Variable Filter Research Articles

Dynamic parameter estimation of the alkaline electrolysis system combining Bayesian inference and adaptive polynomial surrogate models

Utility-scale hydrogen production via alkaline electrolysis (AEL) is a promising pathway toward the decarbonization of the power, transportation, and chemical industries. The efficiency, load flexibility, and operational safety of the AEL system are subject to electrochemical, thermal, and mass transfer dynamics, and the corresponding parameters, including overvoltage coefficients, heat capacities and resistances of the stack and lye-gas separators, thickness and permeability of the diaphragm, etc. The community has developed many models to depict these dynamic behaviors. However, due to the lack of a comprehensive parameter estimation method, these models are generally tuned manually in industrial applications, which can be inaccurate and cannot fit their time-varying nature. To fill this gap, we present a fast and accurate parameter estimation method for the AEL system. Specifically, to address the difficulties of strong nonlinearity of the dynamic electrolyzer models and correlation between different parameters, a Bayesian inference-based Markov chain Monte Carlo method is proposed. To reduce the computing time for online estimation, data-driven adaptive polynomial surrogate models are established to replace repeated time-domain simulations of the electrolyzer model so that estimation can be finished within a few minutes. Experiments on a 5 Nm3/hr-rated AEL system validate the proposed method. Compared with the existing Kalman filter variants, the estimation error is reduced by at most 71.1% in terms of RMSE and NRMSE. In addition, the proposed method provides approaches to fault diagnosis and global sensitivity analysis for operating and designing AEL systems.

Ca2+ and cAMP open differentially dilating synaptic fusion pores.

Neuronal dense-core vesicles (DCVs) contain neuropeptides and much larger proteins that affect synaptic growth and plasticity. Rather than using full collapse exocytosis that commonly mediates peptide hormone release by endocrine cells, DCVs at the Drosophila neuromuscular junction release their contents via fusion pores formed by kiss-and-run exocytosis. Here, we used fluorogen-activating protein (FAP) imaging to reveal the permeability range of synaptic DCV fusion pores and then show that this constraint is circumvented by cAMP-induced extra fusions with dilating pores that result in DCV emptying. These Ca2+-independent full fusions require PKA-R2, a PKA phosphorylation site on Complexin and the acute presynaptic function of Rugose, the homolog of mammalian neurobeachin, a PKA-R2 anchor implicated in learning and autism. Therefore, localized Ca2+-independent cAMP signaling opens dilating fusion pores to release large cargoes that cannot pass through the narrower fusion pores that mediate spontaneous and activity-dependent neuropeptide release. These results imply that the fusion pore is a variable filter that differentially sets the composition of proteins released at the synapse by independent exocytosis triggers responsible for routine peptidergic transmission (Ca2+) and synaptic development (cAMP).

Impulse Noise Suppression in Color Images Using Median Filter and Deep Learning

Abstract: Refining the quality of a noisy image is essential for many image applications. Various median filter variants have been introduced to suppress various noises, but they have their own limitations when detecting noise and restoring noise-free images. Denoising convolutional neural networks (DnCNNs), primarily developed for Gaussian noise removal, are influential nonlinear mapping models in image processing. After alterations in training data, they can be used to suppress other noise with outstanding results. This article suggests a frequency median filter method combined with deep learning for color images polluted by Salt and Pepper (SnP) noise. The analysis presented in this paper has primarily used a frequency median filter to suppress impulse noise wherein the restored value for the center pixel is evaluated by the frequency median rather than the traditional median. After which, the pretrained denoising convolutional neural network is hired to suppress the remaining noise and attain the output image finally. With a visual comparative study, simulation results on the taken test images show that the proposed method surpasses de-noising methods in terms of PSNR, SSIM, NMSE, Entropy, IEF, NCC, PCC and Running Time.

Development of variable 2D FIR filter structures using farrow approximation and row-wise polyphase decomposition

This work proposes pre and post Farrow approximation of row-wise polyphase decomposed 2D finite impulse response (FIR) filters to achieve variability in 2D bandwidths with low implementation costs. The proposed method starts with realizing variable 2D filters using the 2D Farrow structure. The 2D Farrow filter structure is then row-wise polyphase decomposed and approximated in terms of fractional delay again to obtain the final set of coefficients. To illustrate the efficiency of the proposed methodology, design examples of variable circular and elliptical filters are provided in this paper. The simulation results show that the proposed methodology can result in variable 2D FIR filters with a considerable reduction in the number of multipliers w.r.t the existing approaches without much degradation in the performance parameters.

