This document provides information about the Simulation Laboratory course offered at SRI RAMAKRISHNA ENGINEERING COLLEGE. It lists the course outcomes, experiments, and provides details about MATLAB and its features. The key aspects covered are using MATLAB for numerical computation, modeling and simulation. It describes MATLAB's interactive environment, programming language, toolboxes and applications in areas like signal processing and control systems.
As part of our team's enrollment for Data Science Super Specialization course under UpX Academy, we submitted many projects for our final assessments, one of them was Telecom Churn Analysis Model.
The input data was provided by UpX academy and language we used is R. As part of the project, our main objective was :-
-> To predict Customer Churn.
-> To Highlight the main variables/factors influencing Customer Churn.
-> To Use various ML algorithms to build prediction models, evaluate the accuracy and performance of these models.
-> Finding out the best model for our business case & providing executive Summary.
To address the mentioned business problem, we tried to follow a thorough approach. We did a detailed level Exploratory Data Analysis which consists of various Box Plots, Bar Plots etc..
Further we tried our best to build as many Classification models possible which fits our business case (Logistic Regression/kNN/Decision Trees/Random Forest/SVM) and also tried to touch Cox Hazard Survival analysis Model. Later for every model we tried to boost their performances by applying various performance tuning techniques.
As we all are still into our learning mode w.r.t these concepts & starting new, please feel free to provide feedback on our work. Any suggestions are most welcome... :)
Thanks!!
ResNet, short for "Residual Network," is a type of deep neural network architecture that was introduced by Microsoft researchers in 2015. ResNet is designed to address the problem of vanishing gradients, which can occur in deep neural networks that are many layers deep.
The main innovation in ResNet is the use of residual connections, also known as skip connections. These connections allow information from earlier layers of the network to bypass some of the later layers and be directly fed into the later layers. This helps to ensure that the gradient signal from the output can propagate back through the network during training, which can help to prevent the vanishing gradient problem.
ResNet has been shown to be very effective at image recognition and other computer vision tasks. It has achieved state-of-the-art performance on a number of benchmark datasets, such as ImageNet. Since its introduction, many variations and improvements to the original ResNet architecture have been proposed, including ResNeXt, Wide ResNet, and Residual Attention Network (RANet).
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorith...Edureka!
** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training **
This Edureka tutorial on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial:
1. What is Classification?
2. Types of Classification
3. Classification Use Case
4. What is Decision Tree?
5. Decision Tree Terminology
6. Visualizing a Decision Tree
7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm
Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm
As part of our team's enrollment for Data Science Super Specialization course under UpX Academy, we submitted many projects for our final assessments, one of them was Telecom Churn Analysis Model.
The input data was provided by UpX academy and language we used is R. As part of the project, our main objective was :-
-> To predict Customer Churn.
-> To Highlight the main variables/factors influencing Customer Churn.
-> To Use various ML algorithms to build prediction models, evaluate the accuracy and performance of these models.
-> Finding out the best model for our business case & providing executive Summary.
To address the mentioned business problem, we tried to follow a thorough approach. We did a detailed level Exploratory Data Analysis which consists of various Box Plots, Bar Plots etc..
Further we tried our best to build as many Classification models possible which fits our business case (Logistic Regression/kNN/Decision Trees/Random Forest/SVM) and also tried to touch Cox Hazard Survival analysis Model. Later for every model we tried to boost their performances by applying various performance tuning techniques.
As we all are still into our learning mode w.r.t these concepts & starting new, please feel free to provide feedback on our work. Any suggestions are most welcome... :)
Thanks!!
ResNet, short for "Residual Network," is a type of deep neural network architecture that was introduced by Microsoft researchers in 2015. ResNet is designed to address the problem of vanishing gradients, which can occur in deep neural networks that are many layers deep.
The main innovation in ResNet is the use of residual connections, also known as skip connections. These connections allow information from earlier layers of the network to bypass some of the later layers and be directly fed into the later layers. This helps to ensure that the gradient signal from the output can propagate back through the network during training, which can help to prevent the vanishing gradient problem.
