This course is the first part of the education in circuit design, analysis and testing. In the lecture, the students will learn to apply circuit analysis techniques to solve basic electrical networks and to propose electrical network solutions to engineering problems. In the technical skill training the student will learn about electrical measurement equipment and circuits development.

Fundamental concepts from classical and modern physics form the basis for the design and behavior of materials and devices as well as provide an understanding of natural phenomena. 

This course will provide a basis for the specialization courses that deal with physical processes in devices such as telecom systems, electric motors, power plants, lasers, electronics or detectors and sensing systems.

Basic concepts in mechanics, thermodynamics, special relativity, quantum mechanics and atomic and solid‐state structure are introduced.

  • Harmonic oscillators and resonances
  • Force fields and conservation laws
  • Rotational dynamics
  • Relative motion, inertial systems and theory of special relativity
  • Ideal and non‐deal gas models and gas laws
  • Mechanisms of heat transport
  • Laws of thermodynamics and their application
  • Cyclic thermodynamic processes
  • Interference and far‐field diffraction of light waves
  • Particle character of E.M. waves – Wave character of particles
  • Schrödinger formalism in quantum mechanics
  • QM models of 1D structures and 3D H atom
  • Electron spin, identical particles and the Pauli principle
  • Density of states in the solid state and Fermi statistics
  • Band model of the solid state

Learning objectives

At the end of the course, the student will be able to:

  • create software algorithms and programs in the C language from an informal problem description, targeting general purpose processors;
  • escribe the relation between hardware and software of computer systems;
  • illustrate the operation of a single-cycle, multi-cycle and pipelined computer architecture;

Content

1. Programming in the C language

  • functions
  • arrays
  • pointers
  • strings
  • recursion
  • trees
  • I/O from files
  • dynamic memory allocation

2. Computer architecture

  • von Neumann model (datapath, control flow, storage elements)
  • single-cycle, multi-cycle, pipelined processors
  • processor design (design of instruction sets)
  • architecture classification
  • memory organization (including caches)

In this course we review the main concepts from probability; introduce fundamental aspects of random signals and processes; introduce the main concepts and techniques of estimation and detection theory to model and analyze stochastic systems.

The course consists of thee parts:

  1. Probilility, random vectors and processes, that deals with the following topics: Probability spaces, independence, elementary conditional probability. Random variables, distributions of random variables. Useful probability distributions. Random vectors and processes. Distribution of random vectors, independent random variables, conditional distributions. Additive noise. Simple random processes. Expectation and law of large numbers. Second order moments.
  2. Estimation theory, that deals with the following topics: a) Classical estimation: Performance of the estimators, optimal estimators. Cramer‐Rao lower bound. Linear models. Least squares estimation. B) Bayesian estimation and prediction: Bayesian approach. Minimum mean square error(MMSE) and Linear MMSE estimation. Weiner and Kalman filtering.
  3. Detection theory, that deals with the following topics: Detection of deterministic signals in noise, matched filter. Detection of random signals in noise, estimator‐correlator.

Aim of the course is to understand general modelling techniques of Lagrangian and Hamiltonian systems, to perform global analysis of properties of autonomous and non‐autonomous nonlinear dynamical systems including stability, limit cycles, oscillatory behavior and bifurcations and to acquire experience with the simulation of these systems.

Wireless Sensor Networks (WSNs) are composed of numerous (wireless) embedded sensor devices with the aim of sensing particular parameters. There are plenty of interesting applications for these networks such as healthcare, structural and environmental monitoring, disaster management, agriculture, urban supervision, and so on. Networking of such small embedded sensor devices differs from other networks from various aspects. Very stringent power constraints, computation limitations, and short range wireless connections of these devices acquire dedicated efficient networking protocols for WSNs to satisfy the application requirements.
 
In this course, prominent protocols, mechanisms, and services in various networking layers for WSNs are discussed. It includes applications, characteristics of the physical layer and typical sensor nodes, medium access control mechanisms, routing and data dissemination, and services such as synchronization and localization. Also several widely used standard protocols for low power wireless networking, such as IEEE 802.15.4, are discussed. Design, implementation, and simulation of WSNs is an important part of this course.  At the end of the course the students will 
1. have a broad knowledge about Wireless Sensor Networks (WSN), their applications, protocols in different networking layers, and services. 
2. have obtained sufficient skills to design, model, and implement a WSN for commercial or research aims.
3. be aware of the state of the art in various aspects of WSNs and challenging issues to be able to start conducting effective research in this field.

Welcome to 5CCA0 - Semiconductor Physics and Materials

Short course content:

Crystal Structure of Solids; Introduction to Quantum Mechanics;Introduction to quantum theory of solids: conductors, semiconductors and insulators; Semiconductors in equilibrium;

Carrier transport phenomena; Non‐equilibrium excess carriers in semiconductors; P‐n junction; P‐n junction diode; MOSFETs transistors physics and small‐signal modelling; CMOS technology process.