nyuuzyou/kompy huggingface dataset:
Deraza o‘rnini profilli oynalar bilan to‘ldirish

|
Deraza o‘rnini profilli oynalar bilan to‘ldirish
|
bet | 7/8 | Sana | 19.01.2024 | Hajmi | 184,47 Kb. | | #141011 |
Bog'liq « 2 qavatli 8 ta xonodonli turar joy binosini Namangan KURS ISHIGA TAYYOR TUSHUNTIRUV YOZUVIDeraza o‘rnini profilli oynalar bilan to‘ldirish.
a - deraza o‘rni kesimi; b - profilli oyna tutashishini rejada ko‘rinishi; 1- profilli oyna elementlari; 2-germetik; 3-g‘ovak rezina; 4-vint; 5-payvandlangan gorizontal impost; 6-germetik qorishma; 7-shlak paxta; 8-sement qorishma; 9-g‘isht; 10-panel devor; 11-payvand chok.
\
ESHIKLAR.
Eshik o‘rnining to‘ldirmasi eshikkesakisi va tavaqasidan iborat
bo‘ladi.Qo‘llab industrial usulda tayyorlashda eshik kesakisi va tavaqasi birlashtirilib, eshik bloki tarzida ishlab chiqariladi.
Eshik konstruksiyalari.
a-g‘ishtin devorlarda joylashgan eshik qutisi (kesakisi); b-parda devorlarda joylashgan eshik qutisi; 1-chaspak (nalichnik); 2-kursicha; v-eshik tavaqasi shitli; g-filyonkali eshik; d-ramkali shitli eshik; e-ramkasiz shitli eshik; j,z-yog‘och filyonkalar; i-terilgan yog‘och; k-filyonkasi chiqarilgan; 1-duradgorlik plitkasi; 2-fanera listi; 3-romka; 4-elimlangan nagel; 5-quti; 6-chaspak (nalichnik); 7-shponka; 8-planka; 9-filyonka; 10-chiqarilgan filyonka; 11-terilgan yog‘och.
Eshiklarning turlari.Tavaqalar soniga ko‘ra bir, ikki va bir yarim tavaqali eshiklar (eni turlicha bo‘lgan ikki tavaqadan iborat bo‘lib, enlirog‘idan doimiy foydalaniladi) bo‘ladi.
YOg‘och eshiklar xam ichki xam tashqibo‘lishi mumkin, yog‘och eshikarning misollari keltirilgan.
Qo‘llab ko‘riladigan turar-joy binolarining tashqi eshiklari ko‘pincha shitlardan ikki tavaqali yoki bir yarim tavaaqali, yarimi oynavand qilib tayyorlanadi.Katta (asosiy eshiklar yo oynavand qilib yoki dilali qilib tayyorlanadi).
Qo‘llab ko‘riladgan binolarda ichki eshiklar shitli, yaxlit va oynavand bir va ikki tavaqali bo‘ladi.Eshik kesakisi pardevordagi eshik uchun qoldirilgan joyga o‘rnatioladi va unga mixlab maxkamlanadi.
BINONING ICHKI PARDOZI.
Binoning ichki pardoziga oshxona uchun shkaf, gaz plitasi, muzlatgich, mikravolnoe pech, rakavina,xon , stol stul va pardalar.
Xammaom va xojatxona uchun unitas, rakavina, vanna, dush, kir yuvish mashinasi xakazolar.
Mexmonxona uchun kreslo, kanditsioner, qandil, televizor, stol stul, gilam, parda, suniy va tabiiy gullar va boshqalar.
YOtoqxona gul, devan, parda, qandil, televizor, konditsioner, kiyim javoni, tryumo, soat va boshqalar.
|
| |
Deykstra algoritmining tadbiqi - Laboratoriya mashg’uloti. Graflar bilan ishlovchi sodda algoritmlar. Graflarni...

|
Laboratoriya mashg’uloti. Graflar bilan ishlovchi sodda algoritmlar. Graflarni tasvirlash. Eniga va tubiga qarab qidirish
|
bet | 2/3 | Sana | 24.05.2024 | Hajmi | 389,62 Kb. | | #252230 |
Bog'liq 04 3 Graflar bilan ishlovchi sodda algoritmlar Graflarni tasvirlashDeykstra algoritmining tadbiqi
Grafning og’irligini saqlash uchun kvadrat matritsadan foydalaniladi. Satrlar va ustunlar sarlavhalarida grafning uchlari joylashadi. Graf yoylarining og’irligi jadvalning ichki yacheykalariga joylashtiriladi. Graf petlyaga ega emas, shu sababdan ham matritsaning asosiy diagonalida nol qiymatlar joylashgan.
C++ ga tadbiqi
#define _CRT_SECURE_NO_WARNINGS
#include
#include
#define SIZE 6
int main()
{
int a[SIZE][SIZE]; // матрица связей
int d[SIZE]; // минимальное расстояние
int v[SIZE]; // посещенные вершины
int temp, minindex, min;
int begin_index = 0;
system("chcp 1251");
system("cls");
// Инициализация матрицы связей
for (int i = 0; i {
a[i][i] = 0;
for (int j = i + 1; j printf("Введите расстояние %d - %d: ", i + 1, j + 1);
scanf("%d", &temp);
a[i][j] = temp;
a[j][i] = temp;
}
}
// Вывод матрицы связей
for (int i = 0; i {
for (int j = 0; j printf("%5d ", a[i][j]);
printf("\n");
}
//Инициализация вершин и расстояний
for (int i = 0; i {
d[i] = 10000;
v[i] = 1;
}
d[begin_index] = 0;
// Шаг алгоритма
do {
minindex = 10000;
min = 10000;
for (int i = 0; i { // Если вершину ещё не обошли и вес меньше min
if ((v[i] == 1) && (d[i] { // Переприсваиваем значения
min = d[i];
minindex = i;
}
}
// Добавляем найденный минимальный вес
// к текущему весу вершины
// и сравниваем с текущим минимальным весом вершины
if (minindex != 10000)
{
for (int i = 0; i {
if (a[minindex][i] > 0)
{
temp = min + a[minindex][i];
if (temp < d[i])
{
d[i] = temp;
}
}
}
v[minindex] = 0;
}
} while (minindex < 10000);
// Вывод кратчайших расстояний до вершин
printf("\nКратчайшие расстояния до вершин: \n");
for (int i = 0; i printf("%5d ", d[i]);
// Восстановление пути
int ver[SIZE]; // массив посещенных вершин
int end = 4; // индекс конечной вершины = 5 - 1
ver[0] = end + 1; // начальный элемент - конечная вершина
int k = 1; // индекс предыдущей вершины
int weight = d[end]; // вес конечной вершины
while (end != begin_index) // пока не дошли до начальной вершины
{
for (int i = 0; i if (a[i][end] != 0) // если связь есть
{
int temp = weight - a[i][end]; // определяем вес пути из предыдущей вершины
if (temp == d[i]) // если вес совпал с рассчитанным
{ // значит из этой вершины и был переход
weight = temp; // сохраняем новый вес
end = i; // сохраняем предыдущую вершину
ver[k] = i + 1; // и записываем ее в массив
k++;
}
}
}
// Вывод пути (начальная вершина оказалась в конце массива из k элементов)
printf("\nВывод кратчайшего пути\n");
for (int i = k - 1; i >= 0; i--)
printf("%3d ", ver[i]);
getchar(); getchar();
return 0;
}
Dastur bajarilgandan keyin olingan natija
|
|
Bosh sahifa
Aloqalar
Bosh sahifa
Laboratoriya mashg’uloti. Graflar bilan ishlovchi sodda algoritmlar. Graflarni tasvirlash. Eniga va tubiga qarab qidirish
|
Tashqi pardozlar - «bino va inshootlar arxitekturasi» fanidan kurs loyihasiga

