Search results: 1919
- Teacher: Ibrahim Abdullahi
- Teacher: Rahaf Ismail
- Teacher: Shahrzad JazI
- Teacher: Emre Ozbilge
- Teacher: Mahmoud Rob
- Teacher: Sara Salehi
- Teacher: Ouahiba Staf
- Teacher: Basmah Anber
- Teacher: Shahrzad JazI
- Teacher: Ashkan Mohebali
- Teacher: Elnaz Mohebbi
- Teacher: Soheila Saberi
- Teacher: Sara Salehi
The course has an overview of discrete time signals and systems. Sampling/Reconstruction
principles both in time and frequency domains. The Z-Transform and its
properties. Structure of discrete time systems; tapped delay or lattice etc.
Digital filter designs. Realization of digital filters (FIR and IIR). The
discrete Fourier and inverse Fourier transforms. The fast Fourier transform
(FFT) and its analysis. Image acquisition, sampling and quantization. Image
enhancement: Spatial and frequency domain techniques. Image restoration:
Inverse, Wiener and mean filtering. Image compression: Compression models.
- Teacher: Huseyin Oztoprak
- Teacher: Behnood Tabrizi
- Introduction
to Cryptography Concepts - Classical
Encryption Techniques - Block
Ciphers and Data Encryption Standart (DES) - Introduction
to Finite Fields and Advanced Encryption Standard (AES) - Introduction
to Number Theory - Public
Key Cryptography and RSA - Network
Security Applications and System Security
- Teacher: Melike Direkoglu
- Teacher: Rahaf Ismail
- Teacher: Ouahiba Staf
- Teacher: Oyku Akaydin
- Teacher: Ertan Akun
- Teacher: Keyvan Bahlouli
- Teacher: Hilmi DIndar
- Teacher: Melike Direkoglu
- Teacher: Neyre Ersoy
- Teacher: Parvaneh EsmaIlI
- Teacher: Shihab Ibrahim
- Teacher: Rana Kidak
- Teacher: Mehmet Kusaf
- Teacher: Mustafa Mulla
- Teacher: Ali Oztemir
- Teacher: Huseyin Oztoprak
- Teacher: Salahi PehlIvan
- Teacher: Ayse Pekrioglu
- Teacher: Pwadubashiyi PwavodI
- Teacher: Devrim Seral
- Teacher: Ali Shefik
- Teacher: Mustafa Buzun
- Teacher: Devrim Seral
This course introduces the basic knowledge of embedded systems on programmable chips. The given information will help to develop the ability to understand the concept of embedded systems in offline and online applications. The main aim of this course is to give students not only theoretical knowledge but also practical knowledge about different embedded systems. In addition, the common features of embedded systems and partitioning features such as inputs, outputs, interrupts, and scheduling techniques will be covered in the course. Depending on the different embedded system types, these systems will be programmed using the relevant programming languages. Finally, various hardware-software designs and development tools will be introduced to broaden students' fundamental knowledge.
- Teacher: Parvaneh EsmaIlI
The course has the following contents. Basics of data communication and computer networks, ISO/OSI basic reference model. Physical, data link, network and transport layers. Routing, flow control, congestion control. Internetworking. TCP/IP suite of protocols. Higher layer protocols. Contemporary network architectures.
Course Description: Pattern Recognition is a fundamental area of study within the field of artificial intelligence and machine learning, focusing on the automatic discovery and identification of regularities or patterns in data. This course provides an in-depth exploration of the principles, techniques, and applications of pattern recognition.
Course Objectives:
- To introduce students to the basic concepts and terminology of pattern recognition.
- To familiarize students with various pattern recognition techniques and algorithms.
- To develop students' understanding of the theoretical foundations underlying pattern recognition.
- To enable students to apply pattern recognition techniques to solve real-world problems across diverse domains.
- To provide students with hands-on experience through practical assignments and projects.
Topics Covered:
- Polynomial Regression Analysis
- Logistic Regression (binary, categorical and multi-label classifications)
- Decision Tree
- Ensemble Learning Methods
- Support Vector Machines (SVM)
- Clustering Techniques (Kmeans, SOM)
- Multi-Layer Perceptron (MLP) - Backpropagation Algorithm
- Radial Basis Function Network (RBFN)
- Recurrent Neural Network (LSTM, GRU, Elman Network, Bi-directional layers)
- Deep Convoutional Neural Network (DCNN)
- Transfer Learning
- Teacher: Emre Ozbilge