Control System Assignment Help
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I am Rachael S. I am a Control System Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, Durham University, UK. I have been helping students with their homework for the past 8 years. I solve assignments related to Control Systems. Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com. You can also call on +1 678 648 4277 for any assistance with Control System Assignments.
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This document presents a new method called the Matrix Ones Interval Linear Assignment Method (MOILA) for solving assignment problems with interval costs. It begins with definitions of assignment problems and interval analysis concepts. Then it describes the existing Hungarian method and provides an example solved using both Hungarian and MOILA. MOILA involves creating ones in the assignment matrix and making assignments based on the ones. The document outlines algorithms for MOILA as well as extensions to unbalanced and interval assignment problems. It provides an example of applying MOILA to solve a balanced interval assignment problem and compares the solutions to Hungarian. The document introduces MOILA as a systematic alternative to Hungarian for solving a variety of assignment problem types.
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MATLAB DOCUMENTATION ON SOME OF THE MODULES
A.Generate videos in which a skeleton of a person doing the following Gestures.
1.Tilting his head to right and left
2.Tilting his hand to right and left
3.Walking
in matlab.
B. Write a MATLAB program that converts a decimal number to Roman number and vice versa.
C.Using EZ plot & anonymous functions plot the following:
· Y=Sqrt(X)
· Y= X^2
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D.Take your picture and
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E.Record your name pronounced by yourself. Try to display the signal(name) in a plot vs Time, using matlab.
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H.Write any one of the program to do following things using file concept.
1.Create or Open a file
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3. Append some text to already existed file
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2. Intersection of A and B
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MATLAB DOCUMENTATION ON SOME OF THE MODULES
A.Generate videos in which a skeleton of a person doing the following Gestures.
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2.Tilting his hand to right and left
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in matlab.
B. Write a MATLAB program that converts a decimal number to Roman number and vice versa.
C.Using EZ plot & anonymous functions plot the following:
· Y=Sqrt(X)
· Y= X^2
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H.Write any one of the program to do following things using file concept.
1.Create or Open a file
2. Read data from the file and write data to another file
3. Append some text to already existed file
4. Close the file
I.Write a function to perform following set operations
1.Union of A and B
2. Intersection of A and B
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The Fuzzy Assignment Problem (FAP) is a classic combinatorial optimization problem that has received a lot of attention. FAP has a wide range of uses. We suggest a new algorithm that combines to solve the FAP in this paper. Each column is maximized during the optimization process, and the best choice with the lowest cost is selected. The proposed method follows a standard methodology, is simple to execute, and takes less effort to compute. An order to obtain the best solution, the assignment problem is specifically solved here. We looked at how well trapezoidal fuzzy numbers performed. Then, to convert crisp numbers, we use the robust ranking method for trapezoidal fuzzy numbers. The optimality of the result provided by this new method is clarified by a numerical example.
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Cryptography for years. Many crypto-systems use such a big number as their key or part of a
key with the assumption - it is too big that the fastest factorisation algorithms running on the
fastest computers would take impractically long period of time to factorise. Hence, many efforts
have been provided to break those crypto-systems by finding two factors of an integer for
decades. In this paper, a new factorisation technique is proposed which is based on the concept
of backtracking. Binary bit by bit operations are performed to find two factors of a given
integer. This proposed solution can be applied in computing square root, primality test, finding
prime factors of integer numbers etc. If the proposed solution is proven to be efficient enough, it
may break the security of many crypto-systems. Implementation and performance comparison of
the technique is kept for future research.
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This study material is designed to complement your regular coursework. Use it as a valuable resource for exam preparation, revision, and understanding complex concepts. Pay attention to algorithmic explanations, proof of correctness, and time complexity analysis.
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Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Signals and Systems homework.
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Welcome to the comprehensive study material designed specifically for students of Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV) in their 4th semester of the Computer Science Engineering program. In this study material, we will explore the essential concepts of "Analysis & Design of Algorithms," a crucial subject that forms the foundation of efficient problem-solving in computer science and engineering.
Unit 1: Greedy Strategy and Examples
This unit introduces the Greedy strategy, a powerful approach in algorithm design. Greedy algorithms make locally optimal choices at each step, aiming to achieve a globally optimal solution. We will explore its applications through various examples:
Optimal Merge Patterns: Learn how to merge sorted sequences efficiently using the Greedy approach, minimizing the number of comparisons needed.
Huffman Coding: Dive into the world of data compression with Huffman coding, assigning shorter binary codes to frequently occurring characters.
Minimum Spanning Trees (MST): Understand Prim's and Kruskal's algorithms to find the minimum weight spanning tree in a graph, crucial in network design and clustering.
Knapsack Problem: Discover the limitations of the Greedy strategy when solving the Knapsack problem and explore more efficient techniques.
Job Sequencing with Deadlines: Apply the Greedy approach to schedule jobs optimally based on deadlines and profits.
Single Source Shortest Path Algorithm: Master Dijkstra's algorithm to find the shortest paths from a single source vertex to all other vertices in a weighted graph.
Each topic is accompanied by detailed explanations, examples, and step-by-step algorithms to enhance your understanding and problem-solving skills.
About the Author
The study material is meticulously prepared by expert educators with vast experience in computer science and engineering. They have a deep understanding of the RGPV curriculum and the specific needs of 4th-semester students pursuing Computer Science Engineering.
How to Use this Study Material
This study material is designed to complement your regular coursework. Use it as a valuable resource for exam preparation, revision, and understanding complex concepts. Pay attention to algorithmic explanations, proof of correctness, and time complexity analysis.
Why This Study Material?
RGPV-Aligned Content: The material is tailored to align with the RGPV curriculum, ensuring relevance and applicability.
Comprehensive Coverage: All critical topics are covered in detail, providing a holistic understanding of the subject.
Clarity and Simplicity: Complex algorithms and concepts are explained in a clear and straightforward manner, making it accessible to all students.
Real-World Applications: Explore how algorithms are applied to solve real-world problems, enhancing your problem-solving skills.
Practice Questions: Practice questions and exercises are included to reinforce your learning.
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Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. The data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data, you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
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1) Tables of node coordinate and element connectivity data are provided for Cases 1 and 2, along with the results of the finite element analysis showing displacements and stresses.
2) Questions are posed about how renumbering the nodes would affect the structural stiffness matrix and computational efficiency.
3) The algorithm used by the skyline function to calculate half bandwidths is described.
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This document appears to be an exam for a course on machine learning or artificial intelligence. It contains instructions for taking the exam, which is closed book and does not allow calculators. It then lists 6 multiple choice or short answer questions worth various point values that assess knowledge of topics like linear separators, sources of error in machine learning models, designing neural network architectures, and radial basis feature transformations.
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This document presents a hybrid sorting technique called CutShort that aims to optimize the runtime of sorting algorithms. It works by first dividing the input array into subarrays based on the number of bits needed to represent each element. The elements are then repositioned within the input array according to their subarray. Each subarray is then sorted independently using an optimal sorting algorithm like insertion sort. Experimental results on random, worst-case, and favorable data show that combining CutShort with quicksort, mergesort, or insertion sort reduces sorting time significantly compared to using the base algorithms alone. The technique is most effective when the input can be divided into many subarrays of more equal sizes.
Programming problem1. [42 points] in this problem, we write a pro
This programming problem involves using the polyfit() function from the numpy package to estimate the parameters of an unknown polynomial using noisy data. The student is asked to:
1) Plot the noisy data and estimated polynomial for different orders m and select the best m.
2) Plot MSE vs order m to identify the best choice of m.
3) Vary the noise scale and plot the resulting polynomials to analyze the impact of noise on accuracy.
4) Vary the number of samples and plot the resulting polynomials to analyze the impact of fewer samples on accuracy.
20MEMECH Part 3- Classification.pdf
This document discusses various classification algorithms including logistic regression, Naive Bayes, support vector machines, k-nearest neighbors, decision trees, and random forests. It provides examples of using logistic regression and support vector machines for classification tasks. For logistic regression, it demonstrates building a model to classify handwritten digits from the MNIST dataset. For support vector machines, it uses a banknote authentication dataset to classify currency notes as authentic or fraudulent. The document discusses evaluating model performance using metrics like confusion matrix, accuracy, precision, recall, and F1 score.
A Chess Composer Of Two-Move Mate Problems
The document describes a chess composer model called CHESS COMPOSER that aims to automatically generate high-quality two-move mate chess problems. CHESS COMPOSER uses a 64-bit representation and iterative-deepening depth-first search to evaluate all possible closely related positions to an original problem by applying up to three transformations. It tests these modified positions against chess rules and a quality function informed by chess experts to identify improved problems. When tested on 100 known two-move mate problems, CHESS COMPOSER improved the quality of 97 problems, significantly outperforming a previous model.
Basics of Digital Images
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Palm m3 chapter1b
This document provides an overview of MATLAB and introduces its basic functions and commands. It covers topics such as entering commands, arithmetic operations, variables, arrays, plots, functions, scripts, debugging, and problem-solving techniques. Examples are provided throughout to demonstrate key MATLAB skills such as manipulating data, writing scripts, debugging code, and using help resources. The document is intended to teach engineers the fundamental skills needed to use MATLAB.
