1^st Semester

Course Title: Graph Theory

Course Code: CSE-5101

Credit: 3  Credit Hour Course
Prerequisite:   None

Introduction: Graph, Common Graphs, Degrees and Regular Graph and Indigrees and outdegrees in a Digraph. Fundamental concepts: Path and Cycles, Connectivity, Homomorphism and Isomorphism, of Graph and Digraph, Digraph connectivity. Trees and Forests: Basic properties and characterizations of Trees, Inductive proof in Trees, Centers of Trees, Rooted , Binary Trees Levels in Rooted and Binary Trees. Spanning Trees: Spanning trees and forests, spanning trees of the complete graph, The Adjacency and Incidence matrix of a Graph, The Matrix Tree Theorem, Minimum Cost Spanning. Fundamental Properties of Graphs and Digraphs: Bipartite Graphs, Eulerian and Hamiltonian Graphs and Digraphs, Tournament Digraphs, Acyclic Digraphs and Posets. Connectivity and Flow: Edge Cuts and connectivity, Flow in Networks, Menger’s  theorem. Planner graphs: Embedding in Surface, Euler Formula and consequence,, Characterization of planar graphs, Kuratowski and Wagne’s Theorem, Generalization of Euler’s formula, Crossing number. Graph Coloring: The Chromatic number of a graph, Multipartite graphs, Edge coloring of a graph, Tait’s theorem. Independence and Dominence of aVertices, Matchings in a graph, Hall’s Marriage Theorem. Graph Counting: Basic counting result, Generating functions, Partitions of a Finite Set, The level counting Lemma, The Exponential formula and the Number Two or Related Graphs. Graph Algorithms: Algorithm Efficiency, Breadth-First Search, Depth-First Search, Connected Components, Dijkstra’s Shortest path Algorithm.

Course Title: Simulation and Modeling

Course Code: CSE 5102
Credit: 3 Credit Hour Course
Prerequisite:   None

Probability, random variables and their properties, mathematical expectation, specific discrete and continuous random variates (Poisson, exponential, etc.). Simulation tools, random number and variate generation, event serialisation and time advance algorithms; process and resource classes, Performance measures, model instrumentation and result presentation. Simple stochastic processes - discrete time Markov chains, continuous time Markov processes; Poisson process, Birth and Death process and their application to the simple (e.g. M/M/1) queues. More advanced queuing theory - multi-server queues, non-Markovian queues, networks of queues. mean value analysis (analytic derivation of throughput, utilisation, mean queue size and delay).

Course Title: Advanced Cloud Computing

Course Code: CSE 5103
Credit: 3 Credit Hour Course
Prerequisite:   None

Background, Distributed systems, Issues in distributed systems, need for cloud computing, Introduction to cloud computing, Applications, Trends, Types and services of clouds, Enabling Technologies, Cloud Infrastructures and Platforms, Virtualization, Dockers, Kubernetes, Web Services, Cloud Computing Standards, Fog Computing, Edge Computing, Edge cloud and convergence with 5G, Big data and Clouds, Cloud Computing in the era of IoT, Mobile Cloud Computing, Virtual Desktop Infrastructure, Desktop as a Service, Media Cloud, Distributed Clouds, Security Issues in Cloud Computing.

Course Title: Digital Image Processing

Course Code: CSE 5104
Credit: 3 Credit Hour Course
Prerequisite: None

Image sampling and quantization; Image smoothing , sharpening and contrast enhancement in spatial and frequency domains: basic gray level transformation, histogram processing, image subtraction, image averaging, Gaussian and Laplacian filters in spatial and frequency domains, convolution theorem; Image de-noising: noise models, noise reduction by spatial and frequency domain filters, mean filter, adaptive filter, bandpass and band reject filters, notch filter, inverse filter, minimum mean square error filter; Multi-resolution image processing: wavelet transform in one and two dimensions, tree structured wavelet transform, pyramid structured wavelet transform, curvelet transform; Morphological image processing: erosion, dilation, opening, closing, hole filling, connected components, thinning, skeletons, extension of morphological operations to gray scale images; Image segmentation: thresholding, region based segmentation, contour based segmentation, graph based segmentation; Color image processing: color models and transformations, edge detection and segmentation in color images, color image compression; Digital image security; Image content feature extraction, representation and image retrieval; Concept learning and object recognition.

