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.

Abstract
Lung cancer is one of the most common causes of death in the world. The early detection of lung nodules allows an appropriate follow-up, timely treatment and potentially can avoid greater damage in the patient health. The texture is one of the nodule characteristics that is correlated with the malignancy. We developed convolutional neural network architectures to classify automatically the texture of nodules into the non-solid, part-solid and solid classes. The different architectures were tested to determine if the context, the number of slices considered as input and the relation between slices influence on the texture classification performance. The architecture that obtained better performance took into account different scales, different rotations and the context of the nodule, obtaining an accuracy of 0.833 ± 0.041.

Abstract
This paper proposes a method to provide secrecy for digital communications with arbitrarily large quadrature amplitude modulation (QAM) constellations for transmission over a Gaussian fading wiretap channel. This is accomplished by breaking the constellation down into nested quadrature phase-shift keying (QPSK) symbols and randomizing the assignment between message bits and modulated symbols using channel state information (CSI). If enough random bits can be generated from CSI it becomes possible to uniquely map an arbitrary message to any symbol in the large QAM constellation. The proposed method can thereby provide perfect secrecy while maintaining high reliability by exclusively assigning minimum-distance-mapped constellations through the randomization for use by the legitimate decoder. © 2018 IEEE.

Abstract
Performance assessment is critical in today's competitive environments, where companies need to establish trade-offs between key competitive dimensions. The complexity of these environments calls for new approaches to performance assessment. Thus, in this work, we propose a novel conceptual framework for performance assessment in manufacturing environments combining different production strategies. Focus is laid on MTO/ETO combined environments and a three-stage problem analysis is considered. Firstly, a hybrid SD-DES-ABS model approach addresses the needs of a system that handles different types of orders, processes and workforce allocation requirements; secondly, the model results for different demand scenarios are assessed using a one-way ANOVA analysis followed by a Tukey - Kramer's test, with pairwise comparisons for assessment of significant performance variations under different system operating policies. A full factorial Design of Experiments (DOE) analysis follows, for determining the relevant process parameters influencing the system performance. As an example of application of the proposed framework, we consider the case of an advanced manufacturing company, whose manufacturing environment encompasses combined MTO/ETO production strategies.