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We propose a variation of online paging in two-level memory systems where pages in the fast cache get modified and therefore have to be explicitly written back to the slow memory upon evictions. For increased performance, up to alpha arbitrary pages can be moved from the cache to the slow memory within a single joint eviction, whereas fetching pages from the slow memory is still performed on a one-by-one basis. The main objective in this new alpha-paging scenario is to bound the number of evictions. After providing experimental evidence that alpha-paging can adequately model flash-memory devices in the context of translation layers we turn to the theoretical connections between alpha-paging and standard paging. We give lower bounds for deterministic and randomized alpha-paging algorithms. For deterministic algorithms, we show that an adaptation of LRU is strongly competitive, while for the randomized case we show that by adapting the classical Mark algorithm we get an algorithm with a competitive ratio larger than the lower bound by a multiplicative factor of approximately 1.7.
Die Menge digital zur Verfügung stehender Dokumente wächst zunehmend. Umso wichtiger sind adäquate Methoden, um sehr große Dokumentkollektionen durch-suchen zu können. Im Gegensatz zur exakten Suche, bei der nach Dokumenten mit bekannten Dateinamen gesucht wird, werden Techniken des Information Retrieval (IR) dazu eingesetzt, relevante Ergebnisse zu einer Anfrage ausfindig zu machen. Seit einigen Jahren werden verstärkt Kollektionen mit strukturierten Dokumenten durch¬sucht, insbesondere seit Durchsetzung der eXtensible Markup Language (XML) als offizieller Standard des World Wide Web Consortiums (W3C). Mittlerweile gibt es eine Reihe von Forschungsansätzen, bei denen IR-Methoden auf XML-Dokumente angewendet werden. XML Information Retrieval (XML-IR) nutzt dabei die Struktur der Dokumente, um die Suche nach und in denselben effektiver zu machen, d.h. die Qualität von Suchergebnissen zu verbessern, beispielsweise durch Fokussierung auf besonders relevante Dokumentteile. Die bisherigen Lösungen beziehen sich jedoch alle auf zentralisierte Stand-Alone Suchmaschinen zu Forschungszwecken. Sehr große, über eine Vielzahl von Rechnern verteilte Datenkollektionen lassen sich damit nicht durchsuchen. Techniken für verteiltes XML-IR werden in der Praxis auch dort benötigt, wo das zu durchsuchende System aus einer Vielzahl lokaler, heterogener XML-Kollektionen besteht, deren Benutzer ihre Dokumente nicht auf einem zent¬ralen Server speichern wollen oder können; solche Benutzer schließen sich häufig in Form eines dezentralen Peer-to-Peer (P2P) Netzes zusammen. Dennoch gibt es derzeit weder für Systeme im Allgemeinen, noch für P2P-Systeme im Speziellen Suchmaschinen, mit denen nach relevanten Dokumenten gesucht werden kann. In der vorliegenden Dissertation wird daher am Beispiel von P2P-Netzen erstmalig untersucht, inwiefern XML-IR in verteilten Systemen überhaupt effektiv und effizient möglich ist. Dazu wird ein allgemeines Architekturmodell für die Entwick-lung von P2P-Suchmaschinen für XML-Retrieval entworfen, in dem Funktionalität aus den Bereichen XML-IR und P2P in abstrakten Schichten angeordnet ist. Das Modell wird als Grundlage für den Entwurf einer konkreten P2P-Suchmaschine für XML-IR verwendet. Es werden dazu verschiedene Techniken für verteiltes XML-IR entwickelt, um die einzelnen Phasen der Suche umzusetzen: Indizierung der Doku¬mente, Routing der Anfragen, Ranking geeigneter Dokumente und Retrieval von Ergebnissen. Insbesondere die Problematik von aus mehreren Suchbegriffen bestehenden Multitermanfragen sowie Verteilungsaspekte werden berücksichtigt. Neben der zu erzie-lenden Suchqualität steht vor allem der notwendige Kommunikations¬aufwand im Vordergrund. Die entwickelten Methoden werden in Form einer P2P-Suchmaschine für verteiltes XML-Retrieval implementiert, die aus fast 40.000 Zeilen Java-Code besteht. Diese Suchmaschine namens SPIRIX kann voll-funktionsfähig nach XML-Dokumenten in einem P2P-Netz suchen und deren Relevanz inhaltsbasiert bewerten. Für die Kommunikation zwischen Peers wird ein P2P-Protokoll