DLCD Assignment 3 year (2025)

 

Digital Logic and Computer Design

Assignment - 3

SUBJECT CODE-- ECC 207

BATCH- IT 123

SUBMISSION DATE - 7 November 2025

1 Explain cache with set associative and direct mapping. Assume your own example and explain.

2 Explain types of interrupts with an example for each. Explain interrupt cycle with the help of flowchart.

3 Show internal configuration of DMA controller.

4 Explain Computer Organization with respect to computer configuration for micro program, symbolic program and binary microprogram.

5 What is an assembly language? Explain basic computer instruction formats.

6 Define DMA.  Explain its need. Explain DMA transfer in detail with the help of suitable example.

7 Differentiate between hardwired control and microprogram control and explain organization of microprogrammed control unit with neat diagram.

PRCV Assignment 3

                   Subject Name: Pattern Recognition and Computer Vision

Subject Code: ML-411T                 Semester: 7^th

 

Assignment No. 3

 Submission Date - 7 November 2025, Friday

Bloom’s Taxonomy Levels:  1. Remember  2. Understand  3. Apply  4. Analyze  5. Evaluate 6. Create

 

Q. No.	Question	BTL level	CO
1.	What are the two approaches for blind image restoration? Explain in detail.	1	3
2.	Which is the most frequent method to overcome the difficulty of formulating the spatial relocation of pixels?	1	3
3.	Explain additivity property in Linear Operator? A.      How is the degradation process modeled? B.     Explain homogeneity property in Linear Operator?	2	3
4.	Explain the inverse filtering with suitable examples.	1	3
5	Give the relation for degradation model for continuous function? a. Define circulant matrix? b. What is the concept of an algebraic approach?	2	3
6.	What is image degradation and restoration? Explain them with examples.	3	3
7.	Write the properties of Singular value Decomposition (SVD)?	4	3
8.	What are the three methods of estimating the degradation function?	1	3

PRCV Assignment 2

 

Subject Name: Pattern Recognition and Computer Vision

             

Subject Code: ML-411T                 Semester: 7^th

Assignment No. 2

Submission Date - 7 November 2025, Friday

Bloom’s Taxonomy Levels:  1. Remember  2. Understand  3. Apply  4. Analyze  5. Evaluate 6. Create

Q. No.	Question	BTL level	CO
1.	What do you mean by fuzzy decision making? Also discuss the fuzzy classification using suitable examples.	1	2
2.	What do you understand by supervised learning and unsupervised learning? Explain. Discuss any unsupervised learning algorithm with some examples.	2	1
3.	Briefly explain segmentation and grouping.	1	2
4.	Explain Gaussian Mixture Models. If for any two elements A<B given that P(A)=1/3, P(B)=1/5 and P(AUB)=11/30, then find: P(A/B).	1	1
5	Explain chi-square test.	1	1
6.	Explain evaluation of classifiers. Give probability distribution of a random variable.	3	2
7.	Explain why the maximum likelihood estimation is not working with uniformly distributed training sets.	2	2
8.	Show that in the likelihood the sample mean is equal to the mean of samples.	1	1

PRCV NOTES

Subject Name - Pattern Recognition and Computer Vision

Subject Code - ML-411T

Important Docs/ Links 

Syllabus 

PRCV Syllabus

Books

Books

Notes

UNIT 1

UNIT 2

UNIT 3 and 4

Tutorials

Quiz/Tutorials

Beyond Syllabus

Beyond Syllabus

Other Universities Question Papers

Question Papers

PRCV Assignment 1 - 2025

 

Subject Name: Pattern Recognition and Computer Vision

             

Subject Code: ML-411T                 Semester: 7^th

 

Assignment No. 1

 

Bloom’s Taxonomy Levels:  1. Remember  2. Understand  3. Apply  4. Analyze  5. Evaluate 6. Create

 

