Geometric Analysis of Signals for Inference of Multiple Faults in Induction Motors.

Multiple fault identification in induction motors is essential in industrial processes due to the high costs that unexpected failures can cause. In real cases, the motor could present multiple faults, influencing systems that classify isolated failures. This paper presents a novel methodology for detecting multiple motor faults based on quaternion signal analysis (QSA).

Stokes Dynamic Polarimeter for Non-Organic and Organic Samples Characterization.

The light polarization properties provide relevant information about linear-optical media quality and condition. The Stokes-Mueller formalism is commonly used to represent the polarization properties of the incident light over sample tests. Currently, different Stokes Polarimeters are mainly defined by resolution, acquisition rate, and light to carry out accurate and fast measurements.

Artificial Intelligence to Analyze the Cortical Thickness Through Age.

In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer.

Learning Conflicts for C-arm Kinematic Modeling using Artificial Intelligence.

During common surgical tasks related to orthopedic applications, it is necessary to carefully manipulate a mobile C-arm device to achieve the desired position. In this work, we propose the application of learning conflicts analysis to improve the performance of an artificial neural network to compute the inverse kinematics of a C-arm device. Using the forward kinematics equations of a C-arm device (and the respective patient table) a training set for machine learning was generated.

Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson's Disease Patients.

Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson's disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson's disease by the Unified Parkinson's Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands; the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region.