Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red–Green–Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas.

The MIST experiment aims to detect the cosmological 21‐cm signal through sky observations at 25–125 MHz using a wide‐beam antenna. The antenna is mounted above the soil and the beam characteristics are highly dependent on the soil's electrical properties. Accurate models for the beam obtained from electromagnetic simulations are crucial for detecting the 21‐cm signal. Determining the soil properties to inform the beam simulations is therefore a very high priority for MIST. Here we report the first electrical characterization of the MIST observation site in the Canadian High Arctic, which was conducted in July 2022. The electrical parameters were estimated using impedance measurements of the instrument's antenna, which is a very advantageous approach for MIST. Our best‐fit soil model is consistent with a thawed active layer underlain by permafrost, and the parameters were estimated with a precision close to the requirements for the detection of the cosmological 21‐cm signal.

Fiber-optic Mach–Zehnder interferometers are widely used in research areas such as telecommunications, spectroscopy, and quantum information. These optical structures are known to be affected by phase fluctuations that are usually modeled as multiparametric noise. This multidimensional noise must be stabilized or compensated for to enable fiber-optic Mach–Zehnder architectures for practical applications. In this work, we study the effectiveness of a modified Perturb-and-Observe (P&O) algorithm to control multidimensional phase noise in fiber-based multi-arm Mach–Zehnder interferometers. We demonstrate the feasibility of stabilizing multidimensional phase noise by numerical simulations using a simple feedback control scheme and analyze the algorithm’s performance for systems up to dimension $8\times 8$. We achieved minimal steady-state errors that guarantee high optical visibility in complex optical systems with $N\times N$ matrices (with $N=[2,3,4,5,6,7,8]$).

Radio experiments trying to detect the global 21 cm signal from the early Universe are very sensitive to the electrical properties of their environment. For ground-based experiments with the antenna above the soil it is critical to characterize the effect from the soil on the sky observations. This characterization requires estimating the soil’s electrical conductivity and relative permittivity in the same frequency range as the observations. Here we present our initial effort to estimate the conductivity and relative permittivity of the soil using the impedance of an antenna mounted at a distance above the surface. In this technique, the antenna used for soil characterization is the same as the antenna used for sky observations. To demonstrate the technique we use the antenna of the MIST global 21 cm experiment. We measured the antenna impedance at three sites in the Greater Concepción area, Chile. The measurements were done between 25 and 125 MHz, matching the range used by MIST for sky observations. The soil parameters were estimated by fitting the impedance measurements with electromagnetic simulations of the antenna and soil. In this initial effort the soil was modeled as homogeneous. The conductivity at the three sites was found to be between 0.007 and 0.049 Sm⁻¹, and the relative permittivity between 1.6 and 12.7. The percent precision of the estimates at 68% probability is, with one exception, better (lower) than 33%. The best-fit simulations have a better than 10% agreement with the measurements relative to the peak values of the resistance and reactance across our frequency range. For MIST, these results represent a successful proof of concept of the use of the antenna impedance for soil characterization, and are expected to significantly improve in future implementations.

In this paper, an Ambient Intelligence (AmI) architecture for devising and implementing a smart laboratory in a university is presented. The architecture relies on a novel meta-model that abstracts the laboratory ambient using a context model, an adaptation model, an end-user model, and a domain model. Thus, the architecture offers, to the end-users, a set of adaptive AmI services that are supplied by smart agents and wireless sensor networks. The architecture was coded using SQLite, Jade, Protégé, and CLIPs and simulated under Oracle's Solarium environment. The latency and scalability of the proposed architecture, as well as its ability to supply effectively the AmI services, were tested using both qualitative and quantitative standard performance metrics.