Fourier self-deconvolution in coherent anti-Stokes Raman scattering spectrum analysis

Coherent anti-Stokes Raman scattering (CARS) microscopy is rapidly developing into a unique microscopic tool in biophysics, biology and the material sciences. The nonlinear nature of CARS spectroscopy complicates the analysis of the received spectra. There were developed mathematical methods for signal processing and for calculations spectra. Fourier self-deconvolution is a special high pass FFT filter which synthetically narrows the effective trace bandwidth features. As Fourier self-deconvolution can effectively reduce the noise, which may be at a higher spatial frequency than the peaks, without losing peak resolution. The idea of the work is to experiment the possibility of using wavelet decomposition in spectroscopic for background and noise removal, and Fourier transformation for linenarrowing.

RTOS Framework for Real-Time Control System

This thesis is done as a complementary part for the active magnet bearing (AMB) control software development project in Lappeenranta University of Technology. The main focus of the thesis is to examine an idea of a real-time operating system (RTOS) framework that operates in a dedicated digital signal processor (DSP) environment. General use real-time operating systems do not necessarily provide sufficient platform for periodic control algorithm utilisation. In addition, application program interfaces found in real-time operating systems are commonly non-existent or provided as chip-support libraries, thus hindering platform independent software development. Hence, two divergent real-time operating systems and additional periodic extension software with the framework design are examined to find solutions for the research problems. The research is discharged by; tracing the selected real-time operating system, formulating requirements for the system, and designing the real-time operating system framework (OSFW). The OSFW is formed by programming the framework and conjoining the outcome with the RTOS and the periodic extension. The system is tested and functionality of the software is evaluated in theoretical context of the Rate Monotonic Scheduling (RMS) theory. The performance of the OSFW and substance of the approach are discussed in contrast to the research theme. The findings of the thesis demonstrates that the forged real-time operating system framework is a viable groundwork solution for periodic control applications.

Color ad spectral image assessment using novel quality and fidelity techniques

The ongoing development of the digital media has brought a new set of challenges with it. As images containing more than three wavelength bands, often called spectral images, are becoming a more integral part of everyday life, problems in the quality of the RGB reproduction from the spectral images have turned into an important area of research. The notion of image quality is often thought to comprise two distinctive areas – image quality itself and image fidelity, both dealing with similar questions, image quality being the degree of excellence of the image, and image fidelity the measure of the match of the image under study to the original. In this thesis, both image fidelity and image quality are considered, with an emphasis on the influence of color and spectral image features on both. There are very few works dedicated to the quality and fidelity of spectral images. Several novel image fidelity measures were developed in this study, which include kernel similarity measures and 3D-SSIM (structural similarity index). The kernel measures incorporate the polynomial, Gaussian radial basis function (RBF) and sigmoid kernels. The 3D-SSIM is an extension of a traditional gray-scale SSIM measure developed to incorporate spectral data. The novel image quality model presented in this study is based on the assumption that the statistical parameters of the spectra of an image influence the overall appearance. The spectral image quality model comprises three parameters of quality: colorfulness, vividness and naturalness. The quality prediction is done by modeling the preference function expressed in JNDs (just noticeable difference). Both image fidelity measures and the image quality model have proven to be effective in the respective experiments.

D-luokan audiovahvistimen modulointimenetelmien vertailu ja valinta

Tässä työssä esitellään kaksi D-luokan audiovahvistimissa käytettyä modulointimenetelmää ja vertaillaan niiden välisiä ominaisuuksia. Esitellyt menetelmät ovat pulssinleveysmodulaatio sekä sigma-deltamodulaatio. Lisäksi työssä esitellään signaalin käsittelyssä muodostuvan kohinan ja särön syntyä ja menetelmiä niiden välttämiseksi. Menetelmien ominaisuuksien vertailuun muodostetaan simulointimallit Simulink-ohjelmalla. Simulointitulosten perusteella valitaan käytettävä modulointimenetelmä audiovahvistinsovellukseen huomioon ottaen asetetut vaatimukset ja tavoitteet. Lopuksi valitun modulointimenetelmän suunnittelun pääkohtiin kiinnitetään huomiota.

Computational visual quality of digitally printed images

The problem of understanding how humans perceive the quality of a reproduced image is of interest to researchers of many fields related to vision science and engineering: optics and material physics, image processing (compression and transfer), printing and media technology, and psychology. A measure for visual quality cannot be defined without ambiguity because it is ultimately the subjective opinion of an “end-user” observing the product. The purpose of this thesis is to devise computational methods to estimate the overall visual quality of prints, i.e. a numerical value that combines all the relevant attributes of the perceived image quality. The problem is limited to consider the perceived quality of printed photographs from the viewpoint of a consumer, and moreover, the study focuses only on digital printing methods, such as inkjet and electrophotography. The main contributions of this thesis are two novel methods to estimate the overall visual quality of prints. In the first method, the quality is computed as a visible difference between the reproduced image and the original digital (reference) image, which is assumed to have an ideal quality. The second method utilises instrumental print quality measures, such as colour densities, measured from printed technical test fields, and connects the instrumental measures to the overall quality via subjective attributes, i.e. attributes that directly contribute to the perceived quality, using a Bayesian network. Both approaches were evaluated and verified with real data, and shown to predict well the subjective evaluation results.

Kestomagneettitahtikoneen emulointi digitaalisella signaaliprosessorilla

Sähkökäytön suunnittelussa säätöä voidaan testata useassa tapauksessa reaaliaikasimulaattorilla todellisen laitteiston sijaan. Monet reaaliaikasimulaatioiden perustana käytetyt algoritmit soveltuvat täysinohjatulle invertterisillalle. Eräissä sovelluksissa halutaan kuitenkin käyttää puoliksiohjattua siltaa. Puoliksiohjattulla sillalla mallin kausaalisuus voi kääntyä, mitä perinteiset reaaliaikasimulaattorit eivät pysty simuloimaan Tässä työssä oli tavoitteena kehittää reaaliaikasimulaattori puoliksiohjatulle kestomagneettitahtikonekäytölle. Emulaattoriin mallinnettiin todellisen käytön kestomagneettitahtikone ja invertterisilta. Simulaattori toteutettiin digitaaliselle signaaliprosessorille (DSP) ja mittauksiin liittyvät oheislaitteet mallinnettiin FPGA-piirille. Emulaattoriin liitettiin erillinen säätäjä, jota käytettiin myös todellisen sähkökäytön säätämiseen. Emulaattorilla ja todellisella käytöllä tehtyjä mittauksia verrattiin ja emuloimalla saadut tulokset vastasivat melko hyvin todellisesta käytöstä mitattuja.

Akustisen emission anturin signaalin esikäsittelypiirin selvittäminen ja prototyypin toteuttaminen

Tämän työn tavoitteena oli selvittää ja toteuttaa esikäsittelypiirin prototyyppi akustisen emission anturin signaalille. Toteutettu esikäsittelypiiri toimii yksipuoleisella käyttöjännitteellä. Työssä käydään läpi esikäsittelypiirin suunnitteluun liittyvät vaiheet laskelmien ja simulaatioiden muodossa. Lisäksi työssä esitetään mittaustulokset esikäsittelypiirin toiminnasta.

Chaotic Behaviour of Active Magnetic Bearing System by Time Series Analysis

Chaotic behaviour is one of the hardest problems that can happen in nonlinear dynamical systems with severe nonlinearities. It makes the system's responses unpredictable. It makes the system's responses to behave similar to noise. In some applications it should be avoided. One of the approaches to detect the chaotic behaviour is nding the Lyapunov exponent through examining the dynamical equation of the system. It needs a model of the system. The goal of this study is the diagnosis of chaotic behaviour by just exploring the data (signal) without using any dynamical model of the system. In this work two methods are tested on the time series data collected from AMB (Active Magnetic Bearing) system sensors. The rst method is used to nd the largest Lyapunov exponent by Rosenstein method. The second method is a 0-1 test for identifying chaotic behaviour. These two methods are used to detect if the data is chaotic. By using Rosenstein method it is needed to nd the minimum embedding dimension. To nd the minimum embedding dimension Cao method is used. Cao method does not give just the minimum embedding dimension, it also gives the order of the nonlinear dynamical equation of the system and also it shows how the system's signals are corrupted with noise. At the end of this research a test called runs test is introduced to show that the data is not excessively noisy.

