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Rasmussen, Lars KildehöjORCID iD iconorcid.org/0000-0001-7182-9543
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Publications (10 of 207) Show all publications
Li, N., Xiao, M., Rasmussen, L. K., Hu, X. & Leung, V. C. M. (2021). On Resource Allocation of Cooperative Multiple Access Strategy in Energy-Efficient Industrial Internet of Things. IEEE Transactions on Industrial Informatics, 17(2), 1069-1078
Open this publication in new window or tab >>On Resource Allocation of Cooperative Multiple Access Strategy in Energy-Efficient Industrial Internet of Things
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2021 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 17, no 2, p. 1069-1078Article in journal (Refereed) Published
Abstract [en]

An event-triggered attitude control algorithm is developed for quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances. In this article, first an event-triggered supertwisting stabilizing control strategy for a class of second-order nonlinear systems is proposed. Then, a Lyapunov-based stability analysis is provided for the closed-loop system, and the Zeno-free execution of triggering sequence is guaranteed via rigorous analysis. Furthermore, the proposed control strategy is applied on attitude control of UAVs to reduce the computing cost without degrading the performance of the system. Finally, the efficiency of the developed method is validated by numerical simulation.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021
Keywords
Attitude control, Stability analysis, Informatics, Sliding mode control, Closed loop systems, Numerical stability, Manifolds, Event-triggered strategy, quadrotor unmanned aerial vehicles (UAVs), supertwisting control
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-287816 (URN)10.1109/TII.2020.2988643 (DOI)000600967800016 ()2-s2.0-85096690778 (Scopus ID)
Note

QC 20211109

Available from: 2020-12-18 Created: 2020-12-18 Last updated: 2022-06-25Bibliographically approved
Liu, D., Vu, M. T., Chatterjee, S. & Rasmussen, L. K. (2020). Neural Network based Explicit Mixture Models and Expectation-maximization based Learning. In: Proceedings of the International Joint Conference on Neural Networks: . Paper presented at 2020 International Joint Conference on Neural Networks, IJCNN 2020, 19 July 2020 through 24 July 2020. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Neural Network based Explicit Mixture Models and Expectation-maximization based Learning
2020 (English)In: Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc. , 2020Conference paper, Published paper (Refereed)
Abstract [en]

We propose two neural network based mixture models in this work. The proposed mixture models are explicit. The explicit models have analytical forms with the advantages of computing likelihood and efficiency of generating samples. Expectation-maximization based algorithms are developed for learning parameters of the proposed models. We provide sufficient conditions to realize the expectation-maximization based learning. The main requirements are invertibility of neural networks that are used as generators and Jacobian computation of functional form of the neural networks. The requirements are practically realized using a flow-based neural network. In our first mixture model, we use multiple flow-based neural networks as generators. Naturally the model is complex. A single latent variable is used as the common input to all the neural networks. The second mixture model uses a single flow-based neural network as a generator to reduce complexity. The single generator has a latent variable input that follows a Gaussian mixture distribution. The proposed models are verified via training with expectation-maximization based algorithms on practical datasets. We demonstrate efficiency of proposed mixture models through extensive experiments for generating samples and maximum likelihood based classification.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2020
Keywords
classification, expectation maximization, Generative model, mixture models, neural network, Complex networks, Efficiency, Learning systems, Maximum likelihood, Maximum principle, Analytical forms, Expectation - maximizations, Explicit models, Functional forms, Gaussian mixture distribution, Generating samples, Latent variable, Learning parameters, Neural networks
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-291297 (URN)10.1109/IJCNN48605.2020.9207086 (DOI)000626021403121 ()2-s2.0-85093853732 (Scopus ID)
Conference
2020 International Joint Conference on Neural Networks, IJCNN 2020, 19 July 2020 through 24 July 2020
Note

QC 20210322QC 20210721

Available from: 2021-03-22 Created: 2021-03-22 Last updated: 2023-04-03Bibliographically approved
Liu, D., Honore, A., Chatterjee, S. & Rasmussen, L. K. (2020). Powering hidden markov model by neural network based generative models. In: ECAI 2020: 24TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE: . Paper presented at ECAI 2020: 24TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE 29 August 2020 through 8 September 2020 (pp. 1324-1331). IOS Press BV
Open this publication in new window or tab >>Powering hidden markov model by neural network based generative models
2020 (English)In: ECAI 2020: 24TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, IOS Press BV , 2020, p. 1324-1331Conference paper, Published paper (Refereed)
Abstract [en]

