Audio steganalysis has been little explored due to its complexity and randomness, which complicate the analysis. Audio files generate marks in the frequency domain; these marks are known as fingerprints and make the files unique. This allows us to differentiate between audio vectors. In this work, the use of the Goertzel algorithm as a steganalyzer in the frequency domain is combined with the proposed sliding window adaptation to allow the analyzed audio vectors to be compared, enabling the differences between the vectors to be identified. We then apply linear prediction to the vectors to detect any modifications in the acoustic signatures. The implemented Goertzel algorithm is computationally less complex than other proposed stegoanalyzers based on convolutional neural networks or other types of classifiers of lower complexity, such as support vector machines (SVD). These methods previously required an extensive audio database to train the network, and thus detect possible stegoaudio through the matches they find. Unlike the proposed Goertzel algorithm, which works individually with the audio vector in question, it locates the difference in tone and generates an alert for the possible stegoaudio. In this work, we apply the classic Goertzel algorithm to detect frequencies that have possibly been modified by insertions or alterations of the audio vectors. The final vectors are plotted to visualize the alteration zones. The obtained results are evaluated qualitatively and quantitatively. To perform a double check of the fingerprint of the audio vectors, we obtain a linear prediction error to establish the percentage of statistical dependence between the processed audio signals. To validate the proposed method, we evaluate the audio quality metrics (AQMs) of the obtained result. Finally, we implement the stegoanalyzer oriented to AQMs to corroborate the obtained results. From the results obtained for the performance of the proposed stegoanalyzer, we demonstrate that we have a success rate of 100%.

This article presents the design and implementation of a linear actuator based on NiTi Shape Memory Alloys with temperature and position measurements based on a magnetic sensor array and a set of thermistors. The position instrumentation is contact free to avoid friction perturbations; the position signal conditioning is carried out through the calculation of the response of each magnetic sensor, selecting the closest sensor to ensure accurate results on the full range of movement. Experimental results validate the accuracy of the position sensing with a competitive behaviour.

Uncertainty in waiting times is one of the main factors affecting the efficiency of mass public transport. We present a robust model for estimating time of arrival (ETA) on the Mexico City Light Rail (Line 1). Unlike conventional transit models, our proposal employs a nodal segmentation methodology that divides the 13 km route into 17 segments, integrating time series variables and the impact of at-grade road intersections. The results demonstrate an 83% accuracy in predicting arrivals, surpassing the metrics of simple linear regression models when considering time dependence and dwell time at stations. It is important to clarify that this study focuses specifically on the Mexico City Light Rail system and does not address the Mexico City Metro network. Line 1 of the light rail system was selected as the case study because its operational characteristics and available data provide an appropriate environment for evaluating the proposed travel time prediction approach. Consequently, the results presented in this study should be interpreted within the context of the light rail operating conditions considered rather than as a direct representation of the entire urban rail transportation network of Mexico City.