Diego A. Acosta

Statistical tuning of adaptive-weight depth map algorithm

In depth map generation, the settings of the algorithm parameters to yield an accurate disparity estimation are usually chosen empirically or based on unplanned experiments. A systematic statistical approach including classical and exploratory data analyses on over 14000 images to measure the relative influence of the parameters allows their tuning based on the number of bad pixels. Our approach is systematic in the sense that the heuristics used for parameter tuning are supported by formal statistical methods. The implemented methodology improves the performance of dense depth map algorithms. As a result of the statistical based tuning, the algorithm improves from 16.78% to 14.48% bad pixels rising 7 spots as per the Middlebury Stereo Evaluation Ranking Table. The performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury. Future work aims to achieve the tuning by using significantly smaller data sets on fractional factorial and surface-response designs of experiments.

Collaborative networked virtual surgical simulators cnvss implementing hybrid client-server architecture: Factors affecting collaborative performance

Currently, surgical skills teaching in medical schools and hospitals is changing, requiring the development of new tools to focus on (i) the importance of the mentor’s role, (ii) teamwork skills training, and (iii) remote training support. Collaborative Networked Virtual Surgical Simulators (CNVSS) allow collaborative training of surgical procedures where remotely located users with different surgical roles can take part in the training session. To provide successful training involving good collaborative performance, CNVSS should guarantee synchronicity in time of the surgical scene viewed by each user and a quick response time which are affected by factors such as users’ machine capabilities and network conditions. To the best of our knowledge, the impact of these factors on the performance of CNVSS implementing hybrid client–server architecture has not been evaluated. In this paper the development of a CNVSS implementing a hybrid client–server architecture and two statistical designs of experiments (DOE) is described by using (i) a fractional factorial DOE and (ii) a central composite DOE, to determine the most influential factors and how these factors affect the collaboration in a CNVSS. From the results obtained, it was concluded that packet loss, bandwidth, and delay have a larger effect on the consistency of the shared virtual environment, whereas bandwidth, server machine capabilities, and delay and interaction between factors bandwidth and packet loss have a larger effect on the time difference and number of errors of the collaborative task.

Mechanical properties and rheology of polyalkenoate cements using various low-cost fillers

The Publisher apologizes for a misprint that appeared on the Journal of Materials Science webpage for “Mechanical properties and rheology of polyalkenoate cements using various low-cost fillers” by Diego A. Acosta, Gary P. Funkhouser and Brian P.Grady. The last author’s name was spelled incorrectly.

Sensitivity analysis in optimized parametric curve fitting

Purpose – Curve fitting from unordered noisy point samples is needed for surface reconstruction in many applications. In the literature, several approaches have been proposed to solve this problem. However, previous works lack formal characterization of the curve fitting problem and assessment on the effect of several parameters (i.e. scalars that remain constant in the optimization problem), such as control points number (m), curve degree (b), knot vector composition (U), norm degree (k), and point sample size (r) on the optimized curve reconstruction measured by a penalty function (f). The paper aims to discuss these issues. Design/methodology/approach – A numerical sensitivity analysis of the effect of m, b, k and r on f and a characterization of the fitting procedure from the mathematical viewpoint are performed. Also, the spectral (frequency) analysis of the derivative of the angle of the fitted curve with respect to u as a means to detect spurious curls and peaks is explored. Findings – It is more effective to find optimum values for m than k or b in order to obtain good results because the topological faithfulness of the resulting curve strongly depends on m. Furthermore, when an exaggerate number of control points is used the resulting curve presents spurious curls and peaks. The authors were able to detect the presence of such spurious features with spectral analysis. Also, the authors found that the method for curve fitting is robust to significant decimation of the point sample. Research limitations/implications – The authors have addressed important voids of previous works in this field. The authors determined, among the curve fitting parameters m, b and k, which of them influenced the most the results and how. Also, the authors performed a characterization of the curve fitting problem from the optimization perspective. And finally, the authors devised a method to detect spurious features in the fitting curve. Practical implications – This paper provides a methodology to select the important tuning parameters in a formal manner. Originality/value – Up to the best of the knowledge, no previous work has been conducted in the formal mathematical evaluation of the sensitivity of the goodness of the curve fit with respect to different possible tuning parameters (curve degree, number of control points, norm degree, etc.).

Adaptive Architecture to Support Context-Aware Collaborative Networked Virtual Surgical Simulators CNVSS

Stand-alone and networked surgical virtual reality based simulators have been proposed as means to train surgical skills with or without a supervisor nearby the student or trainee. However, surgical skills teaching in medicine schools and hospitals is changing, requiring the development of new tools to focus on: i importance of mentors role, ii teamwork skills and iii remote training support. For these reasons a surgical simulator should not only allow the training involving a student and an instructor that are located remotely, but also the collaborative training session involving a group of several students adopting different medical roles during the training session. Collaborative Networked Virtual Surgical Simulators CNVSS allow collaborative training of surgical procedures where remotely located users with different surgical roles can take part in a training session. Several works have addressed the issues related to the development of CNVSS using various strategies. To the best of our knowledge no one has focused on handling heterogeneity in collaborative surgical virtual environments. Handling heterogeneity in this type of collaborative sessions is important because not all remotely located users have homogeneous Internet connections, nor the same interaction devices and displays, nor the same computational resources, among other factors. Additionally, if heterogeneity is not handled properly, it will have an adverse impact on the performance of each user during the collaborative session. In this paper we describe the development of an adaptive architecture with the purpose of implementing a context-aware model for collaborative virtual surgical simulation in order to handle the heterogeneity involved in the collaboration session.