Optimal Simultaneous Detection and Signal and Noise Power Estimation

Simultaneous detection and estimation is important in many engineering applications. In particular, there are many applications where it is important to perform signal detection and Signal-to-Noise-Ratio (SNR) estimation jointly. Application of existing frameworks in the literature that handle simultaneous detection and estimation is not straightforward for this class of application. This paper therefore aims at bridging the gap between an existing framework, specifically the work by Middleton et al., and the mentioned application class by presenting a jointly optimal detector and signal and noise power estimators. The detector and estimators are given for the Gaussian observation model with appropriate conjugate priors on the signal and noise power. Simulation results affirm the superior performance of the optimal solution compared to the separate detection and estimation approaches.Comment: appears in 2014 IEEE International Symposium on Information Theory (ISIT

Using Stories in Coach Education

The purpose of this paper is to illustrate how storied representations of research can be used as an effective pedagogical tool in coach education. During a series of continuing professional development seminars for professional golf coaches, we presented our research in the form of stories and poems which were created in an effort to evoke and communicate the lived experiences of elite professional golfers. Following these presentations, we obtained written responses to the stories from 53 experienced coaches who attended the seminars. Analysis of this data revealed three ways in which coaches responded to the stories: (i) questioning; (ii) summarising; and (iii) incorporating. We conclude that these responses illustrate the potential of storied forms of representation to enhance professional development through stimulating reflective practice and increasing understanding of holistic, person-centred approaches to coaching athletes in high-performance sport

Recovery of rectified signals from hot-wire/film anemometers due to flow reversal in oscillating flows

Hot-wire/film anemometers have been broadly used in experimental studies in fluid mechanics, acoustics, and ocean engineering. Yet, it is well known that hot-wire/film anemometers rectify the signal outputs due to the lack of sensitivity to flow direction. This main drawback, in turn, makes them less useful for diverse fluctuating flow measurements. To solve this issue, a rectification recovery method has been developed based on reconstruction of the Fourier series expansion in conjunction with signal-squaring approach. This signal recovery method was experimentally examined and proven to be successful for both conventional and microfabricated hot-wire/film anemometers. The method was further applied to dipole field measurements, with data from recovered signals perfectly matching the analytical model of the dipole field

Flow Vision for Autonomous Underwater Vehicles via an Artificial Lateral Line

Most fish have the capability of sensing flows and nearby movements even in dark or murky conditions by using the lateral line organs. This enables them to perform a variety of underwater activities, such as localizing prey, avoiding predators, navigating in narrow spaces, and schooling. To emulate this capability for Autonomous Underwater Vehicles, we developed an artificial lateral line using an array of Micro-Electro-Mechanical-Systems (MEMS) flow sensors. The signals collected via the artificial lateral line are then processed by an adaptive beamforming algorithm developed from Capon\u27s method. The system produces 3D images of source locations for different hydrodynamic activities, including the vibration of a dipole source and the movement of a tail-flicking crayfish. A self-calibration algorithm provides the capability of self-adaptation to different environments. Lastly, we give a Cramer-Rao bound on the theoretical performance limit which is consistent with experimental results

Self-Assembly of Patchy Particles into Polymer Chains: A Parameter-Free Comparison between Wertheim Theory and Monte Carlo Simulation

We numerically study a simple fluid composed of particles having a hard-core repulsion, complemented by two short-ranged attractive (sticky) spots at the particle poles, which provides a simple model for equilibrium polymerization of linear chains. The simplicity of the model allows for a close comparison, with no fitting parameters, between simulations and theoretical predictions based on the Wertheim perturbation theory, a unique framework for the analytic prediction of the properties of self-assembling particle systems in terms of molecular parameter and liquid state correlation functions. This theory has not been subjected to stringent tests against simulation data for ordering across the polymerization transition. We numerically determine many of the thermodynamic properties governing this basic form of self-assembly (energy per particle, order parameter or average fraction of particles in the associated state, average chain length, chain length distribution, average end-to-end distance of the chains, and the static structure factor) and find that predictions of the Wertheim theory accord remarkably well with the simulation results