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e-LiteSense: Self-adaptive energy-aware data sensing in WSN environments
Get PDFCurrently deployed in a wide variety of applicational scenarios, wireless sensor networks (WSNs) are typically a resource-constrained infrastructure. Consequently, characteristics such as WSN adaptability, low-overhead, and low-energy consumption are particularly relevant in dynamic and autonomous sensing environments where the measuring requirements change and human intervention is not viable. To tackle this issue, this article proposes e-LiteSense as an adaptive, energy-aware sensing solution for WSNs, capable of auto-regulate how data are sensed, adjusting it to each applicational scenario. The proposed adaptive scheme is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. In this way, the adaptive algorithm relies on low-complexity rules to establish the sensing frequency weighting the recent drifts of the physical parameter and the levels of remaining energy in the sensor. Using datasets from WSN operational scenarios, we prove e-LiteSense effectiveness in self-regulating data sensing accurately through a low-overhead process where the WSN energy levels are preserved. This constitutes a step-forward for implementing self-adaptive energy-aware data sensing in dynamic WSN environments. © 2019 John Wiley & Sons, Ltd
Precision enology in Tawny Port wine aging process: Monitoring barrel to barrel variation in oxygen, temperature and redox potential
Get PDFTawny Port wine is a category of the famous Portuguese fortified wine commercialized worldwide and produced in the Douro Demarcated Region. Tawny Port wine oxidative aging is a multifactorial process critical for reaching the wanted quality. Real time monitoring of important intrinsic and extrinsic factors that are known to affect both time and quality of the aging process are important to optimize and to manage the natural variability between wines aged in different long-used wood barrels. This study presents the design, development and implementation of a remote distributed system to monitor parameters that are known to be critical for Tawny Port wine aging process. Results indicate that the distributed monitoring system was capable to detect differences between oak wood barrels and between the different storage conditions. Indeed, oxygen and redox potential were the wine's parameters where the differences found between different barrels were greater under the same storage conditions. Considering that Tawny Port wine aging process is oxidative, a variation in the wine's aging process between different wood barrels is to be expected. Actually, significant differences were detected in the oxygen consumption rate amongst the different barrels. Differences in the phenolic composition was also observed in the aged wine (controlled temperature and room temperature).</jats:p
Unraveling the Black Box: Exploring Usage Patterns of a Blended Treatment for Depression in a Multicenter Study
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Reactive Power Provision by the DSO to the TSO considering Renewable Energy Sources Uncertainty
Get PDFThe current coordination between the transmission system operator (TSO) and the distribution system operator (DSO) is changing mainly motivated by the continuous integration of distributed energy resources (DER). The DER technologies are able to provide reactive power services helping the DSOs and TSOs in network operation. This paper follows this trend by proposing a methodology for the reactive power management by the DSO under renewable energy sources (RES) forecast uncertainty, allowing the DSO to coordinate reactive power services to the TSO. The proposed method entails the use of a stochastic AC-OPF, ensuring reliable solutions for the DSO. RES forecast uncertainty is modeled by a set of spatial-temporal trajectories. A 37-bus distribution grid considering realistic generation and consumption data is used to validate the proposed method. An important conclusion is that the method allows the DSO to take advantage of DER full capabilities to provide a new service to the TSO
An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung CT scans
Get PDFThis paper proposes a new methodology to automatically segment and measure the volume of pulmonary nodules in lung computed tomography (CT) scans. Estimating the malignancy likelihood of a pulmonary nodule based on lesion characteristics motivated the development of an unsupervised pulmonary nodule segmentation and volume measurement as a preliminary stage for pulmonary nodule characterization. The idea is to optimally cluster a set of feature vectors composed by intensity and shape-related features in a given feature data space extracted from a pre-detected nodule. For that purpose, a metaheuristic search based on evolutionary computation is used for clustering the corresponding feature vectors. The proposed method is simple, unsupervised and is able to segment different types of nodules in terms of location and texture without the need for any manual annotation. We validate the proposed segmentation and volume measurement on the Lung Image Database Consortium and Image Database Resource Initiative – LIDC-IDRI dataset. The first dataset is a group of 705 solid and sub-solid (assessed as part-solid and non-solid) nodules located in different regions of the lungs, and the second, more challenging, is a group of 59 sub-solid nodules. The average Dice scores of 82.35% and 71.05% for the two datasets show the good performance of the segmentation proposal. Comparisons with previous state-of-the-art techniques also show acceptable and comparable segmentation results. The volumes of the segmented nodules are measured via ellipsoid approximation. The correlation and statistical significance between the measured volumes of the segmented nodules and the ground-truth are obtained by Pearson correlation coefficient value, obtaining an R-value = 92.16% with a significance level of 5%. © 2018 Elsevier Lt