2. Outline of The Presentation….
1
2
Why Electronics & Communication
Engineering??
Why ECE from JIT??
3. • Imagine a world without Electronics!!
• Job opportunities.
• Research scope.
• Curriculum-Based on the new trends
and possibilities.
Why Electronics & Communication Engineering??
4.
5. Research scope.
Have a look at these, in 1956 a
5MB Hard Disk is of the size of a
normalhuman,andin2011, 256
GB, approximately 52,428
times,isofthesizeofhisfinger.
• These changes are the result of research work carried in the field
of electronics, and the credit goes to the ELECTRONICS
ENGINEERS…
6. Curriculum-Based on the new trends and possibilities.
EMAILS
Lets talk about one very important
aspect of over life.
7. Curriculum-Based on the new trends and possibilities.
Have you ever wonder how these things
are possible in this tiny device!!!
We are making it possible..we the
ELECTRONICS ENGINEER…lets see,
how we do this….
8. Let me show you what's inside your phone..
There are many
sensors & other
components.
You will learn about
these components
How these green boards
are made. And this can
be developed in the
department laboratory
We will learn how we communicate.
How information in the form of
images, text, voice, video, is shared
from one location of globe to other.
BASIC ELECTRONICS,
ELECTRONICS CIRCUIT
VLSI DESIGN,
MICROCONTROLLERS &
ITS APPLICAION,
INTEGRATED CIRCUIT
TECHNOLOGY
DATA COMMUNICATION &
NETWORKING, ANALOG
COMMUNICAITON, DIGITAL
COMMUNICATION,
WIRELESS
COMMUNICATION
The memory
elements. Where you
store all your data.
From the designing
to the working.
DIGITAL
ELECTRONICS
9. Why ECE in JIT??
1
2
Our Product.
Our Current students
Our Resource group
Helping future Entrepreneur
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4
11. Our Current students
He has made several award wining projects like LPG gas sensor,
Hybrid street light. Wireless robot.
He has acquired a good knowledge of Arduino (Microcontroller), has
made several projects using it, and has shared his knowledge on
youtube, to facilitate others also.
https://www.youtube.com/channel/UCc3t6zfCF44Fr4x8frkwqgg
Other year students are also working on various minor & major
projects apart from their regular curriculum.
12. Our Resource group with 50 years of cumulative experience
Dr N.R. Kidwai, Ph.D. (ECE)
Dean academics, HOD ECE, Associate Professor
More than 16 years of teaching experience in renowned intuitions
13 Research papers in leading International/National Journals,
Published Books on “Signal & System, Analog & Digital
Electronics.
Ms Ruchi Srivastava, M.Tech (ECE)
Assistant Professor
Research area:- wireless sensor networks
Expertization:- Microprocessors & Microcontrollers
10 years of teaching experience
13. Mr Mohammad Akram, M.Tech (ECE)
Assistant Professor
More than 6 years of teaching experience;
Consistently giving best result in first year, among all the colleges
in the surrounding area.
Mr Mohammad Asif, M.Tech (ECE)
Assistant Professor
More than 6 years of teaching experience with good result.
GATE qualified
Two research papers in renowned International Journals
Mr Himanshu Diwakar,M.Tech(VLSI)
Assistant Professor
Four times GATE qualified
17. CONCLUSION
So if you want to become a
successful ECE engineer you
are welcome at JIT…..
JAHANGIRABAD FORT: JAHANGIRABAD, BARABANKI, U.P. 225203
TEL:05248-243352, 243373, 320800, +91-969539684
EMAIL: Info@jit.edu.in – www.jit.edu.in
The system identification is an approach to model an unknown system. In this configuration the unknown system is in parallel with an adaptive filter, and both are excited with the same signal. When the output MSE(Mean square error) is minimized the filter represents the desired model. The structure used for adaptive system identification is illustrated in figure 1, where P(z) is an unknown system to be identified by an adaptive filter W(z). The signal x(n) excites P(z) and W(z), the desired signal d(n) is the unknown system output, minimizing the difference of output signals y(n) and d(n), the characteristics of P(z) can be determined.
So in This work we have tried to optimize the parameter (step size) for an adaptive Filter…so that we can reduce MSE.
The system identification is an approach to model an unknown system. In this configuration the unknown system is in parallel with an adaptive filter, and both are excited with the same signal. When the output MSE(Mean square error) is minimized the filter represents the desired model. The structure used for adaptive system identification is illustrated in figure 1, where P(z) is an unknown system to be identified by an adaptive filter W(z). The signal x(n) excites P(z) and W(z), the desired signal d(n) is the unknown system output, minimizing the difference of output signals y(n) and d(n), the characteristics of P(z) can be determined.
So in This work we have tried to optimize the parameter (step size) for an adaptive Filter…so that we can reduce MSE.
The system identification is an approach to model an unknown system. In this configuration the unknown system is in parallel with an adaptive filter, and both are excited with the same signal. When the output MSE(Mean square error) is minimized the filter represents the desired model. The structure used for adaptive system identification is illustrated in figure 1, where P(z) is an unknown system to be identified by an adaptive filter W(z). The signal x(n) excites P(z) and W(z), the desired signal d(n) is the unknown system output, minimizing the difference of output signals y(n) and d(n), the characteristics of P(z) can be determined.
So in This work we have tried to optimize the parameter (step size) for an adaptive Filter…so that we can reduce MSE.
The system identification is an approach to model an unknown system. In this configuration the unknown system is in parallel with an adaptive filter, and both are excited with the same signal. When the output MSE(Mean square error) is minimized the filter represents the desired model. The structure used for adaptive system identification is illustrated in figure 1, where P(z) is an unknown system to be identified by an adaptive filter W(z). The signal x(n) excites P(z) and W(z), the desired signal d(n) is the unknown system output, minimizing the difference of output signals y(n) and d(n), the characteristics of P(z) can be determined.
So in This work we have tried to optimize the parameter (step size) for an adaptive Filter…so that we can reduce MSE.
The system identification is an approach to model an unknown system. In this configuration the unknown system is in parallel with an adaptive filter, and both are excited with the same signal. When the output MSE(Mean square error) is minimized the filter represents the desired model. The structure used for adaptive system identification is illustrated in figure 1, where P(z) is an unknown system to be identified by an adaptive filter W(z). The signal x(n) excites P(z) and W(z), the desired signal d(n) is the unknown system output, minimizing the difference of output signals y(n) and d(n), the characteristics of P(z) can be determined.
So in This work we have tried to optimize the parameter (step size) for an adaptive Filter…so that we can reduce MSE.
The system identification is an approach to model an unknown system. In this configuration the unknown system is in parallel with an adaptive filter, and both are excited with the same signal. When the output MSE(Mean square error) is minimized the filter represents the desired model. The structure used for adaptive system identification is illustrated in figure 1, where P(z) is an unknown system to be identified by an adaptive filter W(z). The signal x(n) excites P(z) and W(z), the desired signal d(n) is the unknown system output, minimizing the difference of output signals y(n) and d(n), the characteristics of P(z) can be determined.
So in This work we have tried to optimize the parameter (step size) for an adaptive Filter…so that we can reduce MSE.