3D ellipsoid fitting for multi-view gait recognition

Gait recognition approaches continue to struggle with challenges including view-invariance, low-resolution data, robustness to unconstrained environments, and fluctuating gait patterns due to subjects carrying goods or wearing different clothes. Although computationally expensive, model based techniques offer promise over appearance based techniques for these challenges as they gather gait features and interpret gait dynamics in skeleton form. In this paper, we propose a fast 3D ellipsoidal-based gait recognition algorithm using a 3D voxel model derived from multi-view silhouette images. This approach directly solves the limitations of view dependency and self-occlusion in existing ellipse fitting model-based approaches. Voxel models are segmented into four components (left and right legs, above and below the knee), and ellipsoids are fitted to each region using eigenvalue decomposition. Features derived from the ellipsoid parameters are modeled using a Fourier representation to retain the temporal dynamic pattern for classification. We demonstrate the proposed approach using the CMU MoBo database and show that an improvement of 15-20% can be achieved over a 2D ellipse fitting baseline.

Visual route recognition with a handful of bits

In this paper we use a sequence-based visual localization algorithm to reveal surprising answers to the question, how much visual information is actually needed to conduct effective navigation? The algorithm actively searches for the best local image matches within a sliding window of short route segments or 'sub-routes', and matches sub-routes by searching for coherent sequences of local image matches. In contract to many existing techniques, the technique requires no pre-training or camera parameter calibration. We compare the algorithm's performance to the state-of-the-art FAB-MAP 2.0 algorithm on a 70 km benchmark dataset. Performance matches or exceeds the state of the art feature-based localization technique using images as small as 4 pixels, fields of view reduced by a factor of 250, and pixel bit depths reduced to 2 bits. We present further results demonstrating the system localizing in an office environment with near 100% precision using two 7 bit Lego light sensors, as well as using 16 and 32 pixel images from a motorbike race and a mountain rally car stage. By demonstrating how little image information is required to achieve localization along a route, we hope to stimulate future 'low fidelity' approaches to visual navigation that complement probabilistic feature-based techniques.

Fast and robust stereo matching algorithms for mining automation

The mining environment, being complex, irregular, and time-varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper evaluates a number of matching techniques for possible use in a stereo vision sensor for mining automation applications. Area-based techniques have been investigated because they have the potential to yield dense maps, are amenable to fast hardware implementation, and are suited to textured scenes. In addition, two nonparametric transforms, namely, rank and census, have been investigated. Matching algorithms using these transforms were found to have a number of clear advantages, including reliability in the presence of radiometric distortion, low computational complexity, and amenability to hardware implementation.

Suitability of non-parametric stereo matching algorithms for mining automation

The mining environment, being complex, irregular and time varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper assesses the suitability of a number of matching techniques for use in a stereo vision sensor for close range scenes consisting primarily of rocks. These include traditional area-based matching metrics, and non-parametric transforms, in particular, the rank and census transforms. Experimental results show that the rank and census transforms exhibit a number of clear advantages over area-based matching metrics, including their low computational complexity, and robustness to certain types of distortion.

Fast and robust stereo matching algorithms for mining automation

The mining environment, being complex, irregular and time varying, presents a challenging prospect for stereo vision. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This paper evaluates a number of matching techniques for possible use in a stereo vision sensor for mining automation applications. Area-based techniques have been investigated because they have the potential to yield dense maps, are amenable to fast hardware implementation, and are suited to textured scenes. In addition, two non-parametric transforms, namely, the rank and census, have been investigated. Matching algorithms using these transforms were found to have a number of clear advantages, including reliability in the presence of radiometric distortion, low computational complexity, and amenability to hardware implementation.