namens SpirixDHT entworfen, das auf Basis von Chord arbeitet und speziell für den Einsatz von XML-IR angepasst wird. Für die Evaluierung der entworfenen Techniken wird zunächst die Suchqualität von SPIRIX nachgewiesen. Dies geschieht durch die Teilnahme an INEX, der internationalen Initiative für die Evaluierung von XML-Retrieval. Im Rahmen von INEX werden jedes Jahr XML-IR Lösungen weltweit miteinander verglichen. Für 2008 konnte mit SPIRIX eine Suchpräzision erreicht werden, die vergleichbar mit der Qualität der Top-10 XML-IR Lösungen ist. In weiteren Experimenten werden die entworfenen Methoden für verteiltes XML-Retrieval mit INEX-Werkzeugen evaluiert; dabei werden jeweils die erzielte Such-qualität und der notwendige Aufwand gegenübergestellt. Die gewonnenen Er¬kenn-tnisse werden auf den Routingprozess angewendet; hier ist speziell die Frage-stellung interessant, wie XML-Struktur zur Performanzverbesserung in Bezug auf die Effizienz eines verteilten Systems genutzt werden kann. Die Evaluierung der konzi¬pier¬ten Routingtechniken zeigt eine signifikante Reduzierung der Anzahl versendeter Nachrichten, ihrer Größe und somit der Netzlast, wobei gleichzeitig eine Steigerung der Suchqualität erreicht wird. Im Rahmen der Dissertation wird somit der Nachweis erbracht, dass verteiltes XML-IR sowohl effektiv als auch effizient möglich ist. Zugleich wird gezeigt, wie die Ver¬wendung von XML-IR Techniken beim Routing der Anfragen dazu beitragen kann, den notwendige Suchaufwand – insbesondere den für die Kommunikation zwischen Peers – so weit zu reduzieren, dass das System auch zu einer großen Anzahl von teil¬nehmenden Peers skaliert und trotzdem eine hohe Suchqualität aufrecht erhalten werden kann.
Poster presentation: The analysis of neuronal processes distributed across multiple cortical areas aims at the identification of interactions between signals recorded at different sites. Such interactions can be described by measuring the stability of phase angles in the case of oscillatory signals or other forms of signal dependencies for less regular signals. Before, however, any form of interaction can be analyzed at a given time and frequency, it is necessary to assess whether all potentially contributing signals are present. We have developed a new statistical procedure for the detection of coincident power in multiple simultaneously recorded analog signals, allowing the classification of events as 'non-accidental co-activation'. This method can effectively operate on single trials, each lasting only for a few seconds. Signals need to be transformed into time-frequency space, e.g. by applying a short-time Fourier transformation using a Gaussian window. The discrete wavelet transform (DWT) is used in order to weight the resulting power patterns according to their frequency. Subsequently, the weighted power patterns are binarized via applying a threshold. At this final stage, significant power coincidence is determined across all subgroups of channel combinations for individual frequencies by selecting the maximum ratio between observed and expected duration of co-activation as test statistic. The null hypothesis that the activity in each channel is independent from the activity in every other channel is simulated by independent, random rotation of the respective activity patterns. We applied this procedure to single trials of multiple simultaneously sampled local field potentials (LFPs) obtained from occipital, parietal, central and precentral areas of three macaque monkeys. Since their task was to use visual cues to perform a precise arm movement, co-activation of numerous cortical sites was expected. In a data set with 17 channels analyzed, up to 13 sites expressed simultaneous power in the range between 5 and 240 Hz. On average, more than 50% of active channels participated at least once in a significant power co-activation pattern (PCP). Because the significance of such PCPs can be evaluated at the level of single trials, we are confident that this procedure is useful to study single trial variability with sufficient accuracy that much of the behavioral variability can be explained by the dynamics of the underlying distributed neuronal processes.
