Design optimisation of electromagnetic devices using continuum design sensitivity analysis combined with commercial EM software

This paper deals with two types of optimisation problems: optimised source distribution and the shape optimum design, using Continuum Design Sensitivity Analysis (CDSA) in combination with standard electromagnetic (EM) software. Fast convergence and compatibility with existing EM software are the distinctive features of the proposed implementation. In order to verify the advantages and also to facilitate understanding of the method itself, two design optimisation problems have been tested using both 2D and 3D models: the first is a MRI design problem related to finding an optimal permanent magnet distribution and the second is a pole shape design problem to reduce the cogging torque in a BLDC

Microwave method for high-frequency properties of graphene

Graphene is a remarkable material, which is yet to make the transition from unique laboratory phenomenon to useful industrial material. One missing element in the development process is a quick method of quality control of the electrical properties of graphene which may be applied in, or close to, the graphene growth process on an industrial scale. In this study, the authors describe a non-contact method using microwave resonance which potentially solves this problem. They describe the technique, consider its limitations and accuracy and suggest how the method may have future take up.UK NMS Programme, the EU EMRP project ‘GraphOhm’ and ‘MetNEMS’. The EMRP (European Metrology Research Programme

IEEE Access special section editorial: Artificial intelligence enabled networking

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)

Improvement in Hemodynamic Responses to Metaboreflex Activation after One Year of Training in Spinal Cord Injured Humans

Spinal cord injured (SCI) individuals show an altered hemodynamic response to metaboreflex activation due to a reduced capacity to vasoconstrict the venous and arterial vessels below the level of the lesion. Exercise training was found to enhance circulating catecholamines and to improve cardiac preload and venous tone in response to exercise in SCI subjects. Therefore, training would result in enhanced diastolic function and capacity to vasoconstrict circulation. The aim of this study was to test the hypothesis that one year of training improves hemodynamic response to metaboreflex activation in these subjects. Nine SCI individuals were enrolled and underwent a metaboreflex activation test at the beginning of the study (T0) and after one year of training (T1). Hemodynamics were assessed by impedance cardiography and echocardiography at both T0 and T1. Results show that there was an increment in cardiac output response due to metaboreflex activity at T1 as compared to T0 (545.4 ± 683.9 mL · min(-1) versus 220.5 ± 745.4 mL · min(-1), P < 0.05). Moreover, ventricular filling rate response was higher at T1 than at T0. Similarly, end-diastolic volume response was increased after training. We concluded that a period of training can successfully improve hemodynamic response to muscle metaboreflex activation in SCI subjects

Service Level Agreement-based GDPR Compliance and Security assurance in (multi)Cloud-based systems

Compliance with the new European General Data Protection Regulation (Regulation (EU) 2016/679) and security assurance are currently two major challenges of Cloud-based systems. GDPR compliance implies both privacy and security mechanisms definition, enforcement and control, including evidence collection. This paper presents a novel DevOps framework aimed at supporting Cloud consumers in designing, deploying and operating (multi)Cloud systems that include the necessary privacy and security controls for ensuring transparency to end-users, third parties in service provision (if any) and law enforcement authorities. The framework relies on the risk-driven specification at design time of privacy and security level objectives in the system Service Level Agreement (SLA) and in their continuous monitoring and enforcement at runtime.The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644429 and No 780351, MUSA project and ENACT project, respectively. We would also like to acknowledge all the members of the MUSA Consortium and ENACT Consortium for their valuable help

Intrusion Detection Using Mouse Dynamics

Compared to other behavioural biometrics, mouse dynamics is a less explored area. General purpose data sets containing unrestricted mouse usage data are usually not available. The Balabit data set was released in 2016 for a data science competition, which against the few subjects, can be considered the first adequate publicly available one. This paper presents a performance evaluation study on this data set for impostor detection. The existence of very short test sessions makes this data set challenging. Raw data were segmented into mouse move, point and click and drag and drop types of mouse actions, then several features were extracted. In contrast to keystroke dynamics, mouse data is not sensitive, therefore it is possible to collect negative mouse dynamics data and to use two-class classifiers for impostor detection. Both action- and set of actions-based evaluations were performed. Set of actions-based evaluation achieves 0.92 AUC on the test part of the data set. However, the same type of evaluation conducted on the training part of the data set resulted in maximal AUC (1) using only 13 actions. Drag and drop mouse actions proved to be the best actions for impostor detection.Comment: Submitted to IET Biometrics on 23 May 201

FPGA implementation of a 32x32 autocorrelator array for analysis of fast image series

With the evolving technology in CMOS integration, new classes of 2D-imaging detectors have recently become available. In particular, single photon avalanche diode (SPAD) arrays allow detection of single photons at high acquisition rates (\geq 100 kfps), which is about two orders of magnitude higher than with currently available cameras. Here we demonstrate the use of a SPAD array for imaging fluorescence correlation spectroscopy (imFCS), a tool to create 2D maps of the dynamics of fluorescent molecules inside living cells. Time-dependent fluorescence fluctuations, due to fluorophores entering and leaving the observed pixels, are evaluated by means of autocorrelation analysis. The multi-{\tau} correlation algorithm is an appropriate choice, as it does not rely on the full data set to be held in memory. Thus, this algorithm can be efficiently implemented in custom logic. We describe a new implementation for massively parallel multi-{\tau} correlation hardware. Our current implementation can calculate 1024 correlation functions at a resolution of 10{\mu}s in real-time and therefore correlate real-time image streams from high speed single photon cameras with thousands of pixels.Comment: 10 pages, 7 figure