Subwavelength Grating Waveguide Structures Proposed on the Low-Cost Silica-Titania Platform for Optical Filtering and Refractive Index Sensing Applications.

The sol−gel dip-coating method is a cost-efficient way for the realization of thin films on a planar substrate. In this work, high-quality, low-loss, and low-surface roughness silica−titania thin films are deposited on a glass substrate with the sol−gel dip-coating method. This platform works in the visible to near-IR wavelength ranges and can be useful for several eye-catching photonic components. The paper is comprised of two parts: the first part deals with the development of a low-cost silica−titania waveguide system, whereas the second part provides detail on the numerical modeling of the SWG waveguide filter and SWG waveguide FP-sensor design. The SWG waveguide NIR-stopband filter can achieve an ER of >40 dB and 3-dB bandwidth of 110 nm designed at optimized parameters. The SWG waveguide-FP structure proposed in this work act as a refractive index sensor where the sensitivity is ~120 nm/RIU by reducing the width of the waveguide. This sensitivity can be further enhanced by reducing the waveguide height. We believe that this work is quite important for the realization of low-cost integrated photonic devices based on the silica−titania platform developed via the sol−gel dip-coating method.

Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping.

This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.

Real-time human pose estimation and gesture recognition from depth images using superpixels and SVM classifier.

In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit), so that the algorithm can be operated on a low-cost platform, such as an embedded board. The human pose estimation method is based on an SVM (support vector machine) and superpixels without prior knowledge of a human body model. In the gesture recognition method, gestures are recognized from the pose information of a human body. To recognize gestures regardless of motion speed, the proposed method utilizes the keyframe extraction method. Gesture recognition is performed by comparing input keyframes with keyframes in registered gestures. The gesture yielding the smallest comparison error is chosen as a recognized gesture. To prevent recognition of gestures when a person performs a gesture that is not registered, we derive the maximum allowable comparison errors by comparing each registered gesture with the other gestures. We evaluated our method using a dataset that we generated. The experiment results show that our method performs fairly well and is applicable in real environments.