A low cost one-camera optical tracking system for indoor wide-area augmented and virtual reality environments

In the last years the number of industrial applications for Augmented Reality (AR) and Virtual Reality (VR) environments has significantly increased. Optical tracking systems are an important component of AR/VR environments. In this work, a low cost optical tracking system with adequate attributes for professional use is proposed. The system works in infrared spectral region to reduce optical noise. A highspeed camera, equipped with daylight blocking filter and infrared flash strobes, transfers uncompressed grayscale images to a regular PC, where image pre-processing software and the PTrack tracking algorithm recognize a set of retro-reflective markers and extract its 3D position and orientation. Included in this work is a comprehensive research on image pre-processing and tracking algorithms. A testbed was built to perform accuracy and precision tests. Results show that the system reaches accuracy and precision levels slightly worse than but still comparable to professional systems. Due to its modularity, the system can be expanded by using several one-camera tracking modules linked by a sensor fusion algorithm, in order to obtain a larger working range. A setup with two modules was built and tested, resulting in performance similar to the stand-alone configuration.

Sistema de supervisão aérea baseado em navegação visual para detecção de anomalias em instalações de petróleo e gás

This work deals with the development of a prototype of a helicopter quadrotor for monitoring applications in oil facilities. Anomaly detection problems can be resolved through monitoringmissions performed by a suitably instrumented quadrotor, i.e. infrared thermosensors should be embedded. The proposed monitoring system aims to reduce accidents as well as to make possible the use of non-destructive techniques for detection and location of leaks caused by corrosion. To this end, the implementation of a prototype, its stabilization and a navigation strategy have been proposed. The control strategy is based on dividing the problem into two control hierarchical levels: the lower level stabilizes the angles and the altitude of the vehicle at the desired values, while the higher one provide appropriate references signals to the lower level in order the quadrotor performs the desired movements. The navigation strategy for helicopter quadrotor is made using information provided by a acquisition image system (monocular camera) embedded onto the helicopter. Considering that the low-level control has been solved, the proposed vision-based navigation technique treats the problem as high level control strategies, such as, relative position control, trajectory generation and trajectory tracking. For the position control we use a control technique for visual servoing based on image features. The trajectory generation is done in a offline step, which is a visual trajectory composed of a sequence of images. For the trajectory tracking problem is proposed a control strategy by continuous servovision, thus enabling a navigation strategy without metric maps. Simulation and experimental results are presented to validate the proposal

In silico network topology-based prediction of gene essentiality

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.

Design and development of a low-cost solar tracker

To become competitive, ultimately, photovoltaics should have its costs reduced and use photovoltaic systems of greater efficiency. The main steps in this direction are the use of new materials, the improvement in the manufacture of modules and the adoption of techniques of maximum power point tracking and of solar tracking. This article aims at presenting the project and development of an azimuth and elevation solar tracker, based on a new conception of the positioning sensor, composed of an array of four photoresistors. The two direct current motors that operate in the vertical and horizontal axes are controlled by a proportional-integral microcontroller. The conditions of the project were low cost, small energy consumption and versatility. The microcontroller can also incorporate a maximum power point tracking algorithm. The performance of solar tracker prototype in the initial phase of field tests can be considered appropriate. © Institution of Engineers Australia, 2013.

Composição de dicionários visuais utilizando agrupamento de dados por Florestas de Caminhos Ótimos

