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LESSON PLAN
Teacher Sarita Srivastav File Name : LP- EOE071-SS.doc
Subject Code EOE-071 Subject Name: Enterpreneurship Development
Session 2011-12 Groups : 08CSA, 08CSB,08EC,08IT
Lect. No. Ref. Pt. Topics to be covered
Unit I: Entrepreneurship
1 1.1 Entrepreneurship- Meaning & Defination, growth of SSI in developing
countries.2 1.2 SSI positions vis-a-vis large industries;
3 1.3 Role of small scale industries in the national economy
4 1.4 Characteristics and types of small scale industries
5 1.5 Demand based and resources based ancillaries and sub-control types.
6 1.6 Government policy for small scale industry; stages in starting a small scale
industry.
7 CASE STUDY
Unit II: Project identification
8 2.1 Assessment of viability, formulation, evaluation9 2.2 Financing, field-study and collection of information,
10 2.3 Preparation of project report, demand analysis,
11 2.4 Material balance and output methods
12 2.5 Benefit cost analysis, discounted cash flow,
13 2.6 Internal rate of return and net present value methods.
14 2.7 WORKSHEET
Unit III: Accountancy
15 3.1 Preparation of balance sheets16 3.2 Assessment of economic viability, decision making, expected costs
17 3.3 Planning and production control, quality control, marketing,
18 3.4 Industrial relations, sales and purchases, advertisement
19 3.5 Wages and incentive, inventory control
20 3.6 Preparation of financial reports, accounts and stores studies.
21 .
Unit IV: Project Planning and control
22 4.1 The financial functions, cost of capital approach in project planning and
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control
23 4.2.1 Economic evaluation, risk analysis,
24 4.2.2 capital expenditures, policies and practices in public enterprises
25 4.2.3 profit planning and programming, planning cash flow, capital expenditure
and operations
26 4.3 control of financial flows, control and communication.
Unit V: Laws concerning entrepreneur
27 5.1 Laws concerning entrepreneur viz, partnership laws
28 5.2 , business ownership, sales and income taxes
29 5.3 workman compensation act
30 5.4 Role of various national and state agencies which render assistance tosmall scale industries
31 Doubt Session
LESSON PLANTeacher Krishan Kumar File Name: LP-ECS074-KK.doc
Subject Code ECS-074 Subject Name Pattern Recognition
Session 2011-12 Group 08CSA and 08CSB
Lect.No.
Ref. Pt. Topics to be covered
Unit I: INTRODUCTION
1 1.1
1.2
1.3
Introduction of Pattern Recognition
Basics of pattern recognition
Examples
2 1.4
1.5
1.6
1.7
1.81.9
1.10
1.11
Design principles of pattern recognition system,Model
Preprocessing
Segmentation
Feature ExtractionTraining Samples
Cost
Generalization
3 1.12
1.13
1.14
1.15
1.16
1.17
Leaning and adaptation,
Supervised, unsupervised and reinforcement learning
Pattern recognition approaches:Template matching
Statistical classification
Syntactic or structural matching
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1.18 Neural networks
4
1.13
1.14
Mathematical foundation:-
Linear algebra:Probability theory:
5
1.15
1.16
Mathematical foundation:-
expectation,
mean and covariance,
6
1.17
1.18
Mathematical foundation:-
Normal distribution,
multivariate normal densities,
7
1.19
1.20
Mathematical foundation:-Chi squared test
