Education

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.

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.

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.

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

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.

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.

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 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.

• 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

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

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.

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.

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