The angle dependent ΔE effect in TiN/AlN/Ni micro cantilevers. - In: Sensors and actuators, ISSN 1873-3069, Bd. 345 (2022), 113784, S. 1-12
In this work, magnetoelectric MEMS sensors based on a TiN/AlN/Ni laminate are investigated for the first time in regards of the anisotropic elastic properties when using hard magnetic Nickel as magnetostrictive layer. The implications of crystalline, uniaxial and shape anisotropy are analysed arising from the anisotropic ΔE effect in differently oriented cantilevers with 25 µm length and 15˚ spacing. The ΔE effect is derived analytically to consider the angular dependency of the different anisotropies within the sensors. In the measured frequency spectra complex profiles are observable consisting of contributions from neighbouring structures which are connected by a common electrode. The crosstalk effect is strongly depending on the cantilever orientation and reflects the anisotropic mechanical properties of the material stack. The intensity of the crosstalk effect is increasing for shortened cantilevers and narrowing distance between structures. The ΔE effect is investigated based on cantilevers of different angular spacing and of a single cantilever that is rotated in the magnetic field. The derived peak sensitivities are reaching values of 1.15 and 1.31T-1. The angular dependency of the sensitivity is found to be approximately constant for differently oriented cantilevers. In contrast, for a singly rotated cantilever an angular dependency of the 4th order is observed.
https://doi.org/10.1016/j.sna.2022.113784
Accuracy assessment of simplified computation of active and passive magnetic shielding for optically pumped magnetometers. - In: IEEE transactions on magnetics, ISSN 1941-0069, Bd. 58 (2022), 9, 7401204, insges. 4 S.
A low residual environmental magnetic field is required for the proper operation of most optically pumped magnetometers (OPMs). This is achieved using a combination of passive and active magnetic shielding. Passive magnetic shielding often uses multiple layers of highly permeable materials. A realistic two-layer magnetically shielded room (MSR) for biomagnetic measurements is numerically studied using the finite element method. A measured B-H characteristic of Mumetal is used, which is linearly extrapolated from the Rayleigh region (RR) into the low magnetic field range. This extension of the magnetization curve yields no significant differences compared to using a constant permeability value for the low magnetic field range. Furthermore, we model the MSR with only one shield and apply a simple analytical method of images (MOI). The MOI yields on average a 100-fold reduction in computation time. The relative difference of the magnetic flux density computed with MOI and finite element method (FEM) is smaller than < 1% at the center of a spherical region of interest (ROI) with a radius of 0.3 m desired from the point of magnetoencephalography (MEG) using OPMs. The achieved accuracy of the MOI makes it suitable for the optimization of active shielding coils.
https://doi.org/10.1109/TMAG.2022.3161736
Reweighted low-rank factorization with deep prior for image restoration. - In: IEEE transactions on signal processing, ISSN 1941-0476, Bd. 70 (2022), S. 3514-3529
The low-rank recovery is a powerful tool to restore images from incomplete and corrupted observations. Conventional low-rank recovery techniques employ the reweighted nuclear norm minimization, which requires performing the full singular value decomposition and thus is computationally expensive. Using the scheme of bilinear factorization, we propose the Reweighted Low-rank Matrix Factorization (RLMF) method for single channel image restoration. The RLMF method can not only inherit the computational efficiency of bilinear factorization, but also incorporate the empirical distribution of the singular values in natural images. Then, considering the correlation between image channels, we generalize the reweighted nuclear norm from matrices to tensors, and develop the Reweighted Low-rank Tensor Factorization (RLTF) method for multichannel image restoration. Moreover, we enhance the RLMF and RLTF methods by introducing the deep image prior information, which is capable of capturing the implicit image structure through the neural network architecture to improve restoration accuracy. Experimental results show the computational efficiency of the proposed low-rank factorization scheme, and the superior restoration accuracy of the proposed methods compared with the state-of-the-art methods.
