Audio-visual football video analysis, from structure detection to attention analysis

Sport video is an important video genre. Content-based sports video analysis attracts great interest from both industry and academic fields. A sports video is characterised by repetitive temporal structures, relatively plain contents, and strong spatio-temporal variations, such as quick camera switches and swift local motions. It is necessary to develop specific techniques for content-based sports video analysis to utilise these characteristics. For an efficient and effective sports video analysis system, there are three fundamental questions: (1) what are key stories for sports videos; (2) what incurs viewer’s interest; and (3) how to identify game highlights. This thesis is developed around these questions. We approached these questions from two different perspectives and in turn three research contributions are presented, namely, replay detection, attack temporal structure decomposition, and attention-based highlight identification. Replay segments convey the most important contents in sports videos. It is an efficient approach to collect game highlights by detecting replay segments. However, replay is an artefact of editing, which improves with advances in video editing tools. The composition of replay is complex, which includes logo transitions, slow motions, viewpoint switches and normal speed video clips. Since logo transition clips are pervasive in game collections of FIFA World Cup 2002, FIFA World Cup 2006 and UEFA Championship 2006, we take logo transition detection as an effective replacement of replay detection. A two-pass system was developed, including a five-layer adaboost classifier and a logo template matching throughout an entire video. The five-layer adaboost utilises shot duration, average game pitch ratio, average motion, sequential colour histogram and shot frequency between two neighbouring logo transitions, to filter out logo transition candidates. Subsequently, a logo template is constructed and employed to find all transition logo sequences. The precision and recall of this system in replay detection is 100% in a five-game evaluation collection. An attack structure is a team competition for a score. Hence, this structure is a conceptually fundamental unit of a football video as well as other sports videos. We review the literature of content-based temporal structures, such as play-break structure, and develop a three-step system for automatic attack structure decomposition. Four content-based shot classes, namely, play, focus, replay and break were identified by low level visual features. A four-state hidden Markov model was trained to simulate transition processes among these shot classes. Since attack structures are the longest repetitive temporal unit in a sports video, a suffix tree is proposed to find the longest repetitive substring in the label sequence of shot class transitions. These occurrences of this substring are regarded as a kernel of an attack hidden Markov process. Therefore, the decomposition of attack structure becomes a boundary likelihood comparison between two Markov chains. Highlights are what attract notice. Attention is a psychological measurement of “notice ”. A brief survey of attention psychological background, attention estimation from vision and auditory, and multiple modality attention fusion is presented. We propose two attention models for sports video analysis, namely, the role-based attention model and the multiresolution autoregressive framework. The role-based attention model is based on the perception structure during watching video. This model removes reflection bias among modality salient signals and combines these signals by reflectors. The multiresolution autoregressive framework (MAR) treats salient signals as a group of smooth random processes, which follow a similar trend but are filled with noise. This framework tries to estimate a noise-less signal from these coarse noisy observations by a multiple resolution analysis. Related algorithms are developed, such as event segmentation on a MAR tree and real time event detection. The experiment shows that these attention-based approach can find goal events at a high precision. Moreover, results of MAR-based highlight detection on the final game of FIFA 2002 and 2006 are highly similar to professionally labelled highlights by BBC and FIFA

Eddy current automatic flaw detection system for heat exchanger tubes in steam generators

In this dissertation we present an automatic flaw detection system for heat exchanger tubes in steam generators. The system utilizes two well-known techniques, wavelets and fuzzy logic, to automatically detect the flaws in tubing data. The analysis of eddy current inspection data is a difficult task which requires intensive labor by experienced human analysts. To aid the analysts, an accurate and consistent automatic data analysis package was developed. The software development is divided into three parts: data preprocessing, wavelet analysis, and a fuzzy inference system. The data preprocessing procedure is used to set up a signal analysis standard for different data and also to remove the variations due to lift-off and other geometrical effects. The wavelet technique is used to reduce noise and identify possible flaw indications. Due to multiresolution and the unique time-frequency localization properties of the wavelet transform, the flaw signals have specific characteristics in the wavelet domain. We fully utilize those characteristics to distinguish flaw indications from noise. To further evaluate the flaw candidates and reduce false calls, we invoked fuzzy logic to discriminate between true positives and false positives. A template matching technique and fuzzy inference system were developed. The template matching technique uses signals from artificial flaws as templates to match with possible flaw signals and execute a normalized complex crosscorrelation. Through this process, we obtain both phase and shape information which are placed into a fuzzy inference system for final decision making. A rigorous test of the system using actual inspection data was undertaken. Results from tests indicate that the new techniques show a great deal of promise for automatic flaw detection. Investigating the novel techniques and integrating them into a system are the major contribution of this work

Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

Enhancing the Potential of the Conventional Gaussian Mixture Model for Segmentation: from Images to Videos

Segmentation in images and videos has continuously played an important role in image processing, pattern recognition and machine vision. Despite having been studied for over three decades, the problem of segmentation remains challenging yet appealing due to its ill-posed nature. Maintaining spatial coherence, particularly at object boundaries, remains difficult for image segmentation. Extending to videos, maintaining spatial and temporal coherence, even partially, proves computationally burdensome for recent methods. Finally, connecting these two, foreground segmentation, also known as background suppression, suffers from noisy or dynamic backgrounds, slow foregrounds and illumination variations, to name a few. This dissertation focuses more on probabilistic model based segmentation, primarily due to its applicability in images as well as videos, its past success and mainly because it can be enhanced by incorporating spatial and temporal cues. The first part of the dissertation focuses on enhancing conventional GMM for image segmentation using Bilateral filter due to its power of spatial smoothing while preserving object boundaries. Quantitative and qualitative evaluations are done to show the improvements over a number of recent approaches. The later part of the dissertation concentrates on enhancing GMM towards foreground segmentation as a connection between image and video segmentation. First, we propose an efficient way to include multiresolution features in GMM. This novel procedure implicitly incorporates spatial information to improve foreground segmentation by suppressing noisy backgrounds. The procedure is shown with Wavelets, and gradually extended to propose a generic framework to include other multiresolution decompositions. Second, we propose a more accurate foreground segmentation method by enhancing GMM with the use of Adaptive Support Weights and Histogram of Gradients. Extensive analyses, quantitative and qualitative experiments are presented to demonstrate their performances as comparable to other state-of-the-art methods. The final part of the dissertation proposes the novel application of GMM towards spatio-temporal video segmentation connecting spatial segmentation for images and temporal segmentation to extract foreground. The proposed approach has a simple architecture and requires a low amount of memory for processing. The analysis section demonstrates the architectural efficiency over other methods while quantitative and qualitative experiments are carried out to show the competitive performance of the proposed method

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

Unsupervised multi-scale change detection from SAR imagery for monitoring natural and anthropogenic disasters

