Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Automated identification and behaviour classification for modelling social dynamics in group-housed mice

Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Three Dimensional Bistatic Tomography Using HDTV

The thesis begins with a review of the principles of diffraction and reflection tomography; starting with the analytic solution to the inhomogeneous Helmholtz equation, after linearization by the Born approximation (the weak scatterer solution), and arriving at the Filtered Back Projection (Propagation) method of reconstruction. This is followed by a heuristic derivation more directly couched in the radar imaging context, without the rigor of the general inverse problem solution and more closely resembling an imaging turntable or inverse synthetic aperture radar. The heuristic derivation leads into the concept of the line integral and projections (the Radon Transform), followed by more general geometries where the plane wave approximation is invalid. We proceed next to study of the dependency of reconstruction on the space-frequency trajectory, combining the spatial aperture and waveform. Two and three dimensional apertures, monostatic and bistatic, fully and sparsely sampled and including partial apertures, with controlled waveforms (CW and pulsed, with and without modulation) define the filling of k-space and concomitant reconstruction performance. Theoretical developments in the first half of the thesis are applied to the specific example of bistatic tomographic imaging using High Definition Television (HDTV); the United States version of DVB-T. Modeling of the HDTV waveform using pseudonoise modulation to represent the hybrid 8VSB HDTV scheme and the move-stop-move approximation established the imaging potential, employing an idealized, isotropic 18 scatterer. As the move-stop-move approximation places a limitation on integration time (in cross correlation/pulse compression) due to transmitter/receiver motion, an exact solution for compensation of Doppler distortion is derived. The concept is tested with the assembly and flight test of a bistatic radar system employing software-defined radios (SDR). A three dimensional, bistatic collection aperture, exploiting an elevated commercial HDTV transmitter, is focused to demonstrate the principle. This work, to the best of our knowledge, represents a first in the formation of three dimensional images using bistatically-exploited television transmitters