https://doi.org/10.1109/TSP.2022.3183466
Phase estimation of single tones next to modulated signals in the medium frequency R-mode system. - In: IEEE access, ISSN 2169-3536, Bd. 10 (2022), S. 73309-73316
Position, navigation, and timing information are critical to today’s infrastructures; as a result, the possibility of estimating ranges is being explored in more and more radio systems. One way to achieve this is to extend the modulation with time-synchronised aiding carriers and to estimate their phase at the receiver side. In this paper, we present two ways to minimise the negative influence of the modulation on the phase estimation. We show that the classical maximum likelihood estimator is still an efficient estimator for our problem, using a medium-frequency R-Mode signal as an example, and is therefore used in receiver designs. We then describe two possible ways to precondition the signal to increase the accuracy for short observations. As a first approach, we describe how window functions can positively change the signal-to-noise ratio for our estimation. As a second approach, we show the use of a narrowband bandpass filter. Finally, we show that these approaches, applied to real measurements, improve the variance of the estimate by up to two orders of magnitude.
https://doi.org/10.1109/ACCESS.2022.3190544
Multilinear generalized singular value decomposition (ML-GSVD) and its application to multiuser MIMO systems. - In: IEEE transactions on signal processing, ISSN 1941-0476, Bd. 70 (2022), S. 2783-2797
In this paper, we introduce a Multilinear Generalized Singular Value Decomposition (ML-GSVD) for two or more matrices with one common dimension. The ML-GSVD extends the Generalized Singular Value decomposition (GSVD) of two matrices to higher orders. The proposed decomposition allows us to jointly factorize a set of matrices with one common dimension. In comparison with other approaches that extend the GSVD, the ML-GSVD preserves the essential properties of the original (matrix-based) GSVD, such as orthogonality of the second-mode factor matrices as well as the subspace structure of the third-mode factor matrices. We introduce an ALS-based algorithm to compute the ML-GSVD, which has been inspired by PARAFAC2 decomposition algorithms. In addition, we present an application of the ML-GSVD for transceiver optimization in multicast and unicast MIMO-OFDM systems. Our numerical results show that the proposed ML-GSVD multicast and unicast beamforming outperforms existing state-of-the-art schemes in terms of the sum rate.
https://doi.org/10.1109/TSP.2022.3178902
The influence of white matter lesions on the electric field in transcranial electric stimulation. - In: NeuroImage: Clinical, ISSN 2213-1582, Bd. 35 (2022), 103071, S. 1-12
Background - Transcranial direct current stimulation (tDCS) is a promising tool to enhance therapeutic efforts, for instance, after a stroke. The achieved stimulation effects exhibit high inter-subject variability, primarily driven by perturbations of the induced electric field (EF). Differences are further elevated in the aging brain due to anatomical changes such as atrophy or lesions. Informing tDCS protocols by computer-based, individualized EF simulations is a suggested measure to mitigate this variability. - Objective - While brain anatomy in general and specifically atrophy as well as stroke lesions are deemed influential on the EF in simulation studies, the influence of the uncertainty in the change of the electrical properties of the white matter due to white matter lesions (WMLs) has not been quantified yet. - Methods - A group simulation study with 88 subjects assigned into four groups of increasing lesion load was conducted. Due to the lack of information about the electrical conductivity of WMLs, an uncertainty analysis was employed to quantify the variability in the simulation when choosing an arbitrary conductivity value for the lesioned tissue. - Results - The contribution of WMLs to the EF variance was on average only one tenth to one thousandth of the contribution of the other modeled tissues. While the contribution of the WMLs significantly increased (p≪.01) in subjects exhibiting a high lesion load compared to low lesion load subjects, typically by a factor of 10 and above, the total variance of the EF didnot change with the lesion load. - Conclusion - Our results suggest that WMLs do not perturb the EF globally and can thus be omitted when modeling subjects with low to medium lesion load. However, for high lesion load subjects, the omission of WMLs may yield less robust local EF estimations in the vicinity of the lesioned tissue. Our results contribute to the efforts of accurate modeling of tDCS for treatment planning.
