Fundamentals Series Analog vs. Digital - Polycom -...

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Fundamentals SeriesAnalog vs. Digital

© Polycom, Inc. All rights reserved. 2

Fundamentals Series

Signals

Analog vs. Digital

Defining Quality

Standards

H.323

SIP

Network

Communication I

Network

Communication II

Welcome to Analog vs. Digital, the second module in the Polycom Fundamentals series. This module is approximately 12 minutes long.


Introduction

In order to understand how videoconferencing works it’s important to understand the underlying technologies at work behind the scenes.

In this short module we will compare analog and digital as signaling methods, and find out some basics about how digital signaling works.

To start, let’s recap from the Signals module that an analog signal is one which constantly varies in amplitude and frequency, causing the voltage to vary constantly. In comparison to this, a digital signal is one which only has discrete (ie separate) values.


Digital Basics

One of the most important technological shifts in the last century was that from analog technology to digital technology. It has touched everything we do and it is integral to understanding modern communications, including video. First let’s look at the concept of a digital signal. We can use an analog clock as an example of a basic analog signal. The hand always sweeps through the numbers in continuous motion, never stopping on a specific number. This is how analog works. Now, compare that to a digital clock. It conveys the same information, but very differently. A digital clock clicks from one value directly to the next in a series of steps instead of flowing smoothly between them. This is what we mean when we say that digital uses discrete steps, or values – these are created using binary signals.


Binary Signals

Digital signals use binary for transmitting information. We use a sequence of on and off voltages to represent the value of each bit, either 1 (on) or 0 (off). This is the basis for all digital communications. A string of on and off voltages grouped together to recreate digital values of signals. As it turns out binary is a great numeral system to use when using machines. It is much easier to create a device built on switches with only two positions, on and off, or 1 and 0. So when the first computers were being invented they realized that a binary system was the most efficient way to count using computing machines. So in effect a computer is just a bunch of switches that can be set to on or off. Combine a bunch of them and and voila you can do complicated calculations just by turning things on and off really quickly.


Digital Signals

We looked at an analog signal in the Signals module. Now let’s compare that to a digital one. The digital signal shown here represents the analog waveform above it as a sequence of discrete values. Each discrete value, when connected, gives us a representation of the analog waveform. So rather than dealing with a continuously changing waveform we are charting a series of discrete values and stringing them together. Now we know the difference between analog and digital. Great. Now what? Why do we care about digital?


Why Digital?

One advantage of digital signals when compared to analog signals is that digital signals can be transmitted without the same noise problems as analog signals. Because each step of a digital signal is seen as a specific value some fluctuation can be introduced without changing the perceived value of that part of the signal, within a certain tolerance. If our original 1v value drifts a little and is now 1.12v it is still recognized as the original 1v by the digital system. It’s close enough.

This is quite different from our analog waveform, which reacts to every fluctuation in the wave from noise. And, those changes are often difficult to distinguish from the original analog information. Not to mention that when we then amplify that signal the noise is amplified as well.

With digital we can filter out noise we don’t want and boost the voltage of the signal confidently using a device called a repeater, which makes a new signal from the original but boosts it so it can keep moving and still be usable at the other end.


Why Digital?

Another advantage of digital signals is that they can be more easily stored. If I simply record the sequence of values I can easily recreate the original digital signal, unlike analog which would have to actually record the whole waveform itself and then play it back. This becomes critical not only because we can do things like record a video conference for review later, but also because when we transmit digital data across our networks it can be cached temporarily in devices along the way. Any endpoints and MCUs are able to take advantage of this to buffer the data prior to creating a video image or audio signal. Given the time-critical nature of video and audio data this is a really cool feature, as it means that small network issues have the potential to be evened out and the video and audio played back from the buffer correctly. Without this ability, any data which cannot be processed in time would just be dropped as it would be unusable. This also allows the endpoints and MCUs to make sure the video and audio are in sync when they are sent to each endpoint.


Why Digital?

Another advantage of digital signaling is that it is very flexible. Since the digital signal is made up of a series of numbers, it’s easy to come up with different ways to arrange the numbers to be transmitted. And, as we’re just manipulating numbers, we can also come up with different ways to process the information, especially if we are using computer controlled devices to manipulate the digital signals. Improvements or changes to the signals can be introduced by changes in software even when using the same hardware. Making an improvement to an analog system can be much more difficult since the hardware itself must often be altered and improved to give better performance or a new functionality. This comes into play when we’re doing things like receiving one type of digital audio or video and reformatting it to be another type. This is known as transcoding and it’s invaluable when you want to make different devices compatible. For example an MCU will allow different endpoints to use different audio and video formats in the same call by transcoding the media to send to each one individually.


