Electrical and Computer Engineering

Massive-scale IoT dynamic data updating and compression for a scalable IoT infrastructure

Niloofar Yazdani
Niloofar Yazdani

Future communication networks and storage systems will face tremendous challenges to answer the increasing data traffic. Additionally, upcoming services and applications may also impose very low delay constraints on the transferred data.

Thus, developing technologies that increase the throughput, reduce the delay and operate in a distributed fashion are economically viable, and store and process data close to the end devices is crucial to the design and deployment of future communication networks and cloud computing.

This PhD project will focus on the development of novel coding theory designs for a more efficient management, updating, consistency assurance and storage of Internet of Things data at a massive scale.

In particular, this project will focus on the integration of (network) coding techniques and data deduplication, two approaches for reducing storage costs that have typically been attacked separately. This work is expected to open a new field at the intersection of traditional coding theory and distributed Cloud technologies and systems. The underlying goal of the results and designs of this project is to develop new technologies for Cloud, Edge and Local content management, transmission and consistency assurance.

 

ABOUT THE PROJECT


Project title: 
Massive-scale IoT dynamic data updating and compression for a scalable IoT infrastructure

PhD student: Niloofar Yazdani

Contact: n.yazdani@eng.au.dk

Project period: Sep 2018 to Aug 2021

Main supervisor: Assoc. Prof. Daniel Rötter

Section: Electrical and Computer Engineering


Massive-scale storage compression for a scalable IoT infrastructure

Lars Nielsen
Lars Nielsen

The strict requirements for 5G and the increasing amount of data generated by both end-user and IoT devices present a set of challenges for communication networks and storage systems.

The currently employed infrastructure is unable to handle this load increase, especially with the massive increase of data. Furthermore, with the expected increase of active devices, the cost of transferring data through the current networks to the destination will be problematic. The economical cost of both maintaining and providing networks and storage systems with the ability to handle this will grow significantly. Therefore, it is critical to develop new technologies to handle the increased data load.

The goal of the project is to design technologies and architectures for future storage and communication systems which can handle the increased data loads, using compression to decrease the amount of data actually stored without loss of information.

 

ABOUT THE PROJECT


Project title:
Massive-scale storage compression for a scalable IoT infrastructure (MSCSII)

PhD student: Lars Nielsen

Contact: lani@eng.au.dk

Project period: Aug 2018 to July 2021

Main supervisor: Assoc. Prof. Daniel Rötter

Section: Electrical and Computer Engineering


Adaptive network coding to develop future networks and cloud technologies

Rasmus Vestergaard
Rasmus Vestergaard

Future communication networks and storage systems will need to be able to handle the increasing traffic generated by end users, the strict requirements for 5G communications, and billions of sensing and actuating devices connected to the Internet of Things. 

Current infrastructure is not adequate for these demands, especially since the amount of data will increase massively. With the expected number of devices, the cost of transferring the data all the way through current networks to reach clouds is prohibitive, and the economic cost of deploying a network capable of handling such amounts would be significant with the current technologies. On other factors, such as delay, the current technologies are entirely unable to satisfy the requirements. It is critical to develop new technologies that can increase the throughput in networks, reduce delays and operate in a distributed fashion while being economically viable. 

The goal of the project is to design communication and storage technologies for these future networks and clouds using coding theory constructions. This will be done by applying error-control codes in the network, and developing novel, adaptive codes that are suitable for flexible management of complexity and performance.

 

ABOUT THE PROJECT


Project title:
Adaptive network coding to develop future networks and cloud technologies

PhD student: Rasmus Vestergaard

Contact: rv@eng.au.dk

Project period: Aug 2018 to July 2021

Main supervisor: Assoc. Prof. Daniel Rötter

Co-supervisor: Assoc. Prof. Qi Zhang

Section: Electrical and Computer Engineering


Biomechanical characterisation of aortic root properties for optimising repair techniques in the presence of aneurysmal disease

The pathophysiologic mechanisms governing aortic aneurysm progression in humans are not fully understood. Currently, aneurysm size remains the best criteria for recommending surgery in large aortic aneurysms. This has clear shortcomings as aneurysm size is not an absolute predictor of aneurysm expansion and risk of rupture.

