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Compressive Sensing for the Photonic Mixer Device: Fundamentals, Methods and Results 1st ed. 2017 [Pehme köide]

  • Formaat: Paperback / softback, 496 pages, kõrgus x laius: 210x148 mm, kaal: 679 g, 10 Illustrations, color; 87 Illustrations, black and white; XXXIII, 496 p. 97 illus., 10 illus. in color., 1 Paperback / softback
  • Ilmumisaeg: 26-Apr-2017
  • Kirjastus: Springer Vieweg
  • ISBN-10: 3658180560
  • ISBN-13: 9783658180560
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  • Formaat: Paperback / softback, 496 pages, kõrgus x laius: 210x148 mm, kaal: 679 g, 10 Illustrations, color; 87 Illustrations, black and white; XXXIII, 496 p. 97 illus., 10 illus. in color., 1 Paperback / softback
  • Ilmumisaeg: 26-Apr-2017
  • Kirjastus: Springer Vieweg
  • ISBN-10: 3658180560
  • ISBN-13: 9783658180560
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.     

Acknowledgement vii
Abstract xi
Kurzfassung xv
Nomenclature xxi
List of Figures
xxix
List of Tables
xxxiii
1 Introduction
1(10)
1.1 Motivations and Contributions
4(4)
1.2 Outline
8(3)
2 Phase-Shift-Based Time-of-Flight Imaging Systems
11(78)
2.1 Introduction to Depth Imaging
12(11)
2.2 Phase-Shift-Based Time-of-Flight Imaging Systems
23(26)
2.3 The Photonic Mixer Device (PMD)
49(23)
2.4 Current Limits of the PMD-Based Time-of-Flight Imaging
72(17)
3 Fundamentals of Compressive Sensing
89(118)
3.1 Introduction to Compressive Sensing
89(27)
3.2 Sensing Matrices
116(26)
3.3 Sparsity Bases
142(29)
3.4 Recovery Methods
171(36)
4 Compressive Sensing for the Photonic Mixer Device
207(146)
4.1 Introduction and Application Domains
207(7)
4.2 Solving Preliminary Issues
214(38)
4.3 An Accurate Sensing Model: HR Characterization of PMD Pixels
252(33)
4.4 Sparse Recovery in Spatial Domain
285(33)
4.5 Sparse Recovery in Time-Frequency Domain
318(35)
5 CS-PMD: A Compressive Sensing ToF Camera based on the PMD
353(34)
5.1 General System Description
353(9)
5.2 Hardware
362(9)
5.3 Software: 3D Sparse Recovery from Few Measurements
371(16)
6 Conclusions
387(8)
6.1 Summary
387(3)
6.2 Future Work
390(5)
A Appendix
395(42)
A.1 Cross-Correlation Between Sinusoidal Signals
395(1)
A.2 Cross-Correlation Between Periodic Signals
396(3)
A.3 Phase Shift, Amplitude and Offset Estimation
399(2)
A.4 Depth Measurement Uncertainty
401(1)
A.5 Optical Power Received by a Pixel
402(3)
A.6 Experimental Evaluation of the Delay in the Illumination
405(9)
A.7 Mutual and Matrix Coherences
414(2)
A.8 Adaptive High Dynamic Range: Complementary Material
416(7)
A.9 Inverse Freeman-Tukey Transformation for Poisson Data
423(2)
A.10 Fluorescence Lifetime Microscopy and ToF Imaging
425(3)
A.11 The CS-PMD Camera Prototype
428(7)
A.12 Depth Measurement Uncertainty in the CS-PMD System
435(2)
References 437(58)
Publications 495(1)
First author 495(1)
Coauthor 496
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).