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Afsluttede PhD Projekter

Microwave Photonic Oscillators Integrated on an Optical Chip

Oscillations exist naturally everywhere in our everyday life, from sound waves to the periodic rotations of the earth, creating the dynamics of this world. In order to bring life into our electronic systems, we use electronic oscillators which generate alternating voltages and currents at a desired frequency. They are a fundamental part of almost every piece of electronic circuitry, and they enable, for example, the dynamic behaviour of computers, carriers for the transmission of data and time in clocks.

Electronic oscillators relying on the well-defined resonance of crystals, mainly quartz, have existed for decades. However, the increasing demands for low noise performance and high bandwidth in applications such as high speed analog to digital converters, radars and positioning systems, go beyond the limit of these oscillators.

Another type of oscillator is the optoelectronic oscillator which has gained more and more interest due to its ultra-low noise performance. An optoelectronic oscillator is based on the modulation of an optical carrier wave by a microwave signal which is generated through an optical feedback loop. The current optoelectronic oscillators outperform the crystal oscillators in the giga-hertz regime. However, currently they are based mainly on large discrete components such as optical fibers and are thus applicable to laboratory use only.

The aim of this project is to reduce the size of the optoelectronic oscillator by integrating it onto a photonic chip whilst retaining the ultra-low noise performance.

ABOUT THE PROJECT


Project title:
Microwave Photonic Oscillators Integrated on an Optical Chip

Project period: May 2016 to April 2019

Main supervisor: Assoc. Prof. Martijn Heck

Research section: Electrical and Computer Engineering


Supporting Multidisciplinary Development of Cyber-Physical Systems

Systems consisting of both software and hardware are becoming a vital part of society, where they constitute cars, trains, medical devices and so forth. Such systems can be called Cyber-Physical Systems as they often involve cyber elements controlling physical processes.

When developing Cyber-Physical Systems it can be useful to create models of components, a model being an abstract description of a component. These models are then used in a Co-Simulation which is a simulation of coupled technical systems. Simulating the constituents that make up a given system can help identify undesired behaviour. This study will involve the development of the Co-Simulation Orchestration Engine which is the software responsible for orchestrating a simulation using models of components.

The Co-Simulation Orchestration Engine is part of the INTO-CPS project which is short for Integrated Tool Chain for Model-based Design of Cyber-Physical Systems. The purpose of the INTO-CPS project is to create a family of interlinked tools that support development of Cyber-Physical Systems from requirements to realisation in hardware and software.

ABOUT THE PROJECT

Project title: Supporting Multidisciplinary Development of Cyber-Physical Systems

Project period: Feb. 2016 to Jan. 2019

Main supervisor: Prof. Peter Gorm Larsen

Research section: Electrical and Computer Engineering


Development of Methods for Objective EEG Analysis of Brain Activity induced by Sugar, Salt, Fat and their Substitutes

During the last decade, overweight and obesity have become an increasing global issue. According to WHO, in 2008, around 1.4 billion people over the age of 20 were overweight, at least 500 million were obese and at least 40 million children under the age of five were overweight.

The Food Industry's response to the obesity epidemic has been to produce a number of low fat and sugar food products that enable the consumer to eat the same food while consuming fewer calories. However, an investigation conducted by the Food Administration shows that people tend to consume extra-large servings of the light products, negating any benefits the light products might offer. 

A solution to the above-mentioned obesity epidemic requires a more thorough understanding of the brain's response to varying salt, sugar and fat levels and subjective satiation. Traditionally, food ingredient selection is based on physical and sensory analysis methods. However, in connection with salt, sugar and fat substitution products, objective measurement methods lack the ability to describe what we can register with our senses. In this regard, brain recordings are particularly interesting.

The idea behind the project is to utilise EEG methods to screen salt, sugar and fat substituents when selecting new food ingredients. The goal is to compare EEG results with physical or sensory data for new food ingredients with the hope of supplementing selection criteria for new food ingredients with objective physiological EEG responses.

ABOUT THE PROJECT


Project title:
Development of Methods for Objective EEG Analysis of Brain Activity induced by Sugar, Salt, Fat and their Substitutes

Project period: Sept 2013 to Aug 2018

Main supervisor: Prof. (Docent) Preben Kidmose

Research section: Electrical and Computer Engineering


Compressed Sensing for Machine-type communication in 5G

The future communication system has to cope with extremely diverse and heterogeneous use cases which lead to a number of challenging requirements like massive connections, data deluge, traffic management and inter cell interference. It becomes increasingly apparent that 4G will not be able to meet these requirements. 5G is required to support Mission Critical IoT Communication, Massive Machine-type Communication and Gigabit mobile connectivity.

