Towards Optimal Power Splitting in Simultaneous Power and Information Transmission

This is the author accepted manuscript. the final version is available from IEEE via the DOI in this recordData availability: All code is available under requestSimultaneous wireless information and power transfer (SWIPT) offers novel designs that could enhance the sustainability and resilience of communication systems. Due to the very limited receiving power from radio frequency (RF) signals, optimal splitting strategies play an essential role for many SWIPT systems. This paper investigates optimal power splitting from the outage perspective by formulating the power, information and joint outage performance using a Markov chain, and studying the boundary conditions for achieving an energy-neutral state. Our results show the intrinsic trade-off between power and information outage and propose a novel polynomial method to obtain optimal power splitting. A number of experiments confirm the performance of this method.Royal SocietyRoyal Society of Edinburgh-NSFCHuawei ProjectEuropean Union FP

Systematic infrared image quality improvement using deep learning based techniques

This is the final version. Available from SPIE via the DOI in this recordInfrared thermography (IRT, or thermal video) uses thermographic cameras to detect and record radiation in the longwavelength infrared range of the electromagnetic spectrum. It allows sensing environments beyond the visual perception limitations, and thus has been widely used in many civilian and military applications. Even though current thermal cameras are able to provide high resolution and bit-depth images, there are significant challenges to be addressed in specific applications such as poor contrast, low target signature resolution, etc. This paper addresses quality improvement in IRT images for object recognition. A systematic approach based on image bias correction and deep learning is proposed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. Our main objective is to maximise the useful information on the object to be detected even when the number of pixels on target is adversely small. The experimental results show that our approach can significantly improve target resolution and thus helps making object recognition more efficient in automatic target detection/recognition systems (ATD/R).Centre for Excellence for Sensor and Imaging System (CENSIS)Scottish Funding CouncilDigital Health and Care Institute (DHI)Royal Society of EdinburghNational Science Foundation of Chin

Compressed UAV sensing for flood monitoring by solving the continuous travelling salesman problem over hyperspectral maps

This is the final version. Available from SPIE via the DOI in this record.Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018, 10 - 13 September 2018, Berlin, GermanyUnmanned Aerial Vehicles (UAVs) have shown great capability for disaster management due to their fast speed, automated deployment and low maintenance requirements. In recent years, disasters such as flooding are having increasingly damaging societal and environmental effects. To reduce their impact, real-time and reliable flood monitoring and prevention strategies are required. The limited battery life of small lightweight UAVs imposes efficient strategies to subsample the sensing field. This paper proposes a novel solution to maximise the number of inspected flooded cells while keeping the travelled distance bounded. Our proposal solves the so-called continuous Travelling Salesman Problem (TSP), where the costs of travelling from one cell to another depend not only on the distance, but also on the presence of water. To determine the optimal path between checkpoints, we employ the fast sweeping algorithm using a cost function defined from hyperspectral satellite maps identifying flooded regions. Preliminary results using MODIS flood maps show that our UAV planning strategy achieves a covered flooded surface approximately 4 times greater for the same travelled distance when compared to the conventional TSP solution. These results show new insights on the use of hyperspectral imagery acquired from UAVs to monitor water resourcesThis work was funded by the Royal Society of Edinburgh and National Science Foundation of China within the international project “Flood Detection and Monitoring using Hyperspectral Remote Sensing from Unmanned Aerial Vehicles” (project NNS/INT 15-16 Casaseca)

A reconfigurable supporting connected health environment for people with chronic diseases

Digital healthcare is becoming increasingly important as the ageing population and the number of people diagnosed with chronic diseases is increasing. The face of healthcare delivery has changed radically and at its core is a digital and customer revolution. Connected health is the convergence of medical devices, security devices, and communication technologies. It enables patients to be monitored and treated remotely from their home or primary care facility rather than attend outpatient clinics or be admitted to hospital. This chapter discusses the recent advances in connected health technologies and applications. The authors investigate a reconfigurable supporting connected health solution for people with chronic diseases using reconfigurable hardware and intelligent data interpretation and analysis. In addition, a thorough review of the existing information and communications technologies and challenges in the area of connected health including embedded medical devices, sensors, social networking, knowledge management, data fusion, and cloud computing is presented in this chapter. Finally, future directions and ongoing research in the area of connected health are presented. - 2018 by IGI Global. All rights reserved.Scopu