Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)最新文献

This paper focuses on discrete representation learning for multivariate time series with Gaussian processes. To overcome the challenges inherent in incorporating discrete latent variables into deep learning models, our approach uses a Gumbel-softmax reparameterization trick to address non-differentiability, enabling joint clustering and embedding through learnable discretization of the latent space. The proposed architecture thus enhances interpretability both by estimating a low-dimensional embedding for high dimensional time series and by simultaneously discovering discrete latent states. Empirical assessments on synthetic and real-world fMRI data validate the model's efficacy, showing improved classification results using our representation.

During labor, fetal heart rate (FHR) is monitored externally using Doppler ultrasound. This is done continuously, but for various reasons (e.g., fetal or maternal movements) the system does not record any samples for varying periods of time. In many settings, it would be quite beneficial to estimate the missing samples. In this paper, we propose a (deep) Gaussian process-based approach for estimation of consecutively missing samples in FHR recordings. The method relies on similarities in the state space and on exploiting the concept of attractor manifolds. The proposed approach was tested on a short segment of real FHR recordings. The experimental results indicate that the proposed approach is able to provide more reliable results in comparison to several interpolation methods that are commonly applied for processing of FHR signals.

We propose time-varying Gaussian process state-space models (TVGPSSM) whose hyper-parameters vary with time. The models have the ability to estimate time-varying functions and thereby increase flexibility to extract information from observed data. The proposed inference approach makes use of time-varying inducing points to adapt to changes of the function, and it exploits hierarchical importance sampling. The experimental results show that the approach has better performance than that of the standard Gaussian process.

Myocardial T ₁ mapping is a quantitative MRI technique that has found great clinical utility in the detection of various heart disease. These acquisitions typically require three breath-holds, leading to long scan durations and patient discomfort. Simultaneous multi-slice (SMS) imaging has been shown to reduce the scan time of myocardial T ₁ mapping to a single breath-hold without sacrificing coverage, albeit at reduced precision. In this work, we propose a new reconstruction strategy for SMS imaging that combines the advantages of two different k-space interpolation strategies, while allowing for regularization, in order to improve the precision of accelerated mycordial T ₁ mapping.

The paper provides a theorem for the characterization of numerical stability of spline-type systems. These systems are generated through shifted copies of a given atom over a time lattice. Also, we reformulate the well known Gabor systems via modulated spline-type systems and we apply the corresponding numerical stability to these systems. The numerical stability is tested for consistency against deformations.

Modeling the activity of an ensemble of neurons can provide critical insights into the workings of the brain. In this work we examine if learning based signal modeling can contribute to a high quality modeling of neuronal signal data. To that end, we employ the sparse coding and dictionary learning schemes for capturing the behavior of neuronal responses into a small number of representative prototypical signals. Performance is measured by the reconstruction quality of clean and noisy test signals, which serves as an indicator of the generalization and discrimination capabilities of the learned dictionaries. To validate the merits of the proposed approach, a novel dataset of the actual recordings from 183 neurons from the primary visual cortex of a mouse in early postnatal development was developed and investigated. The results demonstrate that high quality modeling of testing data can be achieved from a small number of training examples and that the learned dictionaries exhibit significant specificity when introducing noise.

This work is motivated by the desire to use image analysis methods to identify and characterize images of food items to aid in dietary assessment. This paper introduces three texture descriptors for texture classification that can be used to classify images of food. Two are based on the multifractal analysis, namely, entropy-based categorization and fractal dimension estimation (EFD), and a Gabor-based image decomposition and fractal dimension estimation (GFD). Our third texture descriptor is based on the spatial relationship of gradient orientations (GOSDM), by obtaining the occurrence rate of pairs of gradient orientations at different neighborhood scales. The proposed methods are evaluated in texture classification and food categorization tasks using the entire Brodatz database and a customized food dataset with a wide variety of textures. Results show that for food categorization our methods consistently outperform several widely used techniques for both texture and object categorization.