This thesis presents the development and evaluation of IT-based methods and systems for supporting assessment of symptoms and enabling remote monitoring of Parkinson‟s disease (PD) patients. PD is a common neurological disorder associated with impaired body movements. Its clinical management regarding treatment outcomes and follow-up of patients is complex. In order to reveal the full extent of a patient‟s condition, there is a need for repeated and time-stamped assessments related to both patient‟s perception towards common symptoms and motor function. In this thesis, data from a mobile device test battery, collected during a three year clinical study, was used for the development and evaluation of methods. The data was gathered from a series of tests, consisting of selfassessments and motor tests (tapping and spiral drawing). These tests were carried out repeatedly in a telemedicine setting during week-long test periods. One objective was to develop a computer method that would process tracedspiral drawings and generate a score representing PD-related drawing impairments. The data processing part consisted of using the discrete wavelet transform and principal component analysis. When this computer method was evaluated against human clinical ratings, the results showed that it could perform quantitative assessments of drawing impairment in spirals comparatively well. As a part of this objective, a review of systems and methods for detecting the handwriting and drawing impairment using touch screens was performed. The review showed that measures concerning forces, accelerations, and radial displacements were the most important ones in detecting fine motor movement anomalies. Another objective of this thesis work was to design and evaluate an information system for delivering assessment support information to the treating clinical staff for monitoring PD symptoms in their patients. The system consisted of a patient node for data collection based on the mobile device test battery, a service node for data storage and processing, and a web application for data presentation. A system module was designed for compiling the test battery time series into summary scores on a test period level. The web application allowed adequate graphic feedback of the summary scores to the treating clinical staff. The evaluation results for this integrated system indicate that it can be used as a tool for frequent PD symptom assessments in home environments.

Localized heating leads to generation of thermal Hotspots that affect performance and reliability of an Integrated Circuit(IC). Functional workloads determine the locations and temperature of hotspots on a die. Programs are classified into phases based on program execution profile. During a phase, spatial power dissipation pattern of an application remains unchanged. In this thesis, we present a systematic approach for developing a synthetic workload from a functional workload to create worst case temperature of a target hotspot in 2D and 3D IC. These synthetic workload are designed to create thermal stress patterns, which would help in characterizing the thermal characteristics of micro architecture to worst case temperature transient which is an important problem in Industry. Our approach is based on the observation that, worst case temperature at a particular location in 2 D IC is determined not only by the current activity in that region, but also by the past activities in the surrounding regions. Therefore, if the surrounding areas were “pre-heated” with a different workload, then the target region may become hotter due to slower rate of lateral heat dissipation Similarly in case of 3D IC, the workload applied to each of the dies in 3D IC keeps on changing continuously, thus the hotspot could be found in any of the stacked layers. Thus the creation of localized hotspot at a particular location in a stacked 3D IC layer depends not only on the present activity at that location but also on the previous activity in the surrounding region and also on the activity of layers below it. Accordingly, (i) we develop a wavelet-based canonical spatio-temporal heat dissipation model for program traces, and use (ii) a novel Integer Linear Programming (ILP) formulation to rearrange program phases to generate target worst case hotspot temperature in 2D and 3D IC. We apply this formulation to target another well-known problem of (iii) maximizing temperature between a pair of co-ordinates in an IC. Experimental results show that by taking the spatio-temporal effect into account and with dynamic phase change behavior, we could raise temperature of a hotspot higher than what is possible otherwise. ICs are often tested at worst-case system operating conditions to assure that, all ICs shipped will function properly in the end system. Thus hotspot temperature maximization is an important in design verification and testing.

Arbetet beskriver möjligheten att kunna utföra prediktivt underhåll med hjälp av information frånCOMTRADE störningsfiler. En mjukvarualgoritm som hämtar tidsförlopp för händelser som uppstårvid störningar och kan ge indikationer på ifall reläer eller strömbrytare faller utanför optimalaarbetsförhållanden har utvecklats. På detta sett kan underhållsarbete utföras vid behov vilket kanspara tid och pengar för att inte göras efter schemalagda tider.Tillsammans med uppdragsgivare från Megger och Ellevio utfördes arbetet med syfte att utvecklaen programvara som stöd för prediktivt underhåll. Programvaran kunde ta ut tidshändelser förregistrerade störningar och kunde hantera flertalet scenarion för vilka typer av information somfunnits tillgängligt ur filen. En användare har tillgång till en automatisk algoritm som gör analysav filen och ett manuellt verktyg där vidare analyser kan göras ifall utfallet från algoritmen inte ärgodtyckligt.Trender över tid är något som finns möjlighet att få ut ur algoritmen, men det kräver en större mängddataset än som varit tillgänglig under arbetet. / The work describes the opportunities to perform preventive maintenance with the help of informationfrom COMTRADE disturbance files. A software algorithm was developed which collects disturbancedata and gives indications if equipment are not working within optimal conditions.Using thisinformation preventive maintenance can be performed based on need instead of scheduling to savetime and money.Together with supervisors from involved companies a software was developed to be used as a supportfor preventive maintenance. The software can extract disturbance times and handle multiple scenariosbased on information collected from disturbance files. A user has access to a algoritm that createsautomatical analysis of the COMTRADE file and a manual tool for extensive analysis when the algoritmdoes not give proper results.Trends over time can be analysed with the algortim, this do require a larger amount of data than whatwas available during the work.

