Abstract
The choice of a suitable method for object manipulation is one of the most critical aspects of virtual environment design. It has been shown that different environments or applications might benefit from direct manipulation approaches, while others might be more usable with indirect ones, exploiting, for example, three dimensional virtual widgets. When it comes to mid-Air interactions, the success of a manipulation technique is not only defined by the kind of application but also by the hardware setup, especially when specific restrictions exist. In this paper we present an experimental evaluation of different techniques and hardware for mid-Air object manipulation in immersive virtual environments (IVE). We compared task performances using both deviceless and device-based tracking solutions, combined with direct and widget-based approaches. We also tested, in the case of freehand manipulation, the effects of different visual feedback, comparing the use of a realistic virtual hand rendering with a simple cursor-like visualization. © 2018 IEEE.

Abstract
Current immersive modeling environments use non-natural tools and interfaces to support traditional shape manipulation operations. In the future, we expect the availability of natural methods of interaction with 3D models in immersive environments to become increasingly important in several industrial applications. In this paper, we present a study conducted on a group of potential users with the aim of verifying if there is a common strategy in gestural and vocal interaction in immersive environments when the objective is modifying a 3D shape model. The results indicate that users adopt different strategies to perform the different tasks but in the execution of a specific activity it is possible to identify a set of similar and recurrent gestures. In general, the gestures made are physically plausible. During the experiment, the vocal interaction was used quite rarely and never to express a command to the system but rather to better specify what the user was doing with gestures.

Abstract
Surgical training for minimal invasive kidney interventions (MIKI) has huge importance within the urology field. Within this topic, simulate MIKI in a patient-specific virtual environment can be used for pre-operative planning using the real patient's anatomy, possibly resulting in a reduction of intra-operative medical complications. However, the validated VR simulators perform the training in a group of standard models and do not allow patient-specific training. For a patient-specific training, the standard simulator would need to be adapted using personalized models, which can be extracted from pre-operative images using segmentation strategies. To date, several methods have already been proposed to accurately segment the kidney in computed tomography (CT) images. However, most of these works focused on kidney segmentation only, neglecting the extraction of its internal compartments. In this work, we propose to adapt a coupled formulation of the B-Spline Explicit Active Surfaces (BEAS) framework to simultaneously segment the kidney and the renal collecting system (CS) from CT images. Moreover, from the difference of both kidney and CS segmentations, one is able to extract the renal parenchyma also. The segmentation process is guided by a new energy functional that combines both gradient and region-based energies. The method was evaluated in 10 kidneys from 5 CT datasets, with different image properties. Overall, the results demonstrate the accuracy of the proposed strategy, with a Dice overlap of 92.5%, 86.9% and 63.5%, and a point-to-surface error around 1.6 mm, 1.9 mm and 4 mm for the kidney, renal parenchyma and CS, respectively. © 2018 IEEE.

Abstract

Abstract
Production scheduling in the presence of real-time events is of great importance for the successful implementation of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various stochastic real-time events which continuously forces reconsideration and revision of pre-established schedules. In an uncertain environment, efficient ways to adapt current solutions to unexpected events, are preferable to solutions that soon become obsolete. This reality motivated us to develop a tool that attempts to start filling the gap between scheduling theory and practice. The developed prototype is connected to the MRP software and uses meta heuristics to generate a predictive schedule. Then, whenever disruptions happen, like arrival of new tasks or cancelation of others, the tool starts rescheduling through a dynamic-event module that combines dispatching rules that best fit the performance measures pre-classified by Kano’s model. The proposed tool was tested in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model. © 2020, Springer Nature Switzerland AG.

Abstract
An early detection of neurodegenerative diseases, such as Parkinson’s disease, can improve therapy effectiveness and, by consequence, the patient’s quality of life. This paper proposes a new methodology for automatic classification of voice samples regarding the presence of acoustic patterns of Parkinson’s disease, using a deep structured neural network. This is a low cost non-invasive approach that can raise alerts in a pre-clinical stage. Aiming to a higher diagnostic detail, it is also an objective to accurately estimate the stage of evolution of the disease allowing to understand in what extent the symptoms have developed. Therefore, two types of classification problems are explored: binary classification and multiclass classification. For binary classification, a deep structured neural network was developed, capable of correctly diagnosing 93.4% of cases. For the multiclass classification scenario, in addition to the deep neural network, a K-nearest neighbour algorithm was also used to establish a reference for comparison purposes, while using a common database. In both cases the original feature set was optimized using principal component analysis and the results showed that the proposed deep structure neural network was able to provide more accurate estimations about the disease’s stage, reaching a score of 84.7%. The obtained results are promising and create the motivation to further explore the model’s flexibility and to pursue better results. © 2020, Springer Nature Switzerland AG.