This thesis describes the construction and implementation of a microprocessor controlled recursive adaptive filter applied as a noise canceller. It describes the concept of the adaptive noise canceller, a method of estimating the received signal corrupted with additive interference (noise). This canceller has two inputs, the primary input containing the corrupted signal and the reference input consisting of the additive noise correlated in some unknown way to the primary noise. The reference input is filtered and subtracted from the primary input without degrading the desired components of the signal. This filtering process is adaptive and based on Widrow-Hoff Least-Mean-Square algorithm. Adaptive filters are programmable and have the capability to adjust their own parameters in situations where minimum piori knowledge is available about the inputs. For recursive filters, these parameters include feed-forward (non-recursive) as well as feedback (recursive) coefficients. A new design and implementation of the adaptive filter is suggested which uses a high speed 68000 microprocessor to accomplish the coefficients updating operation. Many practical problems arising in the hardware implementation are investigated. Simulation results illustrate the ability of the adaptive noise canceller to have an acceptable performance when the coefficients updating operation is carried out once every N sampling periods. Both simulation and hardware experimental results are in agreement.

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In the first part of this dissertation we use an Intermediate Coupled Model to develop a quantitative test to validate the null hypothesis that low-frequency varia- tion of ENSO predictability may be caused by stochastic processes. Three "perfect model scenario" prediction experiments are carried out, where the model is forced ei- ther solely by stochastic forcing or additionally by decadal-varying backgrounds with different amplitudes. These experiments indicate that one can not simply reject the null hypothesis unless the decadal-varying backgrounds are unrealistically strong. The second part of this dissertation investigates the extent to which internal atmospheric variability (IAV) can influence ENSO variation, and examines the un- derlying physical mechanisms linking IAV to ENSO variability with the aid of a newly developed coupled model consisting of an atmospheric general circulation model and a Zebiak-Cane type of reduced gravity ocean model. A novel noise filter algorithm is developed to suppress IAV in the coupled model. A long control coupled simulation, where the filter is not employed, demonstrates that the coupled model captures many statistical properties of the observed ENSO behavior. It further shows that the development of El Ni~no is linked to a boreal spring phenomenon referred to as the Pacific Meridional Model (MM). The MM, character- ized by an anomalous north-south SST gradient and anomalous surface circulation in the northeasterly trade regime with maximum variance in boreal spring, is inherent to thermodynamic ocean-atmosphere coupling in the Intertropical Convergence Zone latitude. The Northern Pacific Oscillation provides one source of external forcing to excite it. This result supports the hypothesis that the MM works as a conduit for extratropical atmospheric influence on ENSO. A set of coupled simulations, where the filter is used to suppress IAV, indicate that reducing IAV in both wind stress and heat flux substantially weakens ENSO variance. Furthermore, the resultant ENSO cycle becomes more regular and no longer shows strong seasonal phase locking. The seasonal phase locking of ENSO is strongly tied to the IAV in surface heat flux. The ENSO cycle is strongly tied to IAV in surface wind stress.

The Gamma Knife is a non-invasive stereotactic radiosurgery system used for treatments of deep targets in the brain. Accurate patient positioning is needed for precise radiation delivery to the target. The two latest versions of the Gamma Knife allow fractionated treatment by co-registering Cone-beam computed tomography (CBCT) images of the patient's position in the Gamma Knife with a diagnostic magnetic resonance (MR) image used for treatment planning. However, CBCT images often suffer from artifacts that degrade image quality, which may result in less accurate co-registration. This thesis project investigates the potential of a new reconstruction framework developed by Elekta, which incorporates scattering correction and noise filters, for the reconstruction of Gamma Knife CBCT images. The performance of the new reconstruction framework, along with its noise filter and scatter correction, is quantified using image quality metrics of phantoms, including contrast, uniformity, spatial resolution, and CT-number accuracy. Additionally, brain CBCT images of five patients are co-registered with their diagnostic MR images, and the mean target registration error is measured. The results indicate that the new reconstruction framework, without using scatter correction and noise filtering, performs equally well as the current framework in reconstructing Gamma Knife CBCT images, as it achieved similar image quality and co-registration accuracy. However, when the scatter correction was used, there were improvements in image uniformity and CT-number accuracy without compromising spatial resolution. Additionally, the introduction of a noise filter resulted in an improved contrast-to-noise ratio and low contrast visibility with minimal compromise of spatial resolution. Despite these image quality enhancements, there were no consistent improvements in co-registration accuracy, indicating that the co-registration is not sensitive to scatter or noise artefacts. / Gammakniven är en medicinteknisk apparat som används för icke-invasiv stereotaktisk strålkirurgi vid behandling av djupa mål i hjärnan. För att uppnå precision i strålbehandlingen krävs noggrann patientpositionering. De två senaste versionerna av Gammakniven tillåter fraktionerad behandling genom att co-registrera cone-beam computed tomography (CBCT)-bilder av patientens position i Gammakniven med en diagnostisk magnetresonans (MR)-bild som används för behandlingsplanering. Tyvärr lider CBCT-bilder ofta av artefakter som kan försämra bildkvaliteten och därmed minska precisionen i co-registreringen. Detta examensarbete undersöker ett nytt rekonstruktionsramverk som utvecklats av Elekta. Det nya rekonstruktionsramverket och dess tillhörande brusfilter och spridningskorrigering utvärderas för rekonstruktion av Gammaknivens CBCT bilder med hjälp av bildkvalitetsmått för fantomer, såsom kontrast, uniformitet, spatial upplösning och noggrannhet i CT-nummer. Dessutom co-registreras CBCT-bilder från fem patienter med deras diagnostiska MR-bilder, och det genomsnittliga registreringsfelet mäts. Resultaten visar att det nya rekonstruktionsramverket, utan användning av spridningskorrigering och brusfiltrering, presterar lika bra som det nuvarande ramverket för rekonstruktion av CBCT-bilder från Gammakniven. Båda ramverken ger liknande bildkvalitet och noggrannhet i co-registreringen av bilderna. Vid användning av spridningskorrigering observerades förbättringar i uniformiteten och noggrannheten i CT-nummer utan att den spatiala upplösningen försämrades. Införandet av brusfilter resulterade i ett förbättrat kontrast-brus-förhållande och synlighet av svaga kontrastskillnader med endast lite avkall på den spatiala upplösningen. Trots dessa förbättringar i bildkvaliteten observerades ingen konsekvent förbättring av noggrannheten i co-registreringen av bilderna, vilket tyder på att co-registreringen inte påverkas av spridnings- eller brusartefakter i stor utsträckning.