Explore key principles such as signal classifications, system properties, and common transforms used in electronics and communications. This quiz covers the foundational knowledge necessary for understanding signals and systems.
Which signal is only defined for integer values and takes the form x[n]?
Explanation: A discrete-time signal is defined only at integer instances and is denoted by x[n]. Continuous-time signals are defined for all real values. A periodic signal repeats itself after a certain interval, but can be either continuous or discrete. Deterministic and random signals describe predictability, not the domain.
What property must a system have if its output to sum of two inputs equals the sum of the outputs to those inputs applied separately?
Explanation: Linearity means the system satisfies both additivity and homogeneity, so the described behavior applies. Time-invariance is about consistency over time. Causality concerns output's dependence on input history. Memoryless refers to output depending only on the present input, and stability is about bounded input-output relations.
Which system property makes sure that the output depends only on present and past input values, not future inputs?
Explanation: Causality ensures output does not rely on future inputs, just present or past inputs. Linearity and time-invariance relate to other system behaviors. Stability ensures bounded input produces bounded output. Invertibility concerns whether outputs can be uniquely traced back to inputs.
If a signal has zero average power but finite energy, how is it classified?
Explanation: An energy signal has finite total energy but zero average power. Power signals have finite nonzero average power. Random and deterministic refer to predictability, not energy properties. The term continuous-time signal is about domain, not power or energy.
Which mathematical tool is primarily used to analyze the frequency spectrum of a continuous-time signal?
Explanation: The Fourier Transform decomposes a signal into its frequency components. Laplace Transform is broader and handles stability/causality. Z-Transform is used for discrete signals. Histogram is unrelated, and DFT Matrix is for discrete transforms (DFT), not general continuous analysis.