A database for publications published by researchers and students at SimulaMet.
Research area
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- All (1014)
- Journal articles (290)
- Books (9)
- Edited books (3)
- Proceedings, refereed (317)
- Book chapters (13)
- Talks, keynote (23)
- PhD theses (9) Remove PhD theses <span class="counter">(9)</span> filter
- Proceedings, non-refereed (19)
- Posters (16)
- Technical reports (15)
- Manuals (1)
- Talks, invited (186)
- Talks, contributed (30)
- Public outreach (62)
- Miscellaneous (21)
PhD theses
SmartIO: Device sharing and memory disaggregation in PCIe clusters using non-transparent bridging
In The University of Oslo. Vol. PhD. University of Oslo (UiO), 2022.Status: Published
SmartIO: Device sharing and memory disaggregation in PCIe clusters using non-transparent bridging
Distributed and parallel computing applications are becoming increasingly compute-heavy and data-driven, accelerating the need for disaggregation solutions that enable sharing of I/O resources between networked machines. For example, in a heterogeneous computing cluster, different machines may have different devices available to them, but distributing I/O resources in a way that maximizes both resource utilization and overall cluster performance is a challenge. To facilitate device sharing and memory disaggregation among machines connected using PCIe non-transparent bridges, we present SmartIO. SmartIO makes all machines in the cluster, including their internal devices and memory, part of a common PCIe domain. By leveraging the memory mapping capabilities of non-transparent bridges, remote resources may be used directly, as if these resources were local to the machines using them. Whether devices are local or remote is made transparent by SmartIO. NVMes, GPUs, FPGAs, NICs, and any other PCIe device can be dynamically shared with and distributed to remote machines, and it is even possible to disaggregate devices and memory, in order to share component parts with multiple machines at the same time. Software is entirely removed from the performance-critical path, allowing remote resources to be used with native PCIe performance. To demonstrate that SmartIO is an efficient solution, we have performed a comprehensive evaluation consisting of a wide range of performance experiments, including both synthetic benchmarks and realistic, large-scale workloads. Our experimental results show that remote resources can be used without any performance overhead compared to using local resources, in terms of throughput and latency. Thus, compared to existing disaggregation solutions, SmartIO provides more efficient, low-cost resource sharing, increasing the overall system performance and resource utilization.
Afilliation | Communication Systems |
Project(s) | Unified PCIe IO: Unified PCI Express for Distributed Component Virtualization, Department of Holistic Systems, Department of High Performance Computing |
Publication Type | PhD Thesis |
Year of Publication | 2022 |
Degree awarding institution | The University of Oslo |
Degree | PhD |
Number of Pages | 236 |
Date Published | 10/2022 |
Publisher | University of Oslo (UiO) |
Thesis Type | Paper Collection |
URL | https://www.duo.uio.no/handle/10852/97351 |
Adaptive Multipath Scheduling for 5G Networks and Beyond: A Learning Perspective
In The University of Oslo. Vol. PhD, 2022.Status: Published
Adaptive Multipath Scheduling for 5G Networks and Beyond: A Learning Perspective
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | PhD Thesis |
Year of Publication | 2022 |
Degree awarding institution | The University of Oslo |
Degree | PhD |
Transparency in Medical Artificial Intelligence
In Oslo Metropolitan University. Vol. PhD, 2022.Status: Published
Transparency in Medical Artificial Intelligence
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | PhD Thesis |
Year of Publication | 2022 |
Degree awarding institution | Oslo Metropolitan University |
Degree | PhD |
Machine Learning-based Classification, Detection, and Segmentation of Medical Images
In UiT The Arctic University of Norway. Vol. PhD, 2022.Status: Published
Machine Learning-based Classification, Detection, and Segmentation of Medical Images
Gastointestinal (GI) cancers are among the most common types of cancers worldwide. In particular, CRC is the most lethal in terms of number of incidences and mortality (third most common cause of cancer and the second common cause of cancer-related deaths). Colonoscopy is the gold standard for screening patients for CRC. During the colonoscopy, gastroenterologists examine the large bowel, detect precancerous abnormal tissue growths like polyps and remove them through the scope if necessary. Although colonoscopy is considered the gold standard, it is an operator-dependent procedure. Previous research has shown large missing rates for GI abnormalities, e.g., polyp miss detection is around 22%-28%. Early detection of GI lesions and cancers at the curable stage can help reduce the mortality rate. The development of automated, accurate, and efficient methods for the detection of the GI cancers could benefit both gastroenterologists and patients. In addition, if integrated into screening programs, an automatic analysis could improve overall GI endoscopy quality.
