Education

Energy Friendly Renovation Processes

Course code:AR0054
Programme:Master Architecture, Urbanism & Building Sciences
InstructorsProf.dr.ir. J.D.M. van Hal and Ir. E.N.M. Stutvoet   

Sustainable housing and neighbourhood transformation are hot topics in the current Dutch building scene. However, too many projects are currently not meeting their goals. This is often not the result of technical or financial issues but the failure of processes and their application. Furthermore, the living environment is an emotionally charged topic, so the understanding of the behaviour and needs of residents plus a cooperative attitude within in the dynamics of the modern-day development team is vital for successful transformations to be achieved. 

Through this blended (partially face-2-face, partially online) course from the Faculty of Architecture and the Built Environment, pioneering knowledge related with (energy) ambitious renovation projects within the existing housing stock, will be made available to master students and simultaneously to professionals from the building sector.

  • Student is able to understand the complexity of the processes of projects with high (environmental) ambitions by taking position on the problem from different perspectives and by cooperating in a dynamic blended learning environment. 
  • Student is able to apply various aspects in the field of energy efficiency within the home renovation process.
  • Student will gather insight and take a position onto different sustainable strategies.
  • Student reflects on the Merger of Interest approach.
  • Student is able to apply and reflect on a broader social context and able to think in terms of sustainable life span.
  • Student is able to apply financial feasibility aspects.
  • Student is able to comprehend and reflect on different perspectives on energy ambitious housing renovation processes.
  • Student is able to comprehend and reflect on the client.
  • Student is able to comprehend and reflect on the role of client and contractor and learn to cooperate in this field.
  • Comprehension of various stakeholders.

Sensing Technologies for the Built Environment

Course code:GEO1001
Programme:Master Geomatics
InstructorsDr.ir. M.J.P.M. Lemmens

The course aims at obtaining basic insight in the sensing technologies and methodologies used in Geomatics. The topics covered are:

  • Geometric and radiometric sensor models of the major types of sensing technologies used in geomatics, including imaging, TLS, airborne Lidar and Radar
  • Generation of Point Clouds and Digital Elevation Models
  • Dynamics of platforms, including satellites, airplanes and ground based stations, which affect the performance of the sensors they carry and how to compensate for these effects
  • Basics of the Electromagnetic (EM) Spectrum and the interaction of EM radiation with atmosphere, land and water
  • Methods for preparing sensor outputs for storage in a GIS, including (1) Georeferencing and the role of GNSS and ground control points; (2) image matching techniques; (3)digital image processing; (4) least squares adjustment (5) surface fitting for blunder detection; (6) statistical pattern recognition

Brief overview of application domains related to the built environment

  • Describe and develop geometric and radiometric models of the sensors used in geomatics for sensing the built environment
  • Describe the basics of the EM spectrum and the interaction of EM radiation with atmosphere, land and water and to evaluate the effects on the data captured by the diverse sensors
  • Analyse the pros and cons of the diverse sensing technologies for creating Point Clouds and Digital Elevation Models
  • Describe the role of, design, compute and apply a covariance matrix
  • Develop a computer program to detect blunders in a DEM data set using least squares adjustment
  • To evaluate which multispectral classification method to choose given the characteristics of the sensor data
  • Describe the basic digital image processing filters and to apply these to a digital image
  • Describe the features and to analyse pros and cons of the diverse image matching techniques

Select the optimal combination of sensing technologies given a decision making problem focussed on the built environment and to motivate the choice

Zero-Energy Design

Course code:AR0092
Programme:Master Architecture, Urbanism & Building Sciences
InstructorsIr. S. Broersma and Prof.dr.ir. A.A.J.F. van den Dobbelsteen

The urgent (inter)national issue of an energetically poor performing existing building stock is the subject of Zero Energy Design. Within the assignment, an existing residential building block has to be transformed into a zero energy building. The focus of the course lies on a well-integrated climate design/energy system with the ambition of energy neutrality and beyond. With the successive steps of reducing the demand, re-using waste streams and producing the remaining demand on site with renewables (of the New Stepped Strategy), a combination of smart measures has to be defined to reach this goal. Smart energy connections with the surrounding built environment will also be considered.

With an energy potential mapping analysis of the neighbourhood and an energy performance calculation program, tools are provided to quantify and prove the final energy performance. To become energy neutral, not only the building related energy (for HVAC: Heating, Ventilation and Air-conditioning) but also the user- and material related energy have to be compensated for by sustainable production at site, making the goal a real challenge.

