Waveform Analysis
Waveform Analysis - Related Videos
06:01
Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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Cited by 1 •
2022
This article describes the semi-automated measurement of the amplitudes and latencies of the first five peaks and troughs in the auditory brainstem response waveform. An additional routine compiles and annotates the data into a spreadsheet for experimenter analysis. These free computer routines are executed using the open-source statistical package R.
01:07
Effective Value of a Periodic Waveform
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2024
The concept of effective value, the root mean square (RMS) value, is crucial in understanding electrical circuits and power delivery. This idea emerges from the necessity to measure the effectiveness of a voltage or current source in supplying power to a resistive load. The effective value of a periodic current represents the direct current (DC) that conveys the same average power to a resistor as the periodic current itself. This concept is crucial when assessing AC circuits. To determine the...
05:11
High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
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2025
This study improves electromagnetic flowmeter accuracy by optimizing excitation waveforms, applying multi-stage filtering, and using Complex Programmable Logic Device (CPLD)-based rectification. A novel waveform-based empty pipe detection method enhances reliability. Experiments show 0.1% accuracy within 0.1-15 m/s, validating industrial applicability.
09:24
Automated 12-Lead Electrocardiograms (ECG) Digitization and WaveForm DataBase (WFDB) Standardization: Insights from Vietnamese Clinical Data
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2026
Here, we present a reproducible protocol for converting 12-lead ECG images from real-world clinical reports into calibrated, WFDB-compliant digital signals for standardized downstream analysis, integrating grid calibration, YOLOv12-based lead localization, waveform extraction (Viterbi or improved centerline), and visual validation checkpoints.
10:39
Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ
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Cited by 3 •
2022
Salmonella invades and replicates inside intestinal epithelial cells both in Salmonella-specific vacuoles and free in the cytosol (hyper-replication). A high-throughput fluorescence microscopy-based protocol is described here to quantify the intracellular phenotypes of Salmonella by two complementary image analyses through ImageJ, reaching single-cell resolution and scoring.
FAQs
Time-domain measurements show when a signal changes, while Fourier analysis reorganizes sampled data by frequency. Engineers can therefore examine periodic behavior through frequency components rather than only through its waveform shape. This view exposes dominant frequencies and harmonics, helping distinguish the intended signal content from distortion or unwanted components during circuit and communication-system evaluation.
Frequency, period, amplitude, and phase describe different aspects of signal behavior and should be interpreted together. Frequency indicates repetition rate, period describes the interval for one cycle, amplitude indicates signal magnitude, and phase provides timing-related information. Rise time adds information about how quickly a transition occurs, making it useful when examining signal quality or transient behavior.
Harmonics are frequency components that accompany a fundamental signal component and can reveal distortion. In waveform analysis, comparing observed component frequencies and harmonic content with expected behavior helps engineers identify whether a circuit or system is producing an altered signal. This assessment is relevant to audio equipment, communication systems, and electrical-circuit testing where signal quality matters.
A practical engineering review begins by inspecting time-domain features, including amplitude, period, phase, rise time, and unexpected transients. When sampled data require deeper separation of components, Fourier analysis provides a frequency-domain view of the signal. Engineers can then relate unusual features to noise, interference, distortion, or instability and use the findings to guide testing or troubleshooting.
Waveform analysis supports several stages of engineering work rather than serving only as a measurement exercise. During design, it helps characterize intended behavior; during testing, it helps validate performance; and during troubleshooting, it helps locate faults. The same approach applies across electrical circuits, sensors, communication systems, audio equipment, and control systems, although relevant signal features may differ.
Unexpected transients, noise, interference, or instability become visible as departures from the expected waveform or frequency content. Identifying these departures gives engineers evidence for diagnosing a system rather than relying only on overall performance observations. The resulting information can support fault localization, performance improvement, and more reliable operation in engineered systems.