P-696 Identification of novel candidate genes associated with meitotic aneuploidy in human embryos by whole-exome sequencing (WES)

Abstract Study question Can certain genetic variants in women cause a genetic predisposition to aneuploidy? Summary answer Our results suggest that the maternal variants identified in genes regulating meiotic processes could be useful genetic biomarkers for predicting a predisposition to embryonic aneuploidies. What is known already Errors in chromosome segregation during meiosis as chromosome synapsis, crossing-over and spindle building, occur frequently in human oocytes and cause aneuploidy in embryos. These errors increase dramatically in the oocytes of older women. However, the rate of producing aneuploidy oocytes varies among IVF patients for a given age. Recent publications have identified genetic variants that are crucial for producing healthy oocytes in female IVF patients. The association between maternal genetic variants and embryonic aneuploidy risk suggests the potential of using genomic data to predict embryonic aneuploidy risk. Our aim was to identify novel variants and candidate genes for embryonic aneuploidy. Study design, size, duration A prospective observational cohort study was done including 127 trophoectoderm biopsies from 29 couples who performed whole-exome sequencing (WES) and PGT-A between November 2019 and March 2022. Women were 35 years old or younger and normal karyotype for men and women. Patients were divided in two groups according to the embryo aneuploidy rate expected by their maternal age: ≤ 50% of aneuploid blastocysts as control group(n = 14), and ≥ 50% of aneuploid blastocysts as study group(n = 15). Participants/materials, setting, methods WES was performed using Trusight One Expanded Sequencing Panel (Illumina®). The following criteria were used for filtering and annotation of candidate variants: (1) minor allele frequency (MAF) <0.05 in the gnomAD and 1000 genomes, (2) variants in genes previously associated with chromosome segregation, chromatin cohesion, meiosis and cell division processes, (3) exonic/splicing boundaries variants, (4) variants having potentially strong/moderate functional effects on the protein evaluated using three in silico prediction algorithms (SIFT, PolyPhen-2 and MutationTaster). Main results and the role of chance Overall, the mean female and male age was 26.21 + 4.11y and 40.69 + 9.07y respectively. No differences were shown between groups for patient characteristics. Regarding PGT-A cycle no differences were seen between the number of oocytes and MII retrieved. However, the mean number of biopsied embryos per patient (5.36 + 2.50 vs 3.47 + 1.36; p < 0.05) as well as the percentage of embryos biopsied on day 5 (85.33 vs 32.69; p < 0.05) were higher in the control group. Finally, as expected, the percentage of aneuploidies was higher in the study group (18.64 vs 71.89; p < 0.05). 45 variants were identified in genes potentially associated to the mechanism related to the phenotype. After the variant filtering six probably pathogenic variants were identified. The variants found in the genes were: c.1397T>C (TLE6), c.169G>A (IKBKG), c.1227A>C (BUB1B), c.277G>A (TP73), c.190C>T (PLXNA3) and c.744C>G (AURKC). All variants were heterozygous and all of them were of maternal origin. The females carrying the candidate variants belonged to the study group, and two variants in different genes were identified in one patient. No potentially pathogenic variants were detected in patients from the control group. Therefore, the average detection rate of genetic variants with potential impact in embryo aneuploidy was 33.33% (5/15). Limitations, reasons for caution A notable strength of our study is the inclusion of both (male and female) genomic data. On the other hand, our study has certain limitations including the inherent challenges of any observational study, also, the pathogenicity of the identified variants should be validated in further functional studies. Wider implications of the findings We described new candidate genes that have never been associated to meiotic embryo aneuploidy and are involved in important biological processes (cell division and chromosome segregation). WES may be considered as an efficient tool to identify patients with higher risk of embryo aneuploidy allowing an individualized genetic counselling in advance. Trial registration number Not applicable

Near‐Infrared InGaAs Intelligent Spectral Sensor by 3D Heterogeneous Hybrid Integration

