For Study Material Call or SMS 9893455006
Email: sonendrag@gmail.com
Online class Module
Download Study Material
1. Module-1 [Linear Algebra-Vector Space]2. Module-2 [Partial Differential Equation and Transform]
3. Module-3 [Probability and Distributions]
4. Module-4 [Stochastic, Markov Process and Queuing Models]
5. Module-5 [Fuzzy and Matlab]
6.Module-6 [Reliability and Decision Theory]
7. Module-7 [Finite Elements Method]
M.Tech RGPV Maths Question Papers
|
|||||||||
Branch
|
Subject
|
Syllabus
|
Paper Code
|
June 2016
|
Dec. 2015
|
June 2015
|
Dec. 2014
|
June 2014
|
Dec. 2013
|
Computer Sci. and Engg.
|
Advanced
Computational
Mathematics
|
Download
|
MCSE
101
|
Paper
|
|||||
Digital Electronics
|
Advanced Mathematics
|
Download
|
MTDE
101
|
Paper
|
|||||
Machine Design & Rob.
|
Advanced Mathematics
|
Download
|
MMMD 101
|
Paper
|
|||||
Power System/Power Electronics
|
Advanced Mathematics
|
Download
|
MEPS
101
|
Paper
|
|||||
Digital Communication
|
Advanced Mathematics
|
Download
|
MEDC
101
|
||||||
Mechanical Engg.
|
Advanced Mathematics
|
Download
|
MMIE
101
|
Paper
|
|||||
Thermal Engg.
|
Advanced Mathematics
|
Download
|
MMTP
101
|
Paper
|
|||||
Software Engg.
|
Advanced
Computational
Mathematics
|
Download
|
MSE
101
|
Paper
|
|||||
Control System
|
Mathematics
|
Download
|
MEIC
101
|
Paper
|
|||||
Bio-Tech
|
Engineering Mathematics
|
Download
|
MBCT
101
|
Paper
|
Paper
|
Paper
|
Paper
|
||
M.Tech RGPV Maths Question Papers
| |||||||||
Branch
|
Subject
|
Syllabus
|
Paper Code
|
Dec. 2016
|
June 2017
|
Dec. 2017
|
June 2018
|
Dec. 2018
|
June 2019
|
Computer Sci. and Engg.
|
Advanced
Computational
Mathematics
|
Download
|
MCSE
101
| Paper | Paper | Paper | Paper | ||
Digital Electronics
|
Advanced Mathematics
|
Download
|
MTDE
101
| Paper | Paper | Paper | Paper | ||
Machine Design & Rob.
|
Advanced Mathematics
|
Download
|
MMMD 101
| Paper | Paper | Paper | Paper | ||
Power System/Power Electronics
|
Advanced Mathematics
|
Download
|
MEPS
101
| Paper | Paper | Paper | Paper | ||
Digital Communication
|
Advanced Mathematics
|
Download
|
MEDC
101
| Paper | Paper | Paper | Paper | ||
Mechanical Engg.
|
Advanced Mathematics
|
Download
|
MMIE
101
| Paper | Paper | Paper | Paper | ||
Thermal Engg.
|
Advanced Mathematics
|
Download
|
MMTP
101
| Paper | Paper | Paper | Paper | ||
Software Engg.
