This paper presents a systematic procedure for the control scheme design for a PVTOL aircraft system with an inverted pendular load, which is a nonlinear underactuated system. The control scheme is based on the use of angular movement as an artificial control in order to propose new auxiliary control inputs. This is achieved by a linear extended state observer-based active disturbance rejection control to reject both nonmodeled dynamics and external disturbances. The flying planar inverted pendulum is then linearized around an unstable equilibrium point, and the resulting system is subdivided into two subsystems: (1) the height system, and (2) the horizontal pendulum system. For the height system, a linear extended state observer-based active disturbance rejection control is proposed in order to accomplish a take-off and landing task in the presence of external disturbances and non-linearities neglected in the linearization process. The flatness property in the horizontal-pendulum system is exploited in order to propose another active disturbance rejection control of linear nature. The flatness of the tangentially linearized model provides a unique structural property that results in an advantageous low-order cascade decomposition of the linear extended state observer design. Numerical simulations show the effectiveness of the proposed control scheme in trajectory tracking tasks in the presence of disturbances caused by crosswinds with random amplitudes.

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%.

Photo-identification (photo-id) is a method used in field studies by biologists to monitor animals according to their density, movement patterns and behavior, with the aim of predicting and preventing ecological risks. However, these methods can introduce subjectivity when manually classifying an individual animal, creating uncertainty or inaccuracy in the data as a result of the human criteria involved. One of the main objectives in photo-id is to implement an automated mechanism that is free of biases, portable, and easy to use. The main aim of this work is to develop an autonomous and portable photo-id system through the optimization of image classification algorithms that have high statistical dependence, with the goal of classifying dorsal fin images of the blue whale through offline information processing on a mobile platform. The new proposed methodology is based on the Scale Invariant Feature Transform (SIFT) that, in conjunction with statistical discriminators such as the variance and the standard deviation, fits the extracted data and selects the closest pixels that comprise the edges of the dorsal fin of the blue whale. In this way, we ensure the elimination of the most common external factors that could affect the quality of the image, thus avoiding the elimination of relevant sections of the dorsal fin. The photo-id method presented in this work has been developed using blue whale images collected off the coast of Baja California Sur. The results shown have qualitatively and quantitatively validated the method in terms of its sensitivity, specificity and accuracy on the Jetson Tegra TK1 mobile platform. The solution optimizes classic SIFT, balancing the results obtained with the computational cost, provides a more economical form of processing and obtains a portable system that could be beneficial for field studies through mobile platforms, making it available to scientists, government and the general public.

When RGB images are processed for the implementation of steganographic algorithms, it is important to study the quality of the cover and retrieved images, since it typically used digital filters, reaching visibly deformed images. The discrete wavelet transform is useful for adjusting the stego- image quality, because one of their principal qualities which are able to analyze different frequency levels (multi-resolution analysis). By decomposing the image using the discrete wavelet transform, we obtain sub-images representing different levels of frequencies, in each of these sub-bands can expand or contract the obtained adjusting sub image well as possible to a desired quality. There by avoiding one of the main problems of steganography: distortion of stego-image. When a steganographic algorithm is employed, numerical calculations performed by the computer cause errors and alterations in the processed images, so we apply a scaling factor depending on the number of bits of the image to adjust these errors. Key words: Steganographic algorithms, RGB images, scaling factor, wavelet.

This paper is focused on the use of radio frequency identification (RFID) technology operating at 125 kHz in a communication layer for a network of mobile and static nodes in marine environments, with a specific focus on the Underwater Internet of Things (UIoT). The analysis is divided into two main sections: characterizing the penetration depth at different frequencies and evaluating the probabilities of data reception between antennas of static nodes and a terrestrial antenna considering the line of sight (LoS) between antennas. The results indicate that the use of RFID technology at 125 kHz allows for data reception with a penetration depth of 0.6116 dB/m, demonstrating its suitability for data communication in marine environments. In the second part of the analysis, we examine the probabilities of data reception between static-node antennas at different heights and a terrestrial antenna at a specific height. Wave samples recorded in Playa Sisal, Yucatan, Mexico, are used for this analysis. The findings show a maximum reception probability of 94.5% between static nodes with an antenna at a height of 0 m and a 100% data reception probability between a static node and the terrestrial antenna when the static-node antennas are optimally positioned at a height of 1 m above sea level. Overall, this paper provides valuable insights into the application of RFID technology in marine environments for the UIoT, considering the minimization of impacts on marine fauna. The results suggest that by adjusting the characteristics of the RFID system, the proposed architecture can be effectively implemented to expand the monitoring area, considering variables both underwater and on the surface of the marine environment.