This document provides details of the curriculum and course modules for a Computer Science degree program across 8 semesters. It lists all courses, their objectives and outcomes, and how they map to the program outcomes. It shows the percentage of courses in different categories like science, humanities, professional core, and electives. It also provides details of student projects done in collaboration with local industries and how they match the program outcomes.
This document provides information about the fourth semester subject "Microprocessor and Programming" for the Computer Engineering group. It includes the teaching and examination scheme, rationale, objectives, learning structure, and contents of the course. The course aims to teach students about the architecture and instruction set of 8085 and 8086 microprocessors. It covers topics such as assembly language programming, procedures, macros, and interfacing with memory devices. The goal is to enable students to design 8086-based programs and systems.
This document provides information about the fourth semester subject "Microprocessor and Programming" for the Computer Engineering group. It includes the teaching and examination scheme, rationale, objectives, learning structure, and contents of the course. The course aims to teach students about the architecture and instruction set of 8085 and 8086 microprocessors. It covers topics such as assembly language programming, procedures, macros, and interfacing with memory. The goal is to enable students to design 8086-based programs and systems. Assessment includes theory exams, practical exams, and sessional work based on bi-weekly tests.
This document provides a syllabus for a B.E. Computer Engineering program from 2012-2015 at Savitribai Phule Pune University. The syllabus aims to blend concepts and advances using open-source technologies. It includes 16 electives in areas like cloud computing, mobile computing, web applications, and cyber security. Laboratory courses focus on utilizing state-of-the-art open-source software. The program objectives are to expose students to computer systems and applications, provide conceptual and interdisciplinary knowledge, and develop professional skills for the IT industry.
The document provides regulations and syllabus details for the Master of Technology in Computer Science and Engineering program at Pondicherry University. It outlines the eligibility criteria, duration of the course, medium of instruction, grading system and curriculum over four semesters. The curriculum includes both hard core and soft core subjects as well as electives in areas like algorithms, software engineering, databases, computer networks and security. Laboratory courses are also included as part of the program.
This document outlines the teaching and examination scheme for the fourth semester subject "Data Structure" in the Computer Engineering group. It includes the course code, teaching hours, examination structure, rationale, objectives, contents, practical skills and learning resources. The subject aims to teach different data structures, algorithms, their applications and comparisons. Students will learn concepts like arrays, stacks, queues, linked lists, trees and graphs through both theory and practical sessions. Assessment includes a theory exam worth 100 marks, practical exam worth 50 marks and term work worth 25 marks.
Tech Jobs Interviews Preparation - GeekGap Webinar #1
Part 1 - Algorithms & Data Structures
What is an algorithm?
What is a data structure (DS)?
Why study algorithms & DS?
How to assess good algorithms?
Algorithm & DS interviews structure
Case study: Binary Search
2 Binary Search variants
Part 2 - System Design
What is system design?
Why study system design?
System design interviews structure
Case study: ERD with Lucidchart
Demo Time: SQLAlchemy
Githu Repo: http://bit.ly/gg-io-webinar-1-github
by www.geekgap.io
This document provides information about the fourth semester subject "Microprocessor and Programming" for the Computer Engineering group. It includes the teaching and examination scheme, rationale, objectives, learning structure, and contents of the course. The course aims to teach students about the architecture and instruction set of 8085 and 8086 microprocessors. It covers topics such as assembly language programming, procedures, macros, and interfacing with memory devices. The goal is to enable students to design 8086-based programs and systems.
This document provides information about the fourth semester subject "Microprocessor and Programming" for the Computer Engineering group. It includes the teaching and examination scheme, rationale, objectives, learning structure, and contents of the course. The course aims to teach students about the architecture and instruction set of 8085 and 8086 microprocessors. It covers topics such as assembly language programming, procedures, macros, and interfacing with memory. The goal is to enable students to design 8086-based programs and systems. Assessment includes theory exams, practical exams, and sessional work based on bi-weekly tests.
This document provides a syllabus for a B.E. Computer Engineering program from 2012-2015 at Savitribai Phule Pune University. The syllabus aims to blend concepts and advances using open-source technologies. It includes 16 electives in areas like cloud computing, mobile computing, web applications, and cyber security. Laboratory courses focus on utilizing state-of-the-art open-source software. The program objectives are to expose students to computer systems and applications, provide conceptual and interdisciplinary knowledge, and develop professional skills for the IT industry.
The document provides regulations and syllabus details for the Master of Technology in Computer Science and Engineering program at Pondicherry University. It outlines the eligibility criteria, duration of the course, medium of instruction, grading system and curriculum over four semesters. The curriculum includes both hard core and soft core subjects as well as electives in areas like algorithms, software engineering, databases, computer networks and security. Laboratory courses are also included as part of the program.
This document outlines the teaching and examination scheme for the fourth semester subject "Data Structure" in the Computer Engineering group. It includes the course code, teaching hours, examination structure, rationale, objectives, contents, practical skills and learning resources. The subject aims to teach different data structures, algorithms, their applications and comparisons. Students will learn concepts like arrays, stacks, queues, linked lists, trees and graphs through both theory and practical sessions. Assessment includes a theory exam worth 100 marks, practical exam worth 50 marks and term work worth 25 marks.
Tech Jobs Interviews Preparation - GeekGap Webinar #1
Part 1 - Algorithms & Data Structures
What is an algorithm?
What is a data structure (DS)?
Why study algorithms & DS?
How to assess good algorithms?
Algorithm & DS interviews structure
Case study: Binary Search
2 Binary Search variants
Part 2 - System Design
What is system design?
Why study system design?
System design interviews structure
Case study: ERD with Lucidchart
Demo Time: SQLAlchemy
Githu Repo: http://bit.ly/gg-io-webinar-1-github
by www.geekgap.io
This document provides an overview of a computer architecture course. It includes:
- The course information, including the web page, examination date, and textbook.
- A preliminary course plan that outlines the topics to be covered in each of the 12 lectures, including instruction pipelining, RISC architectures, superscalar architectures, and architectures for parallel computation.
- Basic introductions and explanations of computer architecture concepts like the von Neumann architecture, instruction execution, data representation, and the instruction cycle.
Mumbai University M.E computer engg syllabusShini Saji
The document outlines the revised syllabus for the Master of Engineering in Computer Engineering program at the University of Mumbai, effective from the 2012-2013 academic year. The program structure is presented across 4 semesters and includes courses in advanced algorithms, parallel computing, operating systems, cyber security, electives, seminars, and dissertation work. Details are provided on the teaching scheme, credit assignments, examination scheme, and syllabi for the core subjects.
Accelrys Announces Experiment Knowledge Base (EKB) for Enterprise Lab ManagementBIOVIA
The document discusses Accelrys' new Experiment Knowledge Base (EKB) solution for enterprise lab management. EKB aims to improve lab efficiency, knowledge capture and reuse by transforming scientific data into actionable knowledge. It integrates with existing lab systems and uses preconfigured interfaces and services to help customers intelligently plan, execute, analyze and manage materials development experiments. EKB is expected to accelerate innovation by reducing experiment repetition and costs while enabling scientists to learn from past experiments.
This document outlines the curriculum and syllabus for a Master of Technology (Computer Science and Engineering) program offered part-time at SRM University. It details the courses offered over six semesters, including topics covered, credit hours, and electives. Courses cover subjects such as parallel computer architecture, object oriented software engineering, database technology, computer communication, and a final project work. The total credits required to earn the MTech degree is 71.
(Paper) Efficient Evaluation Methods of Elementary Functions Suitable for SIM...Naoki Shibata
Naoki Shibata : Efficient Evaluation Methods of Elementary Functions Suitable for SIMD Computation, Journal of Computer Science on Research and Development, Proceedings of the International Supercomputing Conference ISC10., Volume 25, Numbers 1-2, pp. 25-32, DOI:10.1007/s00450-010-0108-2 (May. 2010).
http://ito-lab.naist.jp/~n-sibata/pdfs/isc10simd.pdf
http://freecode.com/projects/sleef
Data-parallel architectures like SIMD (Single Instruction Multiple Data) or SIMT (Single Instruction Multiple Thread) have been adopted in many recent CPU and GPU architectures. Although some SIMD and SIMT instruction sets include double-precision arithmetic and bitwise operations, there are no instructions dedicated to evaluating elementary functions like trigonometric functions in double precision. Thus, these functions have to be evaluated one by one using an FPU or using a software library. However, traditional algorithms for evaluating these elementary functions involve heavy use of conditional branches and/or table look-ups, which are not suitable for SIMD computation. In this paper, efficient methods are proposed for evaluating the sine, cosine, arc tangent, exponential and logarithmic functions in double precision without table look-ups, scattering from, or gathering into SIMD registers, or conditional branches. We implemented these methods using the Intel SSE2 instruction set to evaluate their accuracy and speed. The results showed that the average error was less than 0.67 ulp, and the maximum error was 6 ulps. The computation speed was faster than the FPUs on Intel Core 2 and Core i7 processors.
Colored petri nets theory and applicationsAbu Hussein
This document discusses colored Petri nets (CP-nets) and their applications. CP-nets combine Petri nets with programming languages to model systems involving concurrency, communication, and resource sharing. They allow for simulation and formal verification. The document provides examples of CP-net applications in various domains including protocols, software, hardware, control systems, and military systems. It also describes how CP-net models can be used to automatically generate code for system implementations.
Model Driven Requirements Engineering: Mapping the Field and BeyondSaïd Assar
This document summarizes the results of a study analyzing 29 research papers from past Model Driven Requirements Engineering (MoDRE) workshops. The study found that the papers primarily focused on modeling language representation and transformation techniques for deriving lower-level models. However, areas like elicitation, verification and validation, and requirements processes were underrepresented. The conclusion calls for more empirical research on applying model-driven engineering to all requirements engineering issues and non-functional requirements.
[Harvard CS264] 03 - Introduction to GPU Computing, CUDA Basicsnpinto
1. GPUs have many more cores than CPUs and are very good at processing large blocks of data in parallel.
2. GPUs can provide a significant speedup over CPUs for applications that map well to a data-parallel programming model by harnessing the power of many cores.
