Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
Browse
Search Results
- A minutiae-based indexing algorithm for latent palmprints(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Khodadoust, Javad; Monroy Borja, Raúl; emipsanchez; Aparecida Paulino, Alessandra; Valdes Ramírez, Danilo; Rodríguez Ruiz, Jorge; School of Engineering and Sciences; Campus Monterrey; Medina Pérez, Miguel ÁngelToday, many countries rely on biometric traits for individual authentication, necessitating at least one high-quality sample from each person. However, countries with large populations like China and India, as well as those with high visitor and tourist volumes like France, face challenges such as data storage and database identification. Latent palmprints, comprising about one-third of prints recovered from crime scenes in forensic applications, require inclu sion in law enforcement and forensic databases. Unlike fingerprints, palmprints are larger, and features such as minutiae are approximately ten times more abundant, accompanied by more prominent and wider creases. Consequently, accurately and efficiently identifying la tent palmprints within stored reference palmprints poses significant challenges. Using fre quency domain approaches and deep convolutional neural networks (DCNNs), we present a new palmprint segmentation method in this work that can be used for both latent and full impression prints. The method creates a binary mask. Additionally, we introduce a palmprint quality estimation technique for latent and full impression prints. This method involves parti tioning each palmprint into non-overlapping blocks and considering larger windows centered on each block to derive frequency domain values, effectively accounting for creases and en hancing overall quality mapping. Furthermore, we present a region-growing-based palmprint enhancement approach, starting from high-quality blocks identified through our quality es timation method. Similar to the quality estimation process, this method operates on blocks and windows, transforming high-quality windows into the frequency domain for processing before reverting to the spatial domain, resulting in improved neighboring block outcomes. Finally, we propose two distinct minutiae-based indexing methods and enhance an existing matching-based indexing approach. Our experiments leverage three palmprint datasets, with only one containing latent palmprints, showcasing superior accuracy compared to existing methods
- A generalist reinforcement learning agent for compressing multiple convolutional neural networks(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) González Sahagún, Gabriel; Conant ablos, Santiago Enrique; emipsanchez; Ortíz Bayliss, José Carlos; Cruz Duarte, Jorge Mario; Gutiérrez Rodríguez, Andrés Eduardo; School of Engineering and Sciences; Campus MonterreyDeep Learning has achieved state-of-the-art accuracy in multiple fields. A common practice in computer vision is to reuse a pre-trained model for a completely different dataset of the same type of task, a process known as transfer learning, which reduces training time by reusing the filters of the convolutional layers. However, while transfer learning can reduce training time, the model might overestimate the number of parameters needed for the new dataset. As models now achieve near-human performance or better, there is a growing need to reduce their size to facilitate deployment on devices with limited computational resources. Various compression techniques have been proposed to address this issue, but their effectiveness varies depending on hyperparameters. To navigate these options, researchers have worked on automating model compression. Some have proposed using reinforcement learning to teach a deep learning model how to compress another deep learning model. This study compares multiple approaches for automating the compression of convolutional neural networks and proposes a method for training a reinforcement learning agent that works across multiple datasets without the need for transfer learning. The agents were tested using leaveone- out cross-validation, learning to compress a set of LeNet-5 models and testing on another LeNet-5 model with different parameters. The metrics used to evaluate these solutions were accuracy loss and the number of parameters of the compressed model. The agents suggested compression schemes that were on or near the Pareto front for these metrics. Furthermore, the models were compressed by more than 80% with minimal accuracy loss in most cases. The significance of these results is that by escalating this methodology for larger models and datasets, an AI assistant for model compression similar to ChatGPT can be developed, potentially revolutionizing model compression practices and enabling advanced deployments in resource-constrained environments.
