Modern Optimization and Machine Learning in Acoustics, EM, Radar, and Sonar
This virtual workshop is organized in cooperation between Lund University, Saab, and IEEE Sweden (SP, MTT/AP). The aim is to provide the participants with a venue to discuss and learn about theory and applications of Modern Optimization and Machine Learning.
The focus is on practical problems and the choice of methods.
When: 19 May 2021, 09.00 - 17.00
Where: Online at the zoom platform
Keynote: IEEE DL Professor Pier Luigi Dragotti, Imperial College, London
Topics of Interest
- Inverse Problems, Optimization, Compressive Sensing, and Machine Learning
- Applications in Acoustics, Electromagnetics, Radar, Sonar, and Wave Propagation
IEEE Distinguished Lecturer Professor Pier Luigi Dragotti from Imperial College, London will present the keynote.
Presentations will be held by researchers from academia and the industry.
More information and an updated agenda can be found at https://www.overleaf.com/read/cgtstfkfpphp
A Zoom link will be provided to all participants.
Contact Christer.Larsson@eit.lth.se as soon as possible, if you like to participate in the workshop.
Please let us know if you are an IEEE member.
|När:||2021-05-19 09:00 till 2021-05-19 17:00|
AI Lund lunch seminar: In-memory computing to solve AI?s energy consumption bottle-neck
Title: In-memory computing to solve AI?s energy consumption bottle-neck
When: 5 May at 12.00-13.15
Speaker: Mattias Borg, Dept. of Electrical and Information Technology, LTH , Lund University
The bottle-neck for continued development of Machine Learning lies in the escalating energy consumption during model training. Ultimately, this will require new hardware that implements non-von Neumann architectures, enabling computing-in-memory and even online unsupervised learning by brain-inspired methods.
Memristors based on ferroelectric memory elements are a promising route to such hardware. Here I will introduce memristor-based computing-in-memory, it's benefits in terms of energy-efficiency and our research on the ferroelectric devices that can make it reality.
Please register at: https://ai.lu.se/events/registration-2021-05-05 in order to get an access link to the zoom platform.
|När:||2021-05-05 12:00 till 2021-05-05 13:15|
|Plats:||Online - link by registration|
AI Lund lunch seminar: Machine Learning Opportunities and Challenges in the Internet of Things Era
Title: Machine Learning Opportunities and Challenges in the Internet of Things (IoT) Era
Speaker: Amir Aminifar, Electrical and Information Technology, Lund University
When: 21 April at 12.00-13.15
Where: Online - link by registration
Machine-learning techniques have been considered in many application domains, including Internet of Things (IoT) systems. The adoption of machine learning in IoT systems creates several new opportunities, e.g., detection of health abnormalities using wearable devices. However, enabling machine learning in the IoT domain also involves several challenges inherent to these systems. Here, we highlight the key challenges in the adoption of machine-learning techniques in the IoT domain and briefly discuss how to tackle these challenges.
Amir Aminifar is currently a WASP Assistant Professor in the Department of Electrical and Information Technology at Lund University, Sweden. He received his Ph.D. degrees from the Swedish National Computer Science Graduate School, Linköping University, Sweden. During 2016-2020, he held a Scientist position in the Institute of Electrical Engineering at the Swiss Federal Institute of Technology (EPFL), Switzerland.
Please register at: https://ai.lu.se/events/registration-2021-04-21 in order to get an access link to the zoom platform.
|När:||2021-04-21 12:00 till 2021-04-21 13:15|
|Plats:||Online - link by registration|
ELLIIT Online workshop 2021
This online workshop provides an opportunity for the ELLIIT community, and others with an interest in the ELLIIT Program, to get the latest news from ELLIIT Faculty and ELLIIT projects, as well as an update on coming initiatives.
This online workshop has a focus on recent recruitments and new projects and will be followed by a
traditional two-day on-site workshop in Lund on 26-27 October, later this year.
Please register (by 13 April at the latest): https://www.lyyti.in/ELLIIT_online_workshop_2021
|När:||2021-04-15 13:00 till 2021-04-15 16:00|
Lic. Thesis Seminar: Integration of Clouds to Industrial Communication Networks
Title: Integration of Clouds to Industrial Communication Networks
Presenter: Haorui Peng, Electrical and Information Technology, LTH, Lund University and Wallenberg AI, Autonomous Systems and Software Program (WASP)
Reviewer: Prof. Åke Arvidsson från Kristianstad University
Examiner: Christian Nyberg, Electrical and Information Technology, LTH, Lund University
When: 29 January 2021 at 13.15
Location: Online at the zoom platform. Please register at https://www.lth.se/digitalth/events/register-2021-01-29/ in order to get an access link.