Assignment-18 1.21 Class Test-1 Marks: 10
Unit- II: Statistical Pattern Recognition
9
2.1
2.2
2.3
Bayesian Decision Theory:
Introduction
Bayesian Decision Theory -Continuous FeaturesMinimum-Error-Rate classification
10 2.4
2.5
2.6
Classifiers, discriminants
Multi Category case
Two Category case
11 2.7
2.8
2.9
Normal Density
Univariate Density
Multivariate Density
12 2.10
2.11
2.12
2.13
Normal density and discriminants function
Case-1
Case-2
Case-3
13 2.14
2.15
2.16
Error Bounds for Normal DensitiesChernoff Bound
Bhattacharyya Bound
14 2.17
2.18Bayes Decision Theory Discrete FeaturesIndependent Binary Features
15 2.19
2.20
2.21
Missing and Noisy FeaturesMissing Features
Noisy Features
16 2.22
2.23Compound Bayes Decision Theory and Context
Assingment-2
17 2.24 Class Test-2 Marks: 10
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Unit- III: Parameter Estimation Methods
18
3.1
3.2
3.3
3.4
Parameter Estimation Methods
IntroductionMaximum likelihood estimation,
General PrincipalThe Gaussian case
19 3.5
3.6
3.7
3.8
3.9
3.10
3.11
Bayesian Estimation-
Class Conditional DensitiesParameter Distribution
Bayesian parameter estimation: General TheoryBayesian parameter estimation: Gaussian Case
The Univariate Case:p(|D)
The Univariate Case:p(x|D)
The Multivariate Case20 3.12
3.13
3.14
Dimension Reduction methods-Problems of Dimensionality
Principal component Analysis (PCA),
21 3.15 Fisher Linear discriminants analysis,
22 3.16 Expectation-Maximization (EM),
23 3.17
3.18
3.193.20
Hidden Markov models (HMM),First-order Markov models
First-order hidden Markov models
Hidden Markov Model ComputationEvaluation
Examples
24 3.21 Gaussian mixture models
25 3.22 Revision of whole unitAssignment-3
26 3.23 Class Test-3 Marks: 10
UNIT-4 :Non parametric techniques:
27 4.1
4.2
Introduction
Density estimation,
28 4.3
4.4
4.5
4.6
Parzen windows,IntroductionConvergence of the MeanConvergence of the MeanClassification example
29 4.7
4.8
K-Nearest Neighbor Estimation,Estimation of a posteriori probabilities
30 4.9
4.10
4.11
Nearest Neighbor Rule,Convergence of the Nearest NeighborError Rate for the Nearest-Neighbor RuleError Bounds
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4.12
4.13
The k-Nearest-Neighbor RuleComputational Complexity of the kNearest-Neighbor Rule
31 4.14
4.15
4.16
Fuzzy classificationAre Fuzzy Category Memberships just Probabilities?Assignment-4
32 4.17 Class Test-4 Marks: 10
UNIT-5 Unsupervised learning and clustering:
33 5.1 Creation function for clustering,
34 5.2
5.3
Clustering Techniques:
Iterative square-error partitional clustering-K means,
35 5.4 Agglomerative hierarchical clustering,36 5.5 Clustering validation
37 5.6 Assignment-5, class Test-5 Marks: 10
38 5.7 Revision using PPTs
39 5.8 PPT Showing some development in this field using Research Papers
40 5.9 Queries Handling
BOOKS
1 Pattern Classification by Richard O. Duda, Peter E. Hart, Devid G. Stork,
II Edition
2 Pattern Recognition and Machine Learning by Cristopher M. Bishop
3 Foundation of Statistical Natural Language Processing by Cristopher D.
Manning and Hinrich Schutze
LESSON PLAN
Teacher Sapna Katiyar File Name LP-ECS702-SK
Subject Code ECS702 Subject Name Digital Image Processing
Session 2011 Group 08CSA, 08CSB
Lect.
No.
Ref.
Pt.