The medical field is becoming more interdisciplinary, and the importance of medical image data is increasing rapidly. Medical image analysis can play a central role in disease detection, diagnosis, and treatment. With the increasing number of medical images, there is enormous potential to improve the screening quality. Deep learning (DL), in particular, CNN based models have tremendous potential to automate and enhance the medical image analysis procedure and provide an accurate diagnosis. The automated analysis of the medical images could reduce the burden of the medical experts and provide quality and accessible healthcare to a larger population. In medical imaging, classification, detection, and semantic segmentation tasks are crucial for clinical practice. The development of accurate and efficient CADx or CADe models can help to identify the abnormalities at an early stage and can act as a third eye for the doctors.
To this end, we have studied and designed ml and dl based architectures for gi tract disease classification, detection, and segmentation. Our designed architectures can classify different types of \gls{gi} tract findings and abnormalities accurately with high performance. Our contribution towards the development of cade models for automated polyp detection showed improved performance. Out of three different medical imaging tasks, semantic segmentation of medical imaging data plays a significant role in extracting meaningful information from images by classifying each pixel and segmenting it by class. Using the GI case scenario, we have mainly worked on polyp segmentation and proposed and evaluated different automated polyp segmentation architectures. We have also built architectures for surgical instrument segmentation that showed high performance and real-time speed.
We have collected, annotated, and released several open-access datasets such as HyperKvasir, KvasirCapsule, PolypGen, Kvasir-SEG, Kvasir-instrument, and KvasirCapsule-SEG in collaboration with hospitals in Norway and abroad to address the lack of datasets in the field. We have devised several medical image segmentation architectures (for example, ResUNet++, DoubleU-Net, and ResUNet + CRF + TTA) that provided improved results with the publicly available datasets. Beside that, we have also designed architectures that have the capability of segmenting polyps in real-time with high frame per second (for example, ColonSegNet, NanoNet, PNS-Net, and DDANet). Moreover, we performed extensive studies on the generalizability of our models on public datasets, and by creating a dataset consisting of data from different hospitals, we allow multi-center cross dataset testing. Our results prove that proposed dl based CADx systems might be of great assistance to clinicians in the future.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | PhD Thesis |
Year of Publication | 2022 |
Degree awarding institution | UiT The Arctic University of Norway |
Degree | PhD |
Number of Pages | 400 |
Date Published | 01/2022 |
Thesis Type | Paper-based PhD thesis |
URL | https://munin.uit.no/handle/10037/23693 |
Empirical Analysis of QoS and QoE in Mobile Broadband Networks
In The University of Oslo. Vol. PhD, 2022.Status: Published
Empirical Analysis of QoS and QoE in Mobile Broadband Networks
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | PhD Thesis |
Year of Publication | 2022 |
Degree awarding institution | The University of Oslo |
Degree | PhD |
PhD theses
Understanding and Mitigating the Influence of Delay on Cloud Gaming Quality of Experience
In Technische Universität Berlin, 2021.Status: Published
Understanding and Mitigating the Influence of Delay on Cloud Gaming Quality of Experience
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | PhD Thesis |
Year of Publication | 2021 |
Degree awarding institution | Technische Universität Berlin |
DeepSynthBody: the beginning of the end for data deficiency in medicine
In Oslo Metropolitan University. Vol. PhD, 2021.Status: Published
DeepSynthBody: the beginning of the end for data deficiency in medicine
Recent advancements in technology have made artificial intelligence (AI) a popular tool in the medical domain, especially machine learning (ML) methods, which is a subset of AI. In this context, a goal is to research and develop generalizable and well-performing ML models to be used as the main component in computer-aided diagnosis (CAD) systems. However, collecting and processing medical data has been identified as a major obstacle to produce AI-based solutions in the medical domain. In addition to the focus on the development of ML models, this thesis also aims at finding a solution to the data deficiency problem caused by, for example, privacy concerns and the tedious medical data annotation process.
