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Publications (10 of 94) Show all publications
Wahlström, J., Jaldén, J., Skog, I. & Händel, P. (2018). Alternative em Algorithms for Nonlinear State-Space Models. In: 2018 21st International Conference on Information Fusion, FUSION 2018: . Paper presented at 21st International Conference on Information Fusion, FUSION 2018, 10 July 2018 through 13 July 2018 (pp. 1260-1267). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Alternative em Algorithms for Nonlinear State-Space Models
2018 (English)In: 2018 21st International Conference on Information Fusion, FUSION 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 1260-1267Conference paper, Published paper (Refereed)
Abstract [en]

The expectation-maximization algorithm is a commonly employed tool for system identification. However, for a large set of state-space models, the maximization step cannot be solved analytically. In these situations, a natural remedy is to make use of the expectation-maximization gradient algorithm, i.e., to replace the maximization step by a single iteration of Newton's method. We propose alternative expectation-maximization algorithms that replace the maximization step with a single iteration of some other well-known optimization method. These algorithms parallel the expectation-maximization gradient algorithm while relaxing the assumption of a concave objective function. The benefit of the proposed expectation-maximization algorithms is demonstrated with examples based on standard observation models in tracking and localization. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
Expectation-maximization, Levenberg-Marquardt, system identification, the Gauss-Newton method, trust region, Identification (control systems), Image segmentation, Information fusion, Newton-Raphson method, Positron emission tomography, Religious buildings, Signal receivers, State space methods, Concave objective functions, Expectation - maximizations, Expectation-maximization algorithms, Gauss-Newton methods, Nonlinear state space models, State - space models, Maximum principle
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-236699 (URN)10.23919/ICIF.2018.8455234 (DOI)2-s2.0-85054096276 (Scopus ID)9780996452762 (ISBN)
Conference
21st International Conference on Information Fusion, FUSION 2018, 10 July 2018 through 13 July 2018
Funder
Swedish Foundation for Strategic Research
Note

Conference code: 139346; Export Date: 22 October 2018; Conference Paper; Funding details: SSF, Stiftelsen för Strategisk Forskning; Funding details: SSF, Sjögren’s Syndrome Foundation; Funding text: This research is financially supported by the Swedish Foundation for Strategic Research (SSF) via the project ASSEMBLE. QC 20181112

Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2018-11-12Bibliographically approved
del Aguila Pla, P. & Jaldén, J. (2018). Cell detection by functional inverse diffusion and non-negative group sparsity – Part I: Modeling and Inverse Problems. IEEE Transactions on Signal Processing, 66(20), 5407-5421
Open this publication in new window or tab >>Cell detection by functional inverse diffusion and non-negative group sparsity – Part I: Modeling and Inverse Problems
2018 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 20, p. 5407-5421Article in journal (Refereed) Published
Abstract [en]

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this first part, we start by presenting a physical partial differential equations (PDE) model up to image acquisition for these biochemical assays. Then, we use the PDEs' Green function to derive a novel parametrization of the acquired images. This parametrization allows us to propose a functional optimization problem to address inverse diffusion. In particular, we propose a non-negative group-sparsity regularized optimization problem with the goal of localizing and characterizing the biological cells involved in the said assays. We continue by proposing a suitable discretization scheme that enables both the generation of synthetic data and implementable algorithms to address inverse diffusion. We end Part I by providing a preliminary comparison between the results of our methodology and an expert human labeler on real data. Part II is devoted to providing an accelerated proximal gradient algorithm to solve the proposed problem and to the empirical validation of our methodology.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Inverse problems, Biomedical imaging, Convex optimization, Source localization, Biological modeling
National Category
Signal Processing Medical Image Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-233824 (URN)10.1109/TSP.2018.2868258 (DOI)000444842400007 ()2-s2.0-85052785365 (Scopus ID)
Funder
Swedish Research Council, 2015-04026
Note

QC 20180918

Available from: 2018-08-29 Created: 2018-08-29 Last updated: 2018-11-30Bibliographically approved
del Aguila Pla, P. & Jaldén, J. (2018). Cell detection by functional inverse diffusion and non-negative group sparsity – Part II: Proximal optimization and Performance evaluation. IEEE Transactions on Signal Processing, 66(20), 5422-5437
Open this publication in new window or tab >>Cell detection by functional inverse diffusion and non-negative group sparsity – Part II: Proximal optimization and Performance evaluation
2018 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 20, p. 5422-5437Article in journal (Refereed) Published
Abstract [en]

In this two-part paper, we present a novel framework and methodology to analyze data from certain image-based biochemical assays, e.g., ELISPOT and Fluorospot assays. In this second part, we focus on our algorithmic contributions. We provide an algorithm for functional inverse diffusion that solves the variational problem we posed in Part I. As part of the derivation of this algorithm, we present the proximal operator for the non-negative group-sparsity regularizer, which is a novel result that is of interest in itself, also in comparison to previous results on the proximal operator of a sum of functions. We then present a discretized approximated implementation of our algorithm and evaluate it both in terms of operational cell-detection metrics and in terms of distributional optimal-transport metrics.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Proximal operator, Non-negative group sparsity, Functional optimization, Biomedical imaging, Source localization
National Category
Signal Processing Medical Image Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-233827 (URN)10.1109/TSP.2018.2868256 (DOI)000444842400008 ()2-s2.0-85052808396 (Scopus ID)
Funder
Swedish Research Council, 2015-04026
Note

QC 20180918

Available from: 2018-08-29 Created: 2018-08-29 Last updated: 2018-11-30Bibliographically approved
del Aguila Pla, P. & Jaldén, J. (2018). Cell detection on image-based immunoassays. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018): . Paper presented at 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, April 4-7, 2018 (pp. 431-435). IEEE
Open this publication in new window or tab >>Cell detection on image-based immunoassays
2018 (English)In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, 2018, p. 431-435Conference paper, Published paper (Refereed)
Abstract [en]

Cell detection and counting in the image-based ELISPOT and Fluorospot immunoassays is considered a bottleneck.The task has remained hard to automatize, and biomedical researchers often have to rely on results that are not accurate.Previously proposed solutions are heuristic, and data-based solutions are subject to a lack of objective ground truth data. In this paper, we analyze a partial differential equations model for ELISPOT, Fluorospot, and assays of similar design. This leads us to a mathematical observation model forthe images generated by these assays. We use this model to motivate a methodology for cell detection. Finally, we provide a real-data example that suggests that this cell detection methodology and a human expert perform comparably.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Inverse problems, Optimization, Source localization, Immunoassays
National Category
Signal Processing Medical Image Processing
Research subject
Technology and Health; Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-223933 (URN)10.1109/ISBI.2018.8363609 (DOI)2-s2.0-85048099869 (Scopus ID)978-1-5386-3636-7 (ISBN)978-1-5386-3637-4 (ISBN)978-1-5386-3635-0 (ISBN)
Conference
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA, April 4-7, 2018
Funder
Swedish Research Council, 2015-04026
Note

QC 20180611

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-10-16Bibliographically approved
del Aguila Pla, P. & Jaldén, J. (2018). Convolutional group-sparse coding and source localization. In: : . Paper presented at 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 15-20 April 2018, Calgary, AB, Canada (pp. 2776-2780). IEEE
Open this publication in new window or tab >>Convolutional group-sparse coding and source localization
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a new interpretation of non-negatively constrained convolutional coding problems as blind deconvolution problems with spatially variant point spread function. In this light, we propose an optimization framework that generalizes our previous work on non-negative group sparsity for convolutional models. We then link these concepts to source localization problems that arise in scientific imaging, and provide a visual example on an image derived from data captured by the Hubble telescope.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE International Conference on Acoustics, Speech and Signal Processing, E-ISSN 2379-190X
Keywords
Sparse representation, Source localization, Non-negative group sparsity
National Category
Signal Processing
Research subject
Electrical Engineering; Computer Science
Identifiers
urn:nbn:se:kth:diva-224253 (URN)10.1109/ICASSP.2018.8462235 (DOI)000446384602188 ()2-s2.0-85054240957 (Scopus ID)978-1-5386-4659-5 (ISBN)978-1-5386-4658-8 (ISBN)978-1-5386-4657-1 (ISBN)
Conference
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 15-20 April 2018, Calgary, AB, Canada
Funder
Swedish Research Council, 2015-04026The Royal Swedish Academy of Sciences
Note

