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|a 9783658180577
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|a 10.1007/978-3-658-18057-7
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|a Heredia Conde, Miguel.
|e author.
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|4 http://id.loc.gov/vocabulary/relators/aut
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|a Compressive Sensing for the Photonic Mixer Device
|h [electronic resource] :
|b Fundamentals, Methods and Results /
|c by Miguel Heredia Conde.
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|a 1st ed. 2017.
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|a Wiesbaden :
|b Springer Fachmedien Wiesbaden :
|b Imprint: Springer Vieweg,
|c 2017.
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|a XXXIII, 496 p. 97 illus., 10 illus. in color.
|b online resource.
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|a Phase-Shift-Based Time-of-Flight Imaging Systems -- Fundamentals of Compressive Sensing -- High Resolution Characterization of PMD Pixels -- Compressive Sensing in Spatial and Time-Frequency Domains -- CS-PMD: A Compressive Sensing ToF Camera based on the Photonic Mixer Device.
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|a Miguel Heredia Conde aims at finding novel ways to fit the valuable mathematical results of the Compressive Sensing (CS) theory to the specific case of the Photonic Mixer Device (PMD).To this end, methods are presented that take profit of the sparsity of the signals gathered by PMD sensors. In his research, the author reveals that CS enables outstanding tradeoffs between sensing effort and depth error reduction or resolution enhancement. Contents Phase-Shift-Based Time-of-Flight Imaging Systems Fundamentals of Compressive Sensing High Resolution Characterization of PMD Pixels Compressive Sensing in Spatial and Time-Frequency Domains CS-PMD: A Compressive Sensing ToF Camera based on the Photonic Mixer Device Target Groups< Lecturers and students of computer science, electronic engineering and robotics Researchers and trainees working in application areas where depth sensing is required The Author Dr. Miguel Heredia Conde studied industrial engineering with specialization in automation and electronics at the University of Vigo, Spain. He defended his PhD thesis in engineering at the University of Siegen, Faculty of Science and Technology, Germany. There, he is currently head of the research group on compressive sensing for time-of-flight sensors at the Center for Sensor Systems (ZESS).
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|a Optical data processing.
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|a Data structures (Computer science).
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|a Applied mathematics.
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|a Engineering mathematics.
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|a Computer Imaging, Vision, Pattern Recognition and Graphics.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I22005
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|a Data Structures and Information Theory.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I15009
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|a Mathematical and Computational Engineering.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T11006
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9783658180560
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|i Printed edition:
|z 9783658180584
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|u https://doi.org/10.1007/978-3-658-18057-7
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|a ZDB-2-SCS
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|a ZDB-2-SXCS
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|a Computer Science (SpringerNature-11645)
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|a Computer Science (R0) (SpringerNature-43710)
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