  • develop an integrated energy-neutral climate design
  • make energy calculations and optimise the energy performance of a building

Human Thermal Environments

Course code:ID5455
Programme:IO Electives
InstructorsDr. N. Bogerd

The course will consist of four lectures where the following topics will be discussed:

  • Introduction to the basic principles of human thermoregulation where heat exchange mechanisms between human body and the surrounding environment, thermal perception, body temperature also with relation to age and gender, thermophysiological responses and thermal comfort will be addressed.

  • Introduction to environments and health conditions where human thermoregulation is challenged. Followed by various examples of designing products that support human thermoregulation under such challenging conditions. For example clothing and personal cooling systems used to increase exercise performance in athletes, to relieve neurological symptoms in patients with multiple sclerosis or to relieve heat strain in professionals such as fire-fighters will be discussed.

  • Introduction to the basics of various mathematical models of human thermoregulation and thermal comfort such as Gagge, Stolwijk and Fiala model.

  • Exploring the use of the mathematical models of human thermoregulation to improve the well-being and performance for office occupants and application of personal cooling systems.
  • Explain the basic principles of human thermoregulation.
  • Apply these principles into the design process of products aiming to provide well-being and performance of various end-user groups such as athletes, patients and office occupants.
  • Evaluate the effect of the designed products on physiological responses and thermal comfort of human body using mathematical models.

IOT1: Designing Data-Driven Products and Services for the Internet of Things

Course code:ID5452A
Programme:IO Electives
InstructorsProf. G.W. Kortuem

This course is an introduction to the design of Internet of Things products and services and covers fundamental technologies, user experience design and design processes. Specifically, the course explores emerging practices in data-enabled design and investigates how sensor data can be utilized by designers as creative material for envisioning and creating Internet of Things products and services.

 As part of this course, students will collect behavioural data using mobile and wearable devices and use data-driven insights to design an innovative IoT product or services.

 At the end of the course students will have acquired a grounded understanding of the IoT, data-enabled design and UX design for the IoT, and be proficient in designing data-driven IoT products and services.

Implementing improvement measures is not the last step. An entire, and recurrent, process of managing efficiency measures must be implemented. Periodic measurements are needed to verify the success of the measures and to adjust settings that are not ideal.

    • Designing IoT products and services: Identify and select appropriate IoT tools and technologies
    • IoT user experience: design high-quality user experiences for IoT products and services
    • Data as design material: Integrate data methods in the design process and use IoT data insights for developing innovative products and services
    • Value-sensitive design: Balance the interests of users, business and societal challenges regarding to ethical data issues such as data privacy, data confidentiality, data quality, data ownership etc.
    • Acquire the necessary knowledge and thinking to collaborate with data scientists and engineers throughout the design process.

      IOT2: Developing Connected Products and Services for the Internet of Things

      Course code:ID5452B
      Programme:IO Electives
      InstructorsProf. G.W. Kortuem

      Energy efficiency is the primary means to achieve our climate and emission goals. Every kWh wasted is one too much.
      This course communicates how to achieve efficiency in the energy system by working with dedicated methods. The first step is typically to define goals and to quantify the efficiency targets. This differs from country to country and between sectors and also depends on the stakeholders (governments, city administration, industrial company, etc.).

      Once goals are clear, it is needed to get Information. A system can only be improved if the details are known and understood. Getting the required information involves energy metering, sensor networks, database access, municipal registries, business data, supply chain and manufacturing execution systems and other sophisticated sources of data. The heterogeneity of the data requires pre-processing before it can be used for benchmarking or KPI purposes.

      After the data is converted into information it needs to be visualized, correlated, and communicated to the stakeholders. This course will cover the basic methods to accomplish this.

      Stakeholders that are informed and motivated to improve efficiency will then implement measures. The classical way to improve efficiency is to replace old, inefficient equipment (e.g. windows, machines, insulation) with new, better ones. Additional to that, controls have a large influence on the efficiency of individual equipment and entire systems.

      Implementing improvement measures is not the last step. An entire, and recurrent, process of managing efficiency measures must be implemented. Periodic measurements are needed to verify the success of the measures and to adjust settings that are not ideal.

      • Acquire the necessary knowledge and thinking to collaborate with developers and engineers throughout the design process.
      • Integrate data science and software engineering methods in the design process to develop innovative IoT products and services
      •  Identify and select appropriate development tools and use them effectively throughout the development process
      • Use data science methods to generate insights and knowledge from IoT data and make it intelligible for stakeholders
      • Balance the interests of users, business and societal challenges regarding to ethical data issues such as data privacy, data confidentiality, data quality, data ownership etc.

