Tesis
Permanent URI for this communityhttps://hdl.handle.net/11285/345119
Colección de Tesis y Trabajos de grado (informe final del proyecto de investigación, tesina, u otro trabajo académico diferente a Tesis, sujeto a la revisión y aceptación de una comisión dictaminadora) presentados por alumnos para obtener un grado académico del Tecnológico de Monterrey.
Para enviar tu trabajo académico al RITEC, puedes consultar este Infográfico con los pasos generales para que tu tesis sea depositada en el RITEC.
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- Applications of classical andquantum-optomechanical Light Propagation(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Onah, Francis Emenike; Gutiérrez Vega, Julio César; emipsanchez; Hernández Aranda, Raúl I; Pérez García, Benjamín; López Aguayo, Servando; School of Engineering and Sciences; Campus MonterreyThe theory of electromagnetic waveguides has found applications in many areas of science and technology, ranging from nanotechnology, nano-optics, optics and photonics, sensing, optical communications, plasma physics to astrophysics. One of the challenges facing the application of other waveguides such as the elliptic and parabolic waveguides in the nano-regime is the amount of parameters to be considered in their fabrications and the ability to control these parameters in miniature systems. The equilateral triangular waveguides, just as the circular cylindrical waveguides, has at most just one parameter to consider in their design or fabrication. That is, the triangle side a. The indices m and n (and a third index l, for equilateral triangular waveguide, is dependent on the other two l = −m−n), is a general feature of all cylindrical waveguides. Thus, the equilateral triangular waveguide, has a promising utility for applications in nanotechnology. Since just focusing on this one parameter, one can as precision allows (within the limits of classical light propagation, in essence, before the quantum theory becomes important), produce very tiny equilateral triangular waveguide. Thus the feasibility and promising utility motivates our investigation of the equilateral triangular waveguides, which despite its simplicity, does not have a thorough study of for instance, its attenuation characteristics. This characteristics and other symmetry properties of the modes, especially the surprisingly interesting odd modes of the equilateral triangular waveguide is what we investigate in this project. In the second part of our research, we study Quantum Photonic nano-cavties, which is a typical case of what happens when quantum mechanics becomes important, in propagation of light, in the nano-regime. This also, promises great and novel solutions to the numerous challenges facing the production of versatile and effective nano-machine fabrications. Thus the significance of our research work lies in illustrating and exploring the possibility for a far more reaching industrial applications of quantum photonic-nano cavities, through the quantum optomechanical theoretic formulation and application of multiply synchronized nano-photonic cavities in sensing, information or data storage and distribution in nano-devices, very effective/versatile nano-machines, high level machine learning, artificial intelligence and the future of modern nano-material fabrications and nanotechnology in general. We intend or expect to have a clear cut technological advancement, demonstrated by a fabricated device that harnesses the high quality factorof quantum photonic nano-cavities, in terms of increased capacity or storage power of the nano-device and or a very sensitive light sensor and other technological advancement that has applications in information commutation technology, medicine and the industry in general.
- European option pricing on day-ahead electricity prices: the mexican wholesale electricity market case(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-28) Ramírez García, Alfredo; Saucedo de la Fuente, Eduardo; emipsanchez; Núñez Mora, José Antonio; Amorós Espinosa, José; Contreras Valdez, Mario Iván; Escuela de Graduados en Administración y Dirección de Empresas; Campus Ciudad de MéxicoThe present research proposes a novel European option pricing model with the day-ahead electricity price as an underlying asset which could be implemented as the first day-ahead electricity price hedging financial instrument in the Wholesale Electricity Market (MEM). Therefore, this work represents an essential contribution to the MEM's development since, according to Roy and Basu (2020), MEM should be considered an emerging electricity market owing to its small number of participants, and hedging financial instruments, such as futures or options, cannot be acquired. Hence having an instrument of this kind would allow market participants to implement better risk management strategies to hedge day-ahead electricity price volatility to prevent financial losses. This work is divided into five chapters; each concerns a different component of the proposed model. In Chapter 1, the main characteristics of the MEM, as well as a review of the operating rules that are most closely related to the design of the proposed financial instrument, as well as a general context of the MEM and the growth initiatives proposed by the Mexican government, are described. In Chapter 2 an in-depth review of the probability theory necessary for a complete understanding of the proposed model, starting with basic probability concepts and moving on to the Normal Inverse Gaussian and Multivariate Normal Inverse Gaussian probability distributions, as well as the valuation of a European Option by Monte Carlo valuation is provided. In Chapter 3, two topics are addressed; first, a statistical analysis is performed to confirm that well-known LMP stylized facts, such as seasonality, volatility, and autocorrelation, are observable on MEM's day-ahead electricity prices. Second, Normal Inverse Gaussian (NIG) distribution capability to fit LMP logarithmic returns (Series Returns) is shown as follows: the Seasonal and Trend Decomposition Model (STL), NIG parameter estimation by Maximum Likelihood Estimation (MLE) of Series Returns, simulated NIG series generation from obtained parameters, and goodness-of-fit tests are performed to demonstrate NIG's distribution capabilities to fit and simulate electricity returns series. In Chapter 4, the European option pricing model employing Multivariate Normal Inverse Gaussian (MNIG) is proposed. In order to obtain the European option price for 28 days ahead on an hourly basis (672 hours ahead) by applying this model, each week hour is assumed to be a single independent asset, which produces 168 series for a single week. Four lagged log-prices for each hour are then obtained to be modeled employing MNIG distribution to perform Monte Carlo simulations and generate electricity lagged log-prices trajectories which then are employed to estimate the European option price for the 672 hours ahead by applying the European option pricing methodology. Results show that by applying this valuation model, electricity price correlation and seasonality are modeled by the employment of MNIG distribution, which simplifies modeling complexity and MNIG makes it possible to obtain a correct European option price valuation for each of the forecast values. Finally, the research conclusions are presented in Chapter 5.
