Signalanalys

Signalanalys, or signal analysis, is the discipline that studies methods for extracting information from signals—functions that convey physical quantities such as sound, temperature, or voltage—over time or space. Signals can be continuous or discrete in time and may be one-dimensional or multi-dimensional. The aim is to characterize, transform, and interpret signals to reveal structure, patterns, or features while mitigating noise.

A central distinction is between time-domain and frequency-domain representations. In the time domain, signals are viewed as amplitudes over time; in the frequency domain, spectral content is described using transforms. Key tools include the Fourier transform and Fourier series for periodic signals, the discrete Fourier transform (DFT) and fast Fourier transform (FFT) for sampled data, and, for system analysis, the Laplace transform and the Z-transform. Spectral analysis concerns how signal power distributes across frequencies, using estimators such as the periodogram or methods like Welch’s and multitaper.

For non-stationary signals, time–frequency approaches are used, including the short-time Fourier transform (STFT), wavelet transforms, and the Wigner–Ville distribution. Sampling theory, notably the Nyquist criterion, addresses how continuous signals can be faithfully reconstructed from samples and explains issues of aliasing and anti-aliasing.

Applications span communications, audio and speech processing, radar and sonar, biomedical signals (ECG, EEG), image and video processing, and engineering diagnostics. Tools commonly employed include MATLAB and Python with libraries such as NumPy and SciPy. Signalanalys provides methods to extract meaningful information, reduce noise, and support interpretation, design, and control across disciplines.