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У роботі запропоновано метод очищення від шуму візуальної біометричної інформації за рахунок визначення структури моделі згладжуючої фільтрації візуальної інформації про ідентифіковану особистість на основі статистичного оцінювання якості очищення від шуму двовимірного сигналу. В результаті проведеного системного аналізу сучасних методів очищення зображення від шуму встановлено, що розглянуті методи мають один або більше з таких недоліків: не автоматизовано вибір структури та параметрів моделі фільтра та/або невисока точність очищення від адитивного та мультиплікативного шуму. Тому актуальною є розробка методу очищення від шуму візуальної біометричної інформації для проведення попередньої обробки зображення обличчя людини, що забезпечить необхідну якість зображень і не вимагатиме трудомісткої процедури визначення значень параметрів оператором на основі емпіричного досвіду. Визначено структуру моделі згладжуючої фільтрації. Запропоновано характеристики та критерій якості очищення від шуму візуального сигналу. Для визначення параметра порядку фільтра проведено чисельні дослідження за допомогою бази даних Siblings, що дозволило встановити найефективніший метод: у випадку адитивного гаусового шуму та у випадку мультиплікативного гаусового шуму, найменшу середньоквадратичну помилку, тобто таку, що відповідає критерію якості очищення від шуму візуального сигналу, забезпечує середньо α -усічений фільтр. Запропонований метод дозволяє ставити і вирішувати завдання попередньої обробки візуального сигналу, що використовуються для аналізу і зберігання візуальної інформації в інтелектуальних комп’ютерних системах біометричної ідентифікації особистості по зображенню обличчя. One of the most important problems that exist in security systems today is to increase the effectiveness of identification of a person. Computer biometric identification speeds up and increases the accuracy of the recognition process, which is especially critical in a limited time. A special class of biometric identification of a person is formed by methods based on the analysis of visual information. The first step in processing the visual biometric information for analysis and subsequent recognition of objects such as a human face is digital image filtering or low-frequency noise elimination due to distortion of various imaging devices and their subsequent transmission through various communication channels. The paper proposes the noise cleaning method for visual biometric information by determining the structure of the smoothing filtering model of visual information about the identified person based on statistical evaluation of the noise cleaning quality of two-dimensional signal. The systematic analysis of modern noise cleaning methods for image is carried out. Smoothing adaptive linear time filtering; smoothing adaptive linear frequency filtering, called spectral subtraction; wavelet analysis with threshold processing; smoothing non-adaptive linear time filtering; smoothing non-linear filtration have been studied. It is established that the considered cleaning methods for visual biometric information have one or more of the following disadvantages: not automation of the choice of structure and parameters of the filter model and/or the low accuracy of additive and multiplicative noise cleaning. Therefore, it is important to develop the noise cleaning method of visual biometric information for pre-processing of human face images, which will ensure the necessary image quality and will not require time-consuming procedures to determine parameter values by the operator based on his empirical experience. The structure of the smoothing filtration model, which is reduced to determining the filter order, is determined. The characteristics and quality criterion of visual signal noise cleaning are offered. Numerous studies have been performed to determine the filter order parameter using the Siblings database, which allows to establish the most effective method based on statistical evaluation of the quality of visual information noise cleaning: in the case of additive Gaussian noise and in the case of multiplicative Gaussian noise, the least standard error, that meets the criterion of quality of visual signal noise cleaning, provides a medium α-truncated filter. The proposed method allows to set and solve the problem of the visual signal pre-processing used for analysis and storage of visual information in intelligent computer systems of biometric identification of the person on the face image.

In this work, the Poincaré map numerical method was successfully developed to solve the fourth-order differential equation that describes the flexural vibrations of a beam, within the Timoshenko beam theory. The Euler-Bernoulli continuity conditions were considered, which are valid for frequencies smaller than the critical frequency. As an example, this method was used to design a complex elastic structure, characterized by a flexural frequency spectrum with a broad band gap. Such structure consists of two coupled phononic crystals, which were designed with filling factor values in such a way that in their bending frequency spectra, an allowed band of the first part, overlaps with a band gap of the second one and vice versa. The resulting composed system has a much wider effective gap than its original components, between 4 and 10.5 kHz. This system works as an elastic bending wave filter. Finally, these three structured elastic systems were constructed, and characterized by the acoustic resonance spectroscopy technique. The natural flexural frequencies as well as the corresponding wave amplitudes of each structured beam were measured. The experimental measurements show excellent agreement with the numerical simulation.