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Radar remote sensing can play a critical role in operational monitoring of natural and anthropogenic disasters. Despite its all-weather capabilities, and its high performance in mapping, and monitoring of change, the application of radar remote sensing in operational monitoring activities has been limited. This has largely been due to: (1) the historically high costs associated with obtaining radar data; (2) slow data processing, and delivery procedures; and (3) the limited temporal sampling that was provided by spaceborne radar-based satellites. Recent advances in the capabilities of spaceborne Synthetic Aperture Radar (SAR) sensors have developed an environment that now allows for SAR to make significant contributions to disaster monitoring. New SAR processing strategies that can take full advantage of these new sensor capabilities are currently being developed. Hence, with this PhD dissertation, I aim to: (i) investigate unsupervised change detection techniques that can reliably extract signatures from time series of SAR images, and provide the necessary flexibility for application to a variety of natural, and anthropogenic hazard situations; (ii) investigate effective methods to reduce the effects of speckle and other noise on change detection performance; (iii) automate change detection algorithms using probabilistic Bayesian inferencing; and (iv) ensure that the developed technology is applicable to current, and future SAR sensors to maximize temporal sampling of a hazardous event. This is achieved by developing new algorithms that rely on image amplitude information only, the sole image parameter that is available for every single SAR acquisition. The motivation and implementation of the change detection concept are described in detail in Chapter 3. In the same chapter, I demonstrated the technique's performance using synthetic data as well as a real-data application to map wildfire progression. I applied Radiometric Terrain Correction (RTC) to the data to increase the sampling frequency, while the developed multiscaledriven approach reliably identified changes embedded in largely stationary background scenes. With this technique, I was able to identify the extent of burn scars with high accuracy. I further applied the application of the change detection technology to oil spill mapping. The analysis highlights that the approach described in Chapter 3 can be applied to this drastically different change detection problem with only little modification. While the core of the change detection technique remained unchanged, I made modifications to the pre-processing step to enable change detection from scenes of continuously varying background. I introduced the Lipschitz regularity (LR) transformation as a technique to normalize the typically dynamic ocean surface, facilitating high performance oil spill detection independent of environmental conditions during image acquisition. For instance, I showed that LR processing reduces the sensitivity of change detection performance to variations in surface winds, which is a known limitation in oil spill detection from SAR. Finally, I applied the change detection technique to aufeis flood mapping along the Sagavanirktok River. Due to the complex nature of aufeis flooded areas, I substituted the resolution-preserving speckle filter used in Chapter 3 with curvelet filters. In addition to validating the performance of the change detection results, I also provide evidence of the wealth of information that can be extracted about aufeis flooding events once a time series of change detection information was extracted from SAR imagery. A summary of the developed change detection techniques is conducted and suggested future work is presented in Chapter 6

Speckle Noise Reduction via Homomorphic Elliptical Threshold Rotations in the Complex Wavelet Domain

Many clinicians regard speckle noise as an undesirable artifact in ultrasound images masking the underlying pathology within a patient. Speckle noise is a random interference pattern formed by coherent radiation in a medium containing many sub-resolution scatterers. Speckle has a negative impact on ultrasound images as the texture does not reflect the local echogenicity of the underlying scatterers. Studies have shown that the presence of speckle noise can reduce a physician's ability to detect lesions by a factor of eight. Without speckle, small high-contrast targets, low contrast objects, and image texture can be deduced quite readily. Speckle filtering of medical ultrasound images represents a critical pre-processing step, providing clinicians with enhanced diagnostic ability. Efficient speckle noise removal algorithms may also find applications in real time surgical guidance assemblies. However, it is vital that regions of interests are not compromised during speckle removal. This research pertains to the reduction of speckle noise in ultrasound images while attempting to retain clinical regions of interest. Recently, the advance of wavelet theory has lead to many applications in noise reduction and compression. Upon investigation of these two divergent fields, it was found that the speckle noise tends to rotate an image's homomorphic complex-wavelet coefficients. This work proposes a new speckle reduction filter involving a counter-rotation of these complex-wavelet coefficients to mitigate the presence of speckle noise. Simulations suggest the proposed denoising technique offers superior visual quality, though its signal-to-mean-square-error ratio (S/MSE) is numerically comparable to adaptive frost and kuan filtering. This research improves the quality of ultrasound medical images, leading to improved diagnosis for one of the most popular and cost effective imaging modalities used in clinical medicine

