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Time delay estimation is essential in Acoustic Source Localization (ASL) systems. One of the most used techniques for this purpose is the Generalized Cross Correlation (GCC) between a pair of signals and its use in Steered Response Power (SRP) techniques, which estimate the acoustic power at a specific location. Nowadays, Deep Learning strategies may outperform these methods. However, they are generally dependent on the geometric and sensor configuration conditions that are available during the training phases, thus having limited generalization capabilities when facing new environments if no re-training nor adaptation is applied. In this work, we propose a method based on an encoder-decoder CNN architecture capable of outperforming the well known SRP-PHAT algorithm, and also other Deep Learning strategies when working in mismatched training-testing conditions without requiring a model re-training. Our proposal aims to estimate a smoothed version of the correlation signals, that is then used to generate a refined acoustic power map, which leads to better performance on the ASL task. Our experimental evaluation uses three publicly available realistic datasets and provides a comparison with the SRP-PHAT algorithm and other recent proposals based on Deep Learning.

The International Personality Item Pool (IPIP) five-factor model inventories are widely used for personality research and have been translated into multiple languages. However, the extent of the psychometric assessment of translated scales is variable, often minimal. The lack of psychometric scrutiny is particularly problematic because translation is an inherently complex process. Here, we present a structural analysis of one Spanish translation of the 50-item IPIP five-factor inventory in a sample of Peruvian, non-university educated, working adults (n = 778). A global confirmatory factor analytic (CFA) model of the a priori five factors failed to fit. So too did single factor models for four of the five factors, the exception being Neuroticism. Fit was improved via use of an exploratory structural equation measurement model, but the resultant solution showed very poor theoretical coherence. So, we explored the data for systematic measurement artefacts and sought to model them to improve the psychometric properties of the scale. Specifically, the pattern of factor loadings suggested that the lack of coherence might be due to the effects of the valence of item wording (i.e., positively or negatively worded items). CFA models including five substantive factors and a series of method factors modelling shared covariance based on item wording, improved fit and coherence. This investigation suggests that unless method factors are explicitly modelled the tested Spanish translation may not be suitable for use in certain Spanish-speaking countries or samples composed of non-university educated participants. More broadly, the study has implications for many translated scales, especially when used without thorough psychometric evaluation.

A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements is between 0.4 cm (fusion) and 2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4 &times, 3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

The International Personality Item Pool (IPIP) five-factor model inventories are a widely used resource for personality research and have been translated into multiple languages. However, the extent of the psychometric assessment of translated scales is variable. Here we present a structural analysis of one Spanish translation of the 50-item IPIP five-factor inventory in a sample of Peruvian non-university educated working adults (n=778). A global confirmatory factor analytic (CFA) model of the a priori five factors failed to fit. So too did single factor models for four of the five factors, the exception being Neuroticism. Fit was improved via use of an exploratory structural equation measurement model, but the resultant solution showed very poor theoretical coherence. The pattern of factor loadings suggested that the lack of coherence might be due to the effects of the valence of item wording. CFA models including five substantive factors and a series of method factors modelling shared covariance based on item wording, improved fit and coherence. This investigation suggests that the tested Spanish translation, without explicitly modelling method factors, may not be suitable for use in certain Spanish-speaking countries or samples composed of non-university educated participants.

We present a global and convex formulation for the template-less 3D reconstruction of a deforming object with the perspective camera. We show for the first time how to construct a Second-Order Cone Programming (SOCP) problem for Non-Rigid Structure-from-Motion (NRSfM) using the Maximum-Depth Heuristic (MDH). In this regard, we deviate strongly from the general trend of using affine cameras and factorization-based methods to solve NRSfM, which do not perform well with complex nonlinear deformations. In MDH, the points' depths are maximized so that the distance between neighbouring points in camera space are upper bounded by the geodesic distance. In NRSfM both geodesic and camera space distances are unknown. We show that, nonetheless, given point correspondences and the camera's intrinsics the whole problem can be solved with SOCP. This is the first convex formulation for NRSfM with physical constraints. We further present how robustness and temporal continuity can be included in the formulation to handle outliers and decrease the problem size, respectively. We show with extensive experiments that our methods accurately reconstruct quasi-isometric objects from partial views under articulated and strong deformations. Compared to the previous methods, our approach gives better or similar accuracy. It naturally handles missing correspondences, non-smooth objects and is very simple to implement compared to previous methods, with only one free parameter (the neighbourhood size).

