A Case of Identity for NLMS and Kaczmarz Algorithms

In this paper, we will demonstrate, by employing basic linear algebra, that two seemingly disconnected algorithms, the Kaczmarz algorithm, familiar to mathematics community as an iterative solver of linear systems, and the Normalized LMS (NLMS) algorithm, known to the signal processing community as a self-learning adaptive filter, are identical

Development of advanced algorithms to detect, characterize and forecast solar activities

Study of the solar activity is an important part of space weather research. It is facing serious challenges because of large data volume, which requires application of state-of-the-art machine learning and computer vision techniques. This dissertation targets at two essential aspects in space weather research: automatic feature detection and forecasting of eruptive events. Feature detection includes solar filament detection and solar fibril tracing. A solar filament consists of a mass of gas suspended over the chromosphere by magnetic fields and seen as a dark, ribbon-shaped feature on the bright solar disk in Hα (Hydrogen-alpha) full-disk solar images. In this dissertation, an automatic solar filament detection and characterization method is presented. The investigation illustrates that the statistical distribution of the Laplacian filter responses of a solar disk contains a special signature which can be used to identify the best threshold value for solar filament segmentation. Experimental results show that this property holds across different solar images obtained by different solar observatories. Evaluation of the proposed method shows that the accuracy rate for filament detection is more than 95% as measured by filament number and more than 99% as measured by filament area, which indicates that only a small fraction of tiny filaments are missing from the detection results. Comparisons indicate that the proposed method outperforms a previous method. Based on the proposed filament segmentation and characterization method, a filament tracking method is put forward, which is capable of tracking filaments throughout their disk passage. With filament tracking, the variation of filaments can be easily recorded. Solar fibrils are tiny dark threads of masses in Hα images. It is generally believed that fibrils are magnetic field-aligned, primarily due to the reason that the high electrical conductivity of the solar atmosphere freezes the ionized mass in magnetic field lines and prevents them from diffusing across the lines. In this dissertation, a method that automatically segments and models fibrils from Hα images is proposed. Experimental results show that the proposed method is very successful to derive traces of most fibrils. This is critical for determining the non-potentiality of active regions. Solar flares are generated by the sudden and intense release of energy stored in solar magnetic fields, which can have a significant impact on the near earth space environment (so called space weather). In this dissertation, an automated solar flare forecasting method is presented. The proposed method utilizes logistic regression and SVM (support vector machine) to forecast the occurrences of solar flares based on photospheric magnetic features. Logistic regression is used to derive the probabilities of solar flares occurrence, which are then fed to SVM for determining whether a flare will occur. Comparisons with existing methods show that there is an improvement in the accuracy of X-class solar flare forecasting. It is also found that when sunspot-group classification is combined with photospheric magnetic parameters, the performance of flare forecasting can be further lifted

A combination of motion-compensated cone-beam computed tomography iame reconstruction and electrical impedance tomography

Cone-beam computed tomography (CBCT) is an imaging technique used in conjunction with radiation therapy. CBCT is used to verify the position of tumours just prior to radiation treatment session. The accuracy of the radiation treatment of thoracic and upper abdominal tumours is heavily affected by respiratory movement. Blurring artefacts, due to the movement during a CBCT scanning, cause misregistration between the CBCT image and the planning image. There has been growing interest in the use of motion-compensated CBCT for correcting the breathing-induced artefacts. A wide range of iterative reconstruction methods have been developed for CBCT imaging. The direct motion compensation technique has been applied to algebraic reconstruction technique (ART), simultaneous ART (SART), ordered-subset SART (OS-SART) and conjugate gradient least squares (CGLS). In this thesis a dual modality imaging of electrical impedance tomography (EIT) and CBCT is proposed for the first time. This novel dual modality imaging uses the advantages of high temporal resolution of EIT imaging and high spatial resolution of the CBCT method. The main objective of this study is to combine CBCT with EIT imaging system for motion-compensated CBCT using experimental and computational phantoms. The EIT images were used for extracting motion for a motion-compensated CBCT imaging system. A simple motion extraction technique is used for extracting motion data from the low spatial resolution EIT images. This motion data is suitable for input into the direct motion-compensated CBCT. The performance of iterative algorithms for motion compensation was also studied. The dual modality CBCT-EIT is verified using experimental EIT system and computational CBCT phantom data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development of Analytical Techniques to Monitor Bone Penetration in 3D via Computer Tomography Analysis.

PhDThere has been much work into getting clear and precise images of bone growing within different osteoconductive and osteoinductive scaffolds for the aim of investigating and quantifying the effect the different grafts have on the bone that forms within the graft. Before the bone structure and volume can be quantified, the images produced need to segmented into their different regions. Using images produced from x-ray computed tomography, the samples can be segmented based on their densities. As the voxels have distinct size, if just the density is used to segment out the regions, there will be some miss-identification at the edges of the regions (ghosting). To overcome this problem of misidentification, automated segmentation methods were developed which take not only the intensity of the voxels in the images (which are related to the density) into account for the segmentation but also the local properties. With correct segmentation the volume and surface area are better represented and methods for structure measurement can and were developed. These methods allow for not only the structure of the bone and implants to be quantified, but for the change in structures between the different implants to be compared. This allows for the different structures caused by the different graft materials to be seen and compared. This comparison when used on its own or with other methods such as histology not only allows for the different structures to be identified but all the change in structures due to factors such as remodelling to be identified.EPSRC and ApaTech for their financial support and grant