Course Title: Knowledge Engineering

Course Code: CSE 5105

Credit: 3 Credit Hour Course

Marks: 100
Prerequisite:   None

Introduction to Knowledge Engineering, overview of Knowledge Engineering techniques, and the broadness of the field Knowledge Engineering applications. Knowledge Engineering setting, Knowledge Representation, Cognitive Psychology, biological psychology, (human) perception, learning, memory, planning and problem solving,  artificial intelligence (AI) to concepts in psychology, Construction and Evolution of neural models, theories and interpretations of psychological and neuro-scientific experimental findings. Conceptual Data Modeling: Concepts & Principles of Conceptual Data Modeling, Conceptual Schema Design Steps; Ontology Engineering: Introduction to Description Logics and Reasoning, What is an ontology and Lexical Semantics, Ontology Engineering Methodologies.

Course Title: Computer Arithmetic

Course Code: CSE 5106
Credit: 3 Credit Hour Course
Prerequisite:   None

Integer arithmetic, Floating point arithmetic; Single precision and double precision; Interrupt handling high-speed adders; Standard and recorded multipliers, Booth's multiplier, Canonical and multi bit scanning multipliers, Array multipliers; High radix non-restoring division, SKT division, Robertson division, Convergence division and cellular array dividers; Floating point processors; Binary squares and square roots, evaluation of trigonometric Functions and polynomials, Chen convergence Computation, CORD1C computations, Logarithmic number system (LNS) processor.

Course Title: Digital Signal processing
Course Code: CSE 5107
Credit: 3 Credit Hour Course
Prerequisite:   None

Introduction to DSP, classifications of signals, continuous time and discrete time (DT) sinusoids, concept of frequency, advantages and limitations of DSP, applications of DSP, steps of ADC, sampling theorem, abasing, quantization, coding. Classification of DT signals, classification of DT systems, impulse response, FIR and IIR, block diagram of DT systems, analysis of LTI systems, convolution, properties of convolution, causality and stability of LTI systems, recursive and non-recursive systems, correlation, properties and applications of correlations. Z-transform, ROC, Inverse z-transform, properties of z­-transform, concept of pole-zero, one-sided z-T. Frequency analysis, Fourier series and Fourier transforni for continuous time and discrete time signals, power density and energy density spectrums, DFT, properties of FT and DFT, invertibility of LTI systems, DFT as linear transformation, FFT, divide and conquer approach, radix-2 FFT. Structures of DT systems: Direct form, lattice structure, transposed structure. State-space system analysis. Digital filter: advantages and limitations of digital filters, adaptive filters, applications: inverse modeling, system identification, noise cancellation etc., characteristics of ideal and practical filters. Filter design: designing steps, window method, optimal method, IIR filter design methods.

Course Title: Wireless Ad Hoc and Sensor Networks

Course Code: CSE 5108
Credit: 3 Credit Hour Course
Prerequisite:  None

Introduction: applications and motivations of wireless AdHoc Networks; broadcasting protocols: algorithmic aspect, optimization techniques, power-efficient broadcasting;, routing protocols: DSDV, AODV, DSR, position based routing protocols, load balancing techniques, multi-path routing; medium access control protocols: reservation-based MAC protocols, Bluetooth technology, IEEE 802.11 based MAC protocols; channel propagation models; topology control protocols; power aware protocol design; cross layer design principles; mobility awareness; fairness and security issues: attacks and preventions; stimulating cooperation: self policing schemes, economic incentive based schemes; other state-of-the-art relevant topics.

Applications of Wireless Sensor Networks; Localization and tracking: tracking multiple objects; Medium Access Control: S-MAC, IEEE 802.15.4 and ZigBee; Geographic and energy-aware routing; Attribute-Based Routing: directed diffusion, rumor routing, geographic hash tables; Infrastructure establishment: topology control, clustering, time synchronization; Sensor tasking and control: task-driven sensing, information-based sensor tasking, joint routing and information aggregation; Sensor network databases: challenges, querying the physical environment, in-network aggregation, data indices and range queries, distributed hierarchical aggregation; Sensor network platforms and tools: sensor node hardware, sensor network programming challenges; Other state-of-the-art related topics.

