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Electrical and Computer Engineering

Audiometric Characterization based on Ear-EEG

Anna Sergeeva
Anna Sergeeva

People suffering from hearing loss can benefit from the use of hearing aids. To work properly, it is crucial that the hearing aids are fitted in close accordance with the hearing abilities of the individual hearing aid’s user. In some cases, hearing can deteriorate relatively quickly, especially with increasing age. Thus, it is important to re-fit the hearing aid recurrently.

Traditionally, hearing aid fitting is carried out in the clinic, where different behavioral tests are used to determine hearing thresholds. Alternatively, hearing loss can be characterized based on electrophysiological measures. This is typically based on the auditory steady-state responses (ASSR) recorded from a few electroencephalography (EEG) channels placed on the scalp.

Ear-EEG is a novel EEG recording method in which EEG signals are recorded from electrodes located on an earpiece placed in the ear. Ear-EEG can potentially enable integration of EEG recording into hearing aids and performing of ASSR based hearing threshold estimation in daily life.

Traditionally, ASSR based hearing threshold estimation has been performed using amplitude modulated continuous signals. Hearing tests based on monotonous stimuli of this kind make the user tired and unmotivated and can be inconvenient for the user, especially when the test must carry out for a long period of time.

Natural sounds such as speech is much more pleasant to listen to, thus speech-based hearing tests are more appealing to say yes to and can easier be implemented in daily life. Speech-based hearing test can be performed while the user, for instance, listen to the audio book.

The aim of this Ph.D. project is to investigate the possibilities of using the natural sounds, and in particular speech signals, to estimate hearing thresholds based on the ear-EEG.

ABOUT THE PROJECT

Project title: Audiometric Characterization based on Ear-EEG 

PhD student: Anna Sergeeva 

Contact: ans@eng.au.dk

Project period: August 2020 to July 2023

Main supervisor: Preben Kidmose

Co-supervisor: Christian Bech Christensen 

Section: Electrical and Computer Engineering


Joint processing of ear-EEG and ear-fitted body-coupled microphone signals

Bjarke Lundgaard Gårdbæk
Bjarke Lundgaard Gårdbæk

Physicians are typically concerned with sounds originating from the body, which are traditionally listened to through an acoustic stethoscope. However, interpretation of sounds through such instruments is subject to the training of the individual physician. To overcome this challenge and further advance the method, an electronic version of the stethoscope has been invented, allowing for more objective interpretation of such sounds.

Ear-EEG is a technique specifically designed to monitor brain activity during everyday activities. Through an ear-fitted device, physiological signals reflecting the subject’s brain activity are measured.

This project aims to extend the ear-EEG platform with a body-coupled microphone, i.e. an electronic stethoscope in the ear, enabling recording in real-life environments with a discrete and unobtrusive wearable device. Furthermore, we seek to explore methods for joint processing of ear-EEG signals and sounds picked up from the body-coupled microphone . This would allow for a broader understanding of the current state of the body and is highly applicable in both research and in medical devices for health monitoring.

This project is carried out at the newly founded Center for Ear-EEG

ABOUT THE PROJECT

Project title: Joint processing of ear-EEG and ear-fitted body-coupled microphone signals 

PhD student: Bjarke Lundgaard Gårdbæk 

Contact: blg@eng.au.dk

Project period: August 2020 to July 2025

Main supervisor: Preben Kidmose 

Section: Electrical and Computer Engineering


Narrow-linewidth diode lasers

Mónica Far Brusatori

Lasers are fundamental for modern telecommunication thanks to the reduced spectral distribution of their optical emission as compared to other light sources. This laser ’linewidth’ relates to the statistical notion of 'coherence' and it is inversely proportional to the distance (or time) over which the electromagnetic wave remains with a deterministic phase. However, this wonderful and unique property of lasers can be greatly influenced by the environmental conditions. In addition, advanced modulation formats employed in coherent communication require lasers with increasingly smaller linewidths, power consumption and footprint.