Poster presentation: Introduction The ability of neurons to emit different firing patterns is considered relevant for neuronal information processing. In dopaminergic neurons, prominent patterns include highly regular pacemakers with separate spikes and stereotyped intervals, processes with repetitive bursts and partial regularity, and irregular spike trains with nonstationary properties. In order to model and quantify these processes and the variability of their patterns with respect to pharmacological and cellular properties, we aim to describe the two dimensions of burstiness and regularity in a single model framework. Methods We present a stochastic spike train model in which the degree of burstiness and the regularity of the oscillation are described independently and with two simple parameters. In this model, a background oscillation with independent and normally distributed intervals gives rise to Poissonian spike packets with a Gaussian firing intensity. The variability of inter-burst intervals and the average number of spikes in each burst indicate regularity and burstiness, respectively. These parameters can be estimated by fitting the model to the autocorrelograms. This allows to assign every spike train a position in the two-dimensional space described by regularity and burstiness and thus, to investigate the dependence of the firing patterns on different experimental conditions. Finally, burst detection in single spike trains is possible within the model because the parameter estimates determine the appropriate bandwidth that should be used for burst identification. Results and Discussion We applied the model to a sample data set obtained from dopaminergic substantia nigra and ventral tegmental area neurons recorded extracellularly in vivo and studied differences between the firing activity of dopaminergic neurons in wildtype and K-ATP channel knock-out mice. The model is able to represent a variety of discharge patterns and to describe changes induced pharmacologically. It provides a simple and objective classification scheme for the observed spike trains into pacemaker, irregular and bursty processes. In addition to the simple classification, changes in the parameters can be studied quantitatively, also including the properties related to bursting behavior. Interestingly, the proposed algorithm for burst detection may be applicable also to spike trains with nonstationary firing rates if the remaining parameters are unaffected. Thus, the proposed model and its burst detection algorithm can be useful for the description and investigation of neuronal firing patterns and their variability with cellular and experimental conditions.
Poster presentation: An important challenge in neuroscience is understanding how networks of neurons go about processing information. Synapses are thought to play an essential role in cellular information processing however quantitative and mathematical models of the underlying physiologic processes that occur at synaptic active zones are lacking. We are generating mathematical models of synaptic vesicle dynamics at a well-characterized model synapse, the Drosophila larval neuromuscular junction. This synapse's simplicity, accessibility to various electrophysiological recording and imaging techniques, and the genetic malleability intrinsic to Drosophila system make it ideal for computational and mathematical studies. We have employed a reductionist approach and started by modeling single presynaptic boutons. Synaptic vesicles can be divided into different pools; however, a quantitative understanding of their dynamics at the Drosophila neuromuscular junction is lacking [4]. We performed biologically realistic simulations of high and low release probability boutons [3] using partial differential equations (PDE) taking into account not only the evolution in time but also the spatial structure in two dimensions (the extension to three dimensions will be implemented soon). PDEs are solved using UG, a program library for the calculation of multi-dimensional PDEs solved using a finite volume approach and implicit time stepping methods leading to extended linear equation systems be solvedwith multi-grid methods [3,4]. Numerical calculations are done on multi-processor computers for fast calculations using different parameters in order to asses the biological feasibility of different models. In preliminary simulations, we modeled vesicle dynamics as a diffusion process describing exocytosis as Neumann streams at synaptic active zones. The initial results obtained with these models are consistent with experimental data. However, this should be regarded as a work in progress. Further refinements will be implemented, including simulations using morphologically realistic geometries which were generated from confocal scans of the neuromuscular junction using NeuRA (a Neuron Reconstruction Algorithm). Other parameters such as glutamate diffusion and reuptake dynamics, as well as postsynaptic receptor kinetics will be incorporated as well.
Poster presentation: Introduction Dopaminergic neurons in the midbrain show a variety of firing patterns, ranging from very regular firing pacemaker cells to bursty and irregular neurons. The effects of different experimental conditions (like pharmacological treatment or genetical manipulations) on these neuronal discharge patterns may be subtle. Applying a stochastic model is a quantitative approach to reveal these changes. ...
