Noise suppression algorithms have been used in various tasks such as computer vision, industrial inspection, and video surveillance, among others. The robust image processing systems need to be fed with images closer to a real scene; however, sometimes, due to external factors, the data that represent the image captured are altered, which is translated into a loss of information. In this way, there are required procedures to recover data information closest to the real scene. This research project proposes a Denoising Vanilla Autoencoding (DVA) architecture by means of unsupervised neural networks for Gaussian denoising in color and grayscale images. The methodology improves other state-of-the-art architectures by means of objective numerical results. Additionally, a validation set and a high-resolution noisy image set are used, which reveal that our proposal outperforms other types of neural networks responsible for suppressing noise in images.

An online failure diagnostic test is essential to ensure the robustness and reliability of high-powered systems. Furthermore, the overall design must comprise diagnostic strategies to detect in-service and high-powered module defects. This paper describes the critical failure mechanisms––cross-conduction, inductive avalanche, second turn-on, VS-undershoot, inrush current, and thermal runaway––that directly affect insulated gate bipolar transistor (IGBT) operation. The constructed inverter contains 18 transformer-based taps (six per phase); however, this work studied a single tap (IGBT-based full-bridge module) to understand the reasons for failure and the routes to mitigate them. Moreover, a cost-effective solution using the IR2127STRPBF driver circuit was implemented to reduce the probability of thermal runaway in case of overcurrent, short-circuit, or avalanche events. For this reason, the electrical current state was adjusted using an FPGA digital resource to perform dynamic PWM control signals. The obtained correlation waveforms are valuable for verifying diagnostic data at the design stage to emphasize the significance of evading premature failure events. The comprehensive study on failure diagnosis enabled successful design strategies to construct a robust 45 kVA three-phase multilevel inverter for a 22 kW eolic-photovoltaic generation plant.

There are two main approaches to automatic text classification: content-based classification and style-based classification. With content-based text classification, the topic of a document (politics, sports, health) or fake news is detected. On the other hand, Style-based text classification is used to detect the gender or age of an author, author identification, and authorship attribution. In style-based classification, the set of words defines the author’s vocabulary, which contains several hundred words. In this work, the words are known as dimensions. Texts generate high-dimensional vectors. Multiple works have shown that a large number of dimensions decreases the performance of classifiers. To reduce dimensions there are selection and extraction techniques. This article discusses the use of extraction techniques, which create low-dimensional vectors from combinations of the high-dimensional vector. Due to the development of Deep Learning networks, the use of dimension reduction techniques has decreased because these networks perform dimension reduction automatically. However, in Machine Learning such techniques are still used intensively. Motivated by the above, in this paper, the Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) dimension reduction algorithms are proposed for the identification of texts written by 14 authors of the Corpus PAN 2012. The texts were divided into sequences of 10, 20, and 30 words called sentences. Likewise, blocks of texts made up of 100 sentences were created. The supervised classification was performed with the Nearest Neighbors (KNN), Support Vector Machines (SVM) and Logistic Regression (LR) algorithms using the accuracy metric. The results showed that the reduction of dimensions with PCA and the LR and SVM classifiers achieved better results than other similar works of the state of the art using the same corpus.

The attenuation of resonant frequencies across the entire spectrum of an audio signal is important because it helps to eliminate the harshness, sibilance, clear muddiness, boominess, and proximity effect of any sound source. This paper presents a method for the attenuation of resonant frequencies across the entire spectrum of an audio signal. A spectrum obtained by the Fast Fourier Transform is segmented into bands—one-third octave bands and Equivalent Rectangular Bandwidth-scale bands—in order to obtain the maximum value per band. Additionally, a curve representing the general shape of the spectrum is generated using the standard deviation to create a threshold curve for detecting resonant frequencies. The array with maximum values per bands and the array with the threshold curve are used to detect the resonant frequencies and calculate the attenuation for each filter. Subsequently, the coefficients of a second-order section of IIR-Peak filters are calculated for processing the input signal. Twenty audio files from different sources are utilized to test the algorithm. The output produced is then compared to that produced by the commercially available Soothe2 and RESO plug-ins. The Root Mean Square Level and the Loudness Units Full Scale integrated metrics are reported. The proposed plug-in output is more attenuated than the output from commercial plug-ins under factory conditions. The average RMS attenuation is −2.32 dBFS, while Soothe2 and RESO exhibit −1.27 dBFS and −1.10 dBFS, respectively. The attenuation per octave band over time is calculated using the Wavelet Transform. Finally, an annotator agreement used as a subjective result is made with 40 people related to audio and music in order to verify if the attenuation generated by the present work at resonant frequencies agrees with subjective opinion. The octave band analysis and annotator agreement show that the proposed plug-in performs better on audio from vocal, percussion, and guitar ensembles.

This study addresses the problem of authorship attribution with a novel method for modeling writing style using dependency tree subtree parsing. This method exploits the syntactic information of sentences using mixed syntactic n-grams (mixed sn-grams). The method comprises an algorithm to generate mixed sn-grams by integrating words, POS tags, and dependency relation tags. The mixed sn-grams are used as style markers to feed Machine Learning methods such as a SVM. A comparative analysis was performed to evaluate the performance of the proposed mixed sn-grams method against homogeneous sn-grams with the PAN-CLEF 2012 and CCAT50 datasets. Experiments with PAN 2012 showed the potential of mixed sn-grams to model a writing style by outperforming homogeneous sn-grams. On the other hand, experiments with CCAT50 showed that training with mixed sn-grams improves accuracy over homogeneous sn-grams, with the POS-Word category showing the best result. The study’s results suggest that mixed sn-grams constitute effective stylistic markers for building a reliable writing style model, which machine learning algorithms can learn.

A biological system can emit signals, and if these signals are correctly acquired, they can provide valuable information about the processes occurring within the system, enhancing our knowledge of the biological system. For this reason, we present a prototype system for acquiring various biopotentials using a main module that integrates amplification, high-pass filtering, band-reject filtering, and offset adjustment stages. This configuration allows for adjustable gain when working with different biopotentials and includes dedicated filtering modules for each biopotential type. We also propose a new topology for the shielded controller used in the interconnection between electrodes and the amplification stage to reduce noise introduced by the electrical network. Biopotentials acquired using the proposed topology show improved noise reduction and signal definition compared to those acquired using other topologies found in the literature. The design of the proposed system utilizes basic electronics, making it a low-cost solution. Ultimately, the system is simple, efficient, and suitable for applications requiring the acquisition of multiple types of biopotentials.