Modeling kinect sensor noise for improved 3D reconstruction and tracking

We contribute an empirically derived noise model for the Kinect sensor. We systematically measure both lateral and axial noise distributions, as a function of both distance and angle of the Kinect to an observed surface. The derived noise model can be used to filter Kinect depth maps for a variety of applications. Our second contribution applies our derived noise model to the KinectFusion system to extend filtering, volumetric fusion, and pose estimation within the pipeline. Qualitative results show our method allows reconstruction of finer details and the ability to reconstruct smaller objects and thinner surfaces. Quantitative results also show our method improves pose estimation accuracy. © 2012 IEEE.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Magnetic resonance imaging: An accurate, radiation-free, alternative to computed tomography for the primary imaging and three-dimensional reconstruction of the bony orbit

Purpose: To determine the extent to which the accuracy of magnetic resonance imaging (MRI) based virtual 3-dimensional (3D) models of the intact orbit can approach that of the gold standard, computed tomography (CT) based models. The goal was to determine whether MRI is a viable alternative to CT scans in patients with isolated orbital fractures and penetrating eye injuries, pediatric patients, and patients requiring multiple scans in whom radiation exposure is ideally limited. Materials and Methods: Patients who presented with unilateral orbital fractures to the Royal Brisbane and Women’s Hospital from March 2011 to March 2012 were recruited to participate in this cross-sectional study. The primary predictor variable was the imaging technique (MRI vs CT). The outcome measurements were orbital volume (primary outcome) and geometric intraorbital surface deviations (secondary outcome)between the MRI- and CT-based 3D models. Results: Eleven subjects (9 male) were enrolled. The patients’ mean age was 30 years. On average, the MRI models underestimated the orbital volume of the CT models by 0.50 0.19 cm3 . The average intraorbital surface deviation between the MRI and CT models was 0.34 0.32 mm, with 78 2.7% of the surface within a tolerance of 0.5 mm. Conclusions: The volumetric differences of the MRI models are comparable to reported results from CT models. The intraorbital MRI surface deviations are smaller than the accepted tolerance for orbital surgical reconstructions. Therefore, the authors believe that MRI is an accurate radiation-free alternative to CT for the primary imaging and 3D reconstruction of the bony orbit. �

Tissue-engineered fibroin scaffolds for ocular tissue reconstruction

The silk protein fibroin (Bombyx mori) provides a potential substrate for use in ocular tissue reconstruction. We have previously demonstrated that transparent membranes produced from fibroin support cultivation of human limbal epithelial (HLE) cells (Tissue Eng A. 14(2008)1203-11). We extend this body of work to studies of limbal mesenchymal stromal cell (L-MSC) growth on fibroin. Also, we investigate the ability to produce a fibroin dual-layer scaffold with an upper HLE layer and lower L-MSC layer...

Additive tissue manufacturing for breast reconstruction: Combining CAD/CAM with adipose tissue engineering

The primary aim of this multidisciplinary project was to develop a new generation of breast implants. Disrupting the currently prevailing paradigm of silicone implants which permanently introduce a foreign body into mastectomy patients, highly porous implants developed as part of this PhD project are biodegradable by the body and augment the growth of natural tissue. Our technology platform leverages computer-assisted-design which allows us to manufacture fully patient-specific implants based on a personalised medicine approach. Multiple animal studies conducted in this project have shown that the polymeric implant slowly degrades within the body harmlessly while the body's own tissue forms concurrently.

Reconstruction of a multi-vent kimberlite eruption from deposit and host rock characteristics: Jericho kimberlite, Nunavut, Canada