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

New methods for automated NMD data analysis and protein structure determination

Die Ermittlung von Proteinstukturen mittels NMR-Spektroskopie ist ein komplexer Prozess, wobei die Resonanzfrequenzen und die Signalintensitäten den Atomen des Proteins zugeordnet werden. Zur Bestimmung der räumlichen Proteinstruktur sind folgende Schritte erforderlich: die Präparation der Probe und 15N/13C Isotopenanreicherung, Durchführung der NMR Experimente, Prozessierung der Spektren, Bestimmung der Signalresonanzen ('Peak-picking'), Zuordnung der chemischen Verschiebungen, Zuordnung der NOESY-Spektren und das Sammeln von konformationellen Strukturparametern, Strukturrechnung und Strukturverfeinerung. Aktuelle Methoden zur automatischen Strukturrechnung nutzen eine Reihe von Computeralgorithmen, welche Zuordnungen der NOESY-Spektren und die Strukturrechnung durch einen iterativen Prozess verbinden. Obwohl neue Arten von Strukturparametern wie dipolare Kopplungen, Orientierungsinformationen aus kreuzkorrelierten Relaxationsraten oder Strukturinformationen, die sich in Gegenwart paramagnetischer Zentren in Proteinen ergeben, wichtige Neuerungen für die Proteinstrukturrechnung darstellen, sind die Abstandsinformationen aus NOESY-Spektren weiterhin die wichtigste Basis für die NMR-Strukturbestimmung. Der hohe zeitliche Aufwand des 'peak-picking' in NOESY-Spektren ist hauptsächlich bedingt durch spektrale Überlagerung, Rauschsignale und Artefakte in NOESY-Spektren. Daher werden für das effizientere automatische 'Peak-picking' zuverlässige Filter benötigt, um die relevanten Signale auszuwählen. In der vorliegenden Arbeit wird ein neuer Algorithmus für die automatische Proteinstrukturrechnung beschrieben, der automatisches 'Peak-picking' von NOESY-Spektren beinhaltet, die mit Hilfe von Wavelets entrauscht wurden. Der kritische Punkt dieses Algorithmus ist die Erzeugung inkrementeller Peaklisten aus NOESY-Spektren, die mit verschiedenen auf Wavelets basierenden Entrauschungsprozeduren prozessiert wurden. Mit Hilfe entrauschter NOESY-Spektren erhält man Signallisten mit verschiedenen Konfidenzbereichen, die in unterschiedlichen Schritten der kombinierten NOE-Zuordnung/Strukturrechnung eingesetzt werden. Das erste Strukturmodell beruht auf stark entrauschten Spektren, die die konservativste Signalliste mit als weitgehend sicher anzunehmenden Signalen ergeben. In späteren Stadien werden Signallisten aus weniger stark entrauschten Spektren mit einer größeren Anzahl von Signalen verwendet. Die Auswirkung der verschiedenen Entrauschungsprozeduren auf Vollständigkeit und Richtigkeit der NOESY Peaklisten wurde im Detail untersucht. Durch die Kombination von Wavelet-Entrauschung mit einem neuen Algorithmus zur Integration der Signale in Verbindung mit zusätzlichen Filtern, die die Konsistenz der Peakliste prüfen ('Network-anchoring' der Spinsysteme und Symmetrisierung der Peakliste), wird eine schnelle Konvergenz der automatischen Strukturrechnung erreicht. Der neue Algorithmus wurde in ARIA integriert, einem weit verbreiteten Computerprogramm für die automatische NOE-Zuordnung und Strukturrechnung. Der Algorithmus wurde an der Monomereinheit der Polysulfid-Schwefel-Transferase (Sud) aus Wolinella succinogenes verifiziert, deren hochaufgelöste Lösungsstruktur vorher auf konventionelle Weise bestimmt wurde. Neben der Möglichkeit zur Bestimmung von Proteinlösungsstrukturen bietet sich die NMR-Spektroskopie auch als wirkungsvolles Werkzeug zur Untersuchung von Protein-Ligand- und Protein-Protein-Wechselwirkungen an. Sowohl NMR Spektren von isotopenmarkierten Proteinen, als auch die Spektren von Liganden können für