We evaluated the potential of using convolutional neural networks in classifying spectrograms of Antillean manatee (Trichechus manatus manatus) vocalizations. Spectrograms using binary, linear and logarithmic amplitude formats were considered. Two deep convolutional neural networks (DCNN) architectures were tested: linear (fixed filter size) and pyramidal (incremental filter size). Six experiments were devised for testing the accuracy obtained for each spectrogram representation and architecture combination. Results show that binary spectrograms with both linear and pyramidal architectures with dropout provide a classification rate of 94–99% on the training and 92–98% on the testing set, respectively. The pyramidal network presents a shorter training and inference time. Results from the convolutional neural networks (CNN) are substantially better when compared with a signal processing fast Fourier transform (FFT)-based harmonic search approach in terms of accuracy and F1 Score. Taken together, these results prove the validity of using spectrograms and using DCNNs for manatee vocalization classification. These results can be used to improve future software and hardware implementations for the estimation of the manatee population in Panama.

This study introduces a novel, drone-based approach for the detection and classification of Greater Caribbean Manatees (Trichechus manatus manatus) in the Panama Canal Basin by integrating advanced deep learning techniques. Leveraging the high-performance YOLOv8 model augmented with Sliced Aided Hyper Inferencing (SAHI) for improved small-object detection, our system accurately identifies individual manatees, mother–calf pairs, and group formations across a challenging aquatic environment. Additionally, the use of AltCLIP for zero-shot classification enables robust demographic analysis without extensive labeled data, enhancing model adaptability in data-scarce scenarios. For this study, more than 57,000 UAV images were acquired from multiple drone flights covering diverse regions of Gatun Lake and its surroundings. In cross-validation experiments, the detection model achieved precision levels as high as 93% and mean average precision (mAP) values exceeding 90% under ideal conditions. However, testing on unseen data revealed a lower recall, highlighting challenges in detecting manatees under variable altitudes and adverse lighting conditions. Furthermore, the integrated zero-shot classification approach demonstrated a robust top-2 accuracy close to 90%, effectively categorizing manatee demographic groupings despite overlapping visual features. This work presents a deep learning framework integrated with UAV technology, offering a scalable, non-invasive solution for real-time wildlife monitoring. By enabling precise detection and classification, it lays the foundation for enhanced habitat assessments and more effective conservation planning in similar tropical wetland ecosystems.

Electrical insulation faults produce partial discharges (PD), which can be analysed to identify specific types of defects. PD clustering is a widely used method to identify PD sources, although its success depends largely on the feature maps used. In this paper, three widely used feature maps, or separation maps, are compared: chromatic, energy wavelet with principal component analysis (EW‐PCA), and time–frequency (TF). To compare and evaluate, five scenarios with multi‐PD environments with noise were developed. The clustering ability of the maps was evaluated using two performance indicators: intercluster distance and intracluster distance. The results indicate that the EW‐PCA map performed the best in all scenarios, correctly identifying the largest number of data points and producing the clearest and most distinct clusters. The TF map created distinct clusters in several scenarios, but not all. The chromatic map created distinct clusters in all scenarios but was not as well defined as the other two separation maps. Given the results, it is important in fieldwork to use a wide range of PD clustering, accompanied by performance metrics that support a less biased decision tailored to the test object.

In telecommunications engineering degrees, simulations are used to understand better theoretical concepts. However, in courses related to digital communications or signal processing usually these simulations cannot be easily applied to real data or in real scenarios. This limitation does not allow the students to verify that the learned theoretical hypotheses are valid in practice. Nowadays, software defined radio platforms have been proposed to validate theoretical telecommunications concepts in real situations. Therefore, by combining a software defined radio and simulation tools, students can implement the learned notions over real data in real scenarios. This allows students to work under real applications in order to learn by experience and to see that the theoretical concepts are valid in the real world. This paper presents a project involving real-time decoding of automatic dependent surveillance–broadcast (ADS-B) signals, implemented by using a universal software radio peripheral software defined radio and MATLAB®. The universal software radio peripheral allows to receive real automatic dependent surveillance–broadcast signals sent by the aircraft. Then, by using MATLAB® tools, the automatic dependent surveillance–broadcast baseband signals are decoded. The students are guided through a manual to demodulate the real automatic dependent surveillance–broadcast signals and display in a map the aircraft trajectories. The evaluation is based on a competition between the students, considering execution time of their algorithms, and a final report of the project. This project allows to obtain a game-oriented and novel approach for teaching digital communications and signal processing. The implementation of this project in telecommunications engineering courses motivate the students, allowing them to understand better difficult theoretical concepts, and improve their computer, teamwork, and collaboration skills. The originality of the approach is based on the usage of a prototyping platform, combined with simulation tools, for practical application of telecommunications engineering concepts using real signals.

This study deals with an interweave cognitive‐radio (CR) system, where an uplink orthogonal frequency‐division multiple access system is considered for the primary users (PUs). Our purpose is to estimate the PU carrier frequency offsets (PU‐CFOs) as well as the channels to estimate the transmitted symbols. However, in wideband wireless communications, the PU received‐signal spectrum usually exhibits a localised fading because of the multipath propagation channel. When the PU faded frequencies are used by the CR system, the PU system is contaminated by a CR narrowband interference (CR‐NBI). In that case, if a Kalman filter (KF)‐based approach is used for the estimations of the CFOs and the channels, the state‐space representation does not necessarily take into account the CR‐NBI; this hence has a negative impact on the algorithm performance. Therefore the authors propose to make this solution more robust to the CR‐NBI by detecting when it appears and disappears to avoid using the data disturbed by the CR‐NBI. The authors’ contribution is to assume that the CR‐NBI is because of the transmission of contiguous blocks and to propose various criteria based on the covariance matrix of the KF innovation.