Signal processing python practical (10 Ergebnisse)

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Zustand: New.

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Taschenbuch. Zustand: Neu. Neuware - This book explores the domain of signal processing using Python, with the help of working examples and accompanying code. The book introduces the concepts of Python programming via signal processing with numerous hands-on examples and code snippets. The book will enable readers to appreciate the power of Python in this field and write their code to implement complex signal processing algorithms such as signal compression, cleaning, segmentation, decomposition, and feature extraction and be able to incorporate machine learning models using relevant Python libraries. This book aims to bring together professionals from academia and industry to ignite new developments and techniques in the domain of signal processing with Python.Key Features: - Hands-on Python examples and code for each chapter. - Covers basic to advanced topics. - Focuses on practical applications. - Includes machine learning-based applications.…

Taschenbuch. Zustand: Neu. A Practical Introduction to Digital Signal Processing with Python | Akhtar Nadaf (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206164913 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

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Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware - From raw radar samples to a working sensor application-without piecing together a dozen disconnected tutorials. Does radar theory still feel disconnected from hardware configuration and real measurements Are clutter, multipath, or false detections making results difficult to trust Do unfamiliar settings and incomplete examples slow each new project Thomas Bench's workbench-friendly manual follows a practical path from radar fundamentals and sensor selection through FMCW configuration, raw ADC capture, Python processing, target detection, integration, and troubleshooting. Worked calculations, checkpoints, and reference outputs connect the equations to real engineering decisions.Inside, you'll discover: ? Short-Range Radar Fundamentals? CW Doppler Versus FMCW? 24 GHz and mmWave Sensors? Hardware and Interface Selection? Range-Resolution Calculations? Chirp Configuration and ADC Capture? Python Setup and Data Parsing? FTs, Windowing, and Spectral Peaks? Range-Doppler Map Construction? CFAR Target Detection? Clutter and Multipath Diagnosis? Calibration and Performance Checks? ESP32 and Raspberry Pi Integration? Presence, Movement, and Robotics Projects? Quick-Reference Formulas and Test Checklists No extensive radar background Concepts build progressively, with equations and variables explained. Concerned about hardware cost or compatibility Selection guidance and synthetic-data exercises help readers begin with an appropriate setup. Because modules differ, the book distinguishes transferable principles from device-specific settings rather than promising universal plug-and-play results. Scroll up and click the 'Add to Cart' button now to start building, testing, and understanding your own short-range radar systems with practical FMCW, mmWave, and hands-on project guidance.…

Buch. Zustand: Neu. Neuware - This book explores the domain of signal processing using Python, with the help of working examples and accompanying code. The book introduces the concepts of Python programming via signal processing with numerous hands-on examples and code snippets. The book will enable readers to appreciate the power of Python in this field and write their code to implement complex signal processing algorithms such as signal compression, cleaning, segmentation, decomposition, and feature extraction and be able to incorporate machine learning models using relevant Python libraries. This book aims to bring together professionals from academia and industry to ignite new developments and techniques in the domain of signal processing with Python.Key Features: - Hands-on Python examples and code for each chapter. - Covers basic to advanced topics. - Focuses on practical applications. - Includes machine learning-based applications.…