The goal of this project is to explore both theoretically and experimentally a new type of diode laser with narrow and stable linewidth. It will be designed as a tunable laser with high output power, operating in the telecom C-band. Beside telecommunication, possible applications that will benefit from this work include high-resolution spectroscopy, on-chip nonlinear optics and LIDAR. This laser design intends to reduce significantly the size, the weight, the cost and the complexity of its packaging without affecting its performance. It will be fabricated with mature material systems, thus giving this technology a feasible and credible path towards deployment out of the research laboratory.

ABOUT THE PROJECT

Project title: Narrow-linewidth diode lasers 

PhD student: Mónica Far Brusatori

Contact: mfar@eng.au.dk

Project period: Mar 2020 to Feb 2024 

Main supervisor: Martijn Heck 

Co-supervisor(s): Nicolas Volet  

Section: Electrical and Computer Engineering


Using machine learning for modelling and simulating complex systems

Our society has become increasingly reliant on technical innovation to improve our quality of living. We expect our power grid to reliably supply electricity to our homes and production facilities and we expect our cars to take us safely to work every morning.

These are examples of Cyber-Physical Systems (CPSs), that is systems characterised by a strong coupling between the physical process and the software which controls it.

These types of systems are often safety critical as well, for example the failure of the power grid may cause disruption of other critical infrastructure, whereas the failure of a car may cause it to crash.

To keep up with the rising complexity of CPSs, computer assisted design (CAD) software is commonly used to simulate the individual parts of the system. The next step in this evolution is collaborative simulation (co-sim), where all components of a system are simulated at once. Rather than verifying only the individual components, this methodology makes it possible verify that whole system works as intended.

The central challenge in adopting a simulation-based approach to developing systems is the difficulty of creating the models. Today, this is very labour intensive and requires highly specialized expertise and software. This goal of this project is to develop machine learning based techniques for modelling CPSs, that provides accurate models without requiring knowledge of the internal workings of the system.

 

ABOUT THE PROJECT

Project title: Using machine learning for modelling and simulating complex systems

PhD student: Christian Møldrup Legaard

Contact: cml@eng.au.dk

Project period: Feb 2020 to July 2023

Main supervisor: Peter Gorm Larsen

Co-supervisor(s): Alexandros Iosifidis  

Section: Electrical and Computer Engineering


Open Deep Learning Toolkit for Robotics (OpenDR)

 

In recent years, the utilisation of deep learning has led to remarkable advances in computer vision and robotics applications such as self-driving cars. Despite this success, deep learning remains to become a prime technology for robotics. This is partly due to the prohibitive computational cost of current state of the art deep neural networks, and partly due to the lack of tailored development tools in the field. Open Deep Learning toolkit for Robotics (OpenDR) is an EU funded project aimed at the development of an open toolkit for robotics functionalities. Its focus lies on improving and making available the core AI and Cognition technologies needed in the years to come. Being a multinational project, the development effort is shared among many collaborators in both academia and the industry. My research will focus on real-time and lightweight deep learning architectures for solving computer vision tasks such as human action recognitions and object detection and tracking in recourse-constrained settings.

 

ABOUT THE PROJECT


Project title:
Open Deep Learning Toolkit for Robotics (OpenDR)

PhD student: Lukas Hedegaard Jensen

Contact: lh@eng.au.dk

Project period: Feb 2020 to Jan 2023

Main supervisor: Alexandros Iosifidis

Section: Electrical and Computer Engineering


Open Deep Learning Toolkit for Robotics (OpenDR)

Deep Learning received a lot of attention in last years making lots of tasks be able to solve. However, it requires a lot of computational power to run most Deep Learning methods, making them badly suitable for low-resources systems like robotics. The main goal of my research is to create Deep Learning methods that can achieve both good performance and low computational costs for visual tasks.