Ultra-low latency and high reliability are the key challenges for Mission Critical Communication while for Massive Machine-type Communication the key challenges are massive connections, data deluge and energy efficiency. The conventional techniques are far behind to meet these requirements.  To address these challenges, a new paradigm of communication systems is required. A number of techniques have been regarded as the potential enablers to address these issues. One of the novel techniques is Compressed Sensing. Compressed Sensing exploits the sparsity of the signal to design an efficient system, which has been used in many different fields like astronomy, medical image processing, etc. A 5G system has some basic sources of sparsity like sparse traffic, multipath channels and compressible short messages which can be exploited to cope with the challenges in Mission Critical IoT communication and Massive Machine-type Communication.

The aim of the project is to leverage compressed sensing technique to achieve the challenging performance requirements of Mission Critical IoT Communication and Massive Machine-type Communication in 5G.

ABOUT THE PROJECT


Project title:
Compressed Sensing for Machine-type Communication in 5G

Main supervisor: Assoc. Prof. Qi Zhang

Research section: Electrical and Computer Engineering


Controlling Sound Zones – with perceptually optimised multi-channel signal processing

Sound zone control will lead to a revolution in how we use audio systems: Several groups of people can enjoy different audio contents in a shared space at the same time without interrupting one another. Today this can only be achieved by wearing headphones, which would affect the conversation between people negatively.

Some work has been done in terms of creating separated sound zones [see IEEE SPM 81-91, March 2015, and references herein]. The key to sound zone control is the filtering applied to each loudspeaker signal. Three algorithms are widely adopted for filter design: acoustic contrast control, pressure matching and planarity control. However, there are still many unsolved problems that limit the further development of sound zones. This project will address three unsolved issues:

  • quantify and minimise the influence of nonlinear distortion in loudspeaker drivers.
  • quantify the influence of room reflections on the current sound zone control methods.
  • devise new perception based cost functions for sound zone control and devise accompanying perception optimised regularisation methods.

ABOUT THE PROJECT


Project title: 
Controlling Sound Zones – with perceptually optimised multi-channel signal processing

Main supervisors: Prof. (Docent) Preben Kidmose

Research section: Electrical and Computer Engineering


Open Platform for Big Data Analytics and Information Management

Innovation in agro-technology is expected to be a major facilitator for implementing a sustainable and intensive crop production. The Future Cropping partnership is a collaboration between numerous companies and universities where a main goal is to expand the use of ICT in the agricultural sector.

This project will be investigating the design of an open platform for data mining and analytics, which will integrate data from distributed information sources to provide new technologies for modern high yielding and low emission precision farming. Emphasis will be on designing a robust platform with horizontal scalability for data mining, and to apply machine learning techniques for automatic classification of crop areas exhibiting non-optimal growth. Furthermore, this allows for holistic optimisation of the yield based on previously unknown extracted patterns.

ABOUT THE PROJECT


Project title:
Open Platform for Big Data Analytics and Information Management

Main supervisor: Assoc. Prof. Rune Hylsberg Jacobsen

Research section: Electrical and Computer Engineering


Silicon Photonics for High-Bandwidth Wireless Links and Remote Sensing

The exponential growth of the use of mobile devices, the interconnected environment and ‘smart’ cities require a dramatic increase in network capacity over the next decade. By 2020, it is expected that 50 billion devices are connected to the internet, a large part of these wirelessly. The required wireless bandwidth is, however, far beyond what technologies like WiFi can deliver.

Governments all over the world have opened up new frequency bands for such wireless communications. The current level of technology is not able yet, though, to provide low cost and compact transceivers for these frequency bands, which is a crucial requirement for ubiquitous and mobile interconnected devices. Moreover, no feasible technology exists for the next generation higher frequency bands.

Optical chips are far more suitable than electronics to generate high-bandwidth signals at these frequencies, but until recently this technology was too experimental and not robust. Over the last few years, however, foundries with mature fabrication processes have been set up to fabricate silicon based optical chips. For the first time, we can now use this optical chip technology to realise low cost, compact and high-bandwidth transceivers that enable the next generations of wireless internet. This project aims to achieve scientific and technical breakthroughs required to leverage on silicon photonics co-design of transceivers for high speed wireless communications.

ABOUT THE PROJECT


Project title:
Silicon Photonics for High-Bandwidth Wireless Links and Remote Sensing

Main supervisor: Assoc. Prof. Martijn Heck

Research section: Electrical and Computer Engineering


Utilisation of Radar and Lidar Sensors for Detecting and Classifying Humans and Animals in Autonomous Agriculture

Autonomous farming is the concept of automatic agricultural machines operating safely and efficiently without human intervention. Today, technology is available to automatically navigate and operate agricultural machinery, such as tractors and harvesters, more efficiently and more precisely than by manual human operation. However, a crucial deficiency in this technology concerns the safety aspects. In order for an autonomous vehicle to be certified for unsupervised operation, it must perform automatic real-time risk detection and avoidance in the field with high reliability.