We study Isometric Non-Rigid Shape-from-Motion (Iso-NRSfM): given multiple intrinsically calibrated monocular images, we want to reconstruct the time-varying 3D shape of a thin-shell object undergoing isometric deformations. We show that Iso-NRSfM is solvable from local warps, the inter-image geometric transformations. We propose a new theoretical framework based on the Riemmanian manifold to represent the unknown 3D surfaces as embeddings of the camera's retinal plane. This allows us to use the manifold's metric tensor and Christoffel Symbol (CS) fields. These are expressed in terms of the first and second order derivatives of the inverse-depth of the 3D surfaces, which are the unknowns for Iso-NRSfM. We prove that the metric tensor and the CS are related across images by simple rules depending only on the warps. This forms a set of important theoretical results. We show that current solvers cannot solve for the first and second order derivatives of the inverse-depth simultaneously. We thus propose an iterative solution in two steps. 1) We solve for the first order derivatives assuming that the second order derivatives are known. We initialise the second order derivatives to zero, which is an infinitesimal planarity assumption. We derive a system of two cubics in two variables for each image pair. The sum-of-squares of these polynomials is independent of the number of images and can be solved globally, forming a well-posed problem for $N\geq 3$ images. 2) We solve for the second order derivatives by initialising the first order derivatives from the previous step. We solve a linear system of $4N-4$ equations in three variables. We iterate until the first order derivatives converge. The solution for the first order derivatives gives the surfaces’ normal fields which we integrate to recover the 3D surfaces. The proposed method outperforms existing work in terms of accuracy and computation cost on synthetic and real datasets.

Objective To report the use of augmented reality (AR) in gynecology. Design AR is a surgical guidance technology that enables important hidden surface structures to be visualized in endoscopic images. AR has been used for other organs, but never in gynecology and never with a very mobile organ like the uterus. We have developed a new AR approach specifically for uterine surgery and demonstrated its use for myomectomy. Setting Tertiary university hospital. Patient(s) Three patients with one, two, and multiple myomas, respectively. Intervention(s) AR was used during laparoscopy to localize the myomas. Main Outcome Measure(s) Three-dimensional (3D) models of the patient's uterus and myomas were constructed before surgery from T2-weighted magnetic resonance imaging. The intraoperative 3D shape of the uterus was determined. These models were automatically aligned and "fused" with the laparoscopic video in real time. Result(s) The live fused video made the uterus appear semitransparent, and the surgeon can see the location of the myoma in real time while moving the laparoscope and the uterus. With this information, the surgeon can easily and quickly decide on how best to access the myoma. Conclusion(s) We developed an AR system for gynecologic surgery and have used it to improve laparoscopic myomectomy. Technically, the software we developed is very different to approaches tried for other organs, and it can handle significant challenges, including image blur, fast motion, and partial views of the organ.

Augmented Reality (AR) is a technology that can allow a surgeon to see subsurface structures. This works by overlaying information from another modality, such as MRI and fusing it in real time with the endoscopic images. AR has never been developed for a very mobile organ like the uterus and has never been performed for gynecology. Myomas are not always easy to localize in laparoscopic surgery when they do not significantly change the surface of the uterus, or are at multiple locations. To study the accuracy of myoma localization using a new AR system compared to MRI-only localization. Ten residents were asked to localize six myomas (on a uterine model into a laparoscopic box) when either using AR or in conditions that simulate a standard method (only the MRI was available). Myomas were randomly divided in two groups: the control group (MRI only, AR not activated) and the AR group (AR activated). Software was used to automatically measure the distance between the point of contact on the uterine surface and the myoma. We compared these distances to the true shortest distance to obtain accuracy measures. The time taken to perform the task was measured, and an assessment of the complexity was performed. The mean accuracy in the control group was 16.80 mm [0.1–52.2] versus 0.64 mm [0.01–4.71] with AR. In the control group, the mean time to perform the task was 18.68 [6.4–47.1] s compared to 19.6 [3.9–77.5] s with AR. The mean score of difficulty (evaluated for each myoma) was 2.36 [1–4] versus 0.87 [0–4], respectively, for the control and the AR group. We developed an AR system for a very mobile organ. This is the first user study to quantitatively evaluate an AR system for improving a surgical task. In our model, AR improves localization accuracy.

International audience; We have developed a technique to study how good computers can be at diagnosing gastrointestinal lesions from regular (white light and narrow banded) colonoscopic videos compared to two levels of clinical knowledge (expert and beginner). Our technique includes a novel tissue classification approach which may save clinician's time by avoiding chromoendoscopy, a time-consuming staining procedure using indigo carmine. Our technique also discriminates the severity of individual lesions in patients with many polyps, so that the gastroenterologist can directly focus on those requiring polypectomy. Technically, we have designed and developed a framework combining machine learning and computer vision algorithms, which performs a virtual biopsy of hyperplastic lesions, serrated adenomas and adenomas. Serrated adenomas are very difficult to classify due to their mixed/hybrid nature and recent studies indicate that they can lead to colorectal cancer through the alternate serrated pathway. Our approach is the first step to avoid systematic biopsy for suspected hyperplastic tissues. We also propose a database of colonoscopic videos showing gastrointestinal lesions with ground truth collected from both expert image inspection and histology. We not only compare our system with the expert predictions, but we also study if the use of 3D shape features improves classification accuracy, and compare our technique's performance with three competitor methods.