This study comprises of a biomechanical characterisation and histological mapping of collagen and elastin architecture of exercised human aortic root tissue around its circumference. Knowledge from this study will be applied to develop a conceptual design of a dynamic annuloplasty ring. The expectation is that this ring could mimic and support the native functional and biomechanical properties addressing normal physiological aortic root dynamics. The hope is that these findings will lead to a significantly improved treatment for patients undergoing repair of the aortic root.

ABOUT THE PROJECT


Project title:
Biomechanical characterisation of aortic root properties for optimising repair techniques in the presence of aneurysmal disease - a clinical experimental study

PhD student: Mariam Abdi Noor

Contact: noor@eng.au.dk

Project period: May 2018 to April 2021

Main supervisor: Assoc. Prof. Peter Johansen

Co-supervisor: J. Michael Hasenkam

Section: Electrical and Computer Engineering


Low-power radio-frequency low-noise amplifier for future wireless transceivers

Michele Spasaro

Next-generation (5G and beyond) wireless networks and communications, such as Internet of Things (IoT) and enhanced Mobile Broadband (eMBB), will lead to a trillion devices dealing with the ever-growing demand of data communications between objects and people.

Their realisation requires overcoming severe scientific and technical challenges. Battery-operated devices require extremely low power consumption; next-generation mobile phones require developing wireless transceivers operating at much higher frequencies with respect to current generation; to name a few. The roadmap towards 5G considers the official launch of 5G new-radio phase two in 2020, but such an ambitious goal is far from reached yet.

The aim of the PhD project is to anticipate and develop innovative design solutions for radio-frequency low-noise amplifiers as key enabling step towards the implementation of next-generation low-power wireless transceivers and beyond.

ABOUT THE PROJECT


Project title:
Low-power radio-frequency low-noise amplifier for future wireless transceivers

PhD student: Michele Spasaro

Contact: michele.spasaro@eng.au.dk

Project period: May 2018 to April 2021

Main supervisor: Prof. Domenico Zito

Section: Electrical and Computer Engineering


Machine Learning for optimisation of baggage handling and sorter systems for logistics

René Ahrendt Sørensen

Big data and Machine Learning is currently a very popular field of research. In collaboration with the company BEUMER Group, I will utilise their large amounts of data and their emulator environments to optimise their Baggage Handling System (BHS) in airports.

I will compare their routing algorithms with a self-taught Reinforcement Learning (RL) system not unlike the system that in resent years have been able to beat professional players in games such as chess, backgammon and go, and play at superhuman level in Atari games.

Besides finding the shortest path through the BHS, the RL system might find patterns which could prevent deadlocks and other unwanted events. Currently such events are manually avoided by software developers.

One of the challenges is to describe exactly what such a system should optimise towards. Is it shortest path, lowest delay, highest throughput, etc.

Another very important part of such a system is the transparency, i.e. how well can we explain why the system does what it does. To address this part, I intend to use methods from the field of Explainable Artificial Intelligence.

ABOUT THE PROJECT


Project title:
Machine learning for optimisation of baggage handling and sorter systems for logistics

PhD student: René Ahrendt Sørensen

Contact: ras@eng.au.dk

Project period: May 2018 to April 2021

Main supervisor: Prof. (Docent) Henrik Karstoft

Co-supervisors: Peter Gorm Larsen, Michael Nielsen, Morten Granum

Section: Electrical and Computer Engineering


Ultra-Low power IC design for an implantable ultrasonically-powered device for neural stimulation

Amin Rashidi

Today you can't underestimate the important role of Biomedical Implantable Microsystems (BIM) in modern therapies. For instance, BIMs like cochlear implants and pacemakers have already become successful mass produced products. Nevertheless, other BMIs like deep brain microstimulators for treating malfunctions such as Parkinson Disease (PD) are still facing serious challenges. In 2005, about 4.5 million people in the EU suffered from PD, and it is anticipated that this number will be doubled by 2030.

In the STARDUST project, we will work on design and implementation of a BIM for PD treatment by stimulating neurons in Globous Pallidus through optogentics. To this end, it is proposed to design and fabricate a sub-millimeter biocompatible device to make it as little invasive as possible to the brain. Wireless powering and communications of the device will be done through ultrasonic waves, which is one of the most challenging parts of the project.

Design, fabrication and testing of low-power low-area circuits for optical stimulation of neurologically manipulated neurons, and recording of neural activity associated with PD are also parts of this work.

ABOUT THE PROJECT


Project title:
Ultra-low power IC design for an implantable ultrasonically-powered device for neural stimulation

PhD student: Amin Rashidi

Contact: a.rashidi@eng.au.dk 

Project period: Dec 2017 to Nov 2020

Main supervisor: Assoc. Prof. Farshad Moradi

Research section: Electrical and Computer Engineering


Energy-efficient ultrasonic energy harvesting for biomedical implants for neural stimulation

Seyedsina Hosseini

As time goes by, Wearable, Portable, Implantable (WPI) technologies find their way into the market. Continuous health monitoring with WPI technologies has opened a very promising, although challenging, door to the engineers for developing smarter and smaller technologies, where low-power and low area are the main concerns, especially for the implantable ones.

In this project, “STARDUST”, the main goal is to provide a novel wireless bio-implantable chip enabling in-vivo monitoring, stimulation and local drug delivery specifically for Parkinson's Disease (PD). In Europe, for instance, 4.5 million people suffer from PD.

The project is multidisciplinary and combines the advantages of MicroElectroMechanical Systems (MEMS), local drug delivery, integrated electrodes and micro-scale light emitting diodes (µLEDs). The device consists of a piezoelectric energy harvester, which can be powered with ultrasonic waves.

ABOUT THE PROJECT


Project title: 
Energy-efficient ultrasonic energy harvesting for biomedical implants for neural stimulation

PhD student: Seyedsina Hosseini

Contact: hosseini@eng.au.dk 

Project period: Dec 2017 to Nov 2020

Main supervisor: Assoc. Prof. Farshad Moradi

Research section: Electrical and Computer Engineering


Ultra-low power IC design for ultrasonically-powered implants

Kjeld Laursen

This project is part of a larger project called STARDUST. The broad goal of STARDUST is to create a biomedical implant that can help to alleviate some of the symptoms of Parkinson’s disease. This is to be accomplished by the use of optogenetics, where neurons in a specific area deep in the brain is modified to be sensitive to light of a certain wavelength.

The light source will be an LED in the biomedical implant that will be implanted amongst the modified neurons in the brain. The implant will be powered wirelessly by harvesting the energy from ultrasonic waves transmitted from a transducer placed outside the body.

My task will be on designing an energy harvester integrated circuit (IC) for the implant, and managing the power available to the LED for stimulating neurons. The energy harvesting IC will be used to maximize the energy efficiency of the harvested power by the piezoelectric crystal. The piezoelectric crystal acts as an acoustic power-receiver driven by the ultrasound from an external transducer.

In later stages, I will work on designing chips to monitor the activities of the neurons along with a wireless datalink to transmit the data to an external device for further analysis.

ABOUT THE PROJECT


Project title:
Ultra-low power IC design for ultrasonically-powered implants

PhD student: Kjeld Laursen

Contact: laursen@eng.au.dk 

Project period: Oct 2017 to April 2021

Main supervisor: Assoc. Prof. Farshad Moradi

Research section: Electrical and Computer Engineering


AI-assisted inspection of clover grass fields based on deep learning for targeted fertilisation

This PhD project is supported by the Innovation Fund project SmartGrass and the GUDP project CloverSense. The common goal of the projects is to develop the use of precision agriculture for clover grass fields. Adapting the fertilisation strategies to the local conditions of each field leads to higher quality yields while reducing the impact on the environment.

