Marine Vehicle Sensor Network Architecture and Protocol Designs for Ocean Observation

The micro-scale and meso-scale ocean dynamic processes which are nonlinear and have large variability, have a significant impact on the fisheries, natural resources, and marine climatology. A rapid, refined and sophisticated observation system is therefore needed in marine scientific research. The maneuverability and controllability of mobile sensor platforms make them a preferred choice to establish ocean observing networks, compared to the static sensor observing platform. In this study, marine vehicles are utilized as the nodes of mobile sensor networks for coverage sampling of a regional ocean area and ocean feature tracking. A synoptic analysis about marine vehicle dynamic control, multi vehicles mission assignment and path planning methods, and ocean feature tracking and observing techniques is given. Combined with the observation plan in the South China Sea, we provide an overview of the mobile sensor networks established with marine vehicles, and the corresponding simulation results

Differential Zernike filter for phasing of segmented mirror and image processing

The major objective of this thesis is to study the differential Zernike filter and its applications in phasing segmented mirror and image processing. In terms of phasing, we provide both theoretical analysis and simulation for a differential Zernike filter based phasing technique, and find that the differential Zernike filter perform consistently better than its counterpart, traditional Zernike filter. We also combine the differential Zernike filter with a feedback loop, to represent a gradient-flow optimization dynamic system. This system is shown to be capable of separating (static) misalignment errors of segmented mirrors from (dynamical) atmospheric turbulence, and therefore compress the effects of atmospheric turbulence. Except for segmented mirror phasing, we also apply the Zernike feedback system in image processing. For the same system dynamics as well as in segment phasing, the Zernike filter feedback system is capable of compress the static noisy background, and makes the single particle tracking algorithm even working in case of very low signal-to-noise ratio. Finally, we apply an efficient multiple-particle tracking algorithm on a living cell image sequence. This algorithm is shown to be able to deal with higher particle density, while the single particle tracking methods are not working under this condition

Computer vision and optimization methods applied to the measurements of in-plane deformations

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Development and application of molecular and computational tools to image copper in cells

Copper is a trace element which is essential for many biological processes. A deficiency or excess of copper(I) ions, which is its main oxidation state of copper in cellular environment, is increasingly linked to the development of neurodegenerative diseases such as Parkinson’s and Alzheimer’s disease (PD and AD). The regulatory mechanisms for copper(I) are under active investigation and lysosomes which are best known as cellular “incinerators” have been found to play an important role in the trafficking of copper inside the cell. Therefore, it is important to develop reliable experimental methods to detect, monitor and visualise this metal in cells and to develop tools that allow to improve the data quality of microscopy recordings. This would enable the detailed exploration of cellular processes related to copper trafficking through lysosomes. The research presented in this thesis aimed to develop chemical and computational tools that can help to investigate concentration changes of copper(I) in cells (particularly in lysosomes), and it presents a preliminary case study that uses the here developed microscopy image quality enhancement tools to investigate lysosomal mobility changes upon treatment of cells with different PD or AD drugs. Chapter I first reports the synthesis of a previously reported copper(I) probe (CS3). The photophysical properties of this probe and functionality on different cell lines was tested and it was found that this copper(I) sensor predominantly localized in lipid droplets and that its photostability and quantum yield were insufficient to be applied for long term investigations of cellular copper trafficking. Therefore, based on the insights of this probe a new copper(I) selective fluorescent probe (FLCS1) was designed, synthesized, and characterized which showed superior photophysical properties (photostability, quantum yield) over CS3. The probe showed selectivity for copper(I) over other physiological relevant metals and showed strong colocalization in lysosomes in SH-SY5Y cells. This probe was then used to study and monitor lysosomal copper(I) levels via fluorescence lifetime imaging microscopy (FLIM); to the best of my knowledge this is the first copper(I) probe based on emission lifetime. Chapter II explores different computational deep learning approaches for improving the quality of recorded microscopy images. In total two existing networks were tested (fNET, CARE) and four new networks were implemented, tested, and benchmarked for their capabilities of improving the signal-to-noise ratio, upscaling the image size (GMFN, SRFBN-S, Zooming SlowMo) and interpolating image sequences (DAIN, Zooming SlowMo) in z- and t-dimension of multidimensional simulated and real-world datasets. The best performing networks of each category were then tested in combination by sequentially applying them on a low signal-to-noise ratio, low resolution, and low frame-rate image sequence. This image enhancement workstream for investigating lysosomal mobility was established. Additionally, the new frame interpolation networks were implemented in user-friendly Google Colab notebooks and were made publicly available to the scientific community on the ZeroCostDL4Mic platform. Chapter III provides a preliminary case study where the newly developed fluorescent copper(I) probe in combination with the computational enhancement algorithms was used to investigate the effects of five potential Parkinson’s disease drugs (rapamycin, digoxin, curcumin, trehalose, bafilomycin A1) on the mobility of lysosomes in live cells.Open Acces

STRUCTURE DETERMINATION OF HETEROGENEOUS BIOLOGICAL SPECIMENS IN CROWDED ENVIRONMENTS

The central dogma of molecular biology describes a strictly linear flow of genetic information stored in DNA transferred through RNA and translated into protein products. In the “post-genomic era” however, it is evident that abundant information flows from protein to protein and even protein back to DNA. The field of Structural Biology seeks to understand how the spatial and temporal organization of that information is stored and transmitted via the three-dimensional structure and dynamics of biological macromolecules. X-ray crystallography, nuclear magnetic resonance, and single particle cryo-electron microscopy (cryo-EM) are the primary techniques available to the structural biologist to deduce structure and dynamics at or near atomic resolutions. These tools are generally limited to the study of stable molecules that can be purified biochemically. Other approaches, like super-resolution light microscopy and cryo-electron tomography (cryo-ET), are amenable to the study of more labile macromolecular complexes or those found in situ; however, they are limited to resolutions of tens of nanometers. Improving the resolving capability of cryo-ET with sub-tomogram averaging to routinely reach beyond 10 Å is the primary goal of this work. My unique contribution to the field of structural biology is a suite of software tools called emClarity (enhanced macromolecular classification and alignment for high-resolution in situ tomography) which allows scientists with minimal computational background to probe the structural states of conformationally variable molecules present in complex and crowded environments

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Modeling and Simulation in Engineering

The general aim of this book is to present selected chapters of the following types: chapters with more focus on modeling with some necessary simulation details and chapters with less focus on modeling but with more simulation details. This book contains eleven chapters divided into two sections: Modeling in Continuum Mechanics and Modeling in Electronics and Engineering. We hope our book entitled "Modeling and Simulation in Engineering - Selected Problems" will serve as a useful reference to students, scientists, and engineers

Measurements, Models, Systems and Design

531 s.