Aim To describe a simple and efficacious monitoring system for patients presenting with severe dengue with severe plasma leakage. Method: We reviewed sixty-one out of 110 records of patients presenting with dengue in August and September of 1999 at the Hospital Tony Facio, Limón province, Costa Rica. Results: Dengue virus 3 was the cause of the epidemic. Thirty one (51%) patients presented with warning sings, and thirty (49%) had severe dengue with severe plasma leakage. Those patients presenting with hypotension (low mean arterial blood pressure) were treated with an intravenous polyelectrolyte solution in order to maintain the mean arterial pressure between the lower and mean range. When the mean arterial pressure presented a tendency to rise over the normal range, the infusion was withdrawn. The hematocrit was not the parameter used to monitor the treatment of the patients. There were no deaths. Conclusions: Monitoring the mean arterial blood pressure instead of the hematocrit values in patients with severe dengue with severe plasma leakage represents an easier and more efficacious way of treating this patient population. Resumen: Objetivo: Describir un sistema de monitoreo más simple y eficiente para pacientes con dengue grave con extravasación de plasma severa. Método: Se revisaron 61 de 110 expedientes de pacientes que presentaban dengue, entre agosto y setiembre de 1999, en el Hospital Tony Facio de la provincia de Limón en Costa Rica. Resultados: El virus de dengue tipo 3 causó la epidemia. Treinta y uno (51%) de los pacientes presentaron signos de alarma, treinta (49%) presentaron dengue grave con extravasación de plasma severa. Los pacientes que presentaban hipotensión (baja presión sanguínea media) fueron tratados con solución de polielectrolitos intravenosa para mantener la presión arterial media entre el rango inferior y la media. En cuanto la presión arterial media presentaba una tendencia a incrementar sobre el rango normal, la infusión era retirada. El hematocrito no fue un parámetro para monitorear el manejo de los pacientes. No hubo muertes. Conclusiones: El monitorear la presión arterial media, en lugar de los valores de hematocritos, en pacientes con dengue grave con extravasación severa representa una manera más fácil y eficiente de tratar a esta población de pacientes.

This paper describes an algorithm based on the 2D approach of Allard and Fraley that uses Voronoi tessellation and a non-parametric maximum likelihood estimator. We have designed a 3D version of this algorithm which detects multiple clusters of points immersed in background noise; its application to the detection of galaxy clusters in redshift space, using the astronomical database of the 2-degree Field Galaxy Redshift Survey, is presented and discussed. Adopting as a benchmark a particular set of catalogued clusters of galaxies, we find that the proposed algorithm recognizes the location of ∼ 67% of the clusters. Three variants of the algorithm were assessed to deal with the elongation of the clusters in the radial direction of observation introduced by the astronomical distance indicator; their merits and limitations are discussed. We address separately the detection of the galaxy cluster location and the detection of galaxy cluster members, both of them having an anisotropic space as their search domain. In the case of detection of galaxy cluster members, a second stage of detection was incorporated in order to improve the results. © 2006 IEEE.

Shape-from-Template (SfT) reconstructs the shape of a deforming surface from a single image, a 3D template and a deformation prior. For isometric deformations, this is a well-posed problem. However, previous methods which require no initialization break down when the perspective effects are small, which happens when the object is small or viewed from larger distances. That is, they do not handle all projection geometries. We propose stable SfT methods that accurately reconstruct the 3D shape for all projection geometries. We follow the existing approach of using first-order differential constraints and obtain local analytical solutions for depth and the first-order quantities: the depth-gradient or the surface normal. Previous methods use the depth solution directly to obtain the 3D shape. We prove that the depth solution is unstable when the projection geometry tends to affine, while the solution for the first-order quantities remain stable for all projection geometries. We therefore propose to solve SfT by first estimating the first-order quantities (either depth-gradient or surface normal) and integrating them to obtain shape. We validate our approach with extensive synthetic and real-world experiments and obtain significantly more accurate results compared to previous initialization-free methods. Our approach does not require any optimization, which makes it very fast.

This paper presents a novel approach for indoor acoustic source localization using sensor arrays. The proposed solution starts by defining a generative model, designed to explain the acoustic power maps obtained by Steered Response Power (SRP) strategies. An optimization approach is then proposed to fit the model to real input SRP data and estimate the position of the acoustic source. Adequately fitting the model to real SRP data, where noise and other unmodelled effects distort the ideal signal, is the core contribution of the paper. Two basic strategies in the optimization are proposed. First, sparse constraints in the parameters of the model are included, enforcing the number of simultaneous active sources to be limited. Second, subspace analysis is used to filter out portions of the input signal that cannot be explained by the model. Experimental results on a realistic speech database show statistically significant localization error reductions of up to 30% when compared with the SRP-PHAT strategies.