Q. No.	Question	BTL level	CO
1.	Explain the difference between supervised and unsupervised learning in pattern recognition.	3	1
2.	What is the role of clustering in pattern recognition?	2	1
3.	Discuss the advantages and limitations of using Decision Trees for pattern recognition.	1	1
4.	Explain the concept of feature selection and its importance in pattern recognition.	4	2
5	Analyze the performance of different clustering algorithms (e.g., K-means, Hierarchical) on a gene expression dataset.	2	1
6.	Prove that Naive Bayes classifiers are optimal when features are independent.	4	2
7.	Explain the concept of maximum-margin hyperplane in SVMs.	2	1
8.	Derive the SVM decision boundary equation.	1	1
9.	Describe the role of bias terms in Feed Forward Networks.	2	2
10.	Explain the difference between Fisher Discriminant Analysis and Principal Component Analysis.	2	2
11.	What is the role of regularization in polynomial curve fitting?	1	2

 

 

DIP-AIDS/AIML-302 Notes

Subject Name - Digital Image Processing

Subject Code - AIDS/AIML-302

Important Docs/ Links 

Syllabus 

https://drive.google.com/drive/u/1/folders/1_z3CWqrz3Dj4FU6XYGIYrRWC6CoiCiTt

UNIT 1

https://drive.google.com/drive/u/1/folders/1wfFAUQ2OpfoAI6kOrtp3w5n3KtAgXjaR

UNIT 2

https://drive.google.com/drive/u/1/folders/1B5lhcEGID0scD5ixAbY1X3nKEgvztHN4

UNIT 3

https://drive.google.com/drive/u/1/folders/1yGuzgj1KKaJusYmvnE0HiwGyY6vXJwNo

UNIT 4

https://drive.google.com/drive/u/1/folders/1qbc1KtxV1_oOx4j_pgWNZkGhwEQ9fbe6

Question Bank 

https://drive.google.com/drive/u/1/folders/1hNIXbjNb-3VW0sPVWbZUkqH-BVwRcgIc

DIP ECE -308 T Notes

 

Subject Name - Digital Image Processing

Subject Code - ECE 308T

Important Docs/ Links 

Syllabus 

https://drive.google.com/drive/u/1/folders/15rAOROTQlkBEte4mGkEgFiGAd7tg-tnW

UNIT 1

https://drive.google.com/drive/u/1/folders/1EdBnK_GimSv0FrQJv0TugdA9K8dOkVOt

UNIT 2

https://drive.google.com/drive/u/1/folders/1nY_QwdGYDPP4JKpeDqQu3EI-1o9uF-9p

UNIT 3

https://drive.google.com/drive/u/1/folders/19797fs6oRwaCTRLFhqbZXHivpvEf2Kfc

UNIT 4

https://drive.google.com/drive/u/1/folders/1xrMcHjZZ3mQxHyQNEebDnqoW_zaqE2Ol

Question Bank 

https://drive.google.com/drive/u/1/folders/1hNIXbjNb-3VW0sPVWbZUkqH-BVwRcgIc

Beyond Curriculum Topics

https://drive.google.com/drive/u/1/folders/1K3UdkUNO1iBvQjeG7Yymdce-99NG6YE_

DIP- Assignment-1-2025

 

DIGITAL IMAGE PROESSING - ASSIGNMENT 1

  Date -- 10 Feb 2025

BATCH- ECE- AIML-IX, AIDS
SUBMISSION DATE - 17 Feb 2025

1. Compute the Euclidean Distance (D1), City-block Distance (D2) and Chessboard distance (D3) for points p and q, where p and q be (5, 2) and (1, 5) respectively. Give answer in the form (D1, D2, D3).
2. What do you mean by convolution? Explain all the properties of convolution.
3. Explain CMY model.
4. Write the expression to find the number of bits to store a digital image? Find the number of bits required to store a 256 X 256 image with 32 gray levels.
5. Explain the Quantization process with suitable examples.
6. Write a note on Match Band Effect.
7. Analyze the various parameters of image processing i) Band number ii) Spectrum, iii) wave lengths, iv) applications.
8. Evaluate the various colour models. Explain each of them in detail.

Mail solution at vaibhavnijhawan@mait.ac.in