Pulse Code Modulation

So we know why digital is a good thing. But how do we make something digital? We in the normal universe are analog. Pretty much all things in nature work in an analog fashion as constantly changing variations. Temperature is a great example. It doesn’t just jump from one temperature at 7am to 4 degrees warmer at 7:45am and then step up another 9 degrees at noon. It fluctuates gradually and continuously. In order to take this natural analog information and turn it into digital values we need a mechanism to digitize it (turn the information into numbers). That method is called pulse-code modulation, usually shortened to PCM. PCM creates a digital value representation of an analog signal by recording a value at regular intervals and stringing them together to record discrete values over time. Those values become the basis for digitizing a signal, and PCM is the basis for all analog to digital and digital to analog conversions in the modern world. This creates our ‘connect the dots’ picture of the original analog signal.


Sampling

PCM is based on the idea of sampling, which is simply a measurement of an analog signal at regular intervals. It gives you a representative image of what the analog waveform looked like at the moment the samples were taken, kind of like a ‘connect the dots’ puzzle… only we’re not ending up with the Statue of Liberty or a cartoon character, we end up with a picture of the signal that we’re sampling. We end up with a series of numerical values that tell us what happened to the signal over time. The more often you sample (the higher the frequency) the more accurate your representation is. If we sample more frequently we will have a better idea what the actual analog signal looked like, again just like ‘connect the dots’. As an exact measurement of when a sample should happen is required to make the process accurate, timing is a critical factor in sampling. The number of samples per second is known as the sample rate, and if we’re sampling 1000 times per second it’s a 1000 Hz sample rate or a 1 kHz sample rate.

Along with sample rate, we also need to decide how many bits we use to code each sample. The number of bits we use to code a signal gives us what is called the ‘bit depth’ – CD audio is commonly sampled at a 16-bit bit depth, for example. As the number of bits used increases, the quality increases, but so also does the amount of data required to transmit the whole signal.


Sampling Theorem

A theorem is just a rule which is expressed by a formula. The sampling theorem (also known as the Nyquist Shannon theorem after two of the people who discovered it), is an important part of calculating how often one needs to take samples in order to create an accurate representation of the original signal without introducing too much noise into the process. The theorem states:

‘If a function x(t) contains no frequencies higher than B Hertz, it is completely determined by giving its ordinates at a series of points spaced 1/(2B) seconds apart’. An ordinate is the name for a value on the y axis.

Essentially, all this means is that in order to get the best signal, you need to sample at least twice the frequency of the highest frequency in the analog signal. So, an analog audio signal of 4kHz (4000 cycles per second) needs to be sampled 8000 times in each one second cycle. From that you can be relatively sure that when you recreate the signal from the sequence of values it will be an accurate re-creation.


Hearing Perception

Applying all this to an audio signal, remember that in the Signals module we said that the range of human hearing is between 20 Hz and 20,000 Hz, with human speech centering around 1,000 Hz (1kHz).

Logically, this is also the easiest range of sounds for us to hear, requiring the least effort. Sounds above and below this frequency range are harder to hear. So as telephone system engineers worked on how to make the best quality phone calls they determined that focusing on handling signals in the range from about 300 Hz to 4,000 Hz gave the best performance for cost balance in telephony equipment. Using the sampling theorem to determine how many samples we need to accurately represent that signal would give us what? Let’s figure it out. If our range of frequencies is 300 to 4,000 Hz, our top end audio frequency is then 4000 Hz.

So if our formula is 1/(2B) where B = the highest frequency, B = 4000, and 1/(2B) = 1/(2 x 4000). So the time between samples in seconds is 1/8000, or 8000 samples per second. This is an 8 kHz sampling rate.


Encoding and Decoding

In order to turn our analog signal into a digital signal using PCM we follow this process:

The values of the analog signal are sampled at certain regular times, then Recorded at each point in time as a sequence of numbers Then the values of each sample are converted to binary (using 8 bits) and end up with a series of 0s and 1s in a long line. This is called ‘encoding’ the signal. To transmit the signal, electrical pulses (0v “off” and +5v “on” for each bit) are generated at regular intervals (8 bits per second) onto a wire. Because the far end system knows 8 bit per second sequences are being sent, it can receive the signal, reverse the process and the original analog signal emerges. This is called decoding the signal.

Fundamentals Series Analog vs. Digital - Polycom -...

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