|
«bino va inshootlar arxitekturasi» fanidan kurs loyihasiga
|
bet | 8/8 | Sana | 19.01.2024 | Hajmi | 184,47 Kb. | | #141011 |
Bog'liq « 2 qavatli 8 ta xonodonli turar joy binosini Namangan KURS ISHIGA TAYYOR TUSHUNTIRUV YOZUVITashqi pardozlar marmar tosh, qum suvoq, kraska, sokul va boshqalar.
Foydalanilgan adabiyotlar
ehmorchilik tarixi11-qism”. Toshkent 2010 y
M.M.Vaxitov. “Fuqarolik binolari Mehmorchilik 2-qism”. Toshkent 2010 y
X.M.Ubaydullaev. “Turar joy va jamoat binolarini loyihalashning tipologik asoslari”. Toshkent 2015 y
B.Jo‘raev, O.CHo‘lponov “Fuqaro binolarini arxitekturasi” fanidan o‘quv uslubiy majmuasi. Namangan 2017 y
I.A. SHereshevskiy Jilqe zdaniya
QMQ 2.01.01-94 – «Loyihalash uchun iqlimiy va fizikaviy–geologik mahlumotlar». –T.: 1994 yil.
R.D.Teshabaev «Fukaro binolarining mehmorchilik konstruksiyalari»
QMQ 2.01.04-97 – «Qurilish issiqlik texnikasi». –T.: 1997 yil.
QMQ 2.01.03-96 – «Zilzilaviy xududlarda qurilish». –T.: 1996 yil.
QMQ 2.07.01.-94 – «SHaxarsozlik. SHaxar va qishloq manzilgohlarini rejalashtirish va qurish». –T.: 1994 yil.
SHNQ 2.08.01-05 – «Turar-joy binolari». –T.: 2006 yil.
SHNQ 2.08.02.-09 «Jamoat binolari va inshootlari». –T.: 2009 yil.
QMQ 2.01.08-96 – «SHovqindan himoya». –T.: 1997 yil.
|
| |
-Variant


1.Korxonaning moliyaviy natijalarini tahlil qilish va prognoz qilish

|
-Variant
1.Korxonaning moliyaviy natijalarini tahlil qilish va prognoz qilish
|
bet | 28/38 | Sana | 19.01.2024 | Hajmi | 0,52 Mb. | | #140675 |
Bog'liq 1 dan 7 gaca7-Variant
1.Korxonaning moliyaviy natijalarini tahlil qilish va prognoz qilish.
Moliyaviy natijalar – bu foyda yoki zarar.
Foyda (zarar) – korxonaning jami xarajatlari va jami daromadlari o‘rtasidagi farqni anglatadi.
Foyda - tashkilot faoliyatining ijobiy moliyaviy natijasidir. Salbiy natija zarar deb nomlanadi.
quyidagi foyda ko‘rsatkichlari bilan tavsiflanadi:
•Mahsulot sotishdan yalpi foyda – mahsulot sotishdan sof tushum va sotilgan mahsulot tannarxi o‘rtasidagi ayirma sifatida aniqlanadi:
YaF= SST-SMT.
•Asosiy faoliyatdan foyda – mahsulot sotishdan yalpi foyda bilan davr xarajatlari o‘rtasidagi farq va boshqa daromadlar yoki asosiy faoliyatdan zararlar qo‘shilgan yoki ayirilgan holda aniqlanadi:
AFF = YaF-DX+BD-BZ.
Umumxo‘jalik faoliyatidan foyda (zarar), asosiy faoliyatdan foyda miqdoriga moliyaviy faoliyat bo‘yicha daromadlarni qo‘shish, xarajatlarni ayirish yo‘li bilan hisoblanadi.
UF = AFF+MD-MX.
Daromad (foyda) solig‘i to‘langunga qadar foyda, u umumxo‘jalik faoliyatidan foyda va favqulodda ( ko‘zda tutilmagan) foyda va zararlar saldosining algebraik miqdori sifatida ta’riflanadi.
STF = UF+FP-FZ
Hisobot davrining Sof foydasi soliqlar to‘langandan so‘ng korxona tasarrufida qoladi, daromad yoki foyda solig‘i hisobdan chiqarilgandan so‘ng va yana boshqa qaerdadir hisobga olinmaydigan boshqa soliq va to‘lovlar ayirib tashlangandan so‘ng qoladigan soliqlar to‘langunga qadar foydani o‘zida aks ettiradi:
SF = STF-DS-BS.
Prognozlash - bu korxonaning kelgusidagi faoliyati natijasi va sharoitini aniklashga yo'naltirilgan son va sifat jihatdan rejadan oldingi tizimdir. U korxona oldiga ko'yilgan maksadlarni xakkoniyligini va foydaliligini belgilab beradi. U texnik va iktisodiy prognozlashga bo'linadi.
Bashoratning maqsadi - - bozorga ta`sir qiluvchi omillarni, shu bilan birga, xo`jalikning umumiy ahvoli, tuzilmaviy siljishlar, investitsion faollik, fan-texnika taraqqiyotining iste`molchi va ishlab chiqaruvchilarga ta`sir ko`rsatishi, an`anaviy mahsulotlardan tashqari korxonaning barqarorligi va raqobatbardoshligiga olib keluvchi «pioner» (yangi) mahsulot ishlab chiqarish istiqbolida yuzaga kelishi mumkin bo`lgan holatlarini belgilashdadir. Korxonalar uchun talabni bashorat qilish muhim ahamiyatga ega bo`lib, u ishlab chiqarilayotgan mahsulotlar turi va miqdorining o`zgarishini avvaldan aniqlab beradi. Umuman olganda, bashorat rejalashtirishning ilmiy asosidir.
|
| |
Interfaol ta’limdagi suhbat ishtirokchilari

|
Interfaol ta’limdagi suhbat ishtirokchilari
|
bet | 21/35 | Sana | 02.12.2023 | Hajmi | 134,23 Kb. | | #109920 |
Bog'liq Hamza Hakimzoda Niyoziy va Abdulla Avloniy asarlari misolida mavInterfaol ta’limdagi suhbat ishtirokchilari
|
O’qituvchi – talaba (o’quvchi)
|
O’qituvchi – talaba (o’quvchi)lar guruhi yoki kichik guruhda ishlash
|
O’qituvchi – talaba (o’quvchi)lar jamoasi (guruh, sinf jamoasida ishlash)
|
O’quvchi – axborot-kommunikatsion texnologiyalar
|
Interfaol ta'lim mohiyatiga ko'ra suhbatning "talaba - axborot-kommunikatsion texnologiyalar" shaklida tashkil etilishi talaba (talaba)lar tomonidan mustaqil ravishda yoki o'qituvchi rahbarligida axborot texnologiyalari yordamida bilim, ko'nikma, malakalarning o'zlashtirilishini anglatadi.
O'qitishning interfaol ta'limga asoslanishi bir qarashda nihoyatda oddiy, sodda va hatto "bolalar o'yini" kabi taassurot uyg'otadi. Biroq, bunda o'qituvchining ma'lum darajada quyidagi omillarga ega bo'lishi talab qilinadi:
Asosiy omillar
|
Ish tartibi
|
Metodik malaka
|
Tayanch pedagogik-psixologikbilimlar
|
Tashkilotchilik qobiliyati
|
Didaktik vositalar (axborot, tarqatma materiallar, dars jihozlari)ga ega bo’lish
|
Dars jarayonida demokratik g’oyalarning ustuvor bo’lishi
|
Axborotlarni taqdim etishda xilma-xil metod, vositalarni samarali, o’rinli, maqsadga muvofiq va o’zaro moslikda qo’llashga erishish
|
Shu bilan birga ta'limda interfaol ta'limning samaradorligi quyidagi ikkilamchi omillarga ham bog'liq:
|
| |
-Variant


1.Foyda va rentabellikni tahlil qilish

|
-Variant
1.Foyda va rentabellikni tahlil qilish
|
bet | 24/38 | Sana | 19.01.2024 | Hajmi | 0,52 Mb. | | #140675 |
Bog'liq 1 dan 7 gaca6-Variant
1.Foyda va rentabellikni tahlil qilish
Iqtisodiy tahlilda korxonalarning faoliyati yalpi daromad, sotish, foyda kabi ko'rsatkichlar bilan baholanishi mumkin. Biroq, sanab o'tilgan ko'rsatkichlarning qiymatlari korxona faoliyati to'g'risida fikrni shakllantirish uchun etarli emas. Buning sababi shundaki, ushbu ko'rsatkichlar korxonaning mutlaq xarakteristikasi bo'lib, samaradorlikni baholashda ularni to'g'ri talqin qilish faqat korxonaga kiritilgan mablag'larni aks ettiruvchi boshqa ko'rsatkichlar bilan birgalikda amalga oshirilishi mumkin. Shu sababli, umuman korxonaning samaradorligini tavsiflash uchun iqtisodiy tahlilda turli xil faoliyatning (iqtisodiy, moliyaviy, tadbirkorlik) rentabelligi, rentabellik ko'rsatkichlari hisoblab chiqiladi. Shuni ta'kidlash kerakki, rentabellik ko'rsatkichlari korxonalar foydasini shakllantirish omil omilini aks ettiruvchi muhim elementlardir. Shuning uchun ular korxonaning moliyaviy holatini qiyosiy tahlil qilish va baholashda bo'lishi kerak. Bundan tashqari, rentabellik ko'rsatkichlari korxona menejmenti samaradorligini tahlil qilishda, tashkilotning uzoq muddatli farovonligini aniqlashda, ular investitsiya siyosati va narx belgilash vositasi sifatida ishlatiladi. Rentabellik tijorat korxonasi samaradorligining asosiy sintetik ko'rsatkichi sifatida. Bozor iqtisodiyotini shakllantirish sharoitida korxonalar va ularning birlashmalarining muvaffaqiyatli ishlashi uchun asosiy talablardan biri bu iqtisodiy va boshqa faoliyatning zararsizlantirilishi, xarajatlarni o'z daromadlari bilan qoplash va ma'lum miqdordagi mablag'larni ta'minlashdir. Savdo korxonalarining iqtisodiy faoliyatining moliyaviy natijalarini tavsiflovchi asosiy ko'rsatkichlar yalpi daromad, boshqa daromadlar, foyda va rentabellikdir. Daromadlilik - bu xo'jalik boshqaruvi ko'rsatkichlari va ushlagichlari tizimidagi markaziy o'rinlardan biridir. Bu korxona faoliyatini baholashning mezonidir.
Rentabellikning iqtisodiy mohiyatini faqatgina ko‘rsatkichlar tizimini tavsiflash orqali yoritish mumkin. Ularning umumiy ma’nosi – joylashtirilgan kapitalning bir so‘mi uchun foyda miqdorini aniqlashdan iborat. Rentabellik darajasi turli-tuman funksiyalarni bajaradi, aynan esa:
• korxonaga joylashtirilgan mablag‘larning o‘zini qoplashini aks ettiradi;
• ishlab chiqarishni kengaytirish, modernizatsiya va rekonstruksiya qilishni ta’minlaydi;
• korxonaning kredit va qarzlari qaytarilishini ta’minlaydi, ya’ni uning to‘lov qobiyatiga imkon yaratadi;
• ishlab chiqarishning ijtimoiy tarmog‘ini rivojlantirishga imkon beradi, ya’ni korxona va tashkilotlarning xodimlarini bozor munosabatlari taraqqiyotining salbiq oqibatlaridan himoyalashni ta’minlaydi;
Rentabellikni hisoblashning umumiy formulasi:
R =F r100%/V
Bu yerda, F – korxona foydasi;
V – rentabellik hisoblanadigan ko‘rsatkich.
|
| |
-Variant


1.Tannarxini tahlil qilish va baholash

5-Variant
1.Tannarxini tahlil qilish va baholash.
Tannarx ishlab chiqarish yoki mahsulotlar (ish, xizmatlar) – tabiiy resurslar, xomashyo, materiallar, yoqilg‘i, energiya, asosiy vositalar, mehnat resurslari, shuningdek, ishlab chiqarish bilan bog‘liq boshqa sarflarni qayta ishlash jarayonida foydalaniladigan qiymat bahosini o‘zida aks ettiradi
Mahsulot tannarxi – ishlab chiqarish iqtisodiy samaradorligining eng muhim ko‘rsatkichidir. Unda xo‘jalik faoliyatining barcha jihatlari, barcha ishlab chiqarish resuruslaridan foydalanish natijalari aks ettiriladi. Mahsulot va xizmatlar tannarxi darajasiga foyda miqdori va rentabellik darajasi, korxonaning moliyaviy holati va to‘lov qobiliyati
bog‘liq bo‘ladi.
Tannarxni pasaytirish muammolari ayniqsa hozirgi bosqichda
alohida dolzarb ahamiyat kasb etmoqda. Uni kamaytirish zaxiralarini
izlash ko‘plab korxonalarga o‘z raqobatdoshligini oshirish,
bankrotlikdan qochish va bozor iqtisodiyoti sharoitida yashab qolishga
ko‘maklashadi.
Tannarx tahlili quyidagi asosiy yo‘nalishlar bo‘yicha olib boriladi:
- tannarxning umumlashtiruvchi ko‘rsatkichlari dinamikasi va uning o‘zgarish omillari tahlili;
- sarf-xarajatlarning iqtisodiy elementlari bo‘yicha tannarx tahlili;
- 1 so‘mlik mahsulot uchun sarf-xarajat tahlili;
- sarf-xarajat tuzilmasi va dinamikasi tahlili;
- sarf-xarajatlarning moddalari bo‘yicha tannarx tahlili;
- tannarxning moddalari bo‘yicha omilli tahlili;
- tannarxni pasaytirish zaxiralarini aniqlash.
Tannarx tahlili vazifalari quyidagilar hisoblanadi:
- tannarx bo‘yicha biznes-reja asoslanganligini baholash;
- tannarx bo‘yicha eng muhim ko‘rsatkichlar dinamikasini o‘rganish va ular bo‘yicha tannarxni aniqlash;
- ko‘rsatkichlar dinamikasiga ta’sir qiluvchi omillarni aniqlash va ular asosida haqiqatdagi sarf-xarajatlarning rejadagidan og‘ish miqdori va sabablarini topish;
- mahsulotlar tannarxini pasaytirish zaxiralarini aniqlash
Маҳсулот таннархига киритиладиган харажатлар:
ишлаб чиқаришнинг моддий харажатлари
ишлаб чиқариш хусусиятига эга
ишлаб чиқаришга тегишли асосий воситалар ва номоддий активлар амортизацияси
ишлаб чиқариш аҳамиятига эга бошқахаражатлар
Маҳсулот таннархи бир турдаги маҳсулотни бевосита ва билвосита ишлаб чиқариш харажатларини шу маҳсулот миқдорига бўлиш орқали топилади
МТ = МХ+ИХ+СТ+А+БИХ / М
БИХ-бошқа ишлаб чиқариш харажатлари, сўм
ИХ-ишлаб чиқариш хусусиятидаги меҳнатга ҳақ тўлаш харажатлари, сўм
СТ-ишлаб чиқаришга тегишли бўлган ижтимоий суғурта тўловлари, сўм
МХ-ишлаб чиқаришнинг моддий харажатлари, сўм
|
Material resurslardan foydalanish koeffitsiyenti

