Course Profiles of CSE Elective Courses
CSE313:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE246 Algorithms
Course Objective: This is an elective course and builds up the students’ theoretical understanding of different models of computation and their limitations. The course will address Finite Automata and Regular Expressions, Context-Free Grammars and Pushdown Automata, Turing Machines and Undecidability, and Complexity Theory. Knowledge of this course will be needed as prerequisite knowledge for CSE471 Compiler Design course.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Demonstrate and use Finite Automata and Regular Expressions as model of computation; use these knowledge and write report in real-life problem. |
| CO2 | Demonstrate and use Context-Free Grammar and Pushdown Automata as model of computation; use these knowledge and write report in real-life problem. |
| CO3 | Demonstrate, use, and characterize Turing Machines and Undecidability as model of real world computation and its limitation; use these knowledge and write report in real-life problem. |
| CO4 | Demonstrate and use Complexity Theory to determine resources required by an algorithm; use these knowledge and write report in real-life problem. |
Course Contents
| Course Topic | CO |
|---|---|
| Finite Automata | CO1 |
| Regular Expressions | CO1 |
| Nondeterminism | CO1 |
| Properties of Regular Languages | CO1 |
| Context-Free Grammars | CO2 |
| Pushdown Automata | CO2 |
| Grammars and Equivalences | CO2 |
| Properties of Context-Free Languages | CO2 |
| Turing Machines | CO3 |
| Variations of Turing Machines | CO3 |
| Decidable Problems | CO3 |
| Undecidability | CO3 |
| Time Complexity | CO4 |
| Space Complexity | CO4 |
| NP-Completeness | CO4 |
| Assignments with reports and presentations | CO1, CO2, CO3, CO4 |
CSE 350:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credits | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite:CSE 251 Electronic Circuits
Course Objective: This course includes the evolution trend of computer networks and the procedure of transmitting data over the network by resolving the conflicting issues arising in the course of transmission. The key aspects of transmission, interfacing, link control, and multiplexing are examined.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Interpret data communication concepts, backbone, protocols and architecture. |
| CO2 | Apply and analyze data transmission mechanisms. |
| CO3 | Apply and analyze different aspects of reliability in data communication; demonstrate skills and write report on data communication problems. |
| CO4 | Apply and examine data transformation techniques for effective data communication; demonstrate skills and write report on data communication problems. |
Course Contents
| Course Topic | CO |
|---|---|
| Data communication model, communication tasks | CO1 |
| Introduction to network standard and protocols, Protocol Architecture: OSI standard protocol, TCP/IP protocol suite | CO1 |
| Analog and Digital Transmission | CO2 |
| Transmission impairments, Channel Capacity: Nyquist, Shannons | CO2 |
| Guided transmission media, Wireless transmission media, wireless propagation | CO2 |
| Signal encoding techniques | CO2 |
| Synchronous Transmission, Asynchronous Transmission | CO3 |
| Interfacing | CO3 |
| Types of errors, Error detection: parity check, CRC, checksum
Error correction: Hamming code |
CO3 |
| Flow control techniques– stop-and-wait, sliding window, HDLC | CO3 |
| ARQ techniques– stop-and-wait ARQ, go-back-n ARQ, selective reject | CO3 |
| Multiplexing – FDM, TDM, WDM | CO4 |
| Assignments with reports and presentations | CO3, CO4 |
CSE355:
Credit Hours & Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE345 Digital Logic Design
Course Objective: This course is an elective course and builds up the students’ ability to understand advanced features of digital (combinational and sequential) circuits and design and synthesize digital circuits using computer-aided techniques. The course will address advanced features of digital circuits, designing and synthesizing digital circuits using Verilog Hardware Description Language (HDL), and designing and synthesizing application specific integrated circuits using Programmable Logic Devices (PLDs).