|
Advanced
Computational
Mathematics
|
Download
|
MSE
101
| Paper | Paper | Paper | Paper | ||
Control System
|
Mathematics
|
Download
|
MEIC
101
| Paper | Paper | Paper | Paper | ||
Bio-Tech
|
Engineering Mathematics
|
Download
|
MBCT
101
| Paper | Paper | Paper | Paper | ||
M.Tech RGPV Maths Question Papers | |||||||||
Branch | Subject | Syllabus | Paper Code | Dec. 2019 | June 2020 | Dec. 2020 | June 2022 | Dec. 2022 | June 2023 |
Computer Sci. and Engg. | Advanced Computational Mathematics | Download | MCSE 101 | Paper | Paper | Paper | |||
Digital Electronics | Advanced Mathematics | Download | MTDE 101 | Paper | Paper | Paper | |||
Machine Design & Rob. | Advanced Mathematics | Download | MMMD 101 | Paper | Paper | Paper | |||
Power System/Power Electronics | Advanced Mathematics | Download | MEPS 101 | Paper | Paper | Paper | |||
Digital Communication | Advanced Mathematics | Download | MEDC 101 | Paper | Paper | Paper | |||
Mechanical Engg. | Advanced Mathematics | Download | MMIE 101 | Paper | Paper | Paper | |||
Thermal Engg. | Advanced Mathematics | Download | MMTP 101 | Paper | Paper | Paper | |||
Software Engg. | Advanced Computational Mathematics | Download | MSE 101 | Paper | Paper | Paper | |||
Control System | Mathematics | Download | MEIC 101 | Paper | Paper | Paper | |||
Bio-Tech | Advanced Mathematics | Download | MBCT 101 | Paper | Paper | Paper | |||
Dear students write their comments after downloading about important Questions
S.No.
|
Name of Units
|
Chapters
|
Sample
|
1
|
Vector Space and Transformations
|
Chapter-1
Chapter–2
Chapter–3
| |
2
|
Solution of Partial Differential Equation and their Applications
|
Chapter-1
Chapter–2
Chapter–3
Chapter–4
| |
3
|
Probability Distribution and Their Applications
|
Chapter-1
Chapter–2
Chapter–3
Chapter–4
| |
4
|
Stochastic, Markov and Queuing system
|
Chapter-1
Chapter–2
Chapter–3
| |
5
|
Fuzzy and Matlab
|
Chapter-1
Chapter–2
Chapter–3
Chapter–4
| |
6
|
Reliability, decision theory and goal programming
|
Chapter-1
Chapter–2
Chapter–3
|
Note: Complete solutions of Dec. 2016 paper available @ 300/- and June 2016 paper available @ 300/-
Note: E-notes available Rs.500 per Module/Unit fully Solved.
Module-1 [Linear Algebra-Vector Space]
|
||
Topics
|
E-Notes [
|
Video Link
|
Linear Algebra-Vector Space-Concept of Field & operation
|
Lecture-1
|
|
Linear Algebra-Vector Space Properties of Vector Space and Examples
|
Lecture-2
|
|
Linear Algebra-Vector Subspace-Theorem on Vector Subspace and Examples
|
Lecture-3
|
|
Linear Algebra-Vector Subspace-Theorem on Vector Subspace and Problems
|
Lecture-4
|
|
Linear Algebra-Vector Space-Linear Combination and Linear Span
|
Lecture-5
|
|
Linear Algebra-Linearly Independent and Dependent Set
|
Lecture-6
|
|
Linear Algebra-Linear Transformation and Properties
|
Lecture-7
|
|
Linear Algebra-Linear Transformation-Kernel-Range-Rank-Nullity
|
Lecture-8
|
|
Lecture-9
|
||
Lecture-10
|
||
Lecture-11
|
||
Hash Function
|
||
Heaviside or Unit Step function
|
||
Laplace Transform of Unit step function
|
||
Error Function
|
||
Hermit Polynomial
|
||
Modular Mathematics
|
||
Module-2 [Partial Differential Equation and Transform]
|
||
Topics
|
E-Notes
|
Video Link
|
Fourier Transform
|
Lecture-1
|
|
Fourier Transform
|
Lecture-2
|
|
Fourier Transform- Fourier Sine and Cosine Transform
|
Lecture-3
|
|
Fourier Transform- Fourier Sine and Cosine Transform
|
Lecture-4
|
|
Fourier Transform- Properties of Fourier Transform
|
Lecture-5
|
|
Fourier Transform- Properties of Fourier Transform
|
Lecture-6
|
|
Fourier Transform- Fourier Transform of Signal functions
|
Lecture-7
|
|
Fourier Transform -Application of Fourier Transform to solve ODE
|
Lecture-8
|
|
Discrete Fourier Transform (DFT):
|
||
Properties of DFT
|
||
Fast Fourier Transform (FFT)
|
||
Wavelet and Mother Wavelet or Basic Wavelet
|
||
Wavelet Transform
|
||
Haar Function and Application of Haar Transform
|
||
Partial Differential Equation- Method of Separation variables
|
Lecture
|
|
Partial Differential Equation-One Dimensional Wave Equation
|
Lecture
|
|
Partial Differential Equation-One Dimensional Wave Equation
|
Lecture
|
|
Partial Differential Equation-One Dimensional Heat Equation
|
Lecture
|
|
Numerical Solution of Partial Differential Equation
|
||
Classification of second order differential equation
|
||
Laplace Equation
Standard five point formula (SFPF)
Diagonal five point formula (DFPF)
|
||
Jacobi Iterative method or Point Jacobi Method
|
||
Gauss-Seidal Method
|
||
Module-3 [Probability and Distributions]
|
||
Topics
|
E-Notes
|
Video Link
|
Probability and Distribution -Continues Random Variables
|
Lecture-1
|
|
Probability and Distribution - Random Variables
|
Lecture-2
|
|
Probability and Distribution- Discrete Random Variables
|
Lecture-3
|
|
Probability and Distribution- Discrete Random Variables
|
Lecture-4
|
|
Probability and Distribution - Binomial Distribution
|
Lecture-5
|
|
Probability and Distribution - Binomial Distribution
|
Lecture-6
|
|
Probability and Distribution - Binomial Distribution
|
Lecture-7
|
|
Probability and Distribution -Poisson distribution
|
Lecture-8
|
|
Probability and Distribution -Poisson distribution
|
Lecture-9
|
|
Probability and Distribution -Normal Distribution
|
Lecture-10
|
|
Probability and Distribution -Normal Distribution
|
Lecture-11
|
|
Probability and Distribution -Normal Distribution
|
Lecture-12
|
|
Test of Hypothesis and Large Samples
|
||
Test of Statistics
|
||
Level of Significance
|
||
Error in Hypothesis Testing
|
||
Mean and Standard deviation in simple Sampling of attributes
|
||
Comparison of Large Samples
|
||
Definition of Probability of an Event
|
||
Probability of Odds in favour or against an event
|
||
General Addition theorem
|
||
Conditional Probability
|
||
Module-4 [Stochastic, Markov Process and Queuing Models]
|
||
Topics
|
E-Notes
|
Video Link
|
Probability Vector
|
||
Stochastic Matrix
|
||
Fixed Vector
|
||
Stochastic (Or Random Process)
|
||
Markov Process
|
||
Transition Probability and Matrix
|
||
Transition Diagram
|
||
Markov Chains and Properties of Markov Chain (or Process):
|
||
Stationary distribution
|
||
Absorbing States
|
||
Ergodic Chains
|
||
Queuing System
|
||
Transient and steady states
|
||
Traffic Intensity (utilization factor )
|
||
State and prove the Markovin property of inter-arrival times or Time independent theorem on Exponential distribution
|
||
Queueing Model
|
||
Model I:
(M/M/1 ) : ( ∞ /∞/FCFS ) or Erlang Model (Birth-death model)
|
||
Probability Distribution in Queueing System
|
||
Obtain the steady state equations for the queuing model (M/M/1): (¥/ FCFS).
|
||
Problem-1 A T.V. repairmen finds that the time spend on his jobs has on exponential distribution with mean 30 minutes. If the repairs sets in the order in which they comes in, and if the arrival of sets is approximately Poisson within average rate of 10 per 8 hour in a day. What is repairman’s expected idle time each day? How many jobs are ahead of the average set just brought in?
|
||
Problem-2 In a railway marshalling yard, goods trains arrive at a rate of 30 trains per day. Assuming that the inter-arrival time follows an exponential distribution and the service time distribution is also exponential with an average 36 minutes. Calculating the following,
(i) The mean queue size
(ii) The prob. that the queue size exceeds 10.
