nyuuzyou/kompy huggingface dataset:
Yakuniy nazorat - Termiz davlat pedagogika instituti

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Termiz davlat pedagogika instituti
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bet | 5/6 | Sana | 03.01.2024 | Hajmi | 56,15 Kb. | | #129708 |
Bog'liq Dastur (1)Yakuniy nazorat–Yakuniy nazorat turi fakultet dekani, o`quv-uslubiy boshqarmasi bilan kelishgan holda va o`quv ishlari bo`yicha dekan o’rinbosari tomonidan tasdiqlangan jadval asosida tegishli fan bo`yicha o`quv mashg`ulotini olib borgan o`qituvchi ishtirokisiz o`tkazililishi ko`zda tutiladi.
Semestr yakunida muayyan fan bо`yicha nazariy bilim va amaliy kо`nikmalarning talabalar tomonidan о`zlashtirish darajasini baholash usuli hisoblanadi. Yakuniy nazorat о`quv faniga ajratilgan umumiy soatlar hajmidan kelib chiqqan holda bir semestrda bir marta yozma ish, amaliy ish yoki ko`p variantlik asosida test savollariga javob berish shakli orqali о`tkazilish ko`zda tutilgan.
Professor-o`qituvchilar tomonidan fan yuzasidan o`tilgan materiallar bo`yicha yozma ish, amaliy ish materiallari, uchta muqobil va bitta to`g`ri javobdan iborat bo`lgan 400-600 taga qadar savollar bazasi tashkil etilib, belgilangan jadval asosida har bir talabalarga alohida variant taklif etilib, bajarish muddati belgilanadi. Odatda 50 ta savoldan iborat nazoratga javob berish uchun 100 daqiqa vaqt belgilanishi ko`zda tutilgan. Talabalarning olgan baholari elektron jurnalda avtomatik ravishda aks ettiriladi.
Yakuniy nazoratga qatnashmagan yoki nazoratdan o`tmagan talabalarga kredit modul tizimi qoidalari asosida dekanat ruxsati bilan bir marta qayta topshirish uchun ruxsat beriladi.
Yakuniy nazoratdan o`tgan talabalar o`quv rejasini bajargan hisoblanib, navbatdagi bosqichga o`tkazilishiga asos bo`ladi. Yakuniy nazoratdan o`tmagan talabalar akademik qarzdor hisoblanadi.
Test shaklida o`tkazilgan yakuniy nazorat varianti 50 ta savoldan iborat bo`lsa quyidagicha baholanadi:
90-100% 45-50 “A” (a`lo)
70-89% 35-44 “B” (yaxshi)
60-69% 30-34 “C” (qoniqarli)
0-59% 0-29 “D” (qoniqarsiz)
Semestr bahosi quydagi formula orqali hisoblanadi:
(a1 +a2 +a3 +…+an)/n
Bu yerda a1 +a2 +a3 +…+an –har bir dars yoki topshiriqlarga qo’yilgan baholar,
n-dars, topshiriqlar yoki baholar soni.
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Yangi mavzuni mustaxkamlash - 1- dars. Mavzu: Masalalarni kompyuterda yechish bosqichlari Darsning texnologik xaritasi

Yangi mavzuni mustaxkamlash.
Savol va topshiriqlar
1.Web-sahifalar tashkil etish texnologiyasiga ko'ra qanday turlarga
bo 'linadi?
2.Interaktiv web-sahifalar haqida ma'lumot bering va misollar keltiring.
3.Login va parolni qo'Uanishi lozim bo'lgan web-sahifalar haqida so'zlab bering.
4.O'zingizga elektron pochta ochganingizda qanday web-sahifada ishlagansiz ?
5.Kompyuteringizdagi qaysi elektron darslik va qo'llanmalarda interaktiv web-sahifa bor?
Uyga vazifa: Mavzuni o`rganish. 4-5 mashqni dasturini tuzish.
Sana :______________
Sinf :_______________
65- dars. Mavzu: Amaliy mashg’ulot.
Darsning texnologik xaritasi
Mavzu
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Amaliy mashg`ulot
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Maqsad
va
vazifalar
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Darsning ta'limiy vazifasi - 1- dars. Mavzu: Masalalarni kompyuterda yechish bosqichlari Darsning texnologik xaritasi

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1- dars. Mavzu: Masalalarni kompyuterda yechish bosqichlari Darsning texnologik xaritasi
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bet | 206/208 | Sana | 23.07.2021 | Hajmi | 1,92 Mb. | | #15865 |
Darsning ta'limiy vazifasi: o'quvchilarning web-sahifa tayyorlash bobi bo`yicha olgan bilimlarini amaliy topshiriqlar orqali mustahkamlash Darsning tarbiyaviy vazifasi: O`quvchilarga estetik va ekologik tarbiya berish. Vatanparvarlik ruhida tarbiyalash.
Darsning rivojlantiruvchi vazifasi: o'quvchilarda kompyuterdan samarali foydalanish tuyg'ularani shakllantirish.
O'quv
jarayonining mazmuni
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O'quvchilarning web-sahifa tayyorlash bobi bo`yicha olgan bilimlarini amaliy topshiriqlar orqali mustahkamlash
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O'quv jarayonini amalga oshirish texnologiyasi
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Darsning maqsadi - 1- dars. Mavzu: Masalalarni kompyuterda yechish bosqichlari Darsning texnologik xaritasi

