hybrid-deep-reinforcement-learning-for-multimodal-biomedical-signal

Hybrid Deep Reinforcement Learning for Multimodal Biomedical Signal Fusion

Improving decision-making and diagnostic accuracy in health care requires combining multimodal biomedical signals such as electrocardiograms (ECG), ...

University of Belgrade

In this paper, we propose a massive MIMO (multiple-input-multiple-output) architecture with distributed steerable phased antenna subarrays for position estimation in the mmWave range. We also propose localization algorithms and a multistage/multiresolution search strategy that resolve the problem of high side lobes, which is inherent in spatially coherent localization. The proposed system is intended for use in line-of-sight indoor environments. Time synchronization between the transmitter and the receiving system is not required, and the algorithms can also be applied to a multiuser scenario. The simulation results for the line-of-sight-only and specular multipath scenarios show that the localization error is only a small fraction of the carrier wavelength and that it can be achieved under reasonable system parameters including signal-to-noise ratios, antenna number/placement, and subarray apertures. The proposed concept has the potential of significantly improving the capacity and spectral/energy efficiency of future mmWave massive MIMO systems.

Position estimation with a millimeter-wave massive MIMO system based on distributed steerable phased antenna arrays.

We address wideband direct coherent localization of a radio transmitter by a distributed antenna array in a multipath scenario with spatially-coherent line-of-sight (LoS) signal components. Such a signal scenario is realistic in small cells, especially indoors in the mmWave range. The system model considers collocated time and phase synchronized receiving front-ends with antennas distributed in 3D space at known locations connected to the front-ends via calibrated coaxial cables or analog radio frequency over fiber links. The signal model assumes spherical wavefronts. We propose two ML-type algorithms (for known and unknown transmitter waveforms) and a subspace-based SCM-MUSIC algorithm for wideband direct coherent position estimation. We demonstrate the performance of the methods by Monte Carlo simulations. The results show that even in multipath environments, it is possible to achieve localization accuracy that is much better (by two to three orders of magnitude) than the carrier wavelength. They also suggest that the methods that do not exploit knowledge of the waveform have mean-squared errors approaching the Cramér-Rao bound.

Direct Wideband Coherent Localization by Distributed Antenna Arrays.

Heart diseases (CVDs) are a major cause of morbidity and mortality in all global regions and thus there is the pressing need to develop early detection and effective management approaches. Traditional cardiovascular monitoring systems do not necessarily have real-time analyzing solutions and individual understanding, which leads to delayed interventions. Moreover, one of the greatest issues in digital healthcare applications remains to be data privacy and security.

Article	Year
Position estimation with a millimeter-wave massive MIMO system based on distributed steerable phased antenna arrays. EURASIP journal on advances in signal processing\| PubMed ID: 29904392	2018
Direct Wideband Coherent Localization by Distributed Antenna Arrays. Sensors (Basel, Switzerland)\| PubMed ID: 31640259	2019
Triboostcardio ensemble model for cardiovascular disease detection using advanced blockchain-enabled health monitoring. Frontiers in artificial intelligence\| PubMed ID: 41626575	2025

Milos Janjic

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