Course Title: Advanced Database Management Systems
Course Code: CSE 5109
Credit: 3 Credit Hour Course
Prerequisite:   None

Object Oriented Database; Data Model, Design, Languages; Object Relational Database: Complex data types, Querying with complex data types, Design; Distributed Database: Levels of distribution transparency, Translation of global queries to fragment queries, Optimization of access strategies, Management of distributed transactions, Concurrency control, Reliability, Administration; Parallel Database: Different types of parallelism, Design of parallel database; Multimedia Database Systems Basic concepts, Design, Optimization of access strategies, Management of Multimedia Database Systems, Reliability; Database Wire-housing/Data mining: Basic Concepts and algorithms.

Course Title: Big Data Analytics

Course Code: CSE 5110

Credit: 3 Credit Hour Course

Prerequisite:   None

Introduction to Big Data Analytics ,Big Data Platforms, Big Data Storage and Processing,Big Data Analytics Algorithms ,Spark and Data Analytics, Linked Big Data ,Big Data Applications (TBA), Data models ,Management issues, Hadoop and Weka, Change management ,Knowledge representation ,Finding business value ,Ethical issues in Big Data ,Data bases and Big Data Dealing with unstructured data, Data quality management. Big Data problem: Current challenges, trends, and applications, Algorithms for Big Data analysis, Technologies for Big Data management.

Course Title: Mathematical Programming

Course Code: CSE 5111

Credit: 3 Credit Hour Course

Prerequisite:   None

Course Title: IT Project Management and Entrepreneurship

Course Code: CSE 5112

Credit: 3 Credit Hour Course

Prerequisite:   None

2^nd Semester

Course Title: Future Internet and IoT
Course Code: CSE 5201
Credit: 3 Credit Hour Course
Prerequisite:   None

Course Description

Currently, the Internet is being used for much more things (e.g., entertainment, politics, banking, shopping and trading) than merely web browsing. However, it has many problems with important issues such as quality of service and security. Recently, researchers have been working on redesigning the Internet, which is now being referred to as the Future Internet. There are mainly two approaches: evolutionary and revolutionary (or clean slate). In this course, we review the current Internet and investigate its problems. We will also examine the two approaches for designing the Future Internet. We will focus on the approaches, design and manageability aspects of the Future Internet.

Topics Covered

What is Future Internet?, IoT, BigData, AI, Standardization and Services for Smart City,

Open Mobile Network Platform and its Applications, Artificial Intelligent and 5G,  Research Issues in Future Internet 1 - New Challenges in Security, Edge Computing for beyond 5G, Research Issues in Future Internet 2, Blockchain for Future Internet, Research Issues in Future Internet 3

Course Title: Advanced Algorithms

Course Code: CSE 5202
Credit: 3 Credit Hour Course
Prerequisite:   None

Randomized Algorithms: Las Vegas and Monte Carlo Algorithms; Randomized Data Structures: Skip Lists; Amortized Analysis: Different methods, Applications in Fibonacci Heaps; Lower Bounds: Decision Trees, Information Theoretic Lower Bounds, Adversary Arguments; Approximation Algorithms: Approximation Schemes, Hardness of Approximation; Fixed Parameter Tractability: Parameterized Complexity, Techniques of designing Fixed Parameter Algorithms, Examples; Online Algorithms: Competitive Analysis, Online Paging Problem, k-server Problem; External Memory Algorithms; Advanced Data Structures: Linear and Non-linear Methods

Course Title: Network and OS Security

Course Code: CSE 5203
Credit: 3 Credit Hour Course
Prerequisite:   None

Network Security: Introduction to network security, security models, basic type of attacks, authentications, mutual authentications and authentication protocols, Mediated Authentication (with KDC), Kerberos, public key infrastructure (PKI), secured RTP. Physical network security, LAN security, resilient network topologies, VPN security, IPsec, Secure Socket Layer (SSL) and Transport Layer Security (TLS), electronic mail security, firewalls and web security, DNS security, anomaly detection and traffic analysis, intrusion detection algorithms.

