Computer Graphics

CMSACOR14T: Computer Graphics Theory: 75 Lectures
1. Introduction (5 Lectures) Basic elements of Computer graphics, Applications of Computer Graphics.
2. Graphics Hardware (8 Lectures) Architecture of Raster and Random scan display devices, input/output devices.
3. Fundamental Techniques in Graphics (22 Lectures) Raster scan line, circle and ellipse drawing, thick primitives, Polygon filling, line and polygon clipping algorithms, 2D and 3D Geometric Transformations, 2D and 3D Viewing Transformations (Projections- Parallel and Perspective), Vanishing points.
4. Geometric Modeling (10 Lectures)
Representing curves & Surfaces.
5.Visible Surface determination (8 Lectures) Hidden surface elimination.
6.Surface rendering (7 Lectures) Illumination and shading models. Basic color models and Computer Animation.

Microprocessor

CSMADSE01T:Microprocessor Theory: 60 Lectures Microprocessor architecture: Internal architecture, system bus architecture, memory and I/Ointerfaces. Microprocessor programming: Register Organization, instruction formats, assembly languageprogramming. Interfacing: Memory address decoding, cache memory and cache controllers, I/O interface,keyboard, display, timer, interrupt controller, DMA controller, video controllers, communication interfaces.

Data Mining

CSMADSE02T: Data Mining Theory: 60 lectures
Overview: Predictive and descriptive data mining techniques, supervised and unsupervisedlearning techniques, process of knowledge discovery in databases, pre-processing methods
Data Mining Techniques: Association Rule Mining, classification and regressiontechniques, clustering, Scalability and data management issues in data mining algorithms, measures of interestingness

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, Documentation. (2L)
Techniques ofProblem Solving: Flowcharting, decision table, algorithms, Structured programming concepts,Programming methodologies viz. top-down and bottom-up programming. Overview of Programming : Structure of a Python Program, Elements of Python (2L)
(3L)
Introduction to Python: Python Interpreter, Using Python as calculator, Python shell, Indentation.Atoms, Identifiers and keywords, Literals, Strings, Operators(Arithmetic operator, Relational operator, Logical or Boolean operator, Assignment, Operator, Ternary operator, Bit wise operator, Increment or Decrement operator). (4L)
Creating Python Programs : Input and Output Statements, Control statements(Branching,Looping, Conditional Statement, Exit function, Difference between break, continue and pass.), Defining Functions, default arguments.

CMSSSEC02M: R-Programming

CMSSSEC02M: R-Programming
(1+2 Labs)
Introduction: Overview and History of R, Getting Help, Data Types, Subsetting, Vectorized
Operations, Reading and Writing Data. (5L) Control Structures, Functions, lapply, tapply, split, mapply, apply, Coding Standards. (5L) Scoping Rules, Debugging Tools, Simulation, R Profiler. (5L)

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)