MATLAB Implementation of Low-Pass Filter
A low-pass filter program implemented in MATLAB language, featuring signal processing algorithms for smoothing signals by removing high-frequency components
Fast Fourier Transform (FFT) Filtering
Fast Fourier Transform (FFT) Filtering with capabilities for high-pass, low-pass, and band-pass filtering operations.
MATLAB Implementation of QPSK Using Pseudo-Random Sequence with Low-Pass Filtering
MATLAB implementation of QPSK modulation employing pseudo-random sequences and low-pass filtering techniques, with visual output analysis available through accompanying graphs
MATLAB Code Implementation for Edge Detection and Object Extraction
Edge Detection Implementation: 1. Create a monochrome image containing color blocks and lines, apply low-pass filtering to generate a degraded image with blurred edges, then detect edges using any two edge detection algorithms to produce binary result images. Extract boundary coordinates from the results and compare with original coordinate data to analyze detection errors. 2. Object Extraction: Capture a black/white or color photo containing target objects, apply an edge detection algorithm for automatic object extraction, and analyze the results with implementation insights on MATLAB functions like edge(), imfilter(), and bwboundaries().
MATLAB Implementation of Image Median Filtering with Performance Analysis
Comparative analysis of median filtering and low-pass filtering techniques for image denoising, including PSNR evaluation to quantify performance differences in MATLAB
Reactive Power Separation: Harmonic Components vs. Fundamental Components Using Morphological Filters
Separation of harmonic and fundamental components from reactive power using mathematical morphology filters as low-pass filtering, with implementation details on algorithm structure and key operations
An Efficient Filter: CIC (Cascaded Integrator-Comb Filter) and Its Implementation
A widely adopted efficient filtering solution in engineering uses CIC (cascaded integrator-comb filter) as the first stage for decimation and low-pass filtering, followed by a standard FIR filter as the second stage. This two-stage approach enables backend devices to operate at lower frequencies with reduced hardware costs and power consumption.
Applying Low-pass and High-pass Filters to the Original Signal
In the one-level wavelet decomposition process, the original signal undergoes low-pass and high-pass filtering respectively, followed by binary downsampling to obtain low-frequency and high-frequency coefficients (also referred to as approximation and detail coefficients). Multi-level decomposition recursively applies the same wavelet decomposition to the low-frequency coefficients obtained from the previous level, enabling hierarchical signal analysis.
Time Domain Processing Methods for Vibration Signals
Vibration signal time domain processing techniques including low-pass filtering, band-pass filtering, and frequency domain integration methods with algorithm implementations
Noise Addition and Filtering Processing on Speech Signals
This implementation performs noise addition to speech signals followed by processing using low-pass, band-pass, and high-pass filters. The program runs perfectly, but requires careful attention to audio file path configuration to avoid runtime errors! Includes code descriptions for signal processing workflows and filter implementations.