signalsinformation

Signals information is an interdisciplinary field at the intersection of signal processing and information theory that concerns how signals convey information and how that information can be captured, transmitted, and reconstructed. It covers the representation, manipulation, and interpretation of signals that may be analog or digital, and the information content they bear. The fidelity of information transmission is affected by noise, distortion, and sampling limitations, and depends on how the signal is encoded and decoded.

Signals are time-varying quantities, while information is the content carried by those signals. The same waveform may be used to convey different data depending on encoding, and effective systems separate the task of extracting information from the physical signal from the task of producing or transmitting it. Distortion, interference, and limited bandwidth pose challenges that are addressed through a combination of signal processing and coding techniques.

Core concepts include representation in time and frequency domains, sampling and quantization for digitization, modulation to convey signals over channels, source coding to compress information, and channel coding to detect and correct errors. Information theory provides metrics such as entropy, mutual information, and channel capacity. The Shannon capacity formula expresses the maximum achievable data rate of a channel under a given bandwidth and signal-to-noise ratio: C = B log2(1+SNR) bits per second.

Applications span telecommunication networks, wireless and satellite links, data storage and retrieval, audio and video processing, radar and remote sensing, and medical imaging. The field’s development traces to foundational ideas in sampling theory and information theory, shaping modern digital communications, data compression, and signal processing.