Probabilistic Techniques
Sotiris Nikoletseas - Undergraduate -
(A-)
Computer Engineering and Informatics, University of Patras
The course "PROBABILISTIC TECHNIQUES" is being taught at the 4th year of studies, as an elective course.
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Probability Theory
Sotiris Nikoletseas - Undergraduate -
(A-)
Computer Engineering and Informatics, University of Patras
The course "PROBABILITY THEORY" is being taught at the 2nd year of studies, as an core course.
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Understanding of the appropriate modeling and control system design in order to achieve maximum power extraction, reactive power regulation and other control targets, for all the conventional wind power systems.
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General introduction to the notion of mobile communication. Description of the basic characteristics of the mobile communication channel. Large and small scale phenomena. Categories of mobile channels. General limitations. Digital modulation methods particularly suited to the particularities of the mobile channel. Advanced source coding techniques. Speech coding techniques. Channel coding and equalization. Smart antennas. Spread spectrum communications. Cell based systems (TDMA, FDMA, CDMA). Mobility issues. Static and dynamic channel management. Capacity and micro cells. Algorithms for handoffs. GSM, 2.5G and 3G systems. Medium access protocols for mobile networks (Aloha, CSMA, reservation based, PRMA, polling). Radio package and ad hoc networks: Architectures, protocols, routing algorithms and power allocation methods.
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Discrete Mathematics I
Christos Bouras - Undergraduate -
(A+)
Computer Engineering and Informatics Department, University of Patras
Discrete Mathematics I course includes combinatorics, generating functions, recurrence relations, Polya enumeration theorem and Inclusion - Exclusion principle
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Digital Communications
Kostas Berberidis - Undergraduate -
(A+)
Computer Engineering & Informatics Department, University of Patras
Information Sources and Source Coding; Analog Signal Transmission and Reception; Effect of Noise on Analog Communication Systems; Digital Transmission Through an Additive White Gaussian Noise Channel; Pulse Code Modulation, ADPCM, ADM; Digital Transmission Through Bandlimited AWGN Channels; Digital Transmission via Carrier Modulation (ASK, PSK, FSK, QAM).
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Digital Signal Processing is concerned with the representation, transformation and manipulation of signals on a computer. After half a century advances, DSP has become an important field, and has penetrated a wide range of application systems, such as consumer electronics, digital communications, medical imaging to name a few. Applications of signal processing include some of the hottest current technology trends: internet of things (IoT), cloud computing, software-defined radios, robotics, autonomous vehicles, etc. We are also starting to see higher levels of performance and reduced computational requirements by combining DSP and machine learning techniques.
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Part A: Basic theory and techniques. Overview of stochastic processes. Elements of estimation and detection theory. Emphasis is given on second order estimators, the Wiener estimator and the Kalman Estimator. Recursive estimation techniques, Basic recursive estimation algorithms, Spectrum analysis; Non parametric techniques (Periodogram, Bartlett method), Parametric techniques (e.g. AR models).
Part B: Presentation of selected applications in Signal Processing and Communications, Channel estimation, Channel
Equalization, Symbol Synchronization Algorithms, Spatial Filtering, Smart Antennas.
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Applied Optimisation
Antonio Alexandridis - Undergraduate -
(A-)
Electrical and Computer Engineering, University of Patras
Understanding the basic principles of Optimisation and methods that are used in practice to solve complex problems, concerning the steady state of the system studied.
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This course covers main subjects of bio informatics
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