We identify 1901 galaxy clusters (N g ≥ 2) with the VoML+G algorithm (Paper I) on the Two-Degree Field Galaxy Redshift Survey. We present the 341 clusters with at least 10 galaxies that are within 0.009 < z < 0.14 (the Catalog), of which 254 (~75%) have counterparts in the literature (NED), with the remainder (87) plausibly "new" because of incompleteness of previous searches or unusual galaxy contents. The 207 clusters within z = 0.04–0.09 are used to study the properties of the galaxy systems in the nearby universe, including their galaxy contents parameterized by the late-type galaxy fractions (f L ). For this nearly complete cluster subsample, we find the following: (i) 63% are dominated by early-type galaxies (i.e., the late-type-poor clusters, f L < 0.5) with corresponding mean multiplicity and logarithmic virial mass (in units of M ⊙) of 22 ± 1 and 12.91 ± 0.04, respectively; and (ii) 37% are dominated by late-type galaxies (i.e., the late-type-rich clusters, f L ≥ 0.5) with corresponding mean multiplicity and logarithmic virial mass (in units of M ⊙) of 15.7 ± 0.9 and 12.66 ± 0.07, respectively. The statistical analysis of the late-type fraction distribution supports, with a 3σ confidence level, the presence of two population components. It is suggested that the late-type-poor galaxy systems reflect and extend the class of Abell-APM-EDCC clusters and that the late-type-rich systems (~one-third of the total) belong to a new, previously unappreciated class. The late-type-rich clusters, on average high mass-to-light ratio systems, appear to be more clustered on large scales than the late-type-poor clusters. A class of late-type-rich clusters is not predicted by current theory.

This paper addresses the design and presents the experimental results of an aperiodic remote-controlled mechatronic plant using a MiniDK2 electronic development board. We compare the classic periodic control solution with our self-triggered approach. The triggering mechanism consists of evaluating if the measurement error exceeds a predefined value. This measurement error is defined as the difference between the output signal of a model with and without a sample-and-hold that is activated only at the triggering instants. The minimum inter-execution time is the reference period and the maximum time is set by the designer, guaranteeing the stability of the closed loop system. At each new triggering instant, the remote controller receives a new measurement of the system and sends the actuation signal to the mechatronic plant.

This paper proposes a general framework to solve Non-Rigid Shape-from-Motion (NRSfM) with the perspective camera under isometric deformations. Contrary to the usual low-rank linear shape basis, isometry allows us to recover complex shape deformations from a sparse set of images. Existing methods suffer from ambiguities and may be very expensive to solve. We bring four main contributions. First, we formulate isometric NRSfM as a system of first-order Partial Differential Equations (PDE) involving the shape’s depth and normal field and an unknown template. Second, we show this system cannot be locally resolved. Third, we introduce the concept of infinitesimal planarity and show that it makes the system locally solvable for at least three views. Fourth, we derive an analytic solution which involves convex, linear least-squares optimization only, and outperforms existing works.

[ES] Se presenta un método general para la detección, de forma no invasiva, de la postura corporal de varias personas a partir de la información capturada por múltiples cámaras. Se sigue una filosofía basada en el entrenamiento previo de un modelo articulado y posterior seguimiento. La principal aportación es la posibilidad de detectar varias personas simultáneamente. Se utiliza un modelo articulado para definir las posturas que puede adoptar una persona. Mediante bases de datos de captura de movimiento se selecciona un conjunto de clases o actividades predefinidas. El entrenamiento reduce la complejidad del modelo articulado a partir de técnicas no lineales de reducción de dimensionalidad. Así, las diferentes actividades de una persona quedan definidas de manera compacta por un conjunto de valores de baja dimensionalidad. Posteriormente, un filtro de partículas mixto (estados discretos y continuos) es utilizado para detectar la postura y el tipo de movimiento simultáneamente. Las hipótesis resultantes, seleccionadas automáticamente a partir de la distribución de partículas, son refinadas usando un optimizador no lineal que hace uso de funciones ‘a priori’ del tipo de movimiento entrenado. La propuesta se ha evaluado con un método simple pero estándar, basado en la comparación de volúmenes cilíndricos articulados con volúmenes del cuerpo humano, extraidos automáticamente a partir de las imágenes. Se consigue una precisión cercana a trabajos del estado del arte que no tienen en cuenta a más de una persona y ofrece un marco de trabajo flexible para futuras investigaciones., [EN] This work presents a general framework for tracking simultaneously the body posturas of multiple people from nonintrusive visual sensors. The method is based on a trainingthen-tracking philosophy, with the main addition of being able to handle more than just one person. We train the body postura from labelled motion capture datasets. The training process is based on popular non-linear dimensionality reduction techniques. Then, a mixed, discrete and continuous state particle filter is used to simultaneously detect the postura and the kind of motion performed by each of the human bodies. The resulting hypotheses, automatically selected from the particle distribution, are then refined using non-linear optimization methods with statistical priors. The whole framework is tested using a simple but standard method based on comparing articulated cylindrical models with SfS volumes, taken from several cameras. Our accuracy in public available datasets is near to the state-ofthe-art works that do not take into account multiple people in the problem., Este trabajo ha sido parcialmente respaldado por el Ministerio de Ciencia e Innovacion Español bajo los proyectos VISNU (ref. TIN2009-08984) y SDTEAM-UAH (ref. TIN2008-06856-C05-05).