Cloud computing, owing to its ubiquitousness, scalability and on-demand ac- cess, has transformed into many traditional sectors, such as telecommunication and manufacturing production. As the Fifth Generation Wireless Specifications (5G) emerges, the demand on ubiquitous and re-configurable computing resources for handling tremendous traffic from omnipresent mobile devices has been put forward. And therein lies the adaption of cloud-native model in service delivery of telecommunication networks. However, it takes phased approaches to successfully transform the traditional Telco infrastructure to a softwarized model, especially for Radio Access Networks (RANs), which, as of now, mostly relies on purpose-built Digital Signal Processors (DSPs) for computing and processing tasks.
On the other hand, Industry 4.0 is leading the digital transformation in manufacturing sectors, wherein the industrial networks is evolving towards wireless connectivity and the automation process managements are shifting to clouds. However, such integration may introduce unwanted disturbances to critical industrial automation processes. This leads to challenges to guaran- tee the performance of critical applications under the integration of different systems.
In the work presented in this thesis, we mainly explore the feasibility of inte- grating wireless communication, industrial networks and cloud computing. We have mainly investigated the delay-inhibited challenges and the performance impacts of using cloud-native models for critical applications. We design a solution, targeting at diminishing the performance degradation caused by the integration of cloud computing.
|När:||2021-01-29 13:15 till 2021-01-29 15:00|
|Plats:||Online - link by registration|
PhD defence: Some Notes on Post-Quantum Cryptanalysis (Erik Mårtensson)
Thesis title: Some Notes on Post-Quantum Cryptanalysis
Author: Erik Mårtensson, Department of Electrical and Information Technology, Lund University
Opponent: Prof. Alexander May, Ruhr-Universität Bochum, Germany
When: 22 January 2021 at 9.15
Location: Online at the zoom platform - access by registration
Cryptography as it is used today relies on a foundational level on the assumption that either the Integer Factoring Problem (IFP) or the Discrete Logarithm Problem (DLP) is computationally intractable. In the 1990s Peter Shor developed a quantum algorithm that solves both problems in polynomial time. Since then alternative foundational mathematical problems to replace IFP and DLP have been suggested. This area of research is called post-quantum cryptology.
To remedy the threat of quantum computers the National Institute of Standards and Technology (NIST) has organized a competition to develop schemes for post-quantum encryption and digital signatures. For both categories latticebased cryptography candidates dominate. The second most promising type of candidate for encryption is code-based cryptography.
The lattice-based candidates are based on the difficulty of either the Learning With Errors problem (LWE) or the Nth Degree Truncated Polynomial problem (NTRU), of which LWE is the focus of this thesis. The difficulty of both these
problems in turn relies on the difficulty of variations of the Shortest Vector Problem (SVP). Code-based cryptography is based on the difficulty of decoding random linear codes.
The main focus of this thesis is on solving the LWE problem using the Blum-Kalai-Wasserman algorithm (BKW).We have the following improvements of the algorithm.
- We combined BKW with state-of-the-art lattice sieving methods to improve the complexity of the algorithm. We also elaborate on the similarities and differences between BKW and lattice sieving, two approaches that on a shallow level look very different.
- We developed a new binary approach for the distinguishing phase of the BKW algorithm and showed that it performs favorably compared to previous distinguishers.
- We investigated the Fast Fourier Transform (FFT) approach for the distinguishing part of BKW showing that it performs better than theory predicts and identically with the optimal distinguisher. We showed that we could improve its performance by limiting the number of hypotheses being tested.
- We introduced practical improvements of the algorithm such as nonintegral step sizes, a file-based sample storage solution and an implementation of the algorithm.
We also improved the classical state-of-the-art approaches for k-sieving - lattice sieving where k vectors are combined at a time - by using quantum algorithms. At the cost of a small increase in time complexity we managed to drastically decrease the space requirement compared to the state-of-the-art quantum algorithm for solving the SVP.
Finally, we developed an algorithm for decoding linear codes where the noise is Gaussian instead of binary. We showed how code-based schemes with Gaussian noise are easily broken. We also found other applications for the algorithm in side-channel attacks and in coding theory.
Please register at https://www.lth.se/digitalth/events/register-2021-01-22-9-15 inorder to get an access link for the zoom platform.
|När:||2021-01-22 09:15 till 2021-01-22 12:00|
|Plats:||Online at the zoom platform (Link by registration)|