신호 분석
데시메이션된 1차원 웨이블릿 변환 및 데시메이션되지 않은 1차원 웨이블릿 변환, 1차원 이산 웨이블릿 변환 필터 뱅크, 1차원 이중 트리 변환, 웨이블릿 패킷
이산 웨이블릿 변환, 이중 트리 변환, 웨이블릿 패킷을 사용하여 신호를 분석합니다.
함수
앱
신호 다중분해능 분석기 | 신호를 시간으로 정렬된 성분으로 분해 |
웨이블릿 신호 분석기 | Analyze and compress signals using wavelets (R2023a 이후) |
웨이블릿 신호 잡음 제거기 | 시계열 데이터 시각화 및 잡음 제거 |
도움말 항목
웨이블릿 신호 분석기
- Using Wavelet Signal Analyzer App
Analyze and compress 1-D signals using wavelets.
- 단계 1: Select Signal to Analyze
- 단계 2: Decompose Signal
- 단계 3: Explore Signal Decomposition
- 단계 4: Compress Signal
- 단계 5: Share Results
- Wavelet Decomposition of Complex-Valued Signal Using Wavelet Signal Analyzer
Analyze a complex-valued signal using Wavelet Signal Analyzer. - Visualizing Wavelet Packet Terminal Nodes in Wavelet Signal Analyzer
Learn how Wavelet Signal Analyzer visualizes wavelet packet decompositions.
신호 다중분해능 분석기
- Using Signal Multiresolution Analyzer
Learn how to visualize multilevel wavelet-based decompositions of real-valued signals. - Compare MODWTMRA and EMD Decompositions
Learn how to compare fixed-bandwidth and data-adaptive decompositions using Signal Multiresolution Analyzer. - Share Results Using Signal Multiresolution Analyzer
Learn how to share analyses generated by Signal Multiresolution Analyzer. - Visualize and Recreate EWT Decomposition
Learn how to visualize an empirical wavelet transform decomposition using Signal Multiresolution Analyzer. - Visualize and Recreate TQWT Decomposition
Learn how to visualize a tunable Q-factor wavelet transform decomposition using Signal Multiresolution Analyzer. - Visualize and Recreate VMD Decomposition
Learn how to visualize the intrinsic mode functions and residual of a variational mode decomposition using Signal Multiresolution Analyzer.
임계적으로 샘플링된 DWT
- Critically Sampled and Oversampled Wavelet Filter Banks
Learn about tree-structured, multirate filter banks. - Haar Transforms for Time Series Data and Images
Use Haar transforms to analyze signal variability, create signal approximations, and watermark images. - Border Effects
Compensate for discrete wavelet transform border effects using zero padding, symmetrization, and smooth padding.
데시메이션되지 않은 DWT
- Analytic Wavelets Using the Dual-Tree Wavelet Transform
Create approximately analytic wavelets using the dual-tree complex wavelet transform. - Wavelet Cross-Correlation for Lead-Lag Analysis
Measure the similarity between two signals at different scales. - Comparing MODWT and MODWTMRA
Learn the differences between the maximal overlap discrete wavelet transform (MODWT) and the multiresolution analysis based on the MODWT. - Nondecimated Discrete Stationary Wavelet Transforms (SWTs)
Use the stationary wavelet transform to restore wavelet translation invariance.
프랙털 분석
- 1-D Fractional Brownian Motion Synthesis
Synthesize a 1-D fractional Brownian motion signal. - Multifractal Analysis
Use wavelets to characterize local signal regularity using wavelet leaders.
웨이블릿 패킷 분석
- Wavelet Packets
Use wavelet packets indexed by position, scale, and frequency for wavelet decomposition of 1-D and 2-D signals. - Wavelet Packets: Decomposing the Details
This example shows how wavelet packets differ from the discrete wavelet transform (DWT). - Critically Sampled Wavelet Packet Analysis
Obtain the wavelet packet transform of a 1-D signal and a 2-D image.