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State Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties

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Book Series: Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory ISSN: 18673813 ISBN: 9783731501244 Year: Volume: 14 Pages: XVIII, 257 p. DOI: 10.5445/KSP/1000036878 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:02:00
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State estimation techniques for centralized, distributed, and decentralized systems are studied. An easy-to-implement state estimation concept is introduced that generalizes and combines basic principles of Kalman filter theory and ellipsoidal calculus. By means of this method, stochastic and set-membership uncertainties can be taken into consideration simultaneously. Different solutions for implementing these estimation algorithms in distributed networked systems are presented.

Simultaneous Tracking and Shape Estimation of Extended Objects

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Book Series: Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory ISSN: 18673813 ISBN: 9783731500780 Year: Volume: 13 Pages: XII, 162 p. DOI: 10.5445/KSP/1000035959 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:01:57
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This work is concerned with the simultaneous tracking and shape estimation of a mobile extended object based on noisy sensor measurements. Novel methods are developed for coping with the following two main challenges: i) The computational complexity due to the nonlinearity and high-dimensionality of the problem and ii) the lack of statistical knowledge about possible measurement sources on the extended object.

Nonlinear state and parameter estimation of spatially distributed systems

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Book Series: Karlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory ISSN: 18673813 ISBN: 9783866443709 Year: Volume: 5 Pages: XI, 153 p. DOI: 10.5445/KSP/1000011485 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:01:58
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In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for identifying various model parameters. The Covariance Bounds Filter (CBF) allows the efficient estimation of widely distributed systems in a decentralized fashion.

Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation

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Book Series: Karlsruher Schriftenreihe Fahrzeugsystemtechnik / Institut für Fahrzeugsystemtechnik ISSN: 18696058 ISBN: 9783731508076 Year: Volume: 62 Pages: XXIV, 196 p. DOI: 10.5445/KSP/1000083492 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Technology (General)
Added to DOAB on : 2019-07-28 18:37:01
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This work provides novel robust and regularized algorithms for parameter estimation with applications in vehicle tractive force prediction and mass estimation. Given a large record of real world data from test runs on public roads, recursive algorithms adjusted the unknown vehicle parameters under a broad variation of statistical assumptions for two linear gray-box models.

Probabilistic Framework for Sensor Management

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Book Series: Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Universität Karlsruhe, Intelligent Sensor-Actuator-Systems Laboratory ISSN: 18673813 ISBN: 9783866444058 Year: Volume: 7 Pages: VI, 159 p. DOI: 10.5445/KSP/1000012224 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 19:59:17
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A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions.

Neural Signal Estimation in the Human Brain

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889199235 Year: Pages: 142 DOI: 10.3389/978-2-88919-923-5 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Neurology
Added to DOAB on : 2016-01-19 14:05:46
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The ultimate goal of functional brain imaging is to provide optimal estimates of the neural signals flowing through the long-range and local pathways mediating all behavioral performance and conscious experience. In functional MRI (Magnetic Resonance Imaging), despite its impressive spatial resolution, this goal has been somewhat undermined by the fact that the fMRI response is essentially a blood-oxygenation level dependent (BOLD) signal that only indirectly reflects the nearby neural activity. The vast majority of fMRI studies restrict themselves to describing the details of these BOLD signals and deriving non-quantitative inferences about their implications for the underlying neural activity. This Frontiers Research Topic welcomed empirical and theoretical contributions that focus on the explicit relationship of non-invasive brain imaging signals to the causative neural activity. The articles presented within this resulting eBook aim to both highlight the importance and improve the non-invasive estimation of neural signals in the human brain. To achieve this aim, the following issues are targeted:(1) The spatial limitations of source localization when using MEG/EEG.(2) The coupling of the BOLD signal to neural activity. Articles discuss how animal studies are fundamental in increasing our understanding of BOLD fMRI signals, analyze how non-neuronal cell types may contribute to the modulation of cerebral blood flow, and use modeling to improve our understanding of how local field potentials are linked to the BOLD signal.(3) The contribution of excitatory and inhibitory neuronal activity to the BOLD signal.(4) Assessment of neural connectivity through the use of resting state data, computational modeling and functional Diffusion Tensor Imaging (fDTI) approaches.

Modeling and Analysis of Signal Transduction Networks

ISBN: 9783038421412 9783038421429 Year: Pages: 232 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Added to DOAB on : 2016-05-12 12:19:39
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Biological pathways, such as signaling networks, are a key component of biological systems of each living cell. In fact, malfunctions of signaling pathways are linked to a number of diseases, and components of signaling pathways are used as potential drug targets. Elucidating the dynamic behavior of the components of pathways, and their interactions, is one of the key research areas of systems biology. Biological signaling networks are characterized by a large number of components and an even larger number of parameters describing the network. Furthermore, investigations of signaling networks are characterized by large uncertainties of the network as well as limited availability of data due to expensive and time-consuming experiments. As such, techniques derived from systems analysis, e.g., sensitivity analysis, experimental design, and parameter estimation, are important tools for elucidating the mechanisms involved in signaling networks. This Special Issue contains papers that investigate a variety of different signaling networks via established, as well as newly developed modeling and analysis techniques.

Ultrafast Ultrasound Imaging

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ISBN: 9783038971276 9783038971283 Year: Pages: 184 DOI: 10.3390/books978-3-03897-128-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medical technology
Added to DOAB on : 2018-09-21 09:01:06
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Among medical imaging modalities, such as computed tomography (CT) andmagnetic resonance imaging (MRI), ultrasound imaging stands out due to itstemporal resolution. Owing to the nature of medical ultrasound imaging, it has beenused for not only observation of the morphology of living organs but also functionalimaging, such as blood flow imaging and evaluation of the cardiac function. Ultrafastultrasound imaging, which has recently become widely available, significantlyincreases the opportunities for medical functional imaging. Ultrafast ultrasoundimaging typically enables imaging frame-rates of up to ten thousand frames persecond (fps). Due to the extremely high temporal resolution, this enablesvisualization of rapid dynamic responses of biological tissues, which cannot beobserved and analyzed by conventional ultrasound imaging. This Special Issueincludes various studies of improvements to the performance of ultrafast ultrasound

Umgebungskartenschätzung aus Sidescan-Sonardaten für ein autonomes Unterwasserfahrzeug

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Book Series: Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe ISSN: 18636489 ISBN: 9783731505419 Year: Volume: 26 Pages: XXV, 331 p. DOI: 10.5445/KSP/1000055793 Language: GERMAN
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:01:57
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This work makes several contributions to the process of estimating elevation maps from side-scan sonar data: A new estimation method that recreates sonar measurements by pre-computed known sonar responses (so called kernels) and then derives a height profile from the kernels used. Additionally, a 3D method based on Markov Random Fields and a side-scan sonar simulation environment for arbitraty 3D scenes featuring different sonar modes and several terrain generators have been developed.

Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation

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Book Series: Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory ISSN: 18673813 ISBN: 9783866449527 Year: Volume: 11 Pages: XIV, 210 p. DOI: 10.5445/KSP/1000031356 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:01:58
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This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference.

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