This paper is the first in a series, presenting a new galaxy cluster finder based on a three-dimensional Voronoi Tesselation plus a maximum likelihood estimator, followed by gapping-filtering in radial velocity(VoML+G). The scientific aim of the series is a reassessment of the diversity of optical clusters in the local universe. A mock galaxy database mimicking the southern strip of the magnitude(blue)-limited 2dF Galaxy Redshift Survey (2dFGRS), for the redshift range 0.009 < z < 0.22, is built on the basis of the Millennium Simulation of the LCDM cosmology and a reference catalog of "Millennium clusters," spannning across the 1.0 × 1012–1.0 × 1015 M ⊙ h −1 dark matter (DM) halo mass range, is recorded. The validation of VoML+G is performed through its application to the mock data and the ensuing determination of the completeness and purity of the cluster detections by comparison with the reference catalog. The execution of VoML+G over the 2dFGRS mock data identified 1614 clusters, 22% with N g ≥ 10, 64 percent with 10 > N g ≥ 5, and 14% with N g < 5. The ensemble of VoML+G clusters has a ~59% completeness and a ~66% purity, whereas the subsample with N g ≥ 10, to z ~ 0.14, has greatly improved mean rates of ~75% and ~90%, respectively. The VoML+G cluster velocity dispersions are found to be compatible with those corresponding to "Millennium clusters" over the 300–1000 km s−1 interval, i.e., for cluster halo masses in excess of ~3.0 × 1013 M ⊙ h −1.

This paper describes the development of innovative software and hardware technologies in order to design a system capable of obtaining the pose of robotic wheelchairs through the use of cameras in fixed positions in the environment. Our intelligent space is able of obtaining information from the observed world using its sensors. Robot localization in the intelligent spaces is an essential task to provide a service to users while simplifying the technology embedded on the robot, moving the individual robot intelligence to the environment. The intelligent space is composed of two labs and the corridor between them, simulating a house. Eleven cameras are installed in the intelligent space with USB technology connected onto 11 TCP/IP servers. The identification of the robotic wheelchair in the intelligent space is done by localizing the beacons built with infrared LEDs and positioned on the robot. Thus, the localization algorithm can be simpler and faster. With the points obtained from the location, it is possible to reconstruct the 3D pose of the robotic wheelchair in the environment.

Nonverbal communication plays an important role in many aspects of our lives, such as in job interviews, where vis-α-vis conversations take place. This paper proposes a method to automatically detect body communicative cues by using video sequences of the upper body of individuals in a conversational context. To our knowledge, our work brings novelty by explicitly addressing the recognition of visual activity in a seated, conversational setting from monocular video, compared to most existing work in video-based motion capture, which targets full-body with lower limb activities. We first detect the person hands in the sequence by searching for the higher speed parts along the whole video. Then, aided by training a set of typical conversational movements, we infer the approximate 3D upper body pose, that we transfer to a low-dimensionality space in order to perform action recognition. We test our system in the context of job interviews, with several new databases that we make publicly available.

This paper proposes the development of innovative software and hardware technologies in order to design a system capable of obtaining the pose of a robotic wheelchairs through the use of cameras in fixed positions in the environment. The intelligent space is able of obtaining information from the observed world using its sensors. Robot localization in the intelligent spaces is an essential task to provide a service to users while simplifying the technology embedded on the robot, moving the individual robot intelligence to the environment. The intelligent space is composed of two labs and the corridor between them, simulating a house. Eleven cameras are installed in the intelligent space with USB technology connected onto 11 TCP/IP servers. The identification of the robotic wheelchair in the intelligent space is done by beacons built with infrared LEDs positioned on the top of the robot. Thus, the localization algorithm can be simpler and faster. With the points obtained from the location, it is possible to reconstruct 3-D pose of the robotic wheelchair in the environment.