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Interpret and apply advanced features of digital (combinational and sequential) circuits as the pre-design concepts for computer-aided design of digital circuits. |
| CO2 | Examine, choose, and develop Verilog HDL modeling techniques; perform and demonstrate skills, and write report to design and test digital circuits. |
| CO3 | Examine, choose, and develop Verilog HDL techniques; perform and demonstrate skills, and write report to synthesize digital circuits. |
| CO4 | Examine and choose Programmable Logic Devices (PLDs) and design digital circuits using PLDs as Application Specific Integrated Circuits (ASICs). |
Course Contents
| Course Topic | CO |
|---|---|
| Hazards in Combinational circuits | CO1 |
| Busses and three-state devices | CO1 |
| Mealy and Moor type finite state machines (sequential circuits) | CO1 |
| Register Transfer Logic (RTL) design, Algorithmic State Machines (ASM) | CO1 |
| Structural Verilog modeling of combinational circuits | CO2 |
| Logic simulation, design verification, and test methodology in Verilog-based designs. | CO2 |
| Behavioral Verilog modeling of combinational circuits | CO2 |
| Verilog-based design of datapath elements and datapath controllers | CO2 |
| Logic synthesis using Verilog | CO3 |
| RTL synthesis using Verilog | CO3 |
| High-level synthesis using Verilog | CO3 |
| Verilog-based synthesis of datapath elements and datapath controllers | CO3 |
| Programmable Logic Array (PLA), Verilog modeling of PLA | CO4 |
| Programmable Array Logic (PAL), Verilog modeling of PAL | CO4 |
| Field-Programmable Gate Array (FPGA), Verilog-based design for FPGA | CO4 |
| ASIC synthesis with FPGA | CO4 |
| Mini project | CO2, CO3 |
CSE366:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE246 Algorithms
Course Objective:This course introduces the fundamental concepts and knowledge of Artificial Intelligence (AI) principles and techniques, the state-of-the-art models and algorithms used to undertake these problems. This course is also designed to expose students to the frontiers of AI-intensive computing, while providing a sufficiently strong foundation to encourage further research in machine learning. Knowledge of this course will be needed as prerequisite knowledge for future courses such as CSE475 Machine Learning, CSE477Data Mining and CSE492 Robotics.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Interpret and apply the key components and classical algorithms of artificial intelligence for realistic problem solving. |
| CO2 | Apply and examine game theoretical concepts and practices for solving non-conventional real-life situations. |
| CO3 | Apply and examine Genetic Algorithms and Artificial Neural Networks (ANN) for solving complex search and optimization problems. |
| CO4 | Use statistical methods and machine learning for solving complex AI related problems; perform and demonstrate skills and write report on complex AI related problems. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction to Artificial Intelligence, Intelligent Agents | CO1 |
| Solving problem by searching, Uniformed Search Strategies, Informed Search Strategies | CO1 |
| Constraint Satisfaction Problem | CO1 |
| Games and adversarial search, Games vs. single-agent search, Game tree, Alpha-beta pruning | CO2 |
| Introduction of Genetic Algorithm, GA Terminology | CO3 |
| First-Order Logic, Knowledge Engineering in First-Order Logic | CO2 |
| Planning, The Planning Problem, Planning Algorithms | CO2 |
| Game Theory, Nash Equilibrium and Mixed Strategy equilibrium | CO2 |
| Uncertainty, Acting under Uncertainty, Basic Probability Notation, Bayes’ Rule | CO3 |
| Convolutional Neural Network, Convolution Layer
Pooling Layer, Fully Connected Layer |
CO3 |
| Probabilistic Reasoning over Time, Hidden Markov Models, Kalman Filters | CO3 |
| Learning from Observations, Knowledge in Learning | CO4 |
| Statistical Learning Methods | CO4 |
| Reinforcement Learning | CO4 |
| Mini project | CO4 |
CSE406:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite:CSE405 Computer Network
Course Objective: This coursewill cover the building blocks of Internet of Things (IoTs) and their characteristics. Domain specific IoTsand their real-world applications will be developed here. This course also will introduce the programming aspects of IoTswith a view towards rapid prototyping of complex IoT applications.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Understand the building blocks of IoTs and their characteristics with real-world applications. |
| CO2 | Understand, apply, and examinedifferentarchitectures for different levels of complex IoT applications. |
| CO3 | ExamineIoT data analytics and justifyvarious tools for IoT; perform and demonstrate skills, and write report on realistic data analytics. |
| CO4 | Choose and justify software and hardware tools, developsource code for various IoT domains,perform and demonstrate skills, and write report on realistic IoT development. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction to IoT | CO1 |
| Basics of Networking | CO1 |
| Communication Protocols | CO1 |
| Sensor Networks: Machine-to-Machine Communications | CO1 |
| Interoperability in IoT: Integration of Sensors and Actuators | CO2 |
| Implementation of IoTwith programmable devices | CO2 |
| Software Defined Networking(SDN) for IoT: Data Handling and Analytics | CO3 |
| Cloud Computing | CO3 |
| Fog Computing: Smart Cities and Smart Homes | CO3 |
| Connected Vehicles: Smart Grid | CO3 |
| Industrial IoT: Agriculture, Healthcare, Activity Monitoring and related case studies | CO4 |
| Mini project | CO3, CO4 |
CSE420:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE246 Algorithms
Course Objective: The course focuses on all aspects of fundamental computer graphics, including 2D/3D object representations, transformations, modeling and rendering algorithms. The course also aims to provide a good foundation for OpenGL programming, which is a widely accepted standard for developing graphics applications. The course will assume a good background in programming in C or C++ and a background in mathematics including familiarity with the theory and use of coordinate geometry and of linear algebra such as matrix multiplication.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Understandcomputer graphics system and implement different graphics primitives for drawing a graphics scene. |
| CO2 | Understand, apply, and examinedifferent techniques of clipping, two dimensional transformations, and three-dimensional transformations and viewing for manipulating complex graphics scenario using OpenGL; perform and demonstrate these skills and write reportfor realistic problem solving. |
