In this paper, we obtain the Complex Natural Resonances of an object from the backscattered response in the frequency domain with a novel rational function approximation method based on both Vector Fitting and Cauchy methods. We determine the system order and an initial set of poles, which are used as a basis for a rational function approximation. The results from the simulations and experiments show an improvement in the reconstructed signals and the accuracy of the CNRs calculated, with an increased tolerance to the critical Signal-to-Noise Ratio. This is being used in the problem of GPR landmine humanitarian detection in Colombia.

This paper presents a technique, based on the matrix pencil method (MPM), for the compression of underwater acoustic signals produced by boat engines. The compressed signal, represented by its complex resonance expansion, is intended to be sent over a low-bit-rate wireless communication channel. We demonstrate that the method can provide data compression greater than 60%, ensuring a correlation greater than 93% between the reconstructed and the original signal, at a sampling frequency of 2.2 kHz. Once the signal was reconstituted, a localization process was carried out with the time reversal method (TR) using information from four different sensors in a simulation environment. This process sought to achieve the identification of the position of the ship using only passive sensors, considering two different sensor arrangements.

Attention-Deficit/Hyperactivity Disorder (ADHD) has been associated with altered integration across large-scale brain networks. Temporal fluctuations in functional organization and differences between acquisition centers complicate classification based on resting-state functional magnetic resonance imaging (rs-fMRI). This study used BrainNetCNN as the reference architecture to examine how region-of-interest (ROI) dimensionality, connectivity representation, network design, window configuration and acquisition site influence ADHD discrimination. The sample included 465 participants from four ADHD-200 centers: New York University (NYU; n = 177), Peking University (Peking; n = 183), NeuroIMAGE (n = 39) and Oregon Health & Science University (OHSU; n = 66). Functional connectivity was derived from regional blood-oxygen-level-dependent (BOLD) time series using prespecified 12, 18, 39 and 116-ROI panels from the Automated Anatomical Labeling atlas (AAL116). The reference window was 120 s with an approximately 12-s step. Within each center, classification was assessed through participant-level stratified 10-fold cross-validation repeated five times, with area under the receiver operating characteristic curve (AUC) as the primary metric. BrainNetCNN was configured using NYU data and then kept fixed for evaluation at the remaining centers. Across site-by-ROI combinations, AUC ranged from 50.1% to 64.5%. The 12-ROI configuration reached 63.6% in both NYU and Peking, while the strongest panel differed across centers. Comparisons involving static connectivity, long short-term memory (LSTM), gated recurrent unit (GRU), DeepSets, logistic regression and alternative window settings revealed no consistent advantage. Leave-one-site-out analyses yielded AUCs from 44.4% to 63.9%, with 14 of 16 pointwise 95% confidence intervals including 0.50. Overall, the models showed modest site-dependent discrimination and limited cross-site transportability.

Thermal losses significantly impact the efficiency of photovoltaic modules, particularly under high-temperature and variable cloud cover conditions in tropical climates. This study presents a novel thermal clustering methodology for predicting thermal losses in Monocrystalline Passivated Emitter and Rear Cell (MonoPERC) solar modules. Seven machine learning algorithms were tested using two methods, a baseline approach and a thermal clustering approach, which allow better energy yield forecasting and a more comprehensive understanding of the behavior of PERC modules. The clustering methodology partitions data into distinct thermal regimes, enabling specialized model training for different temperature operating conditions. K-Nearest Neighbors (KNN) was the best model without clustering, achieving a 0.9612 correlation and a mean prediction error of 7.3 W. With the new thermal clustering method, Multi-Layer Perceptron (MLP) was the top performer, with a 0.9561 correlation and an NMAE of 0.1409. Ensemble methods, such as XGBoost and Random Forest, were also highly effective, while linear methods proved inadequate. Results demonstrate that K-Nearest Neighbors achieved superior baseline performance, while the thermal clustering approach improved prediction accuracy across all algorithms. The Multi-Layer Perceptron emerged as the best performer with the clustering methodology.

Complex natural resonances (CNRs) extraction methods such as matrix pencil method (MPM), Cauchy, vector-fitting Cauchy method (VCM), or Prony’s method decompose a signal in terms of frequency components and damping factors based on Baum’s singularity expansion method (SEM) either in the time or frequency domain. The validation of these CNRs is accomplished through a reconstruction of the signal based on these complex poles and residues and a comparison with the input signal. Here, we perform quantitative performance metrics in order to have an evaluation of each method’s hardware suitability factor before selecting a hardware platform using benchmark signals, simulations of backscattering scenarios, and experiments.