Hanning Window Function Interpolation Algorithm
The Hanning window function interpolation algorithm computes amplitude, phase, and frequency parameters of multiple harmonic signals through spectral correction techniques
Digital Electronic Organ Function: Each Musical Note Corresponds to a Specific Frequency Signal
A digital electronic organ generates sound by mapping each musical note to a specific frequency signal. It utilizes a digital signal generator to produce sounds at specified frequencies, simulating a virtual keyboard interface with 15 keys labeled A through O. Sound is triggered on mouse press and stops on release. Additionally, the system can generate common waveforms such as sine, square, and triangle waves through a graphical interface for selecting waveform type, frequency, amplitude, and phase parameters. The implementation involves generating digital signals based on user-defined parameters, writing them to the sound card's buffer, and finally outputting the audio through the sound card hardware.
Virtual Signal Generator
A GUI-based tool that generates (1) periodic functions (including sine, triangular, sawtooth, and square waves) with configurable amplitude, frequency, and phase parameters, (2) squared pulse sequences with adjustable amplitude, frequency, phase, and duty cycle, (3) dual-parameter exponential functions, (4) Gaussian curves with varying ranges, standard deviations, and means, and (5) Gaussian noise with different amplitudes. The implementation utilizes mathematical waveform generation algorithms with real-time parameter modulation capabilities. Users can add offsets or Gaussian noise to any of the first four functions and perform signal arithmetic operations (addition/multiplication) with predefined signals through intuitive GUI controls.
Similar to Prony Algorithm: ESPRIT for Signal Parameter Estimation
Similar to the Prony algorithm, ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) is a parametric signal processing method that enables high-precision identification of frequency, phase, and amplitude parameters for arbitrary combinations of decaying/non-decaying sinusoidal signals in power systems without requiring synchronous sampling.
Structural Modal Parameter Identification in MATLAB Environment
High-precision identification of mode shapes and natural frequencies through MATLAB-based structural modal analysis
Analysis, Processing, and Design of Speech Signals
Speech signal analysis, processing, and design. The MATLAB-based voice changer operates by modifying the input sound's frequency to alter timbre and pitch, creating perceptually different output. This dual modulation of timbre and pitch transforms vocal characteristics. User-generated voice inputs leverage resampling techniques for formant frequency shifting, implementing key DSP algorithms including frequency scaling and spectral envelope modification through functions like resample(), pitch shifting, and formant correction.
Joint Estimation of Signal Azimuth and Frequency Using Delay-Based Methods with Uniform Linear Arrays
This approach utilizes uniform linear arrays and delay-based techniques for joint estimation of signal azimuth and frequency, implementing the MUSIC algorithm with enhanced signal subspace processing.
Ultrasound Image Signal Simulation
A comprehensive ultrasound image signal simulation program capable of modeling various frequencies, uniform depths, and other critical parameters with customizable signal processing algorithms.
Predicting Signal Strength for Ground Mobile Systems Using Propagation Models
This model utilizes field measurements as baseline references for median field strength or path loss in quasi-flat terrain and urban areas, applying correction factors for various propagation environments and terrain conditions to predict ground mobile system signal strength. Operating in the 150MHz to 1000MHz UHF/VHF frequency range, it primarily targets the 800-900MHz band where the Okumura-Hata propagation model is commonly implemented for relatively flat areas. The Hata model provides analytical approximations of empirical data while capturing major influencing factors.
Frequency Estimation of Sinusoidal Signals Using FFT
Estimating the frequency of a sinusoidal signal contaminated with additive white Gaussian noise via FFT involves computing the Fourier transform of x(n) to obtain the spectrum, identifying the frequency corresponding to the maximum magnitude, and calculating the mean squared error over multiple iterations. By varying the signal-to-noise ratio (SNR), simulations demonstrate that the mean squared error decreases as SNR increases, highlighting the method's robustness in noisy environments.