- Environmental assessment of urban rivers through a dual lens approach: machine learning based water quality analysis and metagenomic characterization of contamination effects(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Fernández del Castillo Barrón, Alberto; Gradilla Hernández, Misael Sebastián; emipsanchez; García González, Alejandro; Pacheco Moscoa, Adriana; Brown, Lee; Oscar Alejandro Aguilar Jiménez; School of Engineering and Sciences; Campus Monterrey; Senés Guerrero, CarolinaUrban rivers are critical ecosystems increasingly threatened by pollution. Effective water quality monitoring and contamination assessment are essential for informed management decisions. The Santiago River, a key hydrologic system in Mexico, has become one of the country’s most polluted rivers, posing significant ecological risks and public health concerns for nearby communities. This study underscores the urgent need for comprehensive environmental evaluation and enhanced monitoring approaches. Chapter one introduces the motivation behind monitoring water quality in highly polluted rivers, presenting the problem statement and contextual background of the Santiago River basin. It outlines the research question and provides an overview of the proposed dual-lens approach: combining water quality analysis via machine learning algorithms with metagenomic characterization of contamination effects. Key contributions of this work to the field are also highlighted. Chapter two reviews global monitoring strategies from highly polluted rivers, focusing on nine rivers across developed and developing countries to offer a comparative perspective on water quality management needs. In Chapter three, regression and classification machine learning models are developed to predict the Santiago River Water Quality Index (SR-WQI), designed as complementary tools to strengthen the current monitoring program. Chapter four analyzes the historical water quality patterns of the Santiago River to identify the most variable and representative data for training machine learning models. This chapter also reveals that redundant data can hinder model performance by leading to overfitting. Chapter five investigates spatial variations in the microbial composition of Santiago River sediments and examines correlations with water quality. Using high-throughput sequencing, potential microbial biomarkers were identified and impacts of physicochemical parameters and heavy metals on microbial communities were assessed. Finally, chapter five highlight the main findings of this thesis and covers some limitations, perspectives for future research and final remarks.
- 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
- 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
- Practical inventory models with the warm-up process(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-10) Nobil, Erfan; Cárdenas Barrón, Leopoldo Eduardo; emipsanchez; Loera Hernández, Imelda de Jesús; Treviño Garza,Gerardo; Smith Cornejo, Neale Ricardo; Bourguet Díaz, Rafael Ernesto; School of Engineering and Sciences; Campus MonterreyAs the global population continues to grow, there is an increasing need to enhance the efficiency of production processes. On one hand, manufacturing processes face numerous challenges; on the other hand, various machines in the production line require an initial warm-up phase, which intersects with the fields of operations research and optimization. This dissertation explores the introduction of several concepts along with the warm-up process into the manufacturing workflow. It also addresses a range of issues associated with the warm-up in manufacturing, proposing solutions to these challenges. It tackles common problems in the production line, such as shortages, the environmental impact of carbon emissions, and the production of faulty items. The work at hand employs a diverse set of approaches, from mathematical solutions like the application of the Hessian matrix to the implementation of Karush-Kuhn-Tucker conditions. A variety of methodologies have been applied, ranging from analytical approaches to metaheuristics and innovative deep reinforcement learning techniques. The outcomes of this thesis have resulted in three published papers, with two additional works finished. The publications explore the effect of warm-up process in sustainable EPQ model, the effect of machine downtime on warm-up process, presence of shortage and faulty products with warm-up, machine downtime effect along with shortage on warm-up, and finally multi-product lot scheduling problem with warm-up process. The findings can be regarded as determination of optimal total cost for the system which provides higher revenue for corporations. In case of three published papers, this is done due to analytical approach and mathematical framework, in other words, a closed-form solution represents the whole structure. The solution methodology highlights key concepts, such as shortages and environmental regulations, by comparing results that show how the additional cost of carbon policies and the system’s ability to handle shortages contribute to lower overall costs. In cases involving rework and scrap, rework is shown to incur less cost. Finally, the multi-agent reinforcement learning effectively tackled the stochastic nature of metaheuristic algorithms in fine-tuning the control parameters. Altogether, each paper presents a specific direction within this thesis, and collectively, these provide practical insights for decision-makers in the industry.