| CO3 | Understandand applythe basics of color perception and different color models used in computer graphics. |
| CO4 | Apply and Analyze different modeling, rendering, shading and animation techniques; develop and justify program by integrating these techniques to create 3D objects using OpenGL; perform and demonstrate these knowledge and write report for realistic problem solving. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction: History of computer graphics, graphics architectures and software. | CO1 |
| Efficient implementation of different graphics primitives like Line and circle, and Ellipse. | CO1 |
| Clipping, polygonal fill | CO2 |
| Geometric transformations: 2D transformations (translation, rotation, scaling, shear), homogeneous coordinates, concatenation, current transformation and matrix stacks. | CO2 |
| Geometric transformations: 3D transformations (translation, rotation, scaling) | CO2 |
| Three dimensional viewing, specifying views, affine transformation in 3D, projective transformations. | CO2 |
| Graphics Programming: Getting started with OpenGL, Input and Interaction in OpenGL | CO2 |
| Color perception, color models (RGB, CMY, HLS), color transformations. Color in OpenGL. RGB and Indexed color. | CO3 |
| Introduction to hidden surface removal (z buffer). | CO4 |
| Generate realistic 3D color object using OpenGL | CO4 |
| Curve and surface representation | CO4 |
| Image rendering using ray tracing | CO4 |
| Mini project | CO2
CO4 |
CSE422:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite:CSE246 Algorithms
Course Objective: The course is an elective course and aims at giving the students the knowledge of the basic concepts in the area of modeling and simulation. The course will focus on modeling and simulation of discrete and continuous system. The course will address the modeling and analyzing input and output for a simulation model and generating random numbers for simulation experiments. Simulation tools will be used to conduct experiments.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Understandand usefundamental concepts of computer simulation and its role in engineering problem solving. |
| CO2 | Understand random number variates and apply them to develop simulation models. |
| CO3 | Compute and analyze the output from a terminating simulation of an engineering problem. C3, C4 |
| CO4 | Chooseand examinesoftware tools, perform and demonstrate skills, and write report to model and build a simulation model with basic operations and statistical analysis of output. |
Course Contents and Learning/Assessment Levels
| Course Topic | CO |
|---|---|
| Introduction to simulation modeling | CO1 |
| Review of basic probability and statistics | CO1 |
| Selecting input probability distributions | CO2 |
| Random Number Generators | CO2 |
| Generating Random Variates | CO2 |
| Output Data Analysis for a Single System | CO3 |
| Verification/Validation | CO3 |
| Assignments & Mini Projects with reports and presentations | CO4 |
CSE423:
Credit Hours and Teaching Scheme
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE412 Software Engineering
Course Objective: The objective of this course is to familiarize the students with the fundamental concepts of software architecture, the proprieties and applicability of the different architecture styles. Student will also learn popular design patterns, software components, reusable architectures and the relations of all these concepts with the software reuse.
Course Outcomes (COs)
After completion of this course students will be able to:
| CO1 | Understand and usethe various architecture styles for software systems. |
| CO2 | Choose and evaluate alternative architectures in terms of design and reuse to solve various complex software engineering problems. |
| CO3 | Apply, examine, and justifydesign patterns, and methods and techniques of software reuse. |
| CO4 | Choose, examine, and justify software tools, perform and demonstrate skills, and write report to build a software system following a architecture specification, selecting and applying design patterns and using a component-based development method. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction to fundamentals of software design, concepts, and principles. | CO1 |
| Micro and macro architectures: design patterns, frameworks and production lines | CO1 |
| Types of software patterns: architecture patterns, design patterns, idiomatic structures | CO2 |
| Architecture styles, reference models and architectures: pipes and filters, data abstraction, object-orientation, even-based systems, layered systems, repositories, interpreters, process-control systems. | CO2 |
| Design, evaluation and refinement of software architectures | CO3 |
| Representation and Documentation of software architectures | CO3 |
| Reuse of software architectures: production lines, frameworks, software components | CO3 |
| Case Study: Simple and complex technological architectures with report. | CO4 |
| Assignment and Mini Project with report and presentation | CO4 |
CSE425:
Credit Hours & Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite:CSE103 Structured Programming
Course Objective:This course will emphasize the development of numerical algorithms to provide solutions to common problems formulated in science and engineering. The primary objective of the course is to develop the basic understanding of the construction of numerical algorithms, and more importantly, the applicability and limits of their appropriate use.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Understandthe basic numerical techniques for solving mathematicalsystems. |
| CO2 | Implement numerical techniques to solve computational problems. |
| CO3 | Apply and analyzenumerical techniques considering the accuracy of the techniques. |
| CO4 | Deriveappropriate numerical techniques; perform and demonstrate those skills and write report on solving complex engineering problems. |
Course Contents
| Course Topic | CO |
|---|---|
| Basic idea of Numerical methods | CO1 |
| Data and Information concept | CO1 |
| Root Finding Methods (Bracketing Methods) | CO2 |
| Root Finding Methods (Open-End Methods) | CO3 |
| Introduction to system of linear equations | CO2 |
| Iterative methods for linear equations | CO2 |
| Direct Solution of linear equations (existence of solution) | CO3 |
| Direct analytical methods for linear equations | CO3 |
| Curve fitting and Line curve fitting | CO3 |
| Interpolation with unequal intervals | CO2 |
| Interpolation with equal intervals | CO3 |
