Matrix based Cryptographic Procedure for Efficient Image Encryption

In this paper we propose a cryptographic transformation based on matrix manipulations for image encryption. Substitution and diffusion operations, based on the matrix, facilitate fast conversion of plaintext and images into ciphertext and cipher images. The paper describes the encryption algorithm, discusses the simulation results and compares with results obtained from Advanced Encryption Standard (AES). It is shown that the proposed algorithm is capable of encrypting images eight times faster than AES.

Image Denoising Using Sure-Based Adaptive Thresholding In Directionlet Domain

The standard separable two dimensional wavelet transform has achieved a great success in image denoising applications due to its sparse representation of images. However it fails to capture efficiently the anisotropic geometric structures like edges and contours in images as they intersect too many wavelet basis functions and lead to a non-sparse representation. In this paper a novel de-noising scheme based on multi directional and anisotropic wavelet transform called directionlet is presented. The image denoising in wavelet domain has been extended to the directionlet domain to make the image features to concentrate on fewer coefficients so that more effective thresholding is possible. The image is first segmented and the dominant direction of each segment is identified to make a directional map. Then according to the directional map, the directionlet transform is taken along the dominant direction of the selected segment. The decomposed images with directional energy are used for scale dependent subband adaptive optimal threshold computation based on SURE risk. This threshold is then applied to the sub-bands except the LLL subband. The threshold corrected sub-bands with the unprocessed first sub-band (LLL) are given as input to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the proposed method outperforms the standard wavelet-based denoising methods in terms of numeric and visual quality

Development of 2-D MRT based Image Compression Techniques

The thesis explores the area of still image compression. The image compression techniques can be broadly classified into lossless and lossy compression. The most common lossy compression techniques are based on Transform coding, Vector Quantization and Fractals. Transform coding is the simplest of the above and generally employs reversible transforms like, DCT, DWT, etc. Mapped Real Transform (MRT) is an evolving integer transform, based on real additions alone. The present research work aims at developing new image compression techniques based on MRT. Most of the transform coding techniques employ fixed block size image segmentation, usually 8×8. Hence, a fixed block size transform coding is implemented using MRT and the merits and demerits are analyzed for both 8×8 and 4×4 blocks. The N2 unique MRT coefficients, for each block, are computed using templates. Considering the merits and demerits of fixed block size transform coding techniques, a hybrid form of these techniques is implemented to improve the performance of compression. The performance of the hybrid coder is found to be better compared to the fixed block size coders. Thus, if the block size is made adaptive, the performance can be further improved. In adaptive block size coding, the block size may vary from the size of the image to 2×2. Hence, the computation of MRT using templates is impractical due to memory requirements. So, an adaptive transform coder based on Unique MRT (UMRT), a compact form of MRT, is implemented to get better performance in terms of PSNR and HVS The suitability of MRT in vector quantization of images is then experimented. The UMRT based Classified Vector Quantization (CVQ) is implemented subsequently. The edges in the images are identified and classified by employing a UMRT based criteria. Based on the above experiments, a new technique named “MRT based Adaptive Transform Coder with Classified Vector Quantization (MATC-CVQ)”is developed. Its performance is evaluated and compared against existing techniques. A comparison with standard JPEG & the well-known Shapiro’s Embedded Zero-tree Wavelet (EZW) is done and found that the proposed technique gives better performance for majority of images

Smart electricity networks based on large integration of renewable sources and distributed generation

Das Grünbuch 2006 der Europäischen Kommission "Eine Europäische Strategie für nachhaltige, wettbewerbsfähige und sichere Energie" unterstreicht, dass Europa in ein neues Energie-Zeitalter eingetreten ist. Die vorrangigen Ziele europäischer Energiepolitik müssen Nachhaltigkeit, Wettbewerbsfähigkeit und Versorgungssicherheit sein, wobei sie eine zusammenhängende und logische Menge von Taktiken und Maßnahmen benötigt, um diese Ziele zu erreichen. Die Strommärkte und Verbundnetze Europas bilden das Kernstück unseres Energiesystems und müssen sich weiterentwickeln, um den neuen Anforderungen zu entsprechen. Die europäischen Stromnetze haben die lebenswichtigen Verbindungen zwischen Stromproduzenten und Verbrauchern mit großem Erfolg seit vielen Jahrzehnten gesichert. Die grundlegende Struktur dieser Netze ist entwickelt worden, um die Bedürfnisse großer, überwiegend auf Kohle aufgebauten Herstellungstechnologien zu befriedigen, die sich entfernt von den Verbraucherzentren befinden. Die Energieprobleme, denen Europa jetzt gegenübersteht, ändern die Stromerzeugungslandschaft in zwei Gesichtspunkten: die Notwendigkeit für saubere Kraftwerkstechnologien verbunden mit erheblich verbesserten Wirkungsgraden auf der Verbraucherseite wird es Kunden ermöglichen, mit den Netzen viel interaktiver zu arbeiten; andererseits müssen die zukünftigen europaweiten Stromnetze allen Verbrauchern eine höchst zuverlässige, preiswerte Energiezufuhr bereitstellen, wobei sowohl die Nutzung von großen zentralisierten Kraftwerken als auch kleineren lokalen Energiequellen überall in Europa ausgeschöpft werden müssen. In diesem Zusammenhang wird darauf hingewiesen, dass die Informationen, die in dieser Arbeit dargestellt werden, auf aktuellen Fragen mit großem Einfluss auf die gegenwärtigen technischen und wirtschaftspolitischen Diskussionen basieren. Der Autor hat während der letzten Jahre viele der hier vorgestellten Schlussfolgerungen und Empfehlungen mit Vertretern der Kraftwerksindustrie, Betreibern von Stromnetzen und Versorgungsbetrieben, Forschungsgremien und den Regulierungsstellen diskutiert. Die folgenden Absätze fassen die Hauptergebnisse zusammen: Diese Arbeit definiert das neue Konzept, das auf mehr verbraucherorientierten Netzen basiert, und untersucht die Notwendigkeiten sowie die Vorteile und die Hindernisse für den Übergang auf ein mögliches neues Modell für Europa: die intelligenten Stromnetze basierend auf starker Integration erneuerbarer Quellen und lokalen Kleinkraftwerken. Das neue Modell wird als eine grundlegende Änderung dargestellt, die sich deutlich auf Netzentwurf und -steuerung auswirken wird. Sie fordert ein europäisches Stromnetz mit den folgenden Merkmalen: – Flexibel: es erfüllt die Bedürfnisse der Kunden, indem es auf Änderungen und neue Forderungen eingehen kann – Zugänglich: es gestattet den Verbindungszugang aller Netzbenutzer besonders für erneuerbare Energiequellen und lokale Stromerzeugung mit hohem Wirkungsgrad sowie ohne oder mit niedrigen Kohlendioxidemissionen – Zuverlässig: es verbessert und garantiert die Sicherheit und Qualität der Versorgung mit den Forderungen des digitalen Zeitalters mit Reaktionsmöglichkeiten gegen Gefahren und Unsicherheiten – Wirtschaftlich: es garantiert höchste Wirtschaftlichkeit durch Innovation, effizientes Energiemanagement und liefert „gleiche Ausgangsbedingungen“ für Wettbewerb und Regulierung. Es beinhaltet die neuesten Technologien, um Erfolg zu gewährleisten, während es die Flexibilität behält, sich an weitere Entwicklungen anzupassen und fordert daher ein zuversichtliches Programm für Forschung, Entwicklung und Demonstration, das einen Kurs im Hinblick auf ein Stromversorgungsnetz entwirft, welches die Bedürfnisse der Zukunft Europas befriedigt: – Netztechnologien, die die Stromübertragung verbessern und Energieverluste verringern, werden die Effizienz der Versorgung erhöhen, während neue Leistungselektronik die Versorgungsqualität verbessern wird. Es wird ein Werkzeugkasten erprobter technischer Lösungen geschaffen werden, der schnell und wirtschaftlich eingesetzt werden kann, so dass bestehende Netze Stromeinleitungen von allen Energieressourcen aufnehmen können. – Fortschritte bei Simulationsprogrammen wird die Einführung innovativer Technologien in die praktische Anwendung zum Vorteil sowohl der Kunden als auch der Versorger stark unterstützen. Sie werden das erfolgreiche Anpassen neuer und alter Ausführungen der Netzkomponenten gewährleisten, um die Funktion von Automatisierungs- und Regelungsanordnungen zu garantieren. – Harmonisierung der ordnungspolitischen und kommerziellen Rahmen in Europa, um grenzüberschreitenden Handel von sowohl Energie als auch Netzdienstleistungen zu erleichtern; damit muss eine Vielzahl von Einsatzsituationen gewährleistet werden. Gemeinsame technische Normen und Protokolle müssen eingeführt werden, um offenen Zugang zu gewährleisten und den Einsatz der Ausrüstung eines jeden Herstellers zu ermöglichen. – Entwicklungen in Nachrichtentechnik, Mess- und Handelssystemen werden auf allen Ebenen neue Möglichkeiten eröffnen, auf Grund von Signalen des Marktes frühzeitig technische und kommerzielle Wirkungsgrade zu verbessern. Es wird Unternehmen ermöglichen, innovative Dienstvereinbarungen zu benutzen, um ihre Effizienz zu verbessern und ihre Angebote an Kunden zu vergrößern. Schließlich muss betont werden, dass für einen erfolgreichen Übergang zu einem zukünftigen nachhaltigen Energiesystem alle relevanten Beteiligten involviert werden müssen.

Example Based Image Analysis and Synthesis

Image analysis and graphics synthesis can be achieved with learning techniques using directly image examples without physically-based, 3D models. In our technique: -- the mapping from novel images to a vector of "pose" and "expression" parameters can be learned from a small set of example images using a function approximation technique that we call an analysis network; -- the inverse mapping from input "pose" and "expression" parameters to output images can be synthesized from a small set of example images and used to produce new images using a similar synthesis network. The techniques described here have several applications in computer graphics, special effects, interactive multimedia and very low bandwidth teleconferencing.

