Power-Efficient Joint Compressed Sensing of Multi-Lead ECG Signals

Compressed Sensing (CS) is a new acquisition- compression paradigm for low-complexity energy-aware sensing and compression. By merging both sampling and compression, CS is very promising to develop practical ultra-low power read- out systems for wireless bio-signal monitoring devices, where large amounts of sensor data need to be transferred through power-hungry wireless links. Lately CS has been successfully applied for real-time energy- aware single-lead ECG compression on resource-constrained Wireless Body Sensor Network (WBSN) motes. Building on our previous work, in this paper we propose a new and promising approach for joint compression of multi-lead ECG signals, where strong correlations exist between them. This situation that exhibit strong correlations, can be exploited to reduce even further amount of data to be transmitted wirelessly, thus addressing the important challenge of ultra-low-power embedded monitoring of multi-lead ECG signals

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Recent Techniques for Regularization in Partial Differential Equations and Imaging

abstract: Inverse problems model real world phenomena from data, where the data are often noisy and models contain errors. This leads to instabilities, multiple solution vectors and thus ill-posedness. To solve ill-posed inverse problems, regularization is typically used as a penalty function to induce stability and allow for the incorporation of a priori information about the desired solution. In this thesis, high order regularization techniques are developed for image and function reconstruction from noisy or misleading data. Specifically the incorporation of the Polynomial Annihilation operator allows for the accurate exploitation of the sparse representation of each function in the edge domain. This dissertation tackles three main problems through the development of novel reconstruction techniques: (i) reconstructing one and two dimensional functions from multiple measurement vectors using variance based joint sparsity when a subset of the measurements contain false and/or misleading information, (ii) approximating discontinuous solutions to hyperbolic partial differential equations by enhancing typical solvers with l1 regularization, and (iii) reducing model assumptions in synthetic aperture radar image formation, specifically for the purpose of speckle reduction and phase error correction. While the common thread tying these problems together is the use of high order regularization, the defining characteristics of each of these problems create unique challenges. Fast and robust numerical algorithms are also developed so that these problems can be solved efficiently without requiring fine tuning of parameters. Indeed, the numerical experiments presented in this dissertation strongly suggest that the new methodology provides more accurate and robust solutions to a variety of ill-posed inverse problems.Dissertation/ThesisDoctoral Dissertation Mathematics 201

A subspace approach to accelerated cardiovascular magnetic resonance imaging

Magnetic resonance imaging (MRI) is a uniquely flexible tool for imaging the heart, as it has the potential to perform a significant number of structural and functional cardiovascular assessments. However, the low imaging speed of MRI has limited its clinical application. The assessments that are currently performed in a clinical setting are typically done using gated methodologies, which are complicated by respiration and fail for patients with cardiac arrhythmias. This dissertation describes a subspace approach to accelerate cardiovascular MRI, freeing cardiac MRI from gating techniques and enabling whole-heart 3D dynamic imaging for multiple simultaneous assessments. This imaging approach comprises developments in image modeling, data acquisition, and image reconstruction. A spatiotemporal image model is designed to represent the particular subspace structure of cardiovascular images. The data acquisition development is composed of: a) a sampling strategy which allows integration of the subspace model, parallel imaging, and sparse modeling; b) a novel pulse sequence implementing "self-navigation" for collecting both auxiliary data (for temporal subspace estimation) and imaging data after every excitation; and c) k-space trajectory evaluation and design, replacing Cartesian trajectories which are highly sensitive to readout direction. The image reconstruction work centers on the integration of the subspace model, sensitivity encoding (for parallel imaging), and sparse modeling into one optimization problem; evaluations of strategies for regularizing the image model, adaptively enforcing model order, and for estimating sensitivity maps are also included. The approach is evaluated through simulations on numerical cardiac phantoms and in vivo experiments in human, rat, and mouse subjects. Multiple cardiovascular applications are demonstrated: cine imaging, first-pass myocardial perfusion imaging, late gadolinium enhancement imaging, extracellular volume fraction mapping, and labeled immune cell imaging. Experimental results include human cine images up to 22 fps and 1.0 mm × 1.0 mm spatial resolution, mouse cine images up to 97 fps and 0.12 mm × 0.12 mm spatial resolution, rat images at 74 fps and 0.31 mm × 0.31 mm × 1.0 mm spatial resolution (capturing wall motion, first-pass myocardial perfusion, and late gadolinium enhancement in a single scan), multi-contrast rat images (for extracellular volume fraction mapping) up to 50 fps and 0.42 mm × 0.42 mm × 1.0 mm spatial resolution, and rat images at 98 fps and 0.16 mm × 0.16 mm spatial resolution (depicting labeled immune cells). The end result is an imaging approach capable of ungated, whole-heart 3D cardiovascular MRI in high spatiotemporal resolution. Images can be obtained even for patients with irregular heartbeats, and both cardiac motion and aperiodic contrast dynamics can be imaged in a single scan. These capabilities should enhance the utility of cardiovascular MRI, allowing comprehensive evaluation of the heart

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read