Pós-graduação em Ciência da Computação - IBILCE

Context detection and abstraction in smart environments

Context-aware computing is currently considered the most promising approach to overcome information overload and to speed up access to relevant information and services. Context-awareness may be derived from many sources, including user profile and preferences, network information, sensor analysis; usually context-awareness relies on the ability of computing devices to interact with the physical world, i.e. with the natural and artificial objects hosted within the "environment”. Ideally, context-aware applications should not be intrusive and should be able to react according to user’s context, with minimum user effort. Context is an application dependent multidimensional space and the location is an important part of it since the very beginning. Location can be used to guide applications, in providing information or functions that are most appropriate for a specific position. Hence location systems play a crucial role. There are several technologies and systems for computing location to a vary degree of accuracy and tailored for specific space model, i.e. indoors or outdoors, structured spaces or unstructured spaces. The research challenge faced by this thesis is related to pedestrian positioning in heterogeneous environments. Particularly, the focus will be on pedestrian identification, localization, orientation and activity recognition. This research was mainly carried out within the “mobile and ambient systems” workgroup of EPOCH, a 6FP NoE on the application of ICT to Cultural Heritage. Therefore applications in Cultural Heritage sites were the main target of the context-aware services discussed. Cultural Heritage sites are considered significant test-beds in Context-aware computing for many reasons. For example building a smart environment in museums or in protected sites is a challenging task, because localization and tracking are usually based on technologies that are difficult to hide or harmonize within the environment. Therefore it is expected that the experience made with this research may be useful also in domains other than Cultural Heritage. This work presents three different approaches to the pedestrian identification, positioning and tracking: Pedestrian navigation by means of a wearable inertial sensing platform assisted by the vision based tracking system for initial settings an real-time calibration; Pedestrian navigation by means of a wearable inertial sensing platform augmented with GPS measurements; Pedestrian identification and tracking, combining the vision based tracking system with WiFi localization. The proposed localization systems have been mainly used to enhance Cultural Heritage applications in providing information and services depending on the user’s actual context, in particular depending on the user’s location.

Reconstruction from image correspondences

A single picture provides a largely incomplete representation of the scene one is looking at. Usually it reproduces only a limited spatial portion of the scene according to the standpoint and the viewing angle, besides it contains only instantaneous information. Thus very little can be understood on the geometrical structure of the scene, the position and orientation of the observer with respect to it remaining also hard to guess. When multiple views, taken from different positions in space and time, observe the same scene, then a much deeper knowledge is potentially achievable. Understanding inter-views relations enables construction of a collective representation by fusing the information contained in every single image. Visual reconstruction methods confront with the formidable, and still unanswered, challenge of delivering a comprehensive representation of structure, motion and appearance of a scene from visual information. Multi-view visual reconstruction deals with the inference of relations among multiple views and the exploitation of revealed connections to attain the best possible representation. This thesis investigates novel methods and applications in the field of visual reconstruction from multiple views. Three main threads of research have been pursued: dense geometric reconstruction, camera pose reconstruction, sparse geometric reconstruction of deformable surfaces. Dense geometric reconstruction aims at delivering the appearance of a scene at every single point. The construction of a large panoramic image from a set of traditional pictures has been extensively studied in the context of image mosaicing techniques. An original algorithm for sequential registration suitable for real-time applications has been conceived. The integration of the algorithm into a visual surveillance system has lead to robust and efficient motion detection with Pan-Tilt-Zoom cameras. Moreover, an evaluation methodology for quantitatively assessing and comparing image mosaicing algorithms has been devised and made available to the community. Camera pose reconstruction deals with the recovery of the camera trajectory across an image sequence. A novel mosaic-based pose reconstruction algorithm has been conceived that exploit image-mosaics and traditional pose estimation algorithms to deliver more accurate estimates. An innovative markerless vision-based human-machine interface has also been proposed, so as to allow a user to interact with a gaming applications by moving a hand held consumer grade camera in unstructured environments. Finally, sparse geometric reconstruction refers to the computation of the coarse geometry of an object at few preset points. In this thesis, an innovative shape reconstruction algorithm for deformable objects has been designed. A cooperation with the Solar Impulse project allowed to deploy the algorithm in a very challenging real-world scenario, i.e. the accurate measurements of airplane wings deformations.

