Signal and Information Processing for Intelligent Systems
Our target is to deliver high-quality research to provide innovative solutions by creating theories and algorithms that blend different disciplines. We focus on establishing synergies between fundamental theory, algorithmic solutions, and application-specific implementations with the goal of providing technical contributions that have important societal impacts. By providing a healthy and vibrating working environment, SIGIPRO gives profound importance to attract, consolidate, and encourage internationally recognized human resources devoted to fundamental and applied research.
SIGIPRO aims at generating impact in multiple application domains:
- smart water networks
- energy and communication networks
- e-health
- complex finance engineering systems
- brain networks and cognitive neuroscience
- collaborative robots
- industry 4.0/5.0, digitalization of industry
- embedded intelligence for next-generation batteries
- autonomous self-navigation
- human-machine interfaces
- process optimization in wind farms and hydropower plants
- transportation and biological networks
People at Signal and Information Processing for Intelligent Systems
Who we are?
Simula Metropolitan employees are researchers, postdoctoral fellows, PhD students, engineers and administrative people. We are from all over the world, ranging from newly educated to experienced researchers, all working on making research in digital engineering at the highest international level possible.
Publications at Signal and Information Processing for Intelligent Systems
Journal Article
Efficient Interpretable Nonlinear Modeling for Multiple Time Series
IEEE Transactions on Signal Processing (2023).Status: Submitted
Efficient Interpretable Nonlinear Modeling for Multiple Time Series
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Transactions on Signal Processing |
| Publisher | IEEE Transactions on Signal Processing |
An Online Multiple Kernel Parallelizable Learning Scheme
IEEE Signal Processing Letters (2023).Status: Submitted
An Online Multiple Kernel Parallelizable Learning Scheme
The performance of reproducing kernel Hilbert space-based methods is known to be sensitive to the choice of the reproducing kernel. Choosing an adequate reproducing kernel can be challenging and computationally demanding, especially in data-rich tasks without prior information about the solution domain. In this paper, we propose a learning scheme that scalably combines several single kernel-based online methods to reduce the kernel-selection bias. The proposed learning scheme applies to any task formulated as a regularized empirical risk minimization convex problem. More specifically, our learning scheme is based on a multi-kernel learning formulation that can be applied to widen any single-kernel solution space, thus increasing the possibility of finding higher-performance solutions. In addition, it is parallelizable, allowing for the distribution of the computational load across different computing units. We show experimentally that the proposed learning scheme outperforms the combined single-kernel online methods separately in terms of the cumulative regularized least squares cost metric.
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Signal Processing Letters |
| Publisher | IEEE Signal Processing Letters |
| URL | https://arxiv.org/pdf/2308.10101.pdf |
Energy-efficient online control of a water distribution network based on Deep Reinforcement Learning
IEEE Internet of Things Journal (2023).Status: Submitted
Energy-efficient online control of a water distribution network based on Deep Reinforcement Learning
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Internet of Things Journal |
| Publisher | IEEE Internet of Things Journal |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway. |
Distributed Linear Network Operators via Successive Graph Shift Matrices
IEEE Transactions on Signal and Information Processing over Networks 9 (2023): 315-328.Status: Published
Distributed Linear Network Operators via Successive Graph Shift Matrices
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Transactions on Signal and Information Processing over Networks |
| Volume | 9 |
| Pagination | 315-328 |
| Date Published | 04/2023 |
| Publisher | IEEE Transactions on Signal and Information Processing over Networks |
| ISSN | 2373-776X |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the PETROMAKS Smart-Rig grant 244205 and the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway. |
| DOI | 10.1109/TSIPN.2023.3271148 |
Network-Aware RF-Energy Harvesting for Designing Energy Efficient IoT Networks
Elsevier Internet of Things 22 (2023).Status: Published
Network-Aware RF-Energy Harvesting for Designing Energy Efficient IoT Networks
| Afilliation | Communication Systems |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | Elsevier Internet of Things |
| Volume | 22 |
| Date Published | 07/2023 |
| Publisher | Elsevier Internet of Things |
| ISSN | 2542-6605 |
| DOI | 10.1016/j.iot.2023.100770 |
Online Joint Topology Identification and Signal Estimation from Streams with Missing Data
IEEE Transactions on Signal and Information Processing over Networks (2023).Status: Submitted
Online Joint Topology Identification and Signal Estimation from Streams with Missing Data
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Transactions on Signal and Information Processing over Networks |
| Publisher | IEEE Transactions on Signal and Information Processing over Networks |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway. |
Zero-delay Consistent Signal Reconstruction from Streamed Multi-variate time-series
IEEE Transactions on Signal Processing (2023).Status: Submitted
Zero-delay Consistent Signal Reconstruction from Streamed Multi-variate time-series
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Transactions on Signal Processing |
| Publisher | IEEE Transactions on Signal Processing |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway. |
| URL | ArXiv: https://arxiv.org/pdf/2308.12459.pdf |
A Trainable Approach to Zero-delay Smoothing Spline Interpolation
IEEE Transactions on Signal Processing (2023).Status: Submitted
A Trainable Approach to Zero-delay Smoothing Spline Interpolation
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Transactions on Signal Processing |
| Publisher | IEEE Transactions on Signal Processing |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway. |
| URL | ArXiv: https://arxiv.org/pdf/2203.03776.pdf |
Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs
IEEE Transactions on Signal Processing 71 (2023): 2027-2042.Status: Published
Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs
Online topology estimation of graph-connected time series is challenging in practice, particularly because the dependencies between the time series in many real-world scenarios are nonlinear. To address this challenge, we introduce a novel kernel-based algorithm for online graph topology estimation. Our proposed algorithm also performs a Fourier-based random feature approximation to tackle the curse of dimensionality associated with kernel representations. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group-Lasso based optimization framework, which is solved using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. We provide theoretical guarantees for our algorithm and prove that it can achieve sublinear dynamic regret under certain reasonable assumptions. In experiments conducted on both real and synthetic data, our method outperforms existing state-of-the-art competitors.
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Transactions on Signal Processing |
| Volume | 71 |
| Pagination | 2027-2042 |
| Date Published | 06/2023 |
| Publisher | IEEE |
| ISSN | 1941-0476 |
| Other Numbers | Print ISSN: 1053-587X |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway. |
| URL | https://ieeexplore.ieee.org/document/10141675 |
| DOI | 10.1109/TSP.2023.3282068 |
Proceedings, refereed
Neighborhood Graph Filters based Graph Convolutional Neural Networks for Multi-Agent Deep Reinforcement Learning
In IEEE Conference of Industrial Electronics Society (IECON), 2023.Status: Accepted
Neighborhood Graph Filters based Graph Convolutional Neural Networks for Multi-Agent Deep Reinforcement Learning
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2023 |
| Conference Name | IEEE Conference of Industrial Electronics Society (IECON) |