|
Material resurslardan foydalanish koeffitsiyenti
|
bet | 23/38 | Sana | 19.01.2024 | Hajmi | 0,52 Mb. | | #140675 |
Bog'liq 1 dan 7 gacaMaterial resurslardan foydalanish koeffitsiyenti – bu haqiqatdagi material sarfining rejalashtirilgan kalkulyatsiya bo‘yicha hisoblangan material sarfi miqdori va mahsulotning haqiqatdagi ishlab chiqarishi hamda assortimentiga nisbatidir. Material sig‘imining xususiy ko‘rsatkichlari material resurslarning alohida turlaridan (xomashyo sig‘imi, metall sig‘imi, energiya sig‘imi va boshqalar) foydalanish samaradorligini tavsiflash uchun, shuningdek, alohida mahsulotlarning material sig‘imi darajasini (mahsulot birligiga ishlatilgan barcha materiallar qiymatining uning ulgurji narxiga nisbati) tavsiflash uchun qo‘llaniladi.
Xo'jalik faoliyati barcha tomonlarining natijalarini, birinchi navbatda, uning zaif va oqsoq tomonlarining natijalarini chuqur tahlil qilib, mavjud imkoniyatlarni aniqlash va ularni xo'jalik faoliyatiga jalb qilish asosida sifatli mahsulot ishlab chiqarishni yanada ko'paytirish hamda samaradorligini oshirish yo'llarni ko'rsatishdan iborat.
Material resurslar samaradorligining xususiy ko‘rsatkichlari material resurslarning alohida elementlarini iste’mol qilish samaradorligini tavsiflash, shuningdek, alohida mahsulotlar material sig‘imini baholash uchun qo‘llaniladi. Alohida mahsulotlarning solishtirma material sig‘imi qiymat, natural-qiymat va natural ifodada hisoblanishi mumkin.
Mahsulotning material sig‘imi material sarfining ishlab chiqarilgan mahsulot qiymatiga nisbati sifatida aniqlanadi va ishlab chiqarilgan mahsulotning har bir so‘miga to‘g‘ri keluvchi material sarfini ko‘rsatadi:
Bu yerda Mz – material sarfi; Nv – qiymat yoki natural ko‘rsatkichlardagi mahsulot hajmi. Material sarfi tarkibiga xomashyo, asosiy va qo‘shimcha materiallar, sotib olingan yarimfabrikatlar, yonilg‘i, energiya kiritiladi.
Material qaytimi – material sig‘imiga teskari ko‘rsatkich, iste’mol qilingan resurslarning 1 so‘miga to‘g‘ri keladigan mahsulot ishlab chiqarishni tavsiflaydi:
Mahsulotlarning turlari bo‘yicha solishtirma material sig‘imi mahsulot birligi ishlab chiqarish uchun material sarfining mahsulotning ulgurji narxiga nisbati sifatida aniqlanadi. Bu ko‘rsatkich ham natural, ham qiymat ifodasida ifodalanishi mumkin.
|
| |
"Oyning toq kunlarida to'xtab turish taqiqlanadi"

|
"Oyning toq kunlarida to'xtab turish taqiqlanadi"
|
bet | 18/27 | Sana | 26.12.2019 | Hajmi | 205,4 Kb. | | #5392 |
3.29
"Oyning toq kunlarida to'xtab turish taqiqlanadi"
|
3.30
|
"Oyning juft kunlarida to'xtab turish taqiqlanadi"
|
3.31
|
"Barcha cheklovlarning oxiri" 3.16, 3.20, 3.22, 3.24, 3.26-3.30 belgilaridan bir nechtasiga bir vaqtda amal qiladigan oraliqlarning oxirini bildiradi.
|
3.1-3.3, 3.18.1, 3.18.2, 3.19, 3.27 belgilarining belgilangan yo'nalishli transport vositalariga, 3.2-3.8 belgilari ta'sir oralig'ida yashovchi yoki ishlovchi fuqarolarga tegishli yohud ularga va korxonalarga xizmat qiluvchi transport vositalariga daxli yo'q. Bunday hollarda transport vositalari belgilangan joyga yaqin chorrahadan kirib yoki chiqib ketishlari kerak.
Ushbu Qoidalarning 28.3 bandiga binoan Nogiron taniqlik belgisi bilan belgilangan avtomobillar va kajavali mototsikllarnii boshqarayotgan nogiron haydovchilar 3.2, 3.3 va 3.28 belgilari talablaridan chetga chiqishlari mumkin. 7.18 qo'shimcha axborot belgisi bo'lganda 3.27 belgisining ta'sir oralig'ida to'xtashga ruxsat etiladi.
3.28-3.30 - belgilari taksometri ishlab turgan taksilarga daxli yo'q.
3.18.1 va 3.18.2 belgilari qatnov qismlarining qaysi kesishmasi oldida o'rnatilsa, shu kesishmaga tatbiq etiladi.
3.16, 3.20, 3.22, 3.24, 3.26-3.30 belgilarining ta'siri belgi o'rnatilgan joydan keyingi eng yaqin chorrahagacha, chorrahasi bo'lmagan aholi yashaydigan joylarda aholi yashaydigan hududning oxirigacha, yo'lga yondosh hududlardan dala va o'rmonlarning yo'l bilan tutashgan hamda ikkinchi darajali yo'llardan chiqish joylarda, ularning oldida tegishli belgilar o'rnatilmagan bo'lsa, bu belgilarning ta'siri yo'qolmaydi.
Aholi yashaydigan joylarga etmay o'rnatilgan 3.24 belgisining ta'siri 5.22 belgisigacha kuchini saqlaydi.
Belgilarning ta'sir doirasi:
3.16 va 3.26 belgilari 7.2.1 qo'shimcha belgisi orqali;
3.20, 3.22, 3.24 belgilari 3.21, 3.23, 3.25 va 7.2.1 qo'shimcha belgisi orqali;
3.27-3.30 belgilarining amal qilish oralig'i oxiriga 3.27-3.30 belgilari 7.2.3 qo'shimcha belgisi bilan birga takror o'rnatiladi yoki 7.2.2 bilan qo'llaniladi.
3.27 belgisi 1.4 yotiq chizig'i bilan birgalikda, 3.28 belgisi esa 1.10 yotiq chizig'i bilan birgalikda qo'llanilishi mumkin, bunda belgilarga amal qilish chiziqlar uzunligi bilan aniqlanadi.
3.10, 3.27-3.30 belgilari yo'lning qaysi tomoniga o'rnatilgan bo'lsa, ularga faqat o'sha tomonda amal qilinadi.
4. Buyuruvchi belgilar.
Transport vositalariga ko'rsatilgan yo'nalishda yoki faqat ma'lum yo'nalishlarda harakatlanishlari lozimligini buyuradi.
|
|
| |
Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots - bet 80

