Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Sunwoo Kim

Sunwoo Kim

A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM


Sep 08, 2021
Yu Ge, Ossi Kaltiokallio, Hyowon Kim, Fan Jiang, Jukka Talvitie, Mikko Valkama, Lennart Svensson, Sunwoo Kim, Henk Wymeersch

Figure 1 for A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM
Figure 2 for A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM
Figure 3 for A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM
Figure 4 for A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM

  Access Paper or Ask Questions

Personalized Federated Learning over non-IID Data for Indoor Localization


Jul 21, 2021
Peng Wu, Tales Imbiriba, Junha Park, Sunwoo Kim, Pau Closas

Figure 1 for Personalized Federated Learning over non-IID Data for Indoor Localization
Figure 2 for Personalized Federated Learning over non-IID Data for Indoor Localization
Figure 3 for Personalized Federated Learning over non-IID Data for Indoor Localization
Figure 4 for Personalized Federated Learning over non-IID Data for Indoor Localization

  Access Paper or Ask Questions

Location-aware Channel Estimation for RIS-aided mmWave MIMO Systems via Atomic Norm Minimization


Jul 20, 2021
Hyeonjin Chung, Sunwoo Kim

Figure 1 for Location-aware Channel Estimation for RIS-aided mmWave MIMO Systems via Atomic Norm Minimization
Figure 2 for Location-aware Channel Estimation for RIS-aided mmWave MIMO Systems via Atomic Norm Minimization
Figure 3 for Location-aware Channel Estimation for RIS-aided mmWave MIMO Systems via Atomic Norm Minimization
Figure 4 for Location-aware Channel Estimation for RIS-aided mmWave MIMO Systems via Atomic Norm Minimization
* This paper was submitted to IEEE Transactions on Signal Processing 
  Access Paper or Ask Questions

Atomic Norm Minimization-based Low-Overhead Channel Estimation for RIS-aided MIMO Systems


Jul 20, 2021
Hyeonjin Chung, Sunwoo Kim

Figure 1 for Atomic Norm Minimization-based Low-Overhead Channel Estimation for RIS-aided MIMO Systems
Figure 2 for Atomic Norm Minimization-based Low-Overhead Channel Estimation for RIS-aided MIMO Systems
Figure 3 for Atomic Norm Minimization-based Low-Overhead Channel Estimation for RIS-aided MIMO Systems
Figure 4 for Atomic Norm Minimization-based Low-Overhead Channel Estimation for RIS-aided MIMO Systems
* This paper was submitted to IEEE Globecomm 2021 
  Access Paper or Ask Questions

Dirichlet process approach for radio-based simultaneous localization and mapping


Jul 02, 2021
Jaebok Lee, Hyowon Kim, Henk Wymeersch, Sunwoo Kim

Figure 1 for Dirichlet process approach for radio-based simultaneous localization and mapping
Figure 2 for Dirichlet process approach for radio-based simultaneous localization and mapping
Figure 3 for Dirichlet process approach for radio-based simultaneous localization and mapping
Figure 4 for Dirichlet process approach for radio-based simultaneous localization and mapping

  Access Paper or Ask Questions

Cooperative mmWave PHD-SLAM with Moving Scatterers


Jun 23, 2021
Hyowon Kim, Jaebok Lee, Yu Ge, Fan Jiang, Sunwoo Kim, Henk Wymeersch

Figure 1 for Cooperative mmWave PHD-SLAM with Moving Scatterers
Figure 2 for Cooperative mmWave PHD-SLAM with Moving Scatterers
Figure 3 for Cooperative mmWave PHD-SLAM with Moving Scatterers
Figure 4 for Cooperative mmWave PHD-SLAM with Moving Scatterers

  Access Paper or Ask Questions

Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation


May 08, 2021
Sunwoo Kim, Minje Kim

Figure 1 for Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation
Figure 2 for Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation
Figure 3 for Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation
* 5 pages, 5 figures, under review 
  Access Paper or Ask Questions

Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification


Apr 05, 2021
Aswin Sivaraman, Sunwoo Kim, Minje Kim

Figure 1 for Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification
Figure 2 for Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification
Figure 3 for Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification
* 5 pages, 3 figures, under review 
  Access Paper or Ask Questions

Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility


Feb 19, 2021
Sun Hong Lim, Sunwoo Kim, Byonghyo Shim, Jun Won Choi

Figure 1 for Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility
Figure 2 for Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility
Figure 3 for Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility
Figure 4 for Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility
* 23 pages, 8 figures 
  Access Paper or Ask Questions

State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge


Dec 28, 2020
Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh, Sunwoo Kim, Geunu Jeong, Jingyu Ko, Yohan Jun, Hyungseob Shin, Dosik Hwang, Mahmoud Mostapha, Simon Arberet, Dominik Nickel, Zaccharie Ramzi, Philippe Ciuciu, Jean-Luc Starck, Jonas Teuwen, Dimitrios Karkalousos, Chaoping Zhang, Anuroop Sriram, Zhengnan Huang, Nafissa Yakubova, Yvonne Lui, Florian Knoll

Figure 1 for State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
Figure 2 for State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
Figure 3 for State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
Figure 4 for State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
* Updates to references, minor corrections 
  Access Paper or Ask Questions

State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge


Dec 09, 2020
Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh, Sunwoo Kim, Geunu Jeong, Jingyu Ko, Yohan Jun, Hyungseob Shin, Dosik Hwang, Mahmoud Mostapha, Simon Arberet, Dominik Nickel, Zaccharie Ramzi, Philippe Ciuciu, Jean-Luc Starck, Jonas Teuwen, Dimitrios Karkalousos, Chaoping Zhang, Anuroop Sriram, Zhengnan Huang, Nafissa Yakubova, Yvonne Lui, Florian Knoll

Figure 1 for State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
Figure 2 for State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
Figure 3 for State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
Figure 4 for State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
* 16 pages, 7 figures, 3 tables 
  Access Paper or Ask Questions

Online Exemplar Fine-Tuning for Image-to-Image Translation


Nov 18, 2020
Taewon Kang, Soohyun Kim, Sunwoo Kim, Seungryong Kim

Figure 1 for Online Exemplar Fine-Tuning for Image-to-Image Translation
Figure 2 for Online Exemplar Fine-Tuning for Image-to-Image Translation
Figure 3 for Online Exemplar Fine-Tuning for Image-to-Image Translation
Figure 4 for Online Exemplar Fine-Tuning for Image-to-Image Translation
* 10 pages, 13 figures 
  Access Paper or Ask Questions

Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation


Feb 14, 2020
Sunwoo Kim, Haici Yang, Minje Kim

Figure 1 for Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation
Figure 2 for Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation
Figure 3 for Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation
Figure 4 for Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation

  Access Paper or Ask Questions

Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing


Aug 26, 2019
Sunwoo Kim, Minje Kim

Figure 1 for Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing
Figure 2 for Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing
Figure 3 for Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing
Figure 4 for Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing
* 5 pages, 5 figures 
  Access Paper or Ask Questions

Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Separation


Aug 23, 2019
Sunwoo Kim, Mrinmoy Maity, Minje Kim

Figure 1 for Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Separation
Figure 2 for Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Separation
* 5 pages, 1 figure, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019) 
  Access Paper or Ask Questions