Self-calibrating d-scan: measuring ultrashort laser pulses on-target using an arbitrary pulse compressor

In most applications of ultrashort pulse lasers, temporal compressors are used to achieve a desired pulse duration in a target or sample, and precise temporal characterization is important. The dispersion-scan (d-scan) pulse characterization technique usually involves using glass wedges to impart variable, well-defined amounts of dispersion to the pulses, while measuring the spectrum of a nonlinear signal produced by those pulses. This works very well for broadband few-cycle pulses, but longer, narrower bandwidth pulses are much more difficult to measure this way. Here we demonstrate the concept of self-calibrating d-scan, which extends the applicability of the d-scan technique to pulses of arbitrary duration, enabling their complete measurement without prior knowledge of the introduced dispersion. In particular, we show that the pulse compressors already employed in chirped pulse amplification (CPA) systems can be used to simultaneously compress and measure the temporal profile of the output pulses on-target in a simple way, without the need of additional diagnostics or calibrations, while at the same time calibrating the often-unknown differential dispersion of the compressor itself. We demonstrate the technique through simulations and experiments under known conditions. Finally, we apply it to the measurement and compression of 27.5 fs pulses from a CPA laser.Comment: 11 pages, 5 figures, Scientific Reports, in pres

PLANETCAM-UPV/EHU: A dual channel lucky imaging camera for solar system studies. Performance, Calibration and Scientific applications.

200 p.PlanetCam-UPV/EHU es una cámara astronómica `lucky imaging¿ diseñada para la obtención de imágenes de alta resolución de objetos del Sistema Solar, principalmente como apoyo a la investigación científica en dinámica atmosférica y estudios de estructura vertical de nubes en las atmósferas planetarias. El instrumento trabaja simultáneamente en dos canales, cada uno con su propio detector y filtros del interés en el rango Visible (0.38 - 1 ¿m) e infrarrojo cercano SWIR (1 - 1.7 ¿m). El instrumento contiene varios filtros seleccionados para imágenes de color y una amplia serie de filtros correspondientes a las bandas de absorción de metano y CO2, así como sus longitudes de onda adyacentes, todos ellos seleccionados por su interés en los estudios planetarios. El objetivo principal dentro del alcance de esta tesis ha sido caracterizar el funcionamiento radiométrico y astronómico de PlanetCam, así como su calibración en reflectividad absoluta, proporcionando valores de la reflectividad absoluta de Júpiter y de Saturno a lo largo de cuatro años de observación. Por otra parte, se ha realizado una serie de estudios científicos de los principales planetas del Sistema Solar en términos de dinámica atmosférica así como un modelo de transferencia radiativa para el análisis de la estructura vertical de la atmósfera de Júpiter

PLANETCAM-UPV/EHU: A dual channel lucky imaging camera for solar system studies. Performance, Calibration and Scientific applications.

200 p.PlanetCam-UPV/EHU es una cámara astronómica `lucky imaging¿ diseñada para la obtención de imágenes de alta resolución de objetos del Sistema Solar, principalmente como apoyo a la investigación científica en dinámica atmosférica y estudios de estructura vertical de nubes en las atmósferas planetarias. El instrumento trabaja simultáneamente en dos canales, cada uno con su propio detector y filtros del interés en el rango Visible (0.38 - 1 ¿m) e infrarrojo cercano SWIR (1 - 1.7 ¿m). El instrumento contiene varios filtros seleccionados para imágenes de color y una amplia serie de filtros correspondientes a las bandas de absorción de metano y CO2, así como sus longitudes de onda adyacentes, todos ellos seleccionados por su interés en los estudios planetarios. El objetivo principal dentro del alcance de esta tesis ha sido caracterizar el funcionamiento radiométrico y astronómico de PlanetCam, así como su calibración en reflectividad absoluta, proporcionando valores de la reflectividad absoluta de Júpiter y de Saturno a lo largo de cuatro años de observación. Por otra parte, se ha realizado una serie de estudios científicos de los principales planetas del Sistema Solar en términos de dinámica atmosférica así como un modelo de transferencia radiativa para el análisis de la estructura vertical de la atmósfera de Júpiter

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes

Robustness and capabilities of ultrashort laser pulses characterization with amplitude swing

[EN]In this work we firstly study the influence of different parameters in the temporal characterization of ultrashort laser pulses with the recently developedamplitude swing technique. In this technique, the relative amplitude of two delayed replicas is varied while measuring their second-harmonic spectra. Herewe study the retrieval of noisy traces and the implications of having different delays or phase retardations (relative phases)between the two replicas. Then, we study the capability of the technique to characterize the pulses when the second-harmonic signal is spectrally uncalibrated or incomplete, presenting the analytical calculation of the marginal, which is used to calibrate the traces and to perform the pulse retrievals. We experimentally show the retrieval of different pulses using diverse delays and phase retardationsto perform the amplitude swing trace and demonstrate that, from an uncalibrated trace, both the pulse informationandthe response of the nonlinear process can be simultaneously retrieved.In sum, the amplitude swing technique is shown to be very robust against experimental constraints and limitations, showing a high degree of soundness.Spanish Ministerio de Economía y Competitividad (MINECO) (FIS2017-87970-R, EQC2018-004117-P), Consejería de Educación, Junta de Castilla y León (SA287P18) and FEDER Funds, Fundación General de la Universidad de Salamanca (PC_TCUE18-20_020)

Semantic Segmentation from Sparse Labeling Using Multi-Level Superpixels

Semantic segmentation is a challenging problemthat can benefit numerous robotics applications, since it pro-vides information about the content at every image pixel.Solutions to this problem have recently witnessed a boost onperformance and results thanks to deep learning approaches.Unfortunately, common deep learning models for semanticsegmentation present several challenges which hinder real lifeapplicability in many domains. A significant challenge is theneed of pixel level labeling on large amounts of trainingimages to be able to train those models, which implies avery high cost. This work proposes and validates a simplebut effective approach to train dense semantic segmentationmodels from sparsely labeled data. Labeling only a few pixelsper image reduces the human interaction required. We findmany available datasets, e.g., environment monitoring data, thatprovide this kind of sparse labeling. Our approach is basedon augmenting the sparse annotation to a dense one with theproposed adaptive superpixel segmentation propagation. Weshow that this label augmentation enables effective learning ofstate-of-the-art segmentation models, getting similar results tothose models trained with dense ground-truth

An Expert System to Improve the Energy Efficiency of the Reaction Zone of a Petrochemical Plant

Energy is the most important cost factor in the petrochemical industry. Thus, energy efficiency improvement is an important way to reduce these costs and to increase predictable earnings, especially in times of high energy price volatility. This work describes the development of an expert system for the improvement of this efficiency of the reaction zone of a petrochemical plant. This system has been developed after a data mining process of the variables registered in the plant. Besides, a kernel of neural networks has been embedded in the expert system. A graphical environment integrating the proposed system was developed in order to test the system. With the application of the expert system, the energy saving on the applied zone would have been about 20%.Junta de Andalucía TIC-570

Application of Deep Learning to Classification and Regression Problems in Mechanics

Machine Learning has already become a game-changer in many fields such as Linguistics, Image Processing or Robotics. Becoming themain research topic for the worldwide specialistsin these fields. On the other hand, other fields such as mechanics are just starting to give baby steps on their path to take full advantage of the benefits that Machine Learning could bring.This work pretends to explore the possibilities of how mechanical engineering problems could benefit from Machine Learning. For this purpose, some of the foundation concepts in Machine Learning and more specifically, in Deep Learning, will be developed. Additionally, an example of how the application of Deep Learning to Mechanics will be provided