3. The throughput-oriented nature of GPUs makes them well-suited for algorithms where the same operation can be performed on many data elements independently.
VerticaPy allows users to perform machine learning and data science tasks using Python directly in the Vertica database. It provides tools for data exploration, preparation, modeling, evaluation and visualization. Models can be built and stored within Vertica for scalable deployment and management. VerticaPy aims to bring analytics to the next level by allowing users to leverage Vertica's in-database capabilities while working with Python.
This document outlines the curriculum for the course "Elective Theory II - Data Science and Big Data" for the VI semester of the Diploma in Computer Engineering program. The course covers 5 units over 80 hours on data science fundamentals, data modeling, and big data concepts including storage and processing. The objectives are to understand data science techniques, apply data analysis in Python and Excel, learn about big data characteristics and technologies like Hadoop, and explore applications of big data. Topics include linear regression, classification models, MapReduce, and using big data in fields such as marketing, healthcare, and advertising.
This document outlines the syllabus for an M.Sc. in Computer Science program under the Choice Based Credit System at Periyar University in Salem, India. It provides regulations for admission requirements, program duration, course structure, examinations and evaluations. The program is divided into 4 semesters with core and elective courses, as well as labs and projects. Courses cover topics such as algorithms, computer architecture, databases, data mining and software engineering. Exams include internal and external assessments with theory, practical and viva voce components. Students must complete courses, earn a minimum of 90 credits and meet other requirements to be eligible for classification and university ranking.
Iaetsd march c algorithm for embedded memories in fpgaIaetsd Iaetsd
The document discusses algorithms for testing embedded memories in FPGAs. It introduces the March C algorithm for memory testing and proposes an optimized March C algorithm. The optimized algorithm reduces testing time by applying concurrency - it tests multiple memory subgroups simultaneously. The document implements BIST architectures using both the basic and optimized March C algorithms and compares their performance in terms of time, area and speed for testing embedded memory in FPGAs. The optimized March C algorithm requires less time to test memory compared to other architectures.
This document outlines the curriculum for a Master of Technology (Computer Science and Engineering) degree program. It includes the course requirements and electives for each of the four semesters. In the first semester, students will take courses in mathematical foundations, computer architecture, data structures and algorithms, computer networks, and research methodology. They will also complete labs in network management and a term paper. The subsequent semesters include additional theory courses, labs, and electives in areas such as databases, distributed systems, software engineering, and a capstone project. The degree requires a total of 75 credits over four semesters.
The document provides a resume for Praveen Mehrotra. It summarizes his objective of seeking a full-time position in VLSI circuit design, education including a Master's in electrical engineering and Bachelor's in electronics and communications engineering, and professional experience including design engineering and internship roles. It also lists his skills in HDLs, software languages, and VLSI design tools and provides details of projects implemented during his education.
Zhimeng Luo received an MSEE from the University of Southern California in May 2016 with a 4.0 GPA. He is looking for a full-time position as a hardware engineer or ASIC design engineer. His skills include Verilog HDL, Perl scripting, Cadence tools, and Matlab. For his graduate studies, he implemented a 625MHz DDR3 memory controller in Verilog HDL and a 5-stage pipeline processor. He also designed a 5-stage pipeline including layout and simulation using domino logic, SRAM design, and Cadence. Zhimeng uses Perl scripting to aid SRAM simulation and verification.
This document discusses moving machine learning models from prototype to production. It outlines some common problems with the current workflow where moving to production often requires redevelopment from scratch. Some proposed solutions include using notebooks as APIs and developing analytics that are accessed via an API. It also discusses different data science platforms and architectures for building end-to-end machine learning systems, focusing on flexibility, security, testing and scalability for production environments. The document recommends a custom backend integrated with Spark via APIs as the best approach for the current project.
N. Selvaraj has over 12 years of experience in CAD/CAM/CAE software development and PLM/PDM systems. He has worked as a software engineer and technical lead for companies like Amada Soft India and Renault Nissan Technology and Business Centre India Private Limited. He has expertise in C++, C#, .NET, CATIA V6, Enovia V6, and finite element analysis packages like ANSYS and HyperMesh. Currently, he works as a BIM software development engineer in Singapore.
1. Parallel computation is needed to achieve high performance as modern processors have limitations despite features like caches, buses, and pipelines. Parallel computers use multiple CPUs working together to solve problems faster.
2. Flynn's classification categorizes computer architectures based on their instruction and data flows as single instruction stream single data stream (SISD), single instruction stream multiple data stream (SIMD), or multiple instruction stream multiple data stream (MIMD).
3. Important metrics for measuring parallel performance include speedup, which measures improvement over sequential execution, and efficiency, which relates speedup to number of processors used. According to Amdahl's law, even small amounts of sequential code limit maximum speedup attainable.