      Sustainable Consumer Behaviour

      Course code:ID5136
      Programme:IO Electives
      InstructorsDr.ir. R. Mugge

      Present consumption patterns have a negative effect on the environment. It is designers’ responsibility to tackle this negative effect and design products, packaging, and services that contribute to a sustainable society. However, consumer attitudes towards these sustainable innovations are not by definition positive, which may ultimately reduce their impact on society. In this course, we will touch upon a number of factors that are known to be of high relevance for creating a positive attitude towards new sustainable innovations. Furthermore, we will explore the factors that can influence consumer behaviour in order to learn how designers can stimulate more sustainable consumer behaviour. Using a Capita Selecta format, this course includes the most relevant literature on sustainable consumer behaviour, and as such provides a detailed insight in a number of selected topics, such as: how do consumers develop attitudes towards sustainable innovations, what is the value of product design for consumers’ perception towards sustainable products, how do consumers decide among different sustainable and non-sustainable products, and what factors can change the behaviour of consumers in more sustainable consumer behaviour.

       This course will provide students with detailed knowledge on a set of topics in consumer behaviour that are not sufficiently addressed in the normal curriculum. The topics are chosen on their relevance for design and sustainability.

      • have knowledge of the most relevant consumer theories that relate to sustainability
      • can apply these sustainable consumer theories to analyse consumers’ responses to a new product
      • can apply these sustainable consumer theories to improve the design or promotion strategy of a product

      Control Theory

      Course code:SC42015
      Programme:Master Systems and Control
      InstructorsT. Keviczky
      • State-space description of multivariable linear dynamic systems, interconnections, block diagrams
      • Linearization, equilibria, stability, Lyapunov functions and the Lyapunov equation
      • Dynamic response, relation to modes, the matrix exponential and the variation-of-constants formula
      • Realization of transfer matrix models by state space descriptions, coordinate changes, normal forms
      • Controllability, stabilizability, uncontrollable modes and pole-placement by state-feedback
      • LQ regulator, robustness properties, algebraic Riccati equations
      • Observability, detectability, unobservable modes, state-estimation observer design
      • Output feedback synthesis (one- and two-degrees of freedom) and separation principle
      • Disturbance and reference signal modelling, the internal model principle
      • Translate differential equation models into state-space and transfer matrix descriptions
      • Linearize a system, determine equilibrium points and analyse local stability
      • Describe the effect of pole locations to the dynamic system response in time- and frequency-domain
      • Verify controllability, stabilizability, observability, detectability, minimality of realizations
      • Sketch the relevance of normal forms and their role for controller design and model reduction
      • Describe the procedure and purpose of pole-placement by state-feedback and apply it
      • Apply LQ optimal state-feedback control and analyse the controlled system
      • Reproduce how to solve Riccati equations and describe the solution properties
      • Explain the relevance of state estimation and build converging observers
      • Apply the separation principle for systematic 1dof and 2dof output-feedback controller design
      • Build disturbance and reference models and apply the internal model principle

      Indoor Climate Control Fundamentals

      Course code:ME45110
      Programme:Master Mechanical Engineering
      InstructorsDr. L.C.M. Itard

      This course is an introduction to the fundamentals of indoor climate control and entails four main aspects: thermal comfort & indoor air quality, humid air properties & air handling, thermal behaviour of buildings and finally energy conversion systems for buildings. The course is based on a system modelling & simulation engineering approach and is useful for those interested in lowering the energy consumption of buildings while maintaining a good indoor climate.

       Thermal behaviour of buildings is the core of the course. You will study the thermal behaviour of buildings and the way it can be simulated by heat balances in order to predict indoor temperatures, needed heating and cooling capacities and energy consumption. During the course a dynamic hourly simulation is set up for a simple standard room, based on a set of linearized partial differential equations. Different numerical solving methods are studied. The use of models to achieve low energy buildings is demonstrated.

      • derive the mathematical equations describing the thermal performance of the various components
      • combine these equations in order to simulate the whole system consisting of the weather, building, HVAC equipment and energy conversion system
      • use simulations to answer questions about the design, such as capacities, energy use, comfort, etc.
      • determine the change of air conditions by the various components of an air handing installation
      • translate the fuzzy term "comfort" into design requirements that can be checked afterwards by measurable variables and to describe the limitations of this technical approach learning the theory about the thermal sensation of human bodies
      • calculate the heating and cooling capacity of a confined space by means of simple hand calculation and by dynamic simulation, taking into account the building mass.
      • present the pro and contras of indoor climate systems used in practice, as well as of sustainable alternatives
      • make optimal designs of simple systems based on simulation

      Control System Design

      Course code:SC42000
      Programme:Master Systems and Control and Master Electrical Engineering
      InstructorsDr.ir. A.J.J. van den Boom

      State-space description of single-input, single-output linear dynamic systems, interconnections, block diagrams. Linearization, equilibria, stability, Lyapunov functions and the Lyapunov equation.

      Dynamic response, relation to modes, the matrix exponential.

      Realization of transfer function models by state space descriptions, coordinate changes, canonical forms. Controllability, stabilizability, uncontrollable modes and pole-placement by state-feedback. Application of LQ regulator.

      Observability, detectability, unobservable modes, state-estimation observer design

      Output feedback synthesis and separation principle. Reference signal modelling, integral action for zero steady-state error; Analysis in robust stability and robust performance.

      By taking this course, the student

      • will be able to master the introduced theoretical concepts in systems theory and feedback control design and
      • will be able to practically apply these concepts to design projects and tasks
      • and will be able to relate the learned concepts and techniques to other more specialized ones, to potentially integrate them by taking adjacent courses.
      • will be able to translate a predictive control problem into a standards setting and solve the predictive control problem.

      Adaptive and Predictive Control

      Course code:SC42040
      Programme:Master Systems and Control
      InstructorsS. Baldi

      Adaptive Control covers a set of techniques which provide a systematic approach for automatic adjustment of the controllers in real time, in order to achieve or to maintain a desired level of performance of the control system when the parameters of the plant dynamic model are unknown and/or change in time. Predictive control focuses on the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, coping with amplitude constraints on inputs, outputs and states. One-step ahead predictive controllers enjoy the nice feature that they can cope with the effect of parameter uncertainty upon the performance of the control system. The course presents a basic ground for analysis and design of adaptive and predictive control systems. After an introductory part and an initiation to parameter adaptation algorithms, Model Reference Adaptive Control (MRAC) and Self-Tuning Control (STC) schemes constitute the core part of the course. These techniques are based on one-step ahead predictive strategies, namely model reference and single-step control. Stability analysis in a deterministic environment and convergence analysis in a stochastic environment are both dealt with. Due to historical reasons, the Model Reference Adaptive control will be formulated in a deterministic setting, while the Self-tuning Control in a discrete-time stochastic setting. Multi-step ahead predictive strategies are finally introduced, with finite/infinite horizon predictive control, stability and robustness of predictive control. Hands-on experience is obtained by MATLAB exercises.

      At the end of the course the student should be able to:

      • Design, simulate, and implement parameter adaptation schemes;
      • Design, simulate, and implement single-step ahead adaptive control schemes;
      • Solve the finite and infinite horizon predictive control problem;
      • Master the main analytical details in stability proofs of adaptive and predictive control schemes;
      • Simulate adaptive and predictive control methodologies in Matlab;
      • Discuss simulation results.

      Knowledge Based Control Systems

      Course code:SC42050
      Programme:Master Systems and Control
      InstructorsDr.ing. J. Kober

      Theory and applications of knowledge-based and intelligent control systems, including fuzzy logic control and artificial neural networks:

      • Introduction to intelligent control
      • Fuzzy sets and systems
      • Intelligent data analysis and system identification
      • Knowledge based fuzzy control (direct and supervisory)
      • Artificial neural networks, learning algorithms
      • Control based on fuzzy and neural models
      • Reinforcement learning
      • Examples of real-world applications

      Main objective: understand and be able to apply 'intelligent control' techniques, namely fuzzy logic and artificial neural networks to both adaptive and non-adaptive control.

      Optimization in Systems and Control

      Course code:SC42055
      Programme:Master Systems and Control
      InstructorsDr.ir. A.J.J. van den Boom

      Essentially, almost all engineering problems are optimization problems. If a civil engineer designs a bridge, then one of the main objectives is to obtain the cheapest design or the design that can be implemented most rapidly, where of course several specifications and constraints such as size, strength, safety, etc. have to be taken into account. When developing a new type of engine, we look for the most economical design, the cheapest design, or the design with the highest performance. A process engineer wants a production unit to deliver a final product of maximal quality, with minimal expenditure of energy or with maximal output flow. When composing a portfolio, a financial engineer tries to maximize the expected profits, subject to the given risk constraints. So we encounter optimization problems in almost every engineering field. 