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Mass Spectrometry, especially matrix assisted laser desorption/ionization (MALDI) time of flight (TOF), is emerging as a leading technique in the proteomics revolution. It can be used to find disease-related protein patterns in mixtures of proteins derived from easily obtained samples. In this paper, a novel algorithm for MALDI-TOF MS data processing is developed. The software design includes the application of splines for data smoothing and baseline correction, wavelets for adaptive denoising, multivariable statistics techniques such as clustering analysis, and signal processing techniques to evaluate the complicated biological signals. A MatLab implementation shows the processing steps consecutively including step-interval unification, adaptive wavelet denoising, baseline correction, normalization, and peak detection and alignment for biomarker discovery.

Approximately 20 million people in the United States suffer from irreversible nerve damage and would benefit from a neuroprosthetic device modulated by a Brain-Computer Interface (BCI). These devices restore independence by replacing peripheral nervous system functions such as peripheral control. Although there are currently devices under investigation, contemporary methods fail to offer adaptability and proper signal recognition for output devices. Human anatomical differences prevent the use of a fixed model system from providing consistent classification performance among various subjects. Furthermore, notoriously noisy signals such as Electroencephalography (EEG) require complex measures for signal detection. Therefore, there remains a tremendous need to explore and improve new algorithms. This report investigates a signal-processing model that is better suited for BCI applications because it incorporates machine learning and fuzzy logic. Whereas traditional machine learning techniques utilize precise functions to map the input into the feature space, fuzzy-neuro system apply imprecise membership functions to account for uncertainty and can be updated via supervised learning. Thus, this method is better equipped to tolerate uncertainty and improve performance over time. Moreover, a variation of this algorithm used in this study has a higher convergence speed. The proposed two-stage signal-processing model consists of feature extraction and feature translation, with an emphasis on the latter. The feature extraction phase includes Blind Source Separation (BSS) and the Discrete Wavelet Transform (DWT), and the feature translation stage includes the Takagi-Sugeno-Kang Fuzzy-Neural Network (TSKFNN). Performance of the proposed model corresponds to an average classification accuracy of 79.4 % for 40 subjects, which is higher than the standard literature values, 75%, making this a superior model.

In the field of structural engineering it is of vital importance that the condition of an ageing structure is monitored to detect damages that could possibly lead to failure of the structure. Over the past few years various methods for monitoring the condition of structures have been proposed. With respect to civil and mechanical structures several methods make use of modal parameters such as, natural frequency, damping ratio and mode shapes. In the present work four methods for modal parameter estimation and two methods for have been evaluated for their application to multi degree of freedom structures. The methods evaluated for modal parameter estimation are: Wavelet transform, Hilbert-Huang transform, parametric system identification and peak picking. Through various numerical simulations the effectiveness of these methods is studied. It is found that the simple peak-picking method performs the best and is able to identify modal parameters most accurately in all the simulation cases that were considered in this study. The identified modal parameters are then used for locating the damage. Herein the flexibility and the rotational flexibility approaches are evaluated for damage detection. The approach based on the rotational flexibility is found to be more effective. / Master of Science

Yes / The quantification of corneal endothelial cell (CEC) morphology using manual and semi-automatic software enables an objective assessment of corneal endothelial pathology. However, the procedure is tedious, subjective, and not widely applied in clinical practice. We have developed the CellsDeepNet system to automatically segment and analyse the CEC morphology. The CellsDeepNet system uses Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast of the CEC images and reduce the effects of non-uniform image illumination, 2D Double-Density Dual-Tree Complex Wavelet Transform (2DDD-TCWT) to reduce noise, Butterworth Bandpass filter to enhance the CEC edges, and moving average filter to adjust for brightness level. An improved version of U-Net was used to detect the boundaries of the CECs, regardless of the CEC size. CEC morphology was measured as mean cell density (MCD, cell/mm2), mean cell area (MCA, µm2), mean cell perimeter (MCP, µm), polymegathism (coefficient of CEC size variation), and pleomorphism (percentage of hexagonality coefficient). The CellsDeepNet system correlated highly significantly with the manual estimations for MCD (r = 0.94), MCA (r = 0.99), MCP (r = 0.99), polymegathism (r = 0.92), and pleomorphism (r = 0.86), with p

This thesis deals with copyright protection and content authentication for still images. New blind transform domain block based algorithms using one-level and two-level Discrete Wavelet Transform (DWT) were developed for copyright protection. The mobile number with international code is used as the watermarking data. The robust algorithms used the Low-Low frequency coefficients of the DWT to embed the watermarking information. The watermarking information is embedded in the green channel of the RGB colour image and Y channel of the YCbCr images. The watermarking information is scrambled by using a secret key to increase the security of the algorithms. Due to the small size of the watermarking information comparing to the host image size, the embedding process is repeated several times which resulted in increasing the robustness of the algorithms. Shuffling process is implemented during the multi embedding process in order to avoid spatial correlation between the host image and the watermarking information. The effects of using one-level and two-level of DWT on the robustness and image quality have been studied. The Peak Signal to Noise Ratio (PSNR), the Structural Similarity Index Measure (SSIM) and Normalized Correlation Coefficient (NCC) are used to evaluate the fidelity of the images. Several grey and still colour images are used to test the new robust algorithms. The new algorithms offered better results in the robustness against different attacks such as JPEG compression, scaling, salt and pepper noise, Gaussian noise, filters and other image processing compared to DCT based algorithms. The authenticity of the images were assessed by using a fragile watermarking algorithm by using hash function (MD5) as watermarking information embedded in the spatial domain. The new algorithm showed high sensitivity against any tampering on the watermarked images. The combined fragile and robust watermarking caused minimal distortion to the images. The combined scheme achieved both the copyright protection and content authentication.

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