Automating materials science workflows with pymatgen, FireWorks, and atomateAnubhav Jain
FireWorks is a workflow management system that allows researchers to define and execute complex computational materials science workflows on local or remote computing resources in an automated manner. It provides features such as error detection and recovery, job scheduling, provenance tracking, and remote file access. The atomate library builds on FireWorks to provide a high-level interface for common materials simulation procedures like structure optimization, band structure calculation, and property prediction using popular codes like VASP. Together, these tools aim to make high-throughput computational materials discovery and design more accessible to researchers.
This document provides an overview of a computer architecture course. It includes:
- The course information, including the web page, examination date, and textbook.
- A preliminary course plan that outlines the topics to be covered in each of the 12 lectures, including instruction pipelining, RISC architectures, superscalar architectures, and architectures for parallel computation.
- Basic introductions and explanations of computer architecture concepts like the von Neumann architecture, instruction execution, data representation, and the instruction cycle.
Mumbai University M.E computer engg syllabusShini Saji
The document outlines the revised syllabus for the Master of Engineering in Computer Engineering program at the University of Mumbai, effective from the 2012-2013 academic year. The program structure is presented across 4 semesters and includes courses in advanced algorithms, parallel computing, operating systems, cyber security, electives, seminars, and dissertation work. Details are provided on the teaching scheme, credit assignments, examination scheme, and syllabi for the core subjects.
Accelrys Announces Experiment Knowledge Base (EKB) for Enterprise Lab ManagementBIOVIA
The document discusses Accelrys' new Experiment Knowledge Base (EKB) solution for enterprise lab management. EKB aims to improve lab efficiency, knowledge capture and reuse by transforming scientific data into actionable knowledge. It integrates with existing lab systems and uses preconfigured interfaces and services to help customers intelligently plan, execute, analyze and manage materials development experiments. EKB is expected to accelerate innovation by reducing experiment repetition and costs while enabling scientists to learn from past experiments.
This document outlines the curriculum and syllabus for a Master of Technology (Computer Science and Engineering) program offered part-time at SRM University. It details the courses offered over six semesters, including topics covered, credit hours, and electives. Courses cover subjects such as parallel computer architecture, object oriented software engineering, database technology, computer communication, and a final project work. The total credits required to earn the MTech degree is 71.
(Paper) Efficient Evaluation Methods of Elementary Functions Suitable for SIM...Naoki Shibata
Naoki Shibata : Efficient Evaluation Methods of Elementary Functions Suitable for SIMD Computation, Journal of Computer Science on Research and Development, Proceedings of the International Supercomputing Conference ISC10., Volume 25, Numbers 1-2, pp. 25-32, DOI:10.1007/s00450-010-0108-2 (May. 2010).
http://ito-lab.naist.jp/~n-sibata/pdfs/isc10simd.pdf
http://freecode.com/projects/sleef
Data-parallel architectures like SIMD (Single Instruction Multiple Data) or SIMT (Single Instruction Multiple Thread) have been adopted in many recent CPU and GPU architectures. Although some SIMD and SIMT instruction sets include double-precision arithmetic and bitwise operations, there are no instructions dedicated to evaluating elementary functions like trigonometric functions in double precision. Thus, these functions have to be evaluated one by one using an FPU or using a software library. However, traditional algorithms for evaluating these elementary functions involve heavy use of conditional branches and/or table look-ups, which are not suitable for SIMD computation. In this paper, efficient methods are proposed for evaluating the sine, cosine, arc tangent, exponential and logarithmic functions in double precision without table look-ups, scattering from, or gathering into SIMD registers, or conditional branches. We implemented these methods using the Intel SSE2 instruction set to evaluate their accuracy and speed. The results showed that the average error was less than 0.67 ulp, and the maximum error was 6 ulps. The computation speed was faster than the FPUs on Intel Core 2 and Core i7 processors.
Colored petri nets theory and applicationsAbu Hussein
This document discusses colored Petri nets (CP-nets) and their applications. CP-nets combine Petri nets with programming languages to model systems involving concurrency, communication, and resource sharing. They allow for simulation and formal verification. The document provides examples of CP-net applications in various domains including protocols, software, hardware, control systems, and military systems. It also describes how CP-net models can be used to automatically generate code for system implementations.
Model Driven Requirements Engineering: Mapping the Field and BeyondSaïd Assar
This document summarizes the results of a study analyzing 29 research papers from past Model Driven Requirements Engineering (MoDRE) workshops. The study found that the papers primarily focused on modeling language representation and transformation techniques for deriving lower-level models. However, areas like elicitation, verification and validation, and requirements processes were underrepresented. The conclusion calls for more empirical research on applying model-driven engineering to all requirements engineering issues and non-functional requirements.
[Harvard CS264] 03 - Introduction to GPU Computing, CUDA Basicsnpinto
1. GPUs have many more cores than CPUs and are very good at processing large blocks of data in parallel.
2. GPUs can provide a significant speedup over CPUs for applications that map well to a data-parallel programming model by harnessing the power of many cores.
3. The throughput-oriented nature of GPUs makes them well-suited for algorithms where the same operation can be performed on many data elements independently.
