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.

We present challenges faced deploying a solar-powered wireless sensor network base station and nodes, at a remote oyster farm. It involved installing the base station system and a data server at the shore of a shallow bay, where there is no electrical power available. To solve the problem, we set up a photovoltaic array with an energy monitoring node, from which performance metrics were recorded and plotted. At the water, we deployed two wireless sensor nodes on a raft, a kilometre away from the base station. One node was configured for sea water pH and water temperature (T w ) measurements. The other node was configured for salinity and T w measurements. Furthermore, both nodes measured air temperature and relative humidity, for a more complete characterization. At the salinity node, temperature and relative humidity knowledge was crucial to determine a gain factor for doing a trial of a transmission power control scheme, using a novel temperature and relative humidity algorithm. To enable a fair comparison, the pH nodes transmitter was configured with a fixed power level. The nodes performances were measured locally at the base station, recording metrics such as received signal strength indicator and packet received rates.

A methodology for the selection and determination of electroencephalographic (EEG) signal patterns is presented at the case study level, which can later be used as on-off control signals in other applications. Electroencephalographic signals are acquired through the use of a brain-computer interface (BCI). These systems capture electrical signals from the cortex of the brain and transfer them to a computer so that they can be analyzed by algorithms and some action is taken. In this case, the EEG signals are acquired through the wireless 14-channel Epoc+ platform. The methodology used consists first in acquiring signals from the user sample in three scenarios: in relaxation, thinking about turning on and off. Subsequently, the wavelet transform of each of the channels is obtained for each of the cases and the most significant coefficients are taken into account. Then, through digital signal processing algorithms, descriptive parameters are obtained for the on and off cases, which are used as patterns to describe each of the actions. With this information, a comparison between the incoming signals and the previously stored patterns is made to execute one of the established commands.

Due to the contingency situation that has been generated in various parts of the world and the declaration of a pandemic carried out by the World Health Organization against the Sars-CoV-2 virus, various people, educational institutions and companies are carrying out the development of mechanical ventilators that can meet the need for this equipment in their countries. This paper shows experiences obtained in the design and construction of a transitional mechanical ventilator that allows compliance with the minimum requirements that doctors and healthcare professionals consider when a person is piped. Also, it helps in the seek to comply the regulations that the federal government agency elaborates with the purpose of reviewing the existing proposals for open source mechanical ventilators. It also contains the technical requirements that are need to be covered by the designers. These regulations cover the feasibility for replicating the ventilators proposed, based on certain factors that will be described in this paper. Once the ventilators have been tested, its improvement is carried out from the mechanical part, considering the electrical element to be used, in order to obtain a transitional mechanical ventilator that could be easily replicated with national suppliers.