Parada i Fonda. Figueres 1875-1900

Actualment, a Figueres, hi ha un bon nombre d’hotels, restaurants, cafès i altres establiments relacionats amb l’hostaleria. Aquesta situació no és precisament moderna, sinó que prové d’una llarga tradició hostalera que ha anat evolucionant al llarg dels anys i que ens ha portat on som ara. Aquest treball se centra en un període concret d’aquesta evolució que comprèn els anys que van del 1875 fins al 1900. La primera part és una contextualització de la situació històrica de l’Alt Empordà i la ciutat de Figueres que es complementa amb la informació de les principals vies de comunicació de l’època per tal d’entendre la relació de l’interval estudiat  amb les fondes. La segona part és una anàlisi de la vida i l’evolució dels set hostals que hi havia a la ciutat en aquell moment amb l’objectiu d’entendre la història de la ciutat des del punt de vista polític, econòmic, social i cultural.Today, there is a great number of hotels, restaurants, cafés and other establishments related to the hospitality industry in Figueres. This situation is not exactly modern but comes from a long tradition of hospitality which has evolved over time and has brought us where we are now. This article focuses on a specific period of this evolution that includes the years 1875 to 1900. The first part contextualizes the historical situation of the Alt Empordà county and the town of Figueres with complementary information on the main roads of the time, in order to understand how the studied period relates to inns. The second part analyses the life and evolution of the seven inns that operated in the city at the time to understand the history of Figueres from a political, economic, social and cultural perspective

Numerical resolution of Emden's equation using Adomian polynomials

Purpose: In this paper the authors aim to show the advantages of using the decomposition method introduced by Adomian to solve Emden's equation, a classical non‐linear equation that appears in the study of the thermal behaviour of a spherical cloud and of the gravitational potential of a polytropic fluid at hydrostatic equilibrium. Design/methodology/approach: In their work, the authors first review Emden's equation and its possible solutions using the Frobenius and power series methods; then, Adomian polynomials are introduced. Afterwards, Emden's equation is solved using Adomian's decomposition method and, finally, they conclude with a comparison of the solution given by Adomian's method with the solution obtained by the other methods, for certain cases where the exact solution is known. Findings: Solving Emden's equation for n in the interval [0, 5] is very interesting for several scientific applications, such as astronomy. However, the exact solution is known only for n=0, n=1 and n=5. The experiments show that Adomian's method achieves an approximate solution which overlaps with the exact solution when n=0, and that coincides with the Taylor expansion of the exact solutions for n=1 and n=5. As a result, the authors obtained quite satisfactory results from their proposal. Originality/value: The main classical methods for obtaining approximate solutions of Emden's equation have serious computational drawbacks. The authors make a new, efficient numerical implementation for solving this equation, constructing iteratively the Adomian polynomials, which leads to a solution of Emden's equation that extends the range of variation of parameter n compared to the solutions given by both the Frobenius and the power series methods.This work has been supported by the Ministerio de Ciencia e Innovación, project TIN2009-10581

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.This work has been supported by the Spanish Ministerio de Ciencia e Innovación (www.micinn.es), project TIN2009-10581

Learning a Swarm Foraging Behavior with Microscopic Fuzzy Controllers Using Deep Reinforcement Learning

This article presents a macroscopic swarm foraging behavior obtained using deep reinforcement learning. The selected behavior is a complex task in which a group of simple agents must be directed towards an object to move it to a target position without the use of special gripping mechanisms, using only their own bodies. Our system has been designed to use and combine basic fuzzy behaviors to control obstacle avoidance and the low-level rendezvous processes needed for the foraging task. We use a realistically modeled swarm based on differential robots equipped with light detection and ranging (LiDAR) sensors. It is important to highlight that the obtained macroscopic behavior, in contrast to that of end-to-end systems, combines existing microscopic tasks, which allows us to apply these learning techniques even with the dimensionality and complexity of the problem in a realistic robotic swarm system. The presented behavior is capable of correctly developing the macroscopic foraging task in a robust and scalable way, even in situations that have not been seen in the training phase. An exhaustive analysis of the obtained behavior is carried out, where both the movement of the swarm while performing the task and the swarm scalability are analyzed.This work was supported by the Ministerio de Ciencia, Innovación y Universidades (Spain), project RTI2018-096219-B-I00. Project co-financed with FEDER funds

Modelling Oil-Spill Detection with Swarm Drones

Nowadays, swarm robotics research is having a great increase due to the benefits derived from its use, such as robustness, parallelism, and flexibility. Unlike distributed robotic systems, swarm robotics emphasizes a large number of robots, and promotes scalability. Among the multiple applications of such systems we could find are exploring unstructured environments, resource monitoring, or distributed sensing. Two of these applications, monitoring, and perimeter/area detection of a given resource, have several ecological uses. One of them is the detection and monitoring of pollutants to delimit their perimeter and area accurately. Maritime activity has been increasing gradually in recent years. Many ships carry products such as oil that can adversely affect the environment. Such products can produce high levels of pollution in case of being spilled into sea. In this paper we will present a distributed system which monitors, covers, and surrounds a resource by using a swarm of homogeneous low cost drones. These drones only use their local sensory information and do not require any direct communication between them. Taking into account the properties of this kind of oil spills we will present a microscopic model for a swarm of drones, capable of monitoring these spills properly. Furthermore, we will analyse the proper macroscopic operation of the swarm. The analytical and experimental results presented here show the proper evolution of our system.This work has been supported by the Spanish Ministerio de Ciencia e Innovación, Project TIN2009-10581

Trajectory-Based Morphological Operators: A Model for Efficient Image Processing

Mathematical morphology has been an area of intensive research over the last few years. Although many remarkable advances have been achieved throughout these years, there is still a great interest in accelerating morphological operations in order for them to be implemented in real-time systems. In this work, we present a new model for computing mathematical morphology operations, the so-called morphological trajectory model (MTM), in which a morphological filter will be divided into a sequence of basic operations. Then, a trajectory-based morphological operation (such as dilation, and erosion) is defined as the set of points resulting from the ordered application of the instant basic operations. The MTM approach allows working with different structuring elements, such as disks, and from the experiments, it can be extracted that our method is independent of the structuring element size and can be easily applied to industrial systems and high-resolution images

Vocabulary acquisition through captioned TV series: Are there any aptitude and proficiency effects?

Presentació a: AILA 201

Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.This work has been supported by the Ministerio de Economía y Competitividad (Spain), Project TIN2013-40982-R. Project co-financed with FEDER funds

Soft Optomechanical Systems for Sensing, Modulation, and Actuation

Altres ajuts: CERCA Programme/Generalitat de Catalunya ; the Government of Catalonia's Agency for Business Competitiveness (ACCIÓ) (TECSPR19-1-0021)Soft optomechanical systems have the ability to reversibly respond to optical and mechanical external stimuli by changing their own properties (e.g., shape, size, viscosity, stiffness, color or transmittance). These systems typically combine the optical properties of plasmonic, dielectric or carbon-based nanomaterials with the high elasticity and deformability of soft polymers, thus opening the path for the development of new mechanically tunable optical systems, sensors, and actuators for a wide range of applications. This review focuses on the recent progresses in soft optomechanical systems, which are here classified according to their applications and mechanisms of optomechanical response. The first part summarizes the soft optomechanical systems for mechanical sensing and optical modulation based on the variation of their optical response under external mechanical stimuli, thereby inducing mechanochromic or intensity modulation effects. The second part describes the soft optomechanical systems for the development of light induced mechanical actuators based on different actuation mechanisms, such as photothermal effects and phase transitions, among others. The final section provides a critical analysis of the main limitations of current soft optomechanical systems and the progress that is required for future devices