Fast fictitious surface charge method for calculation of torso surface potentials. - In: IEEE Xplore digital library, ISSN 2473-2001, (2023), insges. 4 S.
Well-established forward modeling methods in electrocardiography (ECG) require fine meshes to calculate the electric scalar potential at the body surface with high accuracy. We introduce a fast fictitious surface charge method (FSCM) with local mesh refinement and smart calculations of elements interactions which improves the accuracy of the calculations and, at the same time, preserves the performance speed.
https://doi.org/10.1109/COMPUMAG56388.2023.10411804
Joint sparse estimation with cardinality constraint via mixed-integer semidefinite programming. - In: IEEE Xplore digital library, ISSN 2473-2001, (2023), S. 106-110
The multiple measurement vectors (MMV) problem refers to the joint estimation of multiple signal realizations where the signal samples share a common sparse support over a known dictionary, which is a fundamental challenge in various applications in signal processing, e.g., direction-of-arrival (DOA) estimation. We consider the maximum a posteriori (MAP) estimation of an MMV problem, which is classically formulated as a regularized least-squares (LS) problem with an ℓ2,0 -norm constraint and derive an equivalent mixed-integer semidefinite program (MISDP) reformulation, which can be solved by state-of-the-art numerical MISDP solvers at an affordable computation time. Numerical simulations in the context of DOA estimation demonstrate the improved error performance of our proposed method in comparison to several popular DOA estimation methods.
https://doi.org/10.1109/CAMSAP58249.2023.10403415
Bayesian estimation of a probability mass function tensor with automatic rank detection. - In: IEEE Xplore digital library, ISSN 2473-2001, (2023), S. 211-215
Estimating the probability mass function (PMF) of a set of discrete random variables using a low-rank model for the PMF tensor has recently gained much attention. However, detecting the rank (model order) of the PMF tensor from observed data is a challenging problem. While classical techniques such as the Akaike and the Bayesian information criteria (AIC and BIC) may be applied in this regard, they require testing a number of candidate model orders before selecting the best one, a procedure which is computationally intensive for large datasets. In this work, we propose an algorithm to estimate the PMF tensor and implicitly detect its rank. We specify appropriate prior distributions for the model parameters and develop a deterministic algorithm which enables the rank to be detected as part of the inference. Numerical results using synthetic data demonstrate that, compared to classical model selection techniques, our approach is more robust against missing observations and is computationally efficient.
https://doi.org/10.1109/CAMSAP58249.2023.10403469
Compressive sensing based high-resolution DoA estimation by beamspace covariance gradient descent. - In: IEEE Xplore digital library, ISSN 2473-2001, (2023), S. 321-325
In this paper, we present a high-resolution direction of arrival (DoA) estimation scheme using compressive measurements for mmWave band communications. We first propose an off-grid refinement algorithm to refine an initial on-grid DoA estimation through a gradient descent search in the beamspace covariance (BSC) domain. Then we integrate the proposed refinement algorithm into the covariance orthogonal matching pursuit (COMP) algorithm, such that an on-grid detected source is firstly refined and then off-grid canceled during the successive iterations. Numerical results show that the proposed method has a lower complexity than the state-of-the-art off-grid compressive DoA estimation method in case of OFDM signals, while it can reliably estimate the DoAs with high accuracy.
https://doi.org/10.1109/CAMSAP58249.2023.10403481
Coupled matrix tensor factorization via a semi-algebraic solution based on simultaneous matrix diagonalization (SECSI-CMTF). - In: IEEE Xplore digital library, ISSN 2473-2001, (2023), S. 431-435
Recent research has shown that the joint analysis of heterogeneous data can be beneficial to understand the underlying structure of the data compared to a separate analysis. This research direction has gained high interest due to the technological progress, where massive amounts of data from multiple sources are collected, e.g., multimodal data from a patient such as EEG (electroencephalogram), MAG (magnetoencephalogram), and other data gathered from laboratory tests. This task of data fusion is challenging due to the heterogeneous structure of the data. In this study, we perform a joint CP decomposition of a heterogeneous data set, i.e., a matrix coupled with a three-dimensional tensor along the first mode, via a new formulation of the coupled matrix and tensor factorization (CMTF) based on the SEmi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization (SECSI). In comparison with the traditional alternating and gradient-based optimization algorithms, the proposed SECSI-CMTF algorithm shows an accurate and robust performance with a significantly increased computational speed. The results are evaluated on synthetic data set and compared to other state-of-the-art approaches, also in ill-conditioned scenarios and in scenarios with different SNRs.