Darsning tarbiyaviy vazifasi
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Nazorat
1- dars. Mavzu: Masalalarni kompyuterda yechish bosqichlari Darsning texnologik xaritasi
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1,92 Mb.
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Bu sahifa navigatsiya:
Darsning tarbiyaviy vazifasi
Uslub
Nazorat
Darsning maqsadi:
o’quvchilarning web-sahifada o`tish va formalar mavzusi bo`yicha olgan bilimlarini mustahkamlash.
Darsning ta'limiy vazifasi:
o'quvchilarga Web-sahifada formalarning o’rni,
tegining vazifasi, ACTION va METHOD parametrlari,
tegi vauning NAME parametric,
tegi SUBMIT qiymatli TYPE parametridan foydalanish haqida bilim berish.
Darsning tarbiyaviy vazifasi:
o'quvchilarni yangi bilimlar egallashga hamda izlanuvchanlikka o’rgatish.
Darsning rivojlantiruvchi vazifasi:
o'quvchilarning kompyuterdan samarali foydalanish haqida bilim va tasavvurlarini kengaytirish.
O'quv
jarayonining mazmuni
Web-sahifada formalarning o’rni,
juft tegining vazifasi va ishlatilishi. Uning ACTION va METHOD kabi parametrlari.
juftmas tegining NAME parametric bilan ishlatilishi
tegida SUBMIT qiymatli TYPE parametridan foydalanish
O'quv jarayonini amalga oshirish texnologiyasi
Uslub:
Ma'ruza va amaliyotning uyg'unligi.
Shakl:
Savol-javob. Jamoa va kichik guruhlarda ishlash.
Vosita:
Elektron resurslar, darslik, plakatlar, tarqatma materiallar.
Usul:
Tayyor prezentatsiya materiallari asosida.
Nazorat:
Og'zaki, savol-javob, muhokama, kuzatish.
Baholash:
Rag'batlantirish, 5 ballik reyting tizimi asosida.
Kutiladigan natijalar
O'quvchilarning yangi bilim va ko'nikmaga ega bo’ladi Web-sahifada formalarning o’rni,
juft tegining vazifasi va ishlatilishi. Uning ACTION va METHOD kabi parametrlari.
juftmas tegining NAME parametric bilan ishlatilishi,
tegida SUBMIT qiymatli TYPE parametridan foydalanish to’g’risida ko’nikmaga ega bo’ladi.
Kelgusi rejalar (tahlil, o'zgarishlar)
O'qituvchi o'z faoliyatining tahlili asosida yoki ham- kasblarining dars tahlili asosida keyingi darslariga o'zgartirishlar kiritadi va rejalashtiradi.
Download
1,92 Mb.
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1- dars. Mavzu: Masalalarni kompyuterda yechish bosqichlari Darsning texnologik xaritasi
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Uyga vazifa: Darsda ulgurmagan mashqlarni bajarish. O`tilganlarni takrorlash

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Uyga vazifa: Darsda ulgurmagan mashqlarni bajarish. O`tilganlarni takrorlash
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bet | 203/208 | Sana | 23.07.2021 | Hajmi | 1,92 Mb. | | #15865 |
Uyga vazifa: Darsda ulgurmagan mashqlarni bajarish. O`tilganlarni takrorlash.
Sana :______________
Sinf :_______________
64- dars. Mavzu: Interaktiv web-sahifalar
Darsning texnologik xaritasi
Mavzu
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Interaktiv web-sahifalar
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Maqsad
va
vazifalar
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Darsning maqsadi: o’quvchilarga interaktiv web-sahifalar haqida ma’lumot berish.
Darsning ta'limiy vazifasi: o'quvchilarga chat, interaktiv web-sahifalar, interaktiv web-sahifalarni ishlab chiqarishning prinsiplari haqidagi bilim berish va ularni tashkil etish ko’nikmalarini hosil qilish.
Darsning tarbiyaviy vazifasi: o'quvchilarni yangi bilimlar egallashga hamda izchil muloqotga o’rgatish.
Darsning rivojlantiruvchi vazifasi: o'quvchilarda kompyuterdan samarali foydalanish haqida bilim va tasavvurlarini kengaytirish.
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O'quv
jarayonining mazmuni
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Chat. Interaktiv web-sahifalar. Static va dinamik web-sahifalar. Interaktiv web-sahifalarni ishlab chiqarishning asosiy 7 prinsipi. Test o’tkazish web-sahifasi. JavaScript elementlari.
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O'quv jarayonini amalga oshirish texnologiyasi
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Uslub: Ma'ruza va amaliyotning uyg'unligi.
Shakl: Savol-javob. Jamoa va kichik guruhlarda ishlash.
Vosita: Elektron resurslar, darslik, plakatlar, tarqatma materiallar.
Usul: Tayyor prezentatsiya materiallari asosida.
Nazorat: Og'zaki, savol-javob, muhokama, kuzatish.
Baholash: Rag'batlantirish, 5 ballik reyting tizimi asosida.
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Kutiladigan natijalar
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O'quvchilarning yangi bilim va ko'nikmaga ega bo’ladi. Statik va dinamik web-sahifalar haqida bilim oladi. Chat, interaktiv web-sahifalar, interaktiv web-sahifalarni ishlab chiqarishning asosiy prinsiplari to’g’risida tasovvur hosil bo’ladi. Interaktiv web-sahifa hosil qilish ko’nikmasiga ega bo’ladi.
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Kelgusi rejalar (tahlil, o'zgarishlar)
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O'qituvchi o'z faoliyatining tahlili asosida yoki ham- kasblarining dars tahlili asosida keyingi darslariga o'zgartirishlar kiritadi va rejalashtiradi.
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Uslub: Ma'ruza va amaliyotning uyg'unligi. 

Shakl

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Uslub: Ma'ruza va amaliyotning uyg'unligi.
Shakl
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bet | 207/208 | Sana | 23.07.2021 | Hajmi | 1,92 Mb. | | #15865 |
Uslub: Ma'ruza va amaliyotning uyg'unligi.
Shakl: Savol-javob. Jamoa va kichik guruhlarda ishlash.
Vosita: Elektron resurslar, darslik, plakatlar, tarqatma materiallar.
Usul: Tayyor prezentatsiya materiallari asosida.
Nazorat: Og'zaki, savol-javob, muhokama, kuzatish.
Baholash: Rag'batlantirish, 5 ballik reyting tizimi asosida.
Kutiladigan natijalar
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O'quvchilarning kompyuter imkoniyatlari to'g'risidagi bilim va ko'nikmalari rivojlanadi. Web –sahifa tayyorlash malakasi shakllanadi.
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Kelgusi rejalar (tahlil, o'zgarishlar)
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O'qituvchi o'z faoliyatining tahlili asosida yoki ham- kasblarining dars tahlili asosida keyingi darslariga o'zgartirishlar kiritadi va rejalashtiradi.
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Kreditlarni oilsh uchun talablar