| Numerical solution of ordinary differential equations | CO4 |
| Numerical Integration | CO4 |
| Mini project | CO4 |
CSE428:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite:CSE412 Software Engineering
Course Objective: This course builds up the students’ ability to design user interfaces based on the capabilities of computer technology and the needs of human factors. They will learn to evaluate and design usable and appropriate software based on the psychological, social, and technical analysis. They will become familiar with the variety of design and evaluation methods used in interaction design and will get experience with these techniques.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Explain, choose and distinguish the capabilities of both humans and computers from the viewpoint of human information processing. |
| CO2 | Analyze and identify user models, user support, socio-organizational issues, and stakeholder requirements of HCI systems. |
| CO3 | Choose, analyze, and justify the HCI design principles, standards, and guidelines for designing HCI systems. |
| CO4 | Apply, examine, select, and design advanced HCI methodologies and technologies for realistic problem solving; perform and demonstrate skills and write report on realistic interaction design. |
Course Contents
| Course Topic | CO |
|---|---|
| Foundations of Human–Computer Interaction:Human Capabilities, The Computer, The Interaction, Paradigms | CO1 |
| HCI Design Process: Interaction Design Basics, HCI in the Software Process, Design Rules, Universal Design | CO1 |
| Implementation Support: Implementation Tools | CO2 |
| Users Models: Cognitive Models, Socio-organizational Issues and Stakeholder Requirements | CO2 |
| Evaluation and User Support: Evaluation, User Support | CO3 |
| Task Models and Dialogs: Analyzing Tasks, Dialog Notations and Design | CO3 |
| Augmented Reality, Hypertext and Multimedia:Groupware and Computer-supported Collaborative Work, Ubiquitous Computing, Virtual Reality and Augmented Reality, Hypertext, Multimedia and the World Wide Web | CO4 |
| Mini project | CO4 |
CSE430:
Credit Hours andTeaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite:CSE412 Software Engineering
Course Objective:This course is designed to enable a clear understanding and knowledge of the software testing and quality control. It explores different SQA components, techniques, and standards practiced as a part of software project management in the industry. Beside the concepts, it will build the capacity of reviewing, planning and designing test cases based on system requirements. It will develop the ability to use different testing techniques (black box and white box) and available tools used in real life software projects.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Understand different software quality assurance and quality control activities and standards for software projects. |
| CO2 | Understand review and inspection techniques and formulate appropriate test plan and test cases based on system specifications. |
| CO3 | Understand different testing techniques and apply and identify appropriate testing for real-life complex software projects. |
| CO4 | Apply and examineautomated testing tools; demonstrate and adapt those skills; justify and compare them for optimized quality control. |
Course Contents
| Course Topic | CO |
|---|---|
| Software Quality, Quality Assurance and Quality Control, SQA components | CO1 |
| Quality standards, CMM and CMMI model, Software Quality factors | CO1 |
| Review and inspections, Formal technical reviews, Cost estimations of review tasks | CO2 |
| Software testing life cycle (STLC), Software test plan preparation, Test case design | CO2 |
| Software testing objectives and strategies, Software test classifications, White box testing, Black box testing | CO3 |
| Test case optimization, All Pair testing, Path testing, Boundary value analysis, Decision table testing | CO3 |
| Assignment &Mini project | CO4 |
CSE438:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE246 Algorithms
Course Objective:This course is an introduction to digital image processing and image analysis techniques and concepts. Topics include intensity transformations for image enhancement, two-dimensional discrete Fourier transform, spatial and frequency domain linear image filtering, nonlinear image filtering, binary image processing, edge detection, image segmentation, and digital video processing basics.This course makes extensive use of MATLAB as an analysis, design, and visualization tool.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Understand the digital image fundamentals and apply different image enhancement techniques for improving the quality of the image. |
| CO2 | Understand the basics of color model and apply them for smoothing, sharpening and/or segmenting color image. |
| CO3 | Understand, choose, examine, and devaluate different image segmentation and morphological operation and image compression techniques; perform and demonstrate these skills and write report for better representation and/or better storage of the image. |
| CO4 | Choose, compare, and justify appropriate object detection and techniques for understanding complex real life images; perform and demonstrate these skills and write report for realistic problem solving. |
Course Contents
| Course Topic | CO |
|---|---|
| Digital Image Fundamentals: Elements of Visual Perception, Light and the Electromagnetic Spectrum, Image Sensing and Acquisition, Image Sampling and Quantization, Some Basic Relationships between Pixels, Linear and Nonlinear Operations. | CO1 |
| Image Enhancement in the Spatial Domain: Basic Gray Level Transformations, Histogram Processing, Basics of Spatial Filtering, Smoothing Spatial Filters, Sharpening Spatial Filters. | CO1 |
| Color Image Processing: Color Fundamentals, Color Models, Pseudocolor Image Processing, Basics of Full-Color Image Processing, Color Transformations, Smoothing and Sharpening, Color Segmentation. | CO2 |
| Image Segmentation: Detection of Discontinuities, Edge Linking and Boundary Detection, Thresholding, Region-Based Segmentation, Segmentation by Morphological Watersheds. | CO3 |
| Morphological Image Processing: Dilation and Erosion, Opening and Closing, Extensions to Gray-Scale Images. | CO3 |
| Image, Video compression: Lossless compression vs. Lossy compression, Image coding JPEG, Video coding and MPEG | CO3 |
| Object Recognition: Representation, Learning, Recognition, BagofWordsmodel | CO4 |
| Mini project | CO3, CO4 |
CSE452:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE 325 Operating Systems.