The applications of near‐infrared spectroscopy (NIRS) are limited due to the bulky size, low integration, and poor intelligence in edge computing of traditional spectrometers. In this work, the authors develop an on‐chip InGaAs intelligent spectral sensor, which consists of a linear variable filter for wavelength selection, a linear focal plane array as detector and a chip processor for neural‐network inferring, based on an advanced 3D heterogeneous hybrid‐integration. More than 200 spectral channels with a spectral resolution of 1.25% central wavelength are acquired in a wide waveband from 900 to 1700 nm. Immediate results are provided onsite for users, especially applicable to nonspecialists, owing to the embedded algorithmic model in‐sensor. As a proof of applications, the authors experimentally detect adulterating green tea and achieve a real‐time identification with accuracy better than 90%. This spectral sensor with edge‐artificial‐intelligence breaks the dependence on external algorithms and paves the way for NIRS into miniaturization, integration, and intelligence, which brings more possibilities in incorporating with the consumer devices and the Internet of Things.

Double-dispersive spatio-spectral scanning for hyperspectral Earth observation

Most instruments for hyperspectral Earth observation rely on dispersive image acquisition via spatial scanning. In such systems, the Earth’s surface is scanned line by line while the satellite carrying the instrument moves over it. The spatial and spectral resolutions of the image acquisition are directly coupled via a slit aperture and are thus difficult to adjust independently. Spatio-spectral scanning systems, on the other hand, can acquire 2D, spectrally coded images with decoupled spatial and spectral resolutions. Despite this advantage, they have so far been given little attention in the literature. Simple architectures using variable filters were proposed, but come with significant caveats. As an alternative, we investigated the use of two dispersion stages for spatio-spectral scanning. We provide a theoretical treatment and show by basic experiments that a double-dispersive system provides robust and flexible image acquisition. Based on our results, we suggest a system concept for the implementation of a demonstrator on a small satellite.

Genetic heterogeneity between paired primary and metastatic solid tumors and implications for neoantigen-based personalized cancer vaccines.

3126 Background: Neoantigen-based personalized cancer vaccines carry significant promise in treating solid malignancies. For the purpose of neoantigen prediction, questions remain on the suitability of the primary versus the metastatic tumor of an individual patient (pt). Here we performed an in-depth analysis of somatic variants of 45 pts with paired primary and metastatic solid tumors. Methods: Pts were enrolled in the Total Cancer Care protocol across the Oncology Research Information Exchange Network (ORIEN). Whole-exome sequencing (WES) of primary and metastatic tumor pairs was performed for 45 pts. These included head and neck (n = 8), renal cell carcinoma (n = 7), non-small cell lung cancer (n = 6), melanoma (n = 5), bladder (n = 3), sarcoma (n = 3), ovary (n = 3), esophageal (n = 2), colorectal (n = 2) and other singletons (n = 5). The data was analyzed through the ORIEN AVATAR Molecular Analysis Pipeline, which consisted of adapter trimming, single sample variant detection, somatic mutation detection and filtering, variant annotation, and filtering of variants from a panel of normal samples. In this analysis, we focused on somatic events that result in an in-frame alteration (such as missense, in-frame insertion/deletion) and out-of-frame protein-altering mutations (such as frameshifts, de novo start and nonstop gain). To assign the clones based on the variant allele frequency, we performed complete linkage clustering. Each cluster was then assigned based on the identified oncogenic driver variant. Results: For in-frame events, we noticed that bladder cancer, melanoma, and gynecological cancers shared close to 75% of the mutations between paired primary and metastatic cases. In contrast, esophageal, brain, and endometrial cancers had a low overlap (< 25%) of variants. For out-of-frame events, we found that these events tend to have a lower proportion of shared somatic variants between primary and metastasis than in-frame variants. Through a closer evaluation of the variant allele frequency in variants shared between primary and metastatic disease, we were able to track several cancer drivers, such as oncogenic drivers of BRAF V600E, RB1 R358*, NRAS G13R, KRAS G12A, and TP53 loss-of-function that were consistently present in paired primary and metastatic tumors. Here, we were able to assign the putative clone based on the driver event in 39 out of 45 pts. Conclusions: Our analysis demonstrates genetic variations that exist when comparing paired primary and metastatic tumors that appear to vary by histology. Variants are potentially undergoing negative selection supported by the preferential loss of out-of-frame events in metastatic tumors. Understanding the clonal structure will be key to neoantigen prediction for effective neoantigen-based vaccines. Additional steps in variant prioritization are ongoing and will be reported at the meeting.

