Compressive Sensing
Applications to Sensor Systems and Image Processing
Seiten
2023
De Gruyter (Verlag)
978-3-11-033531-6 (ISBN)
De Gruyter (Verlag)
978-3-11-033531-6 (ISBN)
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Compressive sensing is a new mathematical technique in signal processing. It enables, for example, optical, radar, MRI and X-ray imaging systems with limited capabilities in bandwidth or resolution to recover "natural" signals with high accuracy. As a consequence, new system architectures can be devised where standard regular sampling is not attainable due to sensor power consumption, size, cost, or technological limitations. This graduate textbook provides detailed background for study and research in compressive sensing, including signal models, measurement schemes, recovery algorithms, highlighting recent theoretical results and showing a broad range of applications. Due to its background information and numerous practical applications, it is an ideal resource for researchers, graduate students and practitioners who want to join this exciting research area.
Joachim Ender, Fraunhofer-Institut für Hochfrequenzphysik und Radartechnik, Bonn, Germany.
Joachim Ender, Fraunhofer Institute for High Frequency Physics and Radar Techniques, Germany.
| Erscheinungsdatum | 10.05.2022 |
|---|---|
| Reihe/Serie | De Gruyter Textbook |
| Zusatzinfo | 250 col. ill. |
| Verlagsort | Berlin/Boston |
| Sprache | englisch |
| Maße | 170 x 240 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Technik ► Elektrotechnik / Energietechnik | |
| Schlagworte | compressive sensing • Compressive Sensing, Sparse matrices, Radar, Sensor Fusion, Optimization, Signal Processing, Optical detectors • Euripides • Fragmente • Komprimierte Abtastung • Optical Detectors • Optimization • Sensor Fusion • Signal Processing • Signalverarbeitung • sparse matrices, radars • Tragödien • TUSC |
| ISBN-10 | 3-11-033531-X / 311033531X |
| ISBN-13 | 978-3-11-033531-6 / 9783110335316 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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