Hidden Markov model (HMM) has been successfully used for sequential data modeling problems. In this work, we propose to power the modeling capacity of HMM by bringing in neural network based generative models. The proposed model is termed as GenHMM. In the proposed GenHMM, each HMM hidden state is associated with a neural network based generative model that has tractability of exact likelihood and provides efficient likelihood computation. A generative model in GenHMM consists of a mixture of generators that are realized by flow models. A learning algorithm for GenHMM is proposed in expectation-maximization framework. The convergence of the learning GenHMM is analyzed. We demonstrate the efficiency of GenHMM by classification tasks on practical sequential data. 

Place, publisher, year, edition, pages
IOS Press BV, 2020
Keywords
Clustering algorithms, Learning algorithms, Maximum principle, Neural networks, Classification tasks, Expectation-Maximization frameworks, Flow model, Generative model, Hidden state, Likelihood computation, Model problems, Sequential data, Hidden Markov models
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-286482 (URN)10.3233/FAIA200235 (DOI)000650971301073 ()2-s2.0-85091756873 (Scopus ID)
Conference
ECAI 2020: 24TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE 29 August 2020 through 8 September 2020
Note

QC 20210621

Available from: 2020-12-17 Created: 2020-12-17 Last updated: 2023-03-30Bibliographically approved
Liu, D., Moghadam, N. N., Rasmussen, L. K., Huang, J. & Chatterjee, S. (2019). alpha Belief Propagation as Fully Factorized Approximation. In: 2019 7TH IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (IEEE GLOBALSIP): . Paper presented at 7th IEEE Global Conference on Signal and Information Processing (IEEE GlobalSIP), NOV 11-14, 2019, Ottawa, CANADA. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>alpha Belief Propagation as Fully Factorized Approximation
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2019 (English)In: 2019 7TH IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (IEEE GLOBALSIP), Institute of Electrical and Electronics Engineers (IEEE) , 2019Conference paper, Published paper (Refereed)
Abstract [en]

Belief propagation (BP) can do exact inference in loop-free graphs, but its performance could be poor in graphs with loops, and the understanding of its solution is limited. This work gives an interpretable belief propagation rule that is actually minimization of a localized alpha-divergence. We term this algorithm as alpha belief propagation (alpha-BP). The performance of alpha-BP is tested in MAP (maximum a posterior) inference problems, where alpha-BP can outperform (loopy) BP by a significant margin even in fully-connected graphs.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Series
IEEE Global Conference on Signal and Information Processing, ISSN 2376-4066
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-279348 (URN)10.1109/GlobalSIP45357.2019.8969545 (DOI)000555454800193 ()2-s2.0-85079273354 (Scopus ID)
Conference
7th IEEE Global Conference on Signal and Information Processing (IEEE GlobalSIP), NOV 11-14, 2019, Ottawa, CANADA
Note

Part of ISBN 978-1-7281-2723-1

QC 20200910

Available from: 2020-09-10 Created: 2020-09-10 Last updated: 2024-03-11Bibliographically approved
Liu, D., Wang, C. & Rasmussen, L. K. (2019). Discontinuous Reception for Multiple-Beam Communication. IEEE Access, 7, 46931-46946
Open this publication in new window or tab >>Discontinuous Reception for Multiple-Beam Communication
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 46931-46946Article in journal (Refereed) Published
Abstract [en]

Discontinuous reception (DRX) techniques have successfully been proposed for energy savings in 4G radio access systems, which are deployed on legacy 2GHz spectrum bands with signal features of omnidirectional propagation. In upcoming 5G systems, higher frequency spectrum bands will also be utilized. Unfortunately higher frequency bands encounter more significant path loss, thus requiring directional beamforming to aggregate the radiant signal in a certain direction. We, therefore, propose a DRX scheme for multiple beam (DRXB) communication scenarios. The proposed DRXB scheme is designed to avoid unnecessary energy-and-time-consuming beam-training procedures, which enables longer sleep periods and shorter wake-up latency. We provide an analytical model to investigate the receiver-side energy efficiency and transmission latency of the proposed scheme. Through simulations, our approach is shown to have clear performance improvements over the conventional DRX scheme where beam training is conducted in each DRX cycle.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Discontinuous reception, beamforming, multiple-beam communication
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-251724 (URN)10.1109/ACCESS.2019.2909808 (DOI)000466485400001 ()2-s2.0-85065090753 (Scopus ID)
Note

QC 20190520

Available from: 2019-05-20 Created: 2019-05-20 Last updated: 2022-06-26Bibliographically approved
Liu, D., Vu, M. T., Chatterjee, S. & Rasmussen, L. K. (2019). ENTROPY-REGULARIZED OPTIMAL TRANSPORT GENERATIVE MODELS. In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP): . Paper presented at 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 12-17, 2019, Brighton, ENGLAND (pp. 3532-3536). IEEE
Open this publication in new window or tab >>ENTROPY-REGULARIZED OPTIMAL TRANSPORT GENERATIVE MODELS
2019 (English)In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2019, p. 3532-3536Conference paper, Published paper (Refereed)
Abstract [en]

We investigate the use of entropy-regularized optimal transport (EOT) cost in developing generative models to learn implicit distributions. Two generative models are proposed. One uses EOT cost directly in an one-shot optimization problem and the other uses EOT cost iteratively in an adversarial game. The proposed generative models show improved performance over contemporary models on scores of sample based test.

Place, publisher, year, edition, pages
IEEE, 2019
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords
Optimal transport, generative models
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-261047 (URN)10.1109/ICASSP.2019.8682721 (DOI)000482554003151 ()2-s2.0-85068999197 (Scopus ID)978-1-4799-8131-1 (ISBN)
Conference
44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 12-17, 2019, Brighton, ENGLAND
Note

QC 20191001

Available from: 2019-10-01 Created: 2019-10-01 Last updated: 2022-06-26Bibliographically approved
Zaki, A., Mitra, P. P., Rasmussen, L. K. & Chatterjee, S. (2019). Estimate exchange over network is good for distributed hard thresholding pursuit. Signal Processing, 156, 1-11
Open this publication in new window or tab >>Estimate exchange over network is good for distributed hard thresholding pursuit
2019 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 156, p. 1-11Article in journal (Refereed) Published
Abstract [en]

We investigate an existing distributed algorithm for learning sparse signals or data over networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal over a network. This learning strategy using exchange of intermediate estimates over the network requires a limited communication overhead for information transmission. Our objective in this article is to show that the strategy is good for learning in spite of limited communication. In pursuit of this objective, we first provide a restricted isometry property (RIP)-based theoretical analysis on convergence of the iterative algorithm. Then, using simulations, we show that the algorithm provides competitive performance in learning sparse signals vis-a-vis an existing alternate distributed algorithm. The alternate distributed algorithm exchanges more information including observations and system parameters.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Sparse learning, Distributed algorithm, Greedy pursuit algorithm, RIP analysis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-240987 (URN)10.1016/j.sigpro.2018.10.010 (DOI)000453494200001 ()2-s2.0-85055577903 (Scopus ID)
Note

QC 20190110

Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2022-06-26Bibliographically approved
Liu, D., Cavarec, B., Rasmussen, L. K. & Yue, J. (2019). On Dominant Interference in Random Networks and Communication Reliability. In: ICC 2019 - 2019 IEEE International Conference on Communications (ICC): . Paper presented at 2019 IEEE International Conference on Communications, ICC 2019; Shanghai International Convention Center, Shanghai; China; 20 May 2019 through 24 May 2019. Institute of Electrical and Electronics Engineers (IEEE), Article ID 8761613.
Open this publication in new window or tab >>On Dominant Interference in Random Networks and Communication Reliability
2019 (English)In: ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Institute of Electrical and Electronics Engineers (IEEE), 2019, article id 8761613Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we study the characteristics of dominant interference power with directional reception in a random network modelled by a Poisson Point Process. Additionally, the Laplace functional of cumulative interference excluding the n dominant interferers is also derived, which turns out to be a generalization of omni-directional reception and complete accumulative interference. As an application of these results, we study the impact of directional receivers in random networks in terms of outage probability and error probability with queue length constraint.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Series
IEEE International Conference on Communications, E-ISSN 1938-1883
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-258168 (URN)10.1109/ICC.2019.8761613 (DOI)000492038803086 ()2-s2.0-85070216996 (Scopus ID)9781538680889 (ISBN)
Conference
2019 IEEE International Conference on Communications, ICC 2019; Shanghai International Convention Center, Shanghai; China; 20 May 2019 through 24 May 2019
Note

