Cloud Computing

Cloud Computing
Overview of Computing Paradigm ( 8 lectures)
Recent trends in Computing : Grid Computing, Cluster Computing, Distributed Computing, Utility Computing, Cloud Computing,
Introduction to Cloud Computing ( 7 lectures) Introduction to Cloud Computing, History of Cloud Computing, Cloud service providers, Benefits and limitations of Cloud Computing,
Cloud Computing Architecture ( 20 lectures) Comparison with traditional computing architecture (client/server), Services provided at various levels, Service Models- Infrastructure as a Service(IaaS), Platform as a Service(PaaS), Software as a Service(SaaS), How Cloud Computing Works, Deployment
Models- Public cloud, Private cloud, Hybrid cloud, Community cloud, Case study of NIST architecture.
Case Studies ( 13 lectures) Case study of Service model using Google App Engine, Microsoft Azure, Amazon EC2 , Eucalyptus. Service Management in Cloud Computing ( 7 lectures) Service Level Agreements(SLAs), Billing & Accounting, Comparing Scaling Hardware: Traditional vs. Cloud, Economics of scaling. Cloud Security ( 5 lectures) Infrastructure Security- Network level security, Host level security, Application level security, Data security and Storage- Data privacy and security Issues, Jurisdictional issues raised by Data location, Authentication in cloud computing.

Big Data

Big Data Theory: 60 lectures
UNDERSTANDING BIG DATA
What is big data – why big data –.Data!, Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big data – risk and big data – credit risk management – big data and algorithmic trading – big data and healthcare – big data in medicine – advertising and big data – big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics.
NOSQL DATA MANAGEMENT
Introduction to NoSQL – aggregate data models – aggregates – key-value and document data models – relationships – graph databases – schema less databases – materialized views – distribution models – shading –– version – map reduce – partitioning and combining – composing map-reduce calculations.
BASICS OF HADOOP Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures.
MAPREDUCE APPLICATIONS MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN – job scheduling – shuffle and sort – task execution – MapReduce types – input formats – output formats
HADOOP RELATED TOOLS Hbase – data model and implementations – Hbase clients – Hbase examples –praxis. Cassandra – Cassandra data model – Cassandra examples – Cassandra clients –Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation – HiveQL queries.

Digital Image Processing

CMSADSE05T: Digital Image Processing Lab Theory: 60 Lectures
1. Introduction (6 Lectures)
Light, Brightness adaption and discrimination, Pixels, coordinate conventions, Imaging Geometry, Perspective Projection, Spatial Domain Filtering, sampling and quantization.
2. Spatial Domain Filtering (7 Lectures) Intensity transformations, contrast stretching, histogram equalization, Correlation and convolution, Smoothing filters, sharpening filters, gradient and Laplacian.
3. Filtering in the Frequency domain (8 Lectures) Hotelling Transform, Fourier Transforms and properties, FFT (Decimation in Frequency and Decimation in Time Techniques), Convolution, Correlation, 2-D sampling, Discrete Cosine Transform, Frequency domain filtering.
4. Image Restoration (8 Lectures) Basic Framework, Interactive Restoration, Image deformation and geometric transformations, image morphing, Restoration techniques, Noise characterization, Noise restoration filters,
Adaptive filters, Linear, Position invariant degradations, Estimation of Degradation functions, Restoration from projections.
5. Image Compression (10 Lectures) Encoder-Decoder model, Types of redundancies, Lossy and Lossless compression, Entropy of an information source, Shannon's 1st Theorem, Huffman Coding, Arithmetic Coding, Golomb Coding, LZW coding, Transform Coding, Sub-image size selection, blocking artifacts, DCT implementation using FFT, Run length coding, FAX compression (CCITT Group-3 and Group-4), Symbol-based coding, JBIG-2, Bit-plane encoding, Bit-allocation, Zonal Coding, Threshold Coding, JPEG, Lossless predictive coding, Lossy predictive coding, Motion Compensation
6. Wavelet based Image Compression (5 Lectures) Expansion of functions, Multi-resolution analysis, Scaling functions, MRA refinement equation, Wavelet series expansion, Discrete Wavelet Transform (DWT), Continuous Wavelet Transform, Fast Wavelet Transform, 2-D wavelet Transform, JPEG-2000 encoding, Digital Image Watermarking.
7. Morphological Image Processing (7 Lectures) Basics, SE, Erosion, Dilation, Opening, Closing, Hit-or-Miss Transform, Boundary Detection, Hole filling, Connected components, convex hull, thinning, thickening, skeletons, pruning, Geodesic Dilation, Erosion, Reconstruction by dilation and erosion.
8. Image Segmentation (9 Lectures) Boundary detection based techniques, Point, line detection, Edge detection, Edge linking, local processing, regional processing, Hough transform, Thresholding, Iterative thresholding, Otsu's method, Moving averages, Multivariable thresholding, Region-based segmentation, Watershed algorithm, Use of motion in segmentation

