OSI and TCP/IP reference models. Physical Layer: Transmission media characteristics: Guided transmission media, Wireless transmission, Public Switched Telephone Network, ISDN, ATM and Communication Satellites. Switching- circuit message and packet, data communication principles – asynchronous and synchronous. Data Link Layer: Framing, Flow & error control, Error detection & correction, Data link protocols. MAC sublayer: Channel allocation problem, Multiple Access protocols, Ethernet, Switching Devices, Wireless LAN, Broadband wireless. Network Layer: Deign issues, Routing Algorithms, Congestion Control algorithms, Internetworking and Internet. Transport Layer: The transport services, Elements of transport Protocols, Introduction to UDP & TCP. Application Layer: Basics of DNS, Email, Web services and introduction to network security. The course includes lab works based on theory taught.
Principles involved in data communication. Modulation techniques, Pulse Modulation, Pulse amplitude modulation, pulse width modulation, pulse position modulation, pulse code modulation, pulse position modulation, quantization, Delta modulation, TDM, FDM, OOK, FSK, PSK, QPSK. Representation of noises, probability of error for pulse system, concept of channel coding and capacity, asynchronous and synchronous communications. Multiplexers, concentrators and buffers, communication medium, fiber optics.
Computer network architectures, protocol layers. Transmission media, encoding systems, error detection, multiplexing, switching. Data link, multiple access channel protocols. Network security, privacy. Applications including network management, electronic mail, virtual terminals, URL, HTTP, Multimedia, distributed operating systems. The course includes lab works based on theory taught. The course includes lab works based on theory taught.
Introduction of Microprocessor and its use, Microprocessor and Memory Basics, Microprocessor: microcontroller & microcomputer, evaluation of microprocessor & application, introduction to 8-bit, 16-bit, and 32-bit microprocessors; addressing modes: absolute addressing, 8086 internal architecture, PIN diagram of 8086, Max-Min mode, register structure; memory read write cycle; Instruction set; pipeline concept: interrupts, programmed I/O, memory mapped I/O, interrupt driven I/O, direct memory access; block transfer; cycle stealing; interleaved; multi-tasking and virtual memory; memory interface; bus interface; arithmetic co-processor; assembly language programming of 8086 microprocessors., serial data transmission, serial communication standards, serial interface implementation. Arduino: Buttons, PWM, and Functions, Arduino: Serial Communication and Processing, I2C, Modbus RTU, TCP/TP Communication and SPI Interfaces, Wireless Communication, Arduino: Interrupts and Hardware Handling, collecting data from external environment via sensors and send/receive data to cloud, learning about python interfacing Program to collect real time, data plotting simultaneously.
Based on theory course.
System Analysis Fundamentals, tools of information system development, information systems development life cycle, tools for analysis; planning phase: systems planning, preliminary planning and investigation, determining IS development requirements, project management, Object Oriented Systems Analysis and Design and Unified Modeling Language.; analysis phase: analysing requirements, evaluating alternatives, information systems analysis principles; design phase: structured information systems design, input design and control, output system design; development phase: information systems development, computer-aided software engineering; implementation phase: systems implementation, systems evaluation and optimization, information systems documentation, Costs and benefits of different approaches to implementing new systems.
Introduction to computer hardware: Processor, RAM, ROM, Motherboard, Hard Disk, DVDs; Assembling and interfacing.
Operating system (OS) installation: Windows 7.0, Linux; Software installation, Disk partitioning and formatting.
Operating system maintenance: OS protection, System restore, System crash repair, Local users and groups, Task manager, Registry, Security policies, etc.
Networking: Networking tools and topologies, TCP/IP, Implementation of a physical network, Introduction to wireless network, Network OS, Data sharing and security, Network printer installation and sharing, Mapping of network drive, Remote desktop, Net meeting.
Trouble shooting: Desktop, Laptop, Networks, Operating System, Printer and Fax.
Importance of AI, Knowledge Representation: Definition and importance of knowledge, representing single facts in logic, resolution non-monotonic reasoning, Dealing within inconsistencies and uncertainties, dempster shafrer theory, Ad-Ho methods, Heuristic reasoning methods, structural representation of knowledge graphs, frames and related structures. Neural Networks: Biological neuron, Artificial neurons and neural networks, Learning processes. Perceptron, multilayer layer perceptron, Bi-directional associative memory, Back propagation method, Self-organizing Kohonen networks, Hopfield neural network. Fuzzy Logic: Fuzzy set and control theory. Fuzzy inference, Fuzzy logic expert systems, Fuzzy associative memory, Fuzzy neural control. General algorithm, Pattern Recognition: Recognition and classification process, learning classification patterns, recognizing and understanding speech. Expert System: architectures, model based system, constraint satisfaction. Introduction to neural networks, learning algorithms and models.
Artificial Intelligence and Intelligent Agents, Problem Solving (Solving Problems by Searching, Adversarial Search, Constraint Satisfaction Problems), Knowledge and Reasoning (Logical Agents, First-Order Logic, Inference in First-Order Logic, Classical Planning, Planning and Acting in the Real World, Knowledge Representation), Uncertain Knowledge and Reasoning (Quantifying Uncertainty, Probabilistic Reasoning, Probabilistic Reasoning over Time, Making Simple Decisions, Making Complex Decisions), Learning (Learning from Examples, Knowledge in Learning, Learning, Probabilistic Models, Reinforcement Learning).
Introduction to machine learning; Regression analysis: Logistic regression, linear regression; Classification techniques: Supervised and unsupervised classification; Neural networks; Support vector machines; Classification trees; Rule based learning; Instance based learning; Reinforcement learning; Ensemble learning; Negative correlation learning; Evolutionary algorithms; Genetic algorithm, Statistical performance evaluation techniques of learning algorithms: bias-variance tradeoff; Practical applications of machine learning recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.