- The impact of loading-unloading zones for freight vehicles on the last-mile logistics for nanostores in emerging markets(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-11) Mora Quiñones, Camilo Andrés; Cárdenas Barrón, Leopoldo Eduardo; emimmayorquin; Fransoo, Jan C.; Smith Cornejo, Neale Ricardo; Loera Hernández, Imelda de Jesús; School of Engineering and Sciences; Campus Monterrey; Veláaquez Martínez, Josue CuauhtémocEvery year, more than 26 billion deliveries are made globally to serve nanostores, the largest grocery retail channel in the world. At each stop, company representatives face a persistent challenge: finding a place to park. While the problem seems simple, it is remarkably complex and far from easy to solve. In emerging markets, where cities have grown rapidly and often without proper planning, fragmented markets and inadequate infrastructure exacerbate the issue. Multiple stakeholders compete for limited curb space, and the lack of dedicated parking disrupts last-mile efficiency, forcing drivers to either cruise for parking or resort to illegal parking. These behaviors lead to increased vehicle emissions, noise pollution, and additional costs. This dissertation provides key insights into last-mile logistics for nanostores in emerging markets, contributing to academic literature and offering practical implications to address the parking problem. The first study addresses the parking challenges faced by freight vehicles serving nanostores, identifying key factors affecting dwell time efficiency and suggesting operational improvements. In the next study, the focus shifts to the implementation of Loading-Unloading Zones (LUZs) as a targeted intervention, analyzing their impact on reducing air and noise pollution in urban areas. The last study extends this analysis by exploring the effects of LUZs on traffic flow, evidencing how their introduction can improve vehicle speed and reduce congestion in densely populated city streets. Together, these studies provide a detailed exploration of the operational, environmental, and infrastructural challenges of last-mile logistics, while offering concrete strategies to improve urban logistics in emerging markets. This dissertation contributes by expanding the body of knowledge and offering actionable managerial insights with the potential to drive meaningful impact. These include enhancing air quality, reducing noise pollution, lowering carbon emissions, improving traffic flow, and achieving substantial cost savings for companies distributing goods to nanostores in emerging markets.
- Processing Applications of Multiple Quantum Teleportation and Structured Light Beams(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Cardoso Isidoro, Carlos; Delgado Cepeda, Francisco Javier; emimmayorquin; Enríquez Flores, Marco Benjamín; Gutiérrez Vega, Julio César; Fernández Cabrera, David José; Vázquez Lepe, Elisa Virginia; Campus Monterrey; Pérez García, Benjamín de JesúsThis dissertation project was designed to work in parallel with the theoretical development of parallel quantum teleportation applications and with the experimental study of structured light beams and their potential application to communications. The study of quantum teleportation has become popular over the years since it was first described by Charles Bennett and his colleagues in the early 1990s. Since then, many processes have emerged for the improvement of the fidelity and exciting applications have arisen, such as those aimed to enhance communication systems. This dissertation explores the development of a scheme for parallel quantum teleportation, starting with the analysis of a double teleportation, where an unknown quantum state is intended to be teleported to two simultaneous receivers, and extending such analysis so that the state is quantumly transmitted in superposition to several receivers supported by other quantum states used as a control. This studied scheme is used for the development of some applications such as for cryptography purposes in the settlement of secure authentication, in Quantum Key Distribution, Quantum Parameter Estimation and database settlement. In these applications, it is highlighted the use of the parallel quantum teleportation for carrying out the process or for some specific stages along the process. Jointly with the theoretical work, the experimental development for structure light applications was made in terms of the study of properties of some vortex beams, such as the Laguerre-Gaussian, Hermite-Gaussian and Bessel beams and their behaviour when the source light is partially coherent, which brings critical advantages against noise and turbulence in the environment but, however, it has the downside to be hard to identify the intensity profile. For this reason, a scheme for characterizing these Partially Coherent Beams as well as the possible effects of turbulence was studied in this work. Finally, a study of the application of entangled photons for learning the phase of unknown unitary operations was also experimentally tested. This document, also presents a proposal for an experimental implementation of parallel double teleportation for communication, involving Structured Light Beams.
