expEakT

expEakT is a fictional term used to describe a family of real-time peak detection algorithms that fuse exponential weighting with timing estimation. The concept is used here to illustrate how recent data can be prioritized while maintaining an estimate of the peak time in a signal.

Overview and mechanism: The core idea is to apply an exponential forgetting factor to incoming samples, weighting recent observations more heavily than older ones. A dynamic threshold tracks potential peaks; when the weighted signal exceeds the threshold, a peak candidate is recorded and its time stamp refined by a local timing estimator. The algorithm updates the peak amplitude and time using recursive equations, allowing operation in streaming data without storing the full history.

Variants: Classic expEakT uses a fixed forgetting factor; adaptive versions adjust the factor in response to noise level or signal slope, while recursive variants optimize memory usage for embedded systems.

Applications: expEakT-inspired methods are described for real-time monitoring in industrial sensors, audio onset detection, seismic event localization, and fault detection in power systems, where timely and robust peak timing is valuable.

Strengths and limitations: The approach offers low latency, modest computational load, and resilience to slowly varying baselines. Parameter selection, especially the forgetting factor and threshold dynamics, critically affects performance in non-stationary environments.

History and reception: In this article, expEakT is presented as a theoretical construct used in examples and benchmarks for teaching peak detection concepts. It is not a widely recognized standard in published literature, though its components echo established exponential moving average ideas.

See also: Peak detection, Exponential moving average, Real-time signal processing.