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)
Рисунок 3 – Сквозные характеристики
Программа устроена так, что в ходе ее выполнения микропроцессор берет данные из определенной области памяти и выдает эти данные на порты через определенный промежуток времени ∆1 и ∆2.
Таким образом надо предварительно ввести в ячейки памяти данные.
Расчет для первого сигнала.
Частота первого сигнала f1= 100 Гц время через которое надо выдавать данные ∆1= 0,1 с, следовательно, количество выдач равно n1=((1/f1)/∆1) = 100. Была построена таблица для синусоиды.
Таблица 1 – Данные для первого сигнала
Угол в градусах
| Синусоида
| Модифицированная синусоида
| Массив
| 0
| 10
| 10
| 64
| 3,6
| 10,62791
| 11
| 6E
| 7,2
| 11,25333
| 11
| 6E
| 10,8
| 11,87381
| 12
| 78
| 14,4
| 12,4869
| 12
| 78
| 18
| 13,09017
| 13
| 82
| 21,6
| 13,68125
| 14
| 8C
| 25,2
| 14,25779
| 14
| 8C
| 28,8
| 14,81754
| 15
| 96
| 32,4
| 15,35827
| 15
| 96
| 36
| 15,87785
| 16
| A0
| 39,6
| 16,37424
| 16
| A0
| 43,2
| 16,84547
| 17
| AA
| 46,8
| 17,28969
| 17
| AA
| 50,4
| 17,70513
| 18
| B4
| 54
| 18,09017
| 18
| B4
| 57,6
| 18,44328
| 18
| B4
| 61,2
| 18,76307
| 19
| BE
| 64,8
| 19,04827
| 19
| BE
| 68,4
| 19,29776
| 19
| BE
| 72
| 19,51057
| 20
| C8
| 75,6
| 19,68583
| 20
| C8
| 79,2
| 19,82287
| 20
| C8
| 82,8
| 19,92115
| 20
| C8
| 86,4
| 19,98027
| 20
| C8
| 90
| 20
| 20
| C8
| 93,6
| 19,98027
| 20
| C8
| 97,2
| 19,92115
| 20
| C8
| 100,8
| 19,82287
| 20
| C8
| 104,4
| 19,68583
| 20
| C8
| Продолжение таблицы 2
108
| 19,51057
| 20
| C8
| 111,6
| 19,29776
| 19
| BE
| 115,2
| 19,04827
| 19
| BE
| 118,8
| 18,76307
| 19
| BE
| 122,4
| 18,44328
| 18
| B4
| 126
| 18,09017
| 18
| B4
| 129,6
| 17,70513
| 18
| B4
| 133,2
| 17,28969
| 17
| AA
| 136,8
| 16,84547
| 17
| AA
| 140,4
| 16,37424
| 16
| A0
| 144
| 15,87785
| 16
| A0
| 147,6
| 15,35827
| 15
| 96
| 151,2
| 14,81754
| 15
| 96
| 154,8
| 14,25779
| 14
| 8C
| 158,4
| 13,68125
| 14
| 8C
| 162
| 13,09017
| 13
| 82
| 165,6
| 12,4869
| 12
| 78
| 169,2
| 11,87381
| 12
| 78
| 172,8
| 11,25333
| 11
| 6E
| 176,4
| 10,62791
| 11
| 6E
| 180
| 10
| 10
| 64
| 183,6
| 9,372095
| 9
| 5A
| 187,2
| 8,746668
| 9
| 5A
| 190,8
| 8,126187
| 8
| 50
| 194,4
| 7,513101
| 8
| 50
| 198
| 6,90983
| 7
| 46
| 201,6
| 6,318754
| 6
| 3C
| 205,2
| 5,742207
| 6
| 3C
| 208,8
| 5,182463
| 5
| 32
| 212,4
| 4,641732
| 5
| 32
| 216
| 4,122147
| 4
| 28
| 219,6
| 3,62576
| 4
| 28
| 223,2
| 3,154529
| 3
| 1E
| 226,8
| 2,710314
| 3
| 1E
| 230,4
| 2,294868
| 2
| 14
| 234
| 1,90983
| 2
| 14
| Продолжение таблицы 2
237,6
| 1,556721
| 2
| 14
| 241,2
| 1,236933
| 1
| 0A
| 244,8
| 0,951729
| 1
| 0A
| 248,4
| 0,702235
| 1
| 0A
| 252
| 0,489435
| 0
| 00
| 255,6
| 0,314168
| 0
| 00
| 259,2
| 0,177127
| 0
| 00
| 262,8
| 0,078853
| 0
| 00
| 266,4
| 0,019733
| 0
| 00
| 270
| 0
| 0
| 00
| 273,6
| 0,019733
| 0
| 00
| 277,2
| 0,078853
| 0
| 00
| 280,8
| 0,177127
| 0
| 00
| 284,4
| 0,314168
| 0
| 00
| 288
