The study of neuroimaging is a very important tool in the diagnosis of central nervous system tumors. This paper presents the evaluation of seven deep convolutional neural network (CNN) models for the task of brain tumor classification. A generic CNN model is implemented and six pre-trained CNN models are studied. For this proposal, the dataset utilized in this paper is Msoud, which includes Fighshare, SARTAJ, and Br35H datasets, containing 7023 MRI images. The magnetic resonance imaging (MRI) in the dataset belongs to four classes, three brain tumors, including Glioma, Meningioma, and Pituitary, and one class of healthy brains. The models are trained with input MRI images with several preprocessing strategies applied in this paper. The CNN models evaluated are Generic CNN, ResNet50, InceptionV3, InceptionResNetV2, Xception, MobileNetV2, and EfficientNetB0. In the comparison of all CNN models, including a generic CNN and six pre-trained models, the best CNN model for this dataset was InceptionV3, which obtained an average Accuracy of 97.12%. The development of these techniques could help clinicians specializing in the early detection of brain tumors.
Autism Spectrum Disorder (ASD) is a neurodevelopmental life condition characterized by problems with social interaction, low verbal and non-verbal communication skills, and repetitive and restricted behavior. People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. Attention is a process that occurs at the cognitive level and allows us to orient ourselves towards relevant stimuli, ignoring those that are not, and act accordingly. This paper presents a methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD. The EEG signals are acquired with an Epoc+ Brain–Computer Interface (BCI) via the Emotiv Pro platform while developing several learning activities and using Matlab 2019a for signal processing. For this article, we propose to use electrodes F3, F4, P7, and P8. Then, we calculate the band power spectrum density to detect the Theta Relative Power (TRP), Alpha Relative Power (ARP), Beta Relative Power (BRP), Theta–Beta Ratio (TBR), Theta–Alpha Ratio (TAR), and Theta/(Alpha+Beta), which are features related to attention detection and neurofeedback. We train and evaluate several machine learning (ML) models with these features. In this study, the multi-layer perceptron neural network model (MLP-NN) has the best performance, with an AUC of 0.9299, Cohen’s Kappa coefficient of 0.8597, Matthews correlation coefficient of 0.8602, and Hamming loss of 0.0701. These findings make it possible to develop better learning scenarios according to the person’s needs with ASD. Moreover, it makes it possible to obtain quantifiable information on their progress to reinforce the perception of the teacher or therapist.
This work presents an electronic sensing approach composed of a pair of Physical–Chemical and Imaging modules to preserve an aquaponic system. These modules offer constant measurements of the physical–chemical characteristics within the fish tank and the grow bed, and an indication of the health of the growing plants through image processing techniques. This proposal is implemented in a low-cost computer, receiving measurements from five sensors, including a camera, and processing the signals using open-source libraries and software. Periodic measurements of the temperature, water level, light, and pH within the system are collected and shared to a cloud platform that allows their display in a dashboard, accessible through a web page. The health of the vegetables growing in the system is estimated by analyzing visible and infrared spectra, applying feature extraction, and computing vegetation indices. This work provides a low-cost solution for preserving sustainable urban farming systems, suitable for new farming communities.
En este artículo se presenta una revisión de los ventiladores mecánicos de código abierto desarrollados en todo el mundo durante el comienzo de la pandemia de COVID 19. Hay muchos proyectos de ventiladores en el mundo, algunos basados en Ambu, turbinas y otros que usan las líneas de aire y oxígeno del hospital, controlando el flujo y la presión. Primero se presentan los conceptos básicos de ventilación mecánica. A continuación, se presenta una descripción general de las principales iniciativas de código abierto del mundo, una descripción breve del ventilador y sus principios de funcionamiento. También se muestran las páginas web de cada uno de los proyectos desarrollados. Este documento pretende dar al lector un punto de partida de los ventiladores mecánicos propuestos. Finalmente, se presenta una compilación desarrollada por la comunidad internacional donde enlistan los principales proyectos de ventiladores desarrollados en todo el mundo, donde obtener su información y la experiencia de los desarrolladores. Es importante notar que la mayoría de ellos aún no han sido aprobados por las autoridades médicas de sus respectivos países.
We present a review of the state of the art of the techniques and algorithms most used in the selection and detection of characteristics of electroencephalographic signals of people when consciously performing activities. These features are numeric parameters that describe the behavior of the signal and are the basis of patterns. In addition, previous experiences in the acquisition of electroencephalographic signals using the Epoc brain-computer interface manufactured by Emotiv are presented. First, some techniques used to eliminate artifacts (disturbances) present in the signal generated by blinking, strong breathing or other movements that contaminate the signal are presented. Later, the algorithms most frequently used in the processing of electroencephalographic signals are shown for the extraction of characteristics that describe the behavior of these patterns and that can be used to detect and recognize patterns in other signals. Finally, we present the lessons that we have acquired as a work team in the recording of electroencephalographic signals in order to be helpful for beginners.
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