      How can we solve such an optimization problem? That is the topic that will be addressed in this course. We will consider both the transformation of real-world design problems into a more mathematical formulation, and the selection of the most efficient numerical algorithms to solve the resulting optimization problem.

      The examples and case studies of this course are primarily oriented towards systems and control. In preceding courses you have already studied modelling, identification and control of systems. However, the examples in these courses were usually limited to simple or small systems, and more complex systems were often dealt with by saying that they can be tackled using optimization. And that is what we will do in this course: you will not only learn how you can identify models and design controllers for complex systems using numerical optimization, but also how this can be done in the most efficient way.

      Networked and Distributed Control Systems

      Course code:SC42100
      Programme:Master Systems and Control
      InstructorsT. Keviczky

      This course starts with the modelling of so-called networked control systems (NCS). Networked control systems are systems in which the communication between plant and controller takes place via a (e.g. wireless) network. Such network-based communication leads to imperfections in sensor and control signals, such as time-varying and uncertain sampling intervals and delays, packet dropouts, scheduling constraints, etc. The modelling framework introduced in the course is subsequently used to support stability analysis, using characterizations based on linear matrix inequalities (LMI). Such stability analysis allows to study trade-offs between requirements on the controller, the network and plant properties.

      The second part of the course deals with the aspect of the distributed control of networked systems. In particular, distributed optimization methods and various decomposition techniques (primal, dual, augmented Lagrangian / proximal point method, ADMM), links to consensus algorithms, and their application in networked multi-vehicle distributed robotics problems. Online optimization-based control approaches such as distributed model predictive control for multivehicle cooperation, distributed LQR and decomposition based methods that are applicable to collections of mobile agents. The methods will be illustrated on application examples including cooperative rendezvous, distributed formation control, spacecraft formation flight, and robotic networks.

      The student must be able to:

      • model networked control systems with network-induced uncertainties / effects / imperfections, including time-varying and uncertain sampling intervals, delays, packet dropouts, and scheduling constraints
      • analyse the stability of NCS (involving the above effects), e.g. by applying LMI-based stability characterizations
      • describe and apply decomposition techniques for distributed optimization to various examples
      • describe and apply consensus algorithms to multi-agent coordination problems
      • solve cooperative control problems by implementing a distributed model predictive control approach
      • analyse the stability and convergence of distributed control methods that rely on online optimization

      Energy Efficiency

      Course code:EE3110TU
      Programme:EWI Electives Service-Education
      InstructorsProf.dr. P. Palensky

      Energy efficiency is the primary means to achieve our climate and emission goals. Every kWh wasted is one too much.
      This course communicates how to achieve efficiency in the energy system by working with dedicated methods. The first step is typically to define goals and to quantify the efficiency targets. This differs from country to country and between sectors and also depends on the stakeholders (governments, city administration, industrial company, etc.).

      Once goals are clear, it is needed to get Information. A system can only be improved if the details are known and understood. Getting the required information involves energy metering, sensor networks, database access, municipal registries, business data, supply chain and manufacturing execution systems and other sophisticated sources of data. The heterogeneity of the data requires pre-processing before it can be used for benchmarking or KPI purposes.

      After the data is converted into information it needs to be visualized, correlated, and communicated to the stakeholders. This course will cover the basic methods to accomplish this.

      Stakeholders that are informed and motivated to improve efficiency will then implement measures. The classical way to improve efficiency is to replace old, inefficient equipment (e.g. windows, machines, insulation) with new, better ones. Additional to that, controls have a large influence on the efficiency of individual equipment and entire systems.

      Implementing improvement measures is not the last step. An entire, and recurrent, process of managing efficiency measures must be implemented. Periodic measurements are needed to verify the success of the measures and to adjust settings that are not ideal.

      • Analyse and improve an energy-relevant technology/process.
      • Conduct an energy efficiency audit of an industrial or commercial customer.
      • Calculate and visualize energy analysis data.

      Ad-hoc Networks

      Course code:ET4388
      Programme:Master Electrical Engineering
      InstructorsR.R. Venkatesha Prasad

      Ad-hoc networks are formed in situations where mobile computing devices require networking applications when a fixed network infrastructure is not available or not preferred to be used. In such cases, mobile devices may possibly set up an ad hoc network themselves. Ad-hoc networks are decentralized, self-organizing networks and are capable of forming a communication network without relying on any fixed infrastructure.