Topics to be covered
Unit I: Introduction & Fundamentals
1 1.1
1.2
Motivation and Perspective & Applications
Components of Image Processing System
2 1.3
1.4
Element of Visual Perception
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A Simple Image Model
3 1.5 Sampling and Quantization
4 1.6
1.7
Basic Gray Level Functions Piecewise-Linear Transformation Functions
Contrast Stretching; Histogram Specification
5 1.8 Histogram Equalization; Local Enhancement
6 1.9 Enhancement using Arithmetic/Logic Operations Image Subtraction, Image
Averaging
7 1.10 Basics of Spatial Filtering; Smoothing - Mean filter, Ordered Statistic Filter
8 1.11 Sharpening The Laplacian
Unit II: Image Enhancement in Frequency Domain
9 2.1
2.2
Fourier Transform and the Frequency Domain
Basis of Filtering in Frequency Domain
10 2.3 Filters Low-pass, High-pass
11 2.4
2.5
Correspondence Between Filtering in Spatial and Frequency Domain
Smoothing Frequency Domain Filters Gaussian Low pass Filters
12 2.6 Sharpening Frequency Domain Filters Gaussian Highpass Filters
13 2.7 Homo morphic FilteringImage Restoration
14 2.8
2.9
A Model of Restoration Process
Noise Models
15 2.10
2.11
Restoration in the presence of Noise only-Spatial Filtering Mean Filters
Arithmetic Mean filter
16 2.12
2.13
2.14
Geometric Mean Filter
Order Statistic Filters Median Filter
Max and Min filters
17 2.15
2.16
Periodic Noise Reduction by Frequency Domain Filtering Band pass Filters
Minimum Mean-square Error RestorationUnit III: Color Image Processing
18 3.1
3.2
3.3
Color Fundamentals,
Color Models
Converting Colors to different models
19 3.4
3.5
Color Transformation
Smoothing and Sharpening
20 3.6 Color Segmentation
Morphological Image Processing
21 3.7 Introduction
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3.8 Logic Operations involving Binary Images
22 3.9
3.10
Dilation and Erosion
Opening and Closing
23 3.11 Morphological Algorithms Boundary Extraction
24 3.12
3.13
Region Filling
Extraction of Connected Components
25 3.14
3.15
Convex Hull
Thinning, Thickening
Unit IV: Registration
26 4.1
4.2
Introduction
Geometric Transformation Plane to Plane transformation
27 4.3 Mapping, Stereo Imaging Algorithms to Establish Correspondence
28 4.4 Algorithms to Recover Depth
Segmentation
29 4.5 Introduction, Region Extraction
30 4.6
4.7
Pixel-Based Approach
Multi-level Thresholding
31 4.8
4.9
Local Thresholding
Region-based Approach
32 4.10
4.11
Edge and Line Detection: Edge Detection, Edge Operators
Pattern Fitting Approach
33 4.12
4.13
Edge Linking and Edge Following
Edge Elements Extraction by Thresholding
34 4.14 Edge Detector Performance
Unit V: Feature Extraction
35 5.1
5.2
5.3
Representation
Topological Attributes
Geometric Attributes
Description
36 5.4 Boundary-based Description
37 5.5 Region-based Description, Relationship
Object Recognition
38 5.6 Deterministic Methods, Clustering
39 5.7
5.8
Statistical Classification
Syntactic Recognition
40 5.9
5.10
Tree Search
Graph Matching
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LESSON PLANTeacher: Shruti Mittal File Name: LP-TCS801-SM.doc
Subject Code: TCS-801 Subject Name : Distributed Systems
Session: 2011-12 Group: 08CSA&08CSB
Lect.
No.
Ref. Pt. Topics to be covered
Unit I:
1 1.1 Characterization of Distributed Systems: Introduction, Examples ofdistributed Systems,Theoretical Foundation for Distributed System: Limitation of
Distributed system, absence of global clock, shared memory2 1.2 Resource sharing and the Web Challenges.
System Models: Architectural models, Fundamental Models
3 1.3 Logical clocks, Lamports logical clocks
4 1.4 Vectors logical clocks
5 1.5 Causal ordering of messages, global state, termination detection.
6 1.6 Distributed Mutual Exclusion: Classification of distributed mutual
exclusion, requirement of mutual exclusion theorem,
7 1.7 Token based and non token based algorithms, Performance metric fordistributed mutual exclusion algorithms.
Unit II:
8 2.1 Distributed Deadlock Detection: system model, resource Vscommunication deadlocks, deadlock prevention
9 2.2 Deadlock avoidance, detection & resolution, centralized dead lockdetection,
10 2.3 distributed dead lock detection, path pushing algorithms, edge chasing
algorithms.
11 2.4 Agreement Protocols: Introduction, System models, classification ofAgreement Problem
12 2.5 Byzantine agreement problem, Consensus problem, InteractiveconsistencyProblem
13 2.6 Solution to Byzantine Agreement problem, Application of Agreementproblem, Atomic Commit in Distributed Database system.