Poster presentation: Introduction The brain is a highly interconnected network of constantly interacting units. Understanding the collective behavior of these units requires a multi-dimensional approach. The results of such analyses are hard to visualize and interpret. Hence tools capable of dealing with such tasks become imperative. ....
The pathogenesis of nodular lymphocyte–predominant Hodgkin lymphoma (NLPHL) and its relationship to other lymphomas are largely unknown. This is partly because of the technical challenge of analyzing its rare neoplastic lymphocytic and histiocytic (L&H) cells, which are dispersed in an abundant nonneoplastic cellular microenvironment. We performed a genome-wide expression study of microdissected L&H lymphoma cells in comparison to normal and other malignant B cells that indicated a relationship of L&H cells to and/or that they originate from germinal center B cells at the transition to memory B cells. L&H cells show a surprisingly high similarity to the tumor cells of T cell–rich B cell lymphoma and classical Hodgkin lymphoma, a partial loss of their B cell phenotype, and deregulation of many apoptosis regulators and putative oncogenes. Importantly, L&H cells are characterized by constitutive nuclear factor {kappa}B activity and aberrant extracellular signal-regulated kinase signaling. Thus, these findings shed new light on the nature of L&H cells, reveal several novel pathogenetic mechanisms in NLPHL, and may help in differential diagnosis and lead to novel therapeutic strategies.
Background Although current molecular clock methods offer greater flexibility in modelling historical evolutionary events, calibration of the clock with dates from the fossil record is still problematic for many groups. Here we implement several new approaches in molecular dating to estimate evolutionary ages of Lacertidae, an Old World family of lizards with a poor fossil record and uncertain phylogeny. Four different models of rate variation are tested in a new program for Bayesian phylogenetic analysis called TreeTime, based on a combination of mitochondrial and nuclear gene sequences. We incorporate paleontological uncertainty into divergence estimates by expressing multiple calibration dates as a range of probabilistic distributions. We also test the reliability of our proposed calibrations by exploring effects of individual priors on posterior estimates. Results According to the most reliable model, as indicated by Bayes factor comparison, modern lacertids arose shortly after the K/T transition and entered Africa about 45 million years ago, with the majority of their African radiation occurring in the Eocene and Oligocene. Our findings indicate much earlier origins for these clades than previously reported, and we discuss our results in light of paleogeographic trends during the Cenozoic. Conclusions This study represents the first attempt to estimate evolutionary ages of a specific group of reptiles exhibiting uncertain phylogenetic relationships, molecular rate variation and a poor fossil record. Our results emphasize the sensitivity of molecular divergence dates to fossil calibrations, and support the use of combined molecular data sets and multiple, well-spaced dates from the fossil record as minimum node constraints. The bioinformatics program used here, TreeTime, is publicly available, and we recommend its use for molecular dating of taxa faced with similar challenges.
Im heutigen Zahlungsverkehr übernehmen in zunehmendem Maße Zahlungen mit Kreditkarten eine entscheidende Rolle. Entsprechend der Verbreitung dieser Art des Zahlungsverkehrs nimmt ebenfalls der Mißbrauch mit diesem bargeldlosen Zahlungsmittel zu. Um die Verluste, die bei dem Kreditkarteninstitut auf diese Weise entstehen, so weit wie möglich einzudämmen, wird versucht, Mißbrauchstransaktionen bei der Autorisierung der Zahlungsaufforderung zu erkennen. Ziel dieser Diplomarbeit ist es zu bestimmen, in wie weit es möglich ist, illegale Transaktionen aus der Menge von Autorisierungsanfragen mit Hilfe adaptiver Algorithmen aufzudecken. Dabei sollen sowohl Methoden aus dem Bereich des Data-Mining, als auch aus den Bereichen der neuronalen Netze benutzt werden. Erschwerend bei der Mißbrauchsanalyse kommt hinzu, daß die Beurteilung der einzelnen Transaktionen in Sekundenbruchteilen abgeschlossen sein muß, um die hohe Anzahl an