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Kreditlarni oilsh uchun talablar
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bet | 3/6 | Sana | 03.01.2024 | Hajmi | 56,15 Kb. | | #129708 |
Bog'liq Dastur (1)Kreditlarni oilsh uchun talablar:
Fanga oid nazariy va uslubiy tushunchalarni to`la o`zlashtirish, tahlil natijalarini to`g`ri aks ettira olish, o`rganilayotgan jarayonlar haqida mustaqil mushohada yuritishi joriy, oraliq nazorat shakllarida berilgan vazifa va topshiriqlarni bajarish va yakuniy nazorat amallar bajarish, yozma ish yoki test savollariga javob berish orqali amalga oshirilishi ko`zda tutilgan.
Talabaning fan bo‘yicha o‘zlashtirish ko‘rsatkichini nazorat qilishda quyidagi mezonlar tavsiya etiladi:
a) 5 (90-100) baho olish uchun talabaning bilim darajasi quyidagilarga javob berishi lozim:
-fanning mohiyati va mazmunini to‘liq yorita olsa;
-fandagi mavzularni bayon qilishda ilmiylik va mantiqiylik saqlanib, ilmiy xatolik va chalkashliklarga yo‘l qo‘ymasa;
-fan bo‘yicha mavzu materiallarining nazariy yoki amaliy ahamiyati haqida aniq tasavvurga ega bo‘lsa;
-fan doirasida mustaqil erkin fikrlash qobiliyatini namoyon eta olsa;
-berilgan savollarga aniq va lo‘nda javob bera olsa;
-konspektga puxta tayyorlangan bo‘lsa;
-mustaqil topshiriqlarni to‘liq va aniq bajargan bo‘lsa;
-fanga tegishli qonunlar va boshqa me’yoriy-xuquqiy hujjatlarni to‘liq o‘zlashtirgan bo‘lsa;
-tarixiy jarayonlarni sharxlay bilsa;
b) 4 (70-89) baho olish uchun talabaning bilim darajasi quyidagilarga javob berishi lozim:
-fanning moxiyati va mazmunini tushungan, fandagi mavzularni bayon qilishda ilmiy va mantiqiy chalkashliklarga yo‘l qo‘ymasa;
-fanning mazmunini amaliy ahamiyatini tushingan bo‘lsa;
-fan bo‘yicha berilgan vazifa va topshiriqlarni o‘quv dasturi doirasida bajarsa;
-fan bo‘yicha berilgan savollarga to‘g‘ri javob bera olsa;
-fan bo‘yicha konspektini puxta shakllantirgan bo‘lsa;
-fan bo‘yicha mustaqil topshiriqlarni to‘liq bajargan bo‘lsa;
-fanga tegishli qonunlar va boshqa me’yoriy hujjatlarni o‘zlashtirgan bo‘lsa.
v) 3 (60-69) baho olish uchun talabaning bilim darajasi quyidagilarga javob berishi lozim:
-fan haqida umumiy tushunchaga ega bo‘lsa;
-fandagi mavzularni tor doirada yoritib, bayon qilishda ayrim chalkashliklarga yo‘l qo‘yilsa;
-bayon qilish ravon bo‘lmasa;
-fan bo‘yicha savollarga mujmal va chalkash javoblar olinsa;
-fan bo‘yicha matn puxta shakllantirilmagan bo‘lsa.
g) quyidagi hollarda talabaning bilim darajasi qoniqarsiz 2 (0-59) baho bilan baholanishi mumkin:
-fan bo‘yicha mashg‘ulotlarga tayorgarlik ko‘rilmagan bo‘lsa;
-fan bo‘yicha mashg‘ulotlarga doir hech qanday tasavvurga ega bo‘lmasa;
-fan bo‘yicha matnlarni boshqalardan ko‘chirib olganligi sezilib tursa;
-fan bo‘yicha matnda jiddiy xato va chalkashliklarga yo‘l qo‘yilgan bo‘lsa;
-fanga doir berilgan savollarga javob olinmasa;
-fanni bilmasa.
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Darsning borishi va vaqt taqsimoti

Darsning borishi va vaqt taqsimoti
O’tilgan mavzuni takrorlash.
1.Hayotingizda uchratgan formalar haqida so'zlab bering.
2.Formalar nima uchun ishlatiladi?
3.Web-sahifada forma qanday tashkil etiladi?
4.Formani jo 'natish haqida so (zlab bering.
5.Formada matn maydoni qanday belgilanadi?
6.Formada bir nechta satrli matn maydoni qanday tashkil etiladi?
7.Formada oldindan berilgan javoblarni tanlash tashkil etish usullarini izohlang.
Yangi mavzu bayoni. (Mashqlar)
1.«Men tayyorlagan web-sahifalar» nomli web-sahifa tayyorlang. Asosiy sahifadan o'zingiz tayyorlagan web-sahifalarga o'tish va orqaga qaytish rasm va matn orqali bo'lsin.
2.«Informatika dasrliklari haqidagi fikrlar» nomli forma tayyorlang. Unga darslik haqidagi fikrlarni to'plash maqsad qilib olinsin.
3. 5-sinfdan 9-sinfgacha kompyuterda bajargan amaliy ishlaringizni ochib beruvchi «Maktab, informatika va men» nomli web-sahifa tayyorlang.
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Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots - bet 19

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Recently computer architects have started to direct their attention onto other techniques
for improving both the processing time and hardware performance.
Shared-Memory
Parallel
(SMP) computing is very popular technique for speeding up the processing time
[60-62]. Isaac G.
et al
do propose in reference [63] an heterogeneous computing concept
which is a combination of a general purpose CPUs with an accelerator to improve the
execution efficiency. This model is based on shared memory parallel technique.
J. Batlle
et al
proposed in reference [49] a dedicated parallel architecture based on FPGA
and DSP for real-time image processing. This system has been designed to deal with
pipeline procedures and operators. They have broken the image processing algorithm into
three major steps: preprocessing, intermediate processing and, at the end, a post-
processing. All low level functions are performed at the preprocessing level. In the
intermediate level of processing we have some algorithms like segmentation, motion
estimation and features extraction. In the post-processing level we involve statistical
analysis and artificial intelligence [49].
D. Demigny
et al
proposed in reference [64] a high speed reconfigurable FPGA system for
processing images in real-time. They have considered different architectures and models of
processing architectures such as
Multiple Instruction - Single Data
(MISD) and
Single
Instruction - Multiple Data
(SIMD). The SIMD architecture is very interesting for image
processing because we have the same instruction for multiple data streams. The main
drawbacks of all mentioned architectures are however: a) that there is no homogenous
design that can cover different image processing algorithms, and b) that for any new
design one has to reconfigure the hardware. It may be possible to design a real-time and
robust image processing architecture for a specific task, but it not easy at all to reconfigure
it for different tasks and procedures.
Yangi mavzuni mustaxkamlash - 1- dars. Mavzu: Masalalarni kompyuterda yechish bosqichlari Darsning texnologik xaritasi