The aim of the PhD project is to develop the method for analysing the local conditions of the clover grass fields. Through the use of machine learning, gathered images of the field are to be transformed into accurate measures, such as yield estimates and clover to grass ratios, without human interaction.

The main focus of the research lies within the field of vision-based Deep Learning - specifically within the topics of semi-supervised learning, semantic segmentation and instance segmentation. To exemplify, this can allow the computer to translate a simple image into a precise map of objects and plant species present in the image.

ABOUT THE PROJECT


Project title: 
AI-assisted inspection of clover grass fields based on deep learning for targeted fertilisation

PhD student: Søren Skovsen

Contact: ssk@eng.au.dk 

Project period: August 2017 to July 2020

Main supervisor: Professor (Docent) Henrik Karstoft

Co-supervisor: Senior Researcher Rasmus Nyholm Jørgensen

Research section: Electrical and Computer Engineering


Smart industrial products

Tomas Kulik

A rising trend in the industry is to build sensors and small computers into the products that otherwise would not have any computing capability. These products can then share data from the sensors among themselves or with a service center. This is what is called an industrial Internet of Things (IoT).

Connecting IoT with the cloud computing provides new opportunities for design and operation of these industrial products. Sharing data within this kind of system, however, poses challenges that are not present in a traditional industrial IT. One of the main challenges is how to ensure that this environment is secure from data theft and system misuse.

The project focuses primarily on addressing this challenge by creating a model of an existing industrial communication system security standard. This model can then be used to verify that the system meets the security requirements needed to withstand specific cyber attacks. This verification in turn acts as a proof to the industry that deployment of such a system does not compromise the security of the business.

ABOUT THE PROJECT


Project title:
Smart industrial products

PhD student: Tomas Kulik

Contact: tomaskulik@eng.au.dk 

Project period: August 2017 to July 2020

Main supervisor: Professor Peter Gorm Larsen

Research section: Electrical and Computer Engineering


In-field traffic intensity reduction as a means to increase yield quantity and quality

Andrés Villa-Henriksen

The overall objective of this project is to study the site-specific relations between yield quantity and quality, and in-field traffic intensity. This information will be used to reduce the negative effects on soil of traffic intensity by in-field optimised route planning.

The forthcoming implementation of IoT applied to farming will allow access to large amounts of data. This site-specific data can be used to relate yield data to the increasing problem of soil degradation induced by in-field trafficking. Therefore, this can now be achieved without the need of the traditional time-consuming data sampling throughout a field.

The main objectives of the project are:

  • Investigate the state of the art of farm machine inter-operability and its potential use in vehicle logistics, documentation and traffic intensity reduction with the goal of increasing crop yields.
  • Research the relation between traffic intensity and yield quantity and quality by the use of site-specific data.
  • Evaluate how route planning and vehicle logistics can be used to reduce in-field traffic intensity, and consequently avoid yield decrease due to soil compaction problems.

ABOUT THE PROJECT


Project title: 
In-field traffic intensity reduction as a means to increase yield quantity and quality

PhD student: Andrés Villa-Henriksen

Contact: avh@eng.au.dk 

Project period: August 2017 to July 2020

Main supervisor: Senior Researcher Claus Grøn Sørensen

Co-supervisor: Gareth Edwards and Ole Green, AgroIntelli

Research section: Electrical and Computer Engineering


Towards autonomy in farming operations: Logistics optimisation

Rene Søndergaard Nilsson

This project aims to improve the efficiency of farming operations through logistics optimisation as a next step towards autonomous systems.

Logistics optimisation in an agricultural setting involves generation and optimisation of:

  • Path plans for all involved vehicles both in-field and out of fields.
  • Inter-vehicle interaction plans, such as loading/unloading of goods (seed, fertilizer, yield, bales, etc.)
  • Resource utilisation plans. For example minimising waiting time at conditioning/drying/storage facilities.