To accomplish the goals of the thesis, we investigated case studies from three different medical branches, namely cardiology, gastroenterology, and andrology. Using data from these case studies, we developed ML models. Addressing the scarcity of medical data, we collected, analyzed, and developed medical datasets and performed benchmark analyses. A framework for generating synthetic medical data has been developed using generative adversarial networks (GANs) as a solution to address the data deficiency problem. Our results indicate that our generated synthetic data may be a solution to the data challenge. As an overarching concept, we introduced the DeepSynthBody as a basis for structured and centralized synthetic medical data generation. The studies presented in the thesis, such as generating synthetic electrocardiograms (ECGs), gastrointestinal (GI)-tract images and videos with and without polyps, and sperm samples, showed that DeepSynthBody can help to overcome data privacy concerns, the time-consuming and costly data annotation process, and the data imbalance problem in the medical domain. Our experiments showed that our generative models generate realistic synthetic data providing comparable results to experiments using real data to tackle the identified problems. The final DeepSynthBody framework is available as an open-source project that allows researchers, industry, and practitioners to use the system and contribute to future developments.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | PhD Thesis |
Year of Publication | 2021 |
Degree awarding institution | Oslo Metropolitan University |
Degree | PhD |
Number of Pages | 387 |
Date Published | 12/2021 |
Thesis Type | Article-based thesis |
PhD theses
Processing Multimedia Workloads on Heterogeneous Multicore Architectures
In University of Oslo. Vol. PhD. UiO, 2015.Status: Published
Processing Multimedia Workloads on Heterogeneous Multicore Architectures
Processor architectures have been evolving quickly since the introduction of the central processing unit. For a very long time, one of the important means of increasing performance was to increase the clock frequency. However, in the last decade, processor manufacturers have hit the so-called power wall, with high heat dissipation. To overcome this problem, processors were designed with reduced clock frequencies but with multiple cores and, later, heterogeneous processing elements. This shift introduced a new challenge for programmers: Legacy applications, written without parallelization in mind, gain no benefits from moving to multicore and heterogeneous architectures. Another challenge for the programmers is that heterogeneous architecture designs are very different with respect to caches, memory types, execution unit organization, and so forth and, in the worst case, a programmer must completely rewrite the application to obtain the best performance on the new architecture.
Multimedia workloads, such as video encoding, are often time sensitive and interactive. These workloads differ from traditional batch processing workloads with no real-time requirements. This work investigates how to
use modern heterogeneous architectures efficiently to process multimedia workloads. To do so, we investigate both simple and complex workloads on multiple architectures to learn about the properties of these architectures. When programing multimedia workloads, it is very important to know how the algorithms perform on the target architecture. In addition, achieving high performance on heterogeneous architectures is not a trivial task, often requiring detailed knowledge about the architecture. We therefore evaluate several optimizations so we can learn how best to write programs for these architectures and avoid potential pitfalls.
We later use the knowledge gained to propose a framework design and language called Parallel Processing Graph (P2G). The P2G framework is designed for multimedia workloads and supports heterogeneous architectures. To demonstrate the feasibility of the framework, we construct a proof-of-concept implementation. Two simple workloads show that we can express multimedia workloads in the system. We also demonstrate the scalability of the designed solution.
Afilliation | Communication Systems |
Project(s) | Department of Holistic Systems |
Publication Type | PhD Thesis |
Year of Publication | 2015 |
Degree awarding institution | University of Oslo |
Degree | PhD |
Date Published | 02/2015 |
Publisher | UiO |
URL | https://www.duo.uio.no/handle/10852/50618 |
PhD theses
An Interdiciplinary Approach to Optimisation in Parallel Computing
2012.Status: Published
An Interdiciplinary Approach to Optimisation in Parallel Computing
Afilliation | , Communication Systems, Communication Systems |
Project(s) | The Center for Resilient Networks and Applications |
Publication Type | PhD Thesis |
Year of Publication | 2012 |
Date Published | 08/12 |