https://doi.org/10.1016/j.nicl.2022.103071
Open sub-granting radio resources in overlay D2D-based V2V communications. - In: EURASIP journal on wireless communications and networking, ISSN 1687-1499, Bd. 2022 (2022), 46, S. 1-29
Richtiger Name des Verfassers: Dariush Mohammad Soleymani
Capacity, reliability, and latency are seen as key requirements of new emerging applications, namely vehicle-to-everything (V2X) and machine-type communication in future cellular networks. D2D communication is envisaged to be the enabler to accomplish the requirements for the applications as mentioned earlier. Due to the scarcity of radio resources, a hierarchical radio resource allocation, namely the sub-granting scheme, has been considered for the overlay D2D communication. In this paper, we investigate the assignment of underutilized radio resources from D2D communication to device-to-infrastructure communication, which are moving in a dynamic environment. The sub-granting assignment problem is cast as a maximization problem of the uplink cell throughput. Firstly, we evaluate the sub-granting signaling overhead due to mobility in a centralized sub-granting resource algorithm, dedicated sub-granting radio resource (DSGRR), and then a distributed heuristics algorithm, open sub-granting radio resource (OSGRR), is proposed and compared with the DSGRR algorithm and no sub-granting case. Simulation results show improved cell throughput for the OSGRR compared with other algorithms. Besides, it is observed that the overhead incurred by the OSGRR is less than the DSGRR while the achieved cell throughput is yet close to the maximum achievable uplink cell throughput.
https://doi.org/10.1186/s13638-022-02128-0
Distortion of electric field homogeneity between two thin toroidal electrodes by a dielectric sphere. - In: Serbian journal of electrical engineering, ISSN 2217-7183, Bd. 19 (2022), 1, S. 45-56
We assess the influence of a radius and a relative permittivity of an isotropic dielectric sphere on electric field homogeneity. The homogenous electric field is generated using two thin toroidal electrodes, charged by equal charges of opposite signs. The Poisson equation is solved by a method of separation of variables. Increase in a relative permittivity of the sphere and in its radius, produces more distortions of the electric field homogeneity.
https://doi.org/10.2298/SJEE2201045M
A novel covariance model for MIMO sensing systems and its identification from measurements. - In: Signal processing, Bd. 197 (2022), 108542
A novel model for the covariance matrix of sampled observations by multiple-input-multiple-output (MIMO) sensing systems with parallel receiver channels will be presented. The model is of shifted Kronecker structure and accounts for two mutually independent noise processes: a coloured and a white one. The maximum-likelihood (ML) estimator is applied to identify this covariance model from observations. The ML estimator gives rise to a non-convex optimisation problem. Since no closed-form solution is available, an iterative, space-alternating Gauss-Newton algorithm is proposed to solve the optimisation problem. This approach repeatedly requires the evaluation of the ML cost function. Since the cost function composes of the inverse and determinant of the covariance matrix, its evaluation can be memory exhaustive, numerically unstable and computationally complex. A computational method is developed to overcome these issues, using the simultaneous matrix diagonalisation and exploiting the properties of the Kronecker product. Measurements by a MIMO radar are used to identify the covariance model and to demonstrate its benefits. The identified covariance model is used to whiten the measurements. The whitening reduces interfering, noise-like components, which enhances the signal-to-interference ratio and hence facilitates the target detection.
https://doi.org/10.1016/j.sigpro.2022.108542
Constrained Cramér-Rao bounds for reconstruction problems formulated as coupled canonical polyadic decompositions. - In: Signal processing, Bd. 198 (2022), 108573
We propose a theoretical performance analysis for a class of reconstruction problems, formulated as coupled canonical polyadic decompositions of two low-resolution tensor observations. We study a particular case when all the modes of the tensors are coupled. Unlike the case of a single coupling constraint, a fully-coupled model requires nonlinear constraints in some estimation scenarios. Thus we introduce two probabilistic scenarios. For each scenario, we derive the constrained Cramér-Rao bounds for the parameters and for the mean-squared error of the reconstructed tensor. We show that with a carefully chosen initialization, the maximum likelihood estimators reach the bounds, even in challenging cases (low signal-to-noise ratio or large tensor rank).
https://doi.org/10.1016/j.sigpro.2022.108573