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The efficient estimation of an approximate model order is essential for applications with multidimensional data if the observed low-rank data is corrupted by additive noise. Certain signal processing applications such as biomedical studies, where the data are collected simultaneously through heterogeneous sensors, share some common features, i.e., coupled factors among multiple tensors. The exploitation of this coupling can lead to a better model order estimation, especially in case of low SNRs.


Bioimpedance is a powerful modality to continuously and non-invasively monitor cardiovascular and respiratory health parameters through the wearable operation. However, for bioimpedance sensors to be utilized in medical-grade settings, the reliability and robustness of the system should be improved. Previous studies provide limited fundamental analyses of the factors involved in the system that impact the sensitivity and the specificity of the modality in capturing the hemodynamics.


Fetal well-being during labor is currently assessed by medical professionals through visual interpretation of the cardiotocogram (CTG), a simultaneous recording of Fetal Heart Rate (FHR) and Uterine Contractions (UC). This method is disputed due to high inter- and intra-observer variability and a resulting increase in the number of unnecessary interventions. A method for computerized interpretation of the CTG, based on Contrastive Predictive Coding (CPC) is presented here.


We present metamer identification plus (metaID+), an algorithm that enhances the performance of brain-computer interface (BCI)-based color vision assessment. BCI-based color vision assessment uses steady-state visual evoked potentials (SSVEPs) elicited during a grid search of colors to identify metamers—light sources with different spectral distributions that appear to be the same color. Present BCI-based color vision assessment methods are slow; they require extensive data collection for each color in the grid search to reduce measurement noise.


Amyotrophic Lateral Sclerosis (ALS) is one of the most common neuromuscular diseases which affects both lower and upper motor neurons. In this paper, a dilated one dimensional convolutional neural network, named ALSNet, is proposed for identifying ALS from raw EMG signal. No hand-crafted feature extraction is required, rather, ALSNet is able to take raw EMG signal as input and detect EMG signals of ALS subjects. This makes the method more feasible for practical implementation by reducing the computational cost required for extracting features.