If the input of trains increase to an average 33 per day. What will be change in (i) and
(ii)?
|
||
Problem-3 Arrivals at telephone booth are considered Poisson with an average time of 10 minutes between one arrival and the next. The length of phone call is assumed to be distributed exponential, with mean 3 minutes:
(a) what is the prob. that person arriving at the booth will have to wait?
(b) what is the average length of non-empty queue that form time to time?
|
||
Problem-4 In a service department manned by one server, on an average one customer arrives every 10 minutes, It has been found out that each customer requires 6 minutes to be served. Find out
i)Average Queue Length
ii)Average time spent in the system
iii)The probability that three would be two customers in the queue.
|
||
Problem-5 A person repairing radios finds that the time spent on the radio sets has exponential distribution with mean 20 minutes. If the radios are repaired in the order in which they come in and their arrival is approximately Poisson with an average rate of 15 for 8-hour day, what is the repairman’s expected idle time each day? How many jobs are ahead of the average set just brought in?
|
||
Model II: (M/M/1: N/FCFS)
|
||
To obtain steady state difference equations:
|
||
To solve the steady state differential equation for P0 and P1
|
||
Module-5 [Fuzzy and Matlab]
|
||
Topics
|
E-Notes
|
Video Link
|
Concept of Fuzzy logic
|
||
Characteristic function
|
||
Fuzzy Set
|
||
Fuzzy Membership function
|
||
Containment or Fuzzy subset
|
||
Height of a fuzzy set
|
||
Normal and Subnormal fuzzy set
|
||
Support of a Fuzzy set
|
||
Cross-over point
|
||
Fuzzy singleton or Nucleus of a fuzzy set
|
||
a-cut set or a-Level set or Cut worthy set
|
||
Strong a-Cut or Strong a-Level set
|
||
Convex Fuzzy set
|
||
Cardinality and Relative Cardinality of a fuzzy set
|
||
Basic Operation on fuzzy sets or Standard operation on Fuzzy sets
|
||
Equality of two sets
|
||
Equivalent sets
|
||
Complement of a fuzzy set
|
||
Union or Disjunction of two sets
|
||
Intersection or Conjunction of two sets
|
||
Difference of two fuzzy sets
|
||
Properties of Fuzzy set operations
|
||
Special Operation on fuzzy sets
|
||
Product of two fuzzy sets
|
||
Product of a fuzzy set with a crisp number
|
||
Power of a fuzzy set
|
||
Disjunctive sum of two fuzzy sets
|
||
Algebraic or Probabilistic Sum of two fuzzy sets
|
||
Bounded difference of two fuzzy sets
|
||
Yager’s Union and Intersection of two fuzzy sets
|
||
Theorem: Suppose A and B be fuzzy sets defined on a universal set X. Prove that
 |
||
Cartesian Product of two Fuzzy sets
|
||
Fuzzy Relation
|
||
Domain of Fuzzy Relation
|
||
Range of Fuzzy Relation
|
||
Height of a fuzzy relation or Total Projection
|
||
Union and intersection of two fuzzy relations
|
||
Inverse of a fuzzy relation
|
||
Fuzzification Technique
|
||
Method of Fuzzification Technique
|
||
1. Singleton fuzzifier
|
||
2. Gaussian fuzzifier
|
||
3. Trapezoidal or Triangular fuzzifier
|
||
Defuzzication Technique
|
||
Methods of Defuzzification
|
||
1. Centre of gravity method or Centroid Diffuzzification method
|
||
2. Centre of Sums Method
|
||
3. Maximum Defuzzification Method Height Defuzzification method
|
||
Use of Fuzzy Logic
|
||
Application of Fuzzy logic
|
||
Fuzzy Rules
|
||
Key Features of MATLAB
|
||
Applications of MATLAB
|
||
MATLAB Windows
1. Command Window
