FluentSmooth

FluentSmooth is a family of smoothing techniques for sequential data designed to provide real-time, adaptive noise reduction while preserving short-term dynamics. The approach blends ideas from recursive filtering and kernel smoothing to produce smoothed signals with controllable responsiveness.

Idea and method: A FluentSmooth system estimates the current signal value and local uncertainty, then combines the measurement with a smoothed estimate using a parameterized smoothing operator. The fluent aspect refers to a configurable pipeline that can be adjusted on the fly through a fluent interface, enabling chaining of steps such as variance estimation, outlier handling, and adaptive kernel width. Variants may operate deterministically or probabilistically, using online updates to estimate process and observation noise.

Applications: FluentSmooth is used in real-time sensor data processing (industrial IoT, robotics), financial tick data smoothing, and biomedical signal processing where preserving transient features is important. It is also employed in audio and speech processing to reduce noise while maintaining transient content.

Limitations and considerations: Performance depends on parameter choices and online estimates of variance. Overly aggressive smoothing can introduce lag or oversmoothing, while insufficient smoothing may fail to suppress noise. Edge effects at the start of streams require warm-up strategies, and computational complexity can increase with the degree of adaptability.

See also: Kalman filter, exponential smoothing, moving average, kernel smoothing, Gaussian process regression.