Analyze a numerically controlled oscillator (NCO) of a digital down-converter (DDC) implemented in fixed-point arithmetic. The example measures the spurious free dynamic range (SFDR)
Use findsignal to find a time-varying signal in your data. It includes examples of how to find exact and closely matching signals by using a distance metric, how to compensate for a slowly
Determine changes or breakouts in signals via cumulative sums and changepoint detection.
Use the functions midcross and dtw to extract features from gait signal data. Gait signals are used to study the walking patterns of patients with neurodegenerative disease. The time
Analyze the harmonic distortion of a weakly non-linear system in the presence of noise.
Use the slew rate as an estimate of the rising and falling slopes of a triangular waveform. Create three triangular waveforms. One waveform has rising-falling slopes of \pm2 , one waveform
Generate 2048 samples of a sinusoid of frequency 2.5 kHz sampled at 50 kHz. Add white Gaussian noise such that the signal-to-noise ratio (SNR) is 80 dB.
Estimate the high and low state levels for digital clock data. In contrast to analog voltage signals, signals in digital circuits have only two states: HIGH and LOW. Information is conveyed
Create a rectangular pulse waveform and measure its duty cycle. You can think of a rectangular pulse waveform as a sequence of on and off states. One pulse period is the total duration of an on
Perform basic peak analysis. It will help you answer questions such as: How do I find peaks in my signal? How do I measure distance between peaks? How do I measure the amplitude of peaks of a
Find the root mean square (RMS) value of a sine wave, a square wave, and a rectangular pulse train using rms . The waveforms in this example are discrete-time versions of their continuous-time
Measure the total harmonic distortion (THD) of a sinusoidal signal. The example uses the following scenario: A manufacturer of audio speakers claims the model A speaker produces less than
Analyze pulses and transitions and compute metrics including rise time, fall time, slew rate, overshoot, undershoot, pulse width, and duty cycle.
Classify heartbeat electrocardiogram (ECG) data from the PhysioNet 2017 Challenge using deep learning and signal processing. In particular, the example uses Long Short-Term Memory