125
labyrinths.
Circuits and Systems I: Fundamental Theory and Applications, IEEE
Transactions on, 1993. 40(3): p. 174-181.
131.
Fernandez, A., et al.
Cellular neural networks simulation on a parallel graphics
processing unit
: IEEE.
132.
Kanwar, R.,
Real-Time Edge Detection using Sundance Video and Image Processing
System.
2009.
133.
Choudhary, A. and S. Ranka,
Guest editor's introduction: parallel processing for
computer vision and image understanding.
Computer, 1992. 25(2): p. 7-10.
134.
Wang, X. and S.G. Ziavras.
HERA: A reconfigurable and mixed-mode parallel
computing engine on platform FPGAs
. 2004: Citeseer.
135.
Tonde, C.,
Hardware and software platforms for computer vision.
136.
Tsuchiyama, R., et al.,
The OpenCL Programming Book.
Group, 2009.
137.
Barak, A., et al.
A package for OpenCL based heterogeneous computing on clusters
with many GPU devices
: IEEE.
138.
Chua, L.O. and T. Roska,
Cellular neural networks and visual computing: foundation
and applications
. 2002: Cambridge Univ Pr.
139.
Montúfar-Chaveznava, R. and D. Guinea,
A Cellular Neural Network System for Real-
Time Image Processing.
140.
Takenouchi, H., T. Watanabe, and A. Hideki,
Development of DT-CNN Emulator
Based on GPGPU.
141.
Arena, P., L. Fortuna, and M. Branciforte,
Reaction-diffusion CNN algorithms to
generate and control artificial locomotion.
Circuits and Systems I: Fundamental
Theory and Applications, IEEE Transactions on, 1999. 46(2): p. 253-260.
142.
Arena, P., et al.
Bio-inspired robotics: application of a CNN-based CPG VLSI chip to
control an autonomous mini-hexapod robot
. 2006: IEEE.
143.
Arena, P., et al.,
An adaptive, self-organizing dynamical system for hierarchical
control of bio-inspired locomotion.
Systems, Man, and Cybernetics, Part B:
Cybernetics, IEEE Transactions on, 2004. 34(4): p. 1823-1837.
144.
Fortuna, L. and L. Patane.
Hexapod locomotion control through a cnn based
decentralized system
. 2002: IEEE.
145.
Arena, P., et al.
An autonomous mini-hexapod robot controlled through a CNN-based
CPG VLSI chip
. 2006: IEEE.
146.
Branciforte, M., et al.
Reaction-Diffusion CNN design for a new class of biologically-
inspired processors in artificial locomotion applications
. 1999: Published by the IEEE
Computer Society.
147.
Horn, J., N. Nafpliotis, and D.E. Goldberg.
A niched Pareto genetic algorithm for
multiobjective optimization
. 1994: Ieee.
148.
Cho, S.B.
Evolving multiple sensory-motor controllers based on cellular neural
network
. 2001: IEEE.
149.
Cigale, B. and D. Zazula,
Segmentation of ovarian ultrasound images using cellular
Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots - bet 78

123
85.
Tarassenko, I. and S. Roberts.
Supervised and unsupervised learning in radial basis
function classifiers
. 1994: IET.
86.
Hu, Y.H., J.N. Hwang, and S.W. Perry,
Handbook of neural network signal processing.
The Journal of the Acoustical Society of America, 2002. 111: p. 2525.
87.
Moody, J. and C.J. Darken,
Fast learning in networks of locally-tuned processing units.
Neural computation, 1989. 1(2): p. 281-294.
88.
Sanger, T.D.,
Optimal unsupervised learning in a single-layer linear feedforward
neural network.
Neural Networks, 1989. 2(6): p. 459-473.
89.
Honkela, A. and H. Valpola,
Unsupervised variational Bayesian learning of nonlinear
models.
Advances in neural information processing systems, 2005. 17: p. 593
–
600.
90.
Malki, S., L. Spaanenburg, and N. Ray.
Image stream processing on a packet-switched
discrete-time CNN
. 2004.
91.
Yang, Z., Y. Nishio, and A. Ushida.
A Two Layer CNN in Image Processing Applications
.
92.
Chua, L.O. and T. Roska,
The CNN paradigm.
Circuits and Systems I: Fundamental
Theory and Applications, IEEE Transactions on, 1993. 40(3): p. 147-156.
93.
Matsumoto, T., et al.
Several image processing examples by CNN
: IEEE.
94.
Kozek, T., T. Roska, and L.O. Chua,
Genetic algorithm for CNN template learning.
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions
on, 1993. 40(6): p. 392-402.
95.
Mohamad, S. and K. Gopalsamy,
Exponential stability of continuous-time and
discrete-time cellular neural networks with delays.
Applied Mathematics and
Computation, 2003. 135(1): p. 17-38.
96.
Harrer, H. and J.A. Nossek,
Discrete time cellular neural networks.
International
Journal of Circuit Theory and Applications, 1992. 20(5): p. 453-467.
97.
Chen, H.C., et al.,
Image-processing algorithms realized by discrete-time cellular
neural networks and their circuit implementations.
Chaos, Solitons & Fractals, 2006.
29(5): p. 1100-1108.
98.
Kawahara, M., T. Inoue, and Y. Nishio.
Image processing application using CNN with
dynamic template
: IEEE.
99.
Zarándy, Á.,
The art of CNN template design.
International Journal of Circuit Theory
and Applications, 1999. 27(1): p. 5-23.
100.
Feiden, D. and R. Tetzlaff,
Cellular neural networks for motion estimation and
obstacle detection.
Advances in Radio Science-Kleinheubacher Berichte. 1.
101.
Feiden, D. and R. Tetzlaff.
Feature extraction in motion estimation with cellular
neural networks using iterative annealing
.
102.
UCAN, O.N., E. BILGILI, and R. COBAN,
Extraction Of Facial Features Using Genetic
Cellular Neural Networks.
network. 1: p. 4.
103.
Wolfram, S.E.W., S. (Ed.),
Theory and Application of Cellular Automata. Reading, MA:
Addison-Wesley, 1986.
104.
Nossek, J.A., et al.,
Cellular neural networks: Theory and circuit design.
International
Journal of Circuit Theory and Applications, 1992. 20(5): p. 533-553.
105.
Mitchell, M.,
An introduction to genetic algorithms
. 1998: The MIT press.
106.
Gacsádi, A., C. Grava, and A. Grava,
Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots - bet 79

