Towards Continuous Subject Identification Using Wearable Devices and Deep CNNs

© 2020 IEEE. Subject identification has several applications. In transportation companies, knowing who is driving their vehicles might prevent theft or other ill-intended actions. On the other hand, privacy concerns, and the respective legislation, hinder the applicability of several traditional recognition techniques based on invasive technologies, such as video cameras. Moreover, in order to keep the driver's distractions to a minimum, this technologies must be non-disruptive, that is, they must be able to identify the subject seamlessly without them taking any action. In this context, we propose using deep learning applied to smart watch data for recognizing the person driving a vehicle based on a training set. Our proposal relies on the possibility of using transfer learning to avoid long training sessions for new drivers and to deliver a solution which can be deployed in practice. In this paper, we describe the convolutional neural network used in the solution and present results according to a real data-set collected by us, achieving accuracies ranging from 75 to 94%

Piracy and armed robbery against ships Annual report : 1 January - 31 December 2001

SIGLEAvailable from British Library Document Supply Centre- DSC:1291. 671(2001) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Backhaul-Aware and Context-Aware User-Cell Association Approach

No abstract available

Targeting fungi: A challenge

Invasive fungal infections are more commonly identified in various categories of patients, mainly in cancer patients but also in those undergoing organ transplantation, patients in intensive care units, and those with AIDS. There is a great need to increase the awareness of practitioners who are still underestimating the morbidity and mortality relating to invasive fungal infections, and to stress the economic burden for the society and healthcare systems of invasive fungal infections. The list of fungal pathogens causing life-threatening complications has also increased recently, with the emergence of unusual fungi being more frequently identified in such settings. Early diagnosis of invasive fungal infections is still a major challenge for the clinician at the bedside. Identification of state-of-the-art management is also a difficult task for the clinical scientists involved in the assessment of optimal strategies to prevent and to treat those invasive fungal infections, although major progress has occurred in the last 5 years with the development of new, safe, and effective antifungal agents. Empiric therapy remains a very controversial issue that should be further investigated in high-quality clinical trials. Overall, clinical research in this difficult field requires independent and objective analysis; only large multi-center clinical trials can address these critical issues and rapidly provide convincing results leading to a better prognosis of patients with invasive fungal infections. These complications still represent too often an obstacle to successful control of severe underlying diseases. Clinical research on the appropriate ways to target fungi will not only define state-of-the-art management but also identify ineffective or redundant treatments. Such an approach will make a substantial contribution to the care of the high-risk patients within the next decade and will preserve our capacity for medical excellence