OS security: Vulnerabilities, threats, exploits and defense mechanisms in operating systems, logging, auditing, address space randomization, memory protection, virtual machine introspection, malware and malware immunization, use of best practice to configure operating systems to industry security standards, distributed OS security.

Course Title: E-Commerce and E-Transaction Security

Course Code: CSE 5204
Credit: 3 Credit Hour Course
Prerequisite:   None

Payment Technologies: EMV & Contactless Chip Technology, Payment Systems & Banking Infrastructures, Secure Online Banking & Payments, Card Payments- Issuing & Acquiring; SecureMobile Technologies: Mobile Payments, Trusted Service Manager, Introduction to NFC,

Smart Cards for GSM, UMTS & CDMA, Mobile Wallet, Cloud based mobile payments; Secure

Transport and Energy Technologies: Automated Fare Collection, Electronic Fee Collection, Secure Smart Metering; ID Management Technologies: ID Management in the Public Domain, Electronic Identity Documents, Public Key Infrastructure for Electronic Identity Documents, Trusted Electronic Identities in Cyberspace; Cryptocurrency: Introduction to Cryptocurrency, Security of Bitcoin, Litecoin and some other well-known cryptocurrencies; Financial cryptography: Automated teller machines (ATM), Point-of-sale (POS), Hardware Security Modules (HSM), Anonymous Internet Banking, Online Auctions, Virtual Goods and Virtual Economies, Identity Theft, Legal and Regulatory

Course Title: Distributed Database Systems

Course Code: CSE 5205
3 Credit Hour Course
Prerequisite:   None

In this course, students will learn different distributed database management algorithms to support concurrency, transaction management, query optimization, replication, recovery, distributed database design and security; implement a client-server DBMS and distributed database applications. Distributed databases - various contemporary issues including data model partitioning, fragmentation, replication issues, query optimization, concurrency control, restart and recovery, distributed database design, client-server and distributed database applications will be discussed in details. Students will build a distributed system with Oracle DBMS. Particular attention will be paid to detailed consideration of distributed database management issues.

Course Title: Neural Networks and Fuzzy Systems

Course Code: CSE 5206
Credit: 3 Credit Hour Course
Prerequisite:   None

Fundamentals of Neural Networks; Back propagation and related training algorithms; Hebbian learning; Cohonen-Grossberg learning; The BAM and the Hopfield Memory; Simulated Annealing; Different types of Neural Networks: Counter propagation, Probabilistic, Radial Basis Function, Generalized Regression, etc; Adaptive Resonance Theory; Dynamic Systems and neural Control; The Boltzmann Machine; Self-organizing Maps; Spatiotemporal Pattern Classification, The Neocognition; Practical Aspects of Neural Networks.

Basic Concepts of Fuzzy set theory; Fuzzy numbers; Aggregation operations of Fuzzy sets; The theory of approximate reasoning; Introduction to Fuzzy logic control; Fuzzy System Models and Developments; Fuzzy logic controllers; De-fuzzification methods; Linguistic descriptions and their analytical forms; The flexible structure of fuzzy systems; Practical Aspects of Neural Networks.

Course Title: Advanced Cellular Mobile Communication

Course Code: CSE 5207
Credit: 3 Credit Hour Course
Prerequisite:   None

Introduction and History of Wireless Systems, Cellular Systems, Wireless LANs, Satellite Systems, Paging Systems; Radio Propagation: free space propagation, propagation mechanisms, link budget design using path loss model, outdoor propagation models, indoor propagation models; Introduction to small-scale fading, impulse response model of multipath fading, parameters of multipath channel, type of small scale fading, Rayleigh and Ricean Distribution; Media Access Control: FDMA, TDMA, and CDMA, Aloha, CSMA, MACA; GSM overview: Standards, services and structure, GSM air interface physical layer: physical channels, logical channels, frame structures, modulation, coding and interleaving, GSM signaling: Data link layer, radio resource management, mobility management, Handover, location update and roaming in GSM; Short message service (SMS), circuit switched data, General Packet Radio Service (GPRS), EnhancedGPRS (EGPRS); CDMA Digital Cellular System (IS-95): Forward CDMA Channel, Reverse CDMA Channel

