Visualization Techniques for the Analysis of Neurophysiological Data

In order to understand the diverse and complex functions of the Human brain, the temporal relationships of vast quantities of multi-dimensional spike train data must be analysed. A number of statistical methods already exist to analyse these relationships. However, as a result of expansions in recording capability hundreds of spike trains must now be analysed simultaneously. In addition to the requirements for new statistical analysis methods, the need for more efficient data representation is paramount. The computer science field of Information Visualization is specifically aimed at producing effective representations of large and complex datasets. This thesis is based on the assumption that data analysis can be significantly improved by the application of Information Visualization principles and techniques. This thesis discusses the discipline of Information Visualization, within the wider context of visualization. It also presents some introductory neurophysiology focusing on the analysis of multidimensional spike train data and software currently available to support this problem. Following this, the Toolbox developed to support the analysis of these datasets is presented. Subsequently, three case studies using the Toolbox are described. The first case study was conducted on a known dataset in order to gain experience of using these methods. The second and third case studies were conducted on blind datasets and both of these yielded compelling results

Multichannel source separation and tracking with phase differences by random sample consensus

Blind audio source separation (BASS) is a fascinating problem that has been tackled from many different angles. The use case of interest in this thesis is that of multiple moving and simultaneously-active speakers in a reverberant room. This is a common situation, for example, in social gatherings. We human beings have the remarkable ability to focus attention on a particular speaker while effectively ignoring the rest. This is referred to as the ``cocktail party effect'' and has been the holy grail of source separation for many decades. Replicating this feat in real-time with a machine is the goal of BASS. Single-channel methods attempt to identify the individual speakers from a single recording. However, with the advent of hand-held consumer electronics, techniques based on microphone array processing are becoming increasingly popular. Multichannel methods record a sound field from various locations to incorporate spatial information. If the speakers move over time, we need an algorithm capable of tracking their positions in the room. For compact arrays with 1-10 cm of separation between the microphones, this can be accomplished by applying a temporal filter on estimates of the directions-of-arrival (DOA) of the speakers. In this thesis, we review recent work on BSS with inter-channel phase difference (IPD) features and provide extensions to the case of moving speakers. It is shown that IPD features compose a noisy circular-linear dataset. This data is clustered with the RANdom SAmple Consensus (RANSAC) algorithm in the presence of strong reverberation to simultaneously localize and separate speakers. The remarkable performance of RANSAC is due to its natural tendency to reject outliers. To handle the case of non-stationary speakers, a factorial wrapped Kalman filter (FWKF) and a factorial von Mises-Fisher particle filter (FvMFPF) are proposed that track source DOAs directly on the unit circle and unit sphere, respectively. These algorithms combine directional statistics, Bayesian filtering theory, and probabilistic data association techniques to track the speakers with mixtures of directional distributions

Waveguide platform and methods for super-resolution fluorescence microscopy of sub-cellular structures

