Lossless compression of hyperspectral images

Technological progress has made a huge amount of data available at increasing spatial and spectral resolutions. Therefore, the compression of hyperspectral data is an area of active research. In somefields, the original quality of a hyperspectral image cannot be compromised andin these cases, lossless compression is mandatory. The main goal of this thesisis to provide improved methods for the lossless compression of hyperspectral images. Both prediction- and transform-based methods are studied. Two kinds of prediction based methods are being studied. In the first method the spectra of a hyperspectral image are first clustered and and an optimized linear predictor is calculated for each cluster. In the second prediction method linear prediction coefficients are not fixed but are recalculated for each pixel. A parallel implementation of the above-mentioned linear prediction method is also presented. Also,two transform-based methods are being presented. Vector Quantization (VQ) was used together with a new coding of the residual image. In addition we have developed a new back end for a compression method utilizing Principal Component Analysis (PCA) and Integer Wavelet Transform (IWT). The performance of the compressionmethods are compared to that of other compression methods. The results show that the proposed linear prediction methods outperform the previous methods. In addition, a novel fast exact nearest-neighbor search method is developed. The search method is used to speed up the Linde-Buzo-Gray (LBG) clustering method.

Stem straightness and compression wood in a 22-year-old stand of container-grown Scots pine trees

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Effect of compression wood on surface roughness and surface absorption of medium density fiberboard

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Siltanosturin teräsrakenteen suunnittelun avuksi luodun ohjelman jatkokehitys

Diplomityössä tehdään jatkokehitystä KCI Konecranes yrityksen siltanosturin laskentaohjelmaan. Ohjelman tärkeimmät jatkokehityskohteet kartoitettiin käyttäjäkyselyn avulla ja niistä valittiin toivotuimmat, sekä diplomityön lujuusopilliseen aihepiiriin parhaiten soveltuvat. Työhön valitut kaksi aihetta ovat koteloprofiilin kaksiosaisen uuman lujuuslaskennan selvittäminen ja siltanosturin kahdeksanpyöräisenpäätykannattajan elementtimallin suunnittelu. Diplomityössä selvitetään jatkokehityskohteisiin liittyvä teoria, mutta varsinainen ohjelmointi jätetään työn ulkopuolelle. Kaksiosaisella uumalla varustetussa koteloprofiilissa nostovaunun kulkukiskon alla olevan uuman yläosa tehdään paksummaksi, jotta uuma kestäisi nostovaunun pyöräkuormasta aiheutuvan paikallisen jännityksen, eliniin sanotun rusennusjännityksen. Rusennusjännityksen määrittäminen uumalevyissä on kaksiosaisen uuman lujuuslaskennan tärkein tehtävä. Rusennuksen aiheuttamankalvojännityksen ja jännityskeskittymien määrittämiseen erilaisissa konstruktioissa etsittiin sopivimmat menetelmät kirjallisuudesta ja standardeista. Kalvojännitys voidaan määrittää luotettavasti käyttäen joko 45 asteen sääntöä tai standardin mukaista menetelmää ja jännityskonsentraatioiden suuruus saadaan kertomallakalvojännitys jännityskonsentraatiokertoimilla. Menetelmien toimivuus verifioitiin tekemällä kymmeniä uuman elementtimalleja erilaisin dimensioin ja reunaehdoin ja vertaamalla elementtimallien tuloksia käsin laskettuihin. Käsin lasketut jännitykset saatiin vastaamaan tarkasti elementtimallien tuloksia. Kaksiosaisen uuman lommahdus- ja väsymislaskentaa tutkittiin alustavasti. Kahdeksanpyöräisiä päätykannattajia käytetään suurissa siltanostureissa pienentämään pyöräkuormia ja radan rusennusjännityksiä. Kahdeksanpyöräiselle siltanosturin päätykannattajalle suunniteltiin elementtimallit molempiin rakenteesta käytettyihin konstruktioihin: nivelöityyn ja jäykkäkehäiseen malliin. Elementtimallien rakentamisessa hyödynnettiin jo olemassa olevia malleja, jolloin niiden lisääminen ohjelmakoodiin nopeutuu ja ne ovat varmasti yhteensopivia muiden laskentamoduuleiden kanssa. Elementtimallien värähtelyanalyysin reunaehtoja tarkasteltiin. Värähtelyanalyysin reunaehtoihin ei tutkimuksen perusteella tarvitse tehdä muutoksia, mutta staattisen analyysin reunaehdot kaipaavat vielä lisätutkimusta.

