The H0H_0 tension in light of vacuum dynamics in the Universe

Despite the outstanding achievements of modern cosmology, the classical dispute on the precise value of $H_0$, which is the first ever parameter of modern cosmology and one of the prime parameters in the field, still goes on and on after over half a century of measurements. Recently the dispute came to the spotlight with renewed strength owing to the significant tension (at $>3\sigma$ c.l.) between the latest Planck determination obtained from the CMB anisotropies and the local (distance ladder) measurement from the Hubble Space Telescope (HST), based on Cepheids. In this work, we investigate the impact of the running vacuum model (RVM) and related models on such a controversy. For the RVM, the vacuum energy density $\rho_{\Lambda}$ carries a mild dependence on the cosmic expansion rate, i.e. $\rho_{\Lambda}(H)$, which allows to ameliorate the fit quality to the overall $SNIa+BAO+H(z)+LSS+CMB$ cosmological observations as compared to the concordance $\Lambda$CDM model. By letting the RVM to deviate from the vacuum option, the equation of state $w=-1$ continues to be favored by the overall fit. Vacuum dynamics also predicts the following: i) the CMB range of values for $H_0$ is more favored than the local ones, and ii) smaller values for $\sigma_8(0)$. As a result, a better account for the LSS structure formation data is achieved as compared to the $\Lambda$CDM, which is based on a rigid (i.e. non-dynamical) $\Lambda$ term.Comment: Accepted for publication in Phys. Lett. B. Significantly extended discussion, two figures and references adde

A proof of Perko's conjectures for the Bogdanov-Takens system

The Bogdanov-Takens system has at most one limit cycle and, in the parameter space, it exists between a Hopf and a saddle-loop bifurcation curves. The aim of this paper is to prove the Perko's conjectures about some analytic properties of the saddle-loop bifurcation curve. Moreover, we provide sharp piecewise algebraic upper and lower bounds for this curve

Flexible system of multiple RGB-D sensors for measuring and classifying fruits in agri-food Industry

The productivity of the agri-food sector experiences continuous and growing challenges that make the use of innovative technologies to maintain and even improve their competitiveness a priority. In this context, this paper presents the foundations and validation of a flexible and portable system capable of obtaining 3D measurements and classifying objects based on color and depth images taken from multiple Kinect v1 sensors. The developed system is applied to the selection and classification of fruits, a common activity in the agri-food industry. Being able to obtain complete and accurate information of the environment, as it integrates the depth information obtained from multiple sensors, this system is capable of self-location and self-calibration of the sensors to then start detecting, classifying and measuring fruits in real time. Unlike other systems that use specific set-up or need a previous calibration, it does not require a predetermined positioning of the sensors, so that it can be adapted to different scenarios. The characterization process considers: classification of fruits, estimation of its volume and the number of assets per each kind of fruit. A requirement for the system is that each sensor must partially share its field of view with at least another sensor. The sensors localize themselves by estimating the rotation and translation matrices that allow to transform the coordinate system of one sensor to the other. To achieve this, Iterative Closest Point (ICP) algorithm is used and subsequently validated with a 6 degree of freedom KUKA robotic arm. Also, a method is implemented to estimate the movement of objects based on the Kalman Filter. A relevant contribution of this work is the detailed analysis and propagation of the errors that affect both the proposed methods and hardware. To determine the performance of the proposed system the passage of different types of fruits on a conveyor belt is emulated by a mobile robot carrying a surface where the fruits were placed. Both the perimeter and volume are measured and classified according to the type of fruit. The system was able to distinguish and classify the 95% of fruits and to estimate their volume with a 85% of accuracy in worst cases (fruits whose shape is not symmetrical) and 94% of accuracy in best cases (fruits whose shape is more symmetrical), showing that the proposed approach can become a useful tool in the agri-food industry.This project has been supported by the National Commission for Science and Technology Research of Chile (Conicyt) under FONDECYT grant 1140575 and the Advanced Center of Electrical and Electronic Engineering - AC3E (CONICYT/FB0008)

Album cover art image generation with Generative Adversarial Networks

Generative Adversarial Networks (GANs) were introduced by Goodfellow in 2014, and since then have become popular for constructing generative artificial intelligence models. However, the drawbacks of such networks are numerous, like their longer training times, their sensitivity to hyperparameter tuning, several types of loss and optimization functions and other difficulties like mode collapse. Current applications of GANs include generating photo-realistic human faces, animals and objects. However, I wanted to explore the artistic ability of GANs in more detail, by using existing models and learning from them. This dissertation covers the basics of neural networks and works its way up to the particular aspects of GANs, together with experimentation and modification of existing available models, from least complex to most. The intention is to see if state of the art GANs (specifically StyleGAN2) can generate album art covers and if it is possible to tailor them by genre. This was attempted by first familiarizing myself with 3 existing GANs architectures, including the state of the art StyleGAN2. The StyleGAN2 code was used to train a model with a dataset containing 80K album cover images, then used to style images by picking curated images and mixing their styles

Fast on-wafer electrical, mechanical, and electromechanical characterization of piezoresistive cantilever force sensors

Validation of a technological process requires an intensive characterization of the performance of the resulting devices, circuits or systems. The technology for the fabrication of Micro and Nanoelectromechanical systems is evolving rapidly, with new kind of device concepts for applications like sensing or harvesting are being proposed and demonstrated. However, the characterization tools and methods for these new devices are still nor fully developed. Here, we present an on-wafer, highly precise and rapid characterization method to measure the mechanical, electrical and electromechanical properties of piezoresistive cantilevers. The set-up is based on a combination of probe-card and atomic force microscopy (AFM) technology, it allows accessing many devices across a wafer and it can be applied to a broad range of MEMS and NEMS. Using this set-up we have characterized the performance of multiple submicron thick piezoresistive cantilever force sensors. For the best design we have obtained a force sensitivity RF=158 uV/nN, a noise of 5.8 uV (1Hz-1kHz) and a minimum detectable force (MDF) of 37 pN with a relative standard deviation of sigma=8%. This small value of sigma, together with a high fabrication yield >95%, validates our fabrication technology. The devices are intended to be used as bio-molecular detectors for the measurement of intermolecular forces between ligand and receptor molecule pairs.This work has been supported by MICINN through projects TEC2011-23600 and NANOSELECT-CSD2007- 00041 (Consolider-Ingenio 2010 Programme).Peer reviewe