Super-resolution fluorescence microscopy is widespread, owing to its demonstrated ability to resolve dynamical processes within cells and to identify the structure and position of specific proteins in the interior of protein complexes. Nowadays, subcellular features can be routinely resolved at the nanoscopic scale thanks to the accessibility of straightforward sample-preparation protocols, simple hardware tools, and open source software. Building on its ability to investigate large-scale macromolecules networks in their natural environment with high resolution, fluorescence microscopy is further evolving by the development of quantitative and high-throughput methods to characterize such networks. Previous implementations of high-throughput microscopy made use of imaging sequentially smaller fields of view (FOV), which makes axial alignment a challenge and extends the imaging time. In our work, we circumvent these problems with our large FOV systems, which are based on flat-field sample illumination over large areas, combined with a CMOS-camera. In this thesis, I present a waveguide platform designed to image a wide area with low background by mean of total internal reflection fluorescence (TIRF) excitation. The waveguide chips for this platform were fabricated at the center of micro-nano technology (CMi) at EPFL, in collaboration with the group of Aleksandra Radenovic (specifically with Evgenii Glushkov). The resulting waveguide-TIRF system is specifically optimized for applications where easy and repetitive buffer exchange is needed. To achieve large and uniform TIRF excitation, I studied some fundamental parameters of the waveguide, developing specific code to simulate, at the first order, its behavior. I then extended light propagation solutions adopted in the field of integrated photonics to our waveguide chip fabrication process. To easily integrate the chip within the commercial stage of an upright microscope, I designed a novel chip holder that ensures aqueous solution sealing, mitigates the presence of scatter light in the imaging area, and facilitates the waveguide alignment during the input beam-coupling phase. On the analysis side, the need for computational tools that are specific to fluorescence microscopy is continuously growing, due to the fact that this technique heavily relies on the treatment of large quantities of data. The automated analysis of images is a fundamental step of the measurement process, necessary for unbiased quantification and statistical validation, especially where repetitive visual inspection would be impractically long. This is particularly critical for single molecule localization microscopy (SMLM), where the quality of the reconstructed super-resolved image actually is a trade-off between the algorithm localization precision and its speed, a key element considering the need of processing tens of thousands of large images to generate the final, super-resolved one. In this work, I present a series of computational tools for CMOS camera characterization developed for large flat-field STORM microscopy, a 3D SMLM reconstruction software specific for Double-Helix (DH) point spread function (PSF) and a set of cell shape analysis tools to study C.Crescentus shape dynamics

On the Breakdown of Helical Wake behind a Rotating Blade in Thermally Stratified Atmosphere

In this thesis, a fundamental study on the breakdown mechanisms of a helical flow is conducted. The helical flow shares common traits with the wind turbine wake flow. The simulation data generated by a Direct Numerical Simulation (DNS) of flow around a rotating blade in a thermally stratified atmosphere were analysed in the physical space before two modal analysis techniques which are Dynamic Mode Decomposition (DMD) and Proper Orthogonal Decomposition (POD) were employed to the dataset. The DMD and POD analyses were able to determine the dynamics of the wake behind the rotating blade, i.e., the dominant mode of the helical flow structures and the corresponding frequency spectrum. As a result, the identification of the coherent structures and dynamics of the helical vortices behind the rotating blade in the stratified atmosphere is presented with the discussion on how the stratified temperature field influenced the deformation and breaking down of the helical wake structure. Various modes showed different characteristics of the flow fields. Focuses on the energy of flow; the POD technique could capture large-scale vortex structures and their organized behaviour, whereas the DMD method focuses on the frequency, and it represented the perturbation dynamics. Overall, the helical wake structure behind a rotating blade was proved to be remarkably influenced by the variation of the thermal stratification in terms of their characteristics, dynamics, and stability. Among all, the most affected is the one from the weakly stable stratified atmosphere

Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

HIGH-THROUGHPUT FLUORESCENCE MICROSCOPY FOR AUTOMATED CLINICAL APPLICATIONS

Fluorescence in situ hybridization (FISH) is a powerful tool for visualizing and detecting genetic abnormalities. Manual scoring FISH analysis is a tedious and labor-and-time-consuming task. Automated image acquisition and analysis provide an opportunity to overcome the difficulties. However, conventional fluorescence microscopes, the mostly used instrument for FISH imaging, have deficiencies. A multi-spectral image modality must be employed in order to visualize fluorescently dyed FISH probes for analysis, and the existing technologies are either two expensive, too slow, or both. Aiming at upgrading the current employed cytogenetic instrumentation, we developed a new imaging technique capable of simultaneously imaging multiple color spectra. Using the principle, we implemented a prototype system and conduct various characterization experiments. Experiment results (<1% peripheral geometric distortion, consistent signal response linearity, and ~2000 lp/mm spatial resolution) show no significant compromise in terms of optical performance. A detector alignment scheme was developed and performed to minimize registration error. The system has significantly faster acquisition speed than conventional fluorescence microscopes albeit the extra cost is quite insignificant

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem