Speech emotion recognition is key to many fields, including human–computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required within successful emotion recognition models. This work aimed to investigate whether splitting the features into two subsets based on their distribution and then applying commonly used feature reduction methods would impact accuracy. Filter reduction was employed using the Kruskal–Wallis test, followed by principal component analysis (PCA) and independent component analysis (ICA). A set of features was investigated to determine whether the indiscriminate use of parametric feature reduction techniques affects the accuracy of emotion recognition. For this investigation, data from three databases—Berlin EmoDB, SAVEE, and RAVDES—were organized into subsets according to their distribution in applying both PCA and ICA. The results showed a reduction from 6373 features to 170 for the Berlin EmoDB database with an accuracy of 84.3%; a final size of 130 features for SAVEE, with a corresponding accuracy of 75.4%; and 150 features for RAVDESS, with an accuracy of 59.9%.

This article introduces a new MfEIT UDESC Mark I system, which consists of a 32-electrode setup featuring a modified Howland current source, low cost, portability, and non-radiation. The system is capable of reconstructing electrical conductivity tomographic images at a rate of 30.624 frames per second, taking about 5 min for imaging. The current source employs a 0.5 mA adjacent current application pattern with frequencies ranging from 10 kHz to 1 MHz. This article outlines the hardware, firmware, and software design specifications, which include the design of the current source, calibration procedures, and image reconstruction process. Tomographic images of conductivity were reconstructed in ex vivo healthy pig lungs and those with pneumonia, as a proof of concept for future applications in live pigs. The high spectral power density, combined with real-time system calibration provides clinical advantages in veterinary medicine. The goal is to identify lung areas affected by Mycoplasma hyopneumoniae in pigs through the analysis of electrical conductivity difference, offering a valuable tool to assist veterinarians to obtain images of respiratory diseases. The modified reconstruction method GREIT (EIDORS) was evaluated with experimental data and was compared with the Gauss–Newton and Total Variation methods, where GREIT 2D proved to be superior.

This article introduces a new MfEIT UDESC Mark I system, which consists of a 32-electrode setup featuring a modified Howland current source, low cost, portable and non radiation. The system is capable of reconstruction of electrical conductivity tomographic images at a rate of 30.624 frames per second and about 5 minutes for imaging. The current source employs 0.5 mA, adjacent current application pattern with frequencies ranging from 10 kHz to 1 MHz. The article outlines the hardware, firmware, and software design specifications, which include the design of the current source, calibration procedures, and image reconstruction process. Tomographic images of conductivity were reconstructed in ex vivo healthy pig lungs and those with pneumonia, as a proof of concept for future applications in live pigs. The high spectral power density, combined with real-time system calibration provides clinical advantages in veterinary medicine. The goal is to identify lung areas affected by Mycoplasma hyopneumoniae in pigs, through the analysis of electrical conductivity difference, offering a valuable tool to assist veterinarians for to obtain images of respiratory diseases. The reconstruction method (GREIT) was evaluated with experimental data and It was compared with the Gauss-Newton and Total Variation methods, where GREIT proved to be superior.

Multifrequency Electrical Impedance Tomography (MfEIT) is a safe, non-invasive, radiation-free technique that visualizes conductivity distribution within an object. Vegetables and animal tissues are often used to calibrate and validate MfEIT systems. This paper presents the calibration of image reconstruction algorithms using eight cucumber samples. An academic MfEIT model obtained edge voltage data at frequencies of 10k, 50k, 100k, and 200 kHz and gains of 5, 10, 60, 150, 200, and 250. Four algorithms were selected for image reconstruction: Graz Consensus for Electrical Impedance Tomography (GREIT); One-Step Gauss-Newton (OSGN); Total Variation Primal-Dual Interior Point Method (TV-PDIPM); and Gauss-Newton Absolute Reconstruction Method (GN-ARM). This work explores key factors influencing image quality, including gain, frequency, hyperparameter values, and reconstruction algorithms. Hyperparameters in algorithms play an important role in fine-tuning image reconstruction models, directly influencing the accuracy, stability, and computational efficiency of the obtained solutions. For this reason, they were systematically varied and tested in this work, aiming to identify the optimal configurations that ensure the best performance of the algorithms in different reconstruction scenarios. The cucumber sample at 250 gain and 100 kHz with GN-ARM produced the best image.