Ana Luna

Retrieval of relevant parameters of natural multilayer systems by means of bio-inspired optimization strategies

Natural photonic structures exhibit remarkable color effects such as metallic appearance and iridescence. A rigorous study of the electromagnetic response of such complex structures requires to accurately determine some of their relevant optical parameters, such as the refractive indices of the materials involved. In this paper, we apply different heuristic optimization strategies to retrieve the real and imaginary parts of the refractive index of the materials comprising natural multilayer systems. Through some examples, we compare the performances of the inversion methods proposed and show that these kinds of algorithms have a great potential as a tool to investigate natural photonic structures.

Structural color in beetles of South America

Photonic microstructures in nature, specifically in endemic species of Coleoptera from Argentina and the south of Chile have been identified, analyzed and modeled. These natural systems produce partial photonic bandgaps (PBGs) as a result of the high periodicity of the microstructures found in some parts of their bodies. With the aid of scanning (SEM) and transmission (TEM) electron microscopy we have identified that the elytron (modified forewing of a beetle that encases the thin hind wings used in flight) of these insects shows a periodic structure which originates diffractive phenomena resulting in extraordinary physical effects such as iridescent or metallic colors. We measured the reflectance spectrum and obtained the chromaticity diagrams of the samples with an Ocean Optics 4000 spectrophotometer. The geometrical parameters of the structure were obtained by processing the SEM images with the ImageJ software, to introduce them in our electromagnetic model. In all cases, a satisfactory agreement between the measurements and the numerical results was obtained. This permits us to explain the mechanism of color production in those specimens. The study of structural colors in the natural world can inspire the development of artificial devices with particular applications in technology, such as intelligent sensors and new kinds of filters.

Characterization of the iridescence-causing multilayer structure of the Ceroglossus suturalis beetle using bio-inspired optimization strategies

We investigate the iridescence exhibited by Ceroglossus suturalis beetles, which mostly live endemically in the southern end of South America. Two differently colored specimens have been studied. We observed and characterized the samples by different microscopy techniques, which revealed a multilayer structure within their cuticle. Using measured reflectance spectra as input data, we applied heuristic optimization techniques to estimate the refractive index values of the constituent materials, to be introduced within the theoretical model. The color of the samples was calculated for different incidence angles, showing that multilayer interference is the mechanism responsible for the observed iridescence.

Characterization of natural photonic structures by means of optimization strategies

Natural photonic structures exhibit remarkable color effects such as metallic appearance and iridescence. A rigorous study of the electromagnetic response of such complex structures requires to accurately determine some of their relevant optical parameters, e.g. the dielectric constants of the materials involved. In a recent work, we have shown that heuristic optimization strategies are suitable tools for the retrieval of the complex refractive index of the materials comprising natural multilayer systems such as the Coleoptera’s cuticle. Moreover, the numerical results obtained illustrate the great potential of this kind of algorithms not only for the study of natural photonic structures, but also for the design of biomimetic photonic devices for lightning, sensing or anti-counterfeiting applications. In a first stage, we assumed that the materials which comprise the layers are characterized by isotropic non-dispersive dielectric permittivities. However, it is well known that the cuticle of many Coleoptera exhibit anisotropy in their constituent materials, and also dispersion has been reported. In this contribution we improve our previous approach in order to have a more realistic and useful computational tool for the retrieval of the relevant parameters of biological structures. For this, we include, within the inversion algorithm, a dispersion model to describe the frequency-dependent dielectric permittivity of the layers’ materials. Also, in order to guarantee the uniqueness of the solution and the convergence to the global optimum, we simultaneously include in the fitness function the information of several angles of incidence, as well as that of the p- and s-polarization states.

Privacy-Aware Data Gathering for Urban Analytics

Nowadays, there are a mature set of tools and techniques for data analytics, which help Data Scientists to extract knowledge from raw heterogeneous data. Nonetheless, there is still a lack of spatiotemporal historical dataset allowing to study everyday life phenomena, such as vehicular congestion, press influence, the effect of politicians comments on stock exchange markets, the relation between food prices evolution and temperatures or rainfall, social structure resilience against extreme climate events, among others. Unfortunately, few datasets are combining from different sources of urban data to carry out studies of phenomena occurring in cities (i.e., Urban Analytics). To solve this problem, we have implemented a Web crawler platform for gathering a different kind of available public datasets.

Alternative setup for estimating reliable frequency values in a ripple tank

In this paper, we introduce an innovative, low-cost and easy experimental setup to be used in a traditional ripple tank when a frequency generator is unavailable. This configuration was carried out by undergraduate students. The current project allowed them to experiment and to study the relationship between the wavelength and the oscillation period of a mechanical wave, among other things. Under this setup, students could evaluate the mechanism not only qualitative but also in a quantitative fashion with a high degree of confidence. The results obtained for the propagation speed of the mechanical waves in different media with this alternative design coincided with those acquired using a commercial device designed for this purpose.

Power demand forecasting through social network activity and artificial neural networks

Long-term and short-term term national power demand forecasting is a well known and open issue for many countries. In this paper, we focus and study the short-term Peruvian national power demand forecasting. Thus, we tackle this problem using indirect and direct method for prediction. The former method relies on Social Network Activity to estimate national needs using regression models. The latter method is based on Artificial Neural Networks (ANNs). The network was used subsequently for predictions of the power for the last day of April, May and June 2016. The result was highly satisfactory with a mean absolute percentage error (MAPE) of 0.36 % for April and 0.34% in May and June. The ANN cumulative model proved to be a fast, reliable and accurate method for predicting power demand in Peril. In the case of the social activity generated by tweets, there is an increase in the MAPE values of an order of magnitude, reaching a maximum value of 7.3% for June. Nevertheless, the power demand forecasting using Twitter posts is a good indicator as a first approximation.