Cooperative Wireless Networks for Localization

This thesis adresses the problem of localization, and analyzes its crucial aspects, within the context of cooperative WSNs. The three main issues discussed in the following are: network synchronization, position estimate and tracking. Time synchronization is a fundamental requirement for every network. In this context, a new approach based on the estimation theory is proposed to evaluate the ultimate performance limit in network time synchronization. In particular the lower bound on the variance of the average synchronization error in a fully connected network is derived by taking into account the statistical characterization of the Message Delivering Time (MDT) . Sensor network localization algorithms estimate the locations of sensors with initially unknown location information by using knowledge of the absolute positions of a few sensors and inter-sensor measurements such as distance and bearing measurements. Concerning this issue, i.e. the position estimate problem, two main contributions are given. The first is a new Semidefinite Programming (SDP) framework to analyze and solve the problem of flip-ambiguity that afflicts range-based network localization algorithms with incomplete ranging information. The occurrence of flip-ambiguous nodes and errors due to flip ambiguity is studied, then with this information a new SDP formulation of the localization problem is built. Finally a flip-ambiguity-robust network localization algorithm is derived and its performance is studied by Monte-Carlo simulations. The second contribution in the field of position estimate is about multihop networks. A multihop network is a network with a low degree of connectivity, in which couples of given any nodes, in order to communicate, they have to rely on one or more intermediate nodes (hops). Two new distance-based source localization algorithms, highly robust to distance overestimates, typically present in multihop networks, are presented and studied. The last point of this thesis discuss a new low-complexity tracking algorithm, inspired by the Fano’s sequential decoding algorithm for the position tracking of a user in a WLAN-based indoor localization system.

Eisströme und Schelfeise an der Küste der Amundsen See (West-Antarktis), beobachtet mit ERS-SAR /// Es stehen 2 Dateien zur Auswahl: pdf-Datei Abbildungen nur schwarz-weiss, zip-Datei Abbildungen farbig

In der vorliegenden Diplomarbeit erfolgte die Beobachtung von Eisströmen und Schelfeisen an der Küste der Amundsen See in der West-Antarktis, unter Verwendung von ERS-SAR-Amplitudenbildprodukten. Bestandteile dieser Beobachtung waren die Erstellung eines Gletscherinventares, die Erstellung von Multitemporalbildern, die Auswertung von Veränderungen der Eisfronpositionen und - schwerpunktmäßig - die Bestimmung von Eisfließgeschwindigkeiten und deren räumlicher und zeitlicher Vergleich.

Dynamik und Klimatologie von diabatischen Rossby-Wellen

Der Wintersturm Lothar zog am 26. Dezember 1999 über Europa und richtete in Frankreich, in Deutschland, in der Schweiz und in Österreich ungewöhnlich hohe Schäden an. Lothar entstand aus einer diabatischen Rossby Welle (DRW) und erreichte erst wenige Stunden vor dem europäischen Kontinent Orkanstärke. DRWs weisen ein interessantes atmosphärisches Strömungsmuster auf. Sie bestehen aus einer positiven PV-Anomalie in der unteren Troposphäre, die sich in einer Region mit starkem meridionalen Temperaturgradient befindet. Die positive PV-Anomalie löst eine zyklonale Strömung aus, dadurch wird östlich der PV-Anomalie warme Luft aus dem Süden herantransportiert. Während des Aufstieg der warmen Luft finden diabatische Prozesse statt, die zur Bildung einer neuen positiven PV-Anomalie in der unteren Troposphäre (PVA) führen. DRWs entstehen unabhängig von PV-Anomalien an der Tropopause. Falls sie jedoch mit ihnen in Wechselwirkung treten, kann - wie im Falle von Lothar - eine explosive Zyklogenese daraus resultieren. Im ersten Teil wird die Dynamik einer DRW am Beispiel des Wintersturms Lothar untersucht. Es wird insbesondere auf das Potential einer DRW zur explosiven Zyklogenese eingegangen. Im zweiten Teil wird das Aufretreten von DRWs in ECMWF-Vorhersagen untersucht. Es werden Unterschiede zwischen DRWs und anderen PV-Anomalien in der unteren Troposphäre hervorgehoben. Die Dynamik von DRWs wird mit Hilfe eines ECMWF-"Ensemble Prediction System" (EPS) des Wintersturms Lothar untersucht. Die 50 Modellläufe des EPS starten am 24. Dezember 1999 um 12 UTC und reichen bis zum 26. Dezember 1999 um 12 UTC. Nur 16 der 50 Modellläufe sagen einen ähnlich starken Sturm wie Lothar vorher. 10 Modellläufen sagen am 26. Dezember keine Zyklone mehr vorher. Die Ausprägung der baroklinen Zone, in der sich die DRW befindet, ist ausschlaggebend für die Intensität der DRW. Weitere wichtige Parameter sind der Feuchtegehalt der unteren Troposphäre und der latente Wärmefluss über dem Ozean. Diejenigen DRWs, die sich zu am 25. Dezember um 12 UTC näher als 400 km am Tropopausenjet befinden, entwickeln sich zu einer starken Zyklone. Alle anderen lösen sich auf oder bleiben schwache Zyklonen. Es ist schwierig, diabatische Prozesse in Wettervorhersagemodellen abzubilden, dementsprechend treten Schwierigkeiten bei der Vorhersage von PVAs auf. In den operationellen ECMWF-Vorhersagen von Juni 2004 bis Mai 2005 werden mit Hilfe eines Tracking- Algorithmus PVAs im Nordpazifik und Nordatlantik bestimmt und in fünf Kategorien eingeteilt. Die fünf Kategorien unterscheiden sich in ihrer Häufigkeit, ihrer Zugbahn und ihrer Gestalt. Im Nordpazifik entstehen doppelt so viele PVAs wie im Nordatlantik. Durchschnittlich werden im Winter weniger PVAs gefunden als im Sommer. Die Baroklinität und die Geschwindigkeit des Tropopausenjets ist in der Nähe von DRWs besonders hoch. Verglichen mit anderen PVAs weisen DRWs eine ähnliche Verteilung des reduzierten Bodendrucks auf. DRWs können in etwa gleich gut vorhergesagt werden wie andere PVAs.

Potential vorticity and moisture in extratropical cyclones : climatology and sensitivity experiments

The development of extratropical cyclones can be seen as an interplay of three positive potential vorticity (PV) anomalies: an upper-level stratospheric intrusion, low-tropospheric diabatically produced PV, and a warm anomaly at the surface acting as a surrogate PV anomaly. In the mature stage they become vertically aligned and form a “PV tower” associated with strong cyclonic circulation. This paradigm of extratropical cyclone development provides the basis of this thesis, which will use a climatological dataset and numerical model experiments to investigate the amplitude of the three anomalies and the processes leading in particular to the formation of the diabatically produced low-tropospheric PV anomaly.rnrnThe first part of this study, based on the interim ECMWF Re-Analysis (ERA-Interim) dataset, quantifies the amplitude of the three PV anomalies in mature extratropical cyclones in different regions in the Northern Hemisphere on a climatological basis. A tracking algorithm is applied to sea level pressure (SLP) fields to identify cyclone tracks. Surface potential temperature anomalies ∆θ and vertical profiles of PV anomalies ∆PV are calculated at the time of the cyclones’ minimum SLP and during the intensification phase 24 hours before in a vertical cylinder with a radius of 200 km around the surface cyclone center. To compare the characteristics of the cyclones, they are grouped according to their location (8 regions) and intensity, where the central SLP is used as a measure of intensity. Composites of ∆PV profiles and ∆θ are calculated for each region and intensity class at the time of minimum SLP and during the cyclone intensification phase.rnrnDuring the cyclones’ development stage the amplitudes of all three anomalies increase on average. In the mature stage all three anomalies are typically larger for intense than for weak winter cyclones [e.g., 0.6 versus 0.2 potential vorticity units (PVU) at lower levels, and 1.5 versus 0.5 PVU at upper levels].rnThe regional variability of the cyclones’ vertical structure and the profile evolution is prominent (cyclones in some regions are more sensitive to the amplitude of a particular anomaly than in other regions). Values of ∆θ and low-level ∆PV are on average larger in the western parts of the oceans than in the eastern parts. In addition, a large seasonal variability can be identified, with fewer and weaker cyclones especially in the summer, associated with higher low-tropospheric PV values, but also with a higher tropopause and much weaker surface potential temperature anomalies (compared to winter cyclones).rnrnIn the second part, we were interested in the diabatic low-level part of PV towers. Evaporative sources were identified of moisture that was involved in PV production through condensation. Lagrangian backward trajectories were calculated from the region with high PV values at low-levels in the cyclones. PV production regions were identified along these trajectories and from these regions a new set of backward trajectories was calculated and moisture uptakes were traced along them. The main contribution from surface evaporation to the specific humidity of the trajectories is collected 12-72 hours prior to therntime of PV production. The uptake region for weaker cyclones with less PV in the centre is typically more localized with reduced uptake values compared to intense cyclones. However, in a qualitative sense uptakes and other variables along single trajectories do not vary much between cyclones of different intensity in different regions.rnrnA sensitivity study with the COSMO model comprises the last part of this work. The study aims at investigating the influence of synthetic moisture modification in the cyclone environment in different stages of its development. Moisture was eliminated in three regions, which were identified as important moisture source regions for PV production. Moisture suppression affected the cyclone the most in its early phase. It led to cyclolysis shortly after its genesis. Nevertheles, a new cyclone formed on the other side of a dry box and developed relatively quickly. Also in other experiments, moisture elimination led to strong intensity reduction of the surface cyclone, limited upper-level development, and delayed or missing interaction between the two.rnrnIn summary, this thesis provides novel insight into the structure of different intensity categories of extratropical cyclones from a PV perspective, which corroborates the findings from a series of previous case studies. It reveals that all three PV anomalies are typically enhanced for more intense cyclones, with important regional differences concerning the relative amplitude of the three anomalies. The moisture source analysis is the first of this kind to study the evaporation-condensation cycle related to the intensification of extratropical cyclones. Interestingly, most of the evaporation occurs during the 3 days prior to the time of maximum cyclone intensity and typically extends over fairly large areas along the track of the cyclone. The numerical model case study complements this analysis by analyzing the impact of regionally confined moisture sources for the evolution of the cyclone.