ABOUT THE PROJECT

Project title: Open Deep Learning Toolkit for Robotics (OpenDR) 

PhD student: Illia Oleksiienko 

Contact: io@eng.au.dk

Project period: Feb 2020 to Jan 2023

Main supervisor: Alexandros Iosifidis 

Section: Electrical and Computer Engineering


Verifiable cryptographic software

Benjamin Salling Hvass
Benjamin Salling Hvass

Zero-knowledge proofs are integral for deploying privacy-preserving cryptocurrencies and other blockchain applications as they represent a fundamental building block for proving statements about confidential data. The most popular framework for such proofs is based on cryptographic pairings defined over elliptic curves, where pairing-based zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) underlie private transactions.

The main aim of my project is to investigate techniques to develop a formally verified efficient software library for pairing-based cryptography as means to support current blockchain projects relying on zero-knowledge proofs.

A verified implementation facilitates trust to the blockchain and increases the robustness of the system and decreases required maintenance.

My project is a part of DIGIT and the Concordium Blockchain Research Center.

 

ABOUT THE PROJECT


Project title:
Verifiable cryptographic software

PhD student: Benjamin Salling Hvass

Contact: bsh@eng.au.dk

Project period: Aug 2019 to Aug 2022

Main supervisor: Assistant prof. Diego F. Aranha and Assoc. Prof. Bas Spitters

Section: Electrical and Computer Engineering


Deep learning spatio-temporal image segmentation in precision farming

Sadaf Farkhani
Sadaf Farkhani

Precision agriculture involves integration of new technologies such as satellite-based images to reduce soil and crop nursing. There are some important indices in agriculture, which are helpful in field caring, such as biomass, leaf area index, protein index, etc. To predict each of these indices, satellite images, multi-spectral images and radar need to be segmented separately. Then, the information of the multi-sensors is fused consequent to a map of the land areas. Deep learning algorithms show promising results in the classification and segmentation domain, and many papers have been working on improving network architectures. However, segmenting radar and satellite data demands a pixel-wise label as well. Hence, RGB ground-based high resolution images taken sparsely are going to be used in this project.

Through this estimation, three challenges will be met. Firstly, sparse ground-based images need to be globalised as the resolution of satellite-based images is much higher than plants' dimension. Secondly, multi-spectral images are going to be segmented based on labels generated in the first step. Finally, segmented results of radar and multi-spectral images will be fused together.

 

ABOUT THE PROJECT


Project title:
Deep learning spatio-temporal image segmentation in precision farming

PhD student: Sadaf Farkhani

Contact: farkhanis@eng.au.dk

Project period: May 2019 to Dec 2021

Main supervisor: Prof. (Docent) Henrik Karstoft

Co-supervisor: Rasmus Nyholm Jørgensen

Section: Electrical and Computer Engineering


Machine learning for pose estimation of soft biological items

Jens Overby
Jens Overby

This PhD project will research methods in Machine Learning for Pose Estimation of multiple, partially occluded, soft biological items in camera images. The application of such poses is in new robotic solutions for the fish and poultry processing industries, where currently, items are transported between processing equipment by static conveyor systems.

In the future, these logistic systems will be dynamic by use of mobile robots at the same high level of automation as in the manufacturing industry today. The handling, alignment and process-infeed of soft items picked from bins and conveyors will be done by robots.

Identifying spatial properties of soft items, however, is challenging from a research point of view. Also, an algorithm for this purpose needs to be flexible and easily configurable for new tasks. Therefore, extended research is needed in the field of unsupervised and semi-supervised machine learning.