This project seeks to apply active sensors in the form of radar and lidar (laser range scanner) to automatically detect humans and animals from a moving vehicle in a farming environment. Radar and lidar both provide precise and robust distance measurements, making three-dimensional positioning of objects possible. In addition, classification of detected objects into categories such as humans, animals and ground/vegetation is investigated.

ABOUT THE PROJECT


Project title:
Utilisation of Radar and Lidar Sensors for Detecting and Classifying Humans and Animals in Autonomous Agriculture

Main supervisor: Senior Researcher Rasmus Nyholm Jørgensen

Research section: Electrical and Computer Engineering


Tool Automation for Model-Based Design of CPSs

This project, as part of the INtegrated Tool chain for model-based design of Cyber Physical Systems (INTO-CPS) project, explores how to automate the process of moving from a discrete abstract model to a realisation in a programming language. Automating the process between a discrete model and its realisation can reduce the risk of human error, when a validated model is manually realized in a programming language and additionally reduce the Time-To-Market for product development.

The focus of this thesis is code generation against distributed hardware architectures, enabling hardware in-the-loop (HiL), software in-the-loop (SiL) and Design Space Exploration (DSE) of Cyber Physical Systems (CPSs).

A model of a CPS is called a co-model, and consists of both discrete and continues models that are connected. This code generator will be part of tools which together enable a detailed and intelligent DSE of all models in co-simulation, which is possible by sweeping over relevant design parameters. Different models from different simulation engines will be connected using the Functional Mock-up Interface (FMI), and extending FMI with information about the design parameters will enable FMI-based co-simulation to cover large parts of the CPSs design life cycle.

ABOUT THE PROJECT


Project title:
Tool Automation for Model-Based Design of CPSs

Main supervisor: Prof. Peter Gorm Larsen

Research section: Electrical and Computer Engineering


Ultra-Low Power Device in Nano-Scale Technology for Biomedical Applications- Seizure Detection

Therapeutic and prosthetic devices have emerged as a promising candidate for treatment of patients with neurological disorders ranging from epilepsy and Parkinson’s disease to motor impairments. The ability to acquire targeted neurological information from the brain is an essential requirement for the advancement of these systems. Thus, brain monitoring introduces key challenges for electronic systems in terms of both instrumentation and information extraction.

The focus of this project is to design a low-power and low-noise mixed-signal IC design in Nano-scale technologies such as CMOS, Fin-FET and Tunnel-FET (TFET). This design will be used to handle brain signal (EEG) acquisition and feature extraction from an analogue channel into the digital domain. The main focus of the project is on designing ultra-low power digital and analogue components especially for neurological disorders such as seizure in a system-on-chip (SOC).

In this system, an Instrumentation Amplifier is used to acquire the microvolt signals from electrodes in the presence of numerous physiological and environmental interferences. These amplified signals are processed using a DSP or custom digital/analogue circuits in CMOS technology in an extremely low power mode. In this project designing high-speed photonic Analog-to-Digital Converters (ADC) will be explored in collaboration with the photonics group.

ABOUT THE PROJECT


Project title:
 Ultra-Low Power Device in Nano-Scale Technology for Biomedical Applications - Seizure Detection

Main supervisor: Prof. (Docent) Jens Kargaard Madsen

Research section: Electrical and Computer Engineering


Optimised Signal Processing of SkyTEM Data

The project is part of a collaboration between the company SkyTEM, the Department of Geoscience and the Department of Engineering at Aarhus University. SkyTEM is a technology leader in groundwater measurements using a helicopter-based TEM (Transient Electromagnetic Method) system for sub-surface exploration.

TEM consists of measuring the earth response to a magnetic signal generated by a large induction coil which, in this case, is towed by a helicopter.

For this project, SkyTEM is currently developing a new hardware platform that enables advanced processing of the measured signals. The purpose of this project is to explore and make use of the possibilities enabled by this new platform.

ABOUT THE PROJECT


Project title:
Optimised Signal Processing of SkyTEM Data

Main supervisor: Assistant Prof. Jakob Juul Larsen

Research section: Electrical and Computer Engineering


Detection and Recognition of Wildlife and Humans in Pasture Fields using Non-stationary Imaging Technologies

The goal of the project is to aid the development of self-driving or autonomous machinery for the agricultural domain. Autonomous machinery is largely possible today but requires, by law, a human operator to ensure human safety. This project will enable autonomous machinery by achieving human safety through automatic detection of humans using a normal and a heat sensitive camera.

Apart from human safety, the farmer needs a cost efficient system by avoiding non-living obstacles that may expose farming machinery, human properties and nature to damages. It is also desirable to ensure wildlife safety in modern farming machinery by automatic detection using a normal and a heat sensitive camera. Wildlife is - in today’s mowing operations - becoming more exposed due to increased working widths and speeds of agricultural machinery leading to the killing of hidden/camouflaged wildlife and contamination of harvested crops due to undetected dead wildlife. Using a camera for automatic detection of wildlife will help farmers avoid the killing of larger animals and contamination of harvested crops.

