arXiv subjects

Mario A. Quiroz-Juarez

Publications and source records attributed to Mario A. Quiroz-Juarez.

6 recordsLinked to original sources

arXiv2025

Compact Neural Network Algorithm for Electrocardiogram Classification

In this paper, we present a powerful, compact electrocardiogram (ECG) classification algorithm for cardiac arrhythmia diagnosis that addresses the current reliance on deep learning and convolutional neural networks (CNNs) in ECG analysis. This work aims to reduce the demand for deep learning, which often requires extensive computational resources and large labeled datasets. Our approach introduces an artificial neural network (ANN) with a simple architecture combined with advanced feature engineering techniques. A key contribution of this work is the incorporation of 17 engineered features that enable the extraction of critical patterns from raw ECG signals. By integrating mathematical transformations, signal processing methods, and data extraction algorithms, our model captures the morphological and physiological characteristics of ECG signals with high efficiency, without requiring deep learning. Our method demonstrates a similar performance to other state-of-the-art models in classifying 4 types of arrhythmias, including atrial fibrillation, sinus tachycardia, sinus bradycardia, and ventricular flutter. Our algorithm achieved an accuracy of 97.36% on the MIT-BIH and St. Petersburg INCART arrhythmia databases. Our approach offers a practical and feasible solution for real-time diagnosis of cardiac disorders in medical applications, particularly in resource-limited environments.

eess.SP↗

arXiv2025

Classical-quantum study of confinement in the chaotic $x^{2}y^{2}$ Yang-Mills Hamiltonian

We analyze how quantum mechanics reinstates confinement in Hamiltonian systems that are classically unstable and exhibit chaotic dynamics. Specifically, we consider two paradigmatic models: the Contopoulos Hamiltonian, an isotropic oscillator perturbed by the quartic coupling $α\, x^{2}y^{2}$, and the purely quartic Yang--Mills Hamiltonian $H=\tfrac{1}{2}(p_{x}^{2}+p_{y}^{2})+α\, x^{2}y^{2}$. Classical dynamics, characterized through Poincaré sections, Lyapunov exponents, and periodic orbits, reveals distinct escape mechanisms: in the Contopoulos system, trajectories destabilize along the diagonal valleys $x=\pm y$ for $α<0$, whereas in the Yang--Mills case with $α>0$, escape occurs along the coordinate axes $x=0$ or $y=0$. In sharp contrast, the quantum Yang--Mills Hamiltonian with $α>0$ admits only discrete, normalizable eigenstates. Semiclassical WKB and full two--dimensional analyses further show that these quantum states are localized along the classical escape channels, illustrating how transverse zero--point motion generates an effective confining barrier. Our study combines global Lyapunov--exponent heat maps with high--precision quantum spectra obtained via variational and Lagrange--mesh methods, providing quantitatively controlled results across regimes. In addition, we corroborate the classical predictions through analog electronic simulations based on operational--amplifier circuit models, offering an experimentally inspired validation of the theoretical framework.

nlin.CD↗

arXiv2024

Exploring the Multifractal Behavior of the Human Genome T2T-CHM13v2.0: Graphical Representations and Cytogenetics

In this work, we applied the Chaos Game Representation (CGR) to the complete human genomic sequence T2T-CHM13v2.0, analyzing the entire chromosome assembly and each chromosome separately, including mitochondrial DNA. Multifractal spectra were determined using two types of box-counting coverage, revealing slight variations across most chromosomes. While the geometric support remained consistent, distinct distributions were observed for each chromosome. Chromosomes 9 and Y exhibited the greatest differences in singularity (Hölder exponent), with minor variations in their fractal support. The CGR distributions generally demonstrated an approximate separation between coding and non-coding sections, as well as CpG or GpC islands. A base-by-base analysis of the fractal support of the CGR uncovered characteristic structural bands in chromosome sequences, which align with patterns identified in cytogenetic studies. Using the complete assembly as a reference, we compared two alternative representations: the Binary Genomic Representation (RGB) and the Markov Chain (MC) representation. Both methods tended toward the same fractal support but displayed differing distributions based on the assigned length parameter. Multifractal analysis highlighted quantitative differences between these representations: RGB aligned more closely with high-frequency components, while MC showed better correspondence with low frequencies. The optimal fit was achieved using MC for twelve-base chains, yielding an average percentage error of 2% relative to the full genomic assembly.

q-bio.OT↗

arXiv2023

Experimental observation of phase transitions of a deformed Dicke model using a reconfigurable, bi-parametric electronic platform

We experimentally study the infinite-size limit of the Dicke model of quantum optics with a parity-breaking deformation strength that couples the system to an external bosonic reservoir. We focus on the dynamical consequences of such symmetry-breaking, which makes the classical phase space asymmetric with non-equivalent energy wells. We present an experimental implementation of the classical version of the deformed Dicke model using a state-of-the-art bi-parametric electronic platform. Our platform constitutes a playground for studying representative phenomena of the deformed Dicke model in electrical circuits with the possibility of externally controlling parameters and initial conditions. In particular, we investigate the dynamics of the ground state, various phase transitions, and the asymmetry of the energy wells as a function of the coupling strength $γ$ and the deformation strength $α$ in the resonant case. Additionally, to characterize the various behavior regimes, we present a two-dimensional phase diagram as a function of the two intrinsic system parameters. The onset of chaos is also analyzed experimentally. Our findings provide a clear connection between theoretical predictions and experimental observations, demonstrating the usefulness of our bi-parametric electronic setup.

quant-ph↗

arXiv2021

Identification of particle mixtures using machine-learning-assisted laser diffraction analysis

We demonstrate a smart laser-diffraction analysis technique for particle mixture identification. We retrieve information about the size, geometry, and ratio concentration of two-component heterogeneous particle mixtures with an efficiency above 92%. In contrast to commonly-used laser diffraction schemes -- in which a large number of detectors is needed -- our machine-learning-assisted protocol makes use of a single far-field diffraction pattern, contained within a small angle ($\sim 0.26^{\circ}$) around the light propagation axis. Because of its reliability and ease of implementation, our work may pave the way towards the development of novel smart identification technologies for sample classification and particle contamination monitoring in industrial manufacturing processes.

eess.IV↗

arXiv2019

Identification of Light Sources using Machine Learning

The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation and molecule fluorescence has relied on the collection of photon statistics or the implementation of quantum state tomography. In general, this task requires an extensive number of measurements to unveil the characteristic statistical fluctuations and correlation properties of light, particularly in the low-photon flux regime. In this article, we exploit the self-learning features of artificial neural networks and naive Bayes classifier to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. We demonstrate robust light identification with tens of measurements at mean photon numbers below one. Our work demonstrates an improvement in terms of the number of measurements of several orders of magnitude with respect to conventional schemes for characterization of light sources. Our work has important implications for multiple photonic technologies such as LIDAR and microscopy.

quant-ph↗