Analog-to-Digital Conversion

РОСЖЕЛДОР

Государственное образовательное учреждение

Высшего профессионального образования

«Ростовский государственный университет путей сообщения»

(РГУПС)

Л.А. Свиридова, М.М. Сорокина

ТЕЛЕКОММУНИКАЦИИ

 Учебно-методическое пособие

по профессионально-ориентированному чтению и обработке информации

Ростов-на-Дону

2008

УДК 42:621. 396.2(07) + 06

Свиридова, Л.А., Сорокина, М.М.

Телекоммуникации: учебно-методическое пособие. /Л.А. Свиридова, М.М. Сорокина; Рост. гос. ун-т путей сообщения. – Ростов н/Д, 2008. – 36 с.

Настоящее учебно-методическое пособие предназначено для обучения профессионально-ориентированному чтению и обработке информации на английском языке студентов факультета АТС. Студенты имеют возможность закрепить и проверить навыки различных видов чтения. Обращение к тому или иному виду чтения регулируется соответствующими заданиями.

Рецензент канд. пед. наук, доц. Черкасова Л.Н. (РГУПС)

                                                         ã Ростовский государственный университет

                                                        путей сообщения, 2008

Задания для изучающего чтения

1 Найдите в статье данные (цифры), которые имеют значение для общего понимания статьи.

2 Отметьте абзацы статьи, которые, по вашему мнению, несут основную информацию.

3 Отметьте факты, которые оказались для вас новыми.

4 Укажите основные проблемы, обсуждаемые в статье.

5 Определите, какие детали статьи не имеют значения для ее общего понимания.

6 На основе статьи объясните, что утверждение «…» правильное.

7 Подготовьтесь к обсуждению следующих тем: «…», «…».

8 Объясните свою точку зрения по проблеме, обсуждаемой в статье.

9 Определите значение термина «…» в статье.

10 Передайте на русский язык смысл предложения, содержащего инфинитивный оборот.

11 Переведите отрывок статьи (текста) на русский язык (по усмотрению преподавателя).

12 Составьте структурно-логическую карту по заданной теме.

13 Заполните структурно-логическую карту по заданной теме.

14 Подготовьте пересказ содержания статьи, используя структурно-логическую карту (схему) по теме статьи.

Задания для ознакомительного чтения

1 Отметьте абзацы, в которых передана главная мысль.

2 Изложите основные мысли в виде пунктов плана.

3 Озаглавьте каждый раздел статьи.

4 Соотнесите все разделы статьи и скажите, правильно ли представлен текст графически.

5 Кратко (в объеме 8 – 10 предложений) изложите содержание статьи.

6 Назовите некоторые факты в тексте, чтобы подтвердить заголовок.

7 Назовите главную идею статьи.

8 Прочитайте статью и ответьте на следующие вопросы.

9 Разделите статью на несколько логических частей и озаглавьте каждую из них.

10 Найдите факты (примеры), подтверждающие следующее: … .

11 Найдите факты (примеры), объясняющие следующее… .

12 Задайте вопросы, которые не нашли ответов в статье.

13 Найдите главные предложения в каждом абзаце статьи.

14 Компрессируйте абзацы, оставляя только предложения, выражающие главную идею.

15 Прочитайте первые предложения абзаца, вычеркните слова, которые не несут важной информации.

Задания для просмотрового чтения

1 Просмотрите статьи, включенные в пособие, и отберите те, которые:    а) относятся к методам кодирования; б) модуляции сигналов; в) доступа к сетям; г) средствам телекоммуникации; д) проводной связи; е) радиосвязи; ж) распространению электромагнитных волн.

2 Из отобранных статей найдите ту, которая информирует о спектре радиочастот.

3 В отобранной статье назовите раздел, описывающий ультравысокие частоты.

4 Прочитайте заголовок и подзаголовки статьи и скажите, какую информацию вы можете получить из нее.

5 Прочитайте заголовок статьи и начало каждого абзаца. Сделайте вывод, что является темой этой статьи.

Типовые задания для самостоятельной внеаудиторной работы

1 Выпишите и переведите незнакомые слова.

2 Запомните значение и произношение подчеркнутых слов.

3 Подготовьте краткое изложение текста.

4 Подготовьтесь к беседе по содержанию текста.

5 Сделайте вывод о возможности использовать информацию текста в вашей профессиональной деятельности; для повышения эффективности и качества связи и т.д.

