Computer Engineering Ph.D. Qualifying Exam Guidelines

The following guidelines are set for the Ph.D. Qualifying Examination in Computer Engineering, in addition to rules and regulations at Middle East Technical University Student Handbook. These are effective as of the first semester of 2006-2007 academic year.

General Information

  • Ph.D. Qualifying exam consists of a written part and an oral part. The candidate is considered successful when he/she passes both parts.
  • Ph.D. Qualifying exam is given twice a year each May and November.
  • The candidate should get the approval of his/her advisor and petition the department at least one month before the exam.
  • The candidate failing to pass the Ph.D. Qualifying exam is given a second chance in the subsequent offering of the exam. Failure in the second attempt leads to the dismissal of the student from the Ph.D. program.

Written Exam

The written exam consists of two parts; Core and Breadth.

Core part

The Core part of the exam covers the following 7 main topics:

  • Data Structures (CENG 213),
  • Algorithms (CENG 315),
  • Discrete Math (CENG 223),
  • Theory of Computation (CENG 280),
  • Programming Languages (CENG 242),
  • Operating Systems (CENG 334),
  • Digital Design and Computer Architecture (CENG 232 & CENG331)

The questions from the Core part will be at the undergraduate level and will cover the content that is listed in the Core subjects table below. In the exam, there will be two questions from each topic, and the student will be asked to attempt only one question from each. Each subject is graded over 20 points and the student is considered successful if her/his total grade is 84 out of 140 (which corresponds to 60% of the maximum grade). In addition, if the student is unsuccessful in the core exam, s/he will be exempt from the subjects on which s/he scored at least 14 out of 20. In the subsequent exam, s/he will be expected to score 60% of the maximum grade from the remaining subjects only. The student reserves the right to “not be exempt” if s/he wishes. The exemption status from a course is valid only for the next exam session.

Breadth part

The Purpose of the Exam To evaluate the student’s ability and potential to conduct research at the doctoral level and to encourage the student to have an earlier involvement in research

Expectations from the Student before the Exam

  • To choose a topic within the student’s research field.
  • To conduct a literature survey on this topic.
  • To make a contribution to this topic as explained below.
  • To prepare the study in the “IEEE conference proceedings format ” in at least 6 pages.
  • To submit this work to the examination committee (jury) latest 1 week before the exam.

Expectations from the Student during the Exam

  • To present the student’s study (30 minutes)
  • Answer questions about the study (10 minutes)
  • Answer general questions in the Ph.D. field of the student (20 minutes, if the jury finds it necessary the question answer part can be extended).

Expected Contribution The student can choose to make one or more of the following types of contributions:

  • Literature evaluation: A literature survey is required in every study, but those students who select this category will be expected to conduct a more detailed literature survey by identifying the advantages and disadvantages of the previous studies, compare them with each other, and provide an analysis-synthesis of the literature in the selected topic.
  • Implementation: The student will implement a paper in the selected topic which should be chosen together with the student’s advisor and produce results by changing various parameter values if applicable.
  • Novel approach: The student will propose a novel approach to a selected problem and implement this approach to produce results. This category includes improving an existing algorithm using a different approach.
  • Comparison: The student will compare two or more algorithms that are selected with the student’s advisor, and discuss the results obtained as a result of this comparison.
  • Theoretical contribution: The student will propose a novel theoretical approach such as a formula, theory, proof, etc. and show the correctness, utility, and reasoning behind this approach.
  • Case study: The student will apply an existing method or process to a realistic problem and discuss the results that are obtained.

Grading The success of the student will be measured according to the table below. The student whose weighted grade total is 60 or higher will be considered successful.

Ratio Point [0-100]
%40 Written work
%20 Presentation and questions related to the presentation
%40 General questions in the selected field
Weighted Total:100

General Principles about the Administration of the Exam

  • For every student who will take this exam, a jury comprised of 5 people with Ph.D. degree and experts in the selected field will be formed by the qualifying exam committee. This jury will include the student’s advisor. It is crucial that the jury members read the study before the exam and prepare questions to be asked during the exam.
  • The written work prepared by the student must be original. It should not be put together by copying and pasting from the previous work.
  • Jury members could check the document submitted by the student against plagiarism using http://www.ithenticate.com/ to which METU has a subscription.
  • A study prepared for the master’s thesis cannot be directly used for his exam. A contribution is expected to be made during the Ph.D. studies even if the topic remains the same.
  • An article (published or unpublished) written by the student as the primary author can be used for this exam. However, the same article cannot be used by more than one student, and if it was published it should not be published more than 12 months before the exam.
  • In case of failure for the first time, a new topic can be chosen or the jury must indicate the expectations from the student for the second exam if the same topic will be used.
  • In case the student is taking this exam for the second time from the same topic, a supporting document explaining the changes from the previous version must be provided by the student to the jury along with the latest version of the study.