 

ABOUT THE PROJECT


Project title:
Machine learning for pose estimation of soft biological items

PhD student: Jens Overby

Contact: jens.overby@eng.au.dk

Project period: May 2019 to April 2022

Main supervisor: Prof. (Docent) Henrik Karstoft

Co-supervisor: Rasmus Skovgaard Andersen, Marel A/S

Section: Electrical and Computer Engineering


Secure data compression and analytics for Internet of Things

Gajraj Kuldeep
Gajraj Kuldeep

The IoT ecosystem enables massive sensor data acquisition, transmission and storage, as well as computation to perform data analytics for diverse smart monitoring, process automation and control services. The ever-increasing data generation brings many critical challenges to the communication, storage and computing infrastructure. The current infrastructure is not adequate to tackle these challenges in the long run. Firstly, the vast amount of generated data has to be represented more efficiently. Secondly, light-weight communication and storage secrecy is needed.

The goal of the project is to design novel schemes which provide both compression and security by using the advanced signal processing techniques. The proposed schemes will be implemented in IoT devices and a prototype will be delivered. The performance of the proposed scheme will be tested and evaluated through standard randomness tests, and energy constraints and computational requirements will be considered.

 

ABOUT THE PROJECT


Project title:
Secure data compression and analytics for Internet of Things

PhD student: Gajraj Kuldeep

Contact: gkuldeep@eng.au.dk

Project period: April 2019 to March 2022

Main supervisor: Assoc. Prof. Qi Zhang

Section: Electrical and Computer Engineering


Low-power hardware implementation of Spiking Neural Networks (SNNs) for implantable neuromorphic chips

Margherita Ronchini
Margherita Ronchini

Epilepsy is a particularly challenging neurological disorder caused by relentless brain damage and aberrant rearrangements of brain wiring. In the light of the dynamic nature of the disease, the medications may stop working and neuromodulation may not completely suppress seizures. Above all, current treatments are symptomatic, while healing the epileptic brain remains unsolved so far.

This project is born from the desire to establish a new paradigm. The goal is to drive self-repair of dysfunctional brain circuits via by the symbiotic integration of bioengineered brain tissue, neuromorphic microeletronics and artificial intelligence.

Our team is taking care of the design, implementation and testing of a low power chip for brain signal recording and processing. The chip’s task is to control the way that the graft tissue develops and interacts with the host. The chip will be neuromorphic, meaning that it will mimic the behaviour of biological neural networks and resemble their structure. The neurons will be implemented through CMOS connected by memristors-based synapses.

 

ABOUT THE PROJECT


Project title:
Low-power hardware implementation of Spiking Neural Networks (SNNs) for implantable neuromorphic chips

PhD student: Margherita Ronchini

Contact:  m.ronchini@eng.au.dk

Project period: April 2019 to March 2022

Main supervisor: Assoc. Prof. Farshad Moradi

Co-supervisor: Prof. (Docent) Preben Kidmose

Section: Electrical and Computer Engineering


Optimal strategies for efficient autonomous drone collaboration

The operation, surveillance and maintenance of high voltage transmission lines in power systems have been a manual and costly process for decades. The process incurs not only operational expenditures but also human capital risks. With the adventure of the Internet of Things (IoT) and drone technology, this surveillance operation can be automated through the collaboration of autonomous systems. This PhD project aims to research the network structure and its protocol architecture of autonomous drones (swarm). Collaboration strategies for drones will be implemented that permit safe and efficient surveillance operation of transmission lines in the power grid.

Analytical and numerical simulations to validate proposed solutions will be considered. Promising solutions obtained from the analytical framework are expected to be prototyped and tested in a laboratory environment. There will be access (drones4energy.dk) to drone equipment and a drone test facility for piloting and field trials.

 

ABOUT THE PROJECT


Project title:
Optimal strategies for efficient autonomous drone collaboration

PhD student: Liping Shi

Contact: liping@eng.au.dk

Project period: March 2019 to Feb 2022

Main supervisor: Assoc. Prof. Rune Hylsberg Jacobsen

Section: Electrical and Computer Engineering


Design of self-powered embedded wireless corrosion instruments

Jaamac Hassan Hire
Jaamac Hassan Hire

This project aims at exploring, researching and developing a novel embeddable, long life-time, ultra-low power, high resolution sensing principle for structural health monitoring (SHM), mainly in reinforced concrete (RC). The objective of the sensor is to output the corrosion rate wirelessly to a remote device, e.g. a computer, where the data is available for the owners of the structure. Corrosion damages reduce the service life of structures and can create serious safety hazards leading to fatal consequences. Today, in corrosion measurements, only very indicative and error-prone sensors exist which are based on technology developed half a century ago with slow processing, hig power usage and large formfactor. They are therefore not an attractive commodity for owners to implement. Thus, this project will challenge this status quo.