ResNet has been shown to be very effective at image recognition and other computer vision tasks. It has achieved state-of-the-art performance on a number of benchmark datasets, such as ImageNet. Since its introduction, many variations and improvements to the original ResNet architecture have been proposed, including ResNeXt, Wide ResNet, and Residual Attention Network (RANet).
Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorith...Edureka!
** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training **
This Edureka tutorial on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial:
1. What is Classification?
2. Types of Classification
3. Classification Use Case
4. What is Decision Tree?
5. Decision Tree Terminology
6. Visualizing a Decision Tree
7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm
Check out our Python Machine Learning Playlist: https://goo.gl/UxjTxm
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Overview of the course. Introduction to image sciences, image processing and computer vision. Basics of machine learning, terminologies, paradigms. No-free lunch theorem. Supervised versus unsupervised learning. Clustering and K-Means. Classification and regression. Linear least squares and polynomial curve fitting. Model complexity and overfitting. Curse of dimensionality. Dimensionality reduction and principal component analysis. Image representation, semantic gap, image features, and classical computer vision pipelines.
Memory Efficient Graph Convolutional Network based Distributed Link Predictionmiyurud
Graph Convolutional Networks (GCN) have found multiple applications of graph-based machine learning. However, training GCNs on large graphs of billions of nodes and edges with rich node attributes consume significant amount of time and memory resources. This makes it impossible to train such GCNs on general purpose commodity hardware. Such use cases demand high-end servers with accelerators and ample amounts of memory. In this paper we implement a memory efficient GCN based link prediction on top of a distributed graph database server called JasmineGraph. Our approach is based on federated training on partitioned graphs with multiple parallel workers. We conduct experiments with three real world graph datasets called DBLP-V11, Reddit, and Twitter. We demonstrate that our approach produces optimal performance for a given hardware setting. JasmineGraph was able to train a GCN on the largest dataset DBLP-V11(>10GB) in 20 hours and 24 minutes for 5 training rounds and 3 epochs by partitioning it into 16 partitions with 2 workers on a single server while the conventional training method could not process it at all due to lack of memory. The second largest dataset Reddit took 9 hours 8 minutes to train with conventional training while JasmineGraph took only 3 hours and 11 minutes with 8 partitions-4 workers in the same hardware giving 3 times improved performance. In case of Twitter dataset JasmineGraph was able to give 5 times improved performance. (10 hours 31 minutes vs 2 hours 6 minutes;16 partitions-16 workers).
A small informative presentation on machine learning.
It contains the following topics:
Introduction to ML
Types of Learning
Regression
Classification
Classification vs Regression
Clustering
Decision Tree Learning
Random Forest
True vs False
Positive vs Negative
Linear Regression
Logistic Regression
Application of Machine Learning
Future of Machine Learning
[PR12] Inception and Xception - Jaejun YooJaeJun Yoo
Introduction to Inception and Xception
video: https://youtu.be/V0dLhyg5_Dw
Papers:
Going Deeper with Convolutions
Rethinking the Inception Architecture for Computer Vision
Inception-v4, Inception-RestNet and the Impact of Residual Connections on Learning
Xception: Deep Learning with Depthwise Separable Convolutions
Activation functions and Training Algorithms for Deep Neural networkGayatri Khanvilkar
Training of Deep neural network is difficult task. Deep neural network train with the help of training algorithms and activation function This is an overview of Activation Function and Training Algorithms used for Deep Neural Network. It underlines a brief comparative study of activation function and training algorithms.
[Webinar Deck] Google Data Studio for Mastering the Art of Data VisualizationsTatvic Analytics
In this webinar, we will take you through the basics of Google Data Studio and enable you to unlock the true value of data when converted into insights. With Google Data Studio, you can turn your data into informative dashboards and monthly reports that are easy to read, easy to share and fully customizable.