Selecting Wavelet Filter for Compression of Spectra from Multispectral Images

The problem of selecting anappropriate wavelet filter is always present in signal compression based on thewavelet transform. In this report, we propose a method to select a wavelet filter from a predefined set of filters for the compression of spectra from a multispectral image. The wavelet filter selection is based on the Learning Vector Quantization (LVQ). In the training phase for the test images, the best wavelet filter for each spectrum has been found by a careful compression-decompression evaluation. Certain spectral features are used in characterizing the pixel spectra. The LVQ is used to form the best wavelet filter class for different types of spectra from multispectral images. When a new image is to be compressed, a set of spectra from that image is selected, the spectra are classified by the trained LVQand the filter associated to the largest class is selected for the compression of every spectrum from the multispectral image. The results show, that almost inevery case our method finds the most suitable wavelet filter from the pre-defined set for the compression.

Data-parallel computation in parallel and distributed environments

The past few decades have seen a considerable increase in the number of parallel and distributed systems. With the development of more complex applications, the need for more powerful systems has emerged and various parallel and distributed environments have been designed and implemented. Each of the environments, including hardware and software, has unique strengths and weaknesses. There is no single parallel environment that can be identified as the best environment for all applications with respect to hardware and software properties. The main goal of this thesis is to provide a novel way of performing data-parallel computation in parallel and distributed environments by utilizing the best characteristics of difference aspects of parallel computing. For the purpose of this thesis, three aspects of parallel computing were identified and studied. First, three parallel environments (shared memory, distributed memory, and a network of workstations) are evaluated to quantify theirsuitability for different parallel applications. Due to the parallel and distributed nature of the environments, networks connecting the processors in these environments were investigated with respect to their performance characteristics. Second, scheduling algorithms are studied in order to make them more efficient and effective. A concept of application-specific information scheduling is introduced. The application- specific information is data about the workload extractedfrom an application, which is provided to a scheduling algorithm. Three scheduling algorithms are enhanced to utilize the application-specific information to further refine their scheduling properties. A more accurate description of the workload is especially important in cases where the workunits are heterogeneous and the parallel environment is heterogeneous and/or non-dedicated. The results obtained show that the additional information regarding the workload has a positive impact on the performance of applications. Third, a programming paradigm for networks of symmetric multiprocessor (SMP) workstations is introduced. The MPIT programming paradigm incorporates the Message Passing Interface (MPI) with threads to provide a methodology to write parallel applications that efficiently utilize the available resources and minimize the overhead. The MPIT allows for communication and computation to overlap by deploying a dedicated thread for communication. Furthermore, the programming paradigm implements an application-specific scheduling algorithm. The scheduling algorithm is executed by the communication thread. Thus, the scheduling does not affect the execution of the parallel application. Performance results achieved from the MPIT show that considerable improvements over conventional MPI applications are achieved.

Introducing massively parallel computers into operational weather forecasting

Numerical weather prediction and climate simulation have been among the computationally most demanding applications of high performance computing eversince they were started in the 1950's. Since the 1980's, the most powerful computers have featured an ever larger number of processors. By the early 2000's, this number is often several thousand. An operational weather model must use all these processors in a highly coordinated fashion. The critical resource in running such models is not computation, but the amount of necessary communication between the processors. The communication capacity of parallel computers often fallsfar short of their computational power. The articles in this thesis cover fourteen years of research into how to harness thousands of processors on a single weather forecast or climate simulation, so that the application can benefit as much as possible from the power of parallel high performance computers. The resultsattained in these articles have already been widely applied, so that currently most of the organizations that carry out global weather forecasting or climate simulation anywhere in the world use methods introduced in them. Some further studies extend parallelization opportunities into other parts of the weather forecasting environment, in particular to data assimilation of satellite observations.

Multispectral image compression using the wavelet transform

Multispectral images contain information from several spectral wavelengths and currently multispectral images are widely used in remote sensing and they are becoming more common in the field of computer vision and in industrial applications. Typically, one multispectral image in remote sensing may occupy hundreds of megabytes of disk space and several this kind of images may be received from a single measurement. This study considers the compression of multispectral images. The lossy compression is based on the wavelet transform and we compare the suitability of different waveletfilters for the compression. A method for selecting a wavelet filter for the compression and reconstruction of multispectral images is developed. The performance of the multidimensional wavelet transform based compression is compared to other compression methods like PCA, ICA, SPIHT, and DCT/JPEG. The quality of the compression and reconstruction is measured by quantitative measures like signal-to-noise ratio. In addition, we have developed a qualitative measure, which combines the information from the spatial and spectral dimensions of a multispectral image and which also accounts for the visual quality of the bands from the multispectral images.

Wavelet Filter Parameterization for Compression of Spectral Images

Vaatimus kuvatiedon tiivistämisestä on tullut entistä ilmeisemmäksi viimeisen kymmenen vuoden aikana kuvatietoon perustuvien sovellutusten myötä. Nykyisin kiinnitetään erityistä huomiota spektrikuviin, joiden tallettaminen ja siirto vaativat runsaasti levytilaa ja kaistaa. Aallokemuunnos on osoittautunut hyväksi ratkaisuksi häviöllisessä tiedontiivistämisessä. Sen toteutus alikaistakoodauksessa perustuu aallokesuodattimiin ja ongelmana on sopivan aallokesuodattimen valinta erilaisille tiivistettäville kuville. Tässä työssä esitetään katsaus tiivistysmenetelmiin, jotka perustuvat aallokemuunnokseen. Ortogonaalisten suodattimien määritys parametrisoimalla on työn painopisteenä. Työssä todetaan myös kahden erilaisen lähestymistavan samanlaisuus algebrallisten yhtälöiden avulla. Kokeellinen osa sisältää joukon testejä, joilla perustellaan parametrisoinnin tarvetta. Erilaisille kuville tarvitaan erilaisia suodattimia sekä erilaiset tiivistyskertoimet saavutetaan eri suodattimilla. Lopuksi toteutetaan spektrikuvien tiivistys aallokemuunnoksen avulla.

Classification-Based compression of Multispectral Images

The purpose of this thesis is to present a new approach to the lossy compression of multispectral images. Proposed algorithm is based on combination of quantization and clustering. Clustering was investigated for compression of the spatial dimension and the vector quantization was applied for spectral dimension compression. Presenting algo¬rithms proposes to compress multispectral images in two stages. During the first stage we define the classes' etalons, another words to each uniform areas are located inside the image the number of class is given. And if there are the pixels are not yet assigned to some of the clusters then it doing during the second; pass and assign to the closest eta¬lons. Finally a compressed image is represented with a flat index image pointing to a codebook with etalons. The decompression stage is instant too. The proposed method described in this paper has been tested on different satellite multispectral images from different resources. The numerical results and illustrative examples of the method are represented too.

Lossless Compression Methods for Multispectral Images: Development and Comparisons

Main purpose of this thesis is to introduce a new lossless compression algorithm for multispectral images. Proposed algorithm is based on reducing the band ordering problem to the problem of finding a minimum spanning tree in a weighted directed graph, where set of the graph vertices corresponds to multispectral image bands and the arcs’ weights have been computed using a newly invented adaptive linear prediction model. The adaptive prediction model is an extended unification of 2–and 4–neighbour pixel context linear prediction schemes. The algorithm provides individual prediction of each image band using the optimal prediction scheme, defined by the adaptive prediction model and the optimal predicting band suggested by minimum spanning tree. Its efficiency has been compared with respect to the best lossless compression algorithms for multispectral images. Three recently invented algorithms have been considered. Numerical results produced by these algorithms allow concluding that adaptive prediction based algorithm is the best one for lossless compression of multispectral images. Real multispectral data captured from an airplane have been used for the testing.

Developing of the Variable Speed Drive -control in simulated parallel pumping systems

This thesis presents briefly the basic operation and use of centrifugal pumps and parallel pumping applications. The characteristics of parallel pumping applications are compared to circuitry, in order to search analogy between these technical fields. The purpose of studying circuitry is to find out if common software tools for solving circuit performance could be used to observe parallel pumping applications. The empirical part of the thesis introduces a simulation environment for parallel pumping systems, which is based on circuit components of Matlab Simulink —software. The created simulation environment ensures the observation of variable speed controlled parallel pumping systems in case of different controlling methods. The introduced simulation environment was evaluated by building a simulation model for actual parallel pumping system at Lappeenranta University of Technology. The simulated performance of the parallel pumps was compared to measured values of the actual system. The gathered information shows, that if the initial data of the system and pump perfonnance is adequate, the circuitry based simulation environment can be exploited to observe parallel pumping systems. The introduced simulation environment can represent the actual operation of parallel pumps in reasonably accuracy. There by the circuitry based simulation can be used as a researching tool to develop new controlling ways for parallel pumps.

Determination of compression behaviour of coating layer

The strength properties of paper coating layer are very important in converting and printing operations. Too great or low strength of the coating can affect several problems in printing. One of the problems caused by the strength of coating is the cracking at the fold. After printing the paper is folded to final form and the pages are stapled together. In folding the paper coating can crack causing aesthetic damage over printed image or in the worst case the centre sheet can fall off in stapling. When folding the paper other side undergoes tensile stresses and the other side compressive stresses. If the difference between these stresses is too high, the coating can crack on the folding. To better predict and prevent cracking at the fold it is good to know the strength properties of coating layer. It has measured earlier the tensile strength of coating layer but not the compressive strength. In this study it was tried to find some way to measure the compressive strength of the coating layer and investigate how different coatings behave in compression. It was used the short span crush test, which is used to measure the in-plane compressive strength of paperboards, to measure the compressive strength of the coating layer. In this method the free span of the specimen is very small which prevent buckling. It was measured the compressive strength of free coating films as well as coated paper. It was also measured the tensile strength and the Bendtsen air permeance of the coating film. The results showed that the shape of pigment has a great effect to the strength of coating. Platy pigment gave much better strength than round or needle-like pigment. On the other hand calcined kaolin, which is also platy but the particles are aggregated, decreased the strength substantially. The difference in the strength can be explained with packing of the particles which is affecting to the porosity and thus to the strength. The platy kaolin packs up much better than others and creates less porous structure. The results also showed that the binder properties have a great effect to the compressive strength of coating layer. The amount of latex and the glass transition temperature, Tg, affect to the strength. As the amount of latex is increasing, the strength of coating is increasing also. Larger amount of latex is binding the pigment particles better together and decreasing the porosity. Compressive strength was increasing when the Tg was increasing because the hard latex gives a stiffer and less elastic film than soft latex.

Error Modeling and Accuracy Analysis of a Novel Mobile Hybrid Parallel Robot

Over the last decades, calibration techniques have been widely used to improve the accuracy of robots and machine tools since they only involve software modification instead of changing the design and manufacture of the hardware. Traditionally, there are four steps are required for a calibration, i.e. error modeling, measurement, parameter identification and compensation. The objective of this thesis is to propose a method for the kinematics analysis and error modeling of a newly developed hybrid redundant robot IWR (Intersector Welding Robot), which possesses ten degrees of freedom (DOF) where 6-DOF in parallel and additional 4-DOF in serial. In this article, the problem of kinematics modeling and error modeling of the proposed IWR robot are discussed. Based on the vector arithmetic method, the kinematics model and the sensitivity model of the end-effector subject to the structure parameters is derived and analyzed. The relations between the pose (position and orientation) accuracy and manufacturing tolerances, actuation errors, and connection errors are formulated. Computer simulation is performed to examine the validity and effectiveness of the proposed method.

New Distance Transforms for Gray Level Image Compression

This thesis deals with distance transforms which are a fundamental issue in image processing and computer vision. In this thesis, two new distance transforms for gray level images are presented. As a new application for distance transforms, they are applied to gray level image compression. The new distance transforms are both new extensions of the well known distance transform algorithm developed by Rosenfeld, Pfaltz and Lay. With some modification their algorithm which calculates a distance transform on binary images with a chosen kernel has been made to calculate a chessboard like distance transform with integer numbers (DTOCS) and a real value distance transform (EDTOCS) on gray level images. Both distance transforms, the DTOCS and EDTOCS, require only two passes over the graylevel image and are extremely simple to implement. Only two image buffers are needed: The original gray level image and the binary image which defines the region(s) of calculation. No other image buffers are needed even if more than one iteration round is performed. For large neighborhoods and complicated images the two pass distance algorithm has to be applied to the image more than once, typically 3 10 times. Different types of kernels can be adopted. It is important to notice that no other existing transform calculates the same kind of distance map as the DTOCS. All the other gray weighted distance function, GRAYMAT etc. algorithms find the minimum path joining two points by the smallest sum of gray levels or weighting the distance values directly by the gray levels in some manner. The DTOCS does not weight them that way. The DTOCS gives a weighted version of the chessboard distance map. The weights are not constant, but gray value differences of the original image. The difference between the DTOCS map and other distance transforms for gray level images is shown. The difference between the DTOCS and EDTOCS is that the EDTOCS calculates these gray level differences in a different way. It propagates local Euclidean distances inside a kernel. Analytical derivations of some results concerning the DTOCS and the EDTOCS are presented. Commonly distance transforms are used for feature extraction in pattern recognition and learning. Their use in image compression is very rare. This thesis introduces a new application area for distance transforms. Three new image compression algorithms based on the DTOCS and one based on the EDTOCS are presented. Control points, i.e. points that are considered fundamental for the reconstruction of the image, are selected from the gray level image using the DTOCS and the EDTOCS. The first group of methods select the maximas of the distance image to new control points and the second group of methods compare the DTOCS distance to binary image chessboard distance. The effect of applying threshold masks of different sizes along the threshold boundaries is studied. The time complexity of the compression algorithms is analyzed both analytically and experimentally. It is shown that the time complexity of the algorithms is independent of the number of control points, i.e. the compression ratio. Also a new morphological image decompression scheme is presented, the 8 kernels' method. Several decompressed images are presented. The best results are obtained using the Delaunay triangulation. The obtained image quality equals that of the DCT images with a 4 x 4