Khuong An Nguyen

Bio: Khuong An Nguyen is an academic researcher from University of Brighton. The author has contributed to research in topics: Computer science & Differential algebraic geometry. The author has an hindex of 9, co-authored 29 publications receiving 207 citations. Previous affiliations of Khuong An Nguyen include Royal Holloway, University of London.

A survey of deep learning approaches for WiFi-based indoor positioning

Abstract: One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a fe...

Epidemic contact tracing with smartphone sensors

Abstract: Contact tracing is widely considered as an effective procedure in the fight against epidemic diseases. However, one of the challenges for technology based contact tracing is the high number of fals...

On the Feasibility of Using Two Mobile Phones and WLAN Signal to Detect Co-Location of Two Users for Epidemic Prediction

Abstract: An epidemic may be controlled or predicted if we can monitor the history of physical human contacts. As most people have a smart phone, a contact between two persons can be regarded as a handshake between the two phones. Our task becomes how to detect the moment the two mobile phones are close. In this paper, we investigate the possibility of using the outdoor WLAN signals, provided by public Access Points, for off-line mobile phones collision detection. Our method does not require GPS coverage, or real-time monitoring. We designed an Android app running in the phone’s background to periodically collect the outdoor WLAN signals. This data are then analysed to detect the potential contacts. We also discuss several approaches to handle the mobile phone diversity, and the WLAN scanning latency issue. Based on our measurement campaign in the real world, we conclude that it is feasible to detect the co-location of two phones with the WLAN signals only.

Solving differential equations

Abstract: The theme of this paper is to `solve' an absolutely irreducible differential module explicitly in terms of modules of lower dimension and finite extensions of the differential field $K$. Representations of semi-simple Lie algebras and differential Galois theory are the main tools. The results extend the classical work of G. Fano.

A review of smartphones-based indoor positioning: Challenges and applications

Abstract: The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing.

Abstract: The newly emergent human virus SARS-CoV-2 (severe acute respiratory syndrome-coronavirus 2) is resulting in high fatality rates and incapacitated health systems. Preventing further transmission is a priority. We analyzed key parameters of epidemic spread to estimate the contribution of different transmission routes and determine requirements for case isolation and contact tracing needed to stop the epidemic. Although SARS-CoV-2 is spreading too fast to be contained by manual contact tracing, it could be controlled if this process were faster, more efficient, and happened at scale. A contact-tracing app that builds a memory of proximity contacts and immediately notifies contacts of positive cases can achieve epidemic control if used by enough people. By targeting recommendations to only those at risk, epidemics could be contained without resorting to mass quarantines ("lockdowns") that are harmful to society. We discuss the ethical requirements for an intervention of this kind.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Abstract: The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

Advances In Kernel Methods Support Vector Learning

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Wearables Can Afford: Light-weight Indoor Positioning with Visible Light (Best Paper Candidate, Best Video Presentation Award)

Abstract: Visible Light Positioning (VLP) provides a promising means to achieve indoor localization with sub-meter accuracy. We observe that the Visible Light Communication (VLC) methods in existing VLP systems rely on intensity-based modulation, and thus they require a high pulse rate to prevent flickering. However, the high pulse rate adds an unnecessary and heavy burden to receiving devices. To eliminate this burden, we propose the polarization-based modulation, which is flicker-free, to enable a low pulse rate VLC. In this way, we make VLP light-weight enough even for resourceconstrained wearable devices, e.g. smart glasses. Moreover, the polarization-based VLC can be applied to any illuminating light sources, thereby eliminating the dependency on LED. This paper presents the VLP system PIXEL, which realizes our idea. In PIXEL, we develop three techniques, each of which addresses a design challenge: 1) a novel color-based modulation scheme to handle users’ mobility, 2) an adaptive downsampling algorithm to tackle the uneven sampling problem of wearables’ low-cost camera and 3) a computational optimization method for the positioning algorithm to enable real-time processing. We implement PIXEL’s hardware using commodity components and develop a software program for both smartphone and Google glass. Our experiments based on the prototype show that PIXEL can provide accurate realtime VLP for wearables and smartphones with camera resolution as coarse as 60 pixel 80 pixel and CPU frequency as low as 300MHz.