Autorisierungsanfragen verarbeiten zu können und den Kundenservice auf Seiten des Benutzers und des Händlers auf diese Weise zu optimieren. Weiter handelt es sich bei einem Großteil der bei der Analyse zu Verfügung stehenden Datensätze um symbolische Daten, also alpha-numerisch kodierte Werte, die stellvertretend für verschiedene Eigenschaften verwendet werden. Nur wenige der Transaktionsdaten sind analoger Natur, weisen also eine Linearität auf, die es erlaubt, "Nachbarschaften" zwischen den Daten bestimmen zu können. Damit scheidet eine reine Analyse auf Basis von neuronalen Netzwerken aus. Diese Problematik führte unter anderem zu dem verfolgten Ansatz. Als Grundlage der Analyse dienen bekannte Mißbrauchstransaktionen aus einem Zeitintervall von ungefähr einem Jahr, die jedoch aufgrund der hohen Anzahl nicht komplett als solche mit den eingehenden Transaktionen verglichen werden können, da ein sequentieller Vergleich zu viel Zeit in Anspruch nähme. Im übrigen würde durch einen einfachen Vergleich nur der schon bekannte Mißbrauch erkannt werden; eine Abstraktion der Erkenntnisse aus den Mißbrauchserfahrungen ist nicht möglich. Aus diesem Grund werden diese Mißbrauchstransaktionen mit Hilfe von Methoden aus dem Bereich des Data-Mining verallgemeinert und damit auf ein Minimum, soweit es die Verläßlichkeit dieser Datensätze zuläßt, reduziert. Desweiteren schließt sich eine Analyse der zu diesem Zeitpunkt noch nicht betrachteten analogen Daten an, um die maximale, enthaltene Information aus den Transaktionsdaten zu beziehen. Dafür werden moderne Methoden aus dem Bereich der neuronalen Netzwerke, sogenannte radiale Basisfunktionsnetze, verwendet. Da eine Mißbrauchsanalyse ohne eine entsprechende Profilanalyse unvollständig wäre, wurde abschließend mit den vorhanden Mitteln auf den zugrunde liegenden Daten in Anlehnung an die bisherige Methodik eine solche Profilauswertung und zeitabhängige Analyse realisiert. Mit dem so implementierten Modell wurde versucht, auf allgemeine Art und Weise, Verhaltens- beziehungsweise Transaktionsmuster einzuordnen und mit bei der Mißbrauchsentscheidung einfließen zu lassen. Aus den vorgestellten Analyseverfahren wurden verschiedene Klassifizierungsmodelle entwickelt, die zu guten Ergebnissen auf den Simulationsdaten führen. Es kann gezeigt werden, daß die Mißbrauchserkennung durch eine kombinierte Anwendung aus symbolischer und analoger Auswertung bestmöglich durchzuführen ist.
FIFO is the most prominent queueing strategy due to its simplicity and the fact that it only works with local information. Its analysis within the adversarial queueing theory however has shown, that there are networks that are not stable under the FIFO protocol, even at arbitrarily low rate. On the other hand there are networks that are universally stable, i.e., they are stable under every greedy protocol at any rate r < 1. The question as to which networks are stable under the FIFO protocol arises naturally. We offer the first polynomial time algorithm for deciding FIFO stability and simple-path FIFO stability of a directed network, answering an open question posed in [1, 4]. It turns out, that there are networks, that are FIFO stable but not universally stable, hence FIFO is not a worst case protocol in this sense. Our characterization of FIFO stability is constructive and disproves an open characterization in [4].
The efficient management of large multimedia databases requires the development of new techniques to process, characterize, and search for multimedia objects. Especially in the case of image data, the rapidly growing amount of documents prohibits a manual description of the images’ content. Instead, the automated characterization is highly desirable to support annotation and retrieval of digital images. However, this is a very complex and still unsolved task. To contribute to a solution of this problem, we have developed a mechanism for recognizing objects in images based on the query by example paradigm. Therefore, the most salient image features of an example image representing the searched object are extracted to obtain a scale-invariant object model. The use of this model provides an efficient and robust strategy for recognizing objects in images independently of their size. Further applications of the mechanism are classical recognition tasks such as scene decomposition or object tracking in video sequences.