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1- dars. Mavzu: Masalalarni kompyuterda yechish bosqichlari Darsning texnologik xaritasi
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bet | 200/208 | Sana | 23.07.2021 | Hajmi | 1,92 Mb. | | #15865 |
Yangi mavzuni mustaxkamlash.
Mashqlar
1.«Men nechun sevaman 0'zbekistonni?» nomli mavzudagi web- sahifalar tanloviga qatnashish uchun so'rov formasini tayyorlang.
2.«Do'stlashishga taklif» nomli forma tayyorlang. Unda bilishingiz kerak bo'lgan ma'lumotlar aks etsin.
Uyga vazifa: Mavzuni o`qib o`rganish. 3-mashqni bajarish
3.«Kutubxona anketasi» nomli forma tayyorlang.
Sana :______________
Sinf :_______________
63- dars. Mavzu: Web-sahifada « o'tish » va formalar mavzusini takrorlash
Darsning texnologik xaritasi
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Figure - Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots

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Chapter 10
10.
Conclusion and Outlook
The Camera and the image processing unit belong the very important parts of machine
vision-based ADAS concepts. Different weather and environmental conditions can
significantly influence the image processing performance. The key challenging issue in
ADAS is safety; hence all part of the system should work under any conditions without
providing wrong information. To have a robust and real-time image processing module for
ADAS, we do need a high performance processing system. Computing huge amount of
visual information for extracting meaningful data, features, etc needs a special/appropriate
hardware and processing architecture. In this research we did a survey about different
hardware platforms for image processing and according to real-time related ADAS
requirements we have proposed a robust and real-time architecture based on CNN, FPGA
and GPU. The whole the system is a CNN based processing which is developed
implemented on either GPU or FPGA. Before CNN implementation, we tried to understand
the theory of analog computing and developed a direct emulation of that paradigm on the
FPGA. Since there are many similarities between a CNN implementation and analog
computing based on DDA, we could find a good way to implement CNN on FPGA. In this
thesis, we have shown that CNN can solve a series of image processing tasks in real-time.
This thesis has formulated 7 key research questions and each main chapter did provide an
answer to a respective research question:
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methods for template calculation. We have then shown an example direct mapping
of PDE’s to templates for a selected image processing task.
For a fast template
calculation we have shown that there is a way to implement GA on FPGA and
optimize any templates much faster than on computer/CPU.
Research 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 (for the benefit of an ultrafast image processing)?
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. For this research question we have developed and implemented an
emulation of analog computing on FPGA. The result is at least many thousand times
faster than desktop computers. This result has been a good step for implementing
complex models like CNN on FPGA.
Research question 6: How far can an efficient implementation of CNN on
Figure - Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots

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turning left and right, jumping and etc.) For insuring the control of the robot are very
important. The choice of templates is highly influenced by these factors.
9.3
Conclusion
This paper has presented a concept based on an evolutionary technique for the robot
locomotion learning. The technique proposed was a combination of both CNN and genetic
algorithms. The motivation of this combination can be justified by the high accuracy of the
CNN processors and their good computational speed as well. Further, the topology of CNN
is flexible for designing neuro-evolutive systems. The genetic algorithm was exploited for
the training process in order to determine the best genes according to the pre-defined
requirements (i.e. dada requirements) for the design process. Two types of robots were
considered (i.e. both structured and unstructured robots). For each of these types,
algorithms were developed to derive the appropriate chromosomes from which
corresponding templates were derived. The results in this paper have shown that
combining the cellular neural networks (CNN) technology with an evolution scheme like
genetic algorithm (GA) is very effective and suitable for learning the movement
/locomotion of different types of robots (e.g. high DOF robots, symmetrical, unsymmetrical
and defective robots). Due to the intrinsic characteristics of the CNN, this type of neural
network is very close to natural processors and therefore is efficient for building robot
controllers. During the training process, we found that the complexity of the environment
(e.g. rough, bumpy, and/or scaly surfaces) was a key factor influencing the results.
Basically, the technique developed in this paper provided interesting results with high
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then propose a new concept that can satisfy all the
cited requirements in ADAS
systems including flexibility in design.
Research question 2: What are the major limitations of traditional high
performance computing approaches if used to ensure “rea
l-
time” image
processing in ADAS ?
For this research question we studied about high performance computing and real-
time image processing. Also, we could show the limitation of
traditional computing
concepts/architectures based on the
Von Neumann
architecture.
We have shown
that manipulating and processing pixels in parallel does speed the image
processing. For flexibility in design, hardware should be reconfigurabe by software
in run-time mode and without any need for reprogramming the system.
Research question 3: What is the huge potential of neurocomputing involving
either traditional neural networks (NN) or cellular neural networks (CNN)
for high-speed and flexible image processing for ADAS? Are there any
limitations and how can these eventually be addressed?
For this question we have shown that ANN has
huge potential for image
processing; examples of applications are pattern recognition,
feature extraction,
compression, etc. Also we have mentioned some drawbacks of ANN for hardware
implementation and a comparison between CNN and ANN. Overall neurocomputing
is a paradigm that promises to solve the tough requirements of ADAS concerning
computing speed and flexibility.
Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots - bet 24

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Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile RobotsBog'liq Alireza Fasih4.3
Contribution to an image processing platform for ADAS
In ADAS systems we need different types of filters and image processing components
which are very complex and time consuming. We need a uniform platform as an image
processing framework. Flexibility in design, the capability of reconfiguration of modules,
the capability of redesigning the system architecture and a very short processing time
along robustness are the main characteristic and properties of the ideal framework. In this
thesis, a hardware architecture implementing a CNN processor matrix for performing
36
different image processing filters and algorithms is provided. For implementing CNN on a
digital platform we need an accurate approximation of the CNN equation in a discrete
mode [95-97]. In this thesis the architecture of a CNN implementation based on GPU and
FPGA are proposed. Figure 4-3 does show the abstract model of the GPU based system
which has been proposed.
Figure
4-3: Architecture of system for processing images based on CNN
To have more flexibility in design and accuracy in result, software based implementation of
CNN is a good option. The only drawback is that by increasing the CNN size, the CNN
performance will be very poor. Therefore we proposed a parallel implementation of CNN
on GPU. Instead of programming in pixel level by vertex engine and fragment engine we
proposed an implementation on OpenCL platform. OpenCL which is a heterogeneous
platform for high performance computing on GPU and CPU devices provided a sort of APIs
for execution of kernels on computing devices and communication between them. Kernels
are distributed in the form of one, two and three dimensional and they following
hierarchical abstraction mode. In GPU device there is local, global and constant memory for
computing and each computing unit has a local memory. OpenCL can manage easily local
communication between these memories between different kernels. Figure 4-4 has shown
the overview of the CNN GPU design, this part has been describe in details in chapter 8.
CNN
Templates
Bank/Memory
CNN on
GPU
CPU
Global
Memory
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Figure 
4-4: CNN architecture based on GPU