Programming in Python Planning the Computer Program

CMSSSEC01M: 

Programming in Python Planning the Computer Program: 

Concept of problem solving,

 Problem definition, 

Programdesign, Debugging,

 Types of errors in programming CLICK

Documentation. (2L)

Techniques of Problem Solving:

 Flowcharting CLICK

 decision table CLICK

algorithms CLICK

Structured programming concepts,

Programming methodologies viz. top-down and bottom-up programming. 

Overview of Programming : 

Structure of a Python Program,

 Elements of Python

 

Introduction to Python:

 Python Interpreter, 

Using Python as calculator,

 Python shell, 

Indentation CLICK

Atoms, 

Identifiers and keywords,

 Literals,

 Strings STRING

Arithmetic operator, Relational operator,  Logical or Boolean operator,  Assignment, Operator, 

Ternary operator, Bit wise operator, Increment or Decrement operator CLICK

Creating Python Programs : 

Input and Output Statements, - CLICK

Control statements:

Branching CLICK

Looping  CLICK

Conditional Statement CLICK

Exit function CLICK

 Difference between break, continue and pass.), CLICK

Defining Functions, CLICK

default arguments. CLICK

ADVANCED PYTHON

LIST CLICK

NUMPY CLICK

PANDAS CLICK

MATPLOTLIB CLICK

CMSSSEC02M: R-Programming

CMSHSE101M/CMSGSE101M: (Credits:3): 

📘 R LANGUAGE TUTORIAL AND NOTES MODULE WISE FOR STUDENTS

🚀 CLICK HERE

R Programming                                                                                                               (45 Classes) 

Module 1: Introduction to R Programming        CLICK FOR NOTES             6 Classes                                    

1. Introduction to R 

● Overview of R and its uses in data analysis and statistics. 

● Installing R and RStudio. 

● Introduction to RStudio interface and its components (Console, Source, Environment, Plots, etc.). 

● Understanding the R command prompt. 

● Basic operations: arithmetic operations, logical operations, and comparison operators. ● Data types in R: numeric, integer, character, logical, and factor. 

● Variable assignment and naming conventions. 

2. Data Structures in R 

● Vectors: creation, indexing, and basic functions (length, class, typeof, etc.). 

● Lists: creation, indexing, and subsetting. 

● Matrices: creation, indexing, and operations. 

● Data frames: creation, importing/exporting data, indexing, and subsetting. 

● Factors: creation, levels, and usage in categorical data. 

Module 2: Data Manipulation and Managemen   CLICK FOR NOTES                            10 Classes 

1. Data Import and Export 

● Reading data from CSV, Excel. 

● Writing data to CSV, Excel. 

2. Data Cleaning and Preparation 

○ Handling missing values and duplicates. 

○ Data type conversions. 

○ Renaming columns and rows 

3. Data Transformation 

○ Selecting columns (select), filtering rows (filter), arranging data (arrange). 

○ Mutating and transforming data (mutate, transmute).

 ○ Summarizing data (summarize, group_by). 

Module 3: Data Visualization                                                                                              10 Classes 

1. Introduction to Data Visualization in R 

○ Understanding the basics of data visualization. 

