Oscilloscope Noise Reduction via Frequency Domain Splitting
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Solution Overview
Problem
Existing oscilloscopes introduce noise that corrupts measurements, especially as noise levels in devices under test become smaller, and current methods for noise reduction are inadequate for non-scalar noise measurements like histograms, frequency spectra, and eye diagrams, often requiring cumbersome channel splitting and prone to distortions.
Innovation Solution
The oscilloscope system employs a processor to split radio frequency signals into low-frequency and high-frequency bands, perform Fourier transforms, and apply inverse Fourier transforms to reduce noise, allowing for noise reduction without channel splitting and minimizing distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If oscilloscope noise is removed using traditional subtraction methods, then measurement accuracy improves for scalar values, but the method fails for non-scalar representations like histograms, frequency spectra, and eye diagrams
Solution Approach 1:
The patent transforms noise reduction from direct time-domain subtraction to frequency-domain processing. By converting waveforms to frequency spectra, applying noise reduction in the frequency domain, and transforming back, the method achieves universal applicability across different noise representations including histograms, frequency spectra, and eye diagrams, not limited to scalar measurements.
Solution Approach 2:
The patent introduces frequency domain transformation as an intermediary step between time-domain measurement and noise reduction. The Fourier transform acts as a mediator that enables noise reduction operations to be applied universally across different signal representations, then transforms the result back to the time domain for final waveform output.
2Measurement precision
If channel splitting is used to remove oscilloscope noise, then noise reduction is achieved, but the process becomes cumbersome and may introduce errors due to cable mismatches and non-ideal connectors
Solution Approach 1:
The patent extracts the oscilloscope noise component from the measured signal through frequency domain analysis. By identifying and removing the noise spectrum from the total measured spectrum, the method achieves noise reduction without requiring physical channel splitting or additional hardware modifications to the measurement setup.
Solution Approach 2:
The patent replaces the mechanical/physical channel splitting approach with a computational signal processing method. Instead of physically dividing signals into separate channels requiring matched cables and connectors, the noise reduction is achieved through mathematical operations in the frequency domain, eliminating hardware-related errors and simplifying the measurement process.
3Adaptability or versatility
If Fast Fourier Transform is used for noise reduction, then frequency domain processing is enabled, but Gibb's phenomenon creates large distortions at waveform edges
Solution Approach 1:
The patent applies windowing functions as a preliminary action before performing the Fast Fourier Transform. By multiplying the time-domain signal by a window function that tapers smoothly toward zero at the edges, the method prepares the signal to minimize spectral leakage and prevent Gibb's phenomenon when the inverse transform is applied, thereby reducing edge distortions.
Solution Approach 2:
The patent dynamically adjusts the frequency domain representation by selectively modifying spectral components. Rather than uniformly processing all frequency components, the method dynamically identifies and removes only the noise portions of the spectrum while preserving signal components, then reconstructs the waveform with minimized edge artifacts through controlled inverse transformation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively lowers the noise floor and improves measurement accuracy by isolating and reducing oscilloscope noise from device under test noise, even in the presence of low-frequency components, while avoiding distortions like Gibb's phenomenon.
Implementation Method 1
perform a first Fourier transform to compute a first new spectrum based on the first spectrum
Implementation Method 2
compute a first waveform of the first new spectrum with noise of the oscilloscope reduced by performing a first inverse Fourier transform based on the first new spectrum
Data Source
AI summary
An oscilloscope includes a memory that stores instructions; and a processor that executes the instructions. When executed by the processor, the instructions cause the oscilloscope to obtain a measurement of a first radio frequency signal; split a first spectrum based on the first radio frequency signal into a first low-frequency band and a first high-frequency band; perform a first Fourier transform to compute a first new spectrum based on the first spectrum; compute a first waveform of the first new spectrum with noise of the oscilloscope reduced by performing a first inverse Fourier transform based on the first new spectrum; and combine the first new spectrum with noise of the oscilloscope reduced with the first low-frequency band.