This project uses a model-based approach such that all farming operations can be simulated and the developed algorithms can be verified in an ‘off-line’ environment.

The developed algorithms will be deployed in a real-time system, providing input to auto-steering systems or guidance to drivers similar to a GPS in cars. Additionally, it will be able to cope with deviations and system failures, such as a vehicle breakdown, by re-planning and re-optimising for the remaining vehicles and resources involved in the operation.

ABOUT THE PROJECT


Project title:
Towards autonomy in farming operations: Logistics optimisation

PhD student: Rene Søndergaard Nilsson

Contact: rn@eng.au.dk

Project period: July 2017 to June 2020

Main supervisor: Prof. Peter Gorm Larsen

Co-supervisor: Senior Researcher Claus Grøn Sørensen

Research section: Electrical and Computer Engineering


Reinforced Weed Classification utilising utilising Context Data and Deep Generative Models

Simon Leminen Madsen

This project is part of the Innovation Fund Denmark project RoboWeedMaPS. The overall goal of RoboWeedMaPS is to substantially reduce the amount of herbicides in modern crop farming, which will benefit society, the environment and the farmer. To achieve this, a more efficient and precise deployment of herbicides is needed. The project will incorporate automated vision systems to assess the optimum weed treatment and thereby eliminate the need for intermediate manual decision-making and data processing.

This project seeks to apply machine learning, specifically deep learning, to automatically classify weed species and their current development stage from images. To improve the certainty of the classification, it should incorporate context relevant data such as site-specific cropping history, past weed registrations, etc. The project will also explore the potential of generating photo realistic image samples of weeds using deep generative models. These artificial samples are expected to be used for creating a more robust weed classification model. Generative models can potentially also be used to improve the quality of unfocused and blurred images.

ABOUT THE PROJECT


Project title:
Reinforced Weed Classification utilising utilising Context Data and Deep Generative Models

PhD student: Simon Leminen Madsen

Contact: slm@eng.au.dk

Project period: Feb 2017 to Jan 2020

Main supervisor: Prof. (Docent) Henrik Karstoft

Co-supervisor: Senior Researcher Rasmus Nyholm Jørgensen 

Research section: Electrical and Computer Engineering


Compact integrated terahertz sources

Pengli An

Science and technologies based on terahertz (THz) frequency electromagnetic radiation (100 GHz-30 THz) have developed rapidly over the last 30 years, to the extent that these now touch many areas from fundamental science to ‘real world’ applications. THz spectroscopy and imaging are currently active and rapidly expanding fields of research with applications in biological and medical fields, for example cancer tumor imaging and defence and security fields like the airport security scanners to detect explosives and illegal drugs.

However, many applications require hand-held or mobile THz devices but these are not commercially available yet. The most important challenge for THz technology is the development of compact and efficient THz sources providing continuous wave output power.

The aim of this project is to develop compact THz emitters. THz radiation can be generated by mixing photonic signals, but these optical components are bulky. Using photonic integration, we can put all these components on a single chip. This provides an efficient and reliable solution for THz sources in terms of cost and size. Therefore, we will develop a photonic integrated circuit (PIC) for THz signal generation in the optical domain. Our PIC will then be coupled to another chip containing THz antennas to convert the optical signal to a narrow beam of THz radiation.

ABOUT THE PROJECT


Project title:
Compact integrated terahertz sources

PhD student: Pengli An

Contact: pengli_an@eng.au.dk

Project period: Jan 2017 to Dec 2019

Main supervisor: Assoc. Prof. Martijn Heck

Research section: Electrical and Computer Engineering


Enabling ultra-reliable low latency communications in 5G

Jianhui Liu

The fifth generation (5G) systems are expected to underpin the Tactile Internet connectivity. The typical use cases of Tactile Internet include industrial automation, remote surgery and autonomous vehicle and so on. To enable these use cases, ultra-reliable and low latency communication (URLLC) is essential to promote the experiences of latency, reliability and availability into an unprecedented new stage.

The URLLC requires reliability of 99.999 percent with latency in the range of 1-10 ms depending on the use cases. It presents enormous challenges at multiple OSI layers and requires disruptive design approaches. One of the prospective solutions is the combination between Fog computing and Radio Access Networks (Fog-RAN). Fog computing extends the computing and storage capacity from distant cloud to the edge of networks which therefore can shorten the end-to-end latency significantly and is beneficial for latency-critical applications. Furthermore, Fog-RAN will provide a flexible network architecture for service-optimised network design and deployment. The aim of this project is to design novel protocols and algorithms to enable URLLC from the aspect of radio access and networking layer.

ABOUT THE PROJECT


Project title:
Enabling ultra-reliable low latency communications in 5G

PhD student: Jianhui Liu

Contact: jianhui.liu@eng.au.dk

Project period: Jan 2017 to Dec 2019

Main supervisor: Assoc. Prof. Qi Zhang

Research section: Electrical and Computer Engineering


Microwave Photonic Oscillators Integrated on an Optical Chip

Peter Tønning

”Timing is everything” is a saying usually reserved for delivering punch-lines. In the field of communication technology, the sentence is literally true and ultra-precise timing is key in advancing the present network society. The timekeepers of technology, oscillators, provide the clock frequency for computing systems, enable carriers for information in wireless communication and provide timing in GPS and radar systems. Oscillators can be realised in a number of different ways, electronic based, crystal based or, more recently, photonic based. 

The optoelectronic oscillator, realised for the first time 20 years ago, offers performance in the GHz-regime way beyond what can be realised by other oscillator types. An optoelectronic oscillator utilises a mix of electronic and photonic components as the name suggests to combine the existing electron-powered technological platform with the superior bandwidth and low-loss capabilities of photons in waveguides/fibers. So far, these systems have been reserved mainly for research purposes due to considerable size and cost as well as lack of robustness. Recent advance in photonic integrated circuits means that the realisation of an optoelectronic oscillator on a chip is within reach.

The goal of this project is to create an optoelectronic oscillator on a chip with a frequency in the GHz regime and phase noise performance better than the crystal based alternative. An oscillator at this frequency would allow for advances in e.g. Doppler radar technology and future high-speed wireless communication.

This research is done in close collaboration with PhD student Lars Nielsen so for another take on this project, see Lars Nielsen's project description.

ABOUT THE PROJECT


Project title:
Microwave Photonic Oscillators Integrated on an Optical Chip

PhD student: Peter Tønning

Contact: toenning@eng.au.dk

Project period: Nov 2016 to Oct 2019

Main supervisor: Assoc. Prof. Martijn Heck

Research section: Electrical and Computer Engineering


Silicon photonics for future spintronic-photonic memory

Hanna Becker

The use of information technology is exponentially growing and has become so frequent that information processing, transmitting and storing accounted for estimated 5 percent of world electricity production in 2012. As this growth is expected to continue, we need to find a way to decrease energy consumption.

The goal of this project is the development of an innovative optical on-chip network. This effort is part of a collaborative project which addresses the need for an energy-efficient data storage device. This network will switch and direct light pulses to spintronic memory elements as well as illuminate them to ‘write’ data. The energy consumption of this device will be reduced by two orders of magnitude compared to present-day memory technology.

Integrated photonics offers a promising platform for new energy-efficient on-chip technologies with the number of photonic elements on optical chips increasing at a rate comparable to Moore’s law. Together with the recent discovery of magnetization reversal by short optical pulses, an optically switchable spintronic memory element becomes feasible. This enables the unique integration of photonics and spintronic memory elements. The project is conducted in collaboration with IMEC/Ghent University (BE), Radboud University (NL), SpinTEC (FR) and QuantumWise (DK).