Course Objective: This course focuses on the principles, techniques, and practices relevant to the design and implementation of distributed systems. Students will study major algorithms and theoretical results and explore them in modern applications like cloud computing, pervasive computing, Google file system, peer-to-peer systems and etc.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Interpret,apply, and examinedifferent theories, models and concepts for the design and implementation of distributed systems. |
| CO2 | Describe, apply, and analyze common problems and their solving algorithms for modern distributed applications. |
| CO3 | Identify, and analyze fundamental limitations and impossibility results for distributed systems to avoid them during realistic problem solving. |
| CO4 | Apply differentdistributed algorithmsfor real-world distributed computing platforms;demonstrate this knowledge and write report for real-world distributed computing platforms. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction to distributed systems and models of distributed computation | CO1 |
| Time, clocks, and synchronization | CO1 |
| Distributed objects and components | CO1 |
| Distributed file system | CO1 |
| Remote invocation and indirect communication | CO2 |
| Global state and snapshot recording algorithms | CO2 |
| Distributed mutual exclusion algorithms | CO2 |
| Deadlock detection in distributed systems | CO2 |
| Check pointing and rollback recover | CO3 |
| Consensus and agreement algorithms | CO3 |
| Assignment with report and presentation (case studies-designing distributed system) | CO4 |
CSE453:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE405 Computer Networks
Course Objective:The objective of this course is to give an introduction to the fundamentals of the wireless communications systems, the wireless network architectures, protocols, and applications. This course will address topics of wireless communications and mobile computing systems, signal propagation characteristics of wireless channels, wireless channel modelling, frequency reuse/cellular/microcellular concepts, spread-spectrum modulation for wireless systems, multiple access techniques, and wireless networking standards.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Comprehend, use, and characterize fundamentals of the wireless communications systems. |
| CO2 | Applyand characterizewireless network architecture’s protocols. |
| CO3 | Understand, use, and examine wireless communications and mobile computing systems, signals propagation characteristics of wireless channels and frequency reuse concepts; implement these skills and write report. |
| CO4 | Understand, apply, and examine satellite communications and wireless networking standards; implement these skills and write report. |
Course Contents
| Course Topic | CO |
|---|---|
| Overview of Wireless Communication Networking and Mobile Computing | CO1 |
| Historical perspectives, first and second generation cellular systems, land mobile vs. satellite vs. indoor wireless systems | CO1 |
| Adaptation and mobility in wireless information systems, challenges of mobile computing, mathematical preliminaries | CO2 |
| Wireless Channel Modelling:Path-loss and shadow fading models | CO3 |
| Rayleigh and Rician fading | CO3 |
| Coherence time, coherence bandwidth, frequency flat and selective fading | CO3 |
| Frequency reuse/cellular/microcellular concepts including sectorization and cell splitting | CO4 |
| Tracking and localization | CO4 |
| Multiple Access Techniques:
TDMA, FDMA, CDMA, ALOHA, Slotted-ALOHA, CSMA/CA, MACA, reservation protocols, 3G systems, wireless LAN standards (IEEE 802.11) |
CO2 |
| WiMAX standards (IEEE 802.16), WPAN standards (IEEE 802.15) | CO2 |
| Satellite communications | CO4 |
| Hidden node problem, exposed node problem, Request to send (RTS),Clear to send (CTS), Network allocation vector (NAV) | CO4 |
| Assignment with report and presentation | CO3, CO4 |
CSE457:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE405 Computer Networks
Course Objective: The objective of this course is to give an introduction to the fundamentals of the cellular concept and communications systems. This course will also address system design fundamentals by introducing frequency reuse and channel assignment and handoff strategies. Besides, co-channel interference and system capacity, trunking and improving coverage and capacity will be its within coverage topic.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Comprehend fundamentals of the cellular communications systems. |
| CO2 | Interpret, apply, and examine cellular architectures and protocols. |
| CO3 | Interpret, apply, and examine cellular geometry to improve cellular capacity and coverage. |
| CO4 | Apply the learnt knowledge; demonstrate this knowledge and write reportfor solving real-life problems. |
Course Contents
| Course Topic | CO |
|---|---|
| Overview of wireless communication networking and mobile computing | CO1 |
| Overview of cellular communications | CO1 |
| Cellular architectures, channels allocations, and assignment strategies | CO2 |
| Channel planning and interference | CO2 |
| Power control for reducing interference | CO2 |
| Trunking and grade of service | CO2 |
| Cell geometry | CO3 |
| Cell splitting, sectoring | CO3 |
| Repeaters for range extension | CO3 |
| Microcell zone concept | CO3 |
| Assignment with report and presentation | CO4 |
CSE471:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite:CSE313 Theory of Computations
Course Objective:The objective of this course is to learn basic principles and advanced techniques of compiler design. The initial part of the course will focus on the classic techniques of lexical analysis and scanning/screening, syntactic analysis like bottom-up and top-down parsing techniques, semantic analysis, type-checking, abstract syntax tree and code generation. The latter part will focus on intermediate representations and simple optimizations like register allocation and instruction scheduling. Students will be exposed to compiler design tools and they will develop a compiler of limited scope.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Understandand use Lexical Analyzer, Regular expression, and Finite Automata in Lexical Analysis. |
| CO2 | Understand, use, and examine various parsing technique, Syntax and Semantic Analysis, Type checking. |
| CO3 | Developand examine concept of Intermediate Code Generation and Code Optimization. |
| CO4 | ChooseCompiler Construction Tools, perform and demonstrate skills, and write report to Design and Build a simple Compiler. |
