Avaliação in vitro da adaptação marginal e interna de coroas de cerâmica e resina composta fabricadas com o sistema CAD/CAM utilizando duas câmeras intraorais de moldagem digital

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Ciências da Saúde, Programa de Pós-Graduação em Ciências da Saúde, 2015.

Quantum of Vision

Visual inputs to artificial and biological visual systems are often quantized: cameras accumulate photons from the visual world, and the brain receives action potentials from visual sensory neurons. Collecting more information quanta leads to a longer acquisition time and better performance. In many visual tasks, collecting a small number of quanta is sufficient to solve the task well. The ability to determine the right number of quanta is pivotal in situations where visual information is costly to obtain, such as photon-starved or time-critical environments. In these situations, conventional vision systems that always collect a fixed and large amount of information are infeasible. I develop a framework that judiciously determines the number of information quanta to observe based on the cost of observation and the requirement for accuracy. The framework implements the optimal speed versus accuracy tradeoff when two assumptions are met, namely that the task is fully specified probabilistically and constant over time. I also extend the framework to address scenarios that violate the assumptions. I deploy the framework to three recognition tasks: visual search (where both assumptions are satisfied), scotopic visual recognition (where the model is not specified), and visual discrimination with unknown stimulus onset (where the model is dynamic over time). Scotopic classification experiments suggest that the framework leads to dramatic improvement in photon-efficiency compared to conventional computer vision algorithms. Human psychophysics experiments confirmed that the framework provides a parsimonious and versatile explanation for human behavior under time pressure in both static and dynamic environments.

Looking at the night sky

There are two series of images in this exhibition: Series # 1: Images 4 – 9 Astronomical observatories in different countries and telescopes which have made some important discoveries in science of astronomy. These images were taken with simple primitive camera including a homemade toy camera and a Holga pinhole camera. The toy camera use a simple plastic lens and film. It produces a very softly focused and blurred image. The Holga Pinhole camera has a panoramic format and also uses film. (The pinhole camera has no lens and only a very small pinhole to lets the light into the camera to produce a simple image) For me the limited resolution of these primitive cameras invoke a sense of wonder, mystery and imagination which the ancient observers must have experienced when looking at the night sky. Series # 2: Images 1 – 3 and 9 - 14 presents individual celestial objects including, the planet Saturn, the Moon, the Sun, a comet and a Star Cluster. All these images have been re-photographed through a number of large primitive lenses. Some are hand made glass lenses and others are hollow and filled with water. These primitive lenses distort and stretch the images and represent the way in which the lens and (the telescope) have changed our vision of the cosmos. They also represent the subjectivity of the lens, something that all photographers know about - just because we see something through a lens, does not mean that all has been revealed and that what finally perceive is both a combination of what we see and what we feel inside and our imagination. The toy and pinhole camera images were made during a 5 year period starting in 2010 and up to 2015. The second series have all been made during 2015.

Response of feral cats to a track-based baiting programme using Eradicat baits

Summary The feral Cat (Felis catus) is a significant threat to Australian fauna, and reducing their impacts is considered an essential action for threatened species conservation. Poison baiting is increasingly being used for the broad scale control of feral cats. In this study, we measured the population response of feral cats to a track-based baiting programme using Eradicat baits in the semi-arid northern wheatbelt region of Western Australia. Over two years, 1500 baits were laid once annually and the response of feral cats was measured using remote cameras in a before–after, control–impact design. There was a significant reduction in feral cat activity in the second year, but not the first. During bait uptake trials, corvids removed the most number of baits, followed by cats and varanids. The lack of a response to baiting in the first year may be due to existing low cat numbers in the baited area and/or the timing of the baiting. We provide a list of key recommendations to help inform future cat baiting programmes and research.

Spiking neural network-based target tracking control for autonomous mobile robots

In this paper, a target tracking controller based on spiking neural network is proposed for autonomous robots. This controller encodes the preprocessed environmental and target information provided by CCD cameras, encoders and ultrasonic sensors into spike trains, which are integrated by a three-layer spiking neural network (SNN). The outputs of SNN are generated based on the competition between the forward/backward neuron pair corresponding to each motor, with the weights evolved by the Hebbian learning. The application to target tracking of a mobile robot in unknown environment verifies the validity of the proposed controller.

Fast vision-based catheter 3D reconstruction

Continuum robots offer better maneuverability and inherent compliance and are well-suited for surgical applications as catheters, where gentle interaction with the environment is desired. However, sensing their shape and tip position is a challenge as traditional sensors can not be employed in the way they are in rigid robotic manipulators. In this paper, a high speed vision-based shape sensing algorithm for real-time 3D reconstruction of continuum robots based on the views of two arbitrary positioned cameras is presented. The algorithm is based on the closed-form analytical solution of the reconstruction of quadratic curves in 3D space from two arbitrary perspective projections. High-speed image processing algorithms are developed for the segmentation and feature extraction from the images. The proposed algorithms are experimentally validated for accuracy by measuring the tip position, length and bending and orientation angles for known circular and elliptical catheter shaped tubes. Sensitivity analysis is also carried out to evaluate the robustness of the algorithm. Experimental results demonstrate good accuracy (maximum errors of  ±0.6 mm and  ±0.5 deg), performance (200 Hz), and robustness (maximum absolute error of 1.74 mm, 3.64 deg for the added noises) of the proposed high speed algorithms.