Unit III:
14 3.1 Distributed Objects and Remote Invocation: Communication between
distributed objects, Remote procedure call, Events and notifications, JavaRMI case study.
15,16 3.2 Security: Overview of security techniques, Cryptographic algorithms,Digital signatures
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17,18 3.2 Cryptography pragmatics, Case studies: Needham Schroeder, Kerberos,SSL & Millicent.
19,20 3.3 Distributed File Systems: File service architecture, Sun Network FileSystem, The Andrew File System, Recent advances.
Unit IV:
21 4.1 Transactions and Concurrency Control: Transactions, Nested
transactions, Locks, Optimistic Concurrency control
22,23 4.2,4.3 Timestamp ordering, Comparison of methods forconcurrency control.
24,25,
26
4.4 Distributed Transactions: Flat and nested distributed transactions,Atomic Commit protocols, Concurrency control in distributed
transactions, Distributed deadlocks,
27,28 4.5 Transaction recovery. Replication: System model and groupcommunication.
29,30 4.6 Fault - tolerant services, highly available services, Transactions with
replicated data.
Unit V:
31,32 5.1 Distributed Algorithms: Introduction to communication protocols,Balanced sliding window protocol,
33,34 5.2 Routing algorithms, Destination based routing, APP problem,
35,36 5.3 Deadlock free Packet switching, Introduction to Wave & traversal
algorithms
37,38 5.4 Election algorithm.CORBA Case Study: CORBA RMI, CORBAservices.
LESSON PLAN
Teacher Rajdev Tiwari
Subject Code ECS-075Subject Name Data Mining and WarehousingSession 2011-12Group 08CS & 08IT
Lect. No(s) Ref. No(s) Topics to be covered with reference points
Unit I: Overview1 1.1 Motivation (for Data Mining),Data Mining-
Definition & Functionalities2 1.2 Data Processing, Form of Data Preprocessing, Data
Cleaning: Missing Values
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3 1.3 Data Cleaning: Missing Values, Noisy
Data,(Binning, Clustering, Regression, Computer
and Human inspection)4 1.4 Inconsistent Data, Data Integration and
Transformation.5 1.5 Data Reduction:-Data Cube Aggregation,
Dimensionality reduction, Data Compression6 1.6 Numerosity Reduction, Clustering, Discretization
and Concept hierarchy generation
Unit II: Concept Description7 2.1 Definition, Data Generalization, Analytical
Characterization, Analysis of attribute relevance8 2.2 Mining Class comparisons, Statistical measures in
large Databases. Measuring Central Tendency9 2.3 Measuring Dispersion of Data, Graph Displays of
Basic Statistical class Description10 2.4 Mining Association Rules in Large Databases,
Association rule mining11 2.5 Mining Single-Dimensional Boolean Association
rules from Transactional Databases Apriori
Algorithm12 2.6 Mining Multilevel Association rules from
Transaction Databases13 2.7
Mining Multi-Dimensional Association rules fromRelational Databases
Unit III: Classification and prediction14 3.1 What is Classification & Prediction, Issues regarding
Classification and prediction15 3.2 Decision tree16 3.3 Bayesian Classification17 3.4 Classification by Back propagation, Multilayer feed-
forward Neural Network, Back propagation
Algorithm
18 3.5 Classification methods K-nearest neighborclassifiers, Genetic Algorithm
19 3.6 Cluster Analysis: Data types in cluster analysis,
Categories of clustering methods, Partitioning
methods20 3.7 Hierarchical Clustering- CURE and Chameleon21 3.8 Density Based Methods-DBSCAN, OPTICS,22 3.9 Grid Based Methods- STING, CLIQUE
23 3.10 Model Based Method Statistical Approach, Neural
Network approach
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24 3.11 Outlier Analysis
Unit IV: Data Warehousing
25 4.1 Overview, Definition, Delivery Process,
26 4.2 Difference between Database System and Data
Warehouse, Multi Dimensional Data Model27 4.3 Data Cubes, Stars, Snow Flakes, Fact Constellations28 4.4 Concept hierarchy, OLAP operations*29 4.5 Process Architecture, 3 Tier Architecture30 4.6 Data Marting31 4.7 Granularity and level of summarization*
