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.
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 student is required to submit the onepage abstract of her/his paper for the oral (area) exam as an attachment to her/his petition for taking the area 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.
Ph.D Qualifying Exam
The exam consists of two parts; Written (Core) exam and Oral (Area) exam.
Written (Core) Exam
This 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.
Oral (Area) Exam
The Purpose of the Exam is 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 make a contribution to this topic as explained below.
 To prepare a paper in the “IEEE conference proceedings format” in at least 6 at most 8 pages.
 An Originality Report for the paper should be prepared and signed by the thesis supervisor. Similarity with the work of others shouldn’t exceed 20%. Originality Report can be prepared using Turnitin (http://www.turnitin.com) or Ithenticate (http://www.ithenticate.com) plagirism check software. It is enough to print the first page of the similarity index for the originality report.
 To submit one page abstract of this paper as an attachment to her/his petition for taking the area exam.
 To submit this paper along with the Originality Report to the examination committee (jury) at least 10 days before the first day of the oral (area) exam period.
Expectations from the Student during the Exam
 To present the student’s study ( at most 20 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 analysissynthesis 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 performance of the student is assessed separately by each jury member according to the table below. A grade of 60 or above is considered as a "pass" vote by that jury member. If at least three jury members vote "pass" the student is considered successful, otherwise the student fails the exam.
Ratio 
Point [0100] 

%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.
 Qualification Exam Committee designates one of the jury members as the chair. The chair is responsible for conducting the examination process.
 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.
Written (Core) Exam 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 GrawGill 
Sorting, Searching 

String Processing 

Graph Algorithms 

Greedy Approach 

Divide and Conquer Algorithms 

Dynamic Programming 

Exhaustive Search 

Complexity Classes, NPcompleteness 

Discrete Mathematics 
Propositional Logic: Logic, Equivalences 
* K.H. Rosen, Discrete Mathematics and its Applications, (Sixth Edition) McGrawHill, 2007. 
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.), PrenticeHall, 1998. 
Pushdown Automata and Context Free Grammars: Parse trees and derivations,Pushdown automata, Pushdown automata versus contextfree grammars, Closure properties,Pumping theorem and its applications, Deterministic PDAs 

Regularity and contextfreeness 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 

ChurchTuring thesis, universal Turing machines 

Halting problem 

Programming Languages 
Storage structures, control structures, scope and binding 
* Programming Language Concepts and Paradigms, D.A. Watt, PrenticeHall, 1990. 
Data and procedural abstraction 

Type systems 

Lexical and syntactic description of languages 

Objectoriented programming languages 

Functional programming languages 

Logic programming languages 

Operating Systems 
Operating Systems Structures 
* Modern Operating Systems, A.S. Tanenbaum, PrenticeHall, ISBN 0135957524, 1992. 
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, PrenticeHall, ISBN 0132129949, 1991. 
Combinational Circuit Minimization: Algebraic and Karnaughmap 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 
Oral (Area) Exam Syllabus
Course 
Topics 
Resources 
Artificial Intelligence 
Uninformed and Heuristic Search 
* Artificial Intelligence: A Modern Approach, S.Russell, P.Norvig, Prentice Hall, 1995. 
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. 
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: Backface elimination, Zbuffer 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, Rayobject intersections (triangle, sphere, plane), Basic ray tracing algorithm, Generating simple shadows with ray tracing, Accelleration structres (bounding boxes, octtree, kdtree) 

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, PrenticeHall, 2000. 
Computational aspects of Morphology 

Syntactic representation in NLP (phrase structure, dependency, unification) 

Parsing strategies for natural languages (bottomup,topdown, mixed) 

Parsing decisions and improvements (determinism, nondeterminism, charts) 

Grammar formalisms (dependency grammars, categorical grammars, phrasestructure grammars) and hierarchy for natural languages 

Handling nonlocal dependencies 

Compositional semantics: Lambdacalculus and logical form 

Basics of dataintensive linguistics (ngrams, language models, classifiers) 

Database Systems 
Physical data organization 
* Database Management Systems, Raghu Ramakrishnan, McGrawHill. 
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, PrenticeHall. 
Linear systems: Norms, matrix norms, Gaussian elimination, forward and backward substitution, pivoting, Householder’s reflection, Given’s rotations, GramSchmidt method, QR, Singular Value Decomposition, Linear Least Squares problems and curve fitting, Relaxation methods (Jacobi, GaussSeidel) 

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.), McGrawHill. 
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, PrenticeHall, 3rd edition, 2008. 
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: Texelbased Texture Descriptions, Quantitative Texture Measures 

Contentbased image retrieval: Image Distance Measures (Color, Texture and Shape Similarity Measures), Precision, Recall and Fscore 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 

Nonparametric techniques: Parzen Window, KNearest Neighbor 

Unsupervised learning: Mixture Resolving, Unsupervised Bayes Method, Maximum Likelihood Method 

Clustering: Kmeans Clustering, Hierarchical Clustering, Component Analysis 

Neurocomputing 
Learning and generalization 
* Neural Computing: Theory and Practice, P.D. Wasserman. 
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. 
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. 
Coordination 

Structuring Distributed Systems 

Process Interaction and Group Communication 

Distributed File Systems 

Concurrency Control 

Distributed Shared Memory 

Basics of FaultTolerance and RealTime Systems 

Programming Languages and Compilers (Advanced) 
Typed lambda calculus 
* Foundations for Programming Languages, (first six chapters) J.C. Mitchell, MIT Press, 1996. 
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 

Syntaxdirected translation, attribute grammars 

Abstract machines, intermediate languages 

Code generation 

Networked Systems 
The principles and techniques employed in computer and wireless networks; the sevenlayer protocol suite known as ISO model 
* Computer Networking: A top down approach, 6th Ed., J.F. Kurose, K.W. Ross, AddisonWesley, 2012. 
Data link layer issues (medium access control, reliable data transfer) 

Network layer issues (packet versus circuitswitching, routing algorithms, IP, QoS) 

Transport layer issues (error control, flow control, congestion control, endtoend 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. 
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: Highthroughput image analysis 

Biological networks and computational systems biology 