Abstract
Shack-Hartmann wavefront sensing relies on accurate spot centre measurement. Several algorithms were developed with this aim, mostly focused on precision, i.e. minimizing random errors. In the solar and extended scene community, the importance of the accuracy (bias error due to peak-locking, quantization, or sampling) of the centroid determination was identified and solutions proposed. But these solutions only allow partial bias corrections. To date, no systematic study of the bias error was conducted. This article bridges the gap by quantifying the bias error for different correlation peak-finding algorithms and types of sub-aperture images and by proposing a practical solution to minimize its effects. Four classes of sub-aperture images (point source, elongated laser guide star, crowded field, and solar extended scene) together with five types of peak-finding algorithms (1D parabola, the centre of gravity, Gaussian, 2D quadratic polynomial, and pyramid) are considered, in a variety of signal-to-noise conditions. The best performing peak-finding algorithm depends on the sub-aperture image type, but none is satisfactory to both bias and random errors. A practical solution is proposed that relies on the antisymmetric response of the bias to the sub-pixel position of the true centre. The solution decreases the bias by a factor of similar to 7 to values of less than or similar to 0.02 pix. The computational cost is typically twice of current cross-correlation algorithms.

Abstract
Sharing location data is becoming more popular as mobile devices become ubiquitous. Location-based service providers use this type of data to provide geographically contextualized services to their users. However, sharing exact locations with possibly untrustworthy entities poses a thread to privacy. Geo-indistinguishability has been recently proposed as a formal notion based on the concept of differential privacy to design location privacy-preserving mechanisms in the context of sporadic release of location data. While adaptations for the case of continuous location updates have been proposed, the study on how the frequency of updates impacts the privacy and utility level is yet to be made. In this paper we address this issue, by analyzing the effect of frequency updates on the privacy and utility levels of four mechanisms: the standard planar Laplacian mechanism suitable for sparse locations, and three variants of an adaptive mechanism that is an adaptation of the standard mechanism for continuous location updates. Results show that the frequency of updates largely impacts the correlation between points. As the frequency of updates decreases, the correlation also decreases. The adaptive mechanism is able to adjust the privacy and utility levels accordingly to the correlation between past positions and current position. However, the estimator function that is used to predict the current location has a great influence in the obtained results.

Abstract
In the later stages of the aging process, an elderly person might need the help of a family member or a caregiver. Technology can be used to help to take care of elderly persons. Autonomous systems, using special interfaces, can collect information from elderly people, which might be useful to predict and recognize health related problems or physical security problems in real time. The emerging technology of image processing, in particular, the emotion recognition, can be a good option to use in elderly care support systems. In this article, we implemented a Microsoft Azure – Emotion SDK to recognize emotion of elderly that able to detect faces and recognize emotions in real time and to be used for elderly care support. The analysis is done with an online video stream, which analyzes facial expression, so that in case of a critical emotion, e.g., if an elderly is very sad or crying, it will inform a caregiver or related entity. From the experiment, we concluded that emotion recognition is a reliable technology to be implemented in real time elderly care. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
The creation of retinal mosaics from sets of fundus photographs can significantly reduce the time spent on the diabetic retinopathy (DR) screening, because through mosaic analysis the ophthalmologists can examine several portions of the eye at a single glance and, consequently, detect and grade DR more easily. Like most of the methods described in the literature, this methodology includes two main steps: image registration and image blending. In the registration step, relevant keypoints are detected on all images, the transformation matrices are estimated based on the correspondences between those keypoints and the images are reprojected into the same coordinate system. However, the main contributions of this work are in the blending step. In order to combine the overlapping images, a color compensation is applied to those images and a distance-based map of weights is computed for each one. The methodology is applied to two different datasets and the mosaics obtained for one of them are visually compared with the results of two state-of-the-art methods. The mosaics obtained with our method present good quality and they can be used for DR grading.

Abstract
Uncontrolled operation of distributed generation (DG) can cause interference with the operation of other equipment such as tap-changers, and non-optimal use of their capability. Thus, having an appropriate scheduling and control on DGs is a crucial issue for distribution system operators. In this paper, a linear multi-objective model for power distribution system scheduling that coordinates tap-changers, photovoltaics (PVs) and battery energy storage operation is proposed. Accordingly, tap-changers experience lower stress, batteries' state of charge is kept in suitable range and DGs are used more effectively. The objective functions of the proposed model encompass improving voltage profile, minimizing losses and peak load. Epsilon-constraint method is employed for solving the multi-objective problem, generating the Pareto set. A new decision-making method is proposed to select the preferred solution from the Pareto set. The 33-bus IEEE test system is used to test the performance of the model. Conclusions are duly drawn.

Abstract
Breast cancer is one of the most common malignanciesaffecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome but, in the case of breast cancer, the surgery aesthetic result has become an important quality indicator for breast cancer patients. In this sense, an adequate surgical planning and prediction tool would empower the patient regarding the treatment decision process, enabling a better communication between the surgeon and the patient and a better understanding of the impact of each surgical option. To develop such tool, it is necessary to create complete 3D model of the breast, integrating both inner and outer breast data. In this review, we thoroughly explore and review the major existing works that address, directly or not, the technical challenges involved in the development of a 3D software planning tool in the field of breast conserving surgery.