Challenges in modern communications technology, baseband and broadband signal transmission, first and second Nyquists criteria for zero inter-symbol interference; robust signal compression and detection techniques, optimum receivers, design of frequency- and time-domain equalizers and echo cancellers; wired and wireless channel characteristics, AWGN channels, time-varying multipath faded channels, channel modeling; advanced source and channel coding techniques, high bit rate digital modulation schemes and MODEMs; SS7 and HDLC protocols, H.323, H.26x, RTP and SCTP; modern high speed communication networks and emerging technologies, access and backbone networks, intelligent networks, NGN; advanced switching and routing principles, VoIP, IP TV, HDTV

Course Title: Advanced Artificial Intelligence

Course Code: CSE 5208
Credit: 3 Credit Hour Course
Prerequisite:   None

In-depth introduction to Artificial Intelligence focusing on techniques that allow intelligent systems to operate in real-time and cope with missing information, uncertainty, and limited computational resources. Topics include: advanced search and problem-solving techniques, resource-bounded search, principles of knowledge representation and reasoning, meta-reasoning, reasoning under uncertainty, Bayesian networks and influence diagrams, decision theory and the value of information, planning and scheduling, intelligent agents architectures, and learning.

Course Title: Advanced Data Mining and Machine Learning

Course Code: CSE 5209
Credit: 3 Credit Hour Course
Prerequisite:   None

Introduction to machine learning and data mining, Designing a learning system, perspective issues in machine learning, Concept of learning and the general to specific Ordering: induction learning hypothesis, Find-S, version space and candidate elimination algorithms, List-then-elimination algorithms, A biased hypothesis space, unbiased hypothesis space, decision tree, Artificial  Neural networks, Multilayer networks and back propagation algorithms, Recurrent network, Evaluation hypothesis, Bayesian learning, Naive bays classifier, Gibbs algorithms, Bayesian belief Networks, EM algorithms, Computational learning theory, probability learning theory, sample complexity finite hypothesis space, sample complexity infinite hypothesis space, Mistake bound model of learning, Instance based learning, K-nearest neighbor learning, Genetic algorithms, Learning sets rules, Analytical learning, Combining inductive and Analytical learning, Reinforcement learning, SVM, Boasting, Clustering, training and testing, cross validation, prediction performance, Data mining tools.

Course Title: Digital Forensics and Investigation

Course Code: CSE 5210
Credit: 3 Credit Hour Course
Prerequisite:   None

Digital Forensics: An overview, Forensics basic and criminalities, Forensic modeling and principles. Understanding Computer Investigations, Basics of Operating system: A review, Data Acquisition File carving and testing, Cyber forensics tools and testing, processing crime and incident scenes, Recovering Graphics Files, Computer Forensics Analysis and validation, Cell Phone and mobile device forensics, Virtual Machine, Network forensics and Live acquisition, network attach trace back and attribution, E-mail Investigation multicast finger printing, multimedia forensics, Intrusion and online frauds detection; Court testimony and report writing skills; Digital Evidence control.

Cyber Law: National ICT Act, National ICT Policy, National e-services rules, National Information security policy guideline, National Copyright, patent, trademark related laws, Laws on document & records retention, UN conventions/Laws related to internet or cyber security, Rights to know, Freedom of Information.

Course Title: Advanced System Analysis and Design

Course Code: CSE 5211
Credit: 3 Credit Hour Course
Prerequisite:   None

3^rd Semester

Course Title: Thesis

Course Code: CSET 5300
Credit: 12 Credit Hour Course
Prerequisite:   None

Students have to complete a Thesis work which will be assigned by the department based on their previous academic records .The work will be carried individually under the direct supervision of an experienced teacher of the department and will be completed within two semesters. Finally students have to face the thesis defense.

Course Title: Project

Course Code: CSEP 5300
Credit: 6 Credit Hour Course
Prerequisite:   None

Students have to complete a Project which will be assigned by the department based on their previous academic records .The work will be carried individually or by a group of normally two students under the direct supervision of an experienced teacher of the department and will be completed within two semesters. Finally students have to face the project defense.