In order to analyze the impact of multisource vibration on the imaging quality of aerospace optical payload during camera scanning, a dynamic imaging quality degradation model fused with satellite vibration filter templates is established. Firstly, according to the motion scanning process of the TDI camera, a filter template combining the amplitude and frequency of the vibration source is designed; Secondly, a piecewise function of satellite vibration with a frequency range of 1 Hz to 1000 Hz is obtained by curve fitting, and the amplitudes of different frequency ranges are brought into the image filter template to simulate the output image affected by the vibration. Finally, the output degraded image is analyzed through structural similarity and mean square error. The simulation results show that during the imaging process of the aerospace scanning mirror, the line frequency of the camera changes regularly with the scanning speed. When platform vibration frequency is similar to the camera’s exposure frequency, it will have a large impact on image quality. Therefore, when the camera’s exposure frequency is 110 Hz, vibration at a frequency of 110 Hz will further degrade the image quality. This article is expected to provide guidance for spatial vibration suppression and image quality degradation assessment in the imaging process of aerospace scanning mirrors.

In this study, we have scrutinized the frequency gap generation by changing the geometrical parameters of a one-dimensional phononic crystal. For this purpose, we have calculated the transmission coefficient of an incident acoustic wave by using the transfer matrix method. We have retained and fixed the total length of the system and changed the system internal geometry not to increase the system length too much. Another reason was to adjust the phononic band gaps and get the desired transmission properties by finding the optimum internal geometry without increasing or decreasing the total length of phononic crystals. In addition, we also propose few structures with the opportunity of applications in acoustical devices such as sonic reflectors. Our results can also be of high interest to design acoustic filters in the case that transmission of certain frequencies is necessary.

Big brown bats emit wideband frequency modulated (FM) ultrasonic pulses for echolocation. They perceive target range from echo delay and target size from echo amplitude. Their sounds contain two prominent down-sweeping harmonic sweeps (FM1, ∼55-22 kHz; FM2, ∼100-55 kHz), which are affected differently by propagation out to the target and back to the bat. FM2 is attenuated more than FM1 during propagation. Bats anchor target ranging asymmetrically on the low frequencies in FM1, while FM2 only contributes if FM1 is present as well. These experiments tested whether the bat’s ability to discriminate target size from the amplitude of echoes is affected by selectively attenuating upper or lower frequencies. Bats were trained to perform an echo amplitude discrimination task with virtual echo targets 83 cm away. While echo delay was held constant and echo amplitude was varied to estimate threshold, either lower FM1 frequencies or higher FM2 frequencies were attenuated. The results parallel effects seen in echo delay experiments; bats’ performance was significantly poorer when the lower frequencies in echoes were attenuated, compared to higher frequencies. The bat’s ability to distinguish between virtual targets at the same simulated range from echoes arriving at the same delay indicates a high level of focused attention for perceptual isolation of one and suppression of the other.

The problem of blind signal separation, namely, the separation of a vocal track from a finished mixed recording, is considered. The purpose of the research is to isolate the characteristics of the vocal signal on the basis of existing methods and software. The existing methods of vocal selection are analyzed: frequency filtering methods, phase subtraction and methods based on artificial intelligence systems. Features of application of each method, their advantages and disadvantages are highlighted. A comparative analysis of the methods considered using Spleeter and iZotope RX7 software is carried out. Artificial intelligence methods are much better at solving the problem, but they are not satisfactory. There are distortions in the timbre of the voice and foreign noises from the remnants of other instruments. Based on this, we conclude that the existing methods of isolating the vocal are not effective due to the lack of consideration of the peculiarities of the timbre of the voice in a particular musical composition.

The elasticity of substitution between labour and capital in the Slovak economy is estimated in the paper. We used an econometric specification of capital and labour demand and data from the National Bank of Slovakia's macroeconomic database. In order to filter the processes caused by short-term shocks, the data were adjusted using the frequency filter. Using the Breusch and Pagan test, we have shown that the stochastic terms of capital and labour demand are related. Therefore, we did not use only the least squares method to estimate both specifications, but we also estimated a system of two equations with seemingly unrelated regression. The paper is extended with a discussion showing that the relatively low value of the input substitution elasticity could theoretically be explained by the transitive nature of the Slovak economy during the studied period. The elasticity of substitution between labour and capital in the Slovak economy was relatively low in the period 1997-2014; we estimated its value in the range from 0.03 to 0.11 depending on the choice of the database version.

We discuss the possibility that the contours or details of images can be obtained by using the low-spatial-frequency or high-spatial-frequency parts of the reference beam in ghost imaging. The influence of the threshold selection which is used to divide the reference pattern into low-frequency and high-frequency parts is quite different. With the increase of the threshold, the signal-to-noise ratio (SNR) of low-frequency ghost imaging increases firstly and then decreases, while SNR of high-frequency ghost imaging decreases gradually. Under a suitable threshold, the results are compared with those by directly filtering ghost-image from the traditional ghost imaging. It is shown that better imaging quality can be obtained by using our scheme. To further improve imaging quality, the method of subtracting background before correlation calculation is also presented.

The implementation of logical operations on signals employing stimulated echo holography is examined for the case when it is excited by an information-carrying object laser pulse and a pulse that serves as a frequency filter. It is shown that a stimulated echo hologram can be used to implement a conjunction operation.

This is the peer reviewed version of the following article: Arvidsson, D., Fridolfsson, J., Börjesson, M., Andersen, L. B., Ekblom, Ö., Dencker, M., & Brønd, J. C. (2019). Re‐examination of accelerometer data processing and calibration for the assessment of physical activity intensity. Scandinavian Journal of Medicine & Science in Sports, 29(10), 1442-1452, which has been published in final form at https://doi.org/10.1111/sms.13470. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This review re‐examines the use of accelerometer and oxygen uptake data for the assessment of activity intensity. Accelerometers capture mechanical work, while oxygen uptake captures the energy cost of this work. Frequency filtering needs to be considered when processing acceleration data. A too restrictive filter attenuates the acceleration signal for walking and, to a higher degree, for running. This measure-ment error affects shorter (children) more than taller (adults) individuals due to their higher movement frequency. Less restrictive filtering includes more movement‐re-lated signals and provides measures that better capture mechanical work, but may include more noise. An optimal filter cut‐point is determined where most relevant acceleration signals are included. Further, accelerometer placement affects what part of mechanical work being captured. While the waist placement captures total me-chanical work and therefore contributes to measures of activity intensity equivalent by age and stature, the thigh and wrist placements capture more internal work and do not provide equivalent measures. Value calibration of accelerometer measures is usually performed using measured oxygen uptake with the metabolic equivalent of task (MET) as reference measure of activity intensity. However, the use of MET is not stringent and is not a measure of activity intensity equivalent by age and stat-ure. A candidate measure is the mass‐specific net oxygen uptake, VO2net (VO2tot − VO2stand). To improve measurement of physical activity intensity using accel-erometers, research developments are suggested concerning the processing of ac-celerometer data, use of energy expenditure as reference for activity intensity, and calibration procedure with absolute versus relative intensity.

Accurate detection of epoch locations is important in extracting the features from the speech signal for automatic detection and assessment of voice disorders. Therefore, this study aimed to compare the various algorithms for detecting epoch locations from the speech associated with voice disorders. In this regard, nine state-of-the-art epoch extraction algorithms were considered, and their performance for different categories of voice disorders was evaluated on the SVD dataset. Experimental results indicate that most of the epoch extraction methods showed better performance for healthy speech; however, their performance was degraded for speech associated with voice disorders. Furthermore, the performance of epoch extraction methods was degraded for the speech of structural and neurogenic disorders compared to the speech of psychogenic and functional disorders. Among the different epoch extraction algorithms, zero phase-zero frequency filtering showed the best performance in terms identification rate (90.37%) and identification accuracy (0.34ms), for speech associated with voice disorders.