Ph.D. Qualifying Exam – Core Part Syllabus

Course Topics Resources
Data Structures Algorithm analysis for data structures * Mark Allen Weiss, Data Structures and Algorithm Analysis in C++ (3rd ed.), Addison Wesley, 2006
Lists, stacks, queues
Trees
Priority queues
Hashing
Algorithms Analysis of Algorithms * Introduction to Algorithms, T. H. Cormen, C. E. Lieserson, R. L. Rivest, C. Stein, Mc Graw-Gill
Sorting, Searching
String Processing
Graph Algorithms
Greedy Approach
Divide and Conquer Algorithms
Dynamic Programming
Exhaustive Search
Complexity Classes, NP-completeness
Discrete Mathematics Propositional Logic: Logic, Equivalences * K.H. Rosen, Discrete Mathematics and its Applications, (Sixth Edition) McGraw-Hill, 2007.
* W.K. Grassmann and J.P. Tremblay, Logic and Discrete Mathematics: A Computer Science Perspective, Prentice Hall, 1996
Predicate Logic: Predicates and Quantifiers, Nested Quantifiers, Methods of Proof
Sets and Functions: Sets, Set Operations, Functions, Growth of Functions, Complexity of Algorithms
Integers: Integers and Division, Integers and Algorithms
Induction and Recursion: Sequences and Summations, Mathematical Induction, Recursive Definitions and Structural Induction, Recursive Algorithms
Counting: Permutations and Combinations, Recurrence Relations, Solving Recurrence Relations, Generating Functions, Inclusion and Exclusion
Relations: Relations and Their Properties, Representing Relations, Closure of Relations, Equivalence Relations, Partial Orderings
Graphs: Int to Graphs, Graph Terminology, Representing Graphs, Connectivity, Euler and Hamiltonian Paths, Shortest Path Problem, Graph Coloring
Trees: Int to Trees, Applications of Trees, Spanning Trees, Min Spanning Trees
Theory of Computation Finite Automata and Regular Expressions: Alphabets and languages, Finite representations of languages,Deterministic finite automata, Nondeterministic finite automata, Equivalence of DFA and NFA, Finite automata versus regular languages, Pumping lemma and its applications, State minimization * Elements of the Theory of Computation, H.R.Lewis, C.H.Papadimitriou, (2nd ed.), Prentice-Hall, 1998.
* Introduction to the Theory of Computation, M.Sipser, Course Technology, 2005.
Push-down Automata and Context Free Grammars: Parse trees and derivations,Pushdown automata, Pushdown automata versus context-free grammars, Closure properties,Pumping theorem and its applications, Deterministic PDAs
Regularity and context-freeness of languages
Turing Machines and unrestricted grammars: Turing machines – definition and examples, Computing with TMs, Recursive and recursively enumerable languages, Extensions of TMs, Nondeterministic TMs, Unrestricted grammars
Church-Turing thesis, universal Turing machines
Halting problem
Programming Languages Storage structures, control structures, scope and binding *Programming Language Concepts and Paradigms, D.A. Watt, Prentice-Hall, 1990.
* Programming Languages: Concepts and Constructs, R. Sethi, Addison Wesley, 1996.
Data and procedural abstraction
Type systems
Lexical and syntactic description of languages
Object-oriented programming languages
Functional programming languages
Logic programming languages
Operating Systems Operating Systems Structures * Modern Operating Systems, A.S. Tanenbaum, Prentice-Hall, ISBN 0-13-595752-4, 1992.
* Operating System Concepts, A. Silberschatz, P.B. Galvin, (4th ed.), Addison-Wesley, ISBN 0-201-50480-4, 1994.
* Design and Implementation of the 4.3BSD Operating System, S.J. Leffler, M.K. McKusick, M.J. Karels, J.S. Quarterman, Addison-Wesley, ISBN 0-201-06196-1, 1989.
Processes, Threads and Their Management
Process and Processor Scheduling
Process Synchronization
Interprocess Communication
Deadlocks
Memory Management
Storage Management (I/O Processing, File Systems)
Protection and Security
Digital Design and Computer Architecture Combinational Circuits * Digital Design, M. Mano, Prentice-Hall, ISBN 0-13-212994-9, 1991.
* Computer Organization, C. Hamacher, Z.G. Vranesic, S. Zaky, (4th ed.) McGraw-Hill, ISBN 0-07-114323-8, 1996.
* Computer Organization and Design, D.A. Patterson, J.L. Hennessy, (2nd ed.), Morgan-Kaufmann, ISBN l-55860-491-X, 1998.
* Computer Systems: A Programmer’s Perspective by Randal E. Bryant and David R. O’Hallaron Prentice Hall, 2003
Combinational Circuit Minimization: Algebraic and Karnaugh-map minimization
Synchronous Sequential Circuits
Registers, Counters
RAM, ROM, PLA, and PAL
Arithmetic Logic Unit, Multiplication and Division, Floating Point operations
Pipelining: Hazards, Forwarding, Branch Prediction
Memory Hierarchy: Interleaving, Cache Memory, Virtual Memory
I/O Systems: Buses, I/O Interfaces, Interrupts, DMA