ABOUT THE PROJECT


Project title:
Detection and Recognition of Wildlife and Humans in Pasture Fields using Non-stationary Imaging Technologies

Main supervisor: Senior Researcher Rasmus Nyholm Jørgensen

Research section: Electrical and Computer Engineering


Scalable Energy Management Infrastructure for Aggregation of Households

The Smart Grid represents an unprecedented opportunity to move the energy industry into a new era of reliability, availability and efficiency that will contribute to our economic and environmental health. The Smart Grid will consist of controls, computers, new technologies and equipment working together and with the electrical grid to respond to our constantly changing electric demands. Also, demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based prices. This project aims at developing a novel ICT infrastructure for the implementation of Demand Response in households. This infrastructure will enable the shifting of energy consumption from high energy-consuming loads to off-peak periods with high generation of electricity from Renewable Energy Sources.

The chief purpose of this project is to develop a novel, comprehensive and optimal scheduling strategy for varied-specific households. In this strategy, the aggregator system will optimise and manage a large number of partial loads simultaneously according to the generation of electricity from Renewable Energy Sources to shift the households’ demands to off-peak hours. The scheduling strategy needs to take into account constraints from household comfort, grid stability, market mechanisms, etc. Also, trying to optimise conflictive objectives of households and aggregator simultaneously creates a multi-objective optimisation problem. As a result, the main question is how much and when the power consumption should be shifted taking into account the inclusion of scalable and diverse-characteristic householders, different appliance types and dynamic energy price strategies. Answering this question helps the householders to benefit financially and the aggregator to balance the system optimally.

ABOUT THE PROJECT


Project title:
Scalable Energy Management Infrastructure for Aggregation of Households

Main supervisor: Assoc. Prof. Rune Hylsberg Jacobsen

Research section: Electrical and Computer Engineering


Low Voltage/Low Power Design in Future Nodes (FinFET and Nanowire-based Devices)

The past few decades have seen the evolution of a semiconductor industry driven by technology scaling. Miniaturisation of bulk Field Effect Transistors (FETs) along with the scaling of power supply voltage have provided the benefits of higher performance, lower power and larger integration density. However, future scaling will face considerable challenges e.g. short-channel effects (SCEs) causing the design and optimisation of circuits to become very challenging.

One approach to counter these effects is to introduce alternate devices which possess inherently better robustness to SCEs in comparison to existing technology. Among these alternatives, multiple-gate FETs such as FinFETs or gate wrap-around FETs are emerging as promising candidates. FinFETs have the potential for analogue applications as well as for improving the performance of digital circuits such as static random access memories (SRAM) which are widely used in most digital and computer systems. In this respect, Intel will use the 3-D trigate transistors commercially in 22-nm technology node and so a strong interest has emerged among semiconductor industries in forming 14 and 10-nm bulk FinFET.

In this project, FinFET devices are utilised for SRAM modules in both circuit and device level designs. Also, we will investigate FinFET and its potentials for low-power applications and design some analogue and mixed-signal building blocks by FinFET in sub 14-nm technologies using the Design Kits provided by IMEC.

ABOUT THE PROJECT


Project title:
Low Voltage/Low Power Design in Future Nodes (FinFET and Nanowire-based Devices)

Main supervisor: Assistant Prof. Farshad Moradi

Research section: Electrical and Computer Engineering


Reliable Communication in Body Area Networks

A Body Area Network (BAN) consists of multiple, tiny, low-power, intelligent, wearable or implanted sensor nodes which are radio-enabled and can communicate wirelessly. The sensor nodes can collect various important physiological data for diagnosis or fast emergency response, and deliver various personalised therapeutic, treatment-related applications and services.

BAN is a new emerging technology that is used in areas such as healthcare, entertainment, games and sport science. It is expected to enhance the patient health care experience by providing independent living solutions for people that need constant healthcare. Furthermore, BAN can reduce the demand on the healthcare infrastructure and medical staff in the hospitals. Essentially, there is a unique combination of four major requirements in a realistic BAN:

  • Energy efficiency
  • Low complexity
  • Robustness against harsh fading conditions
  • Protection against interference and error

The aim of this project is first to investigate the characteristics of the typical BAN applications and the corresponding difficulties and limitations. The next step is to analyse these limitations to provide remedial solutions to achieve the performance requirements. The analysis is meant to address both Physical (PHY) and Medium Access Control (MAC) layers.

ABOUT THE PROJECT


Project title:
Reliable Communication in Body Area Networks

Main supervisors: Assoc. Prof. Qi Zhang, Aarhus University and Eryk Dutkiewicz, Macquarie University

Research section: Electrical and Computer Engineering


An Ultra-Low Power Wireless Biosensing SoC Utilising Low-Voltage Techniques for Body-Worn Healthcare Applications

The need for healthcare devices for everyday life is increasing.  These devices can provide a long-term, unobtrusive monitoring to prevent chronic diseases which are the biggest killers according to the statistics of WHO, e.g. heart disease, stroke, hypertension, diabetes, etc. Past devices are bulky and high-power and have limitations to the use away from specific places e.g. clinics and hospitals. Ultra-low-power, wireless, small-size and user-friendly are the basic requirements of today's healthcare system.

