Signal Processing and Machine Learning Theory: Academic Press Library in Signal Processing
Editat de Paulo S.R. Dinizen Limba Engleză Paperback – 28 noi 2023
- Provides quick tutorial reviews of important and emerging topics of research in signal processing-based tools
- Presents core principles in signal processing theory and shows their applications
- Discusses some emerging signal processing tools applied in machine learning methods
- References content on core principles, technologies, algorithms and applications
- Includes references to journal articles and other literature on which to build further, more specific, and detailed knowledge
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Specificații
ISBN-13: 9780323917728
ISBN-10: 0323917720
Pagini: 1234
Dimensiuni: 191 x 235 x 45 mm
Greutate: 1.83 kg
Editura: ELSEVIER SCIENCE
Seria Academic Press Library in Signal Processing
ISBN-10: 0323917720
Pagini: 1234
Dimensiuni: 191 x 235 x 45 mm
Greutate: 1.83 kg
Editura: ELSEVIER SCIENCE
Seria Academic Press Library in Signal Processing
Public țintă
Upper level undergraduates, Graduate students, researchers in electrical and electronic engineeringCuprins
1. Introduction to Signal Processing and Machine Learning Theory
2. Continuous-Time Signals and Systems
3. Discrete-Time Signals and Systems
4. Random Signals and Stochastic Processes
5. Sampling and Quantization
6. Digital Filter Structures and Their Implementation
7. Multi-rate Signal Processing for Software Radio Architectures
8. Modern Transform Design for Practical Audio/Image/Video Coding Applications
9. Discrete Multi-Scale Transforms in Signal Processing
10. Frames in Signal Processing
11. Parametric Estimation
12. Adaptive Filters
13. Signal Processing over Graphs
14. Tensors for Signal Processing and Machine Learning
15. Non-convex Optimization for Machine Learning
16. Dictionary Learning and Sparse Representation
2. Continuous-Time Signals and Systems
3. Discrete-Time Signals and Systems
4. Random Signals and Stochastic Processes
5. Sampling and Quantization
6. Digital Filter Structures and Their Implementation
7. Multi-rate Signal Processing for Software Radio Architectures
8. Modern Transform Design for Practical Audio/Image/Video Coding Applications
9. Discrete Multi-Scale Transforms in Signal Processing
10. Frames in Signal Processing
11. Parametric Estimation
12. Adaptive Filters
13. Signal Processing over Graphs
14. Tensors for Signal Processing and Machine Learning
15. Non-convex Optimization for Machine Learning
16. Dictionary Learning and Sparse Representation