Ph.D. Qualifying Exam – Breadth Part Syllabus

Course Topics Resources
Artificial Intelligence Uninformed and Heuristic Search * Artificial Intelligence: A Modern Approach, S.Russell, P.Norvig, Prentice Hall, 1995.
* Logical Foundations of Artificial Intelligence, M.R.Genesereth, N.Nilsson, Morgan Kaufmann, 1988.
Game Playing
Constraint Satisfaction and Propagation
Knowledge and Reasoning
Theorem Proving
Planning
Reasoning with Uncertainty
Machine Learning: Learning from examples (supervised learning, decision trees, Regression and classification, ANN, SVM), Learning probabilistic models (Bayesian learning, Naive Bayes classifiers, EM algorithm), Reinforcement Learning (passive RL, active RL)
Computer Graphics Rendering Pipeline: Major stages of the rendering pipeline * Computer Graphics: Principles and Practice, Foley, Van Dam, Feiner, Hughes, (2nd ed.), Addison Wesley, 1995.
* Computer Graphics, Hearn, Baker,(2nd ed.), Prentice Hall, 1994.
* Fundamentals of Computer Graphics, Shirley and Marschner, (3rd ed.), AK Peters, 2009.
* Realistic Ray Tracing, Shirley and Morley, (2nd ed.), AK Peters, 2003.
Geometric Transformations: Homogeneous coordinates, Vectors, points, normals, Translation, scaling, rotation, sheer transformations (2D and 3D)
Raster Algorithms: Line rasterization, Triangle rasterization, Antialiasing
Viewing: Parallel projections, Perspective projections, Clipping, Viewport transformation
Visible Surface Detection: Back-face elimination, Z-buffer algorithm
Phong Shading Model: Ambient Light, Diffuse Reflection, Specular Reflection
Polygonal Surface Shading: Flat shading, Goraud shading, Phong shading
Texturing: Generating of uv coordinates (for both 2D and 3D texture mapping), Mipmapping, Bilinear interpolation, Bump mapping
Volume Rendering: Marching cubes algorithm, Direct volume rendering
Three Dimensional Object Representations: Hermite curve, Natural cubic splines, Bezier curves and surfaces, Geometric continuities, Joining curves and surfaces
Ray tracing: Parametric lines, Parametric and implicit surfaces, Ray-object intersections (triangle, sphere, plane), Basic ray tracing algorithm, Generating simple shadows with ray tracing, Accelleration structres (bounding boxes, oct-tree, kd-tree)
Radiosity: Basic radiosity algorithm, Radiosity equation, Hemicube method for form factor calculations, Jacobi iteration and Gauss Seidel for solving Ax=b
Natural Language Processing Linguistic knowledge representation and propagation * Speech and Language Processing, Jurafsky and Martin, Prentice-Hall, 2000.
* Natural Language Understanding, J.Allen,2.ed,Benjamin-Cummings, 1995.
* Prolog and Natural Language Analysis, F.C.N. Pereira, S.M. Shieber, CSLI, 1987.
Computational aspects of Morphology
Syntactic representation in NLP (phrase structure, dependency, unification)
Parsing strategies for natural languages (bottom-up,top-down, mixed)
Parsing decisions and improvements (determinism, non-determinism, charts)
Grammar formalisms (dependency grammars, categorical grammars, phrase-structure grammars) and hierarchy for natural languages
Handling non-local dependencies
Compositional semantics: Lambda-calculus and logical form
Basics of data-intensive linguistics (n-grams, language models, classifiers)
Database Systems Physical data organization * Database Management Systems, Raghu Ramakrishnan, McGraw-Hill.
* Principles of database and knowledge-base systems, volume 1, Ullman, Computer Science Press.
* Database system concepts, Silberschatz & Korth, McGraw-Hill.
Data models
Relational database design theory (normalization)
Relational query languages
Integrity and security
Transaction management
Concurrency control
Recovery techniques
Query optimization
Numerical Computation Numerical stability of algorithms and conditioning of problems * Numerical Methods, G.Dahlquist, A.Björck, Prentice-Hall.
* Matrix Computations, G. Golub, C.F. Van Loan, THe Johns Hopkins University Press.