Body parts segmentation with attached props using RGB-D imaging

People detection is essential in a lot of different systems. Many applications nowadays tend to require people detection to achieve certain tasks. These applications come under many disciplines, such as robotics, ergonomics, biomechanics, gaming and automotive industries. This wide range of applications makes human body detection an active area of research. With the release of depth sensors or RGB-D cameras such as Micosoft Kinect, this area of research became more active, specially with their affordable price. Human body detection requires the adaptation of many scenarios and situations. Various conditions such as occlusions, background cluttering and props attached to the human body require training on custom built datasets. In this paper we present an approach to prepare training datasets to detect and track human body with attached props. The proposed approach uses rigid body physics simulation to create and animate different props attached to the human body. Three scenarios are implemented. In the first scenario the prop is closely attached to the human body, such as a person carrying a backpack. In the second scenario, the prop is slightly attached to the human body, such as a person carrying a briefcase. In the third scenario the prop is not attached to the human body, such as a person dragging a trolley bag. Our approach gives results with accuracy of 93% in identifying both the human body parts and the attached prop in all the three scenarios.

Cost optimal resource provisioning for live video forwarding across video data centers

Live video forwarding for IP cameras has become a popular service in video data centers. In the forwarding service, requests of end users from different regions arrive in real-time to gain live video streams of IP cameras from inter-connected video data centers. A fundamental scheduling problem is how to assign resources with the global optimal resource cost and forwarding delay to forward live video streams. We introduce the resource provisioning cost as the combination of media server cost, connection bandwidth cost, and forwarding delay cost. In this paper, a multi-objective resource provisioning (MORP) approach is proposed to deal with the online inter-datacenter resource provisioning problem. The approach aims at minimizing the resource provisioning cost during live video forwarding. It adaptively allocates media servers in appropriate video data centers and connects the chosen media servers together to provide system scalability and connectivity. Different from previous works, MORP takes both resource capacity and diversity (e.g. location and price) into consideration during live video forwarding. Finally, the experimental results show that MORP approach not only cuts the resource provisioning cost of 3% to 10% comparing to the bench mark approach, but also shortens the resource provisioning delay.

Regional drivers of clutch loss reveal important trade-offs for beach-nesting birds

Coastal birds are critical ecosystem constituents on sandy shores, yet are threatened by depressed reproductive success resulting from direct and indirect anthropogenic and natural pressures. Few studies examine clutch fate across the wide range of environments experienced by birds; instead, most focus at the small site scale. We examine survival of model shorebird clutches as an index of true clutch survival at a regional scale (∼200 km), encompassing a variety of geomorphologies, predator communities, and human use regimes in southeast Queensland, Australia. Of the 132 model nests deployed and monitored with cameras, 45 (34%) survived the experimental exposure period. Thirty-five (27%) were lost to flooding, 32 (24%) were depredated, nine (7%) buried by sand, seven (5%) destroyed by people, three (2%) failed by unknown causes, and one (1%) was destroyed by a dog. Clutch fate differed substantially among regions, particularly with respect to losses from flooding and predation. ‘Topographic’ exposure was the main driver of mortality of nests placed close to the drift line near the base of dunes, which were lost to waves (particularly during storms) and to a lesser extent depredation. Predators determined the fate of clutches not lost to waves, with the depredation probability largely influenced by region. Depredation probability declined as nests were backed by higher dunes and were placed closer to vegetation. This study emphasizes the scale at which clutch fate and survival varies within a regional context, the prominence of corvids as egg predators, the significant role of flooding as a source of nest loss, and the multiple trade-offs faced by beach-nesting birds and those that manage them.

A remarkably quick habituation and high use of a rope bridge by an endangered marsupial, the western ringtail possum

Rope bridges are being increasingly installed worldwide to mitigate the negative impacts of roads on arboreal animals. However, monitoring of these structures is still limited and an assessment of factors influencing the crossing behaviours is lacking. We monitored the use of a rope bridge near Busselton, Western Australia by the endangered western ringtail possums (Pseudocheirus occidentalis) in order to identify the patterns of use and factors influencing the crossings. We installed motion sensor cameras and microchip readers on the bridge to record the crossings made by individual animals, and analysed these crossing data using generalised linear models that included factors such as days since the installation of the bridge, breeding season, wind speed, minimum temperature and moonlight. Possums started investigating the bridge even before the installation was completed, and the first complete crossing was recorded only 36 days after the installation, which is remarkably sooner than arboreal species studied in other parts of Australia. The possums crossed the bridge increasingly over 270 days of monitoring at a much higher rate than we expected (8.87 ± 0.59 complete crossings per night). Possums crossed the bridge less on windy nights and warm nights probably due to the risk of being blown away and heat stress on warmer days. Crossings also decreased slightly on brighter nights probably due to the higher risk of predation. Breeding season did not influence the crossings. Pseudocheirus occidentalis habituated to the bridge very quickly, and our results demonstrate that rope bridges have a potential as an effective mitigation measure against the negative impacts of roads on this species. More studies and longer monitoring, as well as investigating whether crossing results in the restoration of gene flow are then needed in order to further assess the true conservation value of these crossing structures.

