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$15 USD / hora
Bandera de PAKISTAN
karachi, pakistan
$15 USD / hora
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Se unió el enero 13, 2017
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S R.

@srehant

 5,0 (1 comentario)
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$15 USD / hora
Bandera de PAKISTAN
karachi, pakistan
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Electronics Engineer - Solution Provider

Engineering Graduate and Solution Provider with 18+ years of hands-on in design and development of * NI CRIO and Labview based system automation and control. * System Identification and Adaptive Fuzzy control simulation using MATLAB/SIMULINK * Pattern Recognition using Nueral Network algorithms (SOM SVM etc) using MATLAB * Arduino/Raspberry Pi based Application development * PLC Based Automation and control * Sensor and Track Fusion using Kalman Filtering
Freelancer Electronics Engineers Pakistan
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Portafolio

11590580
Cycloconverters are frequency changers that convert AC power of specific frequency and voltage to different frequency and voltage of AC power without any intermediate DC link. A cycloconverter is a naturally commutated converter in which the output frequency and voltage can be controlled independently and continuously.
3 Phase Cycloconverter Simulation in MATLAB
9957507
Consider relational schema r(A,B,C,D,E,F) Figure attached Proof: AB--> DE Given C-->A Given using pseudo transitivity CB-->DE apply decomposition CB-->D CB-->E CB-->D holds
Using Armstrong axioms
9957480
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
9954640
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
9581052
proof of mathematical Inequalities
proof of mathematical Inequalities
proof of mathematical Inequalities
proof of mathematical Inequalities
9581048
In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method used to solve a system of linear equations. The given system of nonlinear equations converged to desired values of x=1.468 y=0.4 after 6 iterations. The Excel and MATLAB based implementation of Gauss Siedel are also available
GAUSS SIEDEL METHOD FOR SYSTEM OF NONLINEAR EQUATIONS
In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method used to solve a system of linear equations. The given system of nonlinear equations converged to desired values of x=1.468 y=0.4 after 6 iterations. The Excel and MATLAB based implementation of Gauss Siedel are also available
GAUSS SIEDEL METHOD FOR SYSTEM OF NONLINEAR EQUATIONS
In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method used to solve a system of linear equations. The given system of nonlinear equations converged to desired values of x=1.468 y=0.4 after 6 iterations. The Excel and MATLAB based implementation of Gauss Siedel are also available
GAUSS SIEDEL METHOD FOR SYSTEM OF NONLINEAR EQUATIONS
In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method used to solve a system of linear equations. The given system of nonlinear equations converged to desired values of x=1.468 y=0.4 after 6 iterations. The Excel and MATLAB based implementation of Gauss Siedel are also available
GAUSS SIEDEL METHOD FOR SYSTEM OF NONLINEAR EQUATIONS
In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method used to solve a system of linear equations. The given system of nonlinear equations converged to desired values of x=1.468 y=0.4 after 6 iterations. The Excel and MATLAB based implementation of Gauss Siedel are also available
GAUSS SIEDEL METHOD FOR SYSTEM OF NONLINEAR EQUATIONS
11590580
Cycloconverters are frequency changers that convert AC power of specific frequency and voltage to different frequency and voltage of AC power without any intermediate DC link. A cycloconverter is a naturally commutated converter in which the output frequency and voltage can be controlled independently and continuously.
3 Phase Cycloconverter Simulation in MATLAB
9957507
Consider relational schema r(A,B,C,D,E,F) Figure attached Proof: AB--> DE Given C-->A Given using pseudo transitivity CB-->DE apply decomposition CB-->D CB-->E CB-->D holds
Using Armstrong axioms
9957480
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. A system of difference constraints is given in figure. We first model it as a constraint graph. Then we use Bellman-Ford algorithm to either find a feasible solution, or to determine that no feasible solution exists.
Bellman-Ford algorithm on system of diff constraints
9954640
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
An IIR filter is given. Following analytical tasks are performed 1. Calculating difference equation and Z transform. 2. Calculating transfer function H(z) . calculating poles and zeros and filter impulse response. MATLAB tasks 1. Calculating impulse response and compare with the analytic approach. 2. Plotting impulse response, poles and zeros, magnitude and phase of IIR filter
Analysis and simulation of IIR filter
9581052
proof of mathematical Inequalities
proof of mathematical Inequalities
proof of mathematical Inequalities
proof of mathematical Inequalities
9581048
In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method used to solve a system of linear equations. The given system of nonlinear equations converged to desired values of x=1.468 y=0.4 after 6 iterations. The Excel and MATLAB based implementation of Gauss Siedel are also available
GAUSS SIEDEL METHOD FOR SYSTEM OF NONLINEAR EQUATIONS
In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method used to solve a system of linear equations. The given system of nonlinear equations converged to desired values of x=1.468 y=0.4 after 6 iterations. The Excel and MATLAB based implementation of Gauss Siedel are also available
GAUSS SIEDEL METHOD FOR SYSTEM OF NONLINEAR EQUATIONS
In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method used to solve a system of linear equations. The given system of nonlinear equations converged to desired values of x=1.468 y=0.4 after 6 iterations. The Excel and MATLAB based implementation of Gauss Siedel are also available
GAUSS SIEDEL METHOD FOR SYSTEM OF NONLINEAR EQUATIONS
In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method used to solve a system of linear equations. The given system of nonlinear equations converged to desired values of x=1.468 y=0.4 after 6 iterations. The Excel and MATLAB based implementation of Gauss Siedel are also available
GAUSS SIEDEL METHOD FOR SYSTEM OF NONLINEAR EQUATIONS
In numerical linear algebra, the Gauss–Seidel method, also known as the Liebmann method or the method of successive displacement, is an iterative method used to solve a system of linear equations. The given system of nonlinear equations converged to desired values of x=1.468 y=0.4 after 6 iterations. The Excel and MATLAB based implementation of Gauss Siedel are also available
GAUSS SIEDEL METHOD FOR SYSTEM OF NONLINEAR EQUATIONS

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Create Hardware design for Raspberry Pi interacting with an existing machine
good freelancer who solved my problems
Linux
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Software Testing
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Avatar del usuario
Bandera de Dominic S.
@sonntagit
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hace 7 años

Experiencia

Senior Project Manager

Zaniel Automation
nov 2001 - Presente
Engineering Graduate and Solution Provider with 18+ years of hands-on in design and development of * NI CRIO and Labview based system automation and control.

Educación

ME Electrical

Hamdard University, Pakistan 2014 - 2018
(4 años)

BE Electronics

Hamdard University, Pakistan 1995 - 1999
(4 años)

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Raspberry Pi 1
C Programming
Wireless
Engineering

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