Resistance to potato virus A and potato virus Y in potato cultivars grown in Finland

Selostus: Suomessa viljeltävien perunalajikkeiden kestävyys A- ja Y-virusta vastaan

Pathogen derived resistance to Potato virus Y: mechanisms and risks

Selostus: Patogeenivälitteinen, siirtogeeninen kestävyys perunan Y-virusta vastaan: mekanismit ja riskit

Pressure to change and resistance against it: consumer perceptions and acceptance of nutritionally modified foods

Selostus: Muutospaineet ja muutosvastarinta: kuluttajien suhtautuminen ravitsemuksellisesti muunnettuihin elintarvikkeisiin

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

Abstract

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

Abstract

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.

Electromagnetic Design of a Solid Steel Rotor Motor for Demanding Operation Environments

The solid-rotor induction motor provides a mechanically and thermally reliable solution for demanding environments where other rotor solutions are prohibited or questionable. Solid rotors, which are manufactured of single pieces of ferromagnetic material, are commonly used in motors in which the rotationspeeds exceed substantially the conventional speeds of laminated rotors with squirrel-cage. During the operation of a solid-rotor electrical machine, the rotor core forms a conductor for both the magnetic flux and the electrical current. This causes an increase in the rotor resistance and rotor leakage inductance, which essentially decreases the power factor and the efficiency of the machine. The electromagnetic problems related to the solid-rotor induction motor are mostly associated with the low performance of the rotor. Therefore, the main emphasis in this thesis is put on the solid steel rotor designs. The rotor designs studied in thisthesis are based on the fact that the rotor construction should be extremely robust and reliable to withstand the high mechanical stresses caused by the rotational velocity of the rotor. In addition, the demanding operation environment sets requirements for the applied materials because of the high temperatures and oxidizing acids, which may be present in the cooling fluid. Therefore, the solid rotors analyzed in this thesis are made of a single piece of ferromagnetic material without any additional parts, such as copper end-rings or a squirrel-cage. A pure solid rotor construction is rigid and able to keep its balance over a large speed range. It also may tolerate other environmental stresses such as corroding substances or abrasive particles. In this thesis, the main target is to improve the performance of an induction motor equipped with a solid steel rotor by traditional methods: by axial slitting of the rotor, by selecting a proper rotor core material and by coating the rotor with a high-resistive stainless ferromagnetic material. In the solid steel rotor calculation, the rotor end-effects have a significant effect on the rotor characteristics. Thus, the emphasis is also put on the comparison of different rotor endfactors. In addition, a corrective slip-dependent end-factor is proposed. The rotor designs covered in this thesis are the smooth solid rotor, the axially slitted solid rotor and the slitted rotor having a uniform ferromagnetic coating cylinder. The thesis aims at design rules for multi-megawatt machines. Typically, mega-watt-size solidrotor machines find their applications mainly in the field of electric-motor-gas-compression systems, in steam-turbine applications, and in various types of largepower pump applications, where high operational speeds are required. In this thesis, a 120 kW, 10 000 rpm solid-rotor induction motor is usedas a small-scale model for such megawatt-range solid-rotor machines. The performance of the 120 kW solid-rotor induction motors is determined by experimental measurements and finite element calculations.

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.

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.

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.

Consumer resistance to mobile commerce services

Oheinen opinnäytetyö on kvalitatiivinen tutkimus kuluttajavastarinnasta mobiilin kaupankäynnin palveluja kohtaan. Tutkimus kohdistuu läntisiin kulttuureihin, joissa kyseisen innovatiivisen palveluryhmän leviämistä tukevat monet aikaisemmat innovaatiot kuten matkapuhelin, Internet, digitaaliset pankkipalvelut. Tutkimus esittelee innovaatioiden vastarintatekijöitä ihmisen luonnollisena reaktiona tämän vakiintuneita elämäntapoja mullistavia keksintöjä kohtaan nimenomaan läntisissä kulttuureissa, joissa kuluttajat ovat perinteisesti hyvin teknologiamyönteisiä. Toisaalta tutkimusalueella on havaittavissa sosiaalisten ryhmien pirstoutuminen yhä pienemmiksi alaryhmiksi, mikä voi hidastaa sosiaalista oppimista. Tutkimus vastaa todelliseen tutkimusaukkoon. Aihe on samalla sekä ajankohtainen että relevantti vastatessaan nykyisin käytävään utopistiseen keskusteluun digitaalisen informaatioyhteiskunnan kehittymisestä ja merkityksestä modernille ihmiskunnalle. Tutkimuksen teoreettinen eksploratiivinen viitekehys rakentuu valikoiduista uusien tuotteiden ja palvelujen kehittämisen, palvelumarkkinoinnin ja sosiaalisen oppimisen teorioista sekä innovaatio- kommunikaatioteorioista. Empiirisen osan muodostavat kansainvälisten markkinatutkimuslaitosten ja haastateltujen asiantuntijoiden näkemykset alan kehityksestä. Tutkimus osoittaa, että kuluttajat eivät ole valmiita vastaanottamaan kehittyvien teknologioiden mahdollistamia mobiilin kaupankäynnin palveluita ennen kuin ne vastaavat kuluttajien perustarpeisiin ja rakenteelliset vastarintatekijät (alhainen käytettävyys, matala lisäarvo, koetut riskit, perinnevastarinta, palveluryhmän huono mielikuva) on poistettu. Tutkimus esittää, että mobiilin kaupankäynnin alalla toimivien yritysten tulisi työskennellä yhteistyössä keskenään ja kuluttajien kanssa luodakseen kuluttajien tarpeita ja toiveita vastaavia turvallisiksi koettuja mobiilin kaupankäynnin palveluita. Tutkimus ehdottaa, että kyselytutkimusten ohella käytettäisiin havaintomenetelmiä, jotta teknologiat voitaisiin valjastaa kuluttajien tarpeita ja kulutustottumuksia vastaaviksi.