124
109.
Bournez, O., et al.,
The general purpose analog computer and computable analysis
are two equivalent paradigms of analog computation.
Theory and Applications of
Models of Computation, 2006: p. 631-643.
110.
Mullins, B.F.,
A Guide to Solving Simple Ordinary Differential Equations (ODE
’s)
.
Technical report, University of Toronto, Department of Physics. , 2009.
111.
MacLennan, B.J.,
Review of Analog Computing.
Technical Report, Department of
Electrical Engineering & Computer Science University of Tennessee, Knoxville
2007.
112.
Cowan, G.E.R., R.C. Melville, and Y.P. Tsividis,
A VLSI analog computer/digital
computer accelerator.
Solid-State Circuits, IEEE Journal of, 2006. 41(1): p. 42-53.
113.
Lee, J., B.J. Sheu, and R. Chellappa,
A VLSI neuroprocessor for image restoration using
analog computing-based systolic architecture.
The Journal of VLSI Signal Processing,
1993. 5(2): p. 185-199.
114.
Wittig, R.D. and P. Chow.
OneChip: An FPGA processor with reconfigurable logic
.
1997: IEEE.
115.
Stoica, A., et al.,
On-chip evolutionary synthesis of reconfigurable analog computing
circuits.
Internet Source, 2003.
116.
Sivaranjani, K., J. Venkatesh, and P. Janakiraman,
Realization of a digital differential
analyzer using CPLDs.
International Journal of Modelling and Simulation, 2007.
27(3): p. 280.
117.
Clark, C.R., R. Nathuji, and H.H.S. Lee.
Using an fpga as a prototyping platform for
multi-core processor applications
. 2005: Citeseer.
118.
Xu, S. and H. Pollitt-Smith.
A multi-microblaze based SOC system: from SystemC
modeling to FPGA prototyping
. 2008: IEEE.
119.
Ho, T.Y., P.M. Lam, and C.S. Leung,
Parallelization of cellular neural networks on GPU.
Pattern Recognition, 2008. 41(8): p. 2684-2692.
120.
Dolan, R. and G. DeSouza,
GPU-based simulation of cellular neural networks for
image processing.
2009: p. 730-735.
121.
Athanas, P.M. and A.L. Abbott,
Real-time image processing on a custom computing
platform.
Computer, 1995. 28(2): p. 16-25.
122.
Chu, P.P.,
FPGA prototyping by VHDL examples: Xilinx Spartan-3 version
. 2008:
LibreDigital.
123.
miKayaer, K. and V. Tavsanoglu.
A new approach to emulate CNN on FPGAs for real
time video processing
. 2008: IEEE.
124.
Rosinger, H.P.,
Connecting customized IP to the MicroBlaze soft processor using the
Fast Simplex Link (FSL) channel.
Xilinx Application Note, 2004.
125.
Pellerin, D. and M. Saini,
MicroBlaze and PowerPC Cores as Hardware Test
Generators.
Xilinx Xcell journal, 2004. 51.
126.
Pellerin, D., et al.,
Accelerating PowerPC software applications.
Xilinx Xcell
Embedded Magazine.
127.
Aung, Y., et al.,
C-based design methodology for fpga implementation of clustalw msa.
Pattern Recognition in Bioinformatics, 2007: p. 11-18.
128.
Pellerin, D. and S. Thibault,
FPGA and GPU be designed and implemented?

|
FPGA and GPU be designed and implemented?Bog'liq Alireza FasihFPGA and GPU be designed and implemented?
CNN is a complex design in terms of implementation and performance on
traditional architectures of the von Neumann type (such as CPU and sequential
processors). Therefore, we did a survey about CNN implementations on hardware
and later on we have proposed our implementation on FPGA. Due to the limitation
of resources in FPGA, we have implemented a fix-point DT-CNN with high accuracy
in results. Due to the offered flexibility of design by GPUs, we have also
implemented a CNN universal machine on GPU image processing. We have used
OpenCL which is a very strong framework for developing parallel algorithms on
GPUs. The results obtained have shown to be at least 100 times faster than on
normal computer/CPU for processing images.
Research question 7: How far can CNN be used/involved in an evolutionary
computing/control context example (for illustration)?
118
CNN has a great potential for signal processing tasks and it can generate very
complex nonlinear waves and oscillation patterns at the output of CNN cells.
Controlling both the kinematic and the inverse-kinematic of complex robots with
high degree of freedom (DOF) is a very complex scenario whereby classical
solutions fail to solve it easily. In the frame of this research question we have
integrated a CNN processors system to the leg-robot for high level inverse
kinematic controlling. For calculating templates we used GA and a very simple
objective function without considering any inverse or direct kinematic. Different
types of robots are able to move in an optimum way between two points or any
kind of other scenarios and trajectories.
As outlook and future work we do see the integration of all functionalities developed in
this thesis in a working prototype and improve it progressively according to test results in
the real environment.
|
| |
Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots - bet 76

|
Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile RobotsBog'liq Alireza FasihSafety and Convenience Based on All-around Sensing.
Journal of Intelligent and
Robotic Systems, 2008. 51(3): p. 261-287.
42.
Saad, J., A. Baghdadi, and F. Bodereau.
Fpga-based radar signal processing for
automotive driver assistance system
: IEEE.
43.
Wada, M., K.S. Yoon, and H. Hashimoto,
Development of advanced parking assistance
system.
Industrial Electronics, IEEE Transactions on, 2003. 50(1): p. 4-17.
44.
Wu, B.F., et al.,
A DSP-based lane departure warning system.
Proc. of the 8th WSEAS
Int. Conf. on Mathematical Methods and Computational Techniques in Electrical
Engineering, 2006: p. 240-245.
45.
Arth, C., F. Limberger, and H. Bischof,
Real-time license plate recognition on an
embedded DSP-platform.
2007: p. 1-8.
46.
Vitabile, S., S. Bono, and F. Sorbello.
An embedded real-time automatic lane-keeping
system
. 2007: Springer-Verlag.
47.
Doshi, A. and M. Trivedi,
LACASA: A Layered Architecture for Cooperative Active
Safety Applications.
48.
Azman, A., et al.
Optimizing resources of an fpga-based smart camera architecture
.
2007: IEEE Computer Society.
49.
Batlle, J., et al.,
A new FPGA/DSP-based parallel architecture for real-time image
processing.
Real-Time Imaging, 2002. 8(5): p. 345-356.
50.
Martínez-Alvarez, J., et al.,
High Performance Implementation of an FPGA-Based
Sequential DT-CNN.
Nature Inspired Problem-Solving Methods in Knowledge
Engineering, 2007: p. 1-9.
51.
Rudolph, L. and Z. Segall,
Dynamic decentralized cache schemes for MIMD parallel
processors.
ACM SIGARCH Computer Architecture News, 1984. 12(3): p. 340-347.
52.
Greco, J.,
Parallel Image Processing and Computer Vision Architecture
. 2005,
Citeseer.
53.
Risack, R., N. Mohler, and W. Enkelmann,
A video-based lane keeping assistant.
2000:
p. 356-361.
54.
VanderWerf, J., et al.,
Modeling effects of driver control assistance systems on traffic.
Transportation Research Record: Journal of the Transportation Research Board,
2001. 1748(-1): p. 167-174.
55.
Naab, K. and G. Reichart,
Driver assistance systems for lateral and longitudinal
vehicle guidance-heading control and active cruise support.
JSAE Review, 1995.
16(2): p. 220-220.
56.
McCall, J.C. and M.M. Trivedi,
Video-based lane estimation and tracking for driver
assistance: survey, system, and evaluation.
Intelligent Transportation Systems, IEEE
Transactions on, 2006. 7(1): p. 20-37.
57.
Chavda, J., et al.
Performance Measure of Image Processing Algorithms on DSP
Processor & FPGA Based Coprocessor
: IEEE Computer Society.
58.
Chapman, B., G. Jost, and R. Van Der Pas,
Using OpenMP: portable shared memory
parallel programming
. Vol. 10. 2007: The MIT Press.
59.
Hwang,
K.,
Advanced
computer
architecture:
parallelism,
scalability,
programmability
. Vol. 348. 1993: McGraw-Hill.
60.
Hager, G. and G. Wellein,
Introduction to High Performance Computing for Scientists
and Engineers
, CRC Press.
61.
Dowd, K.,
High performance computing
. 1993: O'Reilly & Associates, Inc.
|
| |
Research question 7: How far can CNN be used/involved in an evolutionary

