Abstract
This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3channel RGB image. The challenge was divided into 2 tracks: the "Clean" track sought HS recovery from noiseless RGB images obtained from a known response function (representing spectrally-calibrated camera) while the "Real World" track challenged participants to recover HS cubes from JPEG-compressed RGB images generated by an unknown response function. To facilitate the challenge, the BGU Hyperspectral Image Database [4] was extended to provide participants with 256 natural HS training images, and 5+ 10 additional images for validation and testing, respectively. The "Clean" and "Real World" tracks had 73 and 63 registered participants respectively, with 12 teams competing in the final testing phase. Proposed methods and their corresponding results are reported in this review.

Abstract
The introduction of Industry 4.0 to modernize the existing industrial manufacturing systems has created a wave of disruption all over the world, that changed not only the industry paradigm, but also the research directions in the intelligent manufacturing systems field. After five years since its presentation, similar initiatives have fostered in several countries worldwide to promote the adoption of Industry 4.0 principles. This paper aims to analyze and discuss the current state of adoption of this initiative and to verify the way it changed the research directions, particularly at the level of applying artificial intelligence and ICT technologies in the cyber-physical systems context. For this purpose, an analytical study of the scientific publications related to Industry 4.0 domain has been performed, considering a dataset of scientific publications retrieved from the IEEE Xplore database. This dataset considers two distinct time periods, separated by the introduction of Industry 4.0 as the digitalization threshold: before digital era (B.D.) and after digital era (A.D.). © 2018 IEEE.

Abstract
The Gemini Infrared Multi-Object Spectrograph (GIRMOS) is a powerful new instrument being built to facility- class standards for the Gemini telescope. It takes advantage of the latest developments in adaptive optics and integral field spectrographs. GIRMOS will carry out simultaneous high-angular-resolution, spatially-resolved infrared (1 - 2.4 µm) spectroscopy of four objects within a two-arcminute field-of-regard by taking advantage of multi-object adaptive optics. This capability does not currently exist anywhere in the world and therefore offers significant scientific gains over a very broad range of topics in astronomical research. For example, current programs for high redshift galaxies are pushing the limits of what is possible with infrared spectroscopy at 8 -10- meter class facilities by requiring up to several nights of observing time per target. Therefore, the observation of multiple objects simultaneously with adaptive optics is absolutely necessary to make effective use of telescope time and obtain statistically significant samples for high redshift science. With an expected commissioning date of 2023, GIRMOS's capabilities will also make it a key followup instrument for the James Webb Space Telescope when it is launched in 2021, as well as a true scientific and technical pathfinder for future Thirty Meter Telescope (TMT) multi-object spectroscopic instrumentation. In this paper, we will present an overview of this instrument's capabilities and overall architecture. We also highlight how this instrument lays the ground work for a future TMT early-light instrument.

Abstract
Objective: The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. Materials and methods: The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39 pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). Results and discussion: SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917 +/- 0.0024 and a F-Measure of 0.9925 +/- 0.0019, in comparison with ANN, which reached the values of 0.9847 +/- 0.0032 and 0.9852 +/- 0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. Conclusion: The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW.

Abstract
In this paper, an angular frequency dynamic-based control technique is proposed to control interfaced converters between the power grid and renewable energy sources. The proposed control technique can guarantee a stable operation of power grid under high penetration of renewable energy resources through providing the required inertia properties. The synchronous generator characteristics combined with the basic dynamic model of the interfaced converter can shape a second order derivative of the grid angular frequency consisting of converter power and virtual mechanical power derivative with embedded virtual inertia to prevent instability from the power grid as well as to generate active and reactive power with appropriate inertia. Simulation analyses are performed in Matlab/Simulink to attest the high performance of the proposed control technique.

Abstract
Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem.