With the increase in global life expectancy and the advance of technology, the creation of age-friendly environments is a priority in the design of new products for elderly people healthcare. This paper presents a proposal for a real-time health monitoring system of older adults living in geriatric residences. This system was developed to help caregivers to have a better control in monitoring the health of their patients and have closer communication with their patients’ family members. To validate the feasibility and effectiveness of this proposal, a prototype was built, using a biometric bracelet connected to a mobile application, which allows real-time visualization of all the information generated by the sensors (heart rate, body temperature, and blood oxygenation) in the bracelet. Using these data, caregivers can make decisions about the health status of their patients. The evaluation found that the users perceived the system to be easy to learn and use, providing initial evidence that our proposal could improve the quality of the adult’s healthcare.

This paper presents the usability assessment of the design of an Internet of Medical Things (IoMT) system for older adults; the evaluation, using heuristics, was held early on the design process to assess potential problems with the system and was found to be an efficient method to find issues with the application design and led to significant usability improvements on the IoMT platform.

After an introduction of cognitive radio (CR) technology in communication, the hot research topics are sensing, Primary User Interference (PUI), spectrum management, security, spectrum sharing, and environmental sensing. Among the listed, sensing and Primary User Interference are the bold ones. The base query for these two problems lays finding a means for which and what channel at a particular time is available and avoiding interference with Primary Users (PU). This article presents a novel cognitive radio algorithm called SenPUI for both mentioned main challenges, sensing and PUI. First, energy scan during the inactive portion of communication which is dynamic is done. Second, application packet based primary user identification for PUI avoidance is proposed as base solution. Both techniques described in this work are implemented and resulted in a significant reduction of the target problems estimated around 10-30% reduction in average. Main limitations of Wireless Sensor Network (WSN) such as memory, battery lifetime, and size are considered during the design and implementation of our solutions.

No abstract

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments.