The problem of identifying dynamic errors-in-variables models is of fundamental interest in many areas like process control, array signal processing, astronomical data reduction. In recent years, this field has received increased attention of the research community. In this thesis, some time domain and frequency domain approaches for identifying the errors-in-variables model is studied. The first chapter gives an overview of various methods for identifying dynamic errors-in-variables systems. Several approaches are classified and a qualitative comparison of different existing methods is also presented. The second chapter deals with instrumental variables based approaches. The least squares and the total least squares methods of solving the Yule–Walker equation is of central interest here. The methods are compared from the view point of asymptotic performance, numerical robustness and computation. The method presented in the third chapter uses prefiltered data. The input-output data is passed through a pair of user defined prefilters and the output data from the prefilters is subjected to a least-squares like algorithm. Compared to the IV approach, the proposed method shows a significant improvement in the small-sample properties of the MA parameter estimates, without any increase in the computational load. In the fourth chapter, we show that the two-dimensional process composed of the input-output data admits a finite order ARMA representation. Then we propose a parametric identification algorithm and another non-parametric identification method based on the ARMA representation.

Identification of dynamic errors-in-variables systems, where both inputs and outputs are affected by errors (measurement noises), is a fundamental problem of great interest in many areas, such as process control, econometrics, astronomical data reduction, image processing, etc. This field has received increased attention within several decades. Many solutions have been proposed with different approaches. In this thesis, the focus is on some specific problems concerning two time domain methods for identifying linear dynamic errors-in-variables systems. The thesis is divided into four parts. In the first part, a general introduction to the problem of identifying errors-in-variables systems and different approaches to solve the problem are given. Also, a summary of the contributions and some topics for future works are presented. The second part of the thesis considers the instrumental variables based approaches. They are studied under the periodic excitation condition. The main motivation is to analyze what type of instrumental variables should be chosen to maximally utilize the information of the periodic measurements. A particular overdetermined instrumental variable estimator is proposed, which can achieve optimal performance without weighting. The asymptotic convergence properties of the Bias-eliminating least squares (BELS) methods are investigated in the third part. By deriving an error dynamics equation for the parameter estimates, it is shown that the convergence of the bias-eliminating algorithms is determined by the largest magnitude of the eigenvalues of the system matrix. To overcome the possible divergence of the iteration-type bias-eliminating algorithms under very low signal-to-noise ratio, we reformulate the bias-elimination problem as a minimization problem and develop a variable projection algorithm to perform consistent parameter estimation. Part four contains an analysis of the accuracy properties of the BELS estimates. It is shown that the estimated system parameters and the estimated noise variances are asymptotically Gaussian distributed. An explicit expression for the normalized asymptotic covariance matrix of the estimated system parameters and the estimated noise variances is derived.

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In recent years, the three-dimensional (3D) movie industry has reaped massive commercial success in the theaters. With the advancement of display technologies, more experienced capturing and generation of 3D contents, TV broadcasting, movies, and games in 3D have entered home entertainment, and it is likely that 3D applications will play an important role in many aspects of people's life in a not distant future. 3D video contents contain at least two views from different perspectives for the left and the right eye of viewers. The amount of coded information is doubled if these views are encoded separately. Moreover, for multi-view displays (i.e. different perspectives of a scene in 3D are presented to the viewer at the same time through different angles), either video streams of all the required views must be transmitted to the receiver, or the displays must synthesize the missing views with a subset of the views. The latter approach has been widely proposed to reduce the amount of data being transmitted and make data adjustable to 3D-displays. The virtual views can be synthesized by the Depth Image Based Rendering (DIBR) approach from textures and associated depth images. However, it is still the case that the amount of information for the textures plus the depths presents a significant challenge for the network transmission capacity. Compression techniques are vital to facilitate the transmission. In addition to multi-view and multi-view plus depth for reproducing 3D, light field techniques have recently become a hot topic. The light field capturing aims at acquiring not only spatial but also angular information of a view, and an ideal light field rendering device should be such that the viewers would perceive it as looking through a window. Thus, the light field techniques are a step forward to provide us with a more authentic perception of 3D. Among many light field capturing approaches, focused plenoptic capturing is a solution that utilize microlens arrays. The plenoptic cameras are also portable and commercially available. Multi-view and refocusing can be obtained during post-production from these cameras. However, the captured plenoptic images are of a large size and contain significant amount of a redundant information. An efficient compression of the above mentioned contents will, therefore, increase the availability of content access and provide a better quality experience under the same network capacity constraints. In this thesis, the compression of depth images and of plenoptic contents captured by focused plenoptic cameras are addressed. The depth images can be assumed to be piece-wise smooth. Starting from the properties of depth images, a novel depth image model based on edges and sparse samples is presented, which may also be utilized for depth image post-processing. Based on this model, a depth image coding scheme that explicitly encodes the locations of depth edges is proposed, and the coding scheme has a scalable structure. Furthermore, a compression scheme for block-based 3D-HEVC is also devised, in which diffusion is used for intra prediction. In addition to the proposed schemes, the thesis illustrates several evaluation methodologies, especially the subjective test of the stimulus-comparison method. This is suitable for evaluating the quality of two impaired images, as the objective metrics are inaccurate with respect to synthesized views. For the compression of plenoptic contents, displacement intra prediction with more than one hypothesis is applied and implemented in the HEVC for an efficient prediction. In addition, a scalable coding approach utilizing a sparse set and disparities is introduced for the coding of focused plenoptic images. The MPEG test sequences were used for the evaluation of the proposed depth image compression, and public available plenoptic image and video contents were applied to the assessment of the proposed plenoptic compression. For depth image coding, the results showed that virtual views synthesized from post-processed depth images by using the proposed model are better than those synthesized from original depth images. More importantly, the proposed coding schemes using such a model produced better synthesized views than the state of the art schemes. For the plenoptic contents, the proposed scheme achieved an efficient prediction and reduced the bit rate significantly while providing coding and rendering scalability. As a result, the outcome of the thesis can lead to improving quality of the 3DTV experience and facilitate the development of 3D applications in general.

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LTE (Long Term Evolution) is likely the most complex wireless system ever developed. It incorporates features that could not have been economically implemented as recently as a decade ago. Today, with large-scale ICs, LTE can be easily accommodated in base stations and battery-powered handsets alike. LTE-Advanced is the upgraded version of LTE technology for providing more speed and greater reliability. In this report, the wireless communication between the user and base station is implemented by creating 4G LTE environment in MATLAB. Impact of Coherence time on beam weight computation varies for different delay profiles. Moreover, SNR of the transmitted signal varies significantly by the time gap between two successive uplink frames in TDD configuration. In this report, computationally efficient algorithm for reducing beam weight computations in system level LTE simulations is proposed. The wireless channel is modelled in both Rician and Rayleigh fading channel. Efficiency of beam forming algorithms is observed at different channel conditions like delay profile, fading channel, bandwidth, correlation, modulation technique. The MUSIC algorithm is implemented for detecting the movement of the users in Line of sight condition

This thesis deals with three different applications of ultrasound measurement technology. In process industries like the mining industry, the oil and gas industry, and the paper pulp industry, multiphase flows play an important role. It is of interest to measure several different parameters of these flows, such as the mass fractions and the mass fraction velocities of the different phases. There are currently no single technique available that can measure all of these properties, and commercial multiphase flow meters are in practice a combination of several flow meters that each measure different parameters. The long-term goal of the project presented in this thesis is to develop an ultrasonic technique that can measure all of these properties. The first focus of the work presented in this thesis has been to develop an ultrasonic method that can measure the mass fraction of particles in a solid/liquid multiphase flow. The technique is based on a sensor array that measures an entire cross section of the flow. The use of an array makes it possible to measure the particle distribution. This can then be used to detect static installation effects, thus enabling the use of single point sensor. The sensor array used is clamped on to the outside of the flow pipe which means the technique is completely non-invasive. The second focus is on imaging of opaque flows. While traditional optical techniques such as LDV, etc. does not work for opaque media, there is no such restriction on the ultrasonic method. The imaging technique, called ultrasonic speckle correlation velocimetry (USV) has been applied to image vortices in flows, and to measure particle velocity profiles in multiphase flows. The third and last contribution is in the field of non-destructive evaluation (NDE) of materials. In a biomaterial engineering project, the goal has been to develop an injectable bone cement that can be used to repair or replace fractured bone. During the setting reaction, the cement undergoes a series of phase changes, which have implications on how the cement can be used. The research is motivated by the lack of satisfying standards to measure the setting time. The existing methods are based on mechanical testing and visual examination, which makes them time-consuming and subjective. The ultrasonic technique presented in this thesis provides a non-destructive and objective way to determine both the setting time and some mechanical properties of the cement, during the entire setting process. The thesis consists of an introductory part and a collection of seven papers. / Godkänd; 2002; 20061029 (ysko)

Neuron models are the elementary units which determine the performance of an artificial spiking neural network (ASNN). This study introduces a new Generalized Leaky Integrate-and-Fire (GLIF) neuron model with variable leaking resistor and bias current in order to reproduce accurately the membrane voltage dynamics of a biological neuron. The accuracy of this model is ensured by adjusting its parameters to the statistical properties of the Hodgkin-Huxley model outputs; while the speed is enhanced by introducing a Generalized Exponential Moving Average method that converts the parameterized kernel functions into pre-calculated lookup tables based on an analytic solution of the dynamic equations of the GLIF model. Spike encoding is the initial yet crucial step for any application domain of ASNN. However, current encoding methods are not suitable to process complex temporal signal. Motivated by the modulation relationship found between afferent synaptic currents in biological neurons, this study proposes a biologically plausible spike phase encoding method based on a novel spiking neuron model which could perform wavelet decomposition of the input signal, and encode the wavelet spectrum into synchronized output spike trains. The spike delays in each synchronizing period represent the spectrum amplitudes. The encoding method was tested in encoding of human voice records for speech recognition purposes. Empirical evaluations confirm that encoded spike trains constitute a good representation of the continuous wavelet transform of the original signal. Interictal spike (IS) is a type of transient discharge commonly found in the electroencephalography (EEG) records from epilepsy patients. The detection of IS remains an essential task for 3D source localization as well as in developing algorithms for essential in seizure prediction and guided therapy. We present in this work a new IS detection technology method using the phase encoding method with customized wavelet sensor neuron and a specially designed ASNN structure. The detection results confirm the ability of such ASNN to capture IS automatically from multichannel EEG records.

<p>Godkänd; 1991; 20080410 (ysko)</p>

<p>Godkänd; 1991; 20080407 (ysko)</p>