For a long time, there has been a continued expectation that the biomedical system can be power autonomous which can be facilitated by energy harvesting from the ambient energy sources such as thermal energy, kinetic (motion, vibration) energy, radio frequency and solar power. However, current works have not reached this point yet.

The topic of this project is the utilisation of low-voltage techniques to decrease the system's power as much as possible as the potential of low-voltage techniques to aggressively reduce power has not been well addressed in the literature. To satisfy this task, the system’s total power must decrease to ~100µW including analogue front-end (AFE), µP (digital processing) as well as data transmitting and receiving via radio frequency. A potential application is the Ear-EEG.

ABOUT THE PROJECT


Project title:
An Ultra-Low Power Wireless Biosensing SoC Utilising Low-Voltage Techniques for Body-Worn Healthcare Applications

Main supervisor: Prof. (Docent) Jens Kargaard Madsen

Research section: Electrical and Computer Engineering


Characterisation and Evaluation of Dry-Contact Electrodes for Ear-EEG

Ear-EEG is a novel EEG (electroencephalography) recording approach in which the EEG signal is recorded from electrodes embedded on an ear-piece placed in the ear canal. The ear-EEG has great potentials within continuous brain monitoring in everyday life and will have application within both medical and consumer electronics devices.

The integration of brain monitoring based on EEG into everyday life has been hindered by the limited portability and long set-up time of current wearable systems as well as by the invasiveness of implanted systems.

To address these issues, the ear-EEG has been introduced which is a discreet, unobtrusive and user-centred approach to brain monitoring. The ear-EEG recording concept has been tested by using several standard EEG paradigms and benchmarked against standard on-scalp EEG.

All ear-EEG recordings made so far have been based on wet-electrode technology. In order to improve the usability and user-friendliness, this project will exploit so-called dry-contact electrode technology. This has impact on the design of the electrode itself, the supporting mechanics and the electronic instrumentation for acquiring the EEG signal.

ABOUT THE PROJECT


Project title:
Characterisation and Evaluation of Dry-Contact Electrodes for Ear-EEG

Main supervisor: Prof. (Docent) Preben Kidmose

Research section: Electrical and Computer Engineering


Enhancing Code Generation support in Formal Model Development

In my PhD I work on enhancing tool support for developing mission-critical systems. These are systems whose failure may lead to huge financial costs or loss of human lives. Examples of such systems range from everyday objects like cell phones or online banking systems to safety-critical systems like modern cars and flight control systems.

To ensure that the system is behaving as intended, we use mathematically based techniques to validate the behaviour of the system. One such technique is formal modelling. Based on the formal models the system's software and hardware is developed and integrated to construct a final version of the system.

To increase the chances of the software working correctly I work on developing tools that enable the software implementation to be generated automatically from the formal models. This avoids introducing problems into the software implementation, due to manual translation of model into code.

In my PhD project I explore how to enhance code generation to better support formal model development. Use of code generation enables the insights obtained during early analysis to automatically be transferred to the subsequent phases of development. In particular I am interested in finding new ways to exploit code generation to ensure the correctness of the software implementation. This is particularly important for mission-critical systems like cars and flight control systems, where system failure may have fatal consequences.

ABOUT THE PROJECT


Project title:
Enhancing Code Generation support in Formal Model Development

Main supervisor: Prof. Peter Gorm Larsen

Research section: Electrical and Computer Engineering


Model-Driven Software Development for Agricultural Robotics

Software development for agricultural robotics is complicated and hence time-consuming and expensive.

Agricultural robots are particularly challenging with respect to the development of software due to the combination of an open, unpredictable environment and a complex, open-ended range of tasks.

Model-driven software development is a systematic approach to automatically generated software within a given domain based on a model.

The scientific goal of this project is to improve and develop model-driven software techniques, domain models, underlying software architecture and code generation techniques. This will enable us to create a software platform for automatic generation of control programmes that suit the specific requirements of agricultural robots.

The developmental goal of this project is to provide systematic and automated techniques for the development of a software platform that can be utilised for field robots in various scenarios. This software platform will be directly applicable in the development of control programmes for specific agricultural robots.

ABOUT THE PROJECT

Project title: Model-Driven Software Development for Agricultural Robotics

Main supervisor: Senior Researcher Rasmus Nyholm Jørgensen

Research section: Electrical and Computer Engineering


Virtual Power Plant for Residential Demand Response

In the near future, there will be a higher penetration of renewable energy sources into the electrical grid. The entrance of these distributed energy resources into the system will require smarter solutions to balance the electricity production and consumption. This future grid is known as the Smart Grid.

