Wednesday, 2 September 2015
Friday, 12 June 2015
MATLAB
Matlab introduction
MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language. Developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python.
Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. An additional package, Simulink, adds graphical multi-domain simulation and Model-Based Design for dynamic and embedded systems.
Matlab is a program for doing numerical
computation. It was originally designed for
solving linear algebra type problems using
matrices. It’s name is derived from MATrix
LABoratory.
Matlab is also a programming language that
currently is widely used as a platform for
developing tools for Machine Learning
Why it is useful for prototyping AI projects:
large toolbox of numeric/image library functions
very useful for displaying, visualizing data
high-level: focus on algorithm structure, not on lowlevel
details
allows quick prototype development of algorithms
Some other aspects of Matlab
Matlab is an interpreter -> not as fast as compiled
code
Typically quite fast for an interpreted language
Often used early in development -> can then convert
to C (e.g.,) for speed
Can be linked to C/C++, JAVA, SQL, etc
Commercial product, but widely used in industry
and academia
Many algorithms and toolboxes freely available
Arslan Ali Raza
********************************************
MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language. Developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python.
Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. An additional package, Simulink, adds graphical multi-domain simulation and Model-Based Design for dynamic and embedded systems.
Matlab is a program for doing numerical
computation. It was originally designed for
solving linear algebra type problems using
matrices. It’s name is derived from MATrix
LABoratory.
Matlab is also a programming language that
currently is widely used as a platform for
developing tools for Machine Learning
Why it is useful for prototyping AI projects:
large toolbox of numeric/image library functions
very useful for displaying, visualizing data
high-level: focus on algorithm structure, not on lowlevel
details
allows quick prototype development of algorithms
Some other aspects of Matlab
Matlab is an interpreter -> not as fast as compiled
code
Typically quite fast for an interpreted language
Often used early in development -> can then convert
to C (e.g.,) for speed
Can be linked to C/C++, JAVA, SQL, etc
Commercial product, but widely used in industry
and academia
Many algorithms and toolboxes freely available
Arslan Ali Raza
********************************************
Wednesday, 4 March 2015
Monday, 2 March 2015
Discrete Mathematics___Outline
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GOVT. COLLEGE NO.1 D.I.KHAN
Discrete
Mathematics
By:
Arslan Ali Raza
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Lecture #1
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Mathematics
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Lecture #2
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Introduction to Discrete Mathematics
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Lecture #3
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Introduction to Logic
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Lecture #4
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Propositional Equivalences
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Lecture #5
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Propositions & examples
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Lecture #6
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Predicate & Quantifiers
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Lecture #7
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Sets & Sets operation
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Lecture #8
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Functions and properties of function
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Lecture #9
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Sequence & Series
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Lecture #10
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Summation & examples
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Lecture #11
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Method of proved
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Lecture #12
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Mathematical Induction
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Lecture #13
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Recursion
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Lecture #14
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Recursive definition
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Lecture #15
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The basic of counting
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Lecture #16
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Counting Principles
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Lecture #17
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The pigeonhole principle
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Lecture #18
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Permutations & examples
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Lecture #19
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Combinations & examples
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Lecture #20
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Binomial theorem & examples
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Lecture #21
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Binomial coefficient & examples
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Lecture #22
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Inclusion/Exclusion
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Lecture #23
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Recurrence & examples
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Lecture #24
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Recurrence Relations & examples
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Lecture #25
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Representing relation
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Lecture #26
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Equivalence relations & examples
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Signal Processing __________Course outline
GOVT. COLLEGE NO.1 D.I.KHAN
Introduction
to Signal Processing
By:
Arslan Ali Raza
Lecture #1
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Signal
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Lecture #2
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Introduction to Signal Processing
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Lecture #3
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Signal Processing
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Lecture #4
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Goals of Signal Processing
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Lecture #5
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Continuous time signal & examples
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Lecture #6
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Discrete time signal & examples
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Lecture #7
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System and system properties
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Lecture #8
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Energy signals & examples
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Lecture #9
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Power signals & examples
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Lecture #10
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Impulse response
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Lecture #11
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Convolution sum
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Lecture #12
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Theory & properties of impulse
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Lecture #13
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Even & Odd Signals
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Lecture #14
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Time scaling , Left & Right Signals
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Lecture #15
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Invariance & Linearity
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Lecture #16
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Linear time invariant system
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Lecture #17
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Causal signal & Causal system
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Lecture #18
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Sampling , Pulse code Modulation
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Lecture #19
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Fourier Series & example
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Lecture #20
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Fourier transform & example
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Lecture #21
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Z-Transform, Inverse Z-Transform & example
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Lecture #22
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Laplace Transform & example
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Lecture #23
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Introduction to MATLAB
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