Através do presente estudo objetivou-se realizar uma avaliação da arborização existente em cinco praças de Cachoeira do Sul, onde foram observadas variáveis relativas às espécies arbóreas, tais como: diagnóstico da fitossanidade, riqueza de espécies, análise do CAP (circunferência à altura do peito) e altura, e quanto a presença ou ausência de áreas livres adequadas. Os dados foram coletados em planilha específica, transferidos e analisados no software Excel. O total de indivíduos encontrados foi de 832, distribuídos em 36 famílias botânicas. As cinco espécies de maior ocorrência foram Jacaranda mimosifolia, Ligustrum lucidum, Tipuana tipu, Lagerstroemia indica e Parapiptadenia rigida, representando 35,94% do total. A análise da fitossanidade mostrou que 62,59% das árvores encontram-se saudáveis, 26% estão sendo atacadas por pragas e 11% sofreram injúria mecânica. Na avaliação da altura dos indivíduos, 53% apresentaram alturas maiores que 9 m, observou-se também que 65,75% dessas árvores apresentaram CAP maiores que 55 cm. É possível observar, nas cinco praças, a existência de um mesmo número de espécies exóticas e nativas. Os resultados encontrados para a análise de área livre mostram que 451 indivíduos não possuem espaço adequado para seu bom desenvolvimento, enquanto que 381 possuem esses espaços.

This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices.

This paper proposes the use of a robust loop-shaping H ∞ procedure for the current control loop of VSCs connected to the grid through LCL filters. The proposed method yields robust controllers for systems connected to grids whose equivalent impedance is only known in a predefined range. The design procedure is based on simplified classical loop-shaping principles. The result is a discrete-time controller, suitable for being executed on a Digital Signal Processor and executable in real-time that is robust for the considered conditions. The procedure has been verified by simulation and experimental testing.

This work proposes a novel method based on a prediction error minimization (PEM) algorithm to identify the discrete state-space model of a LCL filter which is used as interface between a Voltage Source Converter and the utility grid. This model is used to design directly a discrete state-space current control based on a servo controller. The so-obtained model makes easier the use of advanced control techniques as Linear Quadratic Regulators, state estimators, Robust Control, etc. The main advantage of using an identification process against to design the controller starting from the manufacturer data is that the control is not sensitive to modeled errors. The method can be applied to closed-loop systems, being suitable for adaptive control designs.

This paper presents a method for obtaining the motion segmentation and 3D localization of multiple mobile robots in an intelligent space. Motion segmentation and positioning are obtained from the images acquired using an array of calibrated and synchronized cameras, without previous knowledge about the number of mobile robots. These cameras are placed in fixed positions within the environment (intelligent space). The proposed algorithm does not rely on previous knowledge or invasive landmarks on board the robots. The proposal presented in this work is based on the minimization of an objective function that includes information from all the cameras. This function depends on three groups of variables: the segmentation boundaries, the 3D rigid motion parameters (linear and angular velocity components) and the depth (distance to the cameras). Before the minimization, it is necessary to initialize the segmentation boundaries and the depth. The use of an extended version of the k-means algorithm allows obtaining a good estimation of the number of robots in the scene during the initialization process. For the objective function minimization, we use a greedy iterative algorithm.

Detecting, tracking and identifying different non-rigid agents in smart spaces is solved with a probabilistic filter proposed in this paper. A camera ring statically mounted in the intelligent space is used as observation system. Besides, three subsystems create the global detection and tracking algorithm: a three-dimensional reconstruction of these entities; a global tracker of all these entities made of a multimodal filter; and a deterministic classifier based on a connectivity map that obtains the individual agents in the smart space. A the end of the paper, different results and reliability statistics are presented in order to demonstrate the global functionality in real executions of the proposal presented.

This paper presents a method for obtaining the motion segmentation and 3D localization of multiple mobile robots in an intelligent space using a multi-camera sensor system. The set of calibrated and synchronized cameras are placed in fixed positions within the environment (intelligent space). The proposed algorithm for motion segmentation and 3D localization is based on the minimization of an objective function. This function includes information from all the cameras, and it does not rely on previous knowledge or invasive landmarks on board the robots. The proposed objective function depends on three groups of variables: the segmentation boundaries, the motion parameters and the depth. For the objective function minimization, we use a greedy iterative algorithm with three steps that, after initialization of segmentation boundaries and depth, are repeated until convergence.

This paper proposes a method based on a prediction error minimization (PEM) algorithm to identify the discrete state-space model of a LCL filter used as interface between a Voltage Source Converter and the electrical grid. This model can be used to design directly a discrete current controller in the state space using techniques as Linear Quadratic Regulator, state estimators, etc. The method can be applied to a closed-loop system, being suitable for adaptive control designs.

