Energy

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In signal processing, the energy E_s of a continuous-time signal x(t) is defined as

Energy in this context is not, strictly speaking, the same as the conventional notion of energy in physics and the other sciences. The two concepts are, however, closely related, and it is possible to convert from one to the other:

where Z represents the magnitude, in appropriate units of measure, of the load driven by the signal.

For example, if x(t) represents the potential (in volts) of an electrical signal propagating across a transmission line, then Z would represent the characteristic impedance (in ohms) of the transmission line. The units of measure for the signal energy E_s would appear as volt²-seconds, which is not dimensionally correct for energy in the sense of the physical sciences. After dividing E_s by Z, however, the dimensions of E would become volt²-seconds per ohm, which is equivalent to joules, the SI unit for energy as defined in the physical sciences.

Spectral Energy Density

Similarly, the spectral energy density of signal x(t) is

where X(f) is the Fourier transform of x(t).

For example, if x(t) represents the magnitude of the electric field component (in volts per meter) of an optical signal propagating through free space, then the dimensions of X(f) would become volt-seconds per meter and E_s(f) would represent the signal's spectral energy density (in volts²-second² per meter²) as a function of frequency f (in hertz). Again, these units of measure are not dimensionally correct in the true sense of energy density as defined in physics. Dividing E_s(f) by Z_o, the characteristic impedance of free space (in ohms), the dimensions become joule-seconds per meter² or, equivalently, joules per meter² per hertz, which is dimensionally correct in SI units for spectral energy density.

Parseval's Theorem

As a consequence of Parseval's theorem, one can prove that the signal energy is always equal to the summation across all frequency components of the signal's spectral energy density.

See also

• Signal processing
• Parseval's theorem
• Inner product