Research question 7: How far can CNN be used/involved in an evolutionary computing/control context example? CNN has a great potential for signal processing tasks and it can generate very complex
nonlinear waves and oscillation patterns at the output of CNN cells. Controlling both the
kinematic and the inverse-kinematic of complex robots with high
Degree of Freedom (DOF)
is a very complex scenario whereby classical solutions fail to solve it easily. Complex ADAS
solutions may also be seen as systems with a high degree of freedom, for example CACC
(cooperative adaptive cruise control). Therefore, the evolution of CNN templates to
generate the optimum wave for both the driving motor(s) and the robot actuators is a
particularly interesting idea for research. In nature, organisms’ systems are evolving as
explained by the theory of natural selection. Overall, the art is to define a fitness function
for evaluating the performance of the actual robot locomotion. Hence, by evolving the CNN
template using a genetic algorithm and a fitness function, we can generate very complex
waves for optimally controlling the r
obot hinges without directly involving the robot’s
kinematic equation in controller design. A two dimensional CNN should be sufficient for
evolving a spatial wave over time for controlling the robot’s actuators and hinges.
A fitness function is a particular type of objective function that quantifies the optimality of
a solution [36]. Input data for the fitness function are based on measurements from robot
parts’ orientations, locations and displacements. In the fitness function we do not define
any behavioral locomotion exactly. On the other hand, we define a function that satisfies
the target or destination. With this method involving genetic algorithms an optimum
template ensures that the robot can move or act according to our desires. The most
15
important point in this learning method is that we do not need to (explicitly) predefine any
robot’s kinematics for movement in the fitness function.
Research question 5: How far can the advantages of "analog computing" be

|
Research question 5: How far can the advantages of "analog computing" beBog'liq Alireza FasihResearch question 5: How far can the advantages of "analog computing" be
used/gained through an emulation of analog computing on
digital hardware platforms like FPGA?
The main advantage of analog computing is that we can “nearly” simultaneously get the
result of complex mathematical differential equations. All signals are generated in parallel
and in real-time and the electronic components do compute simultaneously [30-32]. In the
traditional analog computing method one does face a scaling problem for the dynamic
range of the computing process. The dynamic range of all components is limited and one
additionally faces issues related to noise and high voltage. Therefore, is not possible to
compute any dynamic range and one therefore has to always rescale the ranges
appropriately. Another disadvantage of classical analog computing is that due to the fact
that the solutions appear in real-time one cannot easily record them for further analysis
purposes.
We can cover/overcome all of these problems by using the advantages of digital systems
like FPGA. Traditional analog computers are using operational amplifier based circuits to
model “addition”,” subtraction”, “multiplication” and “integration”
[32, 33]. These functions
are the basis for analog computing. Therefore, since we can realize all of these functions by
a digital circuit an emulation of “analog computing” on FPGA app
ears possible. We do use
the functional block “Digital Differential Analyzer (DDA)” to compute the integral of a
function over time. To get more speedup we can use fixed-point arithmetic calculation
instead of a floating-point one. Therefore, we must thoroughly check the ranges of values
and levels of accuracy for
assigning reserve bits for both “integer” and “fraction” parts. By
emulating analog computing on digital hardware platform like FPGA we become capable of
getting the solution in real-time without any limitation (contrarily to traditional analog
computing) related to scaling voltages. The reason is that instead of voltages we are
dealing with registers and fixed-point operators on data. A further advantage is that we do
not need any additional converter for/before storing data into the memory. We have full
control on the clock rate and this also represents a great advantage of using FPGA and a
digital architecture. Concerning setup time, debugging effort and configurability the digital
emulation of “analog computing” is clearly very superior to the traditional “real electronics
components” based analog computing systems.
|
| |
Research question 6: How far can an efficient implementation of CNN on either FPGA

14
development. In OpenCL there is a standard API for communicating between CPU and GPU.
This API does provide some essential commands for allocating memory,
transferring
memory content between host/CPUs RAM and device/GPUs RAM. There are some
commands also for compiling GPU code which is a kernel file, and executing them. In
OpenCL running multiple kernels is possible, and there is a direct and ultrafast channel for
transferring memory and data between different kernels. These features make GPUs very
flexible and reliable for designing complex architecture and models.
Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots - bet 17

22
hardware resources [25]. Each CNN cell consists of some basic mathematical operator such
as addition, subtraction, integration module, and a sigmoid function. Beside the integration
of CNN and FPGA as a target platform for ultrafast ADAS related image processing we do
also propose and have developed an alternative concept that does integrate CNN and
another also
actually promising technology, the GPU. Details of this additional proposal
(that combines CNN and GPU) are presented in two other chapters of this thesis; see
chapters 7 and 8. The GPU technology does also offer a series of advantages ranging from
design
flexibility, availability, costs, the possibility of an easy integration with other
framework such as
Open Computing Language
(OpenCL), and much more. The company
AMD has released an embedded GPU to provide high performance in mobile and
embedded systems. AMD Radeon E6760 is an embedded discrete graphics processor that
supports OpenCL and it has a good performance for parallel processing. Hence, having a
high-performance system in the scale of embedded system is possible.
Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots - bet 22

|
Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile RobotsBog'liq Alireza Fasih4.2
A survey of the related state-of- the art on image processing
based on ANN.
An artificial neural network is a mathematical model or computational model of biological
neural network. These are essentially a very simple mathematical model for mapping input
to output. The things that make ANN attractive are learning possibility. Means during the
learning phase, by giving a class of function system can find an optimum solution. We have
many different type of neural network such as Feedforward
Multi-layer Perceptron Neural
Network
(MLP),
Radial Basis function
(RBF), Kohonen
Self-Organizion Network
(SOM),
Hopefield Network,
Bi-directional Neural Network
(BRNN),
Associate Neural Network
(ASNN) and etc that are useful for different application. The most common type of artificial
neural network in the field of image processing is MLP. This model as it mentioned in
Figure 4-1, has many input, hidden layer and output. Each neuron in one layer is connected
the other neuron in another layer. Therefore we have fully connection between each layer
to another layer. Depending to the type of data and information we have to reshape them
for feeding them in our network. For example for loading an image into the network we
have to reshape the 2D picture in form of vector. Figure 4-2 has shown this concept. We
have to consider many issues before loading data to the network for training or in
operational mode. For image processing, in many cases we need to perform a color
conversion, normalization and quantization of values before feeding data into the network.
|
| |
ATmega328 texnik parametrlari