* Yousef Saad, Iterative Methods for Sparse Linear Systems, SIAM.
Linear systems: Norms, matrix norms, Gaussian elimination, forward and backward substitution, pivoting, Householder’s reflection, Given’s rotations, Gram-Schmidt method, QR, Singular Value Decomposition, Linear Least Squares problems and curve fitting, Relaxation methods (Jacobi, Gauss-Seidel)
Matrix eigenvalue Problems: Power method, inverse iteration, Rayleigh Quotient, and QR iterations, Jacobi method, Arnoldi and Lanczos processes, Krylov subspace methods for solution of linear systems (GMRES, CG, BiCGStab), preconditioning
Finding roots of nonlinear equations: Bisection, Secand, Newton’s methods, fixed point iteration
Interpolation: Lagrange interpolation, Newton’s interpolation and divided differences, Runge’s phenomenon, Splines, Orthogonal polynomials
Numerical integration: Interpolatory quadrature, Composite quadrature rules
Software Engineering Lifecycles and process models * Software Engineering: a Practitioners Approach, R.S. Pressman, (4th ed.), McGraw-Hill.
* Software Engineering, Sommerville, (4th ed.), Addison-Wesley
Software project management
Specification and modeling techniques
Traditional, object oriented and component based approaches
Software metrics
Software quality
Testing and integration methods
Maintenance
Pattern Recognition and Image Analysis Image Transform: Discrete Fourier transform (FFT excluded), Discrete Haar Wavelet transform * Digital Image Processing, R. C. Gonzales and R. E. Woods, Prentice-Hall, 3rd edition, 2008.
* Pattern Classification, R.O. Duda, P. E. Hart and D. G. Stork, Wiley-Interscience, 2nd edition, 2000.
* Computer Vision, L. G. Shapiro and G. C. Stockman, Prentice Hall, 2001.
Image Enhancement Techniques: Point Processing (basic intensity transformations), Histogram processing, Image negation, power law, log transformations, Spatial Filtering, Convolution (smoothing, sharpening)
Image Compression: Redundancy and measuring image information, Huffman coding
Morphological Operations: Erosion, dilation, opening, closing
Image Segmentation: Edge detection (Canny, Hough transform), Thresholding
Image Representation and Description: Chain codes, Polygons, Regional descriptors
Texture: Texel-based Texture Descriptions, Quantitative Texture Measures
Content-based image retrieval: Image Distance Measures (Color, Texture and Shape Similarity Measures), Precision, Recall and F-score Performance Analysis
Motion from 2D images: Image Subtraction
Stereo Vision: Matching: Cross Correlation, Symbolic Matching, The Epipolar and The Ordering Constraints
Bayesian Decision Theory: Gaussian Density Estimation, Classifier Discriminant Functions
Maximum Likelihood Method: Gaussian Density Estimation
Non-parametric techniques: Parzen Window, K-Nearest Neighbor
Unsupervised learning: Mixture Resolving, Unsupervised Bayes Method, Maximum Likelihood Method
Clustering: K-means Clustering, Hierarchical Clustering, Component Analysis
Neurocomputing Learning and generalization * Neural Computing: Theory and Practice, P.D. Wasserman.
* Introduction to the Theory of Neural Computation, J. Hertz, A. Krogh, and R.G. Palmer, Addison-Wesley, 1991.
* Neural Networks: A Comprehensive Foundation, S. Haykin, Macmillan, 1994.
Multilayer perceptrons and the backpropagation algorithm
Hopfield model
Recurrent networks
Unsupervised learning and self organizing maps
Adaptive resonance theory
Radial basis function networks
Higher order neural networks
Neurodynamics
Parallel Computing Parallelism and classification of parallel computers: Performance bottlenecks, Classification of parallel computers and applications, Programming models for parallel computers* Introduction to Parallel Computing, by Grama, Gupta, Kumar, and Karypis, Addison Wesley, 2003.
* Parallel Programming for Multicore and Cluster Systems, Rauber and Runger, Springer Verlag, 2010.
