Lecture 1
Signals in the Time and Frequency Domains


	1. Introduction
	2. Periodic Signals
	3. Fourier Series Expansion of Periodic Signals
	4. Spectral Representation of Periodic Signals
	5. Duty Cycle of a Rectangular Wave
	6. RMS Voltage and Power Spectrum of Aperiodic Signals
	7. Discrete Fourier Transform (DFT)
	8. Signal Strength
	9. Signal Bandwidth
	10. Conclusion

Introduction


	The tasks of communication is to encode information as a signal level, transmit this signal, then decode the signal at the receiving end.
	An analog signal varies continuously with time, and has an infinite number of possible signal levels.
	A discrete signal changes only once during a certain time interval. The signal value during this time interval is one sample, and the interval length is called the sampling period. Each sample has a infinite number of possible signal levels.
	A digital signal is discrete, but each sample has a finite number of possible signal levels. The limited number of levels means that each sample transmits a single information. It also means that each sample can be represented as digital data, a string of ones and zeroes.
	A digital signal is preferred in computer communications because computers already store and process information digitally.

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"Analog and digital signals in..."


	Analog and digital signals in the time and the frequency
	domain.
	The time domain is simply the signal level expressed as a function of time.
	The frequency domain is comprised of amplitude and a phase for an infinite number of cosine functions. These correspond to the superposition of an infinite number of sinusoidal waveforms in the time domain.
	To convert from the time domain to the frequency domain, we take the Fourier transform of the time domain representation.
	The frequency domain representation of a signal does not change with time, but the Fourier transform for the signal over an infinitely long time period would be impossible. Instead, we assume that the signal has some finite duration, over a sampling interval:
	1. We assume that outside of the sampling interval, the signal repeats itself, so that the signal is periodic.
	2. An alternative assumption is that the time-domain signal has a zero value outside of the sampling interval, so that the signal is aperiodic.

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Fourier Series Expansion of Periodic Signals


	a. There are a finite number of discontinuities in the period T.
	b. It has a finite average value for the period T.
	c. It has a finite number of positive and negative maxima in the
	period T.

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Duty Cycle of a Rectangular Wave

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RMS Voltage Values and Power Spectrum


	The RMS voltage measures the signalÕs power; it is the square root of the average value of the voltage squared, taken over one period of the signal:

Aperiodic Signals


	The Fourier transform of an aperiodic signal can be found from integration of the time-domain waveform:

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"Match the fundamental frequency of..."


	Match the fundamental frequency of the signal to the sampling interval. If the sampling interval contains an integer number of signal periods, the harmonics of the periodic signal will appear as sharp peaks on the amplitude spectrum.
	A rectangular wave with a frequency of 2000 Hz must be sampled with a Digital Spectrum Analyzer that samples at a rate of 500,000 samples per second. How many samples should be taken if the sampling interval must contain 15 complete waveforms?

Signal Strength


	In signal analysis we frequently want to compare the power of two signals.
	The decibel (dB) provides a convenient way to measure the difference of two power levels.
	It measures the logarithmic power difference between two signals.
	If a signal is attenuated by 2 dB in one stage of transmission, then is attenuated by 5 dB in the next stage of transmission, we the two decibel measurements to find the total attenuation of the two stages, in this case 7 dB.
	Signal power equals to waveform power – DC power

Signal Bandwidth


	A baseband signal is any signal which transmits data in the form of the amplitude of the signal voltage. The bandwidth of a baseband signal is measured from zero frequency upwards to f_max.
	A modulated signal transmits data by modifying the amplitude, frequency, or phase of a carrier signal. The bandwidth of a modulated signal is measured from the minimum frequency f_min (below the carrier frequency) upwards to f_max (above the carrier frequency).

	The full bandwidth of a signal is the frequency range that includes all spectrum lines of the signal.
	The absolute bandwidth (ABW) of a signal is the width of the spectrum that contains 98% of the signalÕs total power.

	The effective bandwidth (EBW) (bandwidth) of a signal is the width of the spectrum that contains at least 50% of the signalÕs total power. This is the part of the signal whose power is within 3 dB of the complete signal.

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Conclusion


	A. one-to-one correspondence exists between the representation of a given signal in the time domain and in the frequency domain.
	B. The character of the power spectrum allows us to determine if a signal is aperiodic or periodic in the time domain. A periodic signal has a discrete frequency spectrum while an aperiodic signal has a continuous spectrum.
	C. The amplitude spectrum and phase spectrum together allow us to reconstruct the signal in the time domain. If only the amplitude spectrum or power spectrum is available, it is possible to make some conclusions about features of the signal in the time domain.
	D. The presence of a DC offset means that the signal has a constant component, and the entire signal is shifted along the voltage axis in the time domain.
	I. If all harmonics divisible by a number n are missing for a rectangular periodic signal, the rectangular signal in the time domain has a duty cycle d equal to ¹/_n.
	F. The power spectrum allows us to determine the full bandwidth and the effective bandwidth of the signal.
	G. Bandwidth refers to the range of frequencies represented in an analog signal. The bandwidth of an analog signal determines the maximum sampling rate for a digital signal that is accurately transmitted via this analog signal.

Error Analysis


	A. The absolute error of a measurement is the difference between the ideal value x _theory of a quantity (the measurement predicted by theory) and the experimentally obtained value x _measured.