The capacity of communication networks may be significantly improved by simply enhancing the optical amplifier bandwidth. This paper presents a numerical investigation of an ultra-broadband, low-ripple, two-pump-optical parametric amplifier (2P-OPA) that employs a tellurite glass buried-channel type nano-waveguide as nonlinear medium. The nano-waveguide was designed as a 25-cm-long Archimedean spiral that occupies a footprint of only ~2.5 mm², with a ~0.7 μm² effective cross section. Its zero-dispersion wavelength is ~1550 nm, the nonlinear coefficient is ~3000 W⁻¹ km⁻¹_, and the attenuation coefficient is ~0.5 dB/m (1100 to 1900 nm). Simulations suggest a 2P-OPA based on such waveguide will be able to amplify 243 QPSK input channels modulated at 56 Gbps over 102 nm bandwidth, over metropolitan area network scales.

Robotic applications, such as educational programs, are well-known. Nonetheless, there are challenges to be implemented in other settings, e.g., mine detection, agriculture support, and tasks for industry 4.0. The main challenge consists of robotic operations supported by autonomous decision using sensed-based features extraction. A prototype of a robot assembled using mechanical parts of a LEGO MINDSTORMS Robotic Kit EV3 and a Raspberry Pi controlled through servo algorithms of 2D and 2D1/2 vision approaches was implemented to tackle this challenge. This design is supported by simulations based on image, position, and a hybrid scheme for visual servo controllers. Practical implementation is operated using navigation guided by running up image-based visual servo control algorithms embedded in a Raspberry Pi that uses a control criterion based on error evolution to compute the difference between a target and sensed image. Images are collected by a camera installed on a mobile robotic platform manually and automatically operated and controlled using the Raspberry Pi. An Android application to watch the images by video streaming is shown here, using a smartphone and a video related to the implemented robot’s operation. This kind of robot might be used to complete field reactive tasks in the settings mentioned above, since the detection and control approaches allow self-contained guidance.

A prototype device based on a modified domestic microwave oven for liquid food products pasteurization is presented. This novel design has a coiling glass pipe adapted inside a microwave cavity to allow liquid continuous flow, in which several temperature optical sensors based on Fiber Bragg Grating were installed to measure, by means of contact, the circulating liquid temperature in the pipe, while the oven is on, to obtain the profile temperature as a function of time at different pipe points. The temperatures at liquid input and output were also measured with thermocouples. This device was tested to establish how well it may perform using different standardized liquids with well-known physicochemical and dielectric properties, such as water, water with dissolved sugars, water with dissolved salts, and water with dissolved sugars and salts. It could be observed that the maximum temperature reached was 90 °C for distilled water, 80 °C for water with dissolved salts, 60 °C for water with dissolved sugars and 80 °C for water with dissolved sugars and salts, showing that these data were in agreement with previous results in the literature. This type of device would be potentially useful to establish the device’s efficiency in terms of retention time, energy consumption, and volume of processed liquid, thus, broadening the use of this microwave heating technology with several types of liquid substances.

This research introduces a numerical design of an air-core vortex polymer optical fiber in cyclic transparent optical polymer (CYTOP) that propagates 32 orbital angular momentum (OAM) modes, i.e., it may support up to 64 stable OAM-states considering left- and right-handed circular polarizations. This fiber seeks to be an alternative to increase the capacity of short-range optical communication systems multiplexed by modes, in agreement with the high demand of low-cost, insensitive-to-bending and easy-to-handle fibers similar to others optical fibers fabricated in polymers. This novel fiber possesses unique characteristics: a diameter of 50 µm that would allow a high mechanical compatibility with commercially available polymer optical fibers, a difference of effective index between neighbor OAM modes of around 10⁻⁴ over a bandwidth from 1 to 1.6 µm, propagation losses of approximately 15 × 10⁻³ dB/m for all OAM modes, and a very low dispersion for OAM higher order modes (±l = 16) of up to +2.5 ps/km-nm compared with OAM lower order modes at a telecom wavelength of 1.3 µm, in which the CYTOP exhibits a minimal attenuation. The spectra of mutual coupling coefficients between modes are computed considering small bends of up to 3 cm of radius and slight ellipticity in the ring of up to 5%. Results show lower-charge weights for higher order OAM modes.

Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. Data collection was conducted using a cellphone camera and a handheld VIS-NIR spectrometer. Feature extraction to build the dataset involved image segmentation, performed using the Mahalanobis distance algorithm, as well as spectral processing to calculate multiple vegetation indices. Machine learning models, including linear regression, LASSO, Ridge, ElasticNet, k-nearest neighbors, and decision tree algorithms, were employed for predictive analysis, achieving high accuracy with R² values ranging from 0.938 to 0.998 in predicting biomass and quality traits. A strategy to achieve high performance was implemented by using four spectral captures and computing the reflectance covariance at NIR wavelengths, accounting for the three-dimensional characteristics of the forage. These findings are expected to advance the development of AI-based tools and handheld sensors particularly suited for silvopastoral systems.

This research study compares the performance of different digital image processing algorithms based on computer vision segmentation methods to process satellite multispectral images of Normalized Difference Vegetation Index (NDVI) to estimate agricultural cropland yield as a proposal for supporting spatial planning decisions. NDVI multispectral images were collected from Sentinel-2 L2-A satellite with distinctive features to be processed through these algorithms implemented in an owned and friendly software interface developed in MATLAB App Designer. These are based on image color detection, using three techniques: rectangular thresholding method, simple thresholding method, and segmentation through Mahalanobis discriminant classifier. The segmented images were used to estimate cropland yields as a function of NDVI variations and the characteristics of each analyzed image, employing a linear model that assigned a yield to each segmented area as a function of a specific NDVI range. Algorithm accuracy was determined as a function of expected cropland yield. Results show that the rectangular thresholding method tends to average cropland yield value in slightly non-uniform images. In contrast, thresholding by pixel and Mahalanobis methods performed better on highly non-uniform NDVI images, with deviations less than 8% compared with the expected cropland yield. The rectangular thresholding method could be a more straightforward tool regarding computational cost since, e.g., the demarcation of rectangular areas is easier in any cultivated area, facilitating the implementation of spatial support plans for farmers. The proposal is to use the rectangular thresholding method as a planning tool, as the other methods may be used for more accurate estimations.