CAT-GRAPH+ : towards odometry-driven place consolidation in changing environments

Changing environments present a number of challenges to mobile robots, one of the most significant being mapping and localisation. This problem is particularly significant in vision-based systems where illumination and weather changes can cause feature-based techniques to fail. In many applications only sections of an environment undergo extreme perceptual change. Some range-based sensor mapping approaches exploit this property by combining occasional place recognition with the assumption that odometry is accurate over short periods of time. In this paper, we develop this idea in the visual domain, by using occasional vision-driven loop closures to infer loop closures in nearby locations where visual recognition is difficult due to extreme change. We demonstrate successful map creation in an environment in which change is significant but constrained to one area, where both the vanilla CAT-Graph and a Sum of Absolute Differences matcher fails, use the described techniques to link dissimilar images from matching locations, and test the robustness of the system against false inferences.

Novel keratins identified by quantitative proteomic analysis as the major cytoskeletal proteins of equine (Equus caballus) hoof lamellar tissue

The dermo-epidermal interface that connects the equine distal phalanx to the cornified hoof wall withstands great biomechanical demands, but is also a region where structural failure often ensues as a result of laminitis. The cytoskeleton in this region maintains cell structure and facilitates intercellular adhesion, making it likely to be involved in laminitis pathogenesis, although it is poorly characterized in the equine hoof lamellae. The objective of the present study was to identify and quantify the cytoskeletal proteins present in the epidermal and dermal lamellae of the equine hoof by proteomic techniques. Protein was extracted from the mid-dorsal epidermal and dermal lamellae from the front feet of 5 Standardbred geldings and 1 Thoroughbred stallion. Mass spectrometry-based spectral counting techniques, PAGE, and immunoblotting were used to identify and quantify cytoskeletal proteins, and indirect immunofluorescence was used for cellular localization of K14 and K124 (where K refers to keratin). Proteins identified by spectral counting analysis included 3 actin microfilament proteins; 30 keratin proteins along with vimentin, desmin, peripherin, internexin, and 2 lamin intermediate filament proteins; and 6 tubulin microtubule proteins. Two novel keratins, K42 and K124, were identified as the most abundant cytoskeletal proteins (22.0 ± 3.2% and 23.3 ± 4.2% of cytoskeletal proteins, respectively) in equine hoof lamellae. Immunoreactivity to K14 was localized to the basal cell layer, and that to K124 was localized to basal and suprabasal cells in the secondary epidermal lamellae. Abundant proteins K124, K42, K14, K5, and α1-actin were identified on 1- and 2-dimensional polyacrylamide gels and aligned with the results of previous studies. Results of the present study provide the first comprehensive analysis of cytoskeletal proteins present in the equine lamellae by using mass spectrometry-based techniques for protein quantification and identification.

Cytogenetic alterations in nonmelanoma skin cancer : a review

Since the advent of cytogenetic analysis, knowledge about fundamental aspects of cancer biology has increased, allowing the processes of cancer development and progression to be more fully understood and appreciated. Classical cytogenetic analysis of solid tumors had been considered difficult, but new advances in culturing techniques and the addition of new cytogenetic technologies have enabled a more comprehensive analysis of chromosomal aberrations associated with solid tumors. Our purpose in this review is to discuss the cytogenetic findings on a number of nonmelanoma skin cancers, including squamous- and basal cell carcinomas, keratoacanthoma, squamous cell carcinoma in situ (Bowen's disease), and solar keratosis. Through classical cytogenetic techniques, as well as fluorescence-based techniques such as fluorescence in situ hybridization and comparative genomic hybridization, numerous chromosomal alterations have been identified. These aberrations may aid in further defining the stages and classifications of nonmelanoma skin cancer and also may implicate chromosomal regions involved in progression and metastatic potential. This information, along with the development of newer technologies (including laser capture microdissection and comparative genomic hybridization arrays) that allow for more refined analysis, will continue to increase our knowledge about the role of chromosomal events at all stages of cancer development and progression and, more specifically, about how they are associated with nonmelanoma skin cancer.

Swimmer localization from a moving camera

At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.

Proteomic and other analyses to determine the functional consequences of deregulated kallikrein-related peptidase (KLK) expression in prostate and ovarian cancer

Rapidly developing proteomic tools are improving detection of deregulated kallikrein-related peptidase (KLK) expression, at the protein level, in prostate and ovarian cancer, as well as facilitating the determination of functional consequences downstream. Mass spectrometry (MS)-driven proteomics uniquely allows for the detection, identification and quantification of thousands of proteins in a complex protein pool, and this has served to identify certain KLKs as biomarkers for these diseases. In this review we describe applications of this technology in KLK biomarker discovery, and elucidate MS-based techniques which have been used for unbiased, global screening of KLK substrates within complex protein pools. Although MS-based KLK degradomic studies are limited to date, they helped to discover an array of novel KLK substrates. Substrates identified by MS-based degradomics are reported with improved confidence over those determined by incubating a purified or recombinant substrate and protease of interest, in vitro. We propose that these novel proteomic approaches represent the way forward for KLK research, in order to correlate proteolysis of biological substrates with tissue-related consequences, toward clinical targeting of KLK expression and function for cancer diagnosis, prognosis and therapies.

Carbon nanotube membranes with ultrahigh specific adsorption capacity for water desalination and purification

Development of technologies for water desalination and purification is critical to meet the global challenges of insufficient water supply and inadequate sanitation, especially for point-of-use applications. Conventional desalination methods are energy and operationally intensive, whereas adsorption-based techniques are simple and easy to use for point-of-use water purification, yet their capacity to remove salts is limited. Here we report that plasma-modified ultralong carbon nanotubes exhibit ultrahigh specific adsorption capacity for salt (exceeding 400% by weight) that is two orders of magnitude higher than that found in the current state-of-the-art activated carbon-based water treatment systems. We exploit this adsorption capacity in ultralong carbon nanotube-based membranes that can remove salt, as well as organic and metal contaminants. These ultralong carbon nanotube-based membranes may lead to next-generation rechargeable, point-of-use potable water purification appliances with superior desalination, disinfection and filtration properties. © 2013 Macmillan Publishers Limited.

Low-temperature plasmas in carbon nanostructure synthesis

Plasma-based techniques offer many unique possibilities for the synthesis of various nanostructures both on the surface and in the plasma bulk. In contrast to the conventional chemical vapor deposition and some other techniques, plasma-based processes ensure high level of controllability, good quality of the produced nanomaterials, and reduced environmental risk. In this work, the authors briefly review the unique features of the plasma-enhanced chemical vapor deposition approaches, namely, the techniques based on inductively coupled, microwave, and arc discharges. Specifically, the authors consider the plasmas with the ion/electron density ranging from 10^10 to 10^14 cm−3, electron energy in the discharge up to ∼10 eV, and the operating pressure ranging from 1 to 10^4 Pa (up to 105 Pa for the atmospheric-pressure arc discharges). The operating frequencies of the discharges considered range from 460 kHz for the inductively coupled plasmas, and up to 2.45 GHz for the microwave plasmas. The features of the direct-current arc discharges are also examined. The authors also discuss the principles of operation of these systems, as well as the effects of the key plasma parameters on the conditions of nucleation and growth of the carbon nanostructures, mainly carbon nanotubes and graphene. Advantages and disadvantages of these plasma systems are considered. Future trends in the development of these plasma-based systems are also discussed.

Deterministic control of plasma-assembled self-organized Ge/Si quantum dot arrays

Self-assembly of size-uniform and spatially ordered quantum dot (QD) arrays is one of the major challenges in the development of the new generation of semiconducting nanoelectronic and photonic devices. Assembly of Ge QD (in the ∼5-20 nm size range) arrays from randomly generated position and size-nonuniform nanodot patterns on plasma-exposed Si (100) surfaces is studied using hybrid multiscale numerical simulations. It is shown, by properly manipulating the incoming ion/neutral flux from the plasma and the surface temperature, the uniformity of the nanodot size within the array can be improved by 34%-53%, with the best improvement achieved at low surface temperatures and high external incoming fluxes, which are intrinsic to plasma-aided processes. Using a plasma-based process also leads to an improvement (∼22% at 700 K surface temperature and 0.1 MLs incoming flux from the plasma) of the spatial order of a randomly sampled nanodot ensemble, which self-organizes to position the dots equidistantly to their neighbors within the array. Remarkable improvements in QD ordering and size uniformity can be achieved at high growth rates (a few nms) and a surface temperature as low as 600 K, which broadens the range of suitable substrates to temperature-sensitive ultrathin nanofilms and polymers. The results of this study are generic, can also be applied to nonplasma-based techniques, and as such contributes to the development of deterministic strategies of nanoassembly of self-ordered arrays of size-uniform QDs, in the size range where nanodot ordering cannot be achieved by presently available pattern delineation techniques.

Large arrays and networks of carbon nanotubes : morphology control by process parameters

Large arrays and networks of carbon nanotubes, both single- and multi-walled, feature many superior properties which offer excellent opportunities for various modern applications ranging from nanoelectronics, supercapacitors, photovoltaic cells, energy storage and conversation devices, to gas- and biosensors, nanomechanical and biomedical devices etc. At present, arrays and networks of carbon nanotubes are mainly fabricated from the pre-fabricated separated nanotubes by solution-based techniques. However, the intrinsic structure of the nanotubes (mainly, the level of the structural defects) which are required for the best performance in the nanotube-based applications, are often damaged during the array/network fabrication by surfactants, chemicals, and sonication involved in the process. As a result, the performance of the functional devices may be significantly degraded. In contrast, directly synthesized nanotube arrays/networks can preclude the adverse effects of the solution-based process and largely preserve the excellent properties of the pristine nanotubes. Owing to its advantages of scale-up production and precise positioning of the grown nanotubes, catalytic and catalyst-free chemical vapor depositions (CVD), as well as plasma-enhanced chemical vapor deposition (PECVD) are the methods most promising for the direct synthesis of the nanotubes.

Insomnia : A Self Help Handbook

This book is a practical and useful tool for getting your sleep back on track. Even if you have suffered from insomnia for many years, this book contains simple, easy to learn strategies to manage your sleep loss through evidence-based techniques such as cognitive therapy and stimulus control. Dr. Sacre will guide you through these approaches and explain how they work and why they are recommended above other approaches. There is a chapter on special populations that tells you what to do if you are a shift worker, long distance traveller, parent, older adult, woman (including pregnancy and menopause) or an elite athlete. If you want to enjoy natural, healthy and satisfying sleep again, this handbook gives you all the tools you need to achieve it. You only need to have the motivation and discipline to apply the strategies and stick to them over time. This handbook first explains what normal sleep is all about and challenges some myths about sleep and insomnia. Then you will be guided through a thorough sleep assessment. Insomnia is then described in detail including different types of insomnia and the kinds of factors that contribute to sleep loss. Through the following chapters, you will be shown step-by-step what to do to bring about change in your sleeping patterns and habits, through addressing the factors that perpetuate poor sleep. These factors mainly revolve around unhelpful thinking, compensatory behaviors, poor sleep hygiene and environmental influences. These are all things that are within your control and Dr. Sacre will show you how. Dr. Sacre has worked in the fields of sleep health, mental health and addictive disorders for 25 years and over that time, she has encountered hundreds of people who have struggled with insomnia and sleep loss due to other causes. She currently heads the Therapy Programs department at Belmont Private Hospital in Brisbane, Australia, where there is an emphasis on Cognitive Behavioral Therapy, including a Cognitive Behavioral Therapy for Insomnia (CBT-i) program. A psychologist and nurse, Dr. Sacre is a long-time member of the Australasian Sleep Association and the Australian Psychological Society. She has conducted research into the function of dreaming, online sleep surveys and the usefulness of sleep self-help guides for students, older adults and carers of people with dementia. She has also published on diverse topics, including the management of nightmares in war veterans. She is an Adjunct Associate Professor at Queensland University of Technology, Brisbane and lectures professionals, including psychologists, school counselors and psychiatrists, on sleep disorders and their management as well as Cognitive Behavioral Therapy.