Artificial intelligence, Mobile robot, Computer vision, Evolutionary algorithm and Data mining are his primary areas of study. His Artificial intelligence research includes elements of Machine learning and Pattern recognition. The concepts of his Mobile robot study are interwoven with issues in Simulation and Motion control.
As part of the same scientific family, Andreas Zell usually focuses on Computer vision, concentrating on Simultaneous localization and mapping and intersecting with Reference image and Feature based. His studies in Evolutionary algorithm integrate themes in fields like Multi-objective optimization, Point, Theoretical computer science and Fitness function. His Data mining study integrates concerns from other disciplines, such as Similarity, SBML, Support vector machine, Systems biology and Cluster analysis.
His primary areas of investigation include Artificial intelligence, Computer vision, Mobile robot, Robot and Pattern recognition. Andreas Zell frequently studies issues relating to Machine learning and Artificial intelligence. Computer vision is closely attributed to Simultaneous localization and mapping in his work.
His biological study deals with issues like Control theory, which deal with fields such as Model predictive control. His Robot research is multidisciplinary, incorporating perspectives in Ball and Simulation. His study focuses on the intersection of Support vector machine and fields such as Data mining with connections in the field of Cluster analysis.
His scientific interests lie mostly in Artificial intelligence, Computer vision, Robot, Mobile robot and Algorithm. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition. His study in Computer vision is interdisciplinary in nature, drawing from both Simultaneous localization and mapping, Visual odometry and Benchmark.
His Robot research integrates issues from Ball, Simulation, Human–computer interaction and Modular design. His study looks at the relationship between Mobile robot and topics such as Motion planning, which overlap with Obstacle avoidance. He works mostly in the field of Algorithm, limiting it down to topics relating to Occupancy grid mapping and, in certain cases, Matching, as a part of the same area of interest.
His primary scientific interests are in Artificial intelligence, Computer vision, Robot, Mobile robot and Robustness. Andreas Zell has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition. In most of his Computer vision studies, his work intersects topics such as Curve fitting.
His research in Robot intersects with topics in Convergence, Multi-agent system, Simulation and Stability theory. His study in Mobile robot is interdisciplinary in nature, drawing from both Identification, Lookup table, CAD, Particle filter and Structured light. His work in Particle filter addresses issues such as Probabilistic logic, which are connected to fields such as Data mining.
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Simulation neuronaler Netze
SNNS (Stuttgart Neural Network Simulator)
Andreas Zell;Niels Mache;Ralf Hübner;Günter Mamier.
Preanalytical Aspects and Sample Quality Assessment in Metabolomics Studies of Human Blood
Peiyuan Yin;Andreas Peter;Holger Franken;Xinjie Zhao.
Clinical Chemistry (2013)
Automatic Take Off, Tracking and Landing of a Miniature UAV on a Moving Carrier Vehicle
Karl Engelbert Wenzel;Andreas Masselli;Andreas Zell.
Journal of Intelligent and Robotic Systems (2011)
An Onboard Monocular Vision System for Autonomous Takeoff, Hovering and Landing of a Micro Aerial Vehicle
Shaowu Yang;Sebastian A. Scherer;Andreas Zell.
Journal of Intelligent and Robotic Systems (2013)
Optimal assignment kernels for attributed molecular graphs
Holger Fröhlich;Jörg K. Wegner;Florian Sieker;Andreas Zell.
international conference on machine learning (2005)
Locating Biologically Active Compounds in Medium-Sized Heterogeneous Datasets by Topological Autocorrelation Vectors: Dopamine and Benzodiazepine Agonists
Henri Bauknecht;Andreas Zell;Harald Bayer;Paul Levi.
Journal of Chemical Information and Computer Sciences (1996)
Vibration-based Terrain Classification Using Support Vector Machines
C. Weiss;H. Frohlich;A. Zell.
intelligent robots and systems (2006)
Large-scale generation of computational models from biochemical pathway maps
Finja Büchel;Nicolas Rodriguez;Neil Swainston;Clemens Wrzodek.
arXiv: Molecular Networks (2013)
Loss of mitochondrial peptidase Clpp leads to infertility, hearing loss plus growth retardation via accumulation of CLPX, mtDNA and inflammatory factors
Suzana Gispert;Dajana Parganlija;Michael Klinkenberg;Stefan Dröse.
Human Molecular Genetics (2013)
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