated ZnO nanoparticles. Exposure of the nanowire sensors to the oxidising gas NO2 produced a significant and reproducible response. ZnO and THMA-coated ZnO nanowire sensors both readily detected NO2 down to a concentration in the very low ppm range. Notably, the THMA-coated nanowires consistently displayed a small, enhanced response to NO2 compared to uncoated ZnO nanowire sensors. At the lower concentration levels tested, ZnO nanowire sensors that were coated with THMA-capped ZnO nanoparticles were found to exhibit the greatest enhanced response. ΔR/R was two times greater than that for the as-prepared ZnO nanowire sensors. It is proposed that the ΔR/R enhancement in this case originates from the changes induced in the depletion-layer width of the ZnO nanoparticles that bridge ZnO nanowires resulting from THMA ligand binding to the surface of the particle coating. The heightened response and selectivity to the NO2 target are positive results arising from the coating of these ZnO nanowire sensors with organic-SAM-functionalised ZnO nanoparticles.

Outdoor sensornet design and deployment : experiences from a sugar farm

A wireless sensor network collected real-time water-quality measurements to investigate how current irrigation practices—in particular, underground water salination—affect the environment. New protocols provided high end-to-end packet delivery rates in the hostile deployment environment.

Evaluation of image resolution and super-resolution on face recognition performance

While researchers strive to improve automatic face recognition performance, the relationship between image resolution and face recognition performance has not received much attention. This relationship is examined systematically and a framework is developed such that results from super-resolution techniques can be compared. Three super-resolution techniques are compared with the Eigenface and Elastic Bunch Graph Matching face recognition engines. Parameter ranges over which these techniques provide better recognition performance than interpolated images is determined.