https://doi.org/10.1109/CAMSAP58249.2023.10403496
Autoencoder-based learning of transmission parameters in fast pulse-echo ultrasound imaging employing sparse recovery. - In: IEEE Xplore digital library, ISSN 2473-2001, (2023), S. 516-520
There is recently a notable rise in the exploration of pulse-echo ultrasound image reconstruction techniques that address the inverse problem employing sparse signal and rely on a single measurement cycle. Nevertheless, these techniques continue to pose significant challenges with regard to accuracy of estimations. Previous studies have endeavored to decrease the correlation between received samples in each transducer array in order to enhance accuracy of sparsely approximated solutions to inverse problems. In this paper, our objective is to learn the transmission parameters within a parametric measurement matrix by employing an autoencoder, which encodes sparse spatial data with a parametric measurement matrix and subsequently decodes it using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). Outcomes exhibit superior performance in comparison to both state-of-art random selection of the parameters and conventional plane wave imaging (PWI) scenarios in terms of reconstruction accuracy.
https://doi.org/10.1109/CAMSAP58249.2023.10403443
Probability mass function estimation approaches with application to flow cytometry data analysis. - In: IEEE Xplore digital library, ISSN 2473-2001, (2023), S. 451-455
In this paper, we study three recently proposed probability mass function (PMF) estimation methods for flow cytometry data analysis. By modeling the PMFs as a mixture of simpler distributions, we can reformulate the PMF estimation problem as three different tensor-based approaches: a least squares coupled tensor factorization approach, a least squares partially coupled tensor factorization approach, and a Kullback-Leibler divergence (KLD)-based expectation-maximization (EM) approach. In the coupled methods, the full PMF is estimated from lower-order empirical marginal distributions, while the EM approach estimates the full PMF directly from the observed data. The three approaches are evaluated in the context of simulated and real data experiments.
https://doi.org/10.1109/CAMSAP58249.2023.10403522
LO and calibration signal distribution in a multi-antenna satellite navigation receiver. - In: Engineering proceedings, ISSN 2673-4591, Bd. 54 (2023), 1, 23, S. 1-10
Due to the low signal power of global navigation satellite signals, the receivers are prone to radio frequency interference. Employing multi-antenna arrays is one method to mitigate such effects, by incorporating spatial processing techniques. The large size of the uniform rectangular arrays prevents their use in applications where installation space is limited. Therefore, we proposed a new approach, namely to split one full array into a number of smaller, spatially distributed, sub-arrays to reduce their size and exploit available installation spaces. This concept challenges the distribution of the local oscillator and calibration signals to the respective sub-arrays. This paper compares qualitatively different design concepts for a satellite navigation receiver with two two-element sub-arrays, installed multiple wavelengths apart from each other, in support of establishing an optimal choice for our intended applications in the automotive sector in terms of electrical performance and required hardware and software efforts. In general, weighing the pros and cons of the different concepts, as discussed in the paper, will assist in optimizing the system design approach for a specific application.
https://doi.org/10.3390/ENC2023-15447
Data-driven subsampling matrices design for phased array ultrasound nondestructive testing. - In: IEEE IUS 2023, International Ultrasonics Symposium, Palais des congrès de Montréal, September 3-8, 2023, (2023), insges. 4 S.
By subsampling optimally in the spatial and temporal domains, ultrasound imaging can achieve high performance, while also accelerating data acquisition and reducing storage requirements. We study the design of experiment problem that attempts to find an optimal choice of the subsampling patterns, leading to a non-convex combinatorial optimization problem. Recently, deep learning was shown to provide a feasible approach for solving such problems efficiently by virtue of the softmax function as a differentiable approximation of the one-hot encoded subsampling vectors. We incorporate softmax neural networks into information theory-based and task-based algorithms, respectively, to design optimal subsampling matrices in Full Matrix Capture (FMC) measurements predicated on compressed sensing theory.
https://doi.org/10.1109/IUS51837.2023.10308257
Deep learning-based optimal spatial subsampling in ultrasound nondestructive testing. - In: 31st European Signal Processing Conference (EUSIPCO 2024), (2023), S. 1863-1867
Traditional ultrasound synthetic aperture imaging relies on closely spaced measurement positions, where the pitch size is smaller than half the ultrasound wavelength. While this approach achieves high-quality images, it necessitates the storage of large data sets and an extended measurement time. To address these issues, there is a burgeoning interest in exploring effective subsampling techniques. Recently, Deep Probabilistic Subsampling (DPS) has emerged as a feasible approach for designing selection matrices for multi-channel systems. In this paper, we address spatial subsampling in single-channel ultrasound imaging for Nondestructive Testing (NDT) applications. To accomplish a model-based data-driven spatial subsampling approach within the DPS framework that allows for the optimal selection of sensing positions on a discretized grid, it is crucial to build an adequate signal model and design an adapted network architecture with a reasonable cost function. The reconstructed image quality is then evaluated through simulations, showing that the presented subsampling pattern approaches the performance of fully sampling and substantially outperforms uniformly spatial subsampling in terms of signal recovery quality.
https://doi.org/10.23919/EUSIPCO58844.2023.10289868