Abstract
Some coronagraph masks can be turned into wave front sensing masks thanks to minor modification. For instance, one only has to divide by two the depth of the central well to convert the Roddier & Roddier coronograph into the Zernike wave front sensor (WFS). Physically, the opposition of phase in coronagrapy becomes a quadrature phase in wave front sensing. Here, we replicate this idea to the Four Quadrant Phase Mask (FQPM) coronagraph by introducing a sensor that we call the iQuad WFS, generated by a mask which has the same geometrical structure as the FQPM but with a modified differential piston. An optical and mathematical description of this new WFS is firstly provided showing its great elegance and the central role played by the Hilbert transform in its understanding. We then compare its performance criteria with two classical wave front sensors. We finally show the iQuad sensor has major similarities to the Pyramid sensor making it a wonderful theoretical object to improve our understanding of this sensor.

Abstract
Lung cancer has an increasing preponderance in worldwide mortality, demanding for the development of efficient screening methods. With this in mind, a binary classification method using Lung-RADS (TM) guidelines to warn changes in the screening management is proposed. First, having into account the lack of public datasets for this task, the lung nodules in the LIDC-IDRI dataset were re-annotated to include a Lung-RADS (TM)-based referral label. Then, a wide residual network is used for automatically assessing lung nodules in 3D chest computed tomography exams. Unlike the standard malignancy prediction approaches, the proposed method avoids the need to segment and characterize lung nodules, and instead directly defines if a patient should be submitted for further lung cancer tests. The system achieves a nodule-wise accuracy of 0.87 +/- 0.02.

Abstract

Abstract
Cardiovascular disease is the leading cause of death around the world and its early detection is a key to improving long-term health outcomes. To detect possible heart anomalies at an early stage, an automatic method enabling cardiac health low-cost screening for the general population would be highly valuable. By analyzing the phonocardiogram (PCG) signals, it is possible to perform cardiac diagnosis and find possible anomalies at an early-term. Accordingly, the development of intelligent and automated analysis tools of the PCG is very relevant. In this work, the PCG signals are studied with the main objective of determining whether a PCG signal corresponds to a “normal” or “abnormal” physiological state. The main contribution of this work is the evidence provided that time domain features can be combined with features extracted from a wavelet transformation of PCG signals to improve automatic cardiac disease classification. We empirically demonstrate that, from a pool of alternatives, the best classification results are achieved when both time and wavelet features are used by a Support Vector Machine with a linear kernel. Our approach has obtained better results than the ones reported by the challenge participants which use large amounts of data and high computational power. © Springer Nature Switzerland AG 2019.

Abstract
The IoT (Internet of Things) is a network composed of several devices (things) connected to the Internet and to each other. IoT services are increasingly growing and are allowing companies to deploy scalable solutions with reduced costs and instantaneous data access. These solutions require seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self- maintenance. Such requirements can be given to a company's IoT solution by applying blockchain technology. This paper analyzes the blockchain technology and the advantages and challenges behind its implementation in an IoT environment. A blockchain in IoT scenario was developed to evaluate the performance of different cryptographic hash functions in the IoT device RaspberryPi. Conclusions were drawn when it comes to the viability of some hash functions mainly based on the low resource characteristic shared by the IoT devices, which compromises the performance of the hash function.

Abstract
We present a high-assurance and high-speed implementation of the SHA-3 hash function. Our implementation is written in the Jasmin programming language, and is formally verified for functional correctness, provable security and timing attack resistance in the EasyCrypt proof assistant. Our implementation is the first to achieve simultaneously the four desirable properties (efficiency, correctness, provable security, and side-channel protection) for a non-trivial cryptographic primitive. Concretely, our mechanized proofs show that: 1) the SHA-3 hash function is indifferentiable from a random oracle, and thus is resistant against collision, first and second preimage attacks; 2) the SHA-3 hash function is correctly implemented by a vectorized x86 implementation. Furthermore, the implementation is provably protected against timing attacks in an idealized model of timing leaks. The proofs include new EasyCrypt libraries of independent interest for programmable random oracles and modular indifferentiability proofs.