Search Results - "Huttunen, Janne M. J"

Pulse transit time estimation of aortic pulse wave velocity and blood pressure using machine learning and simulated training data by Huttunen, Janne M J, Kärkkäinen, Leo, Lindholm, Harri

“…Recent developments in cardiovascular modelling allow us to simulate blood flow in an entire human body. Such model can also be used to create databases of…”
Get full text

Estimation of groundwater storage from seismic data using deep learning by Lähivaara, Timo, Malehmir, Alireza, Pasanen, Antti, Kärkkäinen, Leo, Huttunen, Janne M.J., Hesthaven, Jan S.

“…ABSTRACT Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components,…”
Get full text

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach by Huttunen, Janne M.J., Kärkkäinen, Leo, Honkala, Mikko, Lindholm, Harri

“…Deep learning methods combined with large datasets have recently shown significant progress in solving several medical tasks. However, collecting and…”
Get full text

MRI contrasts in high rank rotating frames by Liimatainen, Timo, Hakkarainen, Hanne, Mangia, Silvia, Huttunen, Janne M.J., Storino, Christine, Idiyatullin, Djaudat, Sorce, Dennis, Garwood, Michael, Michaeli, Shalom

“…Purpose MRI relaxation measurements are performed in the presence of a fictitious magnetic field in the recently described technique known as RAFF (Relaxation…”
Get full text

DeepRx: Fully Convolutional Deep Learning Receiver by Honkala, Mikko, Korpi, Dani, Huttunen, Janne M. J.

“…Deep learning has solved many problems that are out of reach of heuristic algorithms. It has also been successfully applied in wireless communications, even…”
Get full text

DeepTx: Deep Learning Beamforming with Channel Prediction by Huttunen, Janne M.J., Korpi, Dani, Honkala, Mikko

“…Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a…”
Get full text

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography by Lähivaara, Timo, Kärkkäinen, Leo, Huttunen, Janne M. J., Hesthaven, Jan S.

“…The feasibility of data based machine learning applied to ultrasound tomography is studied to estimate water-saturated porous material parameters. In this…”
Get full text

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage by Pihlajasalo, Jaakko, Korpi, Dani, Honkala, Mikko, Huttunen, Janne M. J., Riihonen, Taneli, Talvitie, Jukka, Brihuega, Alberto, Uusitalo, Mikko A., Valkama, Mikko

“…In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing…”
Get full text

Deep Learning-Based Pilotless Spatial Multiplexing by Korpi, Dani, Honkala, Mikko, Huttunen, Janne M.J.

“…This paper investigates the feasibility of machine learning (ML)-based pilotless spatial multiplexing in multiple-input and multiple-output (MIMO)…”
Get full text

Deep Learning Based OFDM Physical-Layer Receiver for Extreme Mobility by Pihlajasalo, Jaakko, Korpi, Dani, Honkala, Mikko, Huttunen, Janne M. J., Riihonen, Taneli, Talvitie, Jukka, Uusitalo, Mikko A., Valkama, Mikko

“…In this paper, we propose a machine learning (ML) aided physical layer receiver technique for demodulating OFDM signals that are subject to very high Doppler…”
Get full text

Model reduction in state identification problems with an application to determination of thermal parameters by Huttunen, Janne M.J., Kaipio, Jari P.

“…Large-dimensional parameter estimation problems are often computationally unstable and are therefore characterized as ill-posed inverse problems. Inverse…”
Get full text

Determination of heterogeneous thermal parameters using ultrasound induced heating and MR thermal mapping by Huttunen, Janne M J, Huttunen, Tomi, Malinen, Matti, Kaipio, Jari P

“…In this paper, a method for the determination of spatially varying thermal conductivity and perfusion coefficients of tissue is proposed. The temperature…”
Get more information

HybridDeepRx: Deep Learning Receiver for High-EVM Signals by Pihlajasalo, Jaakko, Korpi, Dani, Honkala, Mikko, Huttunen, Janne M. J., Riihonen, Taneli, Talvitie, Jukka, Brihuega, Alberto, Uusitalo, Mikko A., Valkama, Mikko

“…In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of…”
Get full text

A Bayesian-based approach to improving acoustic Born waveform inversion of seismic data for viscoelastic media by Muhumuza, Kenneth, Roininen, Lassi, Huttunen, Janne M. J, Lähivaara, Timo

“…In seismic waveform inversion, the reconstruction of the subsurface properties is usually carried out using approximative wave propagation models to ensure…”
Get full text

Improving pulse transit time estimation of aortic PWV and blood pressure using machine learning and simulated training data by Huttunen, Janne M. J, Kärkkäinen, Leo, Lindholm, Harri

“…Recent developments in cardiovascular modelling allow us to simulate blood flow in an entire human body. Such model can also be used to create databases of…”
Get full text

Estimation of groundwater storage from seismic data using deep learning by Lähivaara, Timo, Malehmir, Alireza, Pasanen, Antti, Kärkkäinen, Leo, Huttunen, Janne M. J, Hesthaven, Jan S

“…Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components such as the…”
Get full text

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography by Lähivaara, Timo, Kärkkäinen, Leo, Huttunen, Janne M. J, Hesthaven, Jan S

“…We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work,…”
Get full text

DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations by Korpi, Dani, Honkala, Mikko, Huttunen, Janne M.J., Starck, Vesa

“…Recently, deep learning has been proposed as a potential technique for improving the physical layer performance of radio receivers. Despite the large amount of…”
Get full text

Adapting to Reality: Over-the-Air Validation of AI-Based Receivers Trained with Simulated Channels by Luostari, Riku, Korpi, Dani, Honkala, Mikko, Huttunen, Janne M. J

“…Recent research has shown that integrating artificial intelligence (AI) into wireless communication systems can significantly improve spectral efficiency…”
Get full text

Deep Learning-Based Pilotless Spatial Multiplexing by Korpi, Dani, Honkala, Mikko, Huttunen, Janne M. J

“…This paper investigates the feasibility of machine learning (ML)-based pilotless spatial multiplexing in multiple-input and multiple-output (MIMO)…”
Get full text