For the efficient management of large image databases, the automated characterization of images and the usage of that characterization for searching and ordering tasks is highly desirable. The purpose of the project SEMACODE is to combine the still unsolved problem of content-oriented characterization of images with scale-invariant object recognition and modelbased compression methods. To achieve this goal, existing techniques as well as new concepts related to pattern matching, image encoding, and image compression are examined. The resulting methods are integrated in a common framework with the aid of a content-oriented conception. For the application, an image database at the library of the university of Frankfurt/Main (StUB; about 60000 images), the required operations are developed. The search and query interfaces are defined in close cooperation with the StUB project “Digitized Colonial Picture Library”. This report describes the fundamentals and first results of the image encoding and object recognition algorithms developed within the scope of the project.
The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.
Classically, encoding of images by only a few, important components is done by the Principal Component Analysis (PCA). Recently, a data analysis tool called Independent Component Analysis (ICA) for the separation of independent influences in signals has found strong interest in the neural network community. This approach has also been applied to images. Whereas the approach assumes continuous source channels mixed up to the same number of channels by a mixing matrix, we assume that images are composed by only a few image primitives. This means that for images we have less sources than pixels. Additionally, in order to reduce unimportant information, we aim only for the most important source patterns with the highest occurrence probabilities or biggest information called „Principal Independent Components (PIC)“. For the example of a synthetic picture composed by characters this idea gives us the most important ones. Nevertheless, for natural images where no a-priori probabilities can be computed this does not lead to an acceptable reproduction error. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that this definition of PIC implements the classical demand of Shannon’s rate distortion theory.
We study the effect of randomness in the adversarial queueing model. All proofs of instability for deterministic queueing strategies exploit a finespun strategy of insertions by an adversary. If the local queueing decisions in the network are subject to randomness, it is far from obvious, that an adversary can still trick the network into instability. We show that uniform queueing is unstable even against an oblivious adversary. Consequently, randomizing the queueing decisions made to operate a network is not in itself a suitable fix for poor network performances due to packet pileups.
This paper describes a method to treat contextual equivalence in polymorphically typed lambda-calculi, and also how to transfer equivalences from the untyped versions of lambda-calculi to their typed variant, where our specific calculus has letrec, recursive types and is nondeterministic. An addition of a type label to every subexpression is all that is needed, together with some natural constraints for the consistency of the type labels and well-scopedness of expressions. One result is that an elementary but typed notion of program transformation is obtained and that untyped contextual equivalences also hold in the typed calculus as long as the expressions are well-typed. In order to have a nice interaction between reduction and typing, some reduction rules have to be accompanied with a type modification by generalizing or instantiating types.
Motivated by the question of correctness of a specific implementation of concurrent buffers in the lambda calculus with futures underlying Alice ML, we prove that concurrent buffers and handled futures can correctly encode each other. Correctness means that our encodings preserve and reflect the observations of may- and must-convergence. This also shows correctness wrt. program semantics, since the encodings are adequate translations wrt. contextual semantics. While these translations encode blocking into queuing and waiting, we also provide an adequate encoding of buffers in a calculus without handles, which is more low-level and uses busy-waiting instead of blocking. Furthermore we demonstrate that our correctness concept applies to the whole compilation process from high-level to low-level concurrent languages, by translating the calculus with buffers, handled futures and data constructors into a small core language without those constructs.
We show on an abstract level that contextual equivalence in non-deterministic program calculi defined by may- and must-convergence is maximal in the following sense. Using also all the test predicates generated by the Boolean, forall- and existential closure of may- and must-convergence does not change the contextual equivalence. The situation is different if may- and total must-convergence is used, where an expression totally must-converges if all reductions are finite and terminate with a value: There is an infinite sequence of test-predicates generated by the Boolean, forall- and existential closure of may- and total must-convergence, which also leads to an infinite sequence of different contextual equalities.
