Automobiles have increased urban mobility, but traffic accidents have also increased. Therefore, road safety is a significant concern involving academics and government. Transit studies are the main supply for studying road accidents, congestion, and flow traffic, allowing the understanding of traffic flow. They require special equipment (sensors) to measure the car’s speed. With technological advances, artificial intelligence, and videos, it is possible to estimate the speed in real-time without modifying the installed urban infrastructure. We need to employ public databases that provide reliable monocular videos to generate automated traffic studies. The problem of speed estimation with a monocular camera involves synchronizing data recording, tracking, and detecting the vehicles over the road considering the lanes and distance between cars. Usually, a set of constraints are considered, such as camera calibration, flat roads, including methods based on the homography and augmented intrusion lines, patterns or regions, or prior knowledge about the actual dimensions of some of the objects. In this paper, we present a system that generates a dataset from videos recorded from a highway—obtaining 532 samples; we separated the vehicle’s detection by lane, estimating its speed. We use this data set to compare five different statistical methods and three machine learning methods to evaluate their accuracy in estimating the cars’ speed in real-time. Our vehicle estimation requires a feature extraction process using YOLOv3 and Kalman filter to detect and track vehicles. The Linear Regression Model (LRM) yielded the best results obtaining a Mean Absolute Error (MAE) of 1.694 km/h for the center lane and 0.956 km/h for the last lane. The results were compared with several state-of-the-art works, having competitive performance. Hence, LRM is fast estimating speed in real time and does not require high computational resources allowing a future hardware implementation.

The increased criminal activity associated with unauthorized entry into facilities has become a global concern. Traditional mechanical locks suffer from drawbacks such as key loss, theft, duplication risks, and time-consuming operation. Therefore, biometrics has been explored as a key to accessing a restricted area. However, some challenges still need to be solved in developing such systems, including user registration, response speed, maintainability, and the ability to distinguish between real and fake individuals. This paper proposes and develops a biometric lock system (BLS) whose opening is performed by recognizing a person?s face. It solves the challenges of re-training, antispoofing, real-time response, and works inside an embedding system. The proposed BLS overcomes these challenges using a pre-trained network called FaceNet for feature extraction and coding into 128-dimensional vectors.We use the characteristic vector provided by FaceNet and a cosine distance to recognize the persons. It also incorporates ResNet18 + remote photoplethysmography (rPPG) to avoid spoofing. The architecture was implemented in a BLS, demonstrating an impressive false acceptance rate of 0% under varying lighting conditions, with an average response time of 1.68 seconds from facial detection to door opening. The BLS has easy maintainable devices, providing enhanced security by accurately identifying individuals and preventing unauthorized access. The system can distinguish between real and fake people without using specialized hardware. Making it a versatile solution suitable for homes, offices, and commercial spaces. The results underscore the potential efficacy of our proposed BLS in mitigating security challenges related to unwarranted access to restricted facilities.

This paper introduces COVIA (Code + Video + AI), a constructive Video-Based Learning (VBL) platform aiming to address ongoing challenges in computer science education, particularly those related to student engagement and instructor workload. The system was deployed in a blended learning setting as supplementary instruction in mandatory introductory programming courses at a university in Mexico. Curriculum materials and videos were designed using a human-in-the-loop workflow assisted by Generative AI (GenAI) and validated by subject-matter experts. This study employs a longitudinal, quasi-experimental quantitative design, complemented by a quantitative acceptance assessment based on the Technology Acceptance Model (TAM). The evaluation focused on three distinct phases of the introductory Java curriculum: (1) Language Elements, (2) Selective Structures, and (3) Iterative Structures. Statistical analysis using the Wilcoxon Signed-Rank test confirmed that the Experimental Group achieved highly significant learning gains across all three phases ($p<0.001$). In contrast, the Control Group failed to achieve statistically significant growth in Phase 1 ($p=0.119$) and Phase 3 ($p=0.448$). In conclusion, the results suggest that the COVIA platform supports the development of introductory programming competencies as a supportive tool within a blended instructional framework.