- Environmental monitoring to estimate indoor occupancy levels based on Semi-supervised machine learning and data fusion for building management(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Mena Martínez, Alma Rosa; Ceballos Cancino, Hector Gibran; emipsanchez; Alvarado Uribe, Joanna; Cantu, Francisco J.; García, Juan Pablo; School of Engineering and Sciences; Campus Monterrey; Schmitt, JanOccupancy information is essential for space management, energy efficiency, and in times of the COVID-19 pandemic, for crowd control. Obtaining labeled data is challenging due to hardware limitations, privacy considerations, and the required underlying costs. Furthermore, venues over 200 m2 require data fusion techniques. Therefore, this thesis mainly focuses on exploring the potential of Semi-Supervised Learning (SSL), which only needs a few labeled data and a large amount of unlabeled data, to estimate the occupancy levels in enclosed spaces. This study presents an empirical comparison between Supervised ML and SSL models as well as data fusion techniques in real-life university classrooms and offices (uncontrolled conditions) at the University of the West of England, Bristol, UK, and Tecnologico de Monterrey, Mexico. The data was collected for three weeks at each scenario using an in-house developed Internet of Things (IoT) device that measures air temperature, relative humidity, and atmospheric pressure. The ground truth records were gathered through manual logging of occupancy levels. Datasets’ sizes averaged 2350 entries with only 280 labeled instances per dataset. Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP) were used to define a performance baseline for supervised ML. Self-Training (ST) and Label Propagation (LP) were tested for SSL. In addition, several feature fusion methods were explored, including Chi-squared, ANOVA F-test, Spearman and Kendall’s Tau correlation, Mutual Information, Averages, Recursive Feature Elimination, and Principal Component Analysis. The models were evaluated using Accuracy, Precision, Recall, F1-score, Confusion Matrix, and High - Quality Supervised Baseline. ST achieved superior performance compared to baseline models (SVM, RF, MLP) with a highest average accuracy of 90.96% compared to SVM (86.66%). Furthermore, the data fusion results indicated that the Chi-squared approach for feature fusion outperformed others with an F1-score average of 95% and an accuracy average of 99%. These results demonstrate the effectiveness of SSL for indirect occupancy estimation while reducing the need for extensive data collection and labeling.
- Optimization and sustained release of green lentil polyphenols through instant controlled pressure drop and encapsulation in PLGA nanoparticles(2024-12-03) Tienda Vázquez, Mario Adrián; Almanza Arjona, Yara C.; emimmayorquin; Cardador Martínez, Anabertha; Quintus Scheckhuber, Christian; Téllez Pérez, Carmen; School of Engineering and Sciences; Campus Monterrey; Lozano García, OmarThroughout history, legumes have been part of human consumption for their nutritional content and because is an easy crop to cultivate, it can grow in both cold and warm climates. One type of legumes are lentils, consumed worldwide. In Mexico, lentils are consumed by 70% of Mexican adults. Among the lentil varieties, green lentils stand out for having the highest polyphenol content, which makes them an excellent candidate for human consumption. However, the traditional way of cooking lentils requires prolonged times in boiling water. This causes a significant loss of the number of polyphenols present in lentils. Polyphenols have the ability to reduce the prevalence of suffering from chronic degenerative diseases, because they have antioxidants and anti-inflammatories properties. However, the chemical stability of polyphenols is compromised by different factors like the chemical structure, temperature, pH, isomerizations, enzymes, degradation, and oxidation, among others. This study subjected the green lentils to instant controlled pressure drop (DIC) and measured the polyphenol amount, flavonoids and antioxidant capacity 1,1 -diphenyl-2-picrylhydrazyl (DPPH) and Trolox equivalent antioxidant capacity (TEAC and DPPH), with 13 different treatments by varying pressure and time. The results showed that the polyphenols were the only parameter affected by DIC and the best conditions were less than 160 s and less than 0.1 MPa, and the best treatment was the DIC treatment 11, with 0.1 MPa for 135 s. Surprisingly, apparently new polyphenols appeared in the treated lentils due to the physical stress secondary to DIC, and in consequence the biosynthesis of polyphenols. After DIC, the best green lentil treatment was selected (DIC 11). The polyphenolic extract was obtained and nano encapsulated in poly lactic-co-glycolic acid (PLGA) using five different extract volumes (100, 250, 500, 750 and 1000 𝜇L). The nanoparticles were spherical in shape, with negative zeta potential charge (~ 20 mV), and all the syntheses produced particles, with average sizes ranging between 300 to 1100 nm. The polyphenol released was evaluated in PBS at pH 5.5 and 7.4. The release followed a triphasic controlled release, a lag phase of 24 h, a burst and diffusion phase from 24 h to 372 h, up to 15 days, and finally the saturation phase. The combination of the DIC technology as a pretreatment for green lentils and the nanoencapsulation in PLGA nanoparticles, improved the extraction and preserved the polyphenols profile of green lentils, on the other hand, nanoencapsulation protected the polyphenols and reached a controlled polyphenol release for up to 15 days.