Perceptually-based Comparison of Image Similarity Metrics

The image comparison operation ??sessing how well one image matches another ??rms a critical component of many image analysis systems and models of human visual processing. Two norms used commonly for this purpose are L1 and L2, which are specific instances of the Minkowski metric. However, there is often not a principled reason for selecting one norm over the other. One way to address this problem is by examining whether one metric better captures the perceptual notion of image similarity than the other. With this goal, we examined perceptual preferences for images retrieved on the basis of the L1 versus the L2 norm. These images were either small fragments without recognizable content, or larger patterns with recognizable content created via vector quantization. In both conditions the subjects showed a consistent preference for images matched using the L1 metric. These results suggest that, in the domain of natural images of the kind we have used, the L1 metric may better capture human notions of image similarity.

Monocular-based 3-D seafloor reconstruction and ortho-mosaicing by piecewise planar representation

Photo-mosaicing techniques have become popular for seafloor mapping in various marine science applications. However, the common methods cannot accurately map regions with high relief and topographical variations. Ortho-mosaicing borrowed from photogrammetry is an alternative technique that enables taking into account the 3-D shape of the terrain. A serious bottleneck is the volume of elevation information that needs to be estimated from the video data, fused, and processed for the generation of a composite ortho-photo that covers a relatively large seafloor area. We present a framework that combines the advantages of dense depth-map and 3-D feature estimation techniques based on visual motion cues. The main goal is to identify and reconstruct certain key terrain feature points that adequately represent the surface with minimal complexity in the form of piecewise planar patches. The proposed implementation utilizes local depth maps for feature selection, while tracking over several views enables 3-D reconstruction by bundle adjustment. Experimental results with synthetic and real data validate the effectiveness of the proposed approach

Are discourse comprehension and cognitive processes influenced by the type of language language (English and Spanish)? A cross-language study based on elaborative inferences generation

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Compression-based Image Registration

Image registration is an important component of image analysis used to align two or more images. In this paper, we present a new framework for image registration based on compression. The basic idea underlying our approach is the conjecture that two images are correctly registered when we can maximally compress one image given the information in the other. The contribution of this paper is twofold. First, we show that the image registration process can be dealt with from the perspective of a compression problem. Second, we demonstrate that the similarity metric, introduced by Li et al., performs well in image registration. Two different versions of the similarity metric have been used: the Kolmogorov version, computed using standard real-world compressors, and the Shannon version, calculated from an estimation of the entropy rate of the images

Adjoint goal-based error norms for adaptive mesh ocean modelling

Flow in the world's oceans occurs at a wide range of spatial scales, from a fraction of a metre up to many thousands of kilometers. In particular, regions of intense flow are often highly localised, for example, western boundary currents, equatorial jets, overflows and convective plumes. Conventional numerical ocean models generally use static meshes. The use of dynamically-adaptive meshes has many potential advantages but needs to be guided by an error measure reflecting the underlying physics. A method of defining an error measure to guide an adaptive meshing algorithm for unstructured tetrahedral finite elements, utilizing an adjoint or goal-based method, is described here. This method is based upon a functional, encompassing important features of the flow structure. The sensitivity of this functional, with respect to the solution variables, is used as the basis from which an error measure is derived. This error measure acts to predict those areas of the domain where resolution should be changed. A barotropic wind driven gyre problem is used to demonstrate the capabilities of the method. The overall objective of this work is to develop robust error measures for use in an oceanographic context which will ensure areas of fine mesh resolution are used only where and when they are required. (c) 2006 Elsevier Ltd. All rights reserved.

Stress generation in the tension wood of poplar is based on the lateral swelling power of the G-layer

The mechanism of active stress generation in tension wood is still not fully understood. To characterize the functional interdependency between the G-layer and the secondary cell wall, nanostructural characterization and mechanical tests were performed on native tension wood tissues of poplar (Populus nigra x Populus deltoids) and on tissues in which the G-layer was removed by an enzymatic treatment. In addition to the well-known axial orientation of the cellulose fibrils in the G-layer, it was shown that the microfibril angle of the S2-layer was very large (about 36 degrees). The removal of the G-layer resulted in an axial extension and a tangential contraction of the tissues. The tensile stress-strain curves of native tension wood slices showed a jagged appearance after yield that could not be seen in the enzyme-treated samples. The behaviour of the native tissue was modelled by assuming that cells deform elastically up to a critical strain at which the G-layer slips, causing a drop in stress. The results suggest that tensile stresses in poplar are generated in the living plant by a lateral swelling of the G-layer which forces the surrounding secondary cell wall to contract in the axial direction.

A bridge-ship collision avoidance system based on FLIR image sequences

In this paper, a forward-looking infrared (FLIR) video surveillance system is presented for collision avoidance of moving ships to bridge piers. An image preprocessing algorithm is proposed to reduce clutter background by multi-scale fractal analysis, in which the blanket method is used for fractal feature computation. Then, the moving ship detection algorithm is developed from image differentials of the fractal feature in the region of surveillance between regularly interval frames. When the moving ships are detected in region of surveillance, the device for safety alert is triggered. Experimental results have shown that the approach is feasible and effective. It has achieved real-time and reliable alert to avoid collisions of moving ships to bridge piers.