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 electrocardiogram records the heart’s electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classification models are highly complex, reflecting on the use of computational resources. Moreover, the performance of electrocardiogram classification depends on the approach and parameter estimation to generate the model with high accuracy, sensitivity, and precision. Previous works have proposed hybrid approaches and only a few implemented parameter optimization. Instead, they generally applied an empirical tuning of parameters at a data level or an algorithm level. Hence, a scheme, including metrics of sensitivity in a higher precision and accuracy scale, deserves special attention. In this article, a metaheuristic optimization approach for parameter estimations in arrhythmia classification from unbalanced data is presented. We selected an unbalanced subset of those databases to classify eight types of arrhythmia. It is important to highlight that we combined undersampling based on the clustering method (data level) and feature selection method (algorithmic level) to tackle the unbalanced class problem. To explore parameter estimation and improve the classification for our model, we compared two metaheuristic approaches based on differential evolution and particle swarm optimization. The final results showed an accuracy of 99.95%, a F1 score of 99.88%, a sensitivity of 99.87%, a precision of 99.89%, and a specificity of 99.99%, which are high, even in the presence of unbalanced data.

In emergent technologies, data integrity is critical for message-passing communications, where security measures and validations must be considered to prevent the entrance of invalid data, detect errors in transmissions, and prevent data loss. The SHA-256 algorithm is used to tackle these requirements. Current hardware architecture works present issues regarding real-time balance among processing, efficiency and cost, because some of them introduce significant critical paths. Besides, the SHA-256 algorithm itself considers no verification mechanisms for internal calculations and failure prevention. Hardware implementations can be affected by diverse problems, ranging from physical phenomena to interference or faults inherent to data spectra. Previous works have mainly addressed this problem through three kinds of redundancy: information, hardware, or time. To the best of our knowledge, pipelining has not been previously used to perform different hash calculations with a redundancy topic. Therefore, in this work, we present a novel hybrid architecture, implemented on a 3-stage pipeline structure, which is traditionally used to improve performance by simultaneously processing several blocks; instead, we propose using a pipeline technique for implementing hardware and time redundancies, analyzing hardware resources and performance to balance the critical path. We have improved performance at a certain clock speed, defining a data flow transformation in several sequential phases. Our architecture reported a throughput of 441.72 Mbps and 2255 LUTs, and presented an efficiency of 195.8 Kbps/LUT.

Cryptography has become one of the vital disciplines for information technology such as IoT (Internet Of Things), IIoT (Industrial Internet Of Things), I4.0 (Industry 4.0), and automotive applications. Some fundamental characteristics required for these applications are confidentiality, authentication, integrity, and nonrepudiation, which can be achieved using hash functions. A cryptographic hash function that provides a higher level of security is SHA-3. However, in real and modern applications, hardware implementations based on FPGA for hash functions are prone to errors due to noise and radiation since a change in the state of a bit can trigger a completely different hash output than the expected one, due to the avalanche effect or diffusion, meaning that modifying a single bit changes most of the desired bits of the hash; thus, it is vital to detect and correct any error during the algorithm execution. Current hardware solutions mainly seek to detect errors but not correct them (e.g., using parity checking or scrambling). To the best of our knowledge, there are no solutions that detect and correct errors for SHA-3 hardware implementations. This article presents the design and a comparative analysis of four FPGA architectures: two without fault tolerance and two with fault tolerance, which employ Hamming Codes to detect and correct faults for SHA-3 using an Encoder and a Decoder at the step-mapping functions level. Results show that the two hardware architectures with fault tolerance can detect up to a maximum of 120 and 240 errors, respectively, for every run of KECCAK-p, which is considered the worst case. Additionally, the paper provides a comparative analysis of these architectures with other works in the literature in terms of experimental results such as frequency, resources, throughput, and efficiency.

Rock mass characterization is crucial for evaluating slope stability and recommending effective prevention mechanisms. This study presents a comparative analysis of three approaches for discontinuity analysis: (1) conventional field survey, (2) digital manual measurement on 3D models generated with UAV-based photogrammetry, and (3) semi-automatic analysis based on clustering algorithms (K-NN) for point cloud segmentation. All three methods were applied to the same slope, allowing their performance to be evaluated in terms of accuracy, efficiency, and replicability. The results showed that the semi-automatic method achieved the highest coverage (81%) and identified 586 discontinuities, with RMSE values of 2.58° for orientation, 0.087 m for spacing, and 2.05 m for persistence, using the conventional method as a reference. The digital manual method, with 19% coverage, yielded very low error (RMSE of 3.27° for orientation, 0.012 m for spacing, and 0.063 m for persistence), validating it as a complementary and reliable alternative. In contrast, the conventional method required the longest execution time (10 h) and achieved only 19% coverage, being the least replicable due to its dependence on expert judgment. Overall, the comparison highlights the advantages of digital methods, especially the semi-automatic approach, in improving efficiency, safety, and replicability, while providing robust information to recommend prevention strategies for rock slope stability.

In the present study, we analyze the stress, anxiety, state, and academic features of 478 higher-level students. We identify the correlated factors using multiple linear regression and Pearson's correlation coefficients. We use the SISCO and STAI instruments to measure academic stress at the anxiety level. We determine that the most representative stressors in students are the teachers' evaluations with 31% as almost always and 11% as always, physical reactions such as drowsiness or need to sleep 31% as almost always, 12% always. Besides, reactions of depression and sadness show a 9% as almost always, 9% as always. Behavioral and coping reactions show 9% almost always and 9% always. The most outstanding anxiety reactions in the present research revealed that 48% of the students never feel calm, 40% never have happiness like others, 41% are never satisfied or comfortable with themselves. Some psychological changes related to stress and anxiety are closely linked to the suicide rate, derived from the fact that the generated uncertainty becomes a potential source of stress and vulnerability to depressive or anxiety disorders.