Successful data visualization designs are built by smart teams with varied skill sets using advanced visualization tools. This is not an easy combination to find, but we’ll help you get started.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Overview of the course. Introduction to image sciences, image processing and computer vision. Basics of machine learning, terminologies, paradigms. No-free lunch theorem. Supervised versus unsupervised learning. Clustering and K-Means. Classification and regression. Linear least squares and polynomial curve fitting. Model complexity and overfitting. Curse of dimensionality. Dimensionality reduction and principal component analysis. Image representation, semantic gap, image features, and classical computer vision pipelines.
Memory Efficient Graph Convolutional Network based Distributed Link Predictionmiyurud
Graph Convolutional Networks (GCN) have found multiple applications of graph-based machine learning. However, training GCNs on large graphs of billions of nodes and edges with rich node attributes consume significant amount of time and memory resources. This makes it impossible to train such GCNs on general purpose commodity hardware. Such use cases demand high-end servers with accelerators and ample amounts of memory. In this paper we implement a memory efficient GCN based link prediction on top of a distributed graph database server called JasmineGraph. Our approach is based on federated training on partitioned graphs with multiple parallel workers. We conduct experiments with three real world graph datasets called DBLP-V11, Reddit, and Twitter. We demonstrate that our approach produces optimal performance for a given hardware setting. JasmineGraph was able to train a GCN on the largest dataset DBLP-V11(>10GB) in 20 hours and 24 minutes for 5 training rounds and 3 epochs by partitioning it into 16 partitions with 2 workers on a single server while the conventional training method could not process it at all due to lack of memory. The second largest dataset Reddit took 9 hours 8 minutes to train with conventional training while JasmineGraph took only 3 hours and 11 minutes with 8 partitions-4 workers in the same hardware giving 3 times improved performance. In case of Twitter dataset JasmineGraph was able to give 5 times improved performance. (10 hours 31 minutes vs 2 hours 6 minutes;16 partitions-16 workers).
A small informative presentation on machine learning.
It contains the following topics:
Introduction to ML
Types of Learning
Regression
Classification
Classification vs Regression
Clustering
Decision Tree Learning
Random Forest
True vs False
Positive vs Negative
Linear Regression
Logistic Regression
Application of Machine Learning
Future of Machine Learning
[PR12] Inception and Xception - Jaejun YooJaeJun Yoo
Introduction to Inception and Xception
video: https://youtu.be/V0dLhyg5_Dw
Papers:
Going Deeper with Convolutions
Rethinking the Inception Architecture for Computer Vision
Inception-v4, Inception-RestNet and the Impact of Residual Connections on Learning
Xception: Deep Learning with Depthwise Separable Convolutions
Activation functions and Training Algorithms for Deep Neural networkGayatri Khanvilkar
Training of Deep neural network is difficult task. Deep neural network train with the help of training algorithms and activation function This is an overview of Activation Function and Training Algorithms used for Deep Neural Network. It underlines a brief comparative study of activation function and training algorithms.
[Webinar Deck] Google Data Studio for Mastering the Art of Data VisualizationsTatvic Analytics
In this webinar, we will take you through the basics of Google Data Studio and enable you to unlock the true value of data when converted into insights. With Google Data Studio, you can turn your data into informative dashboards and monthly reports that are easy to read, easy to share and fully customizable.
Successful data visualization designs are built by smart teams with varied skill sets using advanced visualization tools. This is not an easy combination to find, but we’ll help you get started.
A basic overview, application and usage of MATLAB for engineers. It covered very basics essential that will help one to get started with MATLAB programming easily.
Provided by IDEAS2IGNITE
Introduction to Matlab
Lecture 1:
Introduction: What is Matlab, History of Matlab, strengths, weakness
Getting familiar with the interface: Layout, Pull down menus
Creating and manipulating objects: Variables (scalars, vectors, matrices, text strings), Operators (arithmetic, relational, logical) and built-in functions
MATLAB assignment help in computation, algorithm development, data analysis and signal processing by MATLAB experts. Also find MATLAB assignment solution for all MATLAB assignment problem.
CETPA INFOTECH PVT LTD is one of the IT education and training service provider brands of India that is preferably working in 3 most important domains. It includes IT Training services, software and embedded product development and consulting services.
CETPA INFOTECH PVT LTD is one of the IT education and training service provider brands of India that is preferably working in 3 most important domains. It includes IT Training services, software and embedded product development and consulting services.
http://www.cetpainfotech.com
programming in c,Overview of C,History, Importance ,Sample Programs ,Basic Structure, Programming Style , Executing ,Unix System , MS-DOS System ,Constants, Variables, and Data Types: Character Set , C Token ,Keyword and Identifiers ,Constants ,Variables ,Data Types ,Declaration of Storage Class ,Assigning Values to Variables ,Defining Symbolic Constants ,Declaration, Overflow and Underflow of Data ,Operators and Expressions: Arithmetic, Relational, Logical, Assignment, Increment and Decrement, Conditional, Bitwise, Special Operators ,Arithmetic Expressions, Evaluation of Expressions ,Precedence of Arithmetic Operators ,Some Computational Problems ,Type Conversions in Expressions ,Operator Precedence and Associativity ,Mathematical Functions,programming in c,simple c concepts,thiruvalluvar university c syllabus,c syllabus,c notes
CETPA INFOTECH PVT LTD is one of the IT education and training service provider brands of India that is preferably working in 3 most important domains. It includes IT Training services, software and embedded product development and consulting services.
http://www.cetpainfotech.com
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
1. 1
SRI RAMAKRISHNA ENGINEERING COLLEGE
[Educational Service: SNR Sons Charitable Trust]
[Autonomous Institution, Accredited by NAAC with ‘A’ Grade]
[Approved by AICTE and Permanently Affiliated to Anna University, Chennai]
[ISO 9001:2015 Certified and all eligible programmes Accredited by NBA]
Vattamalaipalayam, N.G.G.O. Colony Post, Coimbatore – 641 022.
Course Instructors:
Dr. M.EZHILARASI, AP/EEE
No. of Credits: 3
Department of Electrical and Electronics Engineering
20EI271 – SIMULATION LABORATORY
2. Course Outcomes
On successful completion of the course, students will
be able to
• CO1 : Outline the features of mathematical functions
using computational software.
• CO2: Develop a code utilizing basic commands and
functions of simulation software to solve mathematical
problems.
• CO3: Analyze the variables within/import into workspace
of software through plots and display devices.
• CO4 :Analyze the results and analysis of the experiments
through reports.
3. LIST OF EXPERIMENTS
1. Components of computational software and
tool boxes
2. Mathematical operations using arrays and
matrices.
3. Create and manipulate the variables for text and
numeric data types.
4. LIST OF EXPERIMENTS
4. Import and Export data as text files,
spreadsheets, other file formats and web
access.
5. Write basic programs using loops and
conditional statements, such as if, for,
and while.
6. Create variables within /import into Workspace
of software from data files or other programs.
5. LIST OF EXPERIMENTS
6. Solving Mathematical Functions using
simulation.
7. Write basic Programs in Scripts containing
sequence of commands and functions.
8. Create 2D,3D plots and display it in graphical
window
6. LIST OF EXPERIMENTS
10.Program to Read, Display and plot Image using
open CV Library
11.Conversion of original image to Gray scale
image and binary image.
12.Solving Matrices and Vectors problems using
SCILAB.
7. MATLAB
• an abbreviation of "matrix laboratory”
• MATLAB (an abbreviation of "matrix laboratory")
is a proprietary multi-paradigm
programming language and numerical
computing environment developed by
MathWorks.
8. What Is MATLAB?
• MATLAB is a high-performance language for
technical computing.
• It integrates computation, visualization, and
programming in an easy-to-use environment
• where problems and solutions are expressed in
familiar mathematical notation.
9. Typical uses include:
• Math and computation
• Algorithm development
• Modeling, simulation, and prototyping
• Data analysis, exploration, and visualization
• Scientific and engineering graphics
• Application development, including Graphical
User Interface building
10. MATLAB
• MATLAB is an interactive system whose basic
data element is an array that does not require
dimensioning.
• This allows you to solve many technical
computing problems, especially those with
matrix and vector formulations, in a fraction of
the time it would take to write a program in a
scalar non interactive language such as C or
Fortran.
11. The MATLAB System
• The MATLAB system consists of five main parts:
• The MATLAB language
• The MATLAB working environment
• Handle Graphics.
• The MATLAB mathematical function library
• The MATLAB Application Program Interface
(API)
12. The MATLAB language
• This is a high-level matrix/array language with
control flow statements, functions, data
structures, input/output, and object-oriented
programming features.
13. The MATLAB working environment
• This is the set of tools and facilities that you work
with as the MATLAB user or programmer. It
includes facilities for managing the variables in
your workspace and importing and exporting
data.
• It also includes tools for developing, managing,
debugging, and profiling M-files, MATLAB's
applications.
14. Handle Graphics
• This is the MATLAB graphics system. It includes
high-level commands for two-dimensional and
three-dimensional data visualization, image
processing, animation, and presentation
graphics.
• It also includes low-level commands that allow
you to fully customize the appearance of
graphics as well as to build complete Graphical
User Interfaces on your MATLAB applications
16. The MATLAB mathematical function library
• This is a vast collection of computational
algorithms ranging from elementary functions
like,
• sum, sine, cosine, and complex arithmetic, to
more sophisticated functions like,
• matrix inverse, matrix eigenvalues, Bessel
functions, and fast Fourier transforms.
17. The MATLAB Application Program Interface
(API)
• This is a library that allows you to write C and
Fortran programs that interact with MATLAB.
• It include facilities for calling routines from
MATLAB (dynamic linking), calling MATLAB as a
computational engine, and for reading and
writing MAT-files.
18. Applications
• Signal Processing and Communications
• Image and Video Processing
• Control Systems
• Test and Measurement
• Computational Finance
• Computational Biology
19. Local Environment Setup
• Setting up MATLAB environment is a matter of
few clicks. The installer can be downloaded.
• MathWorks provides the licensed product, a trial
version and a student version as well. You need
to log into the site and wait a little for their
approval.
• After downloading the installer the software can
be installed through few clicks.
22. Understanding the MATLAB Environment
• MATLAB development IDE can be launched from
the icon created on the desktop.
• The main working window in MATLAB is called
the desktop. When MATLAB is started, the
desktop appears in its default layout
24. • The desktop has the following panels −
• Current Folder − This panel allows you to access
the project folders and files.
25. • Command Window − This is the main area
where commands can be entered at the
command line. It is indicated by the command
prompt (>>).
26. • Workspace − The workspace shows all the
variables created and/or imported from files.
27. • Command History − This panel shows or return
commands that are entered at the command
line.
28. Use of Semicolon (;) in MATLAB
• Semicolon (;) indicates end of statement.
However, if you want to suppress and hide the
MATLAB output for an expression, add a
semicolon after the expression.
• For example,
• x = 3; y = x + 5
• When you click the Execute button, or type
Ctrl+E, MATLAB executes it immediately and the
result returned is −
• y = 8
29. Adding Comments
• The percent symbol (%) is used for indicating a
comment line. For example,
• x = 9 % assign the value 9 to x
• You can also write a block of comments using the
block comment operators % { and % }.
30. Commonly used Operators and Special
Characters
• + Plus; addition operator.
• - Minus; subtraction operator.
• * Scalar and matrix multiplication operator.
• .* Array multiplication operator.
• ^ Scalar and matrix exponentiation operator.
• .^ Array exponentiation operator.
• Left-division operator.