6 Ознакомьтесь с денотатной картой по специальности «Связь». Какую, по вашему мнению, информацию вы могли бы внести в эту карту.

7 Составьте денотатную карту к тексту «…».

UNIT 1

Telecommunication

Communication ties together the parts of a society just as the nervous  system ties together the parts of an individual. From earliest times, when the only form of communication was speech, to the present, when electronic signals carry information instantly to practically any point on Earth, communication has been the way people have organized their cooperative activities. In the modern world there are two main types of communications media. One type consists of the mass media — such as television, radio, newspapers, and magazines — in which organizations send messages to a large number of people. The other type consists of direct, point-to-point communications — telephone, telegraph, data transmission, and postal service. Of these, the electronic media (all but the postal service) are termed telecommunications.

Telecommunication is science and practice of transmitting information by electromagnetic means. A wide variety of information can be transferred through a telecommunications system, including voice and music, still-frame and full-motion pictures, computer files and applications, and telegraphic data Telecommunication first came into existence with the development of the telegraph in the 1830s and 1840s. For the first time, news and information could be transmitted great distances almost instantaneously. The invention of the telephone in 1876 by Alexander Graham Bell fundamentally transformed telecommunications.

After 1975, however, a new transformation of telecommunications began. The technology used to carry information changed radically.

Principles of telecommunication

Modern telecommunication centres on the problems involved in transmitting large volumes of information over long distances without damaging loss due to noise and interference. Figure 1 shows the basic components of a typical digital telecommunications system capable of transmitting voice, facsimile, data, radio, or television signals. Digital transmission is employed in order to achieve high reliability and because the cost of digital switching systems is much lower than the cost of analog systems. In order to use digital transmission, however, the analog signals that make up most voice, radio, and television communication must be subjected to a process of analog-to-digital conversion. (In data transmission this step is bypassed because the signals are already in digital form; most television, radio, and voice communication, however, use the analog system and must be digitized.) In many cases, the digitized signal is passed through a source encoder, which employs a number of formulas to reduce redundant binary information. After source encoding, the digitized signal is processed in a channel encoder, which introduces redundant information that allows errors to be detected and corrected. The encoded signal is made suitable for transmission by modulation onto a carrier wave and may be made part of a larger signal in a process known as multiplexing. The multiplexed signal is then sent into a multiple-access transmission channel. After transmission, the above process is reversed at the receiving end, and the information is extracted.

UNIT 2

Analog-to-Digital Conversion

 In transmission of speech, audio, or video information, the object is high fidelity — that is, the best possible reproduction of the original message without the degradations imposed by signal distortion and noise. The basis of relatively noise-free and distortion-free telecommunication is the binary signal. The simplest possible signal of any kind that can be employed to transmit messages, the binary signal consists of only two possible values. These values are represented by the binary digits, or bits, 1 and 0. Unless the noise and distortion picked up during transmission are great enough to change the binary signal from one value to another, the correct value can be determined by the receiver so that perfect reception can occur.

If the information to be transmitted is already in binary form (as in data communication), there is no need for the signal to be digitally encoded. But ordinary voice communications taking place by way of a telephone are not in binary form; neither is much of the information gathered for transmission from a space probe, nor are the television or radio signals gathered for transmission through a satellite link. Such signals, which continually vary among a range of values, are said to be analog, and in digital communications systems analog signals must be converted to digital form. The process of making this signal conversion is called analog-to-digital (A/D) conversion.

Sampling

Analog-to-digital conversion begins with sampling, or measuring the amplitude of the analog waveform at equally spaced discrete instants of time. The fact that samples of a continually varying wave may be used to represent that wave relies on the assumption that the wave is constrained in its rate of variation. Because a communications signal is actually a complex wave — essentially the sum of a number of component sine waves, all of which have their own precise amplitudes and phases — the rate of variation of the complex wave can be measured by the frequencies of oscillation of all its components. The difference between the maximum rate of oscillation (or highest frequency) and the minimum rate of oscillation (or lowest frequency) of the sine waves making up the signal is known as the bandwidth (B) of the signal. Bandwidth thus represents the maximum frequency range occupied by a signal. In the case of a voice signal having a minimum frequency of 300 hertz and a maximum frequency of 3,300 hertz, the bandwidth is 3,000 hertz, or 3 kilohertz. Audio signals generally occupy about 20 kilohertz of bandwidth, and standard video signals occupy approximately 6 million hertz, or 6 megahertz.