das 'Screening' nach Inhibitoren benutzt werden. Im ersten Fall wird die Sensitivität der 1H- und 15N-chemischen Verschiebungen des Proteinrückgrats auf kleine geometrische oder elektrostatische Veränderungen bei der Ligandbindung als Indikator benutzt. Als 'Screening'-Verfahren, bei denen Ligandensignale beobachtet werden, stehen verschiedene Methoden zur Verfügung: Transfer-NOEs, Sättigungstransferdifferenzexperimente (STD, 'saturation transfer difference'), ePHOGSY, diffusionseditierte und NOE-basierende Methoden. Die meisten dieser Techniken können zum rationalen Design von inhibitorischen Verbindungen verwendet werden. Für die Evaluierung von Untersuchungen mit einer großen Anzahl von Inhibitoren werden effiziente Verfahren zur Mustererkennung wie etwa die PCA ('Principal Component Analysis') verwendet. Sie eignet sich zur Visualisierung von Ähnlichkeiten bzw. Unterschieden von Spektren, die mit verschiedenen Inhibitoren aufgenommen wurden. Die experimentellen Daten werden zuvor mit einer Serie von Filtern bearbeitet, die u.a. Artefakte reduzieren, die auf nur kleinen Änderungen der chemischen Verschiebungen beruhen. Der am weitesten verbreitete Filter ist das sogenannte 'bucketing', bei welchem benachbarte Punkte zu einen 'bucket' aufsummiert werden. Um typische Nachteile der 'bucketing'-Prozedur zu vermeiden, wurde in der vorliegenden Arbeit der Effekt der Wavelet-Entrauschung zur Vorbereitung der NMR-Daten für PCA am Beispiel vorhandener Serien von HSQC-Spektren von Proteinen mit verschiedenen Liganden untersucht. Die Kombination von Wavelet-Entrauschung und PCA ist am effizientesten, wenn PCA direkt auf die Wavelet-Koeffizienten angewandt wird. Durch die Abgrenzung ('thresholding') der Wavelet-Koeffizienten in einer Multiskalenanalyse wird eine komprimierte Darstellung der Daten erreicht, welche Rauschartefakte minimiert. Die Kompression ist anders als beim 'bucketing' keine 'blinde' Kompression, sondern an die Eigenschaften der Daten angepasst. Der neue Algorithmus kombiniert die Vorteile einer Datenrepresentation im Wavelet-Raum mit einer Datenvisualisierung durch PCA. In der vorliegenden Arbeit wird gezeigt, dass PCA im Wavelet- Raum ein optimiertes 'clustering' erlaubt und dabei typische Artefakte eliminiert werden. Darüberhinaus beschreibt die vorliegende Arbeit eine de novo Strukturbestimmung der periplasmatischen Polysulfid-Schwefel-Transferase (Sud) aus dem anaeroben gram-negativen Bakterium Wolinella succinogenes. Das Sud-Protein ist ein polysulfidbindendes und transferierendes Enzym, das bei niedriger Polysulfidkonzentration eine schnelle Polysulfid-Schwefel-Reduktion katalysiert. Sud ist ein 30 kDa schweres Homodimer, welches keine prosthetischen Gruppen oder schwere Metallionen enthält. Jedes Monomer enhält ein Cystein, welches kovalent bis zu zehn Polysulfid-Schwefel (Sn 2-) Ionen bindet. Es wird vermutet, dass Sud die Polysulfidkette auf ein katalytischen Molybdän-Ion transferiert, welches sich im aktiven Zentrum des membranständigen Enzyms Polysulfid-Reduktase (Psr) auf dessen dem Periplasma zugewandten Seite befindet. Dabei wird eine reduktive Spaltung der Kette katalysiert. Die Lösungsstruktur des Homodimeres Sud wurde mit Hilfe heteronuklearer, mehrdimensionaler NMR-Techniken bestimmt. Die Struktur beruht auf von NOESY-Spektren abgeleiteten Distanzbeschränkungen, Rückgratwasserstoffbindungen und Torsionswinkeln, sowie auf residuellen dipolaren Kopplungen, die für die Verfeinerung der Struktur und für die relative Orientierung der Monomereinheiten wichtig waren. In den NMR Spektren der Homodimere haben alle symmetrieverwandte Kerne äquivalente magnetische Umgebungen, weshalb ihre chemischen Verschiebungen