Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining.

This paper presents a system that is able to monitor the patient vital signs (Heart Rate, SPO ₂ , NIBP, ECG, temperature and respiration rate) and send them continuously to the doctor's android phone device. The system enables multiple patients to be connected to the same doctor. Within the system, the health care professional may activate/deactivate any of the vital signs sensors. He can also set a prescription for the patient, schedule a meeting,... When bad activities are received, a message is directly sent to the doctor and to the patient relatives in order to alert them. Note that the Bluetooth connection is used to send/receive data between the patient platform and its android system. The tested results showed an almost error free system with an accuracy above 95% and a few milliseconds delay between the vital signs reading and their upload over the server.

This paper presents a novel system capable of measuring and analysing the signal coming from six different vital signals. The system is based on a portable remote device, monitored via a mobile application running on an android platform, and connected to the patient via its different sensors. The results are displayed on the patient phone and sent remotely to his doctor's phone. The doctor can read any of the vital signs anytime by enabling the sensor connected to the patient. This system can also help the doctor to compare it to previous results, set new medications, update or define a schedule for a visit. Whenever a faulty signal of any of the vital signs is treated, an automatic message is sent to one of the patient relatives and to his doctor for a fast intervention. The tested results showed an almost error free system with an accuracy above 95%.

In this paper, we propose a solution to simplify reading and understanding medical documents via the automatic demystification of complex medical terms found in web pages. The suggested approach detects those terms using a combination of NLP and heuristics. It computes the probability that a word is a medical and complex term through text processing and analysis. It then makes use of an ad hoc dictionary to simplify the complex terms. Finally, it exploits the Microsoft Bing cognitive API to retrieve images and videos related to the complex medical terms detected and presents them to the users so that they may have a better understanding of the document being read.