Course Title: Advanced Computer Architecture

Course Code: CSE 5301
Credit: 3 Credit Hour Course
Prerequisite:   None

This course examines the structure of modern computer systems. We explore hardware and technology trends that have led to current machine organizations, and then consider specific features and their impact on software and performance. These may include superscalar issue, caches, pipelines, branch prediction, and parallelism.

Course Title: Robotics and Intelligent Systems

Course Code: CSE 5302
Credit: 3 Credit Hour Course
Prerequisite:   None

Introduction: History, robot architectures, technical concepts of robotics, computing and robots, actuation and sensing, robotic system design, applications. Coordinate systems: Cartesian coordinates, transformation matrices, reference frames, relative and general transformations, orientation, inverse transformations, graphs. Rigid-Body Dynamics, Mobile Robots, Personal Assistants, and Games. Kinematics: position: Joints, members, reference frames, trigonometric solution, Homogeneous transformations, direct and inverse kinematics, orientation, precision, efficiency/complexity of kinematics solutions. Kinematics: motion: Derivatives, velocity and acceleration of a rigid bodies, differential movement, Jacobian, and singularities. Sensors, measurements and perception: Sensors hierarchy, Dynamic Systems, Sensors and Actuators, interfaces, internal and external sensors, location, computer vision, applications. Structure of robot brain programs. Input statements. Basic repetition structures: timed, forever, and counting. Sensing from within: Proprioception in the Scribbler: battery, stall, and time sensing. Examples of behaviors using proprioception. Loops with conditions: comparison operations and logical connectives in Python. Sensing the world: camera, light, and proximity. Writing reactive behaviors: making decisions in Python. Sensing light and obstacles.

Control: Basic concepts in control systems, digital control for position, Behavior-based control. Dynamic Effects of Feedback Control, Analog and Digital Control Systems, Optimal Control, Least-Squares Estimation and Numerical Optimization, Monte Carlo Evaluation and Evolutionary Algorithms, Formal Logic and Computing, Predicate Calculus; 1st-order Logic, and Fuzzy Sets, Probability and Statistics, Multivariate Statistics and Stochastic Control, Stochastic, Robust, and Adaptive Control, Classification of Data Sets, Introduction to Neural Networks, Training Neural Networks, Machine Learning and Knowledge Representation, Task Planning and Multi-Agent Systems

System design: System integration: mechanism, actuators and sensors, and software, Designing insect-like behaviors, Braitenberg vehicles, Making decisions, Designing reactive behaviors. Other examples: refrigerator detective, burglar alarm robot,

Course Title: Embedded Systems

Course Code: CSE 5303
Credit: 3 Credit Hour Course
Prerequisite:   None

Introduction to the Embedded Systems. Embedded System Design Specifications     Embedded System Hardware and Hardware/Software Co-design. The 8051/8052 family of Microcontrollers. C programming for Microcontrollers. I/O ports Programming. Timer/Counter hardware and Its Device Driver. Serial communication interface and Its Device Driver. Interrupts Programming. Embedded Software Development Cycle and the Integrated Development Environment. Debugging Techniques for Embedded Software and the Role of Cross Simulators. Real World Interfacing Case Studies: LCD, Sensors, stepper motor, keyboard, PC. Design of Device Driver for Serial Devices. Concept of Finite State Machines and Examples -  Stop Watch, Stepper Motor Control through PC. Remote Control of Systems using IR Remotes Used in Commercial TV Remote Control Modules. Simple Multi Drop Communication Networks with Examples. Simple Wireless Communication with Examples.

Course Title: Cloud and Data Center Security

Course Code: CSE 5304
Credit: 3 Credit Hour Course
Prerequisite:   None

Introduction of several novel security challenges. The co-residency of machines: virtual machines, database engines, hardware resources, storage resources. Cloud Security vulnerabilities: infrastructure level, primary level, user level. Operating system in cloud computing. Security breaches: unauthorized connections, unauthorized leakage of information, unmonitored login attempts, malware propagation. Internal cloud infrastructure. Mount cross-VM side-channel attacks. Security controls for the protection of tenant resources, security concerns of un-authorized disclosure, segregation of tenants, isolation of compute, storage and network resources, Firewalls and License issue.