VerticaPy allows users to perform machine learning and data science tasks using Python directly in the Vertica database. It provides tools for data exploration, preparation, modeling, evaluation and visualization. Models can be built and stored within Vertica for scalable deployment and management. VerticaPy aims to bring analytics to the next level by allowing users to leverage Vertica's in-database capabilities while working with Python.
This document outlines the curriculum for the course "Elective Theory II - Data Science and Big Data" for the VI semester of the Diploma in Computer Engineering program. The course covers 5 units over 80 hours on data science fundamentals, data modeling, and big data concepts including storage and processing. The objectives are to understand data science techniques, apply data analysis in Python and Excel, learn about big data characteristics and technologies like Hadoop, and explore applications of big data. Topics include linear regression, classification models, MapReduce, and using big data in fields such as marketing, healthcare, and advertising.
This document outlines the syllabus for an M.Sc. in Computer Science program under the Choice Based Credit System at Periyar University in Salem, India. It provides regulations for admission requirements, program duration, course structure, examinations and evaluations. The program is divided into 4 semesters with core and elective courses, as well as labs and projects. Courses cover topics such as algorithms, computer architecture, databases, data mining and software engineering. Exams include internal and external assessments with theory, practical and viva voce components. Students must complete courses, earn a minimum of 90 credits and meet other requirements to be eligible for classification and university ranking.
Iaetsd march c algorithm for embedded memories in fpgaIaetsd Iaetsd
The document discusses algorithms for testing embedded memories in FPGAs. It introduces the March C algorithm for memory testing and proposes an optimized March C algorithm. The optimized algorithm reduces testing time by applying concurrency - it tests multiple memory subgroups simultaneously. The document implements BIST architectures using both the basic and optimized March C algorithms and compares their performance in terms of time, area and speed for testing embedded memory in FPGAs. The optimized March C algorithm requires less time to test memory compared to other architectures.
This document outlines the curriculum for a Master of Technology (Computer Science and Engineering) degree program. It includes the course requirements and electives for each of the four semesters. In the first semester, students will take courses in mathematical foundations, computer architecture, data structures and algorithms, computer networks, and research methodology. They will also complete labs in network management and a term paper. The subsequent semesters include additional theory courses, labs, and electives in areas such as databases, distributed systems, software engineering, and a capstone project. The degree requires a total of 75 credits over four semesters.
The document provides a resume for Praveen Mehrotra. It summarizes his objective of seeking a full-time position in VLSI circuit design, education including a Master's in electrical engineering and Bachelor's in electronics and communications engineering, and professional experience including design engineering and internship roles. It also lists his skills in HDLs, software languages, and VLSI design tools and provides details of projects implemented during his education.
Zhimeng Luo received an MSEE from the University of Southern California in May 2016 with a 4.0 GPA. He is looking for a full-time position as a hardware engineer or ASIC design engineer. His skills include Verilog HDL, Perl scripting, Cadence tools, and Matlab. For his graduate studies, he implemented a 625MHz DDR3 memory controller in Verilog HDL and a 5-stage pipeline processor. He also designed a 5-stage pipeline including layout and simulation using domino logic, SRAM design, and Cadence. Zhimeng uses Perl scripting to aid SRAM simulation and verification.
This document discusses moving machine learning models from prototype to production. It outlines some common problems with the current workflow where moving to production often requires redevelopment from scratch. Some proposed solutions include using notebooks as APIs and developing analytics that are accessed via an API. It also discusses different data science platforms and architectures for building end-to-end machine learning systems, focusing on flexibility, security, testing and scalability for production environments. The document recommends a custom backend integrated with Spark via APIs as the best approach for the current project.
N. Selvaraj has over 12 years of experience in CAD/CAM/CAE software development and PLM/PDM systems. He has worked as a software engineer and technical lead for companies like Amada Soft India and Renault Nissan Technology and Business Centre India Private Limited. He has expertise in C++, C#, .NET, CATIA V6, Enovia V6, and finite element analysis packages like ANSYS and HyperMesh. Currently, he works as a BIM software development engineer in Singapore.
1. Parallel computation is needed to achieve high performance as modern processors have limitations despite features like caches, buses, and pipelines. Parallel computers use multiple CPUs working together to solve problems faster.
2. Flynn's classification categorizes computer architectures based on their instruction and data flows as single instruction stream single data stream (SISD), single instruction stream multiple data stream (SIMD), or multiple instruction stream multiple data stream (MIMD).
3. Important metrics for measuring parallel performance include speedup, which measures improvement over sequential execution, and efficiency, which relates speedup to number of processors used. According to Amdahl's law, even small amounts of sequential code limit maximum speedup attainable.
Automating materials science workflows with pymatgen, FireWorks, and atomateAnubhav Jain
FireWorks is a workflow management system that allows researchers to define and execute complex computational materials science workflows on local or remote computing resources in an automated manner. It provides features such as error detection and recovery, job scheduling, provenance tracking, and remote file access. The atomate library builds on FireWorks to provide a high-level interface for common materials simulation procedures like structure optimization, band structure calculation, and property prediction using popular codes like VASP. Together, these tools aim to make high-throughput computational materials discovery and design more accessible to researchers.