| 0,489435
| 0
| 00
| 291,6
| 0,702235
| 1
| 0A
| 295,2
| 0,951729
| 1
| 0A
| 298,8
| 1,236933
| 1
| 0A
| 302,4
| 1,556721
| 2
| 14
| 306
| 1,90983
| 2
| 14
| 309,6
| 2,294868
| 2
| 14
| 313,2
| 2,710314
| 3
| 1E
| 316,8
| 3,154529
| 3
| 1E
| 320,4
| 3,62576
| 4
| 28
| 324
| 4,122147
| 4
| 28
| 327,6
| 4,641732
| 5
| 32
| 331,2
| 5,182463
| 5
| 32
| 334,8
| 5,742207
| 6
| 3C
| 338,4
| 6,318754
| 6
| 3C
| 342
| 6,90983
| 7
| 46
| 345,6
| 7,513101
| 8
| 50
| 349,2
| 8,126187
| 8
| 50
| 352,8
| 8,746668
| 9
| 5A
| 356,4
| 9,372095
| 9
| 5A
| 360
| 10
| 10
| 64
| Данные для синусоиды были получены по формуле U= Аsin(α) + 10, где А – амплитуда, α угол, 10 - смещение синусоиды относительно оси времени.
Модифицированная синусоида состоит из округленных до целых значений синусоиды. Столбец данные содержит значения, которые должен выдавать микропроцессор на порт. Максимальная относительная погрешность при сравнении синусоиды и модифицированной синусоиды достигла 4,894%. Столбец «Массив» таблицы 2 содержит 101 значений для выдачи, но первую сточку нужно отбросить так как она совпадает с последней, а это значит, что после каждого периода синусоида будет смешаться по горизонтали. Таким образом для работы программы надо будет ввести в память микропроцессора данные с столбца «Массив» кроме первой строки.
Расчет для второго сигнала.
Частота первого сигнала f2= 20 Гц время через которое надо выдавать данные ∆1= 0,5 с, следовательно, количество выдач равно n1=((1/f1)/∆1) = 100. Была построена таблица для второго сигнала.
Таблица 3 – Данные для второго сигнала
U, В
| Массив
| U, В
| Массив
| U, В
| Массив
| U, В
| Массив
| 4
| 28
| 2,8
| 1C
| 1,6
| 10
| 2,7
| 1B
| 3,9
| 27
| 2,7
| 1B
| 1,6
| 10
| 2,8
| 1C
| 3,8
| 26
| 2,6
| 1A
| 1,7
| 11
| 2,9
| 1D
| 3,7
| 25
| 2,5
| 19
| 1,8
| 12
| 3
| 1E
| 3,6
| 24
| 2,4
| 18
| 1,9
| 13
| 3,1
| 1F
| 3,5
| 23
| 2,3
| 17
| 2
| 14
| 3,2
| 20
| 3,4
| 22
| 2,2
| 16
| 2,1
| 15
| 3,3
| 21
| 3,3
| 21
| 2,1
| 15
| 2,2
| 16
| 3,4
| 22
| 3,2
| 20
| 2
| 14
| 2,3
| 17
| 3,5
| 23
| 3,1
| 1F
| 1,9
| 13
| 2,4
| 18
| 3,6
| 24
| 3
| 1E
| 1,8
| 12
| 2,5
| 19
| 3,7
| 25
| 2,9
| 1D
| 1,7
| 11
| 2,6
| 1A
| 3,8
| 26
| Продолжение таблицы 3
3,9
| 27
| 0
| 0
| 0
| 0
| 0
| 0
| 4
| 28
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| Расчет количества тактов.
Для временной задержки было рассчитано через сколько тактов микропроцессор должен выдавать данные на порт. Количество тактов для временной задержки выдачи данных на первый порт N1 = ∆1/(1/fт), где N – количество тактов, fт – тактовая частота процессора. Для первого сигнала количество тактов составило 200, а для второго, рассчитанного по аналогичной формуле, составило 1000 тактов.
Распределение адресного пространства.
В ячейках 0000 - 0063 содержится массив данных для второго сигнала, в ячейках 0100 – 0163 для первого.
Разработка блок схемы.
Блок-схема алгоритма решения задачи представлена на рисунке 4.
|