CSE3233-Network Programming

Course Unit Title: Network Programming

Course Unit Code: CSE3233

Course Time : Wednesday [13:30-15:30]/[19:15-21:00]

Course Room: C208

Level of Course: BSc

Course Platforms: CengNetPrg Group // NetPrgClass @Google Classroom

Theoretical: 3

Semester: Spring

Name of Lecturer: Asst. Prof. Dr.Muhammed Cinsdikici

Mode of Delivery: Face-To-Face

Language: Turkish

Assistant: Ahmet Cahit Yaşa

Prerequisities : Algorithms, C/C++ Coding, Data Structures

Objectives of the Course:

Objectives of this course includes providing students with the understanding of current trends in communication protocols and providing them information about socket programming, inter-process communications and network research.

Textbooks:

1. InterProcess Communications in LINUX, John Shapley Gray, Prentice Hall, ISBN-13: 978-0130460424, 2003

 2. C# Network Programming, Richard Blum, Sybex, ISBN: 978-0-7821-4176-4, 2002

3. Java Network Programming, Merlin & Conrad Hughes et.all, Manning, ISBN-13: 978-0138412067, 1997

Learning Outcomes

1. Ability to understand OSI layers and protocols.
2. Ability to develop applications using TCP / ODP protocols
3. Ability to develop multithreaded applications, ensure synchronization in shared memory environments.
4. Ability to design complex network applications and present it.

Course Contents:

Linux Processes and Environment, Primitive Communications, Pipes, Process Message Queues, Semaphores, Shared Memory, Remote Procedure Call, Network Layer Programming [Sockets, TCP/UDP Programming], Application Programming Samples [ICMP, SNMP, SMTP, HTTP]

Grading:

%30 HomeWorks + %20 MidTerm + %20 Final Exam + %30 Final Project 

Late submission of the homeworks and projects are not going to be accepted. The assessment principals are;

On time submission are scored as max.100/100

One week late submissions are scored as max. 80/100

Two weeks late submissions are scored as max. 60/100

Others are scored as 0.

WEEK	NAME	SUBJECTS
	Slides	Laboratory	Suplementary Materials
1	Introduction	Introduction		YoLinux Fork / Exec Page
2	Processing Environments	Processing Env	Research Assignment
3	Process Life Cycle	Process Life Cycle	Process Creation Lab on Linux OS	ProcessesShells / SignalsProcesses and Threads
4	Primitive Communications	Primitive Communications	Java Thread Library Coding
5	Pipes	Pipes – Sample Chapter	Semaphore implementationCode assignment aboutJava threads
6	Message Queues		Research for Queue Systems	Customer Queueing SystemSimulating QueueSabarimala Virtual Queue System 🙂
7	Midterm
8	Semaphores	Semaphore	Deadlock avoidance implementation	OS DeadlockBankers AlgorithmDeadlock in traffic
9	Shared Memory	Memory Management	LitterateurResearch	How CPU Works?Fetch/Decode/ExecutePage Tables
10	Remote Procedure Call / Remote Method Invocation		Book Reading	LRU Approaximation: Second Chance (!)Clock & Second Chance Algorithm s Presentation
11	Sockets		Research on Disk Drives
12	TCP Programming		Linux/Win8 File Management System Information
13	UDP Programming		FAT32/NTFS Info	UDP Protocol Details
14	Case Studies
15	Final Exam

CSE3220 – Machine Learning (Spring, 2022)

Course Time:	Tuesday, 09:55-12:30 / 17:00-19:30
Place:	C208
Instructor:	Assoc. Prof.Dr. Muhammet G. Cinsdikici, cinsdikici@{gmail.com, comvislab.com} Office Hours: Tuesday,13.30-15.30 Office Phone: 2107
Assistant(s):	Ahmet Cahit Yaşa
Class Group	Computer Engineering ML Class Google Group / MLClass@classroom.google.com
Description:	Machine Learning Introduction, Regression, Multivariate Regression, Gradient Descent Learning, Logistic Regression, Regularization, Artficial neural networks, biological foundations, cognitive processes and their modeling using artificial neural networks, problem solving using neural networks, supervised/unsupervised learning, Support Vector Machines, Principle Component Analysis, Naive Bayesian Learning.
Prerequisites:	Python or Matlab or one of the Programing languages (Java, C#), Statistics & Mathematics background for engineering departments
Textbooks:	2021, Zhi-Hu Zhou, “Machine Learning“, Springer, ISBN: 9789811519673 2021, Chris Mattmann, “Machine Learning with Tensorflow “, Manning, ISBN: 9781617297717 2021, Francois Chollet, “Deep Learning with Python“, Manning Publishing, ISBN: 9781617294433 2018, Birol Kuyumcu, “OpenCv Görüntü İşleme ve Yapay Öğrenme“, Level Kitap,  ISBN: 9786056567933 2017, Sebastian Raschka, “Python Machine Learning 2.nd edt”, PACKT Publishing, ISBN: 978-1-783-55513-0 2014, Ethem Alpaydin, “Introduction to Machine Learning – 3rd edt“, MIT Press, ISBN: 978-0-262-02818-9 2012, Kevin P. Murphy, “Machine Learning”, MIT Press, ISBN: 978-0-262-01802-9 2009, Simon Haykin, “Neural Networks and Learning Machines – 3rd edt”, Prentice Hall, ISBN: 978-0-131-47139-9 2006, Christopher Bishop, “Pattern Recognition and Machine Learning”, Springer, ISBN: 978-0-387-31073-2 2004, Mehmet Ö. Efe, “Artificial Neural Networks and Their Applications”, Boğaziçi University Publications, ISBN: 975-5-18223-3 1994, Laurene Fausett, “Fundementals of Neural Networks”, Prentice Hall, ISBN: 978-0-133-34186-7. Free Machine Learning with Matlab
Projects	All Projects are due at the beginning of class. Due dates for projects will be announced at least a week ahead of time. No late submission will be accepted!.
Tests:	Students will have one final exam. Students have to submit their final Technical Report to any journal in SCI or SCI-Expanded or conference in related area.
Grading:	%30 MidTerm + %20 Homeworks + %25 FinalExam + %25 Final Project
Late Submission:	Late submission of projects will not be accepted.
Programming Platforms/Tools:	1. Python 3.5 (or newer versions) + Machine Learning Libraries 2. Matlab R2018b and Simulink Bundled Student Edition 3. Visiual Studio 2015  + {OpenCV / Accord.NET etc..}
Suplimentary Best Video Lectures:	1. Andrew NG – Machine Learning – Stanford 2. Yaser Abu Mostafa – Machine Learning – Caltech 3. Patrick Winston – Artificial Intelligence – MIT 4. Emily Fox – Machine Learning

Lectures:

Lecture01 –  Introduction to Machine Learning
* Introduction to Machine Learning
Lecture02 –  Linear Regression with One Variable
* Linear Regression with One Variable
* Linear Algebra Background
Lecture03 – Linear Regression with Multiple Variables
* Linear Regression with Multiple Variables
* Matlab Scripting Backgound (Optional: Python) 
* Matrix Tutorial (Option)
Lecture04 – Logistic Regression
* Logistic Regression
Lecture05 – Regularization
* Regularization
Lecture06 – Neural Network Fundementals, Components
* Cinsdikici-Introduction Presentation
* Simon Haykin – Introduction to Neural Nets
* Simon Haykin – Perceptron
* Neural Nets Presentation
* Prof. Leslie Smith – Introduction to Neural Network
* Christos Stergiou and Dimitrios Siganos – Introduction to Neural Networks

* Genevieve Orr – Motivation for Neural Networks
* Learning Artificial Neural Networks [Robotics]
* Continuous_Mathematic_Notes.pdf
* Perceptron – Applet Demo
Lecture07 – Neural Network Models
*BackPropagation
* Khonen’s {SOM / LVQ 1, 2, 3}
Lecture08 – NeoCognitron – “Convolutional Neural Net” Deep Learning Model
* Cinsdikici – Neocognitron Variants/LeNet5
Lecture09 – SVM – Support Vector Machines
Lecture10 – Clustering

Lecture11 – Dimensionality Reduction (PCA / LDA)
Lecture12 – Speech Recognition
* AquaPhoenix – Audio Processing with Matlab
* Bern Plannerer – Speech Recognition with Matlab
* Ian McLoughlin – Applied Speech and Audio Processing with Matlab
* David Roberts – Voice Recognition Project
HomeWorks & Project:
HomeWork#1 – Image Segments Classification with Logistic Regression

UBI506 – Operating Systems Course

Course Unit Title	Operating Systems
Course Unit Code	UBE 506
Course Time:	Wednesday [18:00-21:00], Friday [9:30-12:00]
Course Room:	UBE, Room 103
Level of Course Unit	M.Sc.
Number of ECTS Credits Allocated	7,5
Theoretical	3
Semester	Spring
Name of Lecturer(s)	Asst. Prof. Dr.Muhammed Cinsdikici, cinsdikici@gmail.com, office phone: 3205
Mode of Delivery	Face-To-Face
Language	Turkish
Assistant	Kaya Oğuz, kaya.oguz@ege.edu.tr, office phone: 3242
Prerequisities	—
Objectives of the Course	The aim of this course is to introduce the theory and implementation of operating system concepts with emphasis on concepts on sample operating systems like Unix, Linux, Windows and show how the sharing resources.
Textbook	1. Silberschatz Galwin, Operating System Concepts, 9.th Edition, Wiley, 20142. Andrew S. Tanenbaum, Albert S.Woodhull, The Minix book: Operating Systems – Design and Implementation- (3.rd Edition),Prentice Hall, 2006
Learning Outcomes	To conceive the components of operating systems. Ability to explain general working  principles of operating systems. Ability to compare the  kinds and properties of operating systems. To realize the problems about sharing resources of a computer system. Ability to conceive operating system’s services. Ability to explain the concepts of process, deadlock; process scheduling algorithms, memory management and file systems. Ability to investigate the resource sharing methods’ effects to a computer performance Ability to develop software which contains multithreaded architecture and is protected from deadlock Ability to give a presentation, explanation about specific subject related the operating systems by research deeply. Awareness of ethics problems about operating systems.
Course Contents	Process, process communication, Scheduling Synchronization, memory management, virtual memory, paging, swapping, file systems, input-output security, protection, deadlock avoidance algorithms, Unix basic concepts, introduction to distributed systems.
Grading & Projects	%15 HomeWorks + %30 MidTerm + %40 Final Exam + %15 Final Project Late submission of the homeworks and projects are not going to be accepted.  The assessment principals are; ————————————- On time submission are scored as max.100/100 One week late submissions are scored as max. 80/100 Two weeks late submissions are scored as max. 60/100Others are scored as 0.

Weekly Detailed Course Contents

WEEK NAME   SUBJECTS     Theoretical Slides Laboratory Suplementary Materials 1 Introduction Definition of operating system, basis components of an computer system, interrupt handling types, storage hierarchy, structure of an operating system, basis chapter abstracts: process management, memory management. Introduction Computer Pioneers HistoryCharles Babbages Difference MachineHuman’s OS 2 System Structures Operating system services, operating system interfaces  (GUI and CLI), system calls and relation with operating system, system programs, MS-DOS structure, Unix system structure, microkernel  system structure, Mac OS X and Solaris system structure, virtual machines. OS Structures Research Assignment History of GUI MsDOS Source Codes   [Thnx to my student Muhammed Ali Sürücü for the following suplimentary materials] System Calls on Linux with code sample Linux System Call Quick Reference   3 Process Concept Process, process scheduling, process synchronization, interoperating processes, process communication, inter process synchronization on server-client  Processes Process Creation Lab on Linux OS 4 MultiThreaded Programming Threads, multi-thread model, POSIX threads (Pthread),  Windows XP threads, Linux and Java threads  Threads Java Thread Library Coding 5 Synchronization Process synchronization, critical section problem, Peterson’s solution, hardware-base synchronization, semaphores, classical synchronization problems, monitor concept. Process Synchronization Semaphore implementationCode assignment aboutJava threads 6 Process Scheduling CPU scheduling subjects: scheduling criterias, multi CPU scheduling, scheduling algorithms, real-time scheduling, thread scheduling, examples from operating systems, Java thread synchronization code example. CPU Scheduling[Previous Version of the  Slide]  Research for Queue Systems Customer Queueing SystemSimulating QueueSabarimala Virtual Queue System 🙂 7   Midterm     8 Deadlocks Deadlock problem, deadlock prevention methods, avoidance methods from deadlock, deadlock recovery. Classical deadlock problems and solutions, sample codes Deadlock  Deadlock avoidance implementation OS DeadlockBankers AlgorithmDeadlock in traffic 9 Memory Management Strategies Memory management, swapping, contiguous allocation paging, segmentation, segmentation with paging. Memory Management LitterateurResearch  How CPU Works?Fetch/Decode/ExecutePage Tables 10 Virtual-Memory Management Virtual memory, paging on demand, process creation, page reallocation, frame replacement, Segmentation on demand, examples from operating systems. Virtual Memory Management Book Reading LRU Approaximation: Second Chance (!) Clock & Second Chance Algorithm s Presentation 11 Mass Storage Structure Disk Structure, Disk Scheduling, Disk Management, Swap-Space management  Mass Storage Structure  Research on Disk Drives 12 File System Interface File system interface, file concept, file access, file sharing and file protection, file associating.  File System Interface  Linux/Win8 File Management System Information 13 File-Systems Implementation File systems implementation, Allocation methods, free space management  File Systems Implementation  FAT32/NTFS Info 14 Case Studies Linux Systems, Windows 7 15   Final Exam