Faster Interleaved Modular Multiplier Based on Sign Detection
Data Security is the most important issue nowadays. A lot of cryptosystems are introduced to provide security. Public key cryptosystems are the most common cryptosystems used for securing data communication. The common drawback of applying such cryptosystems is the heavy computations which degrade performance of a system. Modular multiplication is the basic operation of common public key cryptosystems such as RSA, Diffie-Hellman key agreement (DH), ElGamal and ECC. Much research is now
directed to reduce overall time consumed by modular multiplication operation. Abd-el-fatah et al. introduced an enhanced architecture for computing modular ultiplication of two large numbers X and Y modulo given M. In this paper, a modification on that architecture is introduced. The proposed design computes modular multiplication by scanning two bits per iteration instead of one bit. The proposed design for 1024-bit precision reduced overall time by 38% compared to the design of Abd-el-fatah et al.
CE344L-200365-Lab2.pdf
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Learning multifractal structure in large networks (Purdue ML Seminar)
This document discusses methods for modeling networks using multifractal network generators (MFNG). MFNG is a recursive model that samples nodes into categories at different levels to generate graphs. The document outlines techniques for estimating MFNG parameters from real networks using method of moments, describes challenges in sampling from MFNG efficiently, and shows MFNG can match properties of Twitter and citation networks.
Signals and Systems Homework Help.pptx
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Chapter 1.ppt
This document provides an overview of MATLAB and introduces its basic functions and commands. It covers topics such as the MATLAB desktop, entering commands, scalar and array arithmetic, variables, functions, plotting, and programming with scripts. Examples are provided to demonstrate key concepts like precedence rules, complex numbers, polynomials, and solving systems of linear equations. The document also discusses getting help, the problem-solving process, and includes homework problems.
Matlab Overviiew 2
This document provides an introduction and overview of key concepts for working with numeric, cell, and structure arrays in MATLAB for geoscientists. It discusses how to create vectors and matrices, perform element-by-element operations on arrays, do matrix multiplication and division, and work with special matrices like identity and zero matrices. It also covers generating regularly spaced vectors using colon notation, appending vectors, and creating logarithmically spaced arrays with logspace. Polynomial operations like multiplication, division, finding roots and coefficients are also summarized.
Design and Analysis of Algorithm Brute Force 1.ppt
The document discusses the brute force algorithm design technique. It provides examples of problems that can be solved using brute force, including swapping variables, computing powers and factorials, sorting, searching, and matrix multiplication. Brute force involves systematically enumerating all possible candidates for solutions and checking if each candidate satisfies the problem's statement. The document outlines brute force algorithms for several problems and discusses the strengths and weaknesses of the brute force approach.
Greedy Algorithms
Greedy algorithms, kruskal's algorithm, merging sorted lists, knapsack problem, union find data structure with path compression
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Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
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Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
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Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
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Control System Assignment Help
- 1. Dynamics & Control For Any Quarries or info, Contact us on :- +1 678 648 4277 You can Mail us on :- info@matlabassignmentexperts.com Visit us on :- https://www.matlabassignmentexperts.com/
- 2. Problem 1 : Matrix creation with loop i) Create a 5×5 matrix in which each element has a value equal to the sum of its row and column numbers. ii) Create a 5×5 matrix in which each element has a value equal to the square of the sum of the element’s row and column numbers. Solution: (i) With nested for loops, control variables i and j indicates row number and column number respectively. ai, j = i + j where i, j = 1, 2,L, 5
- 3. (ii) Similar to i), ai, j = (i + j )2 where i, j = 1, 2,L, 5
- 4. Problem 2 : Matrix creation with conditional Make a one dimensional array that contains all integers in the range of 1 to 100 that are not divisible by 2, 3, and 7. Solution: Mod() function calculates reminder of division. (See mod() in help menu) Unless at least one of a number modulo 2, 3, or 7 is zero, array contains this number.
- 5. Problem 3 : Velocity and acceleration profile calculation from the ball trajectory Use the same trajectory file, ‘ball.mat’ from the last homework. Time(t) and trajectory(x) are from the first and the second columns, respectively. Write an m-file to calculate the ball’s velocity and acceleration in two different ways. You should follow the below guidelines to make m-file. Solution:
- 6. This neighboring point approach is the best to estimate instantaneous velocity and acceleration of ball if trajectory was measured in the noise free environment. However, in the presence of noise, this approach is bad since it is quite sensitive to noise or disturbance when you estimate instantaneous velocity and even more sensitive in the estimate of instantaneous acceleration due to more noise propagation. To minimize noise sensitivity, averaging data over several data points is recommended such as