QC 20180917

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-11-30Bibliographically approved
Saxena, V., Jaldén, J., Bengtsson, M. & Tullberg, H. (2018). DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP): . Paper presented at 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) (pp. 6658-6662). IEEE
Open this publication in new window or tab >>DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE, 2018, p. 6658-6662Conference paper, Published paper (Refereed)
Abstract [en]

In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corresponding frame error probability (FEP) for an arbitrary set of transmission parameters. We propose an abstract model of a bit interleaved coded modulation (BICM) orthogonal frequency division multiplexing (OFDM) link chain and show that the maximum likelihood (ML) estimator of the model parameters estimates the true FEP distribution. Further, we exploit deep neural networks as a general purpose tool to implement our model and propose a training scheme for which, even while training with the binary frame error events (i.e., ACKs/NACKs), the network outputs converge to the FEP conditioned on the input channel state. We provide simulation results that demonstrate gains in the FEP prediction accuracy with our approach as compared to the traditional effective exponential SIR metric (EESM) approach for a range of channel code rates, and show that these gains can be exploited to increase the link throughput.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
FEP, BICM-OFDM, Deep Learning, Neural Networks, Link Adaptation
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-237157 (URN)10.1109/ICASSP.2018.8461864 (DOI)000446384606163 ()2-s2.0-85054259851 (Scopus ID)
Conference
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Funder
Wallenberg Foundations
Note

QC 20181025

Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2018-10-25Bibliographically approved
Maros, M. & Jaldén, J. (2018). DYNAMIC POWER ALLOCATION FOR SMART GRIDS VIA ADMM. In: 2018 IEEE 19TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC): . Paper presented at IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), JUN 25-28, 2018, Kalamata, GREECE (pp. 416-420). IEEE
Open this publication in new window or tab >>DYNAMIC POWER ALLOCATION FOR SMART GRIDS VIA ADMM
2018 (English)In: 2018 IEEE 19TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC), IEEE , 2018, p. 416-420Conference paper, Published paper (Refereed)
Abstract [en]

Electric power distribution systems encounter fluctuations in supply due to renewable sources with high variability in generation capacity. It is therefore necessary to provide algorithms that are capable of dynamically finding approximate solutions. We propose two semi-distributed algorithms based on ADMM and discuss their advantages and disadvantages. One of the algorithms computes a feasible approximate of the optimal power allocation at each time instance. We require coordination between the nodes to guarantee feasibility of each of the iterates. We bound the distance from the approximate solutions to the optimal solution as a function of the variation in optimal power allocation, and we verify our results via experiments.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE International Workshop on Signal Processing Advances in Wireless Communications, ISSN 2325-3789
Keywords
Time varying optimization, Economic Dispatch, ADMM, Smart Grids
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-240039 (URN)000451080200084 ()978-1-5386-3512-4 (ISBN)
Conference
IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), JUN 25-28, 2018, Kalamata, GREECE
Note

QC 20181210

Available from: 2018-12-10 Created: 2018-12-10 Last updated: 2018-12-10Bibliographically approved
Maros, M. & Jaldén, J. (2018). Dynamic Power Allocation for Smart Grids Via ADMM. In: IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC: . Paper presented at 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018, 25 June 2018 through 28 June 2018. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Dynamic Power Allocation for Smart Grids Via ADMM
2018 (English)In: IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper, Published paper (Refereed)
Abstract [en]