Course Contents
| Course Topic | CO |
|---|---|
| Various phases of a Compiler, Lexical Analyzer, Regular Expression, Transition Diagram, | CO1 |
| Finite Automata, NFA, Regular Expression to NFA | CO1 |
| DFA, NFA to DFA (Subset Construction), DFA state minimization | CO2 |
| Context Free Grammar, Ambiguity, Left Recursion | CO2 |
| Top Down Parsing | CO2 |
| Bottom Up Parsing | CO2 |
| Semantic Analysis | CO2 |
| Run Time Environment | CO3 |
| Code Generation & Optimization | CO3 |
| Assignments and miniproject with reports and presentations | CO4 |
CSE475:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE366 Artificial Intelligence
Course Objective:This course will introduce the field of Machine Learning, in particular focusing on the core concepts of supervised and unsupervised learning. Students will learn the algorithms which underpin many popular Machine Learning techniques, as well as developing an understanding of the theoretical relationships between these algorithms. The hands-on exercise will concern the application of machine learning to a range of real-world problems.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Formulateand usemachine learning problems corresponding to different applications. |
| CO2 | Understandand usea range of machine learning algorithms, both supervised and unsupervised along with their strengths and weaknesses. |
| CO3 | Understand, use, and determine appropriate machine learning methods/algorithms suitable for different types of learning problems, i.e. know about their most important weaknesses and advantages. |
| CO4 | Examine and analyze appropriate software tools to implement algorithms in a range of real-world applications; implement and write report to represent the analysis results. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction to Machine Learning | CO1 |
| Supervised Learning Setup | CO1 |
| Linear Prediction: Regression | CO2 |
| Classification: Decision Tree, Logistic Regression | CO2 |
| Probabilistic Modeling: Bayesian Method, Naïve Bias | CO2 |
| Unsupervised Learning: Clustering, Apriori | CO2 |
| Principal component analysis | CO2 |
| Clustering: Partitioning, Hierarchical | CO2 |
| Artificial Neural Networks: perceptron, MLPs, back propagation, introduction to Deep Learning | CO3 |
| Support Vector Machines | CO3 |
| Reinforcement learning and control | CO3 |
| Assignments and Mini Project with reports and presentations | CO4 |
CSE477:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE366 Artificial Intelligence
Course Objective: The objective of the course is to introduce the basic concepts of Data Mining techniques to students. The course focuses on examining the type of data to be mined and applying preprocessing methods to raw data. The course emphasizes on discovering interesting patterns, analyzing supervised and unsupervised models and estimating the accuracy of the algorithms. The students will also be introduced to various Data Mining tools.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Process raw input data to provide suitable input for a range of data mining algorithms. |
| CO2 | Discover and measure interesting patterns from different kinds of databases. |
| CO3 | Evaluate and select appropriate data-mining algorithms and apply, and interpret and report the output appropriately. |
| CO4 | Choose and justify software tools;perform and demonstrate skills, and write report to design and implement Data-mining application using sample, realistic data sets. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction to Data Mining, Data Mining Goals, Stages of the Data Mining Process, Data Mining Techniques, Knowledge Representation Methods | CO1 |
| Data preprocessing: Data cleaning, Data transformation, Data reduction | CO1 |
| Data mining knowledge representation, Representing input data and output knowledge | CO2 |
| Association rules, Correlation analysis | CO2 |
| Classification, Basic learning/mining tasks, Decision trees, Covering rules | CO2 |
| Prediction, The prediction task, Statistical classification, Instance-based methods, Linear models | CO2 |
| Clustering, Basic issues in clustering, First conceptual clustering system, Hierarchical methods, Conceptual clustering. | CO3 |
| Advanced techniques, Data Mining software and applications Text mining, Web mining | CO3 |
| Assignment and Mini Project with Report and Presentation | CO4 |
CSE483:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE246 Algorithms
Course Objective: This course is an elective course and will help the students to gain basic knowledge of the structure of graphs and the techniques used to analyze problems in graph theoryand discretestructures. The course will cover fundamental concepts, such as graphs, cycle, path, circuit, trees, matching and factors, connectivity and coloring, and network.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Understand fundamentals of graphs and analyze different properties of Eulerian and Hamiltonian graphs. |
| CO2 | Explain, and apply different theorem related to spanning tree, maximum matching, and maximum coverage problems. |
| CO3 | Explain, apply andexaminedifferent theorem related to cuts and connectivity, networks flow problem. |
| CO4 | Explain, apply andexaminedifferent graph coloring theorems; perform and demonstrate skill and write report on graph coloring theorems. |
Course Contents
| Course Topic | CO |
|---|---|
| Basic definitions, isomorphisms, walks, cycles and bipartite graphs | CO1 |
| Components, cut-edges, Eulerian graphs, vertex degrees and degree sequences, directed graphs | CO1 |
| Eulerian digraphs, trees and distance | CO1 |
| Counting spanning trees and the matrix tree theorem, minimal spanning trees and shortest paths | CO2 |
| Matchings, Hall’s theorem and coverings, maximum matchings, factors | CO2 |
| Cuts and connectivity | CO3 |
| Network flow problems, max-flow min-cut theorem | CO3 |
| Vertex colorings, bounds on chromatic numbers and Mycielski’s construction | CO4 |
| Chromatic polynomials, chordal graphs, planar graphs | CO4 |
| Euler’s formula and Kuratowski’s theorem, five and four color theorems | CO4 |
| Mini project | CO4 |
CSE484:
Credit Hours and Teaching Scheme
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE246 Algorithms
Course Objective: This course introduces concepts, data structures and techniques of computational geometry, and enhances the students’ ability to mathematically analyze real-life geometric problems. The course covers properties of geometric objects, algorithms, complexity analysis and correctness of algorithms, and real-life applications of algorithms.