The Jericho kimberlite (173.1. ±. 1.3. Ma) is a small (~. 130. ×. 70. m), multi-vent system that preserves products from deep (>. 1. km?) portions of kimberlite vents. Pit mapping, drill core examination, petrographic study, image analysis of olivine crystals (grain size distributions and shape studies), and compositional and mineralogical studies, are used to reconstruct processes from near-surface magma ascent to kimberlite emplacement and alteration. The Jericho kimberlite formed by multiple eruptions through an Archean granodiorite batholith that was overlain by mid-Devonian limestones ~. 1. km in thickness. Kimberlite magma ascended through granodiorite basement by dyke propagation but ascended through limestone, at least in part, by locally brecciating the host rocks. After the first explosive breakthrough to surface, vent deepening and widening occurred by the erosive forces of the waxing phase of the eruption, by gravitationally induced failures as portions of the vent margins slid into the vent and, in the deeper portions of the vent (>. 1. km), by scaling, as thin slabs burst from the walls into the vent. At currently exposed levels, coherent kimberlite (CK) dykes (<. 40. cm thick) are found to the north and south of the vent complex and represent the earliest preserved in-situ products of Jericho magmatism. Timing of CK emplacement on the eastern side of the vent complex is unclear; some thick CK (15-20. m) may have been emplaced after the central vent was formed. Explosive eruptive products are preserved in four partially overlapping vents that are roughly aligned along strike with the coherent kimberlite dyke. The volcaniclastic kimberlite (VK) facies are massive and poorly sorted, with matrix- to clast-supported textures. The VK facies fragmented by dry, volatile-driven processes and were emplaced by eruption column collapse back into the volcanic vents. The first explosive products, poorly preserved because of partial destruction by later eruptions, are found in the central-east vent and were formed by eruption column collapse after the vent was largely cleared of country rock debris. The next active vent was either the north or south vent. Collapse of the eruption column, linked to a vent widening episode, resulted in coeval avalanching of pipe margin walls into the north vent, forming interstratified lenses of country rock-rich boulder breccias in finer-grained volcaniclastic kimberlite. South vent kimberlite has similar characteristics to kimberlite of the north vent and likely formed by similar processes. The final eruptive phase formed olivine-rich and moderately sorted deposits of the central vent. Better sorting is attributed to recycling of kimberlite debris by multiple eruptions through the unconsolidated volcaniclastic pile and associated collapse events. Post-emplacement alteration varies in intensity, but in all cases, has overprinted the primary groundmass and matrix, in CK and VK, respectively. Erosion has since removed all limestone cover.

Analyzing multi-fiber reconstruction in high angular resolution diffusion imaging using the tensor distribution function

High-angular resolution diffusion imaging (HARDI) can reconstruct fiber pathways in the brain with extraordinary detail, identifying anatomical features and connections not seen with conventional MRI. HARDI overcomes several limitations of standard diffusion tensor imaging, which fails to model diffusion correctly in regions where fibers cross or mix. As HARDI can accurately resolve sharp signal peaks in angular space where fibers cross, we studied how many gradients are required in practice to compute accurate orientation density functions, to better understand the tradeoff between longer scanning times and more angular precision. We computed orientation density functions analytically from tensor distribution functions (TDFs) which model the HARDI signal at each point as a unit-mass probability density on the 6D manifold of symmetric positive definite tensors. In simulated two-fiber systems with varying Rician noise, we assessed how many diffusionsensitized gradients were sufficient to (1) accurately resolve the diffusion profile, and (2) measure the exponential isotropy (EI), a TDF-derived measure of fiber integrity that exploits the full multidirectional HARDI signal. At lower SNR, the reconstruction accuracy, measured using the Kullback-Leibler divergence, rapidly increased with additional gradients, and EI estimation accuracy plateaued at around 70 gradients.

Framework for assessing the influence of critical infrastructure interdependencies in post-disaster reconstruction management

Due to ever increasing climate instability, the number of natural disasters affecting society and communities is expected to increase globally in the future, which will result in a growing number of casualties and damage to property and infrastructure. Such damage poses crucial challenges for recovery of interdependent critical infrastructures. Post-disaster reconstruction is a complex undertaking as it is not only closely linked to the well-being and essential functioning of society, but also requires a large financial commitment. Management of critical infrastructure during post-disaster recovery needs to be underpinned by a holistic recognition that the recovery of each individual infrastructure system (e.g. energy, water, transport and information and communication technology) can be affected by the interdependencies that exist between these different systems. A fundamental characteristic of these interdependencies is that failure of one critical infrastructure system can result in the failure of other interdependent infrastructures, leading to a cascade of failures, which can impede post-disaster recovery and delay the subsequent reconstruction process. Consequently, there is a critical need for developing a holistic strategy to assess the influence of infrastructure interdependencies, and for incorporating these interdependencies into a post-disaster recovery strategy. This paper discusses four key dimensions of interdependencies that need to be considered in a post-disaster reconstruction planning. Using key concepts and sub-concepts derived from the notion of interdependency, the paper examines how critical infrastructure interdependencies affect the recovery processes of damaged infrastructures.

