During the visit of our minister of education (Martin Polaschek), Hermann Hellwagner gave an overview of drone research at the AAU and the use of the drone hall.
Read more about Mr. Polaschek´s visit here.
For the quality and timeliness of his reviews, Klaus Schöffmann has been awarded with the Outstanding Reviewer Award at the ACM International Conference on Multimedia Retrieval (ICMR) 2022, which was held at Newark, NJ, USA in June 2022.
Project lead/coordination: Radu Prodan
Project partners: IDC Italia, Peracton Limited, Institut Jozef Stefan, Sintef, Universiteit Twente, metaphacts GmbH, Vrije Universiteit Amsterdam, Cineca, Event Registry, Università di Bologna, Robert Bosch GmbH
Abstract: Graph-Massivizer researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as massive graphs. The tools focus on holistic usability (from extreme data ingestion and massive graph creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation based on the emerging serverless computing paradigm supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through massive graph programming and processing. Graph Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph Massivizer promises 70% more efficient analytics than AliGraph, and 30% improved energy awareness for ETL storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25% lower GHG emissions for basic graph operations. Graph-Massivizer gathers an interdisciplinary group of twelve partners from eight countries, covering four academic universities, two applied research centres, one HPC centre, two SMEs and two large enterprises. It leverages world-leading roles of European researchers in graph processing and serverless computing and uses leadership-class European infrastructure in the computing continuum.
Video streaming is not for online video providers alone. Telco providers are equal players in the game of adaptive video streaming, but to stand out, there is a constant demand for innovations at the edge.
Join us for an interview session between Bitmovin Solution Architect, Adrian Britton, and Alpen Adria University Professor, Dr. Hermann Hellwagner, as they discuss some of the latest research around Edge Computing solutions that will improve functionality for mobile network and CDN providers.
Find here the “Latest Edge Computing Innovations for Video Streaming (ft. ATHENA)” Webinar Recording of 14th June 2022.
Video Encoding Optimizations for Live Video Streaming
20th – 24th June 2022 | Berlin, Germany
Abstract: Live video streaming is expected to become mainstream in the fifth-generation (5G) mobile networks. Optimizing video encoding for live video streaming is challenging due to the latency introduced by any optimization method. In this talk, we introduce low-latency video optimization methods that are utilized to improve the quality of video encodings by predicting optimized encoding parameters.
Hadi Amirpour is a postdoc research fellow at ATHENA directed by Prof. Christian Timmerer. He received his B.Sc. degrees in Electrical and Biomedical Engineering, and he pursued his M.Sc. in Electrical Engineering. He got his Ph.D. in computer science from the University of Klagenfurt in 2022. He was appointed co-chair of Task Force 7 (TF7) Immersive Media Experience (IMEx) at the 15th Qualinet meeting. He was involved in the project EmergIMG, a Portuguese consortium on emerging imaging technologies, funded by the Portuguese funding agency and H2020. Currently, he is working on the ATHENA project in cooperation with its industry partner Bitmovin. His research interests are image processing and compression, video processing and compression, quality of experience, emerging 3D imaging technology, and medical image analysis.
On Saturday (June 18, 2022), Sebastian Uitz presented his game “A Webbing Journey” with his partner Manuel Santner at the A1 Austria eSports Festival in the Austria Center Vienna. The game didn’t fit into the Esports-themed event, but the 2 PCs prepared to play the game were constantly used by players of all ages. Especially the target audience (children 8-12) had so much fun, and the parents had to wait until the kids were done playing. The event was a great success as it was the biggest play session yet and resulted in tons of feedback that will be implemented and bugs that will be fixed until the next event.
Short description: The Intel4EC workshop aims to bring together researchers, developers and practitioners from academia and industry to present their experiences, results and research progress covering architectural designs, methods and applications of AI/ML-enabled Edge-Cloud operations and services. By bringing together these research topics, Intel4EC looks forward to help the community define open standards, AI/ML benchmarks that contribute to experiment reproducibility and systematize the complete management pipeline for a myriad of Cloud-Edge operations.
Organisers: Nishant Saurabh, Zhiming Zhao and Dragi Kimovski
Website and call for papers: https://www.intel4ec-workshop.nl/
IEEE 35th International Symposium on Computer Based Medical Systems (CBMS), July 21-23, Shenzen, China
https://2022.cbms-conference.org
Authors: Natalia Mathá, Klaus Schoeffmann, Stephanie Sarny, Doris Putzgruber-Adamitsch and Yosuf El-Shabrawi
Abstract: In recent years, the utilization intensity and thus the demand for storing cataract surgery videos for different purposes has increased. Hospitals continuously improve their technical recording equipment, i.e., cameras, to enhance the post-operative processing efficiency of the recordings. However, afterward, the videos are stored on hospitals’ internal data servers in their original size, which leads to a massive storage consumption. In this paper, we propose a relevance-based compression scheme. First, we perform a user study with clinicians to define the relevance rates of regular cataract surgery phases. Then, we compress different phases based on the determined relevance rates, using different encoding parameters and two coding standards, namely H.264/AVC and AV1. Afterward, the medical experts evaluate the visual quality of the encoded videos. Our results show a storage-saving potential for H.264/AVC of up to 95.94% and up to 98.82% for AV1, excluding idle phases (no tools are visible).
25th International Conference on Medical Image Computing and Computer Assisted Intervention, September 18-22, 2022, Singapore
https://conferences.miccai.org/2022/en/
Authors: Negin Ghamsarian, Mario Taschwer, Raphael Sznitman, Klaus Schoeffmann
Abstract: Semantic segmentation in cataract surgery has a wide range of applications contributing to surgical outcome enhancement and clinical risk reduction. However, the varying issues in segmenting the different relevant structures in these surgeries make the designation of a unique network quite challenging. This paper proposes a semantic segmentation network termed, DeepPyram, that can deal with these challenges using three novelties: (1) a Pyramid View Fusion module which provides a varying-angle global view of the surrounding region centering at each pixel position in the input convolutional feature map; (2) a Deformable Pyramid Reception module which enables a wide deformable receptive field that can adapt to geometric transformations in the object of interest; and (3) a dedicated Pyramid Loss that adaptively supervises multi-scale semantic feature maps. Compbined we show that these can effectively boost semantic segmentation performance, especially in the case of transparency, deformability, scalability, and blunt edges in objects. We demonstrate that our approach performs at a state-of-the-art level and outperforms a number of existing methods without imposing additional trainable parameters.