|
ATmega328 texnik parametrlari
|
bet | 3/9 | Sana | 19.01.2024 | Hajmi | 1,66 Mb. | | #141179 |
Bog'liq 14 mavzu (1) ATmega328 texnik parametrlari
16 Kbayt dasturlanuvchi Flash xotirali 8-razryadli mikrokontrollerlar
Tejamkor 8-razryadli yuqori samaradorlikga ega AVR mikrokontroller
Progressiv RISC arxitektura
130 yuqori unumli kommanda, ko’plab kommandalar bir takt tsikli davomida bajariladi
32 umumiy belgilangan 8-razryadli ishchi registr
To’liq statik ishchi rejim
Samaradorligi 16 MI‘S (16 MGts takt chastotasi davomida)ga yaqinlashadi
Tarkibiy 2-tsiklik o’z-o’ziga ko’paytirgich
Energiyaga bog’liq bo’lmagan mag’lumotlar va dastur xotirasi
16 Kbayt tizim ichi dasturlanuvchi Flash xotira (In-System Self-‘rogrammable Flash)
1000 tsiklli o’chirish/yozishni tashkil etadi
Mustaqil blokirovka bitlariga ega qo’shimcha yuklovi kodlar sektori
Tarkibiy yuklash dasturini tizimichi dasturlash
Bir vaqtda yozish va o’qish rejimi bilan tag’minlangan (Read-While-Write)
512 bayt EE‘ROM
100000 tsiklli o’chirish/yozish larni tag’minlaydi
1 Kbayt tarkibiy SRAM
Foydalanuvchi dasturiy qurilmalarini ximoya bilan tag’minlovchi dasturiy blokirovka
Interfeys JTAG (IEEE 1149.1 bilan mos)
Qurilmalarni skannerlash, JTAG standartiga ko’ra
Kengaytirilgan tarkibiy sozlashni qo’llash
JTAG interfeysi orqali dasturlash: Flash, EE‘ROM xotirasi, oraliq ulovchi va bitlar blokirovkasi
Tarkibiy periferiya
Aloxida ikki 8-razryadli taymera/schetchik boshlang’ich bo’luvchili va bitta taqqoslash rejimili
Bitta aloxida boshlang’ich bo’lishli va qaydlash (zaxvat) rejimli xamda taqqoslovchi 16- razryadli taymer/schetchik
Aloxida generatorli real vaqtli schetchik
To’rt kanalli ‗WM
8-kanalli 10-razryadli analog - raqamli o’zgartgich
8 nosimmetrik kanal
7 differentsial kanal (faqat TQF‘ korpusda)
1,10 yoki 200 barobar dasturiy kuchayotirishga ega 2 differentsial kanal (faqat TQF‘ korpusda)
Bayt-orientirli 2-o’tkazgichli ketma-ket interfeys
Dasturlanuvchi ketma-ket USART
Ketma-ket interfeys S‘I (boshqaruvchi/boshqarilayotgan)
Aloxida tarkibiy generatorli dasturlanuvchi qo’riqchi taymer
Tarkibiy analogli komparator
Maxsus mikrokontroller funktsiyalari
Tahminot uzatilishi bo’yicha tushish(sbros) va qisqa vaqtli tahminot kuchlanshi pasayishini qayd etuvchi dasturlanadigan detektor
Kalibrlangan tarkibiy RC-generator
Ichki va tashqi uzilish tahminotlari
Kamayuvchi ehstimolchining oltita rejimi: Idle, ‗ower-save, ‗ower-down, Standby, Extended Standby va g’alayonlarni kamaytirish ADC
I/O tarmoqlanib kirish/chiqish va korpus
32 dasturlanuvchi linyalar kirish/chiqish
40-tarmoqlanuvchi korpus ‗DI‘ va 44-tarmoqlanuvchi korpus TQF‘
Ishchi kuchlanish
2,7 - 5,5 V (ATmega328)
4,5 - 5,5 V (ATmega328)
Ishchi chastota
0 - 8 MGts (ATmega328)
0 - 16 MGts (ATmega328)
|
| |
Mikrokontrollerli tizimni texnik qurilmalar bilan bog‘lanishi

|
Mikrokontrollerli tizimni texnik qurilmalar bilan bog‘lanishi
|
bet | 8/9 | Sana | 19.01.2024 | Hajmi | 1,66 Mb. | | #141179 |
Bog'liq 14 mavzu (1) Mikrokontrollerli tizimni texnik qurilmalar bilan bog‘lanishi
Quyida mikrokontrollerni tipik ulanish sxemasini klassik ko’rinishi ―Operator – MK – boshqaruv obekti misolida keltirilgan (5.1rasm).
rasm. MKni bog’lanish usullari:
teskari bog’lanishli avtomatik rostlash va boshqarish tizimi;
avtonom o’lchov qurilmasi; v) intellektual indikator;
g) qo’lda boshqarish tizimi; d) avtomatik boshqarish tizimi;
e) teskari bog’lanishsiz obektni interaktiv boshqarish tizimi;
j) teskari bog’lanishsiz obektni interaktiv o’lchash tizimi;
programmator/otladchik MK; i) avtonom robot.
Istalgan MK ehstemoli uchun minimum ikkita: musbat (‗ower su‘‘ly) va manfiy (Ground reference) tahminot manbai kerak bo’ladi (5.2rasm).
VCC (Voltage Collector-to-Collector) yoki VDD (Voltage Drain-to-Drain);
GND (GrouND) yoki VSS (Voltage Source-to-Source).
rasm. Ikki o’tkazgichli MK manbasi:
bazaviy sxema;
tarkibiy kuchlanishni oshiruvchi DC/DC o’zgartiruvchi rezerv batareya VBAT ehstemol zanjiri.
Avtomatik rostlash va boshqarish tizimini tashkil etish uchun birinchi bo’lib, texnik qurilmalarni va mikrokontrollerni energiya bilan tahminlash zarur. Demak, keyingi navbatda tahminot sxemalarini tanlaymiz. Quyida mikrokontroller uchun 220V elektr tahminot manbaiga ulanuvchi birlamchi tahminot bloki va normallovchi stabilizator sxemasi keltirilgan (5.3rasm). Avtomatlashtirish jarayonini yengillatish uchun ijrochi qurilmalar va datchiklarni tahminotga bog’lanib bo’lingan deb qarqymiz.
a) b)
rasm. Mikrokontroller uchun tahminot blogi(a) va stabilizator(b)
rasmning a sxemasi birlamchi tahminot blogi bo’lib, ikkita bir parametrli parallel transformator (T1 va T2) ehstemol manbai quvvatini ikki barobar oshiradi, ammo qizish miqdori kam bo’ladi. Qo’shilgan kuchlanishlar to’g’irlagich diod (V1...V4) lardan o’tadi.
|
| |