(a). Launch Pad
(b). Workspace
(c). Command History
(d). Current Directory
2. Graphics Window
3. Edit Window
|
||
Files Types
1. M-files
2. Mat-files
3. Mex-files
|
||
Basic Mathematical Functions
|
||
Basic Arithmetic and Logical Operators
|
||
M-file Scripts and M-File functions
|
||
Advantages of MATLAB
|
||
Disadvantages of MATLAB
|
||
Fuzzy Tools box
|
||
1. Command line functions
2. Graphical interactive tools
3. Simulink blocks and examples
|
||
Working with the Fuzzy Logic Toolbox
|
||
Building a Fuzzy Inference System
|
||
Module-6 [Reliability and Decision Theory]
|
||
Topics
|
E-Notes
|
Video Link
|
Definition of Reliability
|
||
Reliability function
|
||
Basic Element of Reliability
|
||
Importance of reliability
1. Reputation
2. Warranty costs
3. Future business
4. Contract requirements
5. Customer satisfaction
6. Safety regulations
|
||
Measurement of Reliability
1. Failure Rate
2. Mean Time between Failures (MTBF):
3. Mean Time To Failure (MTTF)
4. Reliability
5. Availability
6. Mean Time To Repair (MTTR):
7. Unreliability
8. Unavailability
9. Operational Readiness
10. Reliability of the system
|
||
Mean time to system failure (MTSF)
|
||
Mean Sojourn time (MST)
|
||
Failure Distribution
|
||
Decision Theory
|
||
Types of Decision
1. Strategic Decisions
2. Administrative Decision
3. Operating Decision
|
||
Component of Decision Making
1. The Decision maker
2. Objectives
3. The system, or environment
4. Alternative course of action
5. Choice must have unequal efficiencies for the desired outcomes
|
||
Decision Models
|
||
Types of Decision Making Situations
1. Decision making under certainty
2. Decision making under uncertainty
(a) The maximax decision criterion (criterion of optimism)
(b). The minimax decision criterion
(c). The maximin decision criterion (criterion of pessimism)
(d). Laplace criterion (Criterion of equally likelihood)
3. Decision making under risk
(a). Expected monetary value (EMV)
(b.) Expected Opportunity Loss (EOL )
(c). Expected Value of Perfect Information (EVPI
|
||
Goal Programming
|
||
Module-7 [Finite Elements Method]
|
||
Topics
|
E-Notes
|
Video Link
|
Variational functionals
|
||
Euler Lagrange’s equation
|
||
Variational forms
|
||
Ritz method
|
||
Galerkin’s method
|
||
Descretization
|
||
finite element method for one dimensional
problems.
|
||
Thanks for the question bank sir.i am confused that these question are for june 2016 exam or. Recently completed exam in dec 15.i confued about feb 2016 what it means
ReplyDeleteand please tell me about where is study material to download
ReplyDeleteIt's really good to see the material here. Thanks from the bottom of my heart
ReplyDeleteThank u sir for given the study matrial, it is completely solved with RGPV paper solution.
ReplyDeleteTSteachers Thanks
ReplyDeletefor the Blog,
Pragathi Avanigadda Mathematics Bit Bank for TSPSC TRT 2017 SGT Content and Andhra Pradesh Teachers Eligibility Test AP TET Paper I Content Bit Bank.
What is the password for the locked file?
ReplyDeleteSir file password
ReplyDeleteAll materials are protected
ReplyDeleteSir ji rgpv Bhopal ke m.tech digital communication notes 1 sem my Watsup no 7747833402
ReplyDeleteNice Blog. Thanks for sharing with us. Such amazing information.
ReplyDeleteWhat are the disadvantages of a part time M.Tech over full time?
Permutation and combination example formula
ReplyDeleteMCSE Training in Noida
ReplyDelete