Here we report a massively-parallel Fourier-optics convolutional processor accelerated 160x over spatial-light-modulators using digital-mirror-display technology as input and kernel showing an MNIST and CIFAR-10 accuracy of 96% and 54%, respectively.

A metalens opto-electronic camera for spatial frequency filtering under inco- herent illumination is introduced. By inverse-designing the phase on the metasurface, near arbitrarily structured 2D filters are realized for depth-invariant operation.

Lexical simplification (LS) aims at replacing words considered complex in a sentence by simpler equivalents. In this paper, we present the first automatic LS service for French, FrenLys, which offers different techniques to generate, select and rank substitutes. The paper describes the different methods proposed by our tool, which includes both classical approaches (e.g. generation of candidates from lexical resources, frequency filter, etc.) and more innovative approaches such as the exploitation of CamemBERT, a model for French based on the RoBERTa architecture. To evaluate the different methods, a new evaluation dataset for French is introduced.

Active inductors, widely used in filtering and amplification functions, and based on frequency filtering selection, are the subject of several investigations. Through this paper, we propose an analytical study of the active inductor to understand its frequency functioning. To do this, a small transistor signal study is carried out first in order to extract the intrinsic elements that will be used to study the active inductor, by calculating H and Y parameters.

Here we report a massively-parallel Fourier-optics convolutional processor accelerated 160x over spatial-light-modulators using digital-mirror-display technology as input and kernel showing an MNIST and CIFAR-10 accuracy of 96% and 54%, respectively.

Ultrasensitivity is a ubiquitous emergent property of biochemical reaction networks. The design and construction of synthetic reaction networks exhibiting ultrasensitivity has been challenging, but would greatly expand the potential properties of life-like materials. Herein, we exploit a general and modular strategy to reversibly regulate the activity of enzymes using light and show how ultrasensitivity arises in simple out-of-equilibrium enzymatic systems upon incorporation of reversible photoswitchable inhibitors (PIs). Utilizing a chromophore/warhead strategy, PIs of the protease α-chymotrypsin were synthesized, which led to the discovery of inhibitors with large differences in inhibition constants (Ki) for the different photoisomers. A microfluidic flow setup was used to study enzymatic reactions under out-of-equilibrium conditions by continuous addition and removal of reagents. Upon irradiation of the continuously stirred tank reactor with different light pulse sequences, i.e., varying the pulse duration or frequency of UV and blue light irradiation, reversible switching between photoisomers resulted in ultrasensitive responses in enzymatic activity as well as frequency filtering of input signals. This general and modular strategy enables reversible and tunable control over the kinetic rates of individual enzyme-catalyzed reactions and makes a programmable linkage of enzymes to a wide range of network topologies feasible.

Integrated microwave photonics offers new paradigms for processing signals. Microwave photonics advances, including broadband tunable microwave photonic filters, multifunction processors, and high-resolution photonic sensing systems that provide new capabilities for high-performance signal processing, are presented.

Parkinson's disease (PD) is a progressive deterioration of the human central nervous system. Detection of PD (discriminating patients with PD from healthy subjects) from speech is a useful approach due to its non-invasive nature. This study proposes to use novel cepstral coefficients derived from the single frequency filtering (SFF) method, called as single frequency filtering cepstral coefficients (SFFCCs) for the detection of PD. SFF has been shown to provide higher spectro-temporal resolution compared to the short-time Fourier transform. The current study uses the PC-GITA database, which consists of speech from speakers with PD and healthy controls (50 males, 50 females). Our proposed detection system is based on the i-vectors derived from SFFCCs using SVM as a classifier. In the detection of PD, better performance was achieved when the i-vectors were computed from the proposed SFFCCs compared to the popular conventional MFCCs. Furthermore, we investigated the effect of temporal variations by deriving the shifted delta cepstral (SDC) coefficients using SFFCCs. These experiments revealed that the i-vectors derived from the proposed SFFCCs+SDC features gave an absolute improvement of 9% compared to the i-vectors derived from the baseline MFCCs+SDC features, indicating the importance of temporal variations in the detection of PD.

