Towards low power radio localisation

This work investigates the use of super-resolution algorithms for precision localisation and long-term tracking of small subjects, like rodents. An overview is given of a variety of techniques for positioning in use today, namely received signal strength, time of arrival, time difference of arrival and direction of arrival (DoA). Based on the analysis, it is concluded that the direction finding signal subspace based techniques are most appropriate for the purposes of our system. The details of the software defined radio (SDR) antenna array testbed development, build, characterisation and performance evaluation are presented. The results of direction finding experiments in the screened anechoic chamber emulating open-space propagation are discussed. It is shown that such testbed is capable of locating sources in the vicinity of the array with high precision. It can estimate the DoAs of more simultaneously working transmitters than antennas in the array, by employing spread spectrum techniques, and readily accommodates very low power sources. Overall constraints on the system are such that the operational range must be around 50 – 100 m. The transmitter must be small both volumetrically and in terms of weight. It also has to be operational over an extended period of around 1 year. The implications of these are that very small antennas and batteries must be used, which are usually accompanied by very low transmission efficiencies and tiny capacities, respectively. Based on the above, the use of ultra-low power oscillator transmitters, as first cut prototypes of the tag, is proposed. It is shown that the Clapp, Colpitts, Pierce and Cross-coupled architectures are adequate. A thorough analysis of these topologies is provided with full details of tag and antenna co-design. Finally the performance of these architectures is evaluated through simulations with respect to power output, overall efficiency and phase noise.Open Acces

In-ear EEG biometrics for feasible and readily collectable real-world person authentication

The use of EEG as a biometrics modality has been investigated for about a decade, however its feasibility in real-world applications is not yet conclusively established, mainly due to the issues with collectability and reproducibility. To this end, we propose a readily deployable EEG biometrics system based on a `one-fits-all' viscoelastic generic in-ear EEG sensor (collectability), which does not require skilled assistance or cumbersome preparation. Unlike most existing studies, we consider data recorded over multiple recording days and for multiple subjects (reproducibility) while, for rigour, the training and test segments are not taken from the same recording days. A robust approach is considered based on the resting state with eyes closed paradigm, the use of both parametric (autoregressive model) and non-parametric (spectral) features, and supported by simple and fast cosine distance, linear discriminant analysis and support vector machine classifiers. Both the verification and identification forensics scenarios are considered and the achieved results are on par with the studies based on impractical on-scalp recordings. Comprehensive analysis over a number of subjects, setups, and analysis features demonstrates the feasibility of the proposed ear-EEG biometrics, and its potential in resolving the critical collectability, robustness, and reproducibility issues associated with current EEG biometrics

Data from: Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronisation in choir singers and surgical teams

A highly localised data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and nonstationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition (NA-MEMD) and short-time Fourier transform (STFT)-based univariate and multivariate synchrosqueezing transforms (FSST and F-MSST). It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise identification of the synchronised respiratory and HRV frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform (CWT)-based ISC. We also introduce an extension to intrinsic phase synchrony (IPS) measure, referred to as nested intrinsic phase synchrony (N-IPS), for the empirical quantification of physically meaningful and straightforward to interpret trends in phase synchrony. The N-IPS is employed to reveal physically meaningful variations in the levels of cooperation in choir singing and performing a surgical procedure. Both the proposed techniques successfully reveal degrees of synchronisation of the physiological signals in two different aspects: (i) precise localisation of synchrony in time and frequency (ISC), and (ii) large scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS)

Additional file 2: of Self-assessment of surgical ward crisis management using video replay augmented with stress biofeedback

Figure S1. Difference between simulation and resting HRV indices between different experience groups. (PNG 176 kb