Course Outcomes (COs)
After completion of this course students will be able to:
| CO1 | Understand, apply, and analyzepolygon triangulations, orthogonal range searching, and point location algorithms. |
| CO2 | Interpret, apply, and analyzevoronoi diagrams, arrangements and duality, and delaunay triangulations. |
| CO3 | Interpret, apply, and analyze linear programming, randomized algorithms, and graph drawing. |
| CO4 | Apply the learnt knowledge for solving real-life problems; demonstrate this knowledge and write reports. |
Course Contents
| Course Topic | CO |
|---|---|
| Introducing geometric objects, overview of computational geometric algorithms,polygon triangulations | CO1 |
| Convex hull: algorithms, complexity and correctness, real-life applications (both in 2-d and 3-d spaces) | CO1 |
| Polygon triangulations and art gallery theorem | CO1 |
| Orthogonal range searching: data structures and algorithms | CO1 |
| Point location algorithms | CO1 |
| Voronoi diagram: definitions and properties, algorithms | CO2 |
| Arrangements and duality: supersampling in ray tracing | CO2 |
| Delaunay triangulations | CO2 |
| Linear programming | CO3 |
| Randomized algorithms | CO3 |
| Graph drawing | CO3 |
| Assignment with report and presentation | CO4 |
CSE486:
Credit Hours and Teaching Scheme
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE246 Algorithms
Course Objective:This is an interdisciplinary course that introduces computational techniques for solving biological problems. The course also emphasizes understanding of biological problems, computational algorithms to solve these problems, and do some mathematics for the analysis of these algorithms.
Course Outcomes (COs)
After completion of this course students will be able to:
| CO1 | Interpret, apply, and examinedifferent searching and clustering techniques on biological data for answering biological queries. C2, C3, C4 |
| CO2 | Interpret, apply, and examine sequence alignment techniques for understanding protein families, and the emerging field of phylogenomics. C2, C3, C4 |
| CO3 | Interpret, apply, and examine protein structure prediction, classification and analysis techniques for learning the functionalities of proteins. C2, C3, C4 |
| CO4 | Interpret, apply, examine, and justifybiological network for learning relationships within such networks; demonstrate knowledge of this course for real-life problem solving and write reports. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction to biology, bioinformatics, and biological database | CO1 |
| Biological database searching, clustering and evolutionary tree algorithms | CO1 |
| Sequence alignment algorithms: pairwise, MSA, BLAST and FASTA | CO2 |
| Protein folding and protein structure modeling, alignment and prediction | CO3 |
| Gene regulatory network analysis | CO4 |
| Protein networks: montecarlo sampling and randomized graph walk. | CO4 |
| Assignments with report and presentation | CO4 |
CSE487:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite:CSE301 Database Systems
Course Objective: This course builds up the student’s ability to understand the key aspects of big data platform, problems, and applications. The course will emphasize on identification and analysis on large-scale machine learning methods as well as modern tool, such as Hadoop in the context of big data analysis.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Describe the landscape and the V’s of Big Data for real world Big Data problems. |
| CO2 | Apply and examine different tools and techniques for storing, managing, and analyzing big data; adapt the skills and write report to use the necessary tools for handling a variety of big data analytics. |
| CO3 | Applyand examinethe core concepts of machine learning techniques in the context of big data for realistic problem solving; adapt the skills and write report to use the necessary tools for handling a variety of big data analytics. |
| CO4 | Understand, apply, examine, and justifythe relationship among big data components; adapt the skills and write report to use the necessary tools for handling a variety of big data analytics. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction to Big Data, Big Data Skills and Sources of Big Data | CO1 |
| Characteristics of Big Data – The Four V’s | CO1 |
| Key aspects of a Big Data Platform, Storage and Analytics, Governance for Big Data | CO1 |
| Data and Data Science; Relational Databases and SQL | CO2 |
| Data Cleansing and Preparation | CO2 |
| Data Summarization and Visualization; Descriptive Statistics and Correlation | CO2 |
| Association Analysis and Cluster Analysis | CO3 |
| Linear Regression, Principles of Classification; Decision Trees; and Linear Classifiers | CO3 |
| Neural Networks and R | CO4 |
| Introduction to Hadoop, Hadoop components (MapReduce/Pig/Hive/HBase) | CO4 |
| Cloud and Big Data | CO4 |
| Assignments with reports and presentations | CO2, CO3, CO4 |
CSE489:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite:CSE431 Mobile Programming
Course Objective: This course introduces studentsto programming technologies, design and development related to mobile applications. Students are introduced to the survey of current mobile platforms, mobile application development environments, mobile device input methods, as well as developing applications for different mobile platforms. Students will design and build variety of applications throughout the course to reinforce learning and to develop real competency.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Describe and apply different mobile application models and patterns for developing mobile software application. |
| CO2 | Describe, apply, and examinemobile development framework to the development of a mobile application for realistic application creation. |
| CO3 | Use, analyze, and justify advanced programming competency for developing a maintainable and efficient cloud based mobile application. |
| CO4 | Choose software tools,designsoftware for mobile programming;demonstratethese skills, and write report to design, build, and test software for mobile programming. |
Course Contents
| Course Topic | CO |
|---|---|
| Mobile Phones and Network Technologies | CO1 |
| Android Programming, Android Application Frameworks | CO1 |
| Building a Simple User Interface | CO1 |
| Activities and Intents, Services | CO2 |
| Broadcast Receivers | CO2 |
| Data Persistence | CO2 |
| Processes and Threads | CO2 |
| Asynchronous Tasks | CO3 |
| Internet Resources | CO3 |
| Apps Publishing and Business Models | CO3 |
| iOS platform, Objective-C, Application development in iOS | CO3 |
| Mini project | CO4 |
CSE491:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credits | 3 | 1 | 4 |
| Contact Hours | 3 Hours/Week for 13 Weeks | 2 Hours/Week for 13 Weeks | 5 Hours/Week for 13 Weeks |
Prerequisite: CSE345 Digital Logic Design
Course Objective:The course is designed to provide the students with basic theories and techniques of VLSI design in CMOS technology. This course covers the fundamental concepts and structure of designing VLSI systems including CMOS devices and circuits, standard CMOS fabrication processes, CMOS design rules, static and dynamic logic structures, CMOS chip layout, low power techniques and structural design methods of VLSI architecture. This course also emphasizes computer-aided design and synthesis of complex digital circuits using Verilog and physical layout tools.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Interpret the characteristics of CMOS circuit construction and the comparison between different state-of-the-art CMOS technologies and processes. |
| CO2 | Interpret and apply CMOS technology-specific layout rules in the placement and routing of logic components and their interconnect, and examine the functionality, timing, power and parasitic effects. |
| CO3 | Apply and examine hardware modeling techniques for combinational and sequential circuit design. |
| CO4 | Use, examine, and justify Verilog hardware description language and physical layout tools, perform and demonstrate skills and write report to design, test and synthesize complex digital circuits. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction to VLSI and digital IC | CO1 |
| CMOS transistor theory and non-ideal transistor characteristics | CO1 |
| Circuits, fabrication and layout | CO2 |
| DC and transient response | CO2 |
| Logical effort, interconnecting engineering and parasitic | CO2 |
| Combinational and sequential circuit design | CO3 |
| Arithmetic circuits in CMOS VLSI | CO3 |
| Data path functional units | CO3 |
| Memories and programmable logic | CO3 |
| VLSI clocking/low power | CO2 |
| Mini Project | CO4 |
CSE492:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE 442 Microprocessors and Microcontrollers
Course Objective: The objective of this course is to introduce students to the field of Robotics and stimulate their interests through a variety of multidisciplinary topics necessary to understand the fundamentals of designing, building, and programming robots. The course will address fundamental mathematical modeling of robots-kinematics, inverse kinematics, sensors and sensor processing algorithms, different control architectures, their management and programming strategies.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Learn fundamental mathematical and computational models; and solveand analyzekinematic problems involving robot manipulators and mobile robots. |
| CO2 | Familiarizewith robot sensors, sensor processing algorithms and navigation planning approaches; use them andexamine their engineering trade-offs. |
| CO3 | Understand robot actuator movements and controlling managements; and explore the computational challenges within robotic tasks. |
| CO4 | Use, examine, and justify control programming strategies of industrial systems; demonstrate these knowledge and write reportto develop simple mobile robot. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction of Robotics and Robot Mechanical Structure | CO1 |
| Kinematics and inverse kinematic problems | CO1 |
| Actuators and Sensors | CO2 |
| Trajectory Planning | CO2 |
| Motion Planning | CO2 |
| Control Architecture | CO3 |
| Motion Control | CO3 |
| Force Control | CO3 |
| Visual Servoing | CO3 |
| Mini Project | CO4 |
CSE494:
Credit Hours and Teaching Scheme:
|
|
Theory | Laboratory | Total |
|---|---|---|---|
| Credit Hours | 3 | 0 | 3 |
| Contact Hours | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
0 | 3 Hours/Week for 13 Weeks +
Final Exam in the 14th week |
Prerequisite: CSE442 Microprocessors and Microcontrollers
Course Objective: This course is an elective course and build up students’ ability to understand fundamental concepts of embedded systems, techniques and tools for integrating hardware and software components in embedded system design. The course will also emphasize on testing and debugging approaches for verification of embedded systems.
Course Outcomes (COs):
After completion of this course students will be able to:
| CO1 | Understand characteristics and internal architecture of embedded systems. |
| CO2 | Apply and examineprogramming, testing and debugging approaches and tools to develop and verify embedded systems. |
| CO3 | Apply and examine the concepts of real time operating systems to design embedded systems. |
| CO4 | Choose software and hardware tools, perform and demonstrate skills, and write report to develop embedded systems. |
Course Contents
| Course Topic | CO |
|---|---|
| Introduction to embedded systems, Major components and applications of Embedded system | CO1 |
| Architecture of processors in embedded system, memory organization and real-world interfacing | CO1 |
| Device Drivers and interrupt service mechanism | CO1 |
| Develop embedded systems using assembly and high-level languages | CO2 |
| Sequential and Data Flow graph modeling for program analysis | CO2 |
| State machine modeling for program analysis | CO2 |
| Concurrent process modeling for program analysis | CO2 |
| Testing, Simulation and Debugging techniques and tools to design and verify embedded systems | CO2 |
| Task scheduling algorithms in real time operating system environment | CO3 |
| Design procedure of embedded system using real time operating system concepts | CO3 |
| Case studies: Embedded system design for automobile, smart card, digital camera, and home electronics using real time operating system concepts | CO3 |
| Mini project | CO4 |