31. ..contd
• / Right-division operator.
• . Array left-division operator.
• ./ Array right-division operator.
• : Colon; generates regularly spaced elements
and represents an entire row or column.
• ( ) Parentheses; encloses function arguments
and array indices; overrides precedence.
32. • [ ] Brackets; enclosures array elements.
• . Decimal point.
• … Ellipsis; line-continuation operator
• , Comma; separates statements and elements
in a row
• ; Semicolon; separates columns and
suppresses display.
• % Percent sign; designates a comment and
specifies formatting.
34. Special Variables and Constants
• ans - Most recent answer.
• eps - Accuracy of floating-point precision.
• i,j -The imaginary unit √-1.
• Inf - Infinity.
• NaN - Undefined numerical result (not a
number).
• pi -The number π
36. ….contd
• Variable names can be of any length, however,
MATLAB uses only first N characters,
• where N is given by the unction namelengthmax.
37. Saving Your Work
• The save command is used for saving all the
variables in the workspace, as a file with .mat
extension, in the current directory.
38. Commands
• clc Clears command window.
• clear Removes variables from memory.
• exist Checks for existence of file or variable.
• global Declares variables to be global.
• help Searches for a help topic.
• lookfor Searches help entries for a keyword.
• quit Stops MATLAB.
• who Lists current variables.
• whos Lists current variables (long display).
39. Input and Output Commands
• disp Displays contents of an array or string.
• fscanf Read formatted data from a file.
• format Controls screen-display format.
• fprintf Performs formatted writes to screen or
file.
• input Displays prompts and waits for input.
• ; Suppresses screen printing.
40. • %s Format as a string.
• %d Format as an integer.
• %f Format as a floating point value.
• %e Format as a floating point value in scientific
notation.
• %g Format in the most compact form: %f or %e.
• n Insert a new line in the output string.
• t Insert a tab in the output string.
41. • The format function has the following forms used
for numeric display −
• format short Four decimal digits (default).
• format long 16 decimal digits.
• format short eFive digits plus exponent.
• format long e 16 digits plus exponents.
• format bank Two decimal digits.
• format + Positive, negative, or zero.
• format rat Rational approximation.
42. Vector, Matrix and Array Commands
• cat Concatenates arrays.
• find Finds indices of nonzero elements.
• length Computes number of elements.
• linspace Creates regularly spaced vector.
• logspaceCreates logarithmically spaced vector.
• max Returns largest element.
43. …contd
• min Returns smallest element.
• prod Product of each column.
• reshape Changes size.
• size Computes array size.
• sort Sorts each column.
• sum Sums each column.
• eye Creates an identity matrix.
44. ….contd
• ones Creates an array of ones.
• zeros Creates an array of zeros.
• cross Computes matrix cross products.
• dotComputes matrix dot products.
• detComputes determinant of an array.
• inv Computes inverse of a matrix.
45. • pinv Computes pseudoinverse of a matrix.
• rank Computes rank of a matrix.
• rref Computes reduced row echelon form.
• cellCreates cell array.
• celldisp Displays cell array.
46. • cellplot Displays graphical representation of cell
array.
• num2cell Converts numeric array to cell
array.
• deal Matches input and output lists.
• iscell Identifies cell array.
47. Plotting Commands
• axis Sets axis limits.
• fplot Intelligent plotting of functions.
• grid Displays gridlines.
• plot Generates xy plot.
• print Prints plot or saves plot to a file.
• title Puts text at top of plot.
• xlabel Adds text label to x-axis.
• ylabel Adds text label to y-axis.
48. The M Files
• MATLAB allows writing two kinds of program files
• Scripts − script files are program files with .m
extension.
• In these files, you write series of commands,
which you want to execute together. Scripts do
not accept inputs and do not return any outputs.
They operate on data in the workspace.
49. • Functions − functions files are also program files
with .m extension. Functions can accept inputs
and return outputs. Internal variables are local to
the function.