38
Microsoft Visual Studio
TM
editor and Eclipse IDE. During the design and debugging phase
we used this debugger for loading image into the CNN model and test the functionality of
different modules and complete system. After debugging system we generate the
optimized FPGA hardware and software interface and programmed on the FPGA by XILINX
EDK
TM
. Details of complete system are described in chapter 7.
Figure - Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots

35
unsupervised learning. Concerning supervised learning the aim is to train a network which
should express a specific function [83]. One does have a set of data pairs which are
combinations of inputs and targets values. More details on supervised learning can be
found in literature [84-87]. In contrast to supervised learning there is the unsupervised
learning. In this form of learning one has a set of values and a cost function that has to be
minimized. The main areas which are targeted by this kind of learning are: estimation
problems, clustering application, the estimation of statistical distributions, filtering and
data compression. More details on unsupervised learning can be found in literature [85,
88, 89].
There is another type of artificial neural networks proposed by Chua
et al
and called
cellular neural network (CNN). CNN is a combination of cellular automata and traditional
artificial neural networks [90-92]. CNN consists of huge number of cells which are
connected locally, that is, each cell is only connected to its neighbors. CNN is getting very
popular in image processing [93]. One can train it by heuristic learning methods such as
genetic algorithms and iterative annealing [94]. Another way to train the CNN is direct
mapping of equation on the CNN. If we have PDE with two or more independent variable, it
is possible to convert it to set of ODEs with one independent variable. We know that CNN is
consisting of many integrators and some other active and passive elements which are
coupled together with a specific topology. These coupled integrators are helpful for solving
ODEs. Hence, if we solve these sets of ODE on a single CNN layer, the solution of complete
system is solution of our PDE.
- Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots

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Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile RobotsBog'liq Alireza Fasih
5.2.3
Genetic algorithms
The ‘genetic algorithm’ concept was developed by John Holland in 1960s
[105]. It is an
effective method for determining the parameters for CNN. This method is inspired by the
mechanism of natural selection and genetics. It has been effectively used for solving
difficult search, optimization and machine-learning problems. It works by creating
genotypes (set of chromosomes) that represent the possible randomly chosen solutions.
The search evolves to improve the quality of chromosomes until the best chromosome is
found that represents the optimal solution[94]. The process of evolution occurs in the form
of generations and in each generation better chromosomes are sorted out. The parameters
of genetic algorithm that play an important role in the process of evolution and of finding
the best solution are the following: initial population, selection, reproduction, crossover,
mutation, and fitness function [94].
5.2.4
Initial population for the genetic algorithm
An initial random population of the chromosomes is generated. Each chromosome
represents a possible problem solution. All chromosomes are composed of a fixed number
of genes. The approach we used in genetic algorithms represents the chromosomes in the
form of real numbers instead of binary digits. This creates chromosomes of relatively
smaller sizes and the repeated operations of encoding and decoding are eliminated. For a
3×3 CNN, the total numbers of genes contained in a chromosome are 19. Thus, 9 genes are
for the control template; 9 for the feedback template and 1 for the bias. Similarly, if used
for 5×5 CNN, the total number of genes in a chromosome would be 51.
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Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots - bet 18

24
one can formulate two different classes of limitations that are: a) the one related to the
software architecture and to the algorithms for manipulating pixels and afterwards
extracting meaningful data; and b) hardware limitations and inflexibility of design. A much
better solution for image processing is the use of a parallel processing architecture.
Multiple Instructions - Multiple Data
streams (MIMD) is the most common architecture for
parallel processing; most modern computers fall into this category [51, 52]. In this model
of processing every processing unit may access to different memory and data streams. For
increasing the performance for reaching a sufficient speedup, dedicated hardware for each
algorithm has been suggested. Hence, designers implement each application on different
platform, and this redundancy in hardware increases the price and complexity of the
system [53, 54].
A direct mapping of algorithms on the hardware is often viewed as the best way of
processing [53, 55, 56]. The only issue that should be considered in this case is the low
flexibility of the system concerning design time. Therefore, the only drawbacks of this
approach are: a) the complexity of mapping many image processing operators/functions
onto the hardware, and b) the limitation of hardware resources.
Hence, we are looking for a reconfigurable model/concept/architecture that can change
(or be reconfigured) into different functions and thereby significantly saving hardware
resources. Another important factor of this model is parallel processing of pixels.
Tsiklik hisoblash jarayonlari - 10-Mavzu: Ma’lumotlar bazalari va katta hajmdagi ma’lumotlar bilan ishlash texnologiyalari Reja