The concept of bandwidth is central to all telecommunication. In analog-to-digital conversion, there is a fundamental theorem that the analog signal may be uniquely represented by discrete samples spaced no more than one over twice the bandwidth (1/2B) apart. This theorem is commonly referred to as the sampling theorem, and the sampling interval (1/2B seconds) is referred to as the Nyquist interval (after the Swedish-born American electrical engineer Harry Nyquist). In current practice 8,000 samples are taken per second, in order to increase the frequency range and the fidelity of the speech representation.

Quantization

In order for a sampled signal to be stored or transmitted in digital form, each sampled amplitude must be converted to one of a finite number of possible values, or levels. For ease in conversion to binary form, the number of levels is usually a power of 2—that is, 8, 16, 32, 64, 128, 256, and so on, depending on the degree of precision required. In Figure 2, for simplicity of illustration, an analog waveform is shown being quantized on an 8-level scale (0 through 7). In digital transmission of voice, 256 levels are commonly used because tests have shown that this provides adequate fidelity for the average telephone listener.

The input to the quantizer is a sequence of sampled amplitudes for which there are an infinite number of possible values. The output of the quantizer, on the other hand, must be restricted to a finite number of levels. This relation between infinite input and finite output is represented conceptually in Figure 2, where the signal is shown being sampled at amplitude values of 6.5, 3.3, 2.0, 2.5, and so on, but levels of 7, 3, 2, 3, and so on, are assigned in quantization. Assigning infinitely variable amplitudes to a limited number of levels inevitably introduces inaccuracy, and inaccuracy results in a corresponding amount of signal distortion. (For this reason quantization is often called a “lossy” system.) The degree of inaccuracy depends on the number of output levels used by the quantizer. More quantization levels increase the accuracy of the representation, but they also increase the storage capacity or transmission speed required. Better performance with the same number of output levels can be achieved by judicious placement of the output levels and the amplitude thresholds needed for assigning those levels. This placement in turn depends on the nature of the waveform that is being quantized. Generally, an optimal quantizer places more levels in amplitude ranges where the signal is more likely to occur and fewer levels where the signal is less likely. This technique is known as nonlinear quantization. Nonlinear quantization can also be accomplished by passing the signal through a compressor circuit, which amplifies the signal's weak components and attenuates its strong components. The compressed signal, now occupying a narrower dynamic range, can be quantized with a uniform, or linear, spacing of thresholds and output levels. In the case of the telephone signal, the compressed signal is uniformly quantized at 256 levels, each level being represented by a sequence of eight bits. At the receiving end, the reconstituted signal is expanded to its original range of amplitudes. This sequence of compression and expansion, known as companding, can yield an effective dynamic range equivalent to 13 bits.

UNIT 3

Source Encoding

Huffman codes

In general, fewer bits on the average will be needed if the source encoder takes into account the probabilities at which different quantization levels are likely to occur. A simple example will illustrate this concept. Assume a quantizing scale of only four levels: 1, 2, 3, and 4. Following the usual standard of binary encoding, each of the four levels would be mapped by a two-bit code word. But also assume that level 1 occurs 50 percent of the time, that level 2 occurs 25 percent of the time, and that levels 3 and 4 each occur 12.5 percent of the time. Using variable-bit code words, such as those also shown in the table, might cause more efficient mapping of these levels to be achieved. The variable-bit encoding rule would use only one bit 50 percent of the time, two bits 25 percent of the time, and three bits 25 percent of the time. On average it would use 1.75 bits per sample rather than the 2 bits per sample used in the standard code.

The Lempel-Ziv Algorithm

The design and performance of the Huffman code depends on the designers' knowing the probabilities of different levels and sequences of levels. In many cases, however, it is desirable to have an encoding system that can adapt to the unknown probabilities of a source. A very efficient technique for encoding sources without needing to know their probable occurrence was developed in the 1970s by the Israelis Abraham Lempel and Jacob Ziv. The Lempel-Ziv algorithm works by constructing a codebook out of sequences encountered previously. For example, the codebook might begin with a set of four 12-bit code words representing four possible signal levels. If two of those levels arrived in sequence, the encoder, rather than transmitting two full code words (of length 24), would transmit the code word for the first level (12 bits) and then an extra two bits to indicate the second level. The encoder would then construct a new code word of 12 bits for the sequence of two levels, so that even fewer bits would be used thereafter to represent that particular combination of levels. The encoder would continue to read quantization levels until another sequence arrived for which there was no code word. In this case the sequence without the last level would be in the codebook, but not the whole sequence of levels. Again, the encoder would transmit the code word for the initial sequence of levels and then an extra two bits for the last level. The process would continue until all 4,096 possible 12-bit combinations had been assigned as code words.