Automating materials science workflows with pymatgen, FireWorks, and atomate
Kayal
1. Criterion VIII: Curriculum (125)
List all the course modules along with their objectives and outcomes (in Part III):
Additional
Units Science/
theory/lab/
Comments
HSS/
Assignments
Professional Mapping
Lab (no
Course / tests
Theory
of exp)
Core, with POs.
needed to
Elective or
meet
Breadth
objectives
SEMESTER - I
Technical English 5 - HSS g,i Yes -
Engineering Mathematics - I 5 - Science a,e Yes -
Applied Physics 5 - Science a,b,c,e,k,l No -
Applied Chemistry 5 - Science a.l,k, No -
Fundamentals of
5 - Core c,e,a Yes -
Programming
Basic Civil and Mechanical
6 - Core c,e,h,b,i No -
Engineering
Applied Physics Laboratory - 12 Science c,e,k,b, No -
Applied Chemistry
- 12 Science a No -
Laboratory
Programming Laboratory - 12 Core a,b,e No -
Engineering Practice lab - 18 Core a,b,e,f,k No
SEMESTER – II
Technical English – II
Mathematics – II
Engineering Physics – II
Engineering Chemistry – II
Engineering Mechanics
Circuit Theory
Electric Circuits and Electron
Devices
Basic Electrical &
Electronics Engineering
Basic Civil & Mechanical
Engineering
Computer Practice
Laboratory-II
Physics & Chemistry
Laboratory - II
SEMESTER III
Transforms and Partial
5 - Science a,e,k No -
Differential Equations
Page: 1
2. Data Structures 5 - Core a,b,e,m Yes -
Digital Principles and
5 - Core
Systems Design
Object Oriented
5 - Core
Programming
Analog and Digital
5 - Core
Communication
Environmental Science and
5 -
Engineering
Digital Lab - 10 Core
Data Structures Lab - 10 Core
Object Oriented
- 10 Core
Programming Lab
SEMESTER IV
Probability and Queueing
5 - Science a,e No -
Theory
Design and Analysis of
5 - Core a,b,e,h,i,j,k Yes -
Algorithms
Microprocessors and
5 - Core
Microcontrollers
Computer Organization and
5 - Core
Architecture
Operating Systems 5 - Core
Database Management
5 - Core
Systems
Operating Systems Lab - 10 Core
Data Base Management
- 11 Core
Systems Lab
Microprocessors Lab - 06 Core
SEMESTER V
Discrete Mathematics 5 - Core
PC Hardware and Trouble
5 - Core
Shooting
Software Engineering 5 - Core
Formal Languages and
5 - Core
Automata Theory
Java Programming 5 - Core
Computer Networks 5 - Core
Software Engineering Lab - 06 Core
Java Programming Lab - 06 Core
Computer Networks Lab - 08 Core
SEMESTER VI
Page: 2
3. Theory of Computation 5 - Core
Open Source Software 5 - Core
Object Oriented System
5 - Core
Design
Numerical Methods 5 - Core
Computer Graphics 5 - Core
Open Source Lab - 07 Core
Object Oriented Systems
- 04 Core
Lab
Computer Graphics Lab - 03 Core
SEMESTER VII
Artificial Intelligence 5 - Core
Cryptography and
5 - Core
Network Security
Internet Programming 5 - Core
Principles of Compiler
5 - Core
Design
Compiler Design Lab - 10 Core
Internet Programming Lab - 10 Core
System Software Lab - 04 Core
SEMESTER VIII
Open Source Tools and
5 - Core
Components
List of Electives Specified by Affiliated University
Group-A (For VII Semester)
S.No. Course Title
1. Embedded System
2. VLSI Design
3. Visual Programming
4. Optimization Techniques
5. Professional Ethics
6. Mobile Computing
7. Management Information Systems
8. Middleware Technology
Page: 3
4. Group-B (For VII Semester)
S.No. Course Title
1. Software Testing
2. Software Project Management
3. Grid Computing
4. Distributed Computing
5. Parallel Processing
6. Soft Computing
7. ADHOC and Sensor Networks
8. Data Warehousing and Data Mining
9. Client Server Computing
10. Real Time Systems
11. Total Quality Management
12.
13.
Group-C (For VIII Semester)
S.No. Course Title
1. Digital Image Processing
2. Natural Language Processing
3. System Modeling And Simulation
4. Software Quality Management
5. High Speed Networks
6. C # And .Net Framework
7. Network Programming And Management
8. Enterprise Resource Planning
9. Information Security
10 Cloud computing
11. Real Time Systems
12 Semantic Web
13. Service Oriented Architecture
14. Disaster Management
Page: 4
5. VIII-P.1 Contents of Basic sciences, HSS, Professional core and Electives, and Breadth (20)
The Department curriculum provides students with a solid foundation upon which they can build
to meet the challenges associated with their individual career paths, and to adapt to the rapidly changing
technologies faced by today's engineers. In the first year of study, students develop a strong scientific
foundation by taking courses in mathematics, physics, chemistry, basics of mechanical and civil
engineering. From second year onwards, object oriented programming and software development
methodology remain the central theme of the study and henceforth the curriculum is designed to provide
students a sound fundamental education in all areas of computer programming and technologies such as
data structures, computer architecture, C, C++, JAVA programming languages, software engineering,
computer networks, database management systems, operating systems, component based technology, etc.