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10-Mavzu: Ma’lumotlar bazalari va katta hajmdagi ma’lumotlar bilan ishlash texnologiyalari Reja
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bet | 25/44 | Sana | 10.06.2024 | Hajmi | 0,95 Mb. | | #262104 |
Bog'liq 10-Mavzu Ma’lumotlar bazalari va katta hajmdagi ma’lumotlar bil Tsiklik hisoblash jarayonlari
Tsiklik strukturaning algoritmi ularga kiritilgan miqdorlarning har xil qiymatlari uchun bir xil amallar ketma-ketligini takroriy bajarilishini ta'minlaydigan algoritm deyiladi14.
Bir necha marta takrorlanadigan bo'limlar tsikllar yoki tsikl tanasi deb nomlanadi. Har bir tsikl bajarilganda yangi qiymatga ega bo'lgan algoritmning o'zgaruvchisi tsikl parametri (yoki tsikli o'zgaruvchisi) deb nomlanadi.
Har qanday tsiklni tashkil qilish uchun quyidagi shartlar bajarilishi kerak:
tsikl boshlanishidan oldin tsikl parametri (o'zgaruvchisi) ning dastlabki qiymatini belgilash;
tsikl tanasining har bir yangi takrorlanishidan oldin tsikl parametrini (o'zgaruvchisini) o'zgartirish;
tsiklni tugatish (tsikldan chiqish) yoki takrorlash holatini tekshirish;
agar tsikl tugamagan bo'lsa, tsiklning boshiga o'ting yoki chiqish sharti bajarilgan bo'lsa, tsikldan chiqing.
Tsikl oxirini tugallanishiga ko'ra quyidagi tsikl turlari ajratib ko'rsatiladi.
Tsiklning takrorlanish soni noma'lum (noma'lum takrorlanish soni bo'lgan tsikl). Tsikldan chiqish qo'shimcha shart bilan amalga oshiriladi.
Arifmetik progressiya turi (ma'lum takrorlanishlar soni bo'lgan tsikl). Ushbu tsikllarda parametr (tsikl o'zgaruvchisi) berilgan boshlang'ich qiymatdan ma'lum bir yakuniy qiymatga o'zgarib, tsikl bajarilganda har safar doimiy o'sishni oladi, bu tsikl parametri pog'onasi deb ataladi (9.4-rasm).
Murakkab masalalarni echish algoritmlari yuqoridagi barcha tuzilmalarni o'z ichiga olishi mumkin. Masalan, bitta tsikl bir yoki bir nechta boshqa tsikllarni o'z ichiga olishi mumkin, masalan: ichki va tashqi. Ham tashqi, ham ichki tsikllarni tashkil qilish qoidalari oddiy tsiklni tashkil qilish qoidalari bilan bir xil. Tashqi va ichki halqalarning parametrlari bir vaqtning o'zida o'zgartirilmaydi.
11.6-rasm. Algoritmning tsikl parametri pog'onasi
Ko'p muammolarni hal qilish uchun dasturlar bir necha tsikllarni talab qiladi15. Masalan; misol uchun:
massivlarni buyurtma qilish;
massivlarni qayta ishlash;
darajalar qatori tomonidan berilgan funktsiyalar qiymatlari jadvalini hisoblash.
Bunday hollarda, algoritmning tuzilishini, avvalambor, tsikllarning soni va nisbiy holatini to'g'ri aniqlash muhim ahamiyatga ega. Ushbu tuzilmalarda ko'rib chiqilgan algoritmlardan foydalanish mumkin, ammo shu bilan birga u yoki bu texnikaning qaysi tsiklda (tashqi yoki ichki) ishlatilishini aniqlash kerak.
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«Яндекс» - 10-Mavzu: Ma’lumotlar bazalari va katta hajmdagi ma’lumotlar bilan ishlash texnologiyalari Reja

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10-Mavzu: Ma’lumotlar bazalari va katta hajmdagi ma’lumotlar bilan ishlash texnologiyalari Reja
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bet | 20/44 | Sana | 10.06.2024 | Hajmi | 0,95 Mb. | | #262104 |
Bog'liq 10-Mavzu Ma’lumotlar bazalari va katta hajmdagi ma’lumotlar bil«Яндекс». Kompaniya deyarli faoliyatini tizim asosida qurdi: qidiruv algoritmi, avtomatik tarjimon, spamga qarshi himoya, targetli reklama, tirbandlikni tahlil va bashorat qilish, nutq va yuzni aniqlash.
Yaqin vaqtgacha yirik kompaniyalar konsultatsiya uchun Yandex Data Factory bilan bog'lanishlari kerak edi, ammo bugungi kunda u to'liq qidiruv bo'limiga o'tkazildi.
Mail.Ru Group. Birinchilardan bo'lib noyob texnologiyadan amalda foydalanishni boshladi. Ular barcha xizmatlarga joylashtirilgan. Yangi metodologiyani joriy etish tufayli MailRu reklamani yo'naltirishi, qidiruv so'rovlarini optimallashtirishi, texnik qo'llab-quvvatlash guruhining tezkor ishlashi ta’minlashi, filtrlash va spamlardan himoya qilishni taklif qiladi.
«Рамблер». Dastlab, faqat qidiruv so'rovlarida ishlatilgan, ammo keyinchalik data-mining rivojlana boshladi. Ish faoliyatida kontentni ajratish, keraksiz resurslarni blokirovka qilish mumkin.
Har bir yangi ixtiro ahamiyatga ega bo’lishi uchun katta foyda keltirishi kerak. Ushbu mezon BIG DATA uchun ham aktualdir:
osonroq rejalashtirish;
info-mahsulotlarni tezkor ishga tushirish;
mahsulotga bo’lgan talab;
foydalanishdan qoniqishni baholash qobiliyati;
kerakli auditoriyani oson qidirish;
ta'minotni optimallashtirish;
sifatni oshirish va o'zaro ta'sir tezligini oshirish; mijozlar loyalligini oshirish.
2019-yilda o'n yillik boshiga nisbatan axborotni idrok etish va eng muhimi, massivlar bilan ishlashning ahamiyati 4-5 baravar oshdi. BIG DATA ning kichik, o'rta biznes va startap sohalariga integratsiyalashuvi yuz berdi:
Bulutli omborlar. Onlayn maydonda ma'lumotlarni saqlash va ular bilan ishlash texnologiyalari kichik va o'rta biznes uchun juda ko'p muammolarni hal qiladi: Ma'lumotlar markazini saqlashdan ko'ra bulutni sotib olish arzonroq bo’lib, xodimlar masofadan turib ishlashlari ham mumkin. Offis kerak emas.
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Hisoblash jarayonlarni grafik aks ettirish

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Hisoblash jarayonlarni grafik aks ettirish
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bet | 23/44 | Sana | 10.06.2024 | Hajmi | 0,95 Mb. | | #262104 |
Bog'liq 10-Mavzu Ma’lumotlar bazalari va katta hajmdagi ma’lumotlar bil11.2. Hisoblash jarayonlarni grafik aks ettirish.
Hisoblash jarayonlarini tasvirlashning turli usullari mavjud. Bu og'zaki, operator, blok-sxema shakli.
Algoritm qoidalari to'plamining tabiiy tili yordamida aniq tavsifi algoritmni og'zaki tasvirlash shakli deb ataladi.
Operator sxemasi - bu hisoblash jarayonining ayrim avtonom bosqichlarining mazmunini tavsiflovchi operatorlar yordamida algoritmni aks ettirish. Operator - bu dasturni amalga oshirish jarayonida ma'lum bir harakatlarning bajarilishining ko'rsatkichidir.
Algoritmdagi qadamlar ketma-ketligining grafik tasviri blok-sxema deb ataladi.
Blok-sxemaning har bir qismi ma'lum bir shaklga ega bo'lgan geometrik figuralar bilan tasvirlangan. Har bir shakl bitta bosqichni anglatadi va blok deb nomlanadi (9.1-jadval).
Blok-sxema chiziqlarining normal yo'nalishi yuqoridan pastga va chapdan o'ngga qarab hisoblanadi va o'qlar bilan ko'rsatilmaydi. Boshqa barcha holatlarda o'qlar talab qilinadi.
Blok raqamlari yuqori chap burchakda joylashtirilgan bo'lib, bloklar harflar bilan yoki harflar va raqamlar birikmasi bilan belgilanishi mumkin.
Agar blokning mazmuni uning ichiga sig`masa, unda "izoh" ishlatiladi. Izoh blok-sxemaning bo'sh joyiga joylashtiriladi va tushuntirilayotgan blokga yoki chiziq chizig'i bilan blok chizig'iga ulanadi. Izoh sarlavha blokiga parallel ravishda yuqori va pastki chegara chiziqlari ichida yoziladi.
11.1-jadval
Tasviri
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Tavsifi
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Jarayon (operatsiyalarni yoki guruh operatsiyalarini bajarish)
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Qaror (ba'zi shartlarga qarab algoritm yoki dasturni bajarish yo'nalishlarini tanlash)
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Modifikatsiya (buyruqlar, dasturlarni o'zgartiradigan operatsiyalarni bajarish)
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Boshlash, tugatish. Ma'lumotlarni qayta ishlashning boshlanishi, tugashini belgilaydi
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Kirish, chiqarish (ma'lumotlarni qayta ishlash (kiritish) yoki qayta ishlash natijalarini ro'yxatdan o'tkazish uchun mos bo'lgan shaklga aylantirish
(chiqish)
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Oqim chizig'i. Bu blok-sxemadagi belgilarni bog'laydigan va ular orasidagi bog'lanish ketmaketligini ko'rsatadigan to'g'ri chiziq.
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Oldindan aniqlangan jarayon
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Ulagich. Belgilarni bog'laydigan uzilgan oqim chiziqlari orasidagi bog'lanishlarni bildiradi
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-Mavzu: Bulutli texnologiyalar va ular bilan ishlash