In practice, standard algorithms for compressing binary files use code words of 12 bits and transmit 1 extra bit to indicate a new sequence. Using such a code, the Lempel-Ziv algorithm can compress transmissions of English text by about 55 percent, whereas the Huffman code compresses the transmission by only 43 percent.

Run-length Codes

Certain signal sources are known to produce “runs,” or long sequences of only 1s or 0s. In these cases it is more efficient to transmit a code for the length of the run rather than all the bits that represent the run itself. One source of long runs is the fax machine. A fax machine works by scanning a document and mapping very small areas of the document into either a black pixel (picture element) or a white pixel. The document is divided into a number of lines (approximately 100 per inch), with 1,728 pixels in each line (at standard resolution). If all black pixels were mapped into 1s and all white pixels into 0s, then the scanned document would be represented by 1,857,600 bits (for a standard 11-inch page). At older modem transmission speeds of 4,800 bits per second, it would take 6 minutes 27 seconds to send a single page. If, however, the sequence of 0s and 1s were compressed using a run-length code, significant reductions in transmission time would be made.

UNIT 4

Channel Encoding

 As described in Source encoding, one purpose of the source encoder is to eliminate redundant binary digits from the digitized signal. The strategy of the channel encoder, on the other hand, is to add redundancy to the transmitted signal — in this case so that errors caused by noise during transmission can be corrected at the receiver. The process of encoding for protection against channel errors is called error-control coding. Error-control codes are used in a variety of applications, including satellite communication, deep-space communication, mobile radio communication, and computer networking.

 The Hamming code is called a block code because information is blocked into bit sequences of finite length to which a number of redundant bits are added. When k information bits are provided to a block encoder, n − k redundancy bits are appended to the information bits to form a transmitted code word of n bits. The entire code word of length n is thus completely determined by one block of k information bits. In another channel-encoding scheme, known as convolutional encoding, the encoder output is not naturally segmented into blocks but is instead an unending stream of bits. In convolutional encoding, memory is incorporated into the encoding process, so that the preceding M blocks of k information bits, together with the current block of k information bits, determine the encoder output. The encoder accomplishes this by shifting among a finite number of “states,” or “nodes.” There are several variations of convolutional encoding, but the simplest example may be seen in what is known as the (n, 1) encoder, in which the current block of k information bits consists of only one bit. At each given state of the (n, 1) encoder, when the information bit (a 0 or a 1) is received, the encoder transmits a sequence of n bits assigned to represent that bit when the encoder is at that current state. At the same time, the encoder shifts to one of only two possible successor states, depending on whether the information bit was a 0 or a 1. At this successor state, in turn, the next information bit is represented by a specific sequence of n bits, and the encoder is again shifted to one of two possible successor states. In this way, the sequence of information bits stored in the encoder's memory determines both the state of the encoder and its output, which is modulated and transmitted across the channel. At the receiver, the demodulated bit sequence is compared to the possible bit sequences that can be produced by the encoder. The receiver determines the bit sequence that is most likely to have been transmitted, often by using an efficient decoding algorithm called Viterbi decoding (after its inventor, A.J. Viterbi). In general, the greater the memory (i.e., the more states) used by the encoder, the better the error-correcting performance of the code — but only at the cost of a more complex decoding algorithm. In addition, the larger the number of bits (n) used to transmit information, the better the performance — at the cost of a decreased data rate or larger bandwidth.

     In many telecommunications systems, it is necessary to represent an information-bearing signal with a waveform that can pass accurately through a transmission medium. This assigning of a suitable waveform is accomplished by modulation, which is the process by which some characteristic of a carrier wave is varied in accordance with an information signal, or modulating wave. The modulated signal is then transmitted over a channel, after which the original information-bearing signal is recovered through a process of demodulation.Modulation is applied to information signals for a number of reasons, some of which are outlined below.