This module gives an overview of some of the different tiered application architectures (1, 2, 3, N tiers) and some sample designs. The course teaches some of the foundation skills required for building medium to large scale web-based applications, with a B2B e-commerce focus. The course introduces J2EE and Microsoft .NET as two technology architectures for implementing enterprise applications. Java Servlets and Java Server Pages (JSP), Java application servers, integration of data from multiple data sources and distribution of business logic in component-based applications. Multiple application-end delivery formats are considered including web browsers and WAP phones.
Introduction, Digital Image Fundamentals, Image Transform, Image Enhancement, Image Restoration, Image Compression, Image Segmentation, Representation and Description, Recognition and Interpretation.
Introduction to Neural Network; ANN approach to recognition; ANN models, Design and development of ANN; back propagation model.
Introduction to pattern recognition. General pattern recognition concepts. Statistical pattern recognition. Supervised learning using parametric and non-parametric approaches. Linear discriminant functions and the discrete and binary feature cases. Unsupervised learning and clustering. Syntactic Pattern Recognition: Syntactic recognition via parsing and other grammars, graphical approach to syntactic pattern recognition, learning via grammatical inference. Neural Pattern Recognition: Neural pattern associators and matrix approaches, unsupervised learning in neural pattern recognition.
Problems in computational geometry, worst case complexity of geometric algorithms; expected complexity of geometric algorithms and geometric probability, geometric intersection problems, nearest neighbor searching, point inclusion problems, distance between sets, polygon decomposition, the Voronoi diagram and other planner graph, updating and deleting from geometric structures.
Designing an Internet utilizing a range of different technologies. Simplifying the creation and updating web content. Expanding Intranet services by adding client-slide and server-side processing. Interfacing Internet to a database. Querying a database using Cold Fusion.
History of Computing, Social context of computing, Methods and tools of analysis, Professional and ethical responsibilities, Risks and liabilities of computer-based systems, Intellectual property, Privacy and civil liberties, Computer crime, Economic issues in computing, Philosophical frameworks.
Final Year Project/Internship is a subject that must be completed by final year student as a requirement to receive a Bachelor of Science (BSc) degree in Computer Science and Engineering. In this subject the student will be given one semester to work on a task that is related to their field of interest. Students are expected to do their work independently most of the time. But their progress will be monitored closely by their supervisors. At the end of the project/internship, students should document their work in a thesis which must be hard bounded and submitted to the department. Students are also required to submit a soft copy of their thesis to the department.
Circuit variables and elements: Voltage, current, power, energy, independent and dependent sources, resistance. Basic laws: Ohm’s law, Kirchhoff’s current and voltage laws. Simple resistive circuits: Series and parallel circuits, voltage and current division, Wye-delta transformation. Technique of circuit analysis: Nodal and Mesh analysis. Network theorems: Source transformation, Thevenin’s, Norton’s and superposition theorems. Maximum power transfer condition and reciprocity theorem. Energy storage elements: Inductors and capacitors, series and parallel combination of inductors and
capacitors. Response of RL, RC, and RLC circuits: transient and steady state responses. Basic magnetic circuits: magnetic quantities and variables: Flux density, magnetomotive force, magnetic field strength, permeability and B-H curve, reluctance. Laws in magnetic circuits: Ohm’s law and Ampere’s circuital law. Magnetic circuits: Composite series magnetic circuit, parallel, and series-parallel circuits. Analogy between electrical and magnetic circuits. Hysteresis loss and magnetic materials.
In this course students will perform experiments to verify practically the theories and concepts learned in EEE 101.
Definitions of AC voltage, current, power, volt-ampere and various factors including the peak and form factors. Introduction to sinusoidal steady state analysis: Sinusoidal sources, instantaneous and effective voltage and currents, average power, phasors and complex quantities, impedance, real and reactive power, maximum power transfer, power factor and its improvement. Analysis of single-phase AC circuits: Series and parallel RL, RC and RLC circuits, nodal and mesh analysis, application of network theorems in AC circuits, circuits with non-sinusoidal excitations, transients in AC circuits. Passive filters: Basic types. characteristic impedance and attenuation, ladder network, low- and high-pass filters, propagation coefficient and time delay in filter sections, practical composite filters. Resonance in AC circuits: Series and parallel resonance. Magnetically coupled circuits. Analysis of three phase circuits: three phase supply, balanced and unbalanced circuits, power calculation.