Basic Archives - Department Of Physics University of Patras https://physics.upatras.gr/en/elect/basic-en/ School of Natural Sciences Wed, 09 Oct 2024 16:52:29 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 Digital System Design with FPGAs https://physics.upatras.gr/en/pms-courses/digital-system-design-with-fpgas-2/ Wed, 25 Sep 2024 08:57:17 +0000 https://physics.upatras.gr/?post_type=pms-courses&p=7337 FPLDs, CPLDs and FPGAs. The case of Intel’s FPGA s (former altera). Microprocessor systems. Set of commands architectures. Arithmetic for computers. Design of central processing unit. Memory. Input/Output. Design of single processor systems in a programmable circuit. The case of Intel’s Nios II processor. The hardware description language VHDL. Logic circuits design, description and simulation [...]

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  • FPLDs, CPLDs and FPGAs. The case of Intel’s FPGA s (former altera).
  • Microprocessor systems. Set of commands architectures. Arithmetic for computers. Design of central processing unit. Memory. Input/Output.
  • Design of single processor systems in a programmable circuit. The case of Intel’s Nios II processor.
  • The hardware description language VHDL. Logic circuits design, description and simulation of them using VHDL and Intel’s Quartus Prime.
  • Laboratory practice: Design and description using VHDL of very simple central processing unit. Implementation using the Terasic’s DE10-Standard board.

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    ]]>         Master Thesis https://physics.upatras.gr/en/pms-courses/master-thesis-2/ Fri, 31 May 2024 16:02:21 +0000 https://physics.upatras.gr/pms-courses/master-thesis-2/ The post Master Thesis appeared first on Department Of Physics University of Patras.

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    ]]>         Intelligent Data Analysis and Pattern Recognition https://physics.upatras.gr/en/pms-courses/intelligent-data-analysis-and-pattern-recognition-2/ Fri, 31 May 2024 15:57:47 +0000 https://physics.upatras.gr/pms-courses/intelligent-data-analysis-and-pattern-recognition-2/ Basic principles of classic classification theory (Bayes). Probability ratio as criterion in population distinction. Use of the theory in standard (Gaussian) deviate populations. Mahalanobis Distance: Separation of the feature space according to the population statistics and the correlation of the features. Correlation of population features: Degree of correlation. Feature quality. Dimensionality of a classification problem. [...]

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  • Basic principles of classic classification theory (Bayes). Probability ratio as criterion in population distinction. Use of the theory in standard (Gaussian) deviate populations.
  • Mahalanobis Distance: Separation of the feature space according to the population statistics and the correlation of the features.
  • Correlation of population features: Degree of correlation. Feature quality. Dimensionality of a classification problem. Dimensionality reduction and important dimensions.
  • Artificial Neural Networks: Problems that they can solve. Neural networks simple structures.
  • Parameters Estimation. Calculation of the distribution of dependent random variables:
  • Mathematical Morphology
  • Signal detection theory: Basic concepts. Neyman-Pearson criterion. Constant false alarm rate (CFAR) detector.
  • Information synthesis, into simple data, into features and into decisions.
  • Remote sensing
  • Examples of remoting-sensing information synthesis.
  • Decision synthesis.
  • Oversampling – Noise shaping – ΣΔ coders

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    ]]>         Digital Telecommunications https://physics.upatras.gr/en/pms-courses/digital-telecommunications/ Fri, 31 May 2024 15:54:41 +0000 https://physics.upatras.gr/pms-courses/digital-telecommunications/ A. Digital Communications Introduction to Telecommunication Systems and Stochastic Processes. Information Theory and Digital Transmission, Channel Bandwidth. Baseband transmission at AWGN channel. Digital Modulations and Constellations (ASK, PSK, FSK, QPSK, QAM), Geometrical representation of signals. Bandpass Transmission and Linear Compensators, Analytic Signals, Bandpass – Lowpass Signal Transform, Iridium Satellite Interception (ISI), Nyquist Waveforms. Optimum Receiver, [...]

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    ]]>     A. Digital Communications

    1. Introduction to Telecommunication Systems and Stochastic Processes.
    2. Information Theory and Digital Transmission, Channel Bandwidth.
    3. Baseband transmission at AWGN channel.
    4. Digital Modulations and Constellations (ASK, PSK, FSK, QPSK, QAM), Geometrical representation of signals.
    5. Bandpass Transmission and Linear Compensators, Analytic Signals, Bandpass – Lowpass Signal Transform, Iridium Satellite Interception (ISI), Nyquist Waveforms.
    6. Optimum Receiver, Maximum Likelihood Detector, Error Probability.
    7. Spread-Spectrum Systems, Pseudo-random Noise (PN) sequences, Orthogonal Frequency-Division Multiplexing (OFDM) Systems.

    B. Wireless Communications

    1. Electromagnetism – Foundation of digital communications. Introduction to digital communication systems. Overview of analog and digital mobile telephony.
    2. State-of-the-Art wireless networks, Wireless backhaul, Small cells, 5G: Smart World, Internet-of-Things (IoT) and Digital Communications.
    3. Arising wireless technologies and digital communication protocols. Broadband and Digital Communications: Definition and requirements of network design and physical layer. Broadband examples in digital communications

    Arising digital communications: 5G-over-microwave, IoT: Industry 4.0 & Digital/Smart Health.

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    ]]>         Digital Image Processing and Statistical Signal Processing https://physics.upatras.gr/en/pms-courses/digital-image-processing-and-statistical-signal-processing/ Fri, 31 May 2024 15:51:58 +0000 https://physics.upatras.gr/pms-courses/digital-image-processing-and-statistical-signal-processing/ Two-dimensional signals (Principles, two-dimensional image, FFT spectrum) Two-dimensional image receive (sensors, errors, sampling, quantization. Optical sensors, SAR, Near Infrared, Thermal Infrared, X-rays, Electronics: CCDs). Ophthalmic physiology Color, Color spaces, Transforms in color spaces, The behavior of eye in color, Equal color distances. 2-D Linear Filters Three-dimensional signals-video, Spectral content, Velocity filters Image restoration (inverse problems, [...]

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  • Two-dimensional signals (Principles, two-dimensional image, FFT spectrum)
  • Two-dimensional image receive (sensors, errors, sampling, quantization. Optical sensors, SAR, Near Infrared, Thermal Infrared, X-rays, Electronics: CCDs).
  • Ophthalmic physiology
  • Color, Color spaces, Transforms in color spaces, The behavior of eye in color, Equal color distances.
  • 2-D Linear Filters
  • Three-dimensional signals-video, Spectral content, Velocity filters
  • Image restoration (inverse problems, distortion causes-correction method)
  • Image Enhancement
  • Image Analysis
  • Mathematical Morphology
  • Texture
  • Image segmentation
  • Techniques
  • Random sequences, Bernoulli Process, Binary white noise, Random walk, Discrete Wiener Process, Markov processes, Markov chains
  • Hidden Markov models, Viterbi algorithm
  • Estimation, Linear prediction

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    ]]>         Computer Vision – training https://physics.upatras.gr/en/pms-courses/computer-vision-training/ Fri, 31 May 2024 15:51:05 +0000 https://physics.upatras.gr/pms-courses/computer-vision-training/ k-mean clustering, fcm, vlad, Fuzzy logic Linear regression, logistic regression, linear SVM Non-linear regression, Non-linear classification, kernel SVM, k-NN, etc. Image recovery techniques Low-dimensional representations Gabor Filters PCA and LDA for face recognition ICA analysis Compression-codification-sparse representation NMF, Archetypal analysis Spectral clustering-Graphs – MST Neural networks Convolutional neural networks 3-D shapes description – analysis - [...]

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    ]]>     k-mean clustering, fcm, vlad, Fuzzy logic
    Linear regression, logistic regression, linear SVM
    Non-linear regression, Non-linear classification, kernel SVM, k-NN, etc.
    Image recovery techniques
    Low-dimensional representations
    Gabor Filters
    PCA and LDA for face recognition
    ICA analysis
    Compression-codification-sparse representation
    NMF, Archetypal analysis
    Spectral clustering-Graphs – MST
    Neural networks
    Convolutional neural networks
    3-D shapes description – analysis – classification

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    ]]>         Physics Laboratory II (Mechanics – Fluid Mechanics) https://physics.upatras.gr/en/courses/plc108/ Mon, 21 Feb 2022 19:55:27 +0000 https://physics.upatras.gr/?post_type=courses&p=3038 1. Gravity acceleration calculation. 2. Mechanical conservation of energy and Maxwell disk moment of inertia calculation. 3. Torsion modulus of a metallic bar. 4. Viscosity measurement of a liquid with the Ostwald viscometer. 5. Investigation of the relationship between flow resistance and the shape and the shape of the surface condition of a body. 6. [...]

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    ]]>     1. Gravity acceleration calculation.
    2. Mechanical conservation of energy and Maxwell disk moment of inertia calculation.
    3. Torsion modulus of a metallic bar.
    4. Viscosity measurement of a liquid with the Ostwald viscometer.
    5. Investigation of the relationship between flow resistance and the shape and the shape of the surface condition of a body.
    6. Investigation of the pressure distribution on an aerofoil in an air current.
    7. Study of elastic and inelastic collition.
    8. Free damped vibrations and damped vibrations with a driving force.

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    ]]>         Modern Physics https://physics.upatras.gr/en/courses/tac448/ Mon, 28 Jun 2021 11:59:42 +0000 https://physics.upatras.gr/courses/monterna-fysiki/ 1.Quantization of the electromagnetic field, coherent and squeezed states. 2. Photodetection theory. 3. Interaction of the EM field with atoms, Rabi oscillations, the Wigner-Weisskof atom, the optical master equation. 4.  Many-fermion systems, the canonical anticommutation relations, fermionic Fock space, non-relativistic fields. 5. Theory and applications of quantum information. 6. Superfluidity, superconductivity.

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    ]]>     1.Quantization of the electromagnetic field, coherent and squeezed states.
    2. Photodetection theory.
    3. Interaction of the EM field with atoms, Rabi oscillations, the Wigner-Weisskof atom, the optical master equation.
    4.  Many-fermion systems, the canonical anticommutation relations, fermionic Fock space, non-relativistic fields.
    5. Theory and applications of quantum information.
    6. Superfluidity, superconductivity.

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    ]]>         Digital Signal Processing https://physics.upatras.gr/en/courses/elc472/ Fri, 18 Jun 2021 17:34:04 +0000 https://physics.upatras.gr/courses/digital-signal-processing/ Digital signal operations -Introduction Digital signals and systems Discrete Time Fourier Transform-DTFT z-transform Discrete Fourier transform-DFT Design FIR filter Design of IIR filter Median Filters Adaptive Filters

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  • Digital signal operations -Introduction
  • Digital signals and systems
  • Discrete Time Fourier Transform-DTFT
  • z-transform
  • Discrete Fourier transform-DFT
  • Design FIR filter
  • Design of IIR filter
  • Median Filters
  • Adaptive Filters

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    ]]>         Introduction to Microcomputer Architecture https://physics.upatras.gr/en/courses/elc473/ Fri, 18 Jun 2021 17:27:19 +0000 https://physics.upatras.gr/courses/microcomputers-architecture-programming-and-applications/ • Introduction (microcomputer architecture, busses). • Data Coding (fixed/floating point numbers, characters, symbols, instructions) • CPU(arithmetic/logic unit, control unit, register file). • Stack/accumulator/register-based architectures. • Assembly programming (instruction set, addressing modes, stack, subroutines). • Memory (technology, interfacing, hierarchy, cache). • Peripherals (I/O, interrupt/poling). • Microcontrollers(Arduino/Raspberry Pi).

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    ]]>     • Introduction (microcomputer architecture, busses).
    • Data Coding (fixed/floating point numbers, characters, symbols, instructions)
    • CPU(arithmetic/logic unit, control unit, register file).
    • Stack/accumulator/register-based architectures.
    • Assembly programming (instruction set, addressing modes, stack, subroutines).
    • Memory (technology, interfacing, hierarchy, cache).
    • Peripherals (I/O, interrupt/poling).
    • Microcontrollers(Arduino/Raspberry Pi).

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