Julio Ibarra-Fiallo

Temporal super-resolution (SR) aims to reconstruct a high-resolution signal from a low-resolution observation. When hardware limits force low sampling rates, this problem becomes critical for non-stationary signals with abrupt transients and rapid spectral changes. This manuscript reports a deterministic case study using pretrained 1D convolutional models and deterministic evaluation on paired real, synthetic, and mixed EEG-like signals. A compact encoder–linear upsampler–refinement architecture is evaluated at 5× upsampling under four training regimes: synthetic-only, real-only, tuned-real, and mixed. Performance is assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Normalized Mean Absolute Error (NMAE), Log Spectral Distance (LSD), and spectral correlation (SCORR). Across 12 model–dataset combinations, mixed-domain training yields the most robust cross-domain behavior, outperforming single-domain checkpoints on real and mixed evaluation subsets. These findings support the practical value of training corpus composition for temporal SR under distribution shift. A focused morphological event analysis further shows that reconstruction error concentrates at abrupt amplitude and frequency boundaries, confirming that these transient regions are the dominant local challenge. An exploratory hybrid wavelet–superlet pilot is also reported; it achieves competitive pointwise error on selected domains but exhibits a substantial spectral-fidelity gap, indicating that frequency-aware inputs alone do not guarantee spectral reconstruction without auxiliary spectral losses.

Temporal super-resolution (SR) aims to reconstruct a high-resolution signal from a low-resolution observation. When hardware limits force low sampling rates, this problem becomes critical for non-stationary signals with abrupt transients and rapid spectral changes. This manuscript reports a reproducible case study using pretrained 1D convolutional models and deterministic evaluation on paired real, synthetic, and mixed ECG-like signals. A compact encoder–linear upsampler–refinement architecture is evaluated at 5× upsampling under four training regimes: synthetic-only, real-only, tuned-real, and mixed. Performance is assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Log Spectral Distance (LSD), and spectral correlation (SCORR). Across 12 model–dataset combinations, mixed-domain training yields the most robust cross-domain behavior, outperforming single-domain checkpoints on real and mixed evaluation subsets. These findings support the practical value of training-corpus composition for temporal SR under distribution shift. A focused morphological event analysis further shows that reconstruction error concentrates at abrupt amplitude and frequency boundaries, confirming that these transient regions are the dominant local challenge. An exploratory hybrid wavelet–superlet pilot is also reported; it achieves competitive pointwise error on selected domains but exhibits a substantial spectral-fidelity gap, indicating that frequency-aware inputs alone do not guarantee spectral reconstruction without auxiliary spectral losses.

The increasing application of time-series analysis in fields like biomedical engineering or telecommunications emphasizes the need for high-quality data to train and evaluate advanced machine learning models. Acquiring temporal data at suitable resolutions is often limited by ethical, economic, or practical constraints. We introduce CoSiBD (Complex Signal Benchmark Dataset for Super-Resolution), a synthetic dataset designed for reproducible time-series super-resolution research. CoSiBD provides 2,500 high-resolution signals ( N = 5, 000 samples each over a reference domain τ ∈ [0, 4 π ]) with aligned low-resolution versions at four levels (150, 250, 500, and 1,000 samples) obtained via uniform decimation. Signals are generated with diverse non-stationary behaviors through piecewise frequency modulation and spline-based amplitude envelopes, and provides both clean and noisy variants. Signals are distributed as NumPy arrays, plain text, and JSON, with comprehensive metadata describing segment structure, generation parameters, and seeds for full reproducibility. Technical validation analyzes spectral properties and reports baseline SR benchmarking and transfer experiments on EEG and speech data.

Automatic weather recognition using images has important applications in land and air traffic control, autonomous vehicles, road safety, and crop management. Despite its relevance, this field remains underexplored due to the difficulty of extracting robust features for weather conditions that often overlap visually, and the high cost of building large, labeled datasets. To address these challenges, we propose a Multistep Visual Pretraining (MVP) approach, a resource-efficient training pipeline designed to maximize performance with limited supervision. MVP leverages self-supervised pretraining on a large-scale unlabeled weather dataset, followed by supervised fine-tuning on a small labeled dataset, while reusing ImageNet pretrained models to minimize computational requirements. Unlike traditional strategies that demand massive manually labeled data and extensive GPU resources, MVP achieves competitive performance with a compact ResNet-50 architecture, only 1200 labeled images for fine-tuning, and a single mid-range GPU. Our method reached 94% accuracy in multi-class weather recognition, demonstrating that robust, real-world performance can be achieved without the prohibitive costs of large-scale annotation or high-end computing infrastructure. This study extends our previous work, “Weather Recognition Using Self-Supervised Deep Learning”, going from binary to multi-class classification while reducing data and resource dependency, thus offering a scalable and practical solution for real-world deployment.

This work addresses the development of a hybrid method for feature selection and a strategy to classify quite large datasets of handwritten characters. The divide and conquer paradigm, generally, is used to divide a big problem into minor problems. This research applied this concept to recognize handwritten uppercase letters and numbers. As a result, a big problem is split into two nodes or subproblems, one for numbers and one for letters. Then, letters are divided into two nodes representing the straight and curved ones. The division can be called the binary decision tree and allows to obtain a subset with the minimal features of each node called reduct. Here, an improvement of reducts is proposed using the ant colony algorithm as the embedded method. The application of these methods had the following result and conclusions. For each node, subsets of fewer features were obtained with high performance in the classification, considering the morphology of each letter. It is crucial to highlight that the distribution of the samples affects the performance of the classifier and the strategy improves the performance of the reduct.