Abstract
Colorectal cancer is the second most common cancer and the second with the highest associated deaths in the world. Methods used in clinical practice for colon cancer diagnosis are fairly effective but quite unpleasant and not always applicable in situations where the patient has symptoms of colonic obstruction. This problem can be solved by the use of optical methods that can be applied less invasively. This study presents the results of classification of cancerous and healthy colon tissue absorption coefficient spectra. The absorption coefficient was measured using direct calculations from the total reflectance and total transmittance spectra obtained ex vivo. Classification was performed using support vector machine, multilayer perceptron and linear discriminant analysis.

Abstract
The broadband absorption coefficient spectrum of the rabbit lung presents some particular characteristics that allow the identification of the chromophores in this tissue. By performing a weighted combination of the absorption spectra of water, hemoglobin, DNA, proteins and the pigments melanin and lipofuscin, it was possible to obtain a good match to the experimental absorption spectrum of the lung. Such reconstruction provided reasonable information about the contents of the tissue components in the lung tissue, and allowed to identify a similar accumulation of melanin and lipofuscin. The broadband absorption coefficient spectrum of the rabbit lung was reconstructed from the absorption spectra of tissue components. The similar accumulation of melanin and lipofuscin was retrieved from the broadband baseline in the absorption coefficient spectrum, and the calculation of the absorption fold ratios for proteins, DNA and hemoglobin provided good results. The method used is innovative and can be improved to allow the quantification of tissue components concentrations directly. image

Abstract

Abstract

Abstract
The Electroencephalogram (EEG) is a medical tool that captures, in a non-invasive way, electrical signals from the brain activities performed by neurons. EEG signals have been the target of study as a biomarker of Parkinsons disease (PD), where several methods of analysis are applied. The present work aims to evaluate features extracted from EEG signals, through methodologies such as HOS, Haralick descriptors, and Fractal Features, as new biomarkers for PD identification. Data from 50 individuals, available at the Open Neuro repository, who underwent an attentional cognitive task were analyzed. RF and SVM algorithms were employed for the classification of the extracted features. The best accuracy achieved was 79.49% in differentiating between Parkinsons subjects and control subjects using Haralick descriptors and RF classifier, suggesting that these features can identify activations in brain areas caused by dopaminergic medication.

Abstract
The notable expansion of technologies related to automated processes has been observed in recent years, largely driven by the significant advantages they provide across diverse industries. Concurrently, there has been a rise in simulation technologies aimed at replicating these complex systems. Nevertheless, in order to fully leverage the potential of these technologies, it is crucial to ensure the highest possible resemblance of simulations to real-world scenarios. In brief, this work consists of the development of a data acquisition and processing pipeline allowing a posterior search for the optimal physical parameters in MuJoCo simulator to obtain a more accurate simulation of a dexterous robotic hand. In the end, a Random Search optimization algorithm was used to validate this same pipeline.