Electric power distribution systems encounter fluctuations in supply due to renewable sources with high variability in generation capacity. It is therefore necessary to provide algorithms that are capable of dynamically finding approximate solutions. We propose two semi-distributed algorithms based on ADMM and discuss their advantages and disadvantages. One of the algorithms computes a feasible approximate of the optimal power allocation at each time instance. We require coordination between the nodes to guarantee feasibility of each of the iterates. We bound the distance from the approximate solutions to the optimal solution as a function of the variation in optimal power allocation, and we verify our results via experiments.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
ADMM, Economic Dispatch, Smart Grids, Time varying optimization, Electric load dispatching, Electric power distribution, Electric power transmission networks, Scheduling, Signal processing, Wireless telecommunication systems, Approximate solution, Dynamic power allocation, Electric power distribution systems, Optimal power allocation, Smart grid, Time varying, Smart power grids
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-238004 (URN)10.1109/SPAWC.2018.8446012 (DOI)2-s2.0-85053449511 (Scopus ID)9781538635124 (ISBN)
Conference
19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018, 25 June 2018 through 28 June 2018
Note

Conference code: 139030; Export Date: 30 October 2018; Conference Paper; Funding details: ERC, European Research Council; Funding details: 742648; Funding text: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement no. 742648)

QC 20190115

Available from: 2019-01-15 Created: 2019-01-15 Last updated: 2019-01-15Bibliographically approved
Yu, W. & Jaldén, J. (2018). Perspectives in Signal Processing for Communications and Networking. IEEE signal processing magazine (Print), 35(5), 188-+
Open this publication in new window or tab >>Perspectives in Signal Processing for Communications and Networking
2018 (English)In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 35, no 5, p. 188-+Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-235453 (URN)10.1109/MSP.2018.2841413 (DOI)000443991800023 ()
Note

QC 20180927

Available from: 2018-09-27 Created: 2018-09-27 Last updated: 2018-09-27Bibliographically approved
Skog, I., Jaldén, J., Nilsson, J.-O. -. & Gustafsson, F. (2018). Position and orientation estimation of a permanent magnet using a small-scale sensor array. In: I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings. Paper presented at 2018 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2018, 14 May 2018 through 17 May 2018 (pp. 1-5). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Position and orientation estimation of a permanent magnet using a small-scale sensor array
2018 (English)In: I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]

A maximum likelihood estimator for the determination of the position and orientation of a permanent magnet using an array of magnetometers is presented. To reduce the complexity and increase the robustness of the estimator, the likelihood function is concentrated and an iterative solution method for the resulting low-dimensional optimization problem is presented. The performance of the estimator is experimentally evaluated with a miniaturized sensor array that consists of 32 magnetometer triads. The results are compared to the Cramér-Rao bound for the estimation problem at hand. The comparisons show that the performance of the estimator is close to the Cramer-Rao bound; hence, the estimator is close to optimal. Further, the results illustrate that even with a matchbox-sized array and a small magnet with a dipole moment that has a magnitude of 7 2 · 10-3 Am2 the position and orientation of the magnet can, within a 80×80×80 mm volume, be estimated with a root mean square error of less than 10 mm and 15 deg, respectively.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
Cramer-Rao bounds, Iterative methods, Magnetometers, Mean square error, Permanent magnets, Estimation problem, Iterative solution methods, Likelihood functions, Low dimensional, Maximum likelihood estimator, Optimization problems, Position and orientations, Root mean square errors, Maximum likelihood estimation
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-238084 (URN)10.1109/I2MTC.2018.8409526 (DOI)2-s2.0-85050717343 (Scopus ID)9781538622223 (ISBN)
Conference
2018 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2018, 14 May 2018 through 17 May 2018
Note

Conference code: 137883; Export Date: 30 October 2018; Conference Paper

QC 20190111

Available from: 2019-01-11 Created: 2019-01-11 Last updated: 2019-01-11Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6630-243X

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