Driving can be dangerous. Humans become inattentive when performing a monotonous task like driving. Also the risk implied while multi-tasking, like using the cellular phone while driving, can break the concentration of the driver and increase the risk of accidents. Others factors like exhaustion, nervousness and excitement affect the performance of the driver and the response time. Consequently, car manufacturers have developed systems in the last decades which assist the driver under various circumstances. These systems are called driver assistance systems. Driver assistance systems are meant to support the task of driving, and the field of action varies from alerting the driver, with acoustical or optical warnings, to taking control of the car, such as keeping the vehicle in the traffic lane until the driver resumes control. For such a purpose, the vehicle is equipped with on-board sensors which allow the perception of the environment and/or the state of the vehicle. Cameras are sensors which extract useful information about the visual appearance of the environment. Additionally, a binocular system allows the extraction of 3D information. One of the main requirements for most camera-based driver assistance systems is the accurate knowledge of the motion of the vehicle. Some sources of information, like velocimeters and GPS, are of common use in vehicles today. Nevertheless, the resolution and accuracy usually achieved with these systems are not enough for many real-time applications. The computation of ego-motion from sequences of stereo images for the implementation of driving intelligent systems, like autonomous navigation or collision avoidance, constitutes the core of this thesis. This dissertation proposes a framework for the simultaneous computation of the 6 degrees of freedom of ego-motion (rotation and translation in 3D Euclidean space), the estimation of the scene structure and the detection and estimation of independently moving objects. The input is exclusively provided by a binocular system and the framework does not call for any data acquisition strategy, i.e. the stereo images are just processed as they are provided. Stereo allows one to establish correspondences between left and right images, estimating 3D points of the environment via triangulation. Likewise, feature tracking establishes correspondences between the images acquired at different time instances. When both are used together for a large number of points, the result is a set of clouds of 3D points with point-to-point correspondences between clouds. The apparent motion of the 3D points between consecutive frames is caused by a variety of reasons. The most dominant motion for most of the points in the clouds is caused by the ego-motion of the vehicle; as the vehicle moves and images are acquired, the relative position of the world points with respect to the vehicle changes. Motion is also caused by objects moving in the environment. They move independently of the vehicle motion, so the observed motion for these points is the sum of the ego-vehicle motion and the independent motion of the object. A third reason, and of paramount importance in vision applications, is caused by correspondence problems, i.e. the incorrect spatial or temporal assignment of the point-to-point correspondence. Furthermore, all the points in the clouds are actually noisy measurements of the real unknown 3D points of the environment. Solving ego-motion and scene structure from the clouds of points requires some previous analysis of the noise involved in the imaging process, and how it propagates as the data is processed. Therefore, this dissertation analyzes the noise properties of the 3D points obtained through stereo triangulation. This leads to the detection of a bias in the estimation of 3D position, which is corrected with a reformulation of the projection equation. Ego-motion is obtained by finding the rotation and translation between the two clouds of points. This problem is known as absolute orientation, and many solutions based on least squares have been proposed in the literature. This thesis reviews the available closed form solutions to the problem. The proposed framework is divided in three main blocks: 1) stereo and feature tracking computation, 2) ego-motion estimation and 3) estimation of 3D point position and 3D velocity. The first block solves the correspondence problem providing the clouds of points as output. No special implementation of this block is required in this thesis. The ego-motion block computes the motion of the cameras by finding the absolute orientation between the clouds of static points in the environment. Since the cloud of points might contain independently moving objects and outliers generated by false correspondences, the direct computation of the least squares might lead to an erroneous solution. The first contribution of this thesis is an effective rejection rule that detects outliers based on the distance between predicted and measured quantities, and reduces the effects of noisy measurement by assigning appropriate weights to the data. This method is called Smoothness Motion Constraint (SMC). The ego-motion of the camera between two frames is obtained finding the absolute orientation between consecutive clouds of weighted 3D points. The complete ego-motion since initialization is achieved concatenating the individual motion estimates. This leads to a super-linear propagation of the error, since noise is integrated. A second contribution of this dissertation is a predictor/corrector iterative method, which integrates the clouds of 3D points of multiple time instances for the computation of ego-motion. The presented method considerably reduces the accumulation of errors in the estimated ego-position of the camera. Another contribution of this dissertation is a method which recursively estimates the 3D world position of a point and its velocity; by fusing stereo, feature tracking and the estimated ego-motion in a Kalman Filter system. An improved estimation of point position is obtained this way, which is used in the subsequent system cycle resulting in an improved computation of ego-motion. The general contribution of this dissertation is a single framework for the real time computation of scene structure, independently moving objects and ego-motion for automotive applications.