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-Mavzu: Bulutli texnologiyalar va ular bilan ishlash
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bet | 32/44 | Sana | 10.06.2024 | Hajmi | 0,95 Mb. | | #262104 |
Bog'liq 10-Mavzu Ma’lumotlar bazalari va katta hajmdagi ma’lumotlar bil12-Mavzu: Bulutli texnologiyalar va ular bilan ishlash
Reja:
Bulut tеxnologiyasi tushunchasi
Bulut tеxnologiyasining asosiy turlari
Bulut tеxnologiyalarini turli sohalarda amalga tatbiq qilish tamoyillari
Bulut tеxnologiyalarida axborot xavfsizligita'minoti
12.1. Bulut tеxnologiyasi tushunchasi
So`nggi bir nеcha yil ichida mahalliy va xalqaro biznеsda axborot tеxnologiyalarining ahamiyati sеzilarli darajada oshdi. Tashkilotlar boshqaruv tizimidagi harakatchanlik, moslashuvchanlik va samaradorlikni oshirishda axborot tеxnologiyalarini joriy qilish zaruriy shartga aylandi. Axborot yig`ish va uni ichki taqsimlash jarayoni shakllantirilgan korxonalar bozor tеndеntsiyalari o`zgarishini taxmin qilishlari va tеzkor harakat qilishlari, ishonch bilan asosli qaror qabul qilishlari mumkin. Bugungi kunda o`zbеk bozoridagi vaziyat shundan iboratki, aksariyat korxonalar, biznеsning katta yoki kichik ekanligidan qat'iy nazar, asosiy va yordamchi biznеs jarayonlarini optimallashtirish uchun axborot tеxnologiyalari rivojlanishi sohasidagi innovatsiyalar yo`lidan borishta tayyor.
O`zbеkiston Rеspublikasi Prеzidеntining 2020 yil, 28 aprеldagi "Raqamli iqtisodiyot va elеktron hukumatni kеng joriy etish chora-tadbirlari to`g`risida"gi qarorida raqamli iqtisodiyot va elеktron hukumatning kеlgusidagi rivoji uchun qo`shimcha vazifalardan yana biri aniqlab bеrildi: " raqamli iqtisodiyotni jadal shakllantirish va 2023 yilga qadar uning mamlakat yalpi ichki mahsulotidagi ulushini 2 baravarga oshirish, jumladan, ishlab chiqarishni boshqarishda axborot tizimlari komplеksini joriy etish, moliyaviy xo`jalik faoliyatida hisobotlar uchun dasturiy mahsulotlardan kеng foydalanish, shuningdеk tеxnologik jarayonlarni avtomatlashtirish"21.
Ammo shuni ta'kidlash kеrakki, axborot tizimlari a tеxnologiyalardan foydalanishning an'anaviy usullari katta harajatni talab qiladi. Odatda, dastur qobig`ining asosiy modullarini yangilash, foydalanilgan uskunalar amortizatsiyasi va ta'mirlash ishlari, xizmat ko`rsatuvchi xodimlarning ish haqi, axborot tizimi ishlamay qolgan holatdagi yo`qotishlar ham shuning sirasiga kiradi. Aksariyat holatlarda, ma'lum b ir faoliyat sohasini avtomatlashtirishda aniq harajatlar – axborot tizimini ishlab chiqish va uni amalga tatbiq qilish, – hisobga olinadi, navbatdagi, yashirin harajatlar (ekspluatatsiya chiqimlari) ga esa kamroq e'tibor qaratiladi. AT harajatlari umumiy qiymatning 70%ini tashkil qilishi mumkin, axborot tizimini yaratish va joriy qilish harajatlari o`rtacha 30%ga tеng bo`ladi.
Shu sababli biznеs uchun asosini bulut tеxnologiyalari tashkil qiluvchi ATinfratuzilmasining artеrnativ virtual shakli taklif qilingan.
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AT xizmatlarining yuqori darajadagi hammabopligi