The aim of this project is to make a large residential complex and its consumers ”Smart Grid Ready” through the development and operation of a Virtual Power Plant (VPP). A VPP can be understood as a power plant which, instead of just producing electricity, is also able to modify the energy consumption of the end-user, thus providing demand response. The designed VPP will monitor and control the energy usage in the building. Furthermore, the VPP of the building will pool and offer the overall demand response to an external entity at a higher tier in the aggregation hierarchy.

The new student’s residence at Aarhus harbour will be used as a test bed under the name of Grundfos Dormitory Lab. This is a 159 apartment building with a very low consumption profile, equipped with more than 3200 sensors.

The project deals with the Information and Communication Technology (ICT) for the construction of a VPP at building level. Specifically, this involves concept development, demand response engineering, sensing and actuation technology, data mining and forecasting as well as system integration for demonstrating part of the concepts developed. The project aims at advancing the state-of-the-art in VPP technologies for the Smart Grid.

ABOUT THE PROJECT


Project title: 
Virtual Power Plant for Residential Demand Response

Main supervisor: Assoc. Prof. Rune Hylsberg Jacobsen

Research section: Electrical and Computer Engineering


Quality of Context in Ambient Assisted Living Systems

Ambient Assisted Living (AAL) systems integrate different technologies to provide healthier and safer living environments for the elderly. The AAL systems provide AAL services such as monitoring the activities of daily living. It is important to ensure the quality of context information in the AAL system as it reacts and responds to the events that occur by making use of the context information derived from the context data. A lapse in quality could be life-threatening as it leads to a failure in anticipating and reacting to the user's needs, and a failure in adapting to the changes in the environment.

The objective of this project is to investigate how the quality of context data can be assured in high-risk physical activity contexts in the AAL systems. During this project, the context data quality in AAL systems is analysed from different data quality dimensions and novel methods will be developed for quality assurance.

The work being done in this project is part of the European FP7 CareStore project. The CareStore project aims to develop a marketplace for the easy deployment of applications and device drivers in a healthcare scenario, and develop an ambient assisted platform for the home to integrate the CareStore marketplace.

ABOUT THE PROJECT


Project title:
Quality of Context in Ambient Assisted Living Systems

Main supervisor: Assoc. Prof. Stefan Hallerstede

Research section: Electrical and Computer Engineering


Cybersecurity and Privacy Enforcement in an Internet-based Smart Grid

In the battle for lowering CO2 emissions, many countries encourage and financially support the deployment of renewable energy sources for generating electricity. These energy sources are typically non-dispatchable, i.e. they do not produce energy on demand. Currently, no energy storage technology exists that efficiently store the energy when it is produced for later use. Thus, it is necessary to have an Information and Communication Technology (ICT) system that can support the grid operator in accounting for these fluctuations on the grid so the energy can be consumed when it is produced; a so-called smart grid system. This need is only exacerbated further with the advent of solar panels and electric vehicles distributed on the electric grid.

The challenge of balancing the electric grid with non-deterministic generation of electricity calls for ICT solutions that can monitor and manage consumption and production in real-time. Such solutions will be based on data generated from sensors placed e.g. in residential houses. A large majority of the data produced in residential homes is, however, directly related to the residents' behaviour within their own private sphere. If security and privacy going to and from the residential homes are not carefully considered, a possible resistance towards adapting these ICT solutions could emerge. Additionally, the solution must be economically attractive for both the residential consumer and grid operator such that the potential benefit (such as energy cost savings) from getting the monitoring equipment outweighs the price of the equipment.

The aim of this project is to facilitate an economically viable ICT solution for both the residential consumers and the grid operators by using the existing Internet connection in the residential houses as pathway for communicating and transferring data. On top of a service-oriented architecture and an open communication infrastructure, the research will focus on preserving data privacy and enforce cyber-security for the residential consumers while not compromising robustness and service guarantees for the grid operators.

ABOUT THE PROJECT


Project title:
Cybersecurity and Privacy Enforcement in an Internet-based Smart Grid

Main supervisor: Assoc. Prof. Rune Hylsberg Jacobsen

Research section: Electrical and Computer Engineering


Localisation of Wireless Sensor Network Embedded in Biomass

When storing grain and other farm produce, proper drying and storage are important. Using wireless sensors distributed in the storage enables a farmer to monitor how the drying/storage is progressing and to take action if something is not as it should be. This helps to reduce losses and get a higher overall quality.

A number of challenges face a wireless sensor network deployed in such a scenario. Farm produce can contain relatively high amounts of water as well as high concentrations of salts leading to an unfavourable environment for wireless communication.

To help in the process of monitoring storages, the aim of this project is to develop a wireless sensor network capable of functioning reliably under the specific conditions. The system should also be able to localise the sensors of the network as this enables targeted intervention in problem areas.

