Capacities

Experience: Study of the physics and highly non-linear dynamics of the atmosphere. Theoretical aspects of the predictability and prediction of spatial-temporal systems are analyzed, as well as aspects associated with the prediction of modern seasonal probability and climate change, including the problems of assimilation, overall prediction and local downscaling.
The group already has experience in knowledge transfer as a result of different contracts with both public and private sectors. As a result of this activity, a technology-based company was created in 2008, PREDICTIA Intelligent Data Solutions, S.L., which develops customized web-based data mining solutions through the integration of database technologies and advanced automatic learning algorithms. Its main activity is focused on the climate and health sectors.
Projects: Tailor-made analysis and prediction, aimed at companies and organisations that have raw data on their activity, providing advanced analysis and prediction tools.
Services: 

• MeteoLab: Automatic learning tool for meteorology.
  
• Prometeo: Local weather forecasting system from a global model.
• Data Access and Downscaling: Provides climate observations and simulations as well as climate change scenarios.

Experience: The IFCA is an active part of several scientific projects at the forefront of modern astronomy that, due to their complexity and the enormous volume of data they generate, require special attention to the statistical analysis of signals. Thus the group has extensive experience in fuzzy component separation, signal detection and statistical analysis of distributions with a wide range of applications in other sectors apart from astronomy, such as medicine, security, etc.

Among the various analysis techniques used, the following stand out:

Separation of fuzzy components: Development of component separation algorithms based on Maximum Entropy techniques. Other methods include the Independent Component Method (ICA) and Internal Linear Combination and Template Fitting.
Wavelets: Multi-resolution analysis, data compression and denoising.

Detection and estimation of compact signals: These include adapted filters, wavelets, Neyman-Pearson filters based on maximums statistics, linear and non-linear fusion techniques, sparse approximations and Bayesian methods.
Spectral estimation from contaminated data: Using an expectation-maximization (EM) type algorithm, the angular spectrum of powers is recovered from images contaminated by other types of unwanted sources.
Statistical study of distributions: Determination of non-Gaussianity. Some of the techniques developed include: wavelets, goodness-of-fit, morphological and topological indicators, Minkowski's functional and n-pdf study.
Innovative aspects:

• Adaptation of existing signal processing techniques in the literature under non-ideal conditions (correlated, non-Gaussian, non-stationary noise) to specific problems.
• Development of completely new technical solutions, specifically adapted to the type of problem images.
  

Applications: Human and veterinary health, security and defence, telecommunications and information technology. The experience in signal analysis is easily extrapolated to any problem where images or large data sets appear, from medical images to remote sensing.

Experience: As a result of participation in several projects in observational astrophysics, instrumentation for the study of the cosmic background in the microwave range has been developed. With this objective in mind, the group is working on new instrumentation designs to meet the needs of present and future experiments, both on land and on board satellites. In particular, the fields of expertise are:

Design and development of ultra-wide band bow-tie flat antenna arrays with frequencies between 21.65 and 26 GHz and 76 to 81 GHz. We are currently working on designs with different substrates based on thermoplastics, which will be characterized by their small size and low cost.
Applications: Their flat shape together with their small size and the possibility of being manufactured on a wide variety of substrates makes them particularly suitable for integration into the car body, such as dead-point and trajectory deviation sensors.
System-level design and simulation techniques for ultra-low noise radiometric receivers. Precise modeling techniques are available to obtain models that are realistic in terms of behavior but efficient in terms of computational time and consumption of computational resources. They also characterise, test and simulate the behaviour of the systems by means of excitation signals similar to those of their normal operation, thus obtaining precise and conclusive results. Applications: Possible applications are intelligent transport systems, mobile-satellite communications, etc.

Experience: Since 2009 IFCA has been collaborating with the National Institute of Aerospace Technology (INTA) in the development of intelligent supports for the detectors of the LHC experiment of the European Laboratory for Particle Physics (CERN). These supports made of carbon fiber composite material are instrumented with fiber sensors embedded in the material matrix so that they provide real-time information on the shape, temperature and stresses supported by them. IFCA collaborates with the Technological Institute of Aragon (ITA) in the development of these sensors in low electromagnetic noise technologies.

• Intense electromagnetic fields and high voltages, and high doses of nuclear radiation.
• Corrosive media.
• Extreme temperatures.