entartet sind. Die symmetrische Entartung vereinfacht das Problem der Resonanzzuordnung, da nur die Hälfte der Kerne zugeordnet werden müssen. Die NOESY-Zuordnung und die Strukturrechnung werden dadurch erschwert, dass es nicht möglich ist, zwischen den Intra-Monomer-, Inter-Monomer- und Co-Monomer- (gemischten) NOESY-Signalen zu unterscheiden. Um das Problem der Symmetrie-Entartung der NOESY-Daten zu lösen, stehen zwei Möglichkeiten zur Verfügung: (I) asymmetrische Markierungs-Experimente, um die intra- von den intermolekularen NOESY-Signalen zu unterscheiden, (II) spezielle Methoden der Strukturrechnung, die mit mehrdeutigen Distanzbeschränkungen arbeiten können. Die in dieser Arbeit vorgestellte Struktur wurde mit Hilfe der Symmetrie-ADR- ('Ambigous Distance Restraints') Methode in Kombination mit Daten von asymetrisch isotopenmarkierten Dimeren berechnet. Die Koordinaten des Sud-Dimers zusammen mit den NMR-basierten Strukturdaten wur- den in der RCSB-Proteindatenbank unter der PDB-Nummer 1QXN abgelegt. Das Sud-Protein zeigt nur wenig Homologie zur Primärsequenz anderer Proteine mit ähnlicher Funktion und bekannter dreidimensionaler Struktur. Bekannte Proteine sind die Schwefeltransferase oder das Rhodanese-Enzym, welche beide den Transfer von einem Schwefelatom eines passenden Donors auf den nukleophilen Akzeptor (z.B von Thiosulfat auf Cyanid) katalysieren. Die dreidimensionalen Strukturen dieser Proteine zeigen eine typische a=b Topologie und haben eine ähnliche Umgebung im aktiven Zentrum bezüglich der Konformation des Proteinrückgrades. Die Schleife im aktiven Zentrum umgibt das katalytische Cystein, welches in allen Rhodanese-Enzymen vorhanden ist, und scheint im Sud-Protein flexibel zu sein (fehlende Resonanzzuordnung der Aminosäuren 89-94). Das Polysulfidende ragt aus einer positiv geladenen Bindungstasche heraus (Reste: R46, R67, K90, R94), wo Sud wahrscheinlich in Kontakt mit der Polysulfidreduktase tritt. Das strukturelle Ergebnis wurde durch Mutageneseexperimente bestätigt. In diesen Experimenten konnte gezeigt werden, dass alle Aminosäurereste im aktiven Zentrum essentiell für die Schwefeltransferase-Aktivität des Sud-Proteins sind. Die Substratbindung wurde früher durch den Vergleich von [15N,1H]-TROSY-HSQC-Spektren des Sud-Proteins in An- und Abwesenheit des Polysulfidliganden untersucht. Bei der Substratbindung scheint sich die lokale Geometrie der Polysulfidbindungsstelle und der Dimerschnittstelle zu verändern. Die konformationellen Änderungen und die langsame Dynamik, hervorgerufen durch die Ligandbindung können die weitere Polysulfid-Schwefel-Aktivität auslösen. Ein zweites Polysulfid-Schwefeltransferaseprotein (Str, 40 kDa) mit einer fünffach höheren nativen Konzentration im Vergleich zu Sud wurde im Bakterienperiplasma von Wolinella succinogenes entdeckt. Es wird angenommen, dass beide Protein einen Polysulfid-Schwefel-Komplex bilden, wobei Str wässriges Polysulfid sammelt und an Sud abgibt, welches den Schwefeltransfer zum katalytischen Molybdän-Ion auf das aktive Zentrum der dem Periplasma zugewandten Seite der Polysulfidreduktase durchführt. Änderungen chemischer Verschiebungen in [15N,1H]-TROSY-HSQC-Spektren zeigen, dass ein Polysulfid-Schwefeltransfer zwischen Str und Sud stattfindet. Eine mögliche Protein-Protein-Wechselwirkungsfläche konnte bestimmt werden. In der Abwesenheit des Polysulfidsubstrates wurden keine Wechselwirkungen zwischen Sud und Str beobachtet, was die Vermutung bestätigt, dass beide Proteine nur dann miteinander wechselwirken und den Polysulfid-Schwefeltransfer ermöglichen, wenn als treibende Kraft Polysulfid präsent ist

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications