Manufacturing arbitrarily-shaped active waveguides with liquid crystal cladding

Many photonic devices are based on waveguides (WG) whose optical properties can be externally modified. These active WGs are usually obtained with electrooptic materials in either the propagating film (core) or the substrate (cladding). In the second case, the WG tunability is based on the interaction of the active material with the evanescent field of the propagating beam.Liquid crystals (LCs) are an excellent choice as electrooptic active materials since they feature high birefringence, low switching voltage, and relatively simple manufacturing. In this work, we have explored alternative ways to prepare WGs of arbitrary shapes avoiding photolithographic steps. To do this, we have employed a UV laser unit (Spectra Physics)attached to an xyzCNC system mounted on an optical bench. The laser power is 300mW, the spot size can be reduced slightly below 1 µm, and the electromechanicalpositioning is well below that number.Different photoresinshave been evaluated for curing time and uniformity; the results have been compared to equivalent WGs realized by standard photolithographic procedures. Best results have been obtained with several kinds of NOA adhesives (Norland Products Inc.) and SU8 (Microchem). NOA81 optical adhesive has been employed by several groups for the preparation ofmicrochannels [1] and microfluidic systems[2]. In our case, several NOAs having different refractive indices have been tested in order to optimize light coupling and guiding. The adhesive is spinnedonto a substrate, and a number of segmented WGs are written with the laser system. The laser power is attenuated 20 dB. Then the laser spot is swept a number of times (from 1 to 900) on every segment. It has been found that, for example, the optimum number of sweeps for NOA81 is 30-70 times (center of the figure) under these conditions. The WG dimensions obtained with this procedure are about 7 µm high and 12 µm wide.

Superpixel-based roughness measure for multispectral satellite image segmentation

The new generation of artificial satellites is providing a huge amount of Earth observation images whose exploitation can report invaluable benefits, both economical and environmental. However, only a small fraction of this data volume has been analyzed, mainly due to the large human resources needed for that task. In this sense, the development of unsupervised methodologies for the analysis of these images is a priority. In this work, a new unsupervised segmentation algorithm for satellite images is proposed. This algorithm is based on the rough-set theory, and it is inspired by a previous segmentation algorithm defined in the RGB color domain. The main contributions of the new algorithm are: (i) extending the original algorithm to four spectral bands; (ii) the concept of the superpixel is used in order to define the neighborhood similarity of a pixel adapted to the local characteristics of each image; (iii) and two new region merged strategies are proposed and evaluated in order to establish the final number of regions in the segmented image. The experimental results show that the proposed approach improves the results provided by the original method when both are applied to satellite images with different spectral and spatial resolutions.