○ Using base R graphics for plotting. 

2. Using ggplot2 for Advanced Visualization 

○ Introduction to the grammar of graphics. 

○ Creating basic plots (scatter plots, line plots, bar plots, histograms).

○ Customizing plots (titles, labels, themes, colors, and scales). 

○ Faceting and multi-plot layouts. 

3. Interactive Visualizations

○ Introduction to interactive plots using plotly and shiny. 

○ Creating basic interactive plots and dashboards. 

Module 4: Statistical Analysis and Modeling                                                                     3 Classes 

● Descriptive Statistics

○ Calculating measures of central tendency (mean, median, mode). 

○ Calculating measures of dispersion (range, variance, standard deviation). 

Module 5: Advanced R Programming                                                                              10 Classes 

1. Control Structures 

○ Conditional statements (if, else, switch). 

○ Looping constructs (for, while, repeat). 

○ Vectorized operations and the apply family of functions (apply, lapply, sapply, tapply, mapply). 

2. Debugging and Error Handling 

○ Debugging tools in R (debug, trace, browser). 

○ Handling errors and warnings (try, tryCatch). 

Simple Programs :                                                                                                                  6 Classes

 1. Write a R program to take input from the user (name and age) and display the values. Also print the version of R installation. 

2. Write a R program to create a sequence of numbers from 20 to 50 and find the mean of numbers from 20 to 60 and sum of numbers from 51 to 91. 

3. Write a R program to multiply two vectors of integers type and length 3. 

4. Write a R program to find Sum, Mean and Product of a Vector, ignore elements like NA or NaN. 

5. Write a R program to list containing a vector, a matrix and a list and give names to the elements in the list.

 6. Write an R program to extract 3 rd and 5 th rows with 1 st and 3 rd columns from a given data frame. 7. Write a R program to sort a given data frame by multiple column(s). 

8. Write a R program to compare two data frames to find the row(s) in the first data frame that are not present in the second data frame. 

9. Write a program to read a csv file and find min, max and range the data in the file in R 

10. Write a program to find Mean, Median and Mode 

Text Books: 

1.William N. Venables and David M. Smith, An Introduction to R. 2nd Edition. Network Theory Limited.2009 

2. Norman Matloff, The Art of R Programming - A Tour of Statistical Software Design, No Starch Press.2011 i

Reference Books:

1.The Book of R,Tilman M. Davies,No Starch Press,1st edition 2.Discovering Statistics Using R,Andy Field,SAGE Publications Ltd,1st edition

Software Lab Based on R Programming

Software Lab Based on R Programming

 1. Write a program that prints ‗Hello World‘ to the screen.

 2. Write a program that asks the user for a number n and prints the sum of the numbers 1 to n 

 3. Write a program that prints a multiplication table for numbers up to 12. 

 4. Write a function that returns the largest element in a list. 

 5. Write a function that computes the running total of a list. 

 6. Write a function that tests whether a string is a palindrome. 

 7. Implement the following sorting algorithms: Selection sort, Insertion sort, Bubble Sort 

 8. Implement linear search. 

 9. Implement binary search. 

10. Implement matrices addition, subtraction and Multiplication

Software Lab Based on Python:

Section: A ( Simple programs) 1. Write a menu driven program to convert the given temperature from Fahrenheit to Celsius and vice versa depending upon users choice. 2. WAP to calculate total marks, percentage and grade of a student. Marks obtained in each of the three subjects are to be input by the user. Assign grades according to the following criteria : Grade A: Percentage >=80 Grade B: Percentage>=70 and <80 Grade C: Percentage>=60 and <70 Grade D: Percentage>=40 and <60 Grade E: Percentage<40 3. Write a menu-driven program, using user-defined functions to find the area of rectangle, square, circle and triangle by accepting suitable input paramters from user. 4. WAP to display the first n terms of Fibonacci series. 5. WAP to find factorial of the given number. 6. WAP to find sum of the following series for n terms: 1 – 2/2! + 3/3! - - - - - n/n! 7. WAP to calculate the sum and product of two compatible matrices.
Section: B (Visual Python):
All the programs should be written using user defined functions, wherever possible.
1. Write a menu-driven program to create mathematical 3D objects I. curve
II. sphere III. cone IV. arrow V. ring VI. cylinder. 2. WAP to read n integers and display them as a histogram. 3. WAP to display sine, cosine, polynomial and exponential curves.
4. WAP to plot a graph of people with pulse rate p vs. height h. The values of p and h are to be entered by the user. 5. WAP to calculate the mass m in a chemical reaction. The mass m (in gms) disintegrates according to the formula m=60/(t+2), where t is the time in hours. Sketch a graph for t vs. m, where t>=0. 6. A population of 1000 bacteria is introduced into a nutrient medium. The population p grows as follows: P(t) = (15000(1+t))/(15+ e) where the time t is measured in hours. WAP to determine the size of the population at given time t and plot a graph for P vs t for the specified time interval. 7. Input initial velocity and acceleration, and plot the following graphs depicting equations of motion: I. velocity wrt time (v=u+at) II. distance wrt time ( s=u*t+0.5*a*t*t) III. distance wrt velocity ( s=(v*v-u*u)/2*a ) 8. WAP to show a ball bouncing between 2 walls. (Optional)

Digital Image Processing Lab

Digital Image Processing Lab

1. Write program to read and display digital image using MATLAB or SCILAB a. Become familiar with SCILAB/MATLAB Basic commands b. Read and display image in SCILAB/MATLAB c. Resize given image d. Convert given color image into gray-scale image e. Convert given color/gray-scale image into black & white image f. Draw image profile g. Separate color image in three R G & B planes h. Create color image using R, G and B three separate planes i. Flow control and LOOP in SCILAB j. Write given 2-D data in image file
2. To write and execute image processing programs using point processing method a. Obtain Negative image
b. Obtain Flip image c. Thresholding d. Contrast stretching
3. To write and execute programs for image arithmetic operations a. Addition of two images b. Subtract one image from other image c. Calculate mean value of image d. Different Brightness by changing mean value
4. To write and execute programs for image logical operations a. AND operation between two images b. OR operation between two images c. Calculate intersection of two images d. Water Marking using EX-OR operatione. NOT operation (Negative image)
5. To write a program for histogram calculation and equalization using a. Standard MATLAB function b. Program without using standard MATLAB functions c. C Program
6. To write and execute program for geometric transformation of image a. Translation b. Scaling c. Rotation d. Shrinking e. Zooming
7. To understand various image noise models and to write programs for a. image restoration b. Remove Salt and Pepper Noise c. Minimize Gaussian noise d. Median filter and Weiner filter
8. Write and execute programs to remove noise using spatial filters a. Understand 1-D and 2-D convolution process b. Use 3x3 Mask for low pass filter and high pass filter
9. Write and execute programs for image frequency domain filtering a. Apply FFT on given image b. Perform low pass and high pass filtering in frequency domain c. Apply IFFT to reconstruct image
10. Write a program in C and MATLAB/SCILAB for edge detection using different edge detection mask 11. Write and execute program for image morphological operations erosion and dilation. 12. To write and execute program for wavelet transform on given image and perform inverse wavelet transform to reconstruct image.

Cloud Computing Lab

Cloud Computing Lab 1. Create virtual machines that access different programs on same platform. 2. Create virtual machines that access different programs on different platforms . 3. Working on tools used in cloud computing online- a. Storage b. Sharing of data c. manage your calendar, to-do lists, d. a document editing tool 4. Exploring Google cloud 5. Exploring microsoft cloud 6. Exploring amazon cloud

Microprocessor Lab

Microprocessor Lab

ASSEMBLY LANGUAGE PROGRAMMING 1. Write a program for 32-bit binary division and multiplication 2. Write a program for 32-bit BCD addition and subtraction 3. Write a program for Linear search and binary search. 4. Write a program to add and subtract two arrays 5. Write a program for binary to ascii conversion 6. Write a program for ascii to binary conversion