- Assessing digital skills as a job resource: the moderating role of digital skills in the relationship between job stressors and psychological detachment(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-22) Paredes Aguirre, Milton Ismael; Hernández Pozas, Olivia del Roble; emipsanchez; Ayala Millán, Christian Yarid; Campoverde Aguirre, Ronald Enrique; EGADE Business School; Campus MonterreyDigitalization has reshaped work and education, making Digital Skills essential for success in technology-driven environments. Despite their importance, few studies have evaluated Digital Skills as potential job resources that can help manage job stressors and support Psychological Detachment. This doctoral thesis addresses this gap in the literature through four studies. The first study reviewed the interplay between Digital Skills and Well-being, suggesting that these skills may act as a resource that promotes well-being and mitigates stress. The second study evaluated the psychometric properties of the Spanish version of the Digital Self-Efficacy scale, grounded in the DigComp framework, to assess the Digital Skills in Spanish-speaking workers. The third and fourth studies applied the Stressor-Detachment Model to explore whether Digital Skills can reduce the effect of job stressors on Psychological Detachment in digital work environments. Study 3 focused on workers in Ecuador, while Study 4 extended the analysis to university educators using a scale designed for educational contexts. The results showed that Digital Skills help workers and educators manage stress and support mental detachment from work. Some unexpected findings, such as certain stressors being positively linked with Psychological Detachment, suggest that further research is needed to understand stress responses in digital workplaces.
- 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.
- A novel feature extraction methodology using Inter-Trial Coherence framework for signal analysis – A case study applied towards BCI(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) López Bernal, Diego; Ponce Cruz, Pedro; emipsanchez; Ponce Espinosa, Hiram; López Caudana, Edgar Omar; Bustamante Bello, Martín Rogelio; School of Engineering and Sciences; Campus Ciudad de México; Balderas Silva, David ChristopherSignal classification in environments with low signal-to-noise ratio (SNR) presents a significant challenge across various fields, from industrial monitoring to biomedical appli cations. This work explores a novel methodology aimed at improving classification accuracy in such conditions, using EEG-based Brain-Computer Interfaces (BCIs) for inner speech decoding as a case study. EEG-based Brain-Computer Interfaces (BCIs) have emerged as a promising technology for providing communication channels for individuals with speech disabilities, such as those affected by amyotrophic lateral sclerosis (ALS), stroke, or other neurodegenerative diseases. Inner speech classification, a subset of BCI applications, aims to interpret and translate silent, inner speech into meaningful linguistic information. De spite the potential of BCIs, current methodologies for inner speech classification lack the accuracy needed for practical applications. This work investigates the use of inter-trial coherence (ITC) as a novel feature extraction technique to enhance the accuracy of in ner speech classification in EEG-based BCIs. The study introduces a methodology that integrates ITC within a complex Morlet time-frequency representation framework. EEG recordings from ten participants imagining four distinct words (up, down, right, and left) were processed and analyzed. Five different classification algorithms were evaluated: Ran dom Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). The proposed method achieved no table classification accuracies of 75.70% with RF and 66.25% with SVM, demonstrating significant improvements over traditional feature extraction methods. These findings indi cate that ITC is a viable technique for enhancing the accuracy of inner speech classification in EEG-based BCIs. The results suggest practical implications for improving communica tion and navigation capabilities for individuals with ALS or similar conditions. This work lays the foundation for future research on phase-based feature extraction, opening new avenues for understanding the neural mechanisms underlying inner speech and advancing BCI systems’ accuracy and efficiency
- 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