This paper presents a method for obtaining the 3D motion segmentation and 3D localization of mobile robots using an array of calibrated cameras that are placed in fixed positions within the environment. This proposal does not rely on previous knowledge or invasive landmarks on board the robots. The proposal is based on the minimization of an objective function. This function includes information of all the cameras and depends on three groups of variables: the segmentation boundaries, the 3D rigid motion parameters (linear and angular velocity components) and depth (distance to the cameras). For the objective function minimization, we use a greedy algorithm that, after initialization, consists of three iterative steps. The use of multiple cameras increases notably the system robustness against occlusions and lighting changes. The accuracy of the results is also improved with regard to the methods where a single camera is used.

This paper deals with shading and AAMs. Shading is created by lighting change. It can be of two types: self- shading and external shading. The effect of self-shading can be explicitly learned and handled by AAMs. This is not however possible for external shading, which is usually dealt with by robustifying the cost function. We take a different approach: we measure the fitting cost in a so-called Light-Invariant space. This approach naturally handles self-shading and external shading. The framework is based on mild assumptions on the scene reflectance and the cameras. Some photometric camera response parameters are required. We propose to estimate these while fitting an existing color AAM in a photometric 'self-calibration' manner. We report successful results with a face AAM with test images taken indoor under simple lighting change.

In this paper we present a technique for fully automatic, real-time 3D SPECT (Single Photon Emitting Computed Tomography) and 2D ultrasound image registration. We use this technique in the context of kidney lesion diagnosis. Our registration algorithm allows a physician to perform an ultrasound exam after a SPECT image has been acquired and see in real time the registration of both modalities. An automatic segmentation algorithm has been implemented in order to display in 3D the positions of the acquired US images with respect to the organs.

International audience; Using external cameras to achieve robot localization has been widely proposed in the area of Intelligent Spaces. Recently, an online approach that simultaneously obtains robot's pose and its 3D structure using a single external camera has been developed [8]. Such proposal relies on a proper initialization of pose and structure information of the robot. The present paper proposes a solution to initialization which consists of retrieving 3D structure and motion of a rigid object from a set of point matches measured by the camera. A batch Structure from Motion (SFM) approach is proposed along a short path. By incorporating odometry information available in the robot, the ambiguity generated by a single view in the solution is solved. We propose to describe robot's motion and image detection as statistical processes in which the uncertainty is properly modelled. Using a Gaussian equivalence of the processes involved, the SFM cost function is expressed as a Maximum Likelihood optimization. The paper shows the improvements of the approach in the presence of the usual odometry drift noise, compared with those using Euclidean distance as a likelihood. The proposed method is assessed on synthetic and real data.

This paper presents an approach to solve a 3D tracking of mobile robots based on visual information from a fixed and calibrated camera. The proposed algorithm builds a metric model of primitives of the object structure. The approach allows a redefinition of the tracking problem into one of bayesian inference. Robot pose is the result of a sequential algorithm that searchs for a posterior distribution of robot pose and structure model given a set of fiducial points measured in the image plane. The method of correspondence between the model and measurements is supported by a joint distribution compatibility test. The proposed algorithm allows also the addition of new information into the previous model, which converts the present method into a simultaneous pose and reconstruction approach. Metric initialization is solved by combining some amount of odometry information jointly with image measurements. A Maximum Likelihood approach to initialization is described. Real results are shown using a mobile robot and a low cost camera.

This work presents a recursive algorithm to estimate the grid equivalent impedance and generator from the current and voltage measurements performed in the common coupling point, PCC, of a power converter. The method is based in a recursive least squares algorithm performed over the complex space. The described method works on-line, has low computational overhead and does not require the injection of disturbances in the grid.

Direct image registration methods usually treat shadows as outliers. We propose a method which registers images in a 1D shadow invariant space. Shadow invariant image formation is possible by projecting color images, expressed in a log-chromaticity space, onto an 'intrinsic line'. The slope of the line is a camera dependent parameter, usually obtained in a prior calibration step. In this paper, calibration is avoided by jointly determining the 'invariant slope' with the registration parameters. The method deals with images taken by different cameras by using a different slope for each image and compensating for photometric variations. Prior information about the camera is, thus, not required. The method is assessed on synthetic and real data.

This paper proposes a new recursive algorithm to estimate the grid impedance from the current and voltage measurements performed in the common coupling point. The method is based in a recursive least squares algorithm performed over the complex space. The described method has low computational overhead, does not require the injection of disturbances in the grid and simultaneously estimates the grid equivalent impedance and the grid equivalent generator.

In this paper we present a technique for fully automatic, real-time cardiac SPECT (Single Photon Emitting Computed Tomography) and 2D ultrasound image registration. Our registration algorithm allows a physician to perform an ultrasound exam after a series of ECG-gated SPECT images has been acquired and see in real time the registration of both modalities. A specific ECG segmentation algorithm has been developed in order to associate each US image acquired on the fly with the appropriate SPECT volume representing a heart-beat instant.

Two of the most important solutions in position estimation are compared, in this paper, in order to test their efficiency in a multi-tracking application in an unstructured and complex environment. A particle filter is extended and adapted with a clustering process in order to track a variable number of objects. The other approach is to use a Kalman filter with an association algorithm for each of the objects to track. Both algorithms are described in the paper and the results obtained with their real-time execution in the mentioned application are shown. Finally interesting conclusions extracted from this comparison are remarked at the end.

We present here a new non-homogenous illumination correction method of a calibrated incoherent optical fiber bundle (IOFB) for image transmission purpose. The use of IOFB for image transmission in hazardous areas, where conventional camera cannot be employed because of their incorporated electronic, is fully justified. The high fabrication cost of coherent optical fiber bundles makes IOFB useful for large distances images capture applications. Since the bundle is spatially incoherent, the image coming out at the bundle end is a random set of illuminated points, without any spatial correlation, but once the IOFB calibrated, it can be used to transmit optical images without any electronics. To calibrate such cables, a flat LCD screen is used to emit input images. To solve a chromatic aberration problem, generated by the optical system composed by few conventional lens, diaphragms are used, which also reduce the emitted light, and so solve the saturation camera sensor problem too. It unfortunately generates a non-homogenous output picture illumination, which creates noise in the reconstructed images. In this paper we present and compare three methods to correct the cable output Image illumination, once the spatial calibration is achieved, presenting some of the results obtained using this method.

The work described in this paper explores a new solution for tracking multiple and dynamic objects in complex environments. An XPF (extended particle filter) is used to implement a multimodal distribution that represents the most probable estimation for each object position and velocity. A standard PF (particle filter) cannot be used with a variable number of obstacles; some other solutions have been tested in different previous works, but most of them require heavy computational resources at least for a high number of obstacles to be tracked. The solution described here includes a clustering procedure that increases the robustness of the probabilistic process in order to provide on-line adaptation to the variable number of clusters. The result is the XPFCP: extended particle filter with clustering process. The presented algorithm has been tested using stereovision measurements; the results included in the paper show the efficiency of the proposed system.

Diarrhoeal diseases are the major cause of infant mortality in developing countries. Dehydration is the most common complication of diarrhoea, and severe dehydration causes up to 80% of diarrhoeal fatalities. For more than 100 years, physicians focused the treatment of diarrhoeal diseases on the symptom diarrhoea, and there were many 'antidiarrhoeal' drugs, such as water adsorbents (kaolin and pectin) and antiperistaltics (opium, paregoric elixir, diphenoxylate hydrochloride with atropine sulphate and loperamide). This approach focused on a non-dangerous symptom and diverted attention from the real killer, dehydration. A few decades ago, only severely dehydrated patients were treated by intravenous therapy. This treatment was prescribed by a group of professional health workers, administered intravenously by skilled nurses, and reserved for the few patients resident near health facilities. Oral rehydration therapy (ORT), developed 20 years ago, has several advantages over intravenous therapy; it can be administered at home, at health clinics or in modern hospitals, by parents or by nurses or physicians. Most serum disturbances in dehydrated neonates, infants, children, adults and the elderly are resolved by this treatment.

Thirty-five infants with hypernatremic diarrheal dehydration were given "slow" oral rehydration therapy, with deficits replaced over a period of 12 hours. A group of 24 infants received glucose-electrolyte solution for 8 hours, followed by plain water for 4 hours in a volume of 2:1; 11 other infants received equivalent volumes of glucose-electrolyte solution alone over 12 hours. Serum sodium concentrations fell to normal at similar rates in both groups. None of the 35 infants manifested convulsions. These preliminary results indicate that further evaluation of slow oral rehydration in infants with hypernatremic dehydration should be considered.

artículo -- Universidad de Costa Rica, Instituto de Investigaciones en salud. 1983. Este documento de privado debido a limitaciones de derechos de autor. Two hundred thirty-four of 242 neonates (96.7 %) with a mean dehydration of 5.1% body weight were rehydrated with a glucose-electrolyte solution given orally in an average time of 7 44 hours. Eight (3.3 %) neonates required intravenous fluid therapy. Emesis was not an obstacle for complete oral rehydration. Hypernatremia, hyponatremia, and acidosis present on admission were corrected within a few hours. Oral rehydration is an entirely satisfactory choice for treatment of uncomplicated dehydration in neonates. Universidad de Costa Rica, Instituto de investigaciones en Salud UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Instituto de Investigaciones en Salud (INISA)