- A stable real-time implementation model predictive control for fast nonlinear systems(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Rodríguez Guevara, Daniel Orlando; Favela Contreras, Antonio Ramón Xicoténcatl; emipsanchez; Lozoya Gámez, Rafael Camilo; Sotelo Molina, Carlos Gustavo; Sotelo Molina, David Alejandro; School of Engineering and Sciences; Campus Monterrey; Beltrán Carbajal, FranciscoThis dissertation presents two novel approaches for real-time implementation of robust Model Predictive Control (MPC) for fast complex nonlinear systems. These approaches use a linearization step of the model of the system by two different strategies depending on the nature of the nonlinear system. Linear Parameter Varying (LPV) modeling and Differential Flatness representation are the strategies chosen to develop the Model Predictive Controller. LPV modeling consists of the embedding of the nonlinear terms of the system into a series of scheduling parameters. Therefore, the Model Predictive Control is designed using a linear model being a function of the scheduling parameter to predict the behavior of the states of the system along the prediction horizon. The future values of the scheduling parameters are estimated using a recursive least squares algorithm. Both stability and robustness conditions are ensured using Linear Matrix Inequalities (LMI) constraints included in the optimization problem of the MPC. Finally, terminal ellipsoidal sets are introduced in the cost function to improve the performance of the controller. On the other hand, Differential Flatness representation is used to build a linear MPC to exploit the flatness property of some nonlinear systems. In this approach, the nonlinear model is solved as a function of the flat outputs of the system and its derivatives. Thus, a linear optimization problem is solved to predict the future behavior of the flat output and its derivatives as a function of an auxiliary control variable. Afterward, a feedforward controller is designed to define the optimal control action to be inputted into the system as a function of the auxiliary control variable. Finally, the performance of both control strategies is tested with several simulations of complex nonlinear systems using the Matlab-Simulink environment
- Design of an acoustic virtual environment of the mexican archaeological site Edzna(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Navas Reascos, Gustavo Sebastián; Ibarra Zárate, David Isaac; emipsanchez; Recuero López, Manuel; Zalaquett Rock, Francisca Amelia; Lopez Caudana, Edgar Omar; School of Engineering and Sciences; Campus Monterrey; Alonso Valerdi, Luz MaríaArchaeoacoustics is an acoustic field that has great potential in Mexico since the existence of archaeological places inherited from the native people who inhabited these territories in the past. The objective of this project was the design and implementation of a virtual acoustic environment of the archaeological place Edzna. To achieve this goal, the research was conducted as follows: (1) to select a strategically archaeological Mexican place in terms of minimal archaeological deterioration, minimal environmental noise, flexible access, and with both open and enclosed places; (2) to characterize acoustically the selected place; (3) to recreate the recorded sounds; (4) to design and implement an acoustic virtual environment based on the acoustic characterization of the selected place; and (5) to evaluate the User Experience of the acoustic virtual environment from participants in an exposition at MARCO museum in Monterrey. This investigation aimed to contribute to the dissemination and exposure of vivid archaeological sites along in the country, which could help to foster the awareness of Mexican history and heritage
- Design and Development of Conducting Polymer and Carbon Nanostructure based Efficient Thermoelectric Materials(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-02) Ebrahimibagha, Dariush; Mallar, Ray; emimmayorquin; Aguirre Soto, Héctor Alán; Niladri, Banerjee; Gallo Villanueva, Roberto Carlos; School of Engineering and Sciences; Campus Monterrey; Datta, ShubhabrataThermoelectric materials present a promising renewable energy technology for directly converting thermal energy into electricity and vice versa. However, their practical application is hindered by low conversion efficiencies, quantified by the dimensionless figure of merit, 𝑍𝑇 = 𝑆 2 𝜎 𝑘 𝑇 , where 𝑆,𝜎, and 𝑘 are the Seebeck coefficient, electrical onductivity, and thermal conductivity, respectively. Achieving a high 𝑍𝑇 is challenging because enhancing one parameter often degrades the others. Various nanoscale strategies