University of Cyprus

About: University of Cyprus is a education organization based out in Nicosia, Cyprus. It is known for research contribution in the topics: Large Hadron Collider & Context (language use). The organization has 3624 authors who have published 15157 publications receiving 412135 citations.

m-Health e-Emergency Systems: Current Status and Future Directions [Wireless corner]

Abstract: Rapid advances in wireless communications and networking technologies, linked with advances in computing and medical technologies, facilitate the development and offering of emerging mobile systems and services in the healthcare sector. The objective of this paper is to provide an overview of the current status and challenges of mobile health systems (m-health) in emergency healthcare systems and services (e-emergency). The paper covers a review of recent e-emergency systems, including the wireless technologies used, as well as the data transmitted (electronic patient record, bio-signals, medical images and video, subject video, and other). Furthermore, emerging wireless video systems for reliable communications in these applications are presented. We anticipate that m-health e-emergency systems will significantly affect the delivery of healthcare; however, their exploitation in daily practice still remains to be achieved

Studies of dijet transverse momentum balance and pseudorapidity distributions in pPb collisions at √sNN = 5.02 TeV

Abstract: Dijet production has been measured in pPb collisions at a nucleon–nucleon centre-of-mass energy of 5.02 TeV . A data sample corresponding to an integrated luminosity of 35 nb^(−1) was collected using the Compact Muon Solenoid detector at the Large Hadron Collider. The dijet transverse momentum balance, azimuthal angle correlations, and pseudorapidity distributions are studied as a function of the transverse energy in the forward calorimeters ( E^(4<|η|<5.2)_T ). For pPb collisions, the dijet transverse momentum ratio and the width of the distribution of dijet azimuthal angle difference are comparable to the same quantities obtained from a simulated pp reference and insensitive to E^(4<|η|<5.2)_T . In contrast, the mean value of the dijet pseudorapidity is found to change monotonically with increasing E^(4<|η|<5.2)_T , indicating a correlation between the energy emitted at large pseudorapidity and the longitudinal motion of the dijet frame. The pseudorapidity distribution of the dijet system in minimum bias pPb collisions is compared with next-to-leading-order perturbative QCD predictions obtained from both nucleon and nuclear parton distribution functions, and the data more closely match the latter.

An overview of next-generation architectures for machine learning: Roadmap, opportunities and challenges in the IoT era

Abstract: The number of connected Internet of Things (IoT) devices are expected to reach over 20 billion by 2020. These range from basic sensor nodes that log and report the data to the ones that are capable of processing the incoming information and taking an action accordingly. Machine learning, and in particular deep learning, is the de facto processing paradigm for intelligently processing these immense volumes of data. However, the resource inhibited environment of IoT devices, owing to their limited energy budget and low compute capabilities, render them a challenging platform for deployment of desired data analytics. This paper provides an overview of the current and emerging trends in designing highly efficient, reliable, secure and scalable machine learning architectures for such devices. The paper highlights the focal challenges and obstacles being faced by the community in achieving its desired goals. The paper further presents a roadmap that can help in addressing the highlighted challenges and thereby designing scalable, high-performance, and energy efficient architectures for performing machine learning on the edge.