Integration issues are still one of the main concerns regarding hospital information systems (HIS). Hospitals need integration engines capable of guaranteeing that messages are properly dealt with, use standards properly (eg. IHE), are secure, promote integration of legacy systems, enable auditing, monitor failures, evolve to include new standards, have balancing capabilities and are able to compensate problems with the source-projects. This paper describes an architecture for an integration solution for HIS based on open-source components. The proposed architecture is called HealthIRA and is based on a HL7 message router(Mirth), a load balancer (HAProxy), audit-trail (Elasticsearch), HL7 sniffer (GO), message cache, clinical document online translator and databases (MySQL). HealthIRA is partially (all modules but audit-trail, cache and translate) in use at an oncology hospital. Initial results include the analysis of the gathered data and a comparison between the message workflow and the provider's' documentation which revealed important incongruities. With all modules installed, HealthIRA acts as an integrator, a standard's auditor, a translator and a secondary source of data when the primary source is not available, solving some of the integration problems. We identified still the need for an open-source orchestration module with an user-friendly interface for each service.

Stereo confidence measures are important functions for global reconstruction methods and some applications of stereo. In this article we evaluate and compare several models of confidence which are defined at the whole disparity range. We propose a new stereo confidence measure to which we call the Histogram Sensor Model (HSM), and show how it is one of the best performing functions overall. We also introduce, for parametric models, a systematic method for estimating their parameters which is shown to lead to better performance when compared to parameters as computed in previous literature. All models were evaluated when applied to two different cost functions at different window sizes and model parameters. Contrary to previous stereo confidence measure benchmark literature, we evaluate the models with criteria important not only to winner-take-all stereo, but also to global applications. To this end, we evaluate the models on a real-world application using a recent formulation of 3D reconstruction through occupancy grids which integrates stereo confidence at all disparities. We obtain and discuss our results on both indoors' and outdoors' publicly available datasets.

Sensorimotor coupling is ubiquitous in living organisms. Sensory and motor systems are utterly useless if left without the presence of the other. One crucial faculty that organisms have developed with tremendous ecological advantages is the ability to discern between the origins of perceptual input as being originated by the environment or the organism itself, provided by resource efficient sensor and motor systems. This ability has been shown to be implemented through a specialized circuit (forward model) receiving a copy of the motor command (corollary discharge). We propose a fast method to derive a resource constrained forward model by framing sensorimotor coupling as a low-rank approximation of an overly detailed forward model. By framing the problem as a factorization approach we can resort to currently available off-the-shelf solvers for matrix factorization. We experimentally show that by solving the problem as a low-rank approximation we obtain more than an order of magnitude speed up relatively to minimizing the objective function with gradient descent methods. The development of resource constrained and ecologically adapted sensorimotor systems is essential for the deployment of low-cost energy efficient autonomous robots for the execution of specific tasks in particular environments.

We present a grid-based 3D reconstruction method which integrates all costs given by stereo vision into what we call a Cost-Curve Occupancy Grid (CCOG). Occupancy probabilities of grid cells are estimated in a Bayesian formulation, from the likelihood of stereo cost measurements taken at all distance hypotheses. This is accomplished with only a small set of probabilistic assumptions which we discuss in the paper. We quantitatively characterize the method's performance under different conditions of both image noise and number of used stereo pairs, compared also to traditional algorithms. We complement the study by giving insights on design choices of CCOGs such as likelihood model, window size of the cost function and use of a hole filling method. Experiments were made on a real-world outdoors dataset with ground-truth data.

Extensive literature has been written on occupancy grid mapping for different sensors. When stereo vision is applied to the occupancy grid framework it is common, however, to use sensor models that were originally conceived for other sensors such as sonar. Although sonar provides a distance to the nearest obstacle for several directions, stereo has confidence measures available for each distance along each direction. The common approach is to take the highest-confidence distance as the correct one, but such an approach disregards mismatch errors inherent to stereo. In this work, stereo confidence measures of the whole sensed space are explicitly integrated into 3D grids using a new occupancy grid formulation. Confidence measures themselves are used to model uncertainty and their parameters are computed automatically in a maximum likelihood approach. The proposed methodology was evaluated in both simulation and a real-world outdoor dataset which is publicly available. Mapping performance of our approach was compared with a traditional approach and shown to achieve less errors in the reconstruction.

Humanoid robots are complex sensorimotor systems where the existence of internal models are of utmost importance both for control purposes and for predicting the changes in the world arising from the system's own actions. This so-called expected perception relies on the existence of accurate internal models of the robot's sensorimotor chains.

The maintenance of a stable and coherent representation of the surrounding environment is an essential capability in cognitive robotic systems. Most systems employ some form of 3D perception to create internal representations of space (maps) to support tasks such as navigation, manipulation and interaction. The creation and update of such representations may represent a significant effort in the overall computation performed by the robot. In this paper we propose an architecture based on the concept of Expected Perception that allows lightweight map updates whenever the course of action happens according to the robot's expectations. It is only when the robot's predictions and the real world outcomes differ, that corrections must be done at its full extent. We performed experiments and show results in a real robotic platform with stereo (3D) perception where map corrections are proposed by simple image level (2D) comparisons.

This paper deals with the reconstruction of smooth, flexible, flat surfaces isometrically embedded in 3D when observed from uncalibrated camera images. It provides a cost function which captures the global geometry of the problem. The input data consists of a sequence of matched feature points (i.e. known correspondence), but the algorithm allows for missing data so that all points do not need to be visible in all images. Its performance under these conditions is analyzed. A way of inferring the bend radius at each point is provided which can be used back in the cost function, providing a higher order fitting. The performance is validated using both synthetic data and real image examples, where the reconstruction is compared to ground truth and reference points.

This paper deals with the reconstruction of smooth, flexible, isometrically embedded flat surfaces in 3D, such as a sheet of paper or a flag waving in the wind, from a set of 2D projected observations such as camera images. To solve the problem, a set of matched features of the waving object at different poses is needed, which are then applied to the reconstruction algorithm here described. The complete algorithm consists of 2 phases, the first obtaining an initial approximation from local features, the second uses this result to iterate a global cost function, trying to achieve a better estimate. To validate the algorithm, synthetic data with noise is generated, reconstructed and compared to ground truth data. Also, a second experiment consisting of real images of a sheet of paper is shown.

We address the problem of computing the Riemannian centroid of a constellation of points in a naturally reductive homogeneous manifold. We note that many interesting manifolds used in engineering (such as the special orthogonal group, Grassman, sphere, positive definite matrices) possess this structure. We develop an intrinsic Newton scheme for the centroid computation. This is achieved by exploiting a formula that we introduce for obtaining the Hessian of the squared Riemannian distance on naturally reductive homogeneous spaces. Some results of finding the centroid of a constellation of points in these spaces are presented, which evidence the quadratic convergence of the Newton method derived herein. These computer simulation results show that, as expected, the Newton method has a faster convergence rate than the usual gradient-based approaches