Percentage of balanced courses:
Science HSS Professional Core Elective Breadth
16.6 4.5 71.2 7.5 -
In order to take the student above the examination requirements prescribed by the University,
certain advanced topics are also taught as contents beyond the syllabi, in each subject which are indicated
in the course file and the topics covered in the classes. In the course file every subject teacher files, subject
notes, question bank, University Examination Question paper, etc.
VIII P.5 PROJECT WORK (10):
To encourage the development of team-work capabilities one of the program outcomes students
are encouraged to work in teams. Students are doing their project based on their knowledge acquired in
theories and laboratory, or industry needs. Industry has been one of the main drivers of this changing
landscape, especially in engineering education.
Students have done their project in the following industries:
1. Apex Global solutions Chennai.
2. Checktronix, Chennai.
3. Aksaya software, Bangalore.
4. Phoenix Solutions, Chennai.
5. Info valley, Chennai.
6. Stin Soft, Bangalore.
7. Pantech solutions, Chennai.
8. Pragathi Hi Tech Systems, Coimbatore.
9. Crisp System, Coimbatore.
10. Bulk Space 4 u.com, Coimbatore.
11. Pragmatic Systems Inc, Coimbatore.
12. Arrin Solution, Coimbatore.
13. STC Technologies, Coimbatore.
14. Cassiopeia, Coimbatore.
Page: 5
6. Project Areas of Project Contribution/ Achievements / Matching with the
S.NO Name of the Student(s)
Title Specialization Supervisor(s) Research Output stated POs
CAY(2012-2013)
REMOTE CONTROL PC
1 ANJU P. BABY THROUGH INTRANET
JAVA
DECENTRALIZED QOS
AWARE CHECK POINTING
2 ASHOK. R
MOG
.NET
ONLINE SERVICE
3 BALAJI. K.S TRACKING SYSTEM WITH
SMS TECHNOLOGY
SECRET MESSAGE
4 COMMUNICATION
CIBEX. V.S JAVA
DELAY ANALYSIS FOR
WIRELESS
5 DEVARAJ. R
NETWORKS(WITH SINGLE
HUP TRAFFIC)
Page: 6
7. ADAPTIVE SERVICE
BASED SOFTWARE
6 DINESH KUMAR. V
SYSTEM
.NET
ISSUE TRACKING SYSTEM
7 DINESH. A
JAVA
SECURE EMAIL SYSTEM
GURUMOHAN USING ADVANCED
8
REDDY.M ENCRYPTION STANDARD
JAVA
DYNAMIC ROUTING WITH
9 HASHIF ALI. P.K SECURITY
CONSIDERATION
ISSUE TRACKING SYSTEM
10 KARUNYA B.S
JAVA
CAYm1(2011-2012)
TWO WAY PIRACY
AUTHENTICATION
11 KATHIRESAN. T
SOFTWARE
.NET
SECURING DATA
PORTABILITY AND
12 KUMARESAN. R LEAKAGE THROUGH
ADVANCED ENCRYPTION
JAVA
ANT COLONY
13 MANISH. M
OPTIMIZATION
Page: 7
8. NETWORK BASED
TRAITOR TRACING
14 MANORANJITHA. S
TECHNIQUE USING
TRAFFIC PATTERN
FLEXIBLE ROLL BACK
RECOVERY
DYNAMIC
15 MD SALATURAHMAN
HETEROGENEOUS GRID
COMPUTING
JAVA
DYNAMIC ROUTING WITH
SECURITY
16 MOHAMED ASHRAF. M
CONSIDERATION
JAVA
DYNAMIC INTELLIGENT
AGENT FOR NETWORK
17 MOHAN RAJ. K FAULT DETECTION
SYSTEM
.NET
DELAY ANALYSIS FOR
WIRELESS
18 MYILSAMY. S NETWORKS(WITH SINGLE
HUP TRAFFIC)
.NET
Page: 8
9. WEB BASED ATM
19 NAVIN BASKARAN R
.NET
ADAPTIVE SERVICE
BASED SOFTWARE
20 NAVIS SEBASTIAN. J
SYSTEM
.NET
CAYm2(2010-2011)
FACE RECOGNITION
21 NIKHIL PRASAD. C USING LAPLACIAN FACES
JAVA
EFFICIENT MULTICAST
PACKET
22 NITHIN KRISHNAN