The paper describes the processing of ECG signals, which consists of preliminary processing of the original ECG signals, frequency filtering of signals, extraction and calculation of features for determining the heart rate. Provides brief information on the physiological and pathological reactions of the cardiovascular system. The main parameters of heart rate variability have been determined. Algorithmic and software implementation of signal processing and R-peak detection and calculation of RR-intervals in the PYTHON environment are proposed.

Speech intelligibility improvement is a major field of recent research with in audiology and hearing domain. Speech signal usually gets affected due to various environmental and background...

Neoproterozoic rocks are exposed in the southwestern Midyan terrane, which is located in the northwestern Arabian Shield. This terrane contains ophiolitic and metavolcanic rocks and arc-related intrusions. A NW-trending sinistral fault system (Najd Fault System, NFS) affected these Neoproterozoic rocks. The integration of remote sensing techniques, such as band combinations and band ratios of ASTER images, with field observations, helped to discriminate sulfide-rich rocks and to delineate alteration zones, gossans, and quartz veins, which commonly host ore mineralization. The band ratios used were the Al-OH minerals/advanced argillic alteration and the silica and Fe2+ band ratios. ASTER images were used to discriminate different rock units and construct an alteration map of the study area. Additionally, principal component analysis and frequency filtering techniques for ASTER images were used to extract linear faults and shear zone structures. Remote sensing ASTER images helped in the discovery of ancient mines that had not been previously recorded in the study area.

Various representations have been developed for acoustic scene classifications (ASC) task using spectral information. However, there is a wide gap in dealing with acoustic scene representations. In this paper, we propose to use a single frequency filtering (SFF) approach, which provides good temporal and spectral resolution at each instant. Single-frequency filtering cepstral coefficients (SFFCC) with deep neural network (DNN) model as the classifier is used for the experimentation on DCASE 2019 and DCASE 2018 Task 1, development data of subtasks A and B. From the conducted experiments on the development datasets, the usage of the SFFCC features significantly improved ASC performance. This approach has got 35th team rank out of 46 submissions to the corresponding DCASE 2019 Task 1A challenge with a 52.6% classification accuracy on the evaluation dataset. Also, the effect of raw waveforms taken as features for ASC using DNNs was observed.

Atomic-resolution low-dose imaging for beam-sensitive materials is one of the most challenging topics in electron microscopy research. In this study, we theoretically developed a new scanning transmission electron microscopy (STEM) imaging technique by maximizing the signal-to-noise ratio of an obtainable image under the weak phase object approximation (WPOA), which we will call optimum bright-field (OBF) imaging. OBF images are obtained by processing multiple images acquired by segmented/pixelated detectors through complex frequency filtering. This method has been confirmed through a systematic image simulation to be highly dose-efficient. Furthermore, we experimentally demonstrate the high dose efficiency of the OBF technique by visualizing the atomic structure in a lithium-ion battery material using a high-speed segmented detector. Furthermore, it was shown that OBF imaging is usable for real-time imaging, which makes low-dose observations of beam-sensitive materials much easier to achieve.

Hypernasality is an abnormality in speech production observed in subjects with craniofacial anomalies like cleft lip and palate (CLP). Detection and assessment of hypernasality is a primary step in the clinical diagnosis of individuals with CLP. Existing methods explore the short-term spectral information from speech to assess hy-pernasality. The present work examines long-term average spectral (LTAS) features obtained from speech to detect and assess hyper-nasality. This work proposes single frequency filter bank based long-term average spectral (SFFB-LTAS) features for hypernasality detection and assessment. The SFFB is used to extract long-term average spectra with a good spectral resolution. