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Variational nonlinear chirp mode decomposition (VNCMD) is a recently introduced method for nonlinear chirp signal decomposition that has aroused notable attention in various fields. One limiting aspect of the method is that its performance relies heavily on the setting of the bandwidth parameter. To overcome this problem, we here propose a Bayesian implementation of the VNCMD, which can adaptively
In this paper we present methods for triangulation of infinite cylinders from image line silhouettes. We show numerically that linear estimation of a general quadric surface is inherently a badly posed problem. Instead we propose to constrain the conic section to a circle, and give algebraic constraints on the dual conic, that models this manifold. Using these constraints we derive a fast minimal
This paper develops a novel sparse direction-of-arrival (DOA) estimation technique that avoids the common requirement of hyperparameters, which are typically difficult to set suitably in practice. Using the presented minimum mean-square error (MMSE) estimation framework, we propose a computationally efficient super-resolution DOA estimator that is implemented using an alternate updating of the spa
In this work, a computationally efficient evolutionary algorithm is proposed for estimating the direction of arrival (DOA) of coherently distributed (CD) sources corrupted by additive impulsive noise. The typical method, such as distributed signal parameter estimation (DSPE) method, requires a 2-D spectral peak search and cannot allow for coherent signals. The proposed method in this article uses
In this study, we present a newly developed method for the estimation of surface flow paths on a digital elevation model (DEM). The objective is to use a form-based algorithm, analyzing flow over single cells by dividing them into eight triangular facets and to estimate the surface flow paths on a raster DEM. For each cell on a gridded DEM, the triangular form-based multiple flow algorithm (TFM) w
This paper addresses the hybrid quantized signal detection problem in a distributed radar system, where widely separated antennas transmit low-bit quantized data with varying quantization levels depending on the different bandwidth constraint for each channel to a fusion center. To enable a high detection performance with such hybrid quantized data, we formulate the generalized likelihood ratio, R
In this work, we study self-supervised representation learning for 3D skeleton-based action recognition. We extend Bootstrap Your Own Latent (BYOL) for representation learning on skeleton sequence data and propose a new data augmentation strategy including two asymmetric transformation pipelines. We also introduce a multi-viewpoint sampling method that leverages multiple viewing angles of the same
In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distort
This paper considers the problem of jointly designing the transmit waveforms and weights for a frequency diverse array (FDA) in a spectrally congested environment in which unintentional spectral interferences exist. Exploiting the properties of the interference signal induced by the processing of the multi-channel mixing and low-pass filtering FDA receiver, the interference covariance matrix struc
Nonlinear group delay signals with frequency-varying characteristics are common in a wide variety of fields, for instance, structural health monitoring and fault diagnosis. For such applications, the signal is composed of multiple modes, where each mode may overlap in the frequency-domain. The resulting decomposition and forming of time-frequency representations of the nonlinear group delay modes
In this work, we introduce a novel approach for determining a joint sparse spectrum from several non-uniformly sampled data sets, where each data set is assumed to have its own, and only partially known, sampling times. The problem originates in paleoclimatology, where each data point derives from a separate ice core measurement, resulting in that even though all measurements reflect the same peri
The Petit Larousse illustré is a French dictionary first published in 1905. Its division in two main parts on language and on history and geography corresponds to a major milestone in French lexicography as well as a repository of general knowledge from this period. Although the value of many entries from 1905 remains intact, some descriptions now have a dimension that is more historical than cont
Cortical interneuron deficiencies, particularly involving the somatostatin (SST) subtypes, contribute to neurological and neuropsychiatric disorders. These interneurons are difficult to derive in vitro from human embryonic stem cells (hESCs) due to their late embryonic development and dependence on glial interaction. To this end, we developed a three-dimensional co-culture model of hESC-derived ne
Event cameras and LiDARs provide complementary yet distinct data: respectively, asynchronous detections of changes in lighting versus sparse but accurate depth information at a fixed rate. To this day, few works have explored the combination of these two modalities. In this article, we propose a novel neural-network-based method for fusing event and LiDAR data in order to estimate dense depth maps
Background: The Influenza – Monitoring Vaccine Effectiveness in Europe (I-MOVE/I-MOVE+) and Vaccine Effectiveness, Burden and Impact Studies (VEBIS) hospital networks have conducted seasonal multicentre, test-negative, case–control studies in Europe to measure influenza vaccine effectiveness (IVE) since 2015/16. We measured the effect of chronic conditions on VE of influenza A subtypes among older
Introduction Asthma may increase the risk of comorbidities and systemic inflammation, but population data are scarce. This study aimed to compare comorbidities and systemic inflammation between those with and without current asthma and to identify characteristics linked to comorbidities and biomarkers. Methods In a cross-sectional analysis of 28 828 people aged 50–64 in the Swedish CArdioPulmonary
BACKGROUND: Prognostication of recovery in patients who are unconscious following cardiac arrest can be guided by concentrations of brain injury biomarkers in the blood. The optimal biomarker and cutoff concentrations for the prediction of outcome remain unknown. In this study, we aimed to evaluate which biomarker of brain injury is most accurate for predicting functional outcome after cardiac arr
The standard approach for visual place recognition is to use global image descriptors to retrieve the most similar database images for a given query image. The results can then be further improved with re-ranking methods that re-order the top scoring images. However, existing methods focus on re-ranking based on the same image descriptors that were used for the initial retrieval, which we argue pr
In many machine learning tasks, the premise is designed around predetermined targets and clear expectations of model behaviour. In such cases, there is a direct definition of the optimal mappings between inputs and outputs, which can be learned given sufficiently sized datasets and models. However, in many real-world scenarios, tasks are often not as well-posed and instead defined around detecting