In accordance with recent years’ increased development of Wireless Sensor Networks (WSN) and Internet of Things (Iot), utilising such platforms will have tremendous benefits, such as significant lower installation cost and direct real-time access to data. This makes it possible to centralise, process and analyse a large amount of data collected from remotely placed structures, for reliable data interpretation that complies with international standards. This can then be used to launch in-time repair and maintenance operations. By further utilising energy harvesting (EH) from omnipresent sources, as the corrosion process itself, power requirements for the sensor node will be met.

 

ABOUT THE PROJECT


Project title:
Design of self-powered embedded wireless corrosion instruments

PhD student: Jaamac Hassan Hire

Contact: jhh@eng.au.dk 

Project period: Feb 2019 to Jan 2022

Main supervisor: Assoc. Prof. Farshad Moradi

Co-supervisor: Morten Wagner, FORCE Technology

Section: Electrical and Computer Engineering


Meteorological forecasting using deep learning on satellite images

Andreas Holm Nielsen
Andreas Holm Nielsen.

Weather forecasting is an extremely important part of modern society and plays a vital role in many real-world applications. Modern weather forecasting relies on a combination of complex numerical computer models, observations of the atmosphere and pattern-recognition by meteorologists with an exceptional knowledge of the physics underlying atmospheric processes.  The numerical weather prediction (NWP) models use supercomputers to create extremely large simulations of the atmosphere and its evolution in the future based on our best understanding of physics and fluid dynamics. Due to an incomplete theoretical knowledge of these processes and the inherent chaotic nature of the atmosphere, learning latent representations of the atmospheric processes using unsupervised learning can potentially extend our understanding beyond physics and climatology to improve upon the forecast accuracy of the NWP models.

This project is written in collaboration with Danske Commodities A/S and will focus on weather forecasting using deep learning approaches on time-series satellite images, applying state-of-the-art architectures within Generative Adversarial Models and/or convolutional- and recurrent neural networks to generate future sequences of satellite images. From these images, several important meteorological variables can be extracted as features which can be combined with other types of atmospheric measurements to perform supervised learning tasks in relation to weather forecasting.

 

ABOUT THE PROJECT


Project title: 
Meteorological forecasting using deep learning on satellite images

PhD student: Andreas Holm Nielsen

Contact: ahn@eng.au.dk

Project period: Feb 2019 to Feb 2022

Main supervisor: Prof. (Docent) Henrik Karstoft

Co-supervisor: Assoc. Prof. Alexandros Iosifidis

Section: Electrical and Computer Engineering


Efficient Deep Learning approaches for Unmanned Aerial Vehicles

Negar Heidari
Negar Heidari

Recent advances in Machine Learning have enabled us to target and successfully solve many challenging problems, most notably problems related to computer vision applications including image/scene recognition, object detection and recognition and human action localisation and recognition.

However, the current state of the art solutions based on deep neural networks require heavy computations, high memory footprint and long training processes. These requirements are restrictive in many real-life application scenarios like when they are applied in Unmanned Aerial Vehicles – UAVs (e.g. drones).

This project will research new techniques and methodologies for reducing the computational cost of deep neural network architectures (Convolutional Neural Networks and Recurrent Neural Networks). We will focus on the proposal of novel techniques for creating compact network topologies that can achieve the same (or better) performance with the state of the art. Development of the proposed approaches in UAVs and testing in real application scenarios will also lead to exciting research directions for improving existing technology.

 

ABOUT THE PROJECT


Project title:
Efficient Deep Learning approaches for Unmanned Aerial Vehicles

PhD student: Negar Heidari

Contact: negar.heidari@eng.au.dk

Project period: Dec 2018 to Nov 2021

Main supervisor: Assoc. Prof. Alexandros Iosifidis

Co-supervisor: Prof. Peter Gorm Larsen

Section: Electrical and Computer Engineering


An AI-based system for health and safety constraint checking in large public buildings

Beidi Li
Beidi Li

The wellbeing of occupants in the built environment has become a rising issue in today’s society. Primary user concerns in large-scale public buildings include privacy, accessibility, functionality, security, health and safety, etc.

However, modelling dynamic, multifaceted human perception and locomotion often requires complex, specific tools that lack automation and verifiability.

Therefore, my PhD is aiming at formally representing and reasoning about occupants’ experiences and behavior in a transparent, traceable, and scalable way while providing interoperability with existing approaches and standards such as Building Information Modelling (BIM) and Industry Foundation Classes (IFC).

Specifically, I propose to extend the declarative logic programming framework Answer Set Programming (ASP) with spatio-temporal ontologies to natively support in-depth architectural analysis. Using spatial artefacts – semantically meaningful spaces carrying information about human perception, cognition, and behavior - I propose an evidence-based modelling of human-centric concepts such as vision, sound, wayfinding, egress, crowds, etc. Moreover, I intend to incorporate a series of specialized optimization techniques directly within the ASP reasoning engine to provide a robust, reliable, and portable framework for checking spatial consistencies.

I am currently investigating the automatic compliance checking of various descriptive building and construction codes including the Danish Building Regulations (BR18), the New Zealand Building Code, and the German construction safety code on Fall Protection B100 (Absturzsicherungen auf Baustellen).
My PhD project is funded by the Independent Danish Research Fund (DFF) and supervised by Asst. Prof. Carl Peter Leslie Schultz and Prof. Peter Gorm Larsen.
I am currently doing a research exchange within the Chair of Computational Modeling and Simulation at Technical University of Munich, led by Prof. Dr.-Ing. André Borrmann.

 

 

ABOUT THE PROJECT


Project title: 
An AI-based system for health and safety constraint checking in large public buildings 

PhD student: Beidi Li

Contact: beidi.li@eng.au.dk

Project period: Dec 2018 to Nov 2021

Main supervisor: Prof. Peter Gorm Larsen

Co-supervisor: Assistant Prof. Carl Peter Leslie Schultz

Section: Electrical and Computer Engineering


Vision-based anomaly detection in unmanned aerial vehicle and ground robots

Ilker Bozcan
Ilker Bozcan

Anomaly detection is the classification of objects and events that are labeled as suspicious. Autonomous surveillance systems should be aware of what entities are anomaly in the environment. During this project, we will develop an autonomous surveillance system using cost-effective visual sensors and deep neural networks that are state-of-the-art object detection algorithms.

The system is suited for anomaly detection for both types of data: aerial and ground. Unmanned Aerial/Ground Vehicles (UAV/UGV) are possible robotic platforms to operate anomaly detection systems. As a use case, UGVs will be used for plant classification where weeds are considered as anomaly. In another use case, UAVs will be used for flying object detection where other drones are marked as anomaly.

 

ABOUT THE PROJECT


Project title:
Vision-based anomaly detection in unmanned aerial vehicle and ground robots

PhD student: Ilker Bozcan

Contact: ilker@eng.au.dk

Project period: Oct 2018 to Sep 2021

Main supervisor: Assoc. Prof. Erdal Kayacan

Section: 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


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 March 2021

Main supervisor: Senior Researcher Claus Grøn Sørensen

Co-supervisor: Gareth Edwards and Ole Green, AgroIntelli

Research section: Electrical and Computer Engineering