have been explored, yet a comprehensive framework for improving 𝑍𝑇 remains elusive. Recently, polymer-based nanocomposites, particularly carbon nanotubes (CNTs) dispersed in polyaniline (PANI), have gained attention due to their flexibility, non-toxicity, and processability, key traits for next-generation flexible electronic devices. Despite this potential, optimizing thermoelectric performance in PANI-CNT systems is complex, as it depends on numerous factors, including CNT dimensions, functionality, and PANI's doping and morphology. This research employs machine learning (ML) and genetic algorithms (GA) to model and optimize the thermoelectric properties of PANI-CNT nanocomposites. By analyzing structural and compositional variables—such as CNT length, diameter, type, and PANI morphology—we identified strategies that enhance electrical conductivity and the Seebeck coefficient while minimizing thermal conductivity. Our ML models revealed that selecting appropriate dopants for PANI and using single-walled CNT (SWCNT) improves overall thermoelectric performance. Multi-objective GA optimization further refined these findings, demonstrating that SWCNTs help reduce thermal conductivity and that CNT length plays a dual role: shorter CNTs decrease 𝑘, while longer ones enhance both 𝑆 and 𝜎. Experimental validation was performed by fabricating PANI-CNT nanocomposite pellets, but achieving high 𝑍𝑇 remained elusive due to limitations in dataset quality and the variability introduced by diverse synthesis techniques. The synthesis method influences PANI dimensionality (e.g., 0D, 1D, 2D) and the morphology of PANI-CNT composites (core-shell vs. dispersed), complicating performance consistency. While the experiments confirmed the general trend of model predictions, they highlighted the necessity of cleaner, more comprehensive datasets for future research. Ultimately, this study lays the groundwork for designing high-efficiency thermoelectric nanocomposites and outlines the next steps in developing more accurate predictive models and synthesis methods for improved thermoelectric performance.
- Development of chitosan films using lemon Juice and impact of bimetallic and trimetallic nanoparticles on their physical properties(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-02) Hassan, Dilawar; Torres Huerta, Ana Laura; emipsanchez; Ehsan, Muhammad; Sánchez Rodríguez, Elvia Patricia; Talha Khalil, Ali; School of Engineering and Sciences; Campus Ciudad de México; Antonio Pérez AuroraThe global challenge of plastic pollution has driven the search for biodegradable and sustainable materials. This thesis explores the development of chitosan (CH) films, synthesized using a green chemical approach that employs lemon juice and lemon peel extract as natural alternatives to synthetic acids. The incorporation of nanoparticles, explicitly zinc ferrite (ZnFe₂O₄ NPs) and nickel zinc ferrite (NiZnFe₂O₄NPs), further manipulate the functional properties of the films, making them suitable for diverse applications. The ZnFe₂O₄ NPs, synthesized using lemon peel extract, presented a crystalline size of 16 nm and significantly improved the mechanical (TS) and barrier properties of 1.5% CH films. The TS of the films increased from 0.641 MPa for bare CH to 0.835 MPa with 2% ZnFe₂O₄ NPs, while puncture strength improved by 2.7 times. The water vapor permeability (WVP) decreased by 28%, establishing enhanced barrier properties. Conversely, NiZnFe₂O₄ NPs (crystalline size 29 nm), enhanced 2% CH film flexibility, achieving a 36.83% elongation at break with 2% NP reinforcement. These films also exhibited enhanced resistance to moisture, making them suitable for applications that require better barrier properties. Morphological testing, including SEM and AFM, revealed that NPs incorporation altered the surface texture of the films, increasing roughness uniformly with NP concentration. FTIR spectra confirmed successful NPs’ integration, with characteristic metal-oxygen bond vibrations appearing at specific wavenumbers. Optical properties showed minimal color changes after NPs addition, with both ZnFe₂O₄ and NiZnFe₂O₄ films maintaining suitable transparency for practical applications. This thesis highlights the potential of green-synthesized CH films as eco-friendly substitutes for conventional plastics. ZnFe₂O₄ films demonstrated superior mechanical strength and barrier properties, while NiZnFe₂O₄ films provided improved flexibility and moisture resistance. The integration of green chemistry with nanotechnology establishes a sustainable pathway for the development of highperformance polymeric materials, addressing pressing environmental and industrial needs.