ABOUT THE PROJECT


Project title:
Localisation of Wireless Sensor Network Embedded in Biomass

Main supervisor: Assoc. Prof. Rune Hylsberg Jacobsen

Research section: Electrical and Computer Engineering


On the Extensibility of Formal Methods Tools

Extensibility is a useful property of software systems. It can be informally defined as the ability of a system to support the addition of new and unplanned functionalities.

My PhD project explores extensibility of formal methods tools, specifically the Overture tool for VDM (Vienna Development Method). I analyse and improve the extensibility of Overture through various vectors, such as software architecture and coding patterns.

My work revolves around extensions to the Overture tool that act as case studies and drive the research. Particular focus is given to the proof obligation generator extension and integration with theorem proving. Outside of specific extensions, I am interested in tool support for extending the notation itself.

In parallel to the above, I will also investigate a connection between extensibility and formal modelling by applying extensibility techniques from software engineering to the construction of extensible formal models.

ABOUT THE PROJECT


Project title:
On the Extensibility of Formal Methods Tools

Main supervisor: Prof. Peter Gorm Larsen

Research section: Electrical and Computer Engineering


Intelligent Soil Tillage using Image Sensors

The aim of this project is to research and develop a system for sensor based online control of the degree of tillage for a seedbed cultivator moving in the field.

The system will include a vision system for characterising the seedbed quality. The vision system will consist of a series of laser range scanners positioned on the seedbed cultivator which continuously generate a map of the raw and processed soil. The quality of the seedbed is correlated with aggregate size in the soil surface, i.e. the cloddiness of the soil. Large clods result in a too airy seedbed and thus the risk of drying out the seeds. On the other hand, a too processed seedbed has the risk of sealing the soil surface after heavy rain and thus resulting in bad germination. Additionally, it is a sign of too intensive seedbed cultivation and thus of an unnecessarily high energy consumption while cultivating.

The online control of tillage intensity is based on image analysis, and the seedbed cultivator is adjusted continuously while running in the field. Since the same field often consists of many types of soil, the system must be able to react to the changes in soil type and adjust the tillage intensity accordingly.

ABOUT THE PROJECT


Project title:
Intelligent Soil Tillage using Image Sensors

Main supervisor: Prof. (Docent) Henrik Karstoft

Research section: Electrical and Computer Engineering


Modelling, Simulation and Evaluation of Autonomous Agricultural Vehicle Guidance

This project deals with the challenges faced when designing and deploying an autonomous vehicle guidance system in the agricultural domain. Automatic guidance systems for agricultural machines, i.e. auto-steering systems, employ sensory input from the Global Navigation Satellite System (GNSS) and/or other localisation systems. The auto-steering systems can increase efficiency in terms of reduced overlap, less operational time and fuel.

Before an auto-steering solution can be operational on a given vehicle, it is necessary to calibrate several (control) parameters of the auto-steering system. These calibration parameters are dependent on the technical performance of the vehicle. We believe that better control parameters could be selected by utilising a simulated model of an agricultural machine. The simulated model will be able to execute and evaluate multiple candidate solutions using optimisation and search algorithms. The conjecture is that modelling and simulation would be less costly than selecting parameters and try to manually fine-tune an auto-steering system.

ABOUT THE PROJECT


Project title:
Modelling, Simulation and Evaluation of Autonomous Agricultural Vehicle Guidance

Main supervisor: Senior Researcher Rasmus Nyholm Jørgensen

Research section: Electrical and Computer Engineering


System Level Power-Aware Design for Mission-Critical Embedded Systems

An embedded system is a special purpose computing device commonly operating with low computing power and sometimes under limited energy availability.

One of the techniques that can be used to develop these kinds of systems is model-driven engineering. Under this design approach, the engineer uses modelling technologies to represent the system at a high level of abstraction. Following this initial representation, the model is progressively transformed into a final implementation.

The aim of this project is to adopt this approach to design and implement embedded systems that operate under tight energy budgets. The challenge is to incorporate the notion of energy consumption into different modelling paradigms. The reason behind applying several paradigms resides in the fact that embedded systems’ energy consumption can be studied from different angles, i.e. the physical, communication or computation angle.

By using different modelling approaches, one can select the most suitable for each angle and obtain energy estimations from different points of view.

The results of this project are being applied progressively in a case study in which an electronic compression stocking to treat leg venous insufficiency is developed. The aim is to evaluate how the energy consumption predictions determined by the models relate to the actual implementation, and how the modelling process benefits system development.

ABOUT THE PROJECT


Project title:
System Level Power-Aware Design for Mission-Critical Embedded Systems

Main supervisor: Prof. Peter Gorm Larsen

Research section: Electrical and Computer Engineering


Visual Awareness Negativity and Spatial Attention

For most of the population, our eyes have become the primary access to the world around us. However, much of the visual stimuli that enter our eyes is filtered out before it reaches our consciousness/visual awareness. Our visual system is extraordinarily good at this filtering process, though sometimes important information is filtered away which can be devastating e.g. in traffic situations.