AUTHENTICATION
JAVA
SECRET MESSAGE
23 NIYAS K.V COMMUNICATION
JAVA
Page: 9
10. NETWORK BASED
TRAITOR TRACING
24 RAJA NARENDRAN. R TECHNIQUE USING
TRAFFIC PATTERN
.NET
NETWORK BASED
TRAITOR TRACING
25 RAJ VISHNU.B TECHNIQUE USING
TRAFFIC PATTERN
.NET
TWO WAY PIRACY
AUTHENTICATION
26 RAM PRASATH.S
SOFTWARE
.NET
TRAJECTORY WARE
27 RAMYA. P HOUSE
.NET
LAYERED APPROACH
USING CONDITIONAL
28 ROSE MARY PAUL RANDOM FIELD FOR
INTRUSION DETECTION
JAVA
IMAGE PROCESSING WITH
29 SANGAMESWARAN. N VISUAL CRYPTOGRAPHY
.NET
Page: 10
11. TWO WAY PIRACY
AUTHENTICATION
30 SARAVANA KUMAR. B
SOFTWARE
.NET
ANT COLONY
31 SATHEESH.T OPTIMIZATION
.NET
CONTINUOUS
MONITORING OF SPATIAL
QUERIES IN WIRELESS
32 SATHISH KUMAR. M
BROADCAST
ENVIRONMENTS
JAVA
ANT COLONY
33 SATHIS KUMAR. S OPTIMIZATION
.NET
DECENTRALIZED QOS
AWARE CHECK POINTING
34 SATHISH KUMAR. S.R
MOG
.NET
DECENTRALIZED QOS
AWARE CHECK POINTING
35 SATHISH KUMAR. T
MOG
.NET
36 SHALINI. R
DYNAMIC ROUTING WITH
SECURITY
37 SHYAMJITH. K
CONSIDERATION
JAVA
LAYERED APPROACH
USING CONDITIONAL
38 SONA PAUL RANDOM FIELD FOR
INTRUSION DETECTION
JAVA
Page: 11
12. FACE RECOGNITION
39 SONU. P.S USING LAPLACIAN FACES
JAVA
EFFICIENT MULTICAST
PACKET
40 SONY P.J
AUTHENTICATION
JAVA
41 SREEKUTTAN. P.S
FLEXIBLE ROLL BACK
RECOVERY
DYNAMIC
42 SRIDHAR. S
HETEROGENEOUS GRID
COMPUTING
JAVA
LAYERED APPROACH
USING CONDITIONAL
SOORYA
43 RANDOM FIELD FOR
SREEDHARAN. K.V
INTRUSION DETECTION
JAVA
CONTINUOUS
MONITORING OF SPATIAL
QUERIES IN WIRELESS
44 TAMILARASAN. M
BROADCAST
ENVIRONMENTS
JAVA
IMAGE PROCESSING WITH
45 THIRUMURUGAN. A VISUAL CRYPTOGRAPHY
.NET
SECURE EMAIL SYSTEM
USING ADVANCED
46 THIYAGARAJAN. P
ENCRYPTION STANDARD
JAVA
Page: 12
13. FLEXIBLE ROLL BACK
RECOVERY
DYNAMIC
48 VANATHI. S
HETEROGENEOUS GRID
COMPUTING
JAVA
REMOTE CONTROL PC
49 VIGNESH. P THROUGH INTRANET
JAVA
SECRET MESSAGE
50 VISHNU. T COMMUNICATION
JAVA
51 YUVARAJ. K
DELAY ANALYSIS FOR
WIRELESS
52 YUVARAJ. M NETWORKS(WITH SINGLE
HUP TRAFFIC)
.NET
WEB BASED ATM
53 ABBASS ALI.K
.NET
EFFICIENT MULTICAST
PACKET
54 ANOOP JOSE
AUTHENTICATION
JAVA
SECURING DATA
PORTABILITY AND
55 BALRAJ.S LEAKAGE THROUGH
ADVANCED ENCRYPTION
JAVA
FACE RECOGNITION
56 CHRISTY RAJU USING LAPLACIAN FACES
JAVA
ISSUE TRACKING SYSTEM
57 ENIYA.M
JAVA
Page: 13
14. ONLINE SERVICE
TRACKING SYSTEM WITH
58 KAMALAKKANNAN.G
SMS TECHNOLOGY
.NET
DYNAMIC INTELLIGENT
AGENT FOR NETWORK
59 KARTHIK.R FAULT DETECTION
SYSTEM
.NET
DYNAMIC INTELLIGENT
AGENT FOR NETWORK
60 MEIYALAGAN.L FAULT DETECTION
SYSTEM
.NET
TRAJECTORY WARE
61 PRABHAKARAN.P HOUSE
.NET
CONTINUOUS
MONITORING OF SPATIAL
QUERIES IN WIRELESS
62 PRADEEP.S
BROADCAST
ENVIRONMENTS
JAVA
ONLINE SERVICE
TRACKING SYSTEM WITH
63 RAJKUMAR.M
SMS TECHNOLOGY
.NET
TRAJECTORY WARE
64 RAMESH KUMAR.S HOUSE
.NET
SECURING DATA
PORTABILITY AND
65 STANLY.W LEAKAGE THROUGH
ADVANCED ENCRYPTION
JAVA
Page: 14
15. SECURE EMAIL SYSTEM
USING ADVANCED
66 VENKATACHALAM.P
ENCRYPTION STANDARD
JAVA
CALLTAXI MAINTENACE
67 LAVANYA.R SYSTEM
JAVA
IG-Internal Guide EG-External Guide
Page: 15