Variable speed drive in fan system monitoring

Fan systems are responsible for approximately 10% of the electricity consumption in industrial and municipal sectors, and it has been found that there is energy-saving potential in these systems. To this end, variable speed drives (VSDs) are used to enhance the efficiency of fan systems. Usually, fan system operation is optimized based on measurements of the system, but there are seldom readily installed meters in the system that can be used for the purpose. Thus, sensorless methods are needed for the optimization of fan system operation. In this thesis, methods for the fan operating point estimation with a variable speed drive are studied and discussed. These methods can be used for the energy efficient control of the fan system without additional measurements. The operation of these methods is validated by laboratory measurements and data from an industrial fan system. In addition to their energy consumption, condition monitoring of fan systems is a key issue as fans are an integral part of various production processes. Fan system condition monitoring is usually carried out with vibration measurements, which again increase the system complexity. However, variable speed drives can already be used for pumping system condition monitoring. Therefore, it would add to the usability of a variablespeed- driven fan system if the variable speed drive could be used as a condition monitoring device. In this thesis, sensorless detection methods for three lifetime-reducing phenomena are suggested: these are detection of the fan contamination build-up, the correct rotational direction, and the fan surge. The methods use the variable speed drive monitoring and control options for the detection along with simple signal processing methods, such as power spectrum density estimates. The methods have been validated by laboratory measurements. The key finding of this doctoral thesis is that a variable speed drive can be used on its own as a monitoring and control device for the fan system energy efficiency, and it can also be used in the detection of certain lifetime-reducing phenomena.

Linear prediction in optical spectroscopy

A linear prediction procedure is one of the approved numerical methods of signal processing. In the field of optical spectroscopy it is used mainly for extrapolation known parts of an optical signal in order to obtain a longer one or deduce missing signal samples. The first is needed particularly when narrowing spectral lines for the purpose of spectral information extraction. In the present paper the coherent anti-Stokes Raman scattering (CARS) spectra were under investigation. The spectra were significantly distorted by the presence of nonlinear nonresonant background. In addition, line shapes were far from Gaussian/Lorentz profiles. To overcome these disadvantages the maximum entropy method (MEM) for phase spectrum retrieval was used. The obtained broad MEM spectra were further underwent the linear prediction analysis in order to be narrowed.

Steganografia ja kuvien digitaalinen vesileimaus

Steganografian tarkoituksena on salaisen viestin piilottaminen muun informaation sekaan. Tutkielmassa perehdytään kirjallisuuden pohjalta steganografiaan ja kuvien digitaaliseen vesileimaamiseen. Tutkielmaan kuuluu myös kokeellinen osuus. Siinä esitellään vesileimattujen kuvien tunnistamiseen kehitetty testausjärjestelmä ja testiajojen tulokset. Testiajoissa kuvasarjoja on vesileimattu valituilla vesileimausmenetelmillä parametreja vaihdellen. Tunnistettaville kuville tehdään piirreirrotus. Erotellut piirteet annetaan parametreina luokittimelle, joka tekee lopullisen tunnistamispäätöksen. Tutkimuksessa saatiin toteutettua toimiva ohjelmisto vesileiman lisäämiseen ja vesileimattujen kuvien tunnistamiseen kuvajoukosta. Tulosten perusteella, sopivalla piirreirrottimella ja tukivektorikoneluokittimella päästään yli 95 prosentin tunnistamistarkkuuteen.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Control of Bubbling Fluidized Bed Conditions

The objectives of this master’s thesis were to understand the importance of bubbling fluidized bed (BFB) conditions and to find out how digital image processing and acoustic emission technology can help in monitoring the bed quality. An acoustic emission (AE) measurement system and a bottom ash camera system were evaluated in acquiring information about the bed conditions. The theory part of the study describes the fundamentals of BFB boiler and evaluates the characteristics of bubbling bed. Causes and effects of bed material coarsening are explained. The ways and methods to monitor the behaviour of BFB are determined. The study introduces the operating principles of AE technology and digital image processing. The empirical part of the study describes an experimental arrangement and results of a case study at an industrial BFB boiler. Sand consumption of the boiler was reduced by optimization of bottom ash handling and sand feeding. Furthermore, data from the AE measurement system and the bottom ash camera system was collected. The feasibility of these two systems was evaluated. The particle size of bottom ash and the changes in particle size distribution were monitored during the test period. Neither of the systems evaluated was ready to serve in bed quality control accurately or fast enough. Particle size distributions according to the bottom ash camera did not correspond to the results of manual sieving. Comprehensive interpretation of the collected AE data requires much experience. Both technologies do have potential and with more research and development they may enable acquiring reliable and real-time information about the bed conditions. This information could help to maintain disturbance-free combustion process and to optimize bottom ash handling system.