* Sourcebook of parallel computing, Jack Dongarra, et.al. Kaufmann, 2002.
Pipelining and vector processing: Instruction pipelining, superscalar execution, and instruction scheduling, Pipelining arithmetic operations, Performance analysis of pipelined operations
Interconnection topologies and implementing various communication operations: Metrics for evaluating performance of interconnection networks, Point to point and collective communication operations and their implementation
Task decomposition and design of parallel algorithms: Principles of parallel algorithm design, Task interaction and dependency graphs, Graph partitioning/clustering, Load balancing
Analysis of parallel algorithms: Speed improvement and efficiency, Amdhal’s law, Gustafson’s law, Weak and strong scalability
Parallelism in various applications (e.g. matrix problems in scientific applications, sorting and searching, etc.)
Distributed Systems Time Synchronization * Distributed Systems: Principles and Paradigms, 2nd edition, A.S. Tanenbaum, M. Van Steen, Pearson Higher Education, 2007.
* Distributed Systems: Concepts and Design 4th edition, J. Dollimore, T. Kindberg, G. Coulouris, Addison-Wesley, 2006.
* Principles of Concurrent and Distributed Programming, 2nd edition, B. Ben-Ari, Addison-Wesley, 2006.
Coordination
Structuring Distributed Systems
Process Interaction and Group Communication
Distributed File Systems
Concurrency Control
Distributed Shared Memory
Basics of Fault-Tolerance and Real-Time Systems
Programming Languages and Compilers (Advanced) Typed lambda calculus * Foundations for Programming Languages, (first six chapters) J.C. Mitchell, MIT Press, 1996.
* Compilers: Principles, Techniques, and Tools, A.V. Aho, R. Sethi, J.D. Ullman, Addison-Wesley, 1986.
Semantic specification of languages: Operational, denotational and axiomatic approaches
Algebraic specification of data types
Partial correctness proofs with before and after assertions
Lexical and syntactic analysis of languages
Syntax-directed translation, attribute grammars
Abstract machines, intermediate languages
Code generation
Networked Systems The principles and techniques employed in computer and wireless networks; the seven-layer protocol suite known as ISO model * Computer Networking: A top down approach, 6th Ed., J.F. Kurose, K.W. Ross, Addison-Wesley, 2012.
* Computer Networks, 5th Ed., A.S. Tanenbaum, Prentice Hall, 2011.
* Cryptography and Network Security: Principles and Practice, 5th Ed., W. Stallings, Prentice Hall, 2011.
Data link layer issues (medium access control, reliable data transfer)
Network layer issues (packet- versus circuit-switching, routing algorithms, IP, QoS)
Transport layer issues (error control, flow control, congestion control, end-to-end argument, TCP, UDP)
Network programming (socket interface)
Performance evaluation of computer networks
Security of computer networks (confidentiality, integrity, and authentication)
Wireless networks (Cellular networks, mobility management, WLAN)
Bioinformatics Sequence analysis, next generation sequencing: Genome annotation, Computational evolutionary biology, Comparative genomics, Genetics of disease, Analysis of mutations in cancer * Understanding Bioinformatics, M. Zvelebil and J.O. Baum, Garland Science, 2008.
* Bioinformatics: the machine learning approach, 2nd Ed., Baldi and S. Brunak, MIT press, 2001.
* Principles of Computational Cell Biology, V. Helms, Wiley-Blackwell, 2008.
Gene and protein expression, gene regulation
Structural bioinformatics: Protein folding problem, prediction of secondary/tertiary structure, Structural alignment, Multiple structural alignment, Protein docking
Functional classification of proteins, human genome annotation
Statistical modeling of biological data
Biological Text Mining
Bioimage Informatics: High-throughput image analysis
Biological networks and computational systems biology
grad/phdqual.txt · Last modified: 2016/03/23 07:33 by Tolga CAN