Sparsity‐optimised farrow structure variable fractional delay filter for wideband array

AbstractIn this paper, a new sparsity‐optimised Farrow structure variable fractional delay (SFS‐VFD) filter is proposed to address the aperture effect in wideband array. Our method is based on coefficient (anti‐)symmetry and optimises the number and orders of its sub‐filters, greatly reducing the non‐zero coefficients. The established cost function is formulated as a parametric minimisation problem with multiple regularisation constraints, and solved by the modified three‐block alternating direction multiplier method (MTB‐ADMM), which is improved by introducing core variable correction items to ensure stable and fast convergence. Experimental results show that the SFS‐VFD filter reduces the complexity of the system by decreasing the use of multipliers and adders while ensuring high delay accuracy. In wideband array, the SFS‐VFD filter effectively corrects the aperture effect and achieves precise beam pointing.

Efficient Digital Implementation of a Multirate-Based Variable Fractional Delay Filter for Wideband Beamforming

In this brief, we propose a FPGA implementation of a novel Variable Fractional Delay (VFD) filter architecture for digital wideband (hundred of MHz or more) beamforming applications. The proposed architecture is based on a multirate structure that allows reaching high performance in terms of amplitude and group delay with a significant reduction in design complexity. Usually, the antenna’s beams are modified by introducing precise delays that can be multiples or fractions of the sampling period. The most used structures for realizing this operation are Farrow filters but, for wideband applications, the classical Farrow structure may require a huge amount of resources. A common solution to simplify the Farrow filter architecture is interpolation. However, this solution requires an increase of the clock frequency which is not always possible. In this context, our multirate approach mitigates the limitations in terms of computational complexity avoiding the requirement to increase of the clock frequency, while still exhibiting great performance. A comparison between a reference design based on the proposed approach and the literature is also provided. The experimental results confirm that the proposed architecture, characterized by a computational complexity similar to other works, exhibits better performance without the need to increase the clock frequency.

Coupled-mode theory for resonant gratings with a varying period

We propose a coupled-mode theory for resonant diffraction gratings with a varying period. We consider diffractive structures, in which the reciprocal lattice vector, a quantity inversely proportional to the period, varies linearly in the direction of periodicity. It is shown that optical properties of such a structure essentially depend on the period change rate. On the basis of a comparison with the results of rigorous numerical simulations using the rigorous coupled-wave analysis, high accuracy of the proposed theoretical model is demonstrated. In particular, the developed coupled-mode theory describes the broadening of the resonant peak and the appearance of secondary maxima caused by a non-zero period change rate. The obtained results can be used for the development of linear variable filters based on resonant diffraction gratings with varying parameters.

Aperture-encoded snapshot hyperspectral imaging with a lensless camera

We present a lensless snapshot hyperspectral camera that is capable of hyperspectral imaging over a broad spectrum using a compact and low-cost hardware configuration. We leverage the multiplexing capability of a lensless camera, a novel type of computational imaging device that replaces the lens with a thin mask. Our device utilizes a linear variable filter and a phase mask to encode spectral information onto a monochromatic image sensor, enabling recovery of hyperspectral image stacks from a single measurement by utilizing spectral information encoded in different parts of the 2D point spread function. We perform spectral calibration using a reference color chart and verify the prototype device’s spectral and spatial resolution, as well as its imaging field of view. We report on the design and construction of the device, the image reconstruction algorithm, and spectral calibration methods and present hyperspectral images ranging from 410 to 800 nm obtained with our prototype device.

Augmented Kalman Filter Design in a Localization System Using Onboard Sensors With Intrinsic Delays

This paper presents a novel approach to address the limitations of the conventional Augmented Kalman Filter (AKF) when only on-board incremental sensors are used, which is a more realistic strategy for diverse robot configurations. The proposed AKF focuses on an online calibration of odometric parameters based on velocity measurements. Moreover, it shows the unsuitability of the AKF when data is not perfectly synchronized and introduces a real-time synchronization scheme to enhance the accuracy of parameter adjustments. The effectiveness of the method is demonstrated using an autonomous wheelchair equipped with encoders, a LIDAR and an IMU. A pre-module is added to synchronize LIDAR and IMU signals with respect to the encoders. The lags are included in the state vector and updated based on their reliability to synchronize the data in real time, which addresses the problem of real sensors where the delay period can change over time. The system also incorporates a fuzzy controller that designs a variable filter covariance inversely proportional to measurement reliability, enabling a dynamic parameter adjustment. The experiments show that the proposed localization outperforms the Extended Kalman Filter and the new AKF results provide better parameter estimation compared to when the implementation improvements are not taken into account.

An Adaptive Beam Forming Algorithm for Reduction of Coexistence- Interference and Improved Performance in 5G Communication Systems

Abstract: A couple of Wi-Fi systems coexisting in a 5G community might produce interference within the same frequency band, degrading the obtained sign's overall performance. This paper proposes a singular set of rules in antenna array processing to address interference-coexistence verbal exchange. We adopt a linear filter which is referred to as a Linearly confined minimal Variance (LCMV) filter out. We impose a log-sum penalty at the coefficients and upload it to the price feature based on traditional singly linearly restrained least suggest square (LC-LMS). The iterative formula for filter weights is derived. We show that the brand-new approach's convergence price is quicker than the conventional one in the usage of simulations in an antenna environment with a signal of hobby, noise, and interference. Furthermore, the proposed approaches suggest rectangular errors (MSE) are shown. Our approach has a lower MSE than the traditional LC-LMS set of rules, in line with the findings of the experiments. The cautioned adaptive beam forming approach may be used in a 5G gadget to cope with sign and interference coexistence.

Depth-Enhanced Holographic Super Multi-View Maxwellian Display Based on Variable Filter Aperture.

The super multi-view (SMV) near-eye display (NED) effectively provides depth cues for three-dimensional (3D) displays by projecting multiple viewpoint images or parallax images onto the retina simultaneously. Previous SMV NED suffers from a limited depth of field (DOF) due to the fixed image plane. Aperture filtering is widely used to enhance the DOF; however, an invariably sized aperture may have opposite effects on objects with different reconstruction depths. In this paper, a holographic SMV display based on the variable filter aperture is proposed to enhance the DOF. In parallax image acquisition, multiple groups of parallax images, each group recording a part of the 3D scene on a fixed depth range, are captured first. In the hologram calculation, each group of wavefronts at the image recording plane (IRP) is calculated by multiplying the parallax images with the corresponding spherical wave phase. Then, they are propagated to the pupil plane and multiplied by the corresponding aperture filter function. The size of the filter aperture is variable which is determined by the depth of the object. Finally, the complex amplitudes at the pupil plane are back-propagated to the holographic plane and added together to form the DOF-enhanced hologram. Simulation and experimental results verify the proposed method could improve the DOF of holographic SMV display, which will contribute to the application of 3D NED.

Extracting the most discriminating functional connections in mild traumatic brain injury based on machine learning

BackgroundMild traumatic brain injury (mTBI) is characterized as brain microstructural damage, which may cause a wide range of brain functional disturbances and emotional problems. Brain network analysis based on machine learning is an important means of neuroimaging research. Obtaining the most discriminating functional connection is of great significance to analyze the pathological mechanism of mTBI. MethodsTo better obtain the most discriminating features of functional connection networks, this study proposes a hierarchical feature selection pipeline (HFSP) composed of Variance Filtering (VF), Lasso, and Principal Component Analysis (PCA). Ablation experiments indicate that each module plays a positive role in classification, validating the robustness and reliability of the HFSP. Furthermore, the HFSP is compared with recursive feature elimination (RFE), elastic net (EN), and locally linear embedding (LLE), verifying its superiority. In addition, this study also utilizes random forest (RF), SVM, Bayesian, linear discriminant analysis (LDA), and logistic regression (LR) as classifiers to evaluate the generalizability of HFSP. ResultsThe results show that the indexes obtained from RF are the highest, with accuracy = 89.74%, precision = 91.26%, recall = 89.74%, and F1 score = 89.42%. The HFSP selects 25 pairs of the most discriminating functional connections, mainly distributed in the frontal lobe, occipital lobe, and cerebellum. Nine brain regions show the largest node degree.Limitations: The number of samples is small. This study only includes acute mTBI. ConclusionsThe HFSP is a useful tool for extracting discriminating functional connections and may contribute to the diagnostic processes.

