This project arises in response to the growing global need for inclusive technological solutions aimed at improving the quality of life of people with visual impairments. The motivation is based on addressing the specific barriers these individuals face, with a particular focus on university environments. The methodology adopted focuses on the convergence of web services and networked communication technologies, seeking to promote the autonomy and safety of visually impaired people. A distinctive aspect of this project is the implementation of low power consumption and long-range technology, marking a pioneering approach in this field, which not only addresses the immediate need for inclusive solutions; but contributes to technological advancement by introducing efficient and long-range solutions. Through testing, very short response times are achieved in terms of identifying dangerous movements such as falls and detecting nearby objects, while character recognition still requires further refinement. The key innovation lies in the effective convergence of technologies, setting a new standard for inclusive and sustainable solutions. In other words, this project not only responds to a pressing need, but also marks a significant milestone in improving inclusion for the visually impaired, highlighting its relevance at the local level and contributing to global progress in this crucial field.

El presente trabajo consiste en el estudio de herramientas que permiten configurar un entorno de simulación o emulación de redes dentro de un mismo sistema facilitando de cierta forma plantear un escenario de red que cuenta con topologías y flujos de tráfico. Específicamente se enfocó en el modelado de tráfico en redes SDN (Redes Definidas por Software) que permite que un solo equipo administre una red en su totalidad, haciéndolo capaz de tolerar cambios en toda la estructura de la red sin necesidad de detener los servicios. Se realiza una investigación bibliográfica de fuentes de carácter documental sobre simuladores que permiten el modelado de redes para determinar la más adecuada; los instrumentos analizadas fueron: NS-3, EstiNet y MiniNet. Posteriormente de acuerdo a la información obtenida y el estudio del mismo, se realizó una tabla comparativa con los parámetros que distinguen a cada una, permitiendo de esta forma concluir de manera idónea la mejor opción para emulación de topologías en ambientes SDN.

The convergence between Artificial Intelligence (AI) and sustainable technologies is redefining the efficiency of energy and environmental systems. This study examines the impact of AI on renewable energy optimization, waste management and carbon footprint reduction. Using a mixed research approach, combining systematic literature review, quantitative data analysis and predictive modeling, key trends and emerging opportunities are identified. The findings confirm that AI improves operational efficiency by up to 35%, reduces resource waste by 30%, and contributes to the smart management of power grids and recycling systems. Challenges related to the high energy consumption of AI models and the need for adequate infrastructure for large-scale deployment are also discussed. It is shown that the use of machine learning algorithms can increase the operational efficiency of energy systems and improve waste management through computer vision techniques. Statistical analysis has also revealed a significant reduction in resource waste through the application of AI in the prediction and optimization of energy consumption.

Este trabajo proporciona un análisis de las estrategias de técnicas de modulación en sistemas de comunicaciones inalámbricas en redes de próxima generación con el objetivo de optimizar la eficiencia espectral y la calidad de transmisión. La alta demanda de conectividad en las redes de quinta generación 5G y futuras sexta generación 6G requieren técnicas de modulación avanzadas que permitan mejorar la transmisión de datos, reducir la latencia y aumentar la capacidad de usuarios conectados simultáneamente. Para analizar el desempeño de estas estrategias se realizó simulaciones en entornos como OMNET++, diseñando escenarios virtuales que analizan el impacto de la modulación en la transmisión con ancho de banda. Además, se modela un entorno de red con antenas y celdas de distintos tamaños considerando diversos factores de propagación y condiciones de conectividad.

Ecuador experienced one of the fastest escalations of lethal violence in Latin America, with the intentional homicide rate rising from 7.8 per 100,000 inhabitants in 2020 to 47.25 in 2023. This study develops a replicable, algorithm-selection framework for forecasting the temporal evolution of four key patterns of intentional homicide (monthly case volume, firearm share, mean victim age, and geographic concentration in the province of Guayas) using official open data comprising 24,000+ incident records (January 2020–December 2024). Individual incidents were aggregated into monthly time series, and three forecasting algorithms with distinct inductive biases (SARIMA, Prophet, and Holt–Winters exponential smoothing) were compared under an out-of-sample protocol that reserves the 12 months of 2024 as a test set, using MAE, RMSE, and MAPE. No single algorithm dominated: SARIMA was the most accurate for the volume and geographic series, Holt–Winters for the firearm-share series (MAPE 2.85%), and Prophet for the volatile mean-age series (MAPE 5.73%). Re-trained on the full sample, the selected models project a five-year horizon (2025–2029) in which homicides stabilize at historically high levels, roughly four times the 2020 baseline, with firearm involvement consolidating above 85% and violence remaining concentrated in Guayas. The results support pattern-specific algorithm selection for criminological forecasting and provide a quantitative baseline for security policy evaluation in Ecuador.