Unit V:
32 5.1 Aggregation, Historical information, Query Facility33 5.2 OLAP function and Tools. OLAP Servers, ROLAP,
MOLAP, HOLAP34 5.3 Data Mining interface, Security35 5.3 Backup and Recovery, Tuning Data Warehouse36 5.4 Testing Data Warehouse
References
1. M.H.Dunham,Data Mining:Introductory and
Advanced Topics PearsonEducation
2. Jiawei Han, Micheline Kamber, Data Mining
Concepts & Techniques Elsevier3. Sam Anahory, Dennis Murray, Data
Warehousing in the Real World : A Practical Guide
for Building Decision Support Systems, Pearson
Education
4. Mallach,Data Warehousing System,McGraw
Hill
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LAB PLAN
Teacher MANISH MISHRA
Subject Code ECS-752
Subject Name DIGITAL IMAGE PROCESSING LAB
Session 2011-12
Group 08CSA
A. LIST OF EXPERIMENTS AS PER UNIVERSITY SYLLABUS
Class
No.
Name of experiment Tentative Date
1. Write a program in c to rotate a line in animated
form
5Aug
2. Write a c program to fill a rectangle using eight
neighbor pixel algo
12Aug
3. Write a c program to display intensity array ofoutput image for given input image using salt andpeper noise method
19Aug
4. Write a c program to display intensity array of
output image for given input image using histogram
equalization method
26Aug
5. Write a c program to implement image enhancementusing arithmetic and logical operator (i.e.subtraction
averaging of image)
2Sep
6. Write a c program to implement imageSegmentation
9Sep
7. Write a c program to implement Region extractionof an input image
16Sep
8. Write a c program to implement morphologicalimage processing 23Sep
9. Write a c program to implement Colortransformation on input image
14Oct
10. Write a c program to implement2Dtransformation(translation and rotation) on input
image
21Oct
11. Implement all the above methods by using
MATLAB
4Nov,11Nov,18Nov
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B. LAB CONDUCT PLAN:All the groups shall conduct same experiment on a day (when implement with c).
ORThe students shall be divided in groups of 2/3(when implement with MATLAB) and
each group shall conduct different experiments.
C. LAB EVALUATION/MARKING SCHEMETotal internal marks: 25
Attendance marks: 5
Lab Record: 5
Quiz 5
Internal Viva 10
Teacher MANISH MISHRA
Subject Code ECS-752
Subject Name DIGITAL IMAGE PROCESSING LAB
Session 2011-12
Group 08CSB
D. LIST OF EXPERIMENTS AS PER UNIVERSITY SYLLABUS
Class
No.
Name of experiment Tentative
Date
1. Write a program in c to rotate a line in animated
form
1Aug
2. Write a c program to fill a rectangle using eight
neighbor pixel algo
8Aug
3. Write a c program to display intensity array ofoutput image for given input image using salt andpeper noise method
29Aug
4. Write a c program to display intensity array of
output image for given input image using histogram
equalization method
12Sep
5. Write a c program to implement image enhancement
using arithmetic and logical operator (i.e.subtraction
averaging of image)
19Sep
6. Write a c program to implement image
Segmentation
26Sep
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7. Write a c program to implement Region extractionof an input image
10Oct
8. Write a c program to implement morphologicalimage processing 10Oct
9. Write a c program to implement Colortransformation on input image
17Oct
10. Write a c program to implement
2Dtransformation(translation and rotation) on input
image
31Oct
11. Implement all the above methods by using
MATLAB
31Oct,14Nov
E. LAB CONDUCT PLAN:All the groups shall conduct same experiment on a day (when implement with c).
OR
The students shall be divided in groups of 2/3(when implement with MATLAB) and
each group shall conduct different experiments.
F. LAB EVALUATION/MARKING SCHEMETotal internal marks: 25
Attendance marks: 5Lab Record: 5
Quiz 5
Internal Viva 10