ABOUT THE PROJECT


Project title:
Silicon photonics for future spintronic-photonic memory

PhD student: Hanna Becker

Contact: hanna.becker@eng.au.dk

Project period: Oct 2016 to Sept 2019

Main supervisor: Assoc. Prof. Martijn Heck

Research section: Electrical and Computer Engineering


Microwave Photonic Oscillators Integrated on an Optical Chip

Lars Nielsen

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

PhD student: Lars Nielsen

Contact: ln@eng.au.dk

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

Casper Thule Hansen

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

PhD student: Casper Thule Hansen

Contact: casper.thule@eng.au.dk

Project period: Feb. 2016 to Jan. 2019

Main supervisor: Prof. Peter Gorm Larsen

Research section: Electrical and Computer Engineering


Intraocular pressure measurement for the treatment of primary open angle glaucoma using self-powered System-On-contact-Lens (SOL)

Katrine Lundager

Primary open-angle glaucoma (POAG) is one of the leading causes of visual impairment and blindness, which affected more than 57.5 million people globally in 2015 especially the middle-aged and older people.

Increased intraocular pressure (IOP) is known to be a risk factor for the development and progression of the optic nerve degeneration and visual field loss characterising the disease. Therefore, patients at risk are subjected to regular examinations with an ophthalmologist to see if the changes in the optic nerve and the visual field are progressing. However, it is known that the intraocular pressure shows diurnal variations, and especially IOP peaks during night are suspected to constitute a particular risk factor for progression of the disease. Therefore, it might improve the treatment considerably if the IOP is continuously measured.

The purpose of this project is therefore to design an eye contact lens for continuous monitoring of the intraocular pressure, which envisions integratability with a drug-delivery contact lens in the future. The proposed system includes sensors, electronics (processing and communication) that is powered by solar cells, a Radio-Frequency Energy harvester and a piezoelectric harvester for sleeping state. By the use of this technique, primary open angle glaucoma can be monitored more closely and treated more accurately.

ABOUT THE PROJECT


Project title: 
Intraocular pressure measurement for the treatment of primary open angle glaucoma using self-powered System-On-contact-Lens (SOL)

PhD student: Katrine Lundager

Contact: klundager@eng.au.dk

Project period: Feb. 2016 to July 2019

Main supervisor: Assoc. Prof. Farshad Moradi

Co-supervisor: Michael Heimlich

Research section: Electrical and Computer Engineering


Compressed Sensing for Machine-type communication in 5G

Mehmood Alam

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

PhD student: Mehmood Alam

Contact: mehmood.alam@eng.au.dk

Project period: Jan. 2016 to Dec. 2018

Main supervisor: Assoc. Prof. Qi Zhang

Research section: Electrical and Computer Engineering


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

Xiaohui Ma

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

PhD student: Xiaohui Ma

Contact: xma@eng.au.dk

Project period: Nov. 2015 to Oct. 2018

Main supervisors: Prof. (Docent) Preben Kidmose, Jan A. Pedersen (Dynaudio)

Co-supervisors: Assistant Prof. Jakob Juul Larsen, Patrick J. Hegarty (Dynaudio)

Research section: Electrical and Computer Engineering


Open Platform for Big Data Analytics and Information Management

Jacob Høxbroe Jeppesen

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

PhD student: Jacob Høxbroe Jeppesen

Contact: jhj@eng.au.dk

Project period: Nov. 2015 to Oct. 2018

Main supervisor: Assoc. Prof. Rune Hylsberg Jacobsen

Co-supervisor: Prof. Thomas S. Toftegaard

Research section: Electrical and Computer Engineering


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

Camilla Arndal Rotvel

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

PhD student: Camilla Arndal Rotvel

Contact: caro@eng.au.dk

Project period: Sept 2013 to Aug 2018

Main supervisor: Prof. (Docent) Preben Kidmose

Co-supervisors: Stine Møller, DuPont Nutrition BioSciences, Ole Næsby Larsen, University of Southern Denmark and Troels W. Kjær, Roskilde Sygehus

Research section: Electrical and Computer Engineering