Daily organ tracking in intensity-modulated radiotherapy of prostate cancer using an electronic portal imaging device with a dose saving acquisition mode

BACKGROUND AND PURPOSE: Daily use of conventional electronic portal imaging devices (EPID) for organ tracking is limited due to the relatively high dose required for high quality image acquisition. We studied the use of a novel dose saving acquisition mode (RadMode) allowing to take images with one monitor unit per image in prostate cancer patients undergoing intensity-modulated radiotherapy (IMRT) and tracking of implanted fiducial gold markers. PATIENTS AND METHODS: Twenty five patients underwent implantation of three fiducial gold markers prior to the planning CT. Before each treatment of a course of 37 fractions, orthogonal localization images from the antero-posterior and from the lateral direction were acquired. Portal images of both the setup procedure and the five IMRT treatment beams were analyzed. RESULTS: On average, four localization images were needed for a correct patient setup, resulting in four monitor units extra dose per fraction. The mean extra dose delivered to the patient was thereby increased by 1.2%. The procedure was precise enough to reduce the mean displacements prior to treatment to < o =0.3 mm. CONCLUSIONS: The use of a new dose saving acquisition mode enables to perform daily EPID-based prostate tracking with a cumulative extra dose of below 1 Gy. This concept is efficiently used in IMRT-treated patients, where separation of setup beams from treatment beams is mandatory.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Total Variation Blind Deconvolution: The Devil Is in the Details

In this paper we study the problem of blind deconvolution. Our analysis is based on the algorithm of Chan and Wong [2] which popularized the use of sparse gradient priors via total variation. We use this algorithm because many methods in the literature are essentially adaptations of this framework. Such algorithm is an iterative alternating energy minimization where at each step either the sharp image or the blur function are reconstructed. Recent work of Levin et al. [14] showed that any algorithm that tries to minimize that same energy would fail, as the desired solution has a higher energy than the no-blur solution, where the sharp image is the blurry input and the blur is a Dirac delta. However, experimentally one can observe that Chan and Wong's algorithm converges to the desired solution even when initialized with the no-blur one. We provide both analysis and experiments to resolve this paradoxical conundrum. We find that both claims are right. The key to understanding how this is possible lies in the details of Chan and Wong's implementation and in how seemingly harmless choices result in dramatic effects. Our analysis reveals that the delayed scaling (normalization) in the iterative step of the blur kernel is fundamental to the convergence of the algorithm. This then results in a procedure that eludes the no-blur solution, despite it being a global minimum of the original energy. We introduce an adaptation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the state of the art.