      Ad-hoc networks form a relatively new field of research. In this lecture, besides general introduction to ad-hoc networks and their applications, we will focus on state-of-the-art methods and technologies for forming an ad-hoc network and maintaining its stability despite the dynamics of the network.

      • Model the ad-hoc networks using Graphs.
      • Describe the working principles of medium access control protocols for ad-hoc networks
      • Explain the working principles, advantages and disadvantages of different classes of routing protocols for ad-hoc networks
      • Choose various components to form a coherent ad hoc networking architecture
      • Develop a simulator to evaluate the MAC and routing protocols for ad hoc networks
      • Assess the suitability of ad-hoc networks for different communication needs and scenarios

      Systems and Control

      Course code:EE2S21
      Programme:Bachelor Electrical Engineering
      InstructorsProf.dr.ir. B.H.K. De Schutter

      We study properties of dynamic systems and how these properties can be influenced via control. To this aim we first develop mathematical models for the dynamic behaviour of systems based on first principles. This includes both linear and nonlinear models as well as linearization. Next, we analyse several important system properties in the time and the frequency domain. We introduce the concept of feedback control and discuss its effects on the system properties. Finally, we present several feedback control approaches to influence the system properties, with an emphasis on PID controllers.

      • to describe simple physical systems (in particular electrical, mechanical, electro-mechanical, fluid, and thermodynamic systems) using bond graphs
      • to derive mathematical dynamics models for such systems
      • to linearize non-linear models
      • to analyse the dynamic behaviour of closed-loop systems described by state space models or transfer functions in terms of step and impulse responses and zeros and poles
      • interpret Bode diagrams of 1st and 2nd order systems, and sketch Bode diagrams of systems given via a transfer function
      • design PID controllers, in particular for 1st and 2nd order systems
      • apply the above approaches and methods using Matlab

      Networking

      Course code:EE4C06
      Programme:Master Electrical Engineering
      InstructorsProf.dr.ir. P.F.A. Van Mieghem

      Introduction to the fundamentals of wireless communications (e.g. sampling, down/up-conversion, filtering, carrier recovery, error detection and control, digital modulations, Fourier transform) from the perspective of software defined radio (https://en.wikipedia.org/wiki/Software-defined_radio). Students will learn to apply theory of software-defined radio receivers first to design their own FM and digital radio receivers. Then, to design their own networking standard receivers such as LORA. This course is different from other wireless communication courses as it strives to be very practical in nature: students will test the discussed wireless concepts using their own software defined radio receivers.

      The above module will be complemented with the high-level introduction to modern wireless networking systems of Cognitive Radio and RFID.

      Students at the end of the course will be able to:

      • employ their own analysis methodology to assess new wireless network systems (especially at the physical layer)
      • understand how practical wireless systems work and get deeper understanding of how theory of wireless communications applies to practice;
      • implement existing and new wireless systems with software defined radio.

      Wireless Networking

      Course code:ET4394
      Programme:Master Electrical Engineering
      InstructorsDr. P. Pawelczak

      Introduction to the fundamentals of wireless communications (e.g. sampling, down/up-conversion, filtering, carrier recovery, error detection and control, digital modulations, Fourier transform) from the perspective of software defined radio (https://en.wikipedia.org/wiki/Software-defined_radio). Students will learn to apply theory of software-defined radio receivers first to design their own FM and digital radio receivers. Then, to design their own networking standard receivers such as LORA. This course is different from other wireless communication courses as it strives to be very practical in nature: students will test the discussed wireless concepts using their own software defined radio receivers.

      The above module will be complemented with the high-level introduction to modern wireless networking systems of Cognitive Radio and RFID.

      Students at the end of the course will be able to:

      • employ their own analysis methodology to assess new wireless network systems (especially at the physical layer)
      • understand how practical wireless systems work and get deeper understanding of how theory of wireless communications applies to practice;
      • implement existing and new wireless systems with software defined radio.

      Advanced Microelectronics packaging

      Course code:ET4391
      Programme:Master Electrical Engineering
      InstructorsProf.dr. G.Q. Zhang

      As the bridge between devices and various multi-funcational electronics systems, microelectronic system integration and packaging control more than 90% of the size, 60% of the cost, and largely the system performance and reliability. It is one of the most fascinating and rapid developing technology and business fields of Semiconductors, and playing a dominant role in the development of future micro/nanoelectronics and systems.

      To master the knowledge of advanced micro/nanoelectronics system integration and packaging technologies