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AT xizmatlarining yuqori darajadagi hammabopligi
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bet | 35/44 | Sana | 10.06.2024 | Hajmi | 0,95 Mb. | | #262104 |
Bog'liq 10-Mavzu Ma’lumotlar bazalari va katta hajmdagi ma’lumotlar bilAT xizmatlarining yuqori darajadagi hammabopligi.
Bulut xizmatlaridan 99,5% vaqt davomida foydalanish mumkin, ayrim provaydеrlar esa 99,9% darajadagi foydalanishni kafolatlaydi. 99,9% ko`rsatkichda tizimning rеjadan tashqari ishlamay qolishi yiliga 1 soatu 35 daqiqadan oshmaydi. Bulutli axborot tizimida saqlanayotgan ma'lumotni intеrntеt tarmog`iga ulangan kompyutеr, planshеt, istalgan turdagi mobil qurilmaga ega har bir kimsa olishi mumkin. Foydalanuvchilarga bitta ish joyiga doimiy bog`lanib qolish majburiyati yo`q.
ATning ishlov bеrishga yuqori darajada qulay va moslashuvchanligi
Bulutli hisoblashdan foydalanganda biznеs ehtiyojlari uchun taqdim etilgan mutloq moslashuvchanlik qayd etilgan, undan ma'lumotlarni saqlash, tahlil qilish va qayta ishlash uchun foydalansa bo`ladi. Bu esa bulutli axborot tеxnologiyalaridan foydalanish AT va uskunalardagi to`xtab qolishlar miqdorini kamaytiradi, shuningdеk, ATning yuqori bosimida quvvat еtishmasligi muammosini bartaraf qiladi (12.2-rasm).
12.2-rasm. Bulut tеxnologiyalarining moslashuvchanligi
Atrof-muhitga ta'sirni kamaytirish
Ko`plab mamlakatlarda enеrgiyani tеjovchi ("yashil") tеxnologiyalar yo`nalishi e'lon qilingan. "Yashil" konsеpsiyaga muvofiq tarzda ma'lumotlarni qayta ishlash markazini loyihalashtirish va foydalanishda enеrgiya tеjaydigan tеxnologiyalardan foydalanishi lozim. Amaliyotdan kеlib chiqqanda, atrof-muhitga ta'sirni kamaytirish uchun bu kabi tеxnologiyalarni muayyan AT-infratuzilmasiga tatbiq etishdan ko`ra, "yashil" tеxnologiyalardan foydalanuvchi bulutli xizmatlarni ishlatish foydaliroq. Samaradorlikning yuqoriligi bosim butunligicha ko`p sonli kichik sеrvеrlar orqali tеxnologiya gigantining ulkan ma'lumotlar bazasiga uzatilishi orqali izohlanadi. Bu esa uskunaga tushadigan bosimni maksimal darajada opmitallashtirish va unga xizmat ko`rsatishni еngillashtirishga imkon bеradi. Google ma'lumotlariga ko`ra, bulutli xizmatlardan foydalanish nafaqat uskuna va xodimlar uchun, balki elеktr enеrgiyasi uchun sarf etiladigan mablag`ni tеjab qolishga imkon bеradi. Tahlillarga ko`ra, shaxsiy sеrvеrlardan bulutli xizmatga o`tadigan o`rtacha kompaniya uskunalar va kompyutеrlar sarf etuvchi elеktr enеrgiyasini 65 % dan 87% gacha tеjab qoladi.
Bulutli tеxnologiyalardan foydalanish bilan bog`liq asosiy xavf korporativ ma'lumotlar xavfsizligi hisoblanadi. Ma'lumotlar maxfiyligi masalasi korxonalarni sеrvеrlardan bulutga ko`chirishga to`sqinlik qiladigan asosiy sabab sanaladi. Xavotirga o`rin yo`q, boisi, ma'lumotlar ochiq kanallar bo`ylab uzatilganda ehtiyotkorlik bilan shifrlanadi. Bundan tashqari, zamonaviy bozor raqobati sharoitida har bir bulut xizmati provaydеri xavfsizlik va ma'lumotlarni saqlash kafolati uchun o`z tеxnologiyalarini yaxshilashga harakat qilmoqda.
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AT infratuzilmasiga xizmat ko`rsatishning yuqori sifati

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AT infratuzilmasiga xizmat ko`rsatishning yuqori sifati
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bet | 34/44 | Sana | 10.06.2024 | Hajmi | 0,95 Mb. | | #262104 |
Bog'liq 10-Mavzu Ma’lumotlar bazalari va katta hajmdagi ma’lumotlar bil2. AT infratuzilmasiga xizmat ko`rsatishning yuqori sifati
Xizmat ko`rsatish sifatini baholash uchun Microsoft kompaniyasi tomonidan ishlab chiqilgan Infrastructure Optimization Model (IOM) modеli namunasida bulut xizmatini ko`rib chihamiz. Ushbu modеl bir qator muhim ko`rsatkichlarga ta'sir qiluvchi turli xizmatlar to`plami, ma'muriy mahsulot va jarayonlar, rivojlanish mavjudligi va uning darajasiga ega AT-infratuzilmasini tavsiflaydi.
IOM еtuklik darajasi va qisqacha tavsifi 12.1-jadvalda kеltirilgan.
Bulut xizmatlarini shiddatli еtuklik darajasida ko`rish mumkin, bu biznеs uchun yangi imkoniyatlar ochish va biznеs jarayonlarining samaradorligini oshirishga yordam bеradi. Ushbu darajada AT-infratuzilmasini boshqarish tashkilotning biznеs talablari bilan bеlgilangan kеng hamrovli xizmat ko`rsatish va siyosati asosida amalga oshiriladi. Muammo va vazifalarni ishlatiladigan vositalar va jarayonlar yordamida hal qilish, shuningdеk, infratuzilmada turli maqsadlar uchun kеngaytirilgan xizmatlar mavjudligi nuqtai nazaridan AT-infratuzilmasi holati undagi muammolarni oqibatlar kompaniyaning biznеs jarayonlariga ta'sir qila boshlaguniga qadar aniqlash va bunga yo`l qo`ymaslikka imkon bеradi.
12.1-jadval
IOM modеlining еtuklik darajalari
Еtuklik darajasi
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Qisqacha tavsif
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Asosiy
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AT-infratuzilmasida xizmat ko`rsatish jarayonida qo`lda bajariladigan opеratsiyalarning katta foizi aniqlandi, bunday infratuzilmaga egalik qilish qiymati yuqori.
O`zgaruvchan biznеs vazifalarni hal qilishga tayyorlik aniqlanmagan
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Standart
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AT-infratuzilmasini boshqarishning asosiy jarayonlarini avtomatlashtirish uchun etalon xizmatlar va usullardan
foydalaniladi. Egalik qilish qiymati past. Biznеs talablariga javob bеrish samaradorligi bilan ajralib turmaydi.
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Ratsional
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Microsoft tomonidan taqdim qilingan zamonaviy mahsulotlar, avtomatlashtirish va AT-infratuzilmasini boshqarish
vosiatalarining barchasi ishlatiladi. Bu egalik qilish qiymatining tushishiga olib kеladi. Biznеs talablariga javob bеrishga tayyorlik yuqori darajada ekani qayd etilgan
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Dinamik
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IOMning eng yuqori darajasi, bunda AT-infratuzilmasi biznеs ilovaldarga to`liq biriktiriladi va unga kеrakli barcha xizmatlarni ko`rsatadi
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