QC 20191007

Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2022-06-26Bibliographically approved
Li, N., Xiao, M. & Rasmussen, L. K. (2019). Spectrum Sharing With Network Coding for Multiple Cognitive Users. IEEE Internet of Things Journal, 6(1), 230-238
Open this publication in new window or tab >>Spectrum Sharing With Network Coding for Multiple Cognitive Users
2019 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 1, p. 230-238Article in journal (Refereed) Published
Abstract [en]

In this paper, an intelligently cooperative communication network with cognitive users is considered, where in a primary system and a secondary system, respectively, a message is communicated to their respective receiver over a packet-based wireless link. The secondary system assists in the transmission of the primary message employing network coding, on the condition of maintaining or improving the primary performance, and is granted limited access to the transmission resources as a reward. The users in both systems exploit their previously received information in encoding and decoding the binary combined packets. Considering the priority of legitimate users, a selective cooperation mechanism is investigated and the system performance based on an optimization problem is analyzed. Both the analytical and numerical results show that the condition for the secondary system accessing the licensed spectrum resource is when the relay link performs better than the direct link of the primary transmission. We also extend the system model into a network with multiple secondary users and propose two relay selection algorithms. Jointly considering the related link qualities, a best relay selection and a best relay group selection algorithm are discussed. Overall, it is found that the throughput performance can be improved with multiple secondary users, especially with more potential users cooperating in the best relay group selection algorithm.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Cooperative communication, multiuser, network coding, spectrum sharing
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-246273 (URN)10.1109/JIOT.2017.2728626 (DOI)000459709500021 ()2-s2.0-85028923057 (Scopus ID)
Note

QC 20190325

Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2022-06-26Bibliographically approved
Liu, D., Fodor, V. & Rasmussen, L. K. (2019). Will Scale-Free Popularity Develop Scale-Free Geo-Social Networks?. IEEE Transactions on Network Science and Engineering, 6(3), 587-598
Open this publication in new window or tab >>Will Scale-Free Popularity Develop Scale-Free Geo-Social Networks?
2019 (English)In: IEEE Transactions on Network Science and Engineering, E-ISSN 2327-4697, Vol. 6, no 3, p. 587-598Article in journal (Refereed) Published
Abstract [en]

Empirical results show that spatial factors such as distance, population density and communication range affect our social activities, also reflected by the development of ties in social networks. This motivates the need for social network models that take these spatial factors into account. Therefore, in this paper we propose a gravity-low-based geo-social network model, where connections develop according to the popularity of the individuals, but are constrained through their geographic distance and the surrounding population density. Specifically, we consider a power-law distributed popularity, and random node positions governed by a Poisson point process. We evaluate the characteristics of the emerging networks, considering the degree distribution, the average degree of neighbors and the local clustering coefficient. These local metrics reflect the robustness of the network, the information dissemination speed and the communication locality. We show that unless the communication range is strictly limited, the emerging networks are scale-free, with a rank exponent affected by the spatial factors. Even the average neighbor degree and the local clustering coefficient show tendencies known in non-geographic scale-free networks, at least when considering individuals with low popularity. At high-popularity values, however, the spatial constraints lead to popularity-independent average neighbor degrees and clustering coefficients.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-258825 (URN)10.1109/TNSE.2018.2841942 (DOI)000484296800027 ()2-s2.0-85047804112 (Scopus ID)
Note

QC 20220322

Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2024-01-05Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-7182-9543

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