Investigación en IBERO Puebla

This paper presents an adaptive detection framework for DDoS attacks using a hyper-parameter-tuned Random Forest classifier on high-fidelity synthetic network traffic, achieving an AUROC of 0.994. The study provides a rigorous baseline for future validation on live traffic.

This study presents an AI-assisted prototype to enhance efficiency in surgical procedures, addressing the critical shortage of surgical instrument nurses. The system uses YOLOv8 and MediaPipe for object and hand recognition, enabling a UR5e collaborative robot to assist in surgical instrumentation.

This study compares two AI models for fire detection in images for security drone integration. It evaluates Google Teachable Machine and a custom Google Colab model, finding the Colab model has a higher detection rate while Teachable Machine is easier to implement.

This paper presents a portable IoT seizure detection system attached to a waist belt. It uses a gyroscope to identify abrupt movements and an ESP32-S3 with an MPU6500 IMU for real-time analysis, sending immediate alerts via a Telegram bot.

This project presents a wearable vest to monitor and prevent thoracic kyphosis by measuring spinal tilt angles using an MPU-6050 accelerometer. The system, which integrates an ESP32 microcontroller, provides real-time feedback to improve postural awareness and has been validated for clinical and ergonomic assessment.

This study presents the design and manufacturing of a 3D-printed front limb wheelchair for dogs with congenital malformations. The modular and adjustable structure, made with PLA and TPU, enhances mobility and provides physical assistance, significantly improving the quality of life for animals with mobility impairments.

This study presents the development of a monitoring system to track and visualize key environmental parameters for amaranth cultivation. The system integrates an ESP32 microcontroller with DHT22, HD-38, and DS18B20 sensors to measure air and soil temperature, and humidity. The collected data is displayed in real-time, facilitating optimal crop management.

The control and movement of automated robots is a fundamental field in mobile robotics. This system uses algorithms to guide robot navigation, ensuring the robot accurately follows a desired trajectory. Any deviation is progressively corrected, maintaining the course towards the destination.