Recommended or Required Reading

2. Silberschatz Galwin, Operating System Concepts with JAVA, Wiley, 20041. Andrew S. Tanenbaum, Albert S.Woodhull, The Minix book: Operating Systems – Design and Implementation- (3.rd Edition),Prentice Hall, 20062. Silberschatz Galwin, Operating System Concepts with JAVA, Wiley, 2004

UBI621 – Digital Image Processing

Course Unit Title	Digital Image Processing
Course Unit Code	UBI 621
Course Time:	Friday [09:00-12:00]
Course Room:	UBE, Room 103
Level of Course Unit	PhD
Number of ECTS Credits Allocated	7,5
Theoretical	3
Semester	Fall
Lecturer	Assoc. Prof. Dr.Muhammed Cinsdikici,
Contact Info	cinsdikici@gmail.com, office phone: 3205
Mode of Delivery	Face-To-Face
Language	Turkish
Assistant
Prerequisities	Matlab Coding, C# coding, Linear Algebra,  No prior knowledge of vision is assumed.
Objectives of the Course	– to make the student identify image processing methods,- comprehend importance of using computer in image processing,- construct basic image implementations with MATLAB program packet- develop image processing algorithms.
Textbook(s)	Computer Vision: A modern Approach (2nd edt), David A. Forsyth, Jean Ponce, Prentice Hall, ISBN: 978-0136085928, 2011 Computer Vision: Algorithms and Applications , Richard Szeliski, ISBN: 978-1-84882-934-3, Springer, 2011 Digital Image Processing 3rd Edition (DIP/3e), Rafael C. Gonzalez, Richard E. Woods, Prentice Hall, ISBN:978-0135052679, 2008. Practical Image and Video Processing, Oge Marques, Wiley, ISBN: 978-0470048153 ,2011
Learning Outcomes	At the end of this course, a student should be able to: Calculate image histograms and gray-scale transformation for image enhancement, explain histogram equilazation method Identify filtering methods in spatial and frequency domains and develop algorithms Calculate 2-dimensional Fourier Transformation of an image; calculate and analyse 2-dimensional convolution and apply it for the filtering purpose Comprehend mathematical morphology fundamentals and apply them for image processing purposes Recall basic MATLAB instructions and functions for basic image processing operations Apply image processing algorithms for real life problems Have the motivation to propose new solutions for image processing problems Realize the advantages of MATLAB program packet in algorithm development and develop image processing algorithms in MATLAB environment
Course Contents	Objectives of this course are; to make the student identify image processing methods, comprehend importance of using computer in image processing, construct basic image implementations with MATLAB program packet and develop image processing algorithms.
Grading & Projects	%30 HomeWorks + %20 MidTerm + %20 Final Exam + %30 Final Project Late submission of the homeworks and projects are not going to be accepted. The assessment principals are;————————————-On time submission are scored as max.100/100One week late submissions are scored as max. 80/100Two weeks late submissions are scored as max. 60/100Others are scored as 0.

 

ekly Detailed Course Contents

WEEK NAME   SUBJECTS     Theoretical Slides Laboratory Suplementary Materials 1 Introduction /Fundementals  Image Representation, Image Reading/Writing, Indexing Images, Converting Image Classes  Research Allignment  PinHole Camera (Obscura) 2 Intensity Transformations & Spatial Filtering  Image Intensity Transformations, Histogram Processing, Equalization/Matching, Linear/NonLinear Filtering a. Lecture-2-Filtering_Derivative_Noise   b. Intensity Transformations   c1. Histogram Equalization   c2. Histogram Equalization with Step by Step Exp Base Paper Definition + Literatur Searching  a. Histogram Equalization Open Matlab Code   b. Local Histogram Equalization Open Matlab Code 3 Edge Detectors Primitive Detectors, Marr Hilderith, Canny Edge Detectors  Lecture-3-EdgeDetection Hmw#1: Histogram Matching a.  Marr Hilderith MatlabCode_Mohamed Athiqb. Canny Edge Detector Code Explanationc. Canny Edge Detector with Javad. Canny Edge Detector Tutorial Pagee. Canny Edge Detector without Matlabs functions 4 Image Features  Harris Corners, SIFT Features, SURF FEatures a.Lecture-4-Harris Corner Detection_Interest Points   b. Lecture-5-SIFT Hmw#2: Detecting Corners with Harris Operator  a. Harris Corner Detector Logic   b. Edge & Corner Detection Lecture   c. Original SIFT code   d. SIFT Tutorial by Utkarsh Sinhae. ASIFT Method   f. SIFT Matlab Code 5 Frequency Domain Processing  Fundemental Concepts, BandPass (Low, High, Specified Intervals) Filters, Fourier Transforms a.Chapter 5 – Fourier Transformb. FFT Tutorial Video   c. Fourier for Beginners. Hmw#3: SIFT Feature detection  a. Fourier Series and Transformation – Part 2   b. Fourier Series and Transformation – Part 3   c. Fourier Series Simpler Explanation Part I   d. Fourier Series Simpler Explanation Part II 6 Image Restoration Adding Noise, Noise Characteristics, Noise Filtering, Wiener Filtering, Geometric Transformations 7   Midterm     8 Color Image Processing  Color Spaces (RGB, HSI, HSV, Lab) 9 Wavelet  Wavelet Transform, Inverse Wavelet 10 Image Compression  Huffman Codes (Encoding/Decoding), Color Compression, JPEG Compression 11 Morphological Image Procesing  Dilation, Erosion, Opening, Closing, Mophological Reconstruction 12 Image Segmentation  Point/Line/Edge Detection, Hough Transform, Thresholding, Region-Based Segmentation 13 Object Recognition  Distance Measure, Shape/Pattern Matching 14 Case Studies 15   Final Exam

Additional  Reading

Video & Image Processing Tutorial Using C#/VS.Net, http://www.cogitance.com/files/videoprocessing/videoprocessing.htm

UBI 544 – Distributed Systems

Course Time:	Wednesday, 09:30-12:00
Place:	UBE, Room 103
Instructor:	Assoc.Prof.Dr.Muhammed G. Cinsdikici, cinsdikici@gmail.com Office Hours: Wednesday, 13.30-15.30 Office Phone: 3205
Assistant:	Elif Acar Haytaoglu, elifacar101@yahoo.com Office Phone: 3238
Platforms:	dissys@googlegroups.com
Description:	Overview of Operating Systems, Process Synchronization and Deadlocks, Threads and Thread Synchronization, Synchronization In Distributed Systems, Management of Time: Logical Clocks, Vector Clocks, Global States, Termination Detection. Distributed Mutual Exclusion: Central Algorithms, Lamport’s Distributed Algorithm, Token Based and Heuristic Algorithms. Distributed Deadlock Detection: Centralized and Distributed Algorithms, Election Algorithms, Agreement Protocols : Byzantine Agreement, Dynamic Scheduling in Distributed Systems, Static Scheduling in Distributed Systems, Distributed File Systems, Fault Tolerance in Distributed Systems, Distributed Real-Time Systems.
Prerequisites:	Operating Systems and Network Programing graduate/undergraduate level.
Textbooks:	1.** Advanced Concepts in Operating System, Mukesh Singhall and Niranjan Shivaratri, McGraw-Hill, 1994.2.** Distributed Computing: Principles, Algorithms, and Systems, Ajay D. Kshemkalyani, Chicago Mukesh Singhal, Cambridge Press, 2008, ISBN-13: 97805218763463.* Distributed Systems: Principles and Paradigms 2/E, Andrew S. Tanenbaum, Maarten van Steen, Prentice-Hall, 20084. Advanced .Net Remoting (C# Edition), Ingo Rammer & Mario Szpuszta, Apress, 2001. 5. InterProcess Communications in LINUX, John Shapley Gray, Prentice Hall, 2003 Bookstore Info –> http://pandora.com.tr
Projects	All Projects are due at the beginning of class. Due dates for projects will be announced at least a week ahead of time. No late submission will be accepted!.
Tests:	Students will have one final exam. Students will be tested on all material covered from lectures and text. Exams will test student’s ability of solving problems and understanding of concepts.
Grading:	%15 Hmwrks + %15 Final Project + %30 MidTerm Exam + %40 Final Exam
Late Submission:	Late submission of projects will not be accepted.
Course Topics:
Lectures:	Lecture01 – Introduction / Definition / Concepts * Expanded by Paul Barry and Muhammed Cinsdikici chapter 01.ppt * Tanenbaum Chapter 1-Introduction * Tanenbaum Chapter 2-Architecture * Kayhan_Erciye? CS633 03 chapter 1.ppt * Prof.Dr.Stefan Leue (University of Freiburg)chapter 1.pdf * Ihor Kuz (Urniversity_of_new_south_wales)chapter1.pdf Lecture02 – Communication * Cisco Systems OSI Model Presentation * Expanded by Paul Barry and Muhammed Cinsdikici chapter 02.ppt * Tanenbaum Chapter 4-Communication * Prof.Dr.Stefan Leue (University of Freiburg)chapter 2.pdf * Ihor Kuz (Urniversity_of_new_south_wales)chapter2.pdf * Muhammed Cinsdikici MPI-Programming-Presentation.ppt Lecture03 – Processes / Threads * Threads * Processes, *Migration (University of Victoria Lecture Notes for Tanenbaum) * Tanenbaum Chapter 3-Threads * Expanded by Paul Barry and Muhammed Cinsdikici chapter 03.ppt Thread Synchronization Sample Codes Lecture04 – RPC/RMI * RPC.ppt, * RPC_Programming.ppt, * Advanced-Socket Programming.ppt * Jhon Shapley Gray, “IPC on Linux”, Chapter RPC. * Geylani-RMI-Presentation.ppt * Geylani-RMI Sample Codes Lecture05 – Naming * Expanded by Paul Barry and Muhammed Cinsdikici chapter 04.ppt * Tanenbaum Chapter 5-Naming * Ihor Kuz (Urniversity_of_new_south_wales)chapter11.pdf Lecture06 – Synchronization * Expanded by Paul Barry and Muhammed Cinsdikici chapter 05.ppt * Tanenbaum Chapter 6-Synchronization / Elections / Mutual Exclusion * Kayhan_Erciye? CS633 03 chapter 4.ppt * Prof.Dr.Stefan Leue (University of Freiburg)chapter 3.pdf * Ihor Kuz (Urniversity_of_new_south_wales) Sync-Part I.pdf & Sync-Part II.pdf * Clocks (University of Victoria Lecture Notes for Tanenbaum) * Supper-Clocks-Presentation-aos-5.ppt * Clocks-Presentation-ch2.pdf * MainClocks.htm Lecture07 – Election Algorithms / Mutual Exclusion * Mutual Exclusion-UVA-Comp.Science.pdf * Tanenbaum Chapter 6-Synchronization / Elections / Mutual Exclusion * Daniel Manasce-Sync in Dis.Systems.pdf * Andrew Tanenbaum-7/e . chapter 06.ppt * A Toolkit for the Simulation of Distributed Algorithms in Java.html Lecture08 – Distributed Deadlock * Distributed Deadlock * Deadlocks in Distributed Systems * Chandy-Misra-Haar’s Distributed Deadlock Detection * Distributed DBMS Advanced Topics Lecture09 – Agreement Protocols * Agreement Protocols * Byzantine Generals * Consensus Concepts Lecture10 –Fault Tolerance
Projects / Assignments:	Assignment 01 – Assignment#0Assignment 02 – Assignment#1Assignment 03 – Assignment#2Assignment 04 – Assignment#3Assignment 05 – Assignment#4Final Project – FINAL PROJECT