Community participation in post-disaster reconstruction

When a community already torn by a prolonged war is subsequently subjected to being hit by a natural disaster, the combined impact of such disasters can be extremely devastating. Affected communities often face enormous challenges during the long-term reconstruction, mainly due to the lack of a viable community involvement process. In post-war settings, affected communities are often conceived as being disabled and are hardly ever consulted when reconstruction projects are instigated. This lack of community involvement often leads to poor project planning, decreased community support and an unsustainable completed project. The impact of war, coupled with the tensions created by the poor housing provisions, often hinder the affected residents from integrating permanently into their home communities. This paper identifies a number of fundamental factors that act as barriers to community participation in reconstruction projects. The paper is based on a statistical analysis of a questionnaire survey administered in 2012 in Afghanistan.

The influence of critical infrastructure interdependencies on post-disaster reconstruction: Elements of infrastructure interdependency that impede the post-disaster recovery effort

The importance of developing effective disaster management strategies has significantly grown as the world continues to be confronted with unprecedented disastrous events. Factors such as climate instability, recent urbanization along with rapid population growth in many cities around the world have unwittingly exacerbated the risks of potential disasters, leaving a large number of people and infrastructure exposed to new forms of threats from natural disasters such as flooding, cyclones, and earthquakes. With disasters on the rise, effective recovery planning of the built environment is becoming imperative as it is not only closely related to the well-being and essential functioning of society, but it also requires significant financial commitment. In the built environment context, post-disaster reconstruction focuses essentially on the repair and reconstruction of physical infrastructures. The reconstruction and rehabilitation efforts are generally performed in the form of collaborative partnerships that involve multiple organisations, enabling the restoration of interdependencies that exist between infrastructure systems such as energy, water (including wastewater), transport, and telecommunication systems. These interdependencies are major determinants of vulnerabilities and risks encountered by critical infrastructures and therefore have significant implications for post-disaster recovery. When disrupted by natural disasters, such interdependencies have the potential to promote the propagation of failures between critical infrastructures at various levels, and thus can have dire consequences on reconstruction activities. This paper outlines the results of a pilot study on how elements of infrastructure interdependencies have the potential to impede the post-disaster recovery effort. Using a set of unstructured interview questionnaires, plausible arguments provided by seven respondents revealed that during post-disaster recovery, critical infrastructures are mutually dependent on each other’s uninterrupted availability, both physically and through a host of information and communication technologies. Major disruption to their physical and cyber interdependencies could lead to cascading failures, which could delay the recovery effort. Thus, the existing interrelationship between critical infrastructures requires that the entire interconnected network be considered when managing reconstruction activities during the post-disaster recovery period.

Separable spatiotemporal priors for convex reconstruction of time-varying 3D point clouds

Reconstructing 3D motion data is highly under-constrained due to several common sources of data loss during measurement, such as projection, occlusion, or miscorrespondence. We present a statistical model of 3D motion data, based on the Kronecker structure of the spatiotemporal covariance of natural motion, as a prior on 3D motion. This prior is expressed as a matrix normal distribution, composed of separable and compact row and column covariances. We relate the marginals of the distribution to the shape, trajectory, and shape-trajectory models of prior art. When the marginal shape distribution is not available from training data, we show how placing a hierarchical prior over shapes results in a convex MAP solution in terms of the trace-norm. The matrix normal distribution, fit to a single sequence, outperforms state-of-the-art methods at reconstructing 3D motion data in the presence of significant data loss, while providing covariance estimates of the imputed points.