 Data Center architectures, critical computing infrastructure, hardware devices, computers, firewalls, routers, switches, software applications, email systems, Web servers, computer desktop operating systems. Critical systems: intranet, extranet and Internet. Critical data: applications and servers, data centers’ core assets, customer information, intellectual property, and other business-critical data. Big Data, mobility, and global online collaboration.

Course Title: Advanced Microprocessor and Microcomputer Design
Course Code: CSE 5305
Credit: 3 Credit Hour Course
Prerequisite:   None

Review of different microprocessors: 80486, 68040, V70, Gmicro processors; Comparing the architectures: RISC and CISC; Instruction set of machines: SPARC, INTEL, and MIPS; Study of microprocessors: Pentium II, Alpha 21064, MIS 6400, PA-RISC; Math coprocessors and microprocessors. Design a microcomputer with supporting components.

Course Title: Special Topics Related to Computer Science and Engineering
Course Code: CSE 5306
Credit: 3 Credit Hour Course
Prerequisite:   None

Syllabus should be approved by Academic committee and committee of courses  prior to the commencement of the term. In each term only one such course title under this course number can be offered. Furthermore one student can take such course only once.

Course Title: Advanced Cellular Mobile Communication

Course Code: CSE 5307
Credit: 3 Credit Hour Course
Prerequisite:   None

Introduction and History of Wireless Systems, Cellular Systems, Wireless LANs, Satellite Systems, Paging Systems; Radio Propagation: free space propagation, propagation mechanisms, link budget design using path loss model, outdoor propagation models, indoor propagation models; Introduction to small-scale fading, impulse response model of multipath fading, parameters of multipath channel, type of small scale fading, Rayleigh and Ricean Distribution; Media Access Control: FDMA, TDMA, and CDMA, Aloha, CSMA, MACA; GSM overview: Standards, services and structure, GSM air interface physical layer: physical channels, logical channels, frame structures, modulation, coding and interleaving, GSM signaling: Data link layer, radio resource management, mobility management, Handover, location update and roaming in GSM; Short message service (SMS), circuit switched data, General Packet Radio Service (GPRS), EnhancedGPRS (EGPRS); CDMA Digital Cellular System (IS-95): Forward CDMA Channel, Reverse CDMA Channel

Challenges in modern communications technology, baseband and broadband signal transmission, first and second Nyquists criteria for zero inter-symbol interference; robust signal compression and detection techniques, optimum receivers, design of frequency- and time-domain equalizers and echo cancellers; wired and wireless channel characteristics, AWGN channels, time-varying multipath faded channels, channel modeling; advanced source and channel coding techniques, high bit rate digital modulation schemes and MODEMs; SS7 and HDLC protocols, H.323, H.26x, RTP and SCTP; modern high speed communication networks and emerging technologies, access and backbone networks, intelligent networks, NGN; advanced switching and routing principles, VoIP, IP TV, HDTV

Course Title: Network Performance and Analysis

Course Code: CSE 5308
Credit: 3 Credit Hour Course
Prerequisite:   None

Course Title: Software Quality assurance and Testing

Course Code: CSE 5309
Credit: 3 Credit Hour Course
Prerequisite:   None

Course Title: Internet Security and Policy

Course Code: CSE 5310
Credit: 3 Credit Hour Course
Prerequisite:   None

TCP/IP Protocols, Vulnerabilities, Attacks, and Countermeasures: ARP protocol and ARP cache poisoning, IP protocols, packet sniffering, IP Spoofing, IP fragmentation attacks, IP traceback, ICMP protocol and ICMP misbehaviors, TCP protocol, TCP session hijacking, SYN flooding attacks, DoS attacks, and DDoS attacks, IP Routing protocols and Attacks, DNS and Pharming attacks, BGP protocols and Attacks, Port scanning and signature identification, Web Attacks and Defenses, Botnet, spams. Authentication and authentication protocols. Privacy values and interest, Privacy Norms, privacy and e-commerce, Internet policy routing.

Course Title: Research Methodology

Course Code: CSE 5311
Credit: 3 Credit Hour Course
Prerequisite:   None