This project seeks to explore the mechanisms that control whether visual information reaches our visual awareness or is filtered out. We will do this by measuring magnetic signals (magnetoencephalography – MEG) generated from specific areas in the brain when this process occurs and see how these mechanisms modulate the activity in the brain.

The first such mechanism we explore is how spatial attention, i.e. where we are directing our eyes, modulates this filtering process. To do this, we need to be able to control how much spatial attention the test subjects are able to allocate to a given visual stimulus. This will be achieved through the use of a gaze contingent display which can change the visual content on the screen depending on where the subject is looking.

The development of such a gaze contingent display with a faster response time than the human eye and which can be used while recording MEG signals is a prerequisite for the examination of the effect of spatial attention on our visual awareness.

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Project title:
Visual Awareness Negativity and Spatial Attention

Main supervisor: Prof. (Docent) Henrik Karstoft

Research section: Electrical and Computer Engineering


Well-Founded Engineering of System of Systems

A System of Systems (SoS) is a system type that has risen from the increased complexity found in present-day system engineering. An SoS denotes a collaborative system which consists of many independent, heterogeneous constituent systems that combine information and functionality in order to reach a synergistic functionality that is greater than the sum of the constituent systems' abilities.

The SoS Engineering field faces the challenge of its constituent systems being individually owned and developed, which means that there is no centralised authority to take action or make decisions. As a result, decisions have to be agreed on, collaboratively, between the individual owners/developers of the constituent systems. Therefore, the interfaces, data types, communication channels and interactions between the systems need to be agreed upon during development.

The purpose of the project is to research how existing and new methods can be adapted, combined and innovated to improve the foundation of SoS engineering. The research is focused on aiding collaboration by utilising a combination between the field of software engineering and the field of systems engineering.

ABOUT THE PROJECT


Project title:
Well-Founded Engineering of System of Systems

Main supervisor: Prof. Peter Gorm Larsen

Research section: Electrical and Computer Engineering


Pattern Recognition Methods for Reduction of Human-Wildlife Conflicts

The purpose of this project is to develop methods and algorithms for automation in agriculture.

The main focus is wildlife management systems where new technology could automate or improve existing methods.

The main contribution of the work is the development of pattern recognition algorithms which are capable of detecting and recognising wildlife in an agricultural setting. This includes audio and video based systems that are capable of measuring the presence and behaviour of wildlife.

The expected result of this work is a proof of concept solution which can be used in further development and full-scale tests.

ABOUT THE PROJECT


Project title:
Pattern Recognition Methods for Reduction of Human-Wildlife Conflicts

Main supervisor: Prof. (Docent) Henrik Karstoft

Research section: Electrical and Computer Engineering


Fiber Optical Load Sensors for Wind Turbines

This project is in the field of fiber optical sensors for measuring the load on a wind turbine blade.

The advantages of using an optical sensor instead of typical electronic sensors in a wind turbine blade are the immunity to lightning and the longer longevity of the sensor. A system that measures the load of the blades can provide information which can be used to optimise the production and design of a wind turbine.

The focus is on applied research on a new type of sensor that can measure the load on a wind turbine blade and provide better information about the shape of a blade during operation of the wind turbine.

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Project title:
Fiber Optical Load Sensors for Wind Turbines

Main supervisor: Prof. Martin Kristensen

Research section: Electrical and Computer Engineering


Ear-EEG and Applications to Brain Computer Interface (BCI)

Brain Computer Interfaces (BCIs) are an emerging technology that uses electrical activity in the brain, measured by non-invasive electroencephalography (EEG), to enable direct communication between the human brain and external devices such as computers. Over the past several years, BCIs have become an important research topic because of their ability to help patients suffering from severe loss of motor functions such as ALS, stroke, etc. Indeed, such interfaces can increase an individual’s independence leading to an improved quality of life and reduced healthcare costs.

The recently introduced ear-EEG methodology is a promising enabling technology for wearable EEG systems. Ear-EEG records EEG signals within the ear canal by embedding electrodes on a customised ear piece similar to ear-plugs used in hearing-aid applications. Ear-EEG is, compared to alternative technologies, a discreet, unobtrusive, robust and user-friendly technology.

Whereas most BCIs are based on visually evoked potentials, the objective of this project is to develop auditory BCIs based on the Ear-EEG platform. This is feasible because it has been shown that auditory evoked potentials (AEP) can be recorded with good signal quality from ear-EEG.

The aim of the project is to develop new auditory paradigms where the AEPs can be easily controlled by the user’s attention, and to develop advanced signal processing algorithms for detection of the AEP modulations.

ABOUT THE PROJECT


Project title:
Ear-EEG and Applications to Brain Computer Interface (BCI)

PhD student: Faisal Farooq

Main supervisor: Prof. (Docent) Preben Kidmose

Research section: Electrical and Computer Engineering