Positron Emission Tomography (PET) Tumor Segmentation and Quantification: Development of New Algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera’s point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ~10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera’s PSF. The algorithm can also improve dose estimation and treatment planning.

MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices. Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines. The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers. We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain. In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem. In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

A NOVEL MODELING AND STATE OF CHARGE ESTIMATION SCHEME FOR LITHIUM-ION BATTERIES

Lithium Ion (Li-Ion) batteries have got attention in recent decades because of their undisputable advantages over other types of batteries. They are used in so many our devices which we need in our daily life such as cell phones, lap top computers, cameras, and so many electronic devices. They also are being used in smart grids technology, stand-alone wind and solar systems, Hybrid Electric Vehicles (HEV), and Plug in Hybrid Electric Vehicles (PHEV). Despite the rapid increase in the use of Lit-ion batteries, the existence of limited battery models also inadequate and very complex models developed by chemists is the lack of useful models a significant matter. A battery management system (BMS) aims to optimize the use of the battery, making the whole system more reliable, durable and cost effective. Perhaps the most important function of the BMS is to provide an estimate of the State of Charge (SOC). SOC is the ratio of available ampere-hour (Ah) in the battery to the total Ah of a fully charged battery. The Open Circuit Voltage (OCV) of a fully relaxed battery has an approximate one-to-one relationship with the SOC. Therefore, if this voltage is known, the SOC can be found. However, the relaxed OCV can only be measured when the battery is relaxed and the internal battery chemistry has reached equilibrium. This thesis focuses on Li-ion battery cell modelling and SOC estimation. In particular, the thesis, introduces a simple but comprehensive model for the battery and a novel on-line, accurate and fast SOC estimation algorithm for the primary purpose of use in electric and hybrid-electric vehicles, and microgrid systems. The thesis aims to (i) form a baseline characterization for dynamic modeling; (ii) provide a tool for use in state-of-charge estimation. The proposed modelling and SOC estimation schemes are validated through comprehensive simulation and experimental results.

Examination of the Effect of Time Delay on Fragmentation

The main objective of blasting is to produce optimum fragmentation for downstream processing. Fragmentation is usually considered optimum when the average fragment size is minimum and the fragmentation distribution as uniform as possible. One of the parameters affecting blasting fragmentation is believed to be time delay between holes of the same row. Although one can find a significant number of studies in the literature, which examine the relationship between time delay and fragmentation, their results have been often controversial. The purpose of this work is to increase the level of understanding of how time delay between holes of the same row affects fragmentation. Two series of experiments were conducted for this purpose. The first series involved tests on small scale grout and granite blocks to determine the moment of burden detachment. The instrumentation used for these experiments consisted mainly of strain gauges and piezoelectric sensors. Some experiments were also recorded with a high speed camera. It was concluded that the time of detachment for this specific setup is between 300 and 600 μs. The second series of experiments involved blasting of a 2 meter high granite bench and its purpose was the determination of the hole-to-hole delay that provides optimum fragmentation. The fragmentation results were assessed with image analysis software. Moreover, vibration was measured close to the blast and the experiments were recorded with high speed cameras. The results suggest that fragmentation was optimum when delays between 4 and 6 ms were used for this specific setup. Also, it was found that the moment at which gases first appear to be venting from the face was consistently around 6 ms after detonation.

Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences

Fully articulated hand tracking promises to enable fundamentally new interactions with virtual and augmented worlds, but the limited accuracy and efficiency of current systems has prevented widespread adoption. Today's dominant paradigm uses machine learning for initialization and recovery followed by iterative model-fitting optimization to achieve a detailed pose fit. We follow this paradigm, but make several changes to the model-fitting, namely using: (1) a more discriminative objective function; (2) a smooth-surface model that provides gradients for non-linear optimization; and (3) joint optimization over both the model pose and the correspondences between observed data points and the model surface. While each of these changes may actually increase the cost per fitting iteration, we find a compensating decrease in the number of iterations. Further, the wide basin of convergence means that fewer starting points are needed for successful model fitting. Our system runs in real-time on CPU only, which frees up the commonly over-burdened GPU for experience designers. The hand tracker is efficient enough to run on low-power devices such as tablets. We can track up to several meters from the camera to provide a large working volume for interaction, even using the noisy data from current-generation depth cameras. Quantitative assessments on standard datasets show that the new approach exceeds the state of the art in accuracy. Qualitative results take the form of live recordings of a range of interactive experiences enabled by this new approach.