• Description: 

    Spectral analysis is a fundamenal tool in a broad range of scientific disciplines, including
    telecommunications, chemistry, power electronics, and speech and audio processing. The course gives an overview of modern methods for spectral analysis for both stochastic and deterministic signals, including both parametric and non-parametric methods. More specifically, a number of methods based on different principles will be discussed, namely classical methods based on the Fourier transform, methods based on concepts from linear algebra such as shift-invariance and subspaces, optimal distortion-less filtering methods, and sparsity-based methods based on convex optimization and statistical principles. The properties of these methods will be analyzed and their application to real signal will be discussed.

  • Organizer: Mads Græsbøll Christensen, mgc@create.aau.dk
  • Lecturers: Mads Græsbøll Christensen
  • ECTS: 3

  • Time: November 2020

  • Place:

  • Zip code:

  • City:

  • Number of seats: 50

  • Deadline: October 2020

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: Nonlinear dynamics are found in all practical systems. If a detailed model of the behavior of a system is known, it may be possible to design a nonlinear control strategy, which improves the stability and performance of the system. However, although nonlinear control theory is precise, it is often mathematically complicated and deemed hard to implement in a practical system.

An example of a nonlinear system is a power converter connected to a renewable energy source. Power converters are becoming key devices in modern power systems due to the rapid introduction of renewable energy sources. Due to their nonlinear behavior, classical controllers (e.g., PID controllers) are generally not able to provide good performance; and to make matters worse, the nonlinear dynamics will not only degrade the performance of the device itself, but also the stability and reliability of the connected electrical power grid.
The course aims at building a fundamental understanding how to implement nonlinear control methods in a real system (power converters in power system) and demonstrate the aforementioned performance improvement relative to linear control.

Prerequisites: This course is intended for researchers and engineers for control engineers exploring newapplications of control theory in power electronics, and for advanced university students in these fields. General knowledge basic control theory is preferred. Couse exercises will be performed on MATLAB/Simulink.

Organizer: Associate Professor Jan Dimon Bendtsen, dimon@es.aau.dk

Lecturers: Associate Professor Jan Dimon Bendtsen, Yonghao Gui

ECTS: 2.0

Time: 23-24 March 2020

Place: Aalborg University

Number of seats: 30

Deadline: 2 March 2020

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: 
The course has two main parts, which in headlines are: (1) Foundation of optimal control, and (2) Special topics. If time permits we will also discuss numerical implementation. In the first part of the course, we will concentrate on the foundation of optimal control. We will discuss necessary and su􀂨cient condition for optimality, and various types of constraints. We will address the question of existence of optimal strategies. We cover two main results in optimal control theory, the Hamilton-Jacobi-Bellman (HJB) equation and the (Pontryagin) maximum principle. We show how the dynamic programming principle works for an optimal control problem by using the HJB equation to solve linear quadratic control problems. Moreover, we apply the maximum principle to linear quadratic control problems.
We end this part by introducing the notion of viscosity solution to the HJB equation.
In the second part of the course we will give an introduction to two areas of optimal control:
singular optimal control where higher order conditions such as the generalized Legendre–Clebsch condition is used to obtain suficient condition for local optimality, and optimal control of Markov processes where the state variables are not known with certainty (they are the outcome of stochastic differential equations). Finally, if time permits we will discuss software solutions for optimal control problems.

Prerequisites: A basic knowledge of mathematics as obtained through undergraduate engineering studies.

Organizer: Associate Professor John Leth, jjl@es.aau.dk 

Lecturers: Associate Professor John Leth, jjl@es.aau.dk

ECTS: 4.0

Time: August 2020

Place: Aalborg University

Number of seats: 30

Deadline: 3 weeks prior to start

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description:

In this course, we will study fundamental concepts related to (finite dimensional) linear time-invariant control system such as controllability, observability and stabilizability. These basic concepts will be introduced via the "geometric approach" meaning that they will be related to various subspaces related to the matrices appearing in the system equations. This approach will enable us to introduce the important notion of (A,B)-invariant subspace (and its dual concept, (C,A)-invariant subspace), which will be used to solve the disturbance decoupling problem (and can be used to solve the problem of tracking and regulation).
Moreover, the notion of (A,B)-invariant subspace and (C,A)-invariant subspace also turn out to be instrumental in other synthesis problems like observer design, system invertibility, the minimum phase property, and output stabilizability.

Prerequsites: A basic knowledge of mathematics as obtained through undergraduate engineering studies. Knowledge of control is an advantage but not a prerequisite.

Organizer: Associate Professor John Leth, jjl@es.aau.dk 

Lecturers: John Leth, Rafael Wisniewski, Mihaly Petreczky (CNRS (French National Center for Scientific Research))

ECTS: 2.0

Time: August 17-21

Place: Aalborg University

Number of seats: 30

Deadline: July 27

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

 

Description:

In the space of the last two decades or so, Linear Matrix Inequalities (LMIs) have become a de facto standard tool in numerical analysis and design in Control Engineering. Many standard problems, such as stability, robustness, performance and state feedback design, are naturally formulated as LMIs. The advantage of formulating a controller design problem with LMIs is that additional constraints can easily be added. As an example, a linear quadratic regulator (lqr) can be designed subject to constraints on the domain of the closed-loop poles. In addition, various efficient numerical toolboxes have been developed over the years, permitting straightforward usage of LMIs in practical setting.

Organizer: Associate Professor John Leth, jjl@es.aau.dk 

Lecturers: Associate Professor John Leth, Jan Dimon Bendtsen

ECTS: 3.0

Time: TBA

Place: Aalborg University

Number of seats: 25

Deadline: TBA

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

 

Description:
Much of technical scientific research has as an ultimate goal to develop
technological devices that will be used by people. Either as services, or products that may end up
being an integral part of peoples’ lives. In order to investigate if a given technology can be used in a given context, experiments with human subjects are indispensable. These types of experiments can be time consuming and expensive and if not properly designed and executed the results may not be reliable.

This course takes both a theoretical and practical approach to data collection from human subjects,
in order to avoid mistakes and common pitfalls while improving the reliability of the results. From
an understanding of cognitive and perceptual processes of human interaction with the environment, we will investigate implications of these processes in different data collection scenarios.
The following scenarios will be presented based on the research activities of the section:
1. Evaluation of sound environments and interactive control (sound zones).
2. Hearing aid aided performance ratings (BEAR).
3. EEG differences between active and passive listening.
The course consist of lectures and practical work, where you will design and participate in
experiments.

Prerequisites: Knowledge of basic data analysis models, such as CHI-square, T-tests, ANOVA is
an advantage, but not strictly required. Programming in any language capable of making simple
user interfaces.

Learning Objectives:
- Understand ethical and practical implications of using humans as test subjects.
- Understand and learn to work with rules and regulations for personal data (GDPR).
- Understand how to avoid common mistakes in the experimental design.
- Understand and know the importance of familiarisation and training.
- Understand and learn to apply methods for balancing levels of independent variables.
- Understand and be able to work with variability in subject responses.
- Be able to implement methods for data collection that can minimise subject bias.
- Be able to implement and work with difference judgements, rating scales and forced choice
methods.
- Be able to derive scales based on human responses

Organizer: Lars Bo Larsen

Lecturers: Lars Bo Larsen, Rodrigo Ordonez

ECTS: 2.0

Time: TBA

Place: Aalborg University

Number of seats: 30

Deadline: TBA

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description:
Hearing assistive devices (HADs) are ubiquitous. They include, for example, devices such as headsets for speech communication in noisy environments (air plane crews, emergency/rescue teams, combat soldiers, police forces, etc.), headsets for offi ce use, gaming, etc., and hearing care systems, e.g. hearing aids and cochlear implants.

The course consists of lectures and hands-on exercises, which allow the course participants to understand in-depth the technical problems related to HADs and their potential solutions. The course has three main parts. The first part is an introductory part, which lays the foundation for the rest of the course, covering fundamental topics such as auditory perception (normal and impaired hearing) and a discussion of the basic principles of HADs. The second part provides an overview of fundamental signal processing problems encountered in HADs, and an in-depth treatment of state-of-the-art solutions. These include methods for beamforming and noise reduction, direction-of-arrival estimation, feedback and echo control, hearing loss compensation, etc. Furthermore, an overview is given of methodologies for evaluating HADs with a particular focus on methods for intelligibility assessment and estimation. The third part of the course presents emerging technologies for hearing assistive devices, including machine learning techniques for processing of speech in noise, audio-visual signal processing, user-aware/symbiotic signal processing, methods for own-voice processing, etc. While the course focuses on the HAD application, many of the discussed techniques are very general and find use in the much broader field of general sound processing. The course is multi-disciplinary, including topics such as basic auditory perception, statistical signal processing, deep learning, practical do’s and don’ts.

The course also bridges the gap between theoretical background and practical/robust application in practice. The course is a one-week concentrated course to be held in the Fall, 2020. The course involves course preparation (approximately 1 ECTS), course presence (1 ECTS), assignment finalization and hand-in (1 ECTS)

Prerequisites: Basic knowledge of statistical signal processing, stochastic processes, and linear algebra. Familiarity/handy with Matlab/Python.

Organizer: Professor Jesper Jensen

Lecturers: Prof. Jesper Jensen, AAU. Prof. Jan Østergaard, AAU. Prof. Zheng-Hua Tan, AAU. Dr. Meng Guo, Oticon (invited talk). Dr. Jan Mark de Haan, Sennheiser Comm. (invited talk). Dr.

Asger Heidemann Andersen, Oticon (invited talk). Dr. Svend Feldt, Sennheiser Comm. (invited talk). Dr. Carina Graversen, Eriksholm (invited talk).

ECTS: 3.0

Time: November 2020

Place: Aalborg University

Number of seats: 50

Deadline: 3 weeks prior to start

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description:
Deep learning is an emerged area of research in machine learning and has recently shown huge success in a variety of areas. The impact on many applications is revolutionary, which ignites intensive studies of this topic.

During the past few decades, the prevalent machine learning methods, including support vector machines, conditional random flelds, hidden Markov models, and one-hidden-layer multi-layer perceptron, have found a broad range of applications. While being effective in solving simple or well-constrained problems, these methods have one drawback in common, namely they all have shallow architectures. They in general have no more than one or two layers of nonlinear feature transformations, which limits their performance on many real-world applications. 

On the contrary, the human brain and its cognitive process, being far more complicated, have deep architectures that are organized into many hierarchical layers. The information gets more abstract while going up along the hierarchy. Interests in using deep architectures have been reignited by many amazing applications enabled by deep learning techniques.

During the past years, deep learning methods and applications have witnessed unprecedented success.

This course will give an introduction to deep learning both by presenting valuable methods and by addressing speciflc applications. This course covers both theory and practices for deep learning. The students will also have hands-on exercises experimenting a variety of deep learning architectures for applications.

Topics will include:

  • Machine learning fundamentals
  • Deep learning concepts
  • Deep learning methods including deep autoencoders, deep neural networks, recurrent neural networks, long short-term memory recurrent networks, convolutional neural networks, and generative adversarial networks.
    • Selected applications of deep learning

Teaching methods: The course will be taught through a combination of lectures, demos of applications and small exercises.

Criteria for assessment: Acceptable exercise solutions and at-least 75% participation are required to pass the course

Prerequisites: Basic probability and statistics theory, linear algebra and machine learning

Organizer: Zheng-Hua Tan, zt@es.aau.dk

Lecturers: Zheng-Hua Tan, zt@es.aau.dk 

ECTS: 2.0

Time: 20, 23, 25 March 2020

Place: Aalborg University

Number of seats: 50

Deadline: 28 February

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.