A robust iterative receiver for single carrier underwater acoustic communications under impulsive noise

Achieving reliable underwater acoustic (UWA) communications is challenging due to the rapid changes of UWA channels and presence of impulsive noise. The soft decision feedback equalization (SDFE) has attracted much attention due to its capability of tackling the rapid changes of UWA channels. However, the existing SDFE exhibits error floor and even divergence due to the presence of severe impulsive noise. To overcome this problem, an improved SDFE (ISDFE) is proposed, which incorporates impulsive noise detection, clipping level estimation and damping to handle the impulsive noise. As the performance of the ISDFE also depends on the accuracy of channel estimation (CE), we propose a variable kernel width maximum complex correntropy criterion Kalman filter (VKW-MCCC-KF) algorithm to achieve robust and accurate CE under impulsive noise. With the proposed ISDFE and VKW-MCCC-KF, an iterative receiver for single carrier (SC) UWA mobile communication under impulsive noise is proposed, which incorporates the adaptive broadband Doppler compensation, VKW-MCCC-KF based CE and ISDFE, working in an iterative manner. Experimental results show that, compared with existing receivers, the proposed receiver achieves better performance and robustness to impulsive noise under fast changing UWA channels.

Online estimation of state-of-charge inconsistency for lithium-ion battery based on SVSF-VBL

It is of great significance to estimate cell inconsistency for improving the service life and safety performance of the power battery pack. To accurately estimate the state-of-charge (SOC) inconsistency of cells with different performance parameters and working states, a method combining adaptive robust unscented Kalman filter and smooth variable structure filter with time-varying smoothing boundary layer (SVSF-VBL) is proposed. The cell mean-difference model is used to simulate the behavior characteristics of the battery module, including the cell mean model expressed by the dual polarization (DP) model and the cell difference model characterized by the hypothetical Rint model. Firstly, the improved forgetting factor recursive least square is applied to identify parameters of the DP model, and the unscented Kalman filter incorporating robust estimation and adaptive filter tuning is employed to estimate the SOC of the battery module. Then, SVSF-VBL is used to estimate the SOC difference between each cell and module based on the Rint model for improving the estimation accuracy and robustness. In addition, the comprehensive inconsistency of the cells can be captured by the secondary performance indicator inherent in SVSF-VBL, which contributes to the in-depth study of cell inconsistency. Finally, a series of tests are carried out to verify the performance of the proposed method, and the results show that the method can improve the estimation accuracy and convergence performance while effectively suppressing the system disturbance.

Design and development of Fabry–Perot etalon based color filter using ebeam GLAD technique: Simulation and experiment

The design of Spatial Variable Color Filter (SVCF), based on the principle of Fabry–Perot (FP) etalon, has been analyzed in detail considering Ag as reflecting metallic layer and TiO2 as cavity layer. The effect of different thickness ranges of cavity layer for different order of interference peaks and the effect of varying thickness of reflecting layer has been investigated through simulation. A SVCF has been designed in the broad visible wavelength range of 464 to 670 nm based on the 2nd order interference peak of FP etalon for optimized spectral performance and it has been experimentally demonstrated using ebeam evaporation technique. The reflecting Ag layer has been deposited at normal angle and the tapered cavity layer of TiO2 has been deposited at 86° glancing angle. The distribution of thickness and dispersion relationship at various locations of the glancing angle deposited (GLAD) TiO2 thin films has been analyzed by spectroscopic ellipsometry measurements. The effect of lateral variation of dispersion properties of cavity layer caused by GLAD, on the spectral performance of the developed SVCF, has been investigated by both simulation and through spectral transmittance measurement. Overall results demonstrate the potential of the axial glancing angle deposition technique for the first time for development of SVCF in visible wavelength with high peak transmittance (∼67%) and a peak wavelength tunability of ∼4 nm/mm.

Electrochemical photonics: a pathway towards electrovariable optical metamaterials

Abstract This review article focuses on the latest achievements in the creation of a class of electrotuneable optical metamaterials for switchable mirrors/windows, variable colour mirrors, optical filters, and SERS sensors, based on the voltage-controlled self-assembly of plasmonic nanoparticles at liquid/liquid or solid/liquid electrochemical interfaces. Practically, these experimental systems were navigated by physical theory, the role of which was pivotal in defining the optimal conditions for their operation, but which itself was advanced in feedback with experiments. Progress and problems in the realisation of the demonstrated effects for building the corresponding devices are discussed. To put the main topic of the review in a wider perspective, the article also discusses a few other types of electrovariable metamaterials, as well as some of those that are controlled by chemistry.