On Body Characterization for On-Body Radio Propagation Channel using Wearable Textile Monopole Antenna

This paper presents the experimental investigation of the characterization of the narrowband on-body radio propagation channel at 2.45 GHz. Wearable planar textile monopole antennas (TM) were used in this measurement campaign. The measurements were conducted in the RFshielded room environment, considering eight on-body radio links. A statistical analysis was conducted on the spectral parameters of the channel to enable the prediction and modeling of dynamic on-body radio propagation characteristics. The empirical data were fitted to several wellknown statistical models to determine the model that provided the best fit for the data. The results showed that the path loss exponent was consistent with the results of previous studies. The results also demonstrated that lognormal distribution was found to be the best fit for path loss in dynamic on-body radio propagation channel

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

Compressed Sensing for Open-ended Waveguide Non-Destructive Testing and Evaluation

Ph. D. ThesisNon-destructive testing and evaluation (NDT&E) systems using open-ended waveguide (OEW) suffer from critical challenges. In the sensing stage, data acquisition is time-consuming by raster scan, which is difficult for on-line detection. Sensing stage also disregards demand for the latter feature extraction process, leading to an excessive amount of data and processing overhead for feature extraction. In the feature extraction stage, efficient and robust defect region segmentation in the obtained image is challenging for a complex image background. Compressed sensing (CS) demonstrates impressive data compression ability in various applications using sparse models. How to develop CS models in OEW NDT&E that jointly consider sensing & processing for fast data acquisition, data compression, efficient and robust feature extraction is remaining challenges. This thesis develops integrated sensing-processing CS models to address the drawbacks in OEW NDT systems and carries out their case studies in low-energy impact damage detection for carbon fibre reinforced plastics (CFRP) materials. The major contributions are: (1) For the challenge of fast data acquisition, an online CS model is developed to offer faster data acquisition and reduce data amount without any hardware modification. The images obtained with OEW are usually smooth which can be sparsely represented with discrete cosine transform (DCT) basis. Based on this information, a customised 0/1 Bernoulli matrix for CS measurement is designed for downsampling. The full data is reconstructed with orthogonal matching pursuit algorithm using the downsampling data, DCT basis, and the customised 0/1 Bernoulli matrix. It is hard to determine the sampling pixel numbers for sparse reconstruction when lacking training data, to address this issue, an accumulated sampling and recovery process is developed in this CS model. The defect region can be extracted with the proposed histogram threshold edge detection (HTED) algorithm after each recovery, which forms an online process. A case study in impact damage detection on CFRP materials is carried out for validation. The results show that the data acquisition time is reduced by one order of magnitude while maintaining equivalent image quality and defect region as raster scan. (2) For the challenge of efficient data compression that considers the later feature extraction, a feature-supervised CS data acquisition method is proposed and evaluated. It reserves interested features while reducing the data amount. The frequencies which reveal the feature only occupy a small part of the frequency band, this method finds these sparse frequency range firstly to supervise the later sampling process. Subsequently, based on joint sparsity of neighbour frame and the extracted frequency band, an aligned spatial-spectrum sampling scheme is proposed. The scheme only samples interested frequency range for required features by using a customised 0/1 Bernoulli measurement matrix. The interested spectral-spatial data are reconstructed jointly, which has much faster speed than frame-by-frame methods. The proposed feature-supervised CS data acquisition is implemented and compared with raster scan and the traditional CS reconstruction in impact damage detection on CFRP materials. The results show that the data amount is reduced greatly without compromising feature quality, and the gain in reconstruction speed is improved linearly with the number of measurements. (3) Based on the above CS-based data acquisition methods, CS models are developed to directly detect defect from CS data rather than using the reconstructed full spatial data. This method is robust to texture background and more time-efficient that HTED algorithm. Firstly, based on the histogram is invariant to down-sampling using the customised 0/1 Bernoulli measurement matrix, a qualitative method which only gives binary judgement of defect is developed. High probability of detection and accuracy is achieved compared to other methods. Secondly, a new greedy algorithm of sparse orthogonal matching pursuit (spOMP)-based defect region segmentation method is developed to quantitatively extract the defect region, because the conventional sparse reconstruction algorithms cannot properly use the sparse character of correlation between the measurement matrix and CS data. The proposed algorithms are faster and more robust to interference than other algorithms.China Scholarship Counci

Variational Multi-Task Models for Image Analysis: Applications to Magnetic Resonance Imaging

This thesis deals with the study and development of several variational multi-task models for solving inverse problems in imaging, with a particular focus on Magnetic Resonance Imaging (MRI). In most image processing problems, one usually deals with the reconstruction task, i.e., the task of reconstructing an image from indirect measurements, and then performs various operations, one after the other (i.e. sequentially), to improve the quality of the reconstruction and to extract useful information. However, recent developments in a variational context, have shown that performing those tasks jointly (i.e. in a multi-task framework) offers great benefits, and this is the perspective that we follow in this thesis. We go beyond traditional sequential approaches and set a new basis for variational multi-task methods for MRI analysis. We demonstrate that by sharing representation between tasks and carefully interconnecting them, one can create synergies across challenging problems and reduce error propagation. More precisely, firstly we propose a multi-task variational model to tackle the problems of image reconstruction and image segmentation using non-convex Bregman iteration. We describe theoretical and numerical details of the problem and its optimisation scheme. Moreover, we show that our multi-task model achieves better results in several examples and MRI applications than existing approaches in the same context. Secondly, we show that our approach can be extended to a multi-task reconstruction and segmentation model for the nonlinear inverse problem of velocity-encoded MRI. In this context, the aim is to estimate not only the magnitude from MRI data, but also the phase and its flow information, whilst simultaneously identify regions of interest through the segmentation task. Finally, we go beyond two-task frameworks and introduce for the first time a variational multi-task model to handle three imaging tasks. To this end, we design a variational multi-task framework addressing reconstruction, super-resolution and registration for improving the quality of MRI reconstruction. We demonstrate that our model is theoretically well-motivated and it outperforms sequential models whilst requiring less computational cost. Furthermore, we show through experimental results the potential of this approach for clinical applications

Scalable Bayesian methods for the analysis of neuroimaging data

The recent surge in large-scale population health datasets, such as the UK Biobank or the Adolescent Brain Cognitive Development (ABCD) study, requires the development of scalable statistical methods that are capable of analysing the rich multitude of data sources. This thesis focuses on the scalable analysis of Magnetic Resonance Imaging (MRI) neuroimaging data, such as binary lesion masks and task-based functional Magnetic Resonance Imaging (fMRI). In particular, we introduce two Bayesian spatial models with sparsity priors on the spatially varying coefficients and extend our work to suit the large sample sizes found in population health studies. Firstly, we propose a scalable hierarchical Bayesian image-on-scalar regression model, called BLESS, capable of handling binary responses and of placing continuous spike-and-slab mixture priors on spatially varying parameters. Thereby, enforcing spatial dependency on the parameter dictating the amount of sparsity within the probability of inclusion. The use of mean-field variational inference with dynamic posterior exploration, which is an annealing-like strategy that improves optimisation, allows our method to scale to large sample sizes. We validate our results via simulation studies and an application to binary lesion masks from the UK Biobank. Secondly, we extend our method to account for underestimation of posterior variance due to variational inference by providing an approximate posterior sampling approach inspired by Bayesian bootstrap ideas and spike-and-slab priors with random shrinkage targets. Besides accurate uncertainty quantification, this approach is capable of producing novel cluster size-based imaging statistics, such as credible intervals of cluster size, and measures of reliability of cluster occurrence. Thirdly, we develop a Bayesian nonparametric scalar-on-image regression model with a relaxed-thresholded Gaussian process prior on the spatially varying coefficients in order to introduce sparsity and smoothness into the model. Our main contribution is the improved scalability, allowing for larger sample sizes and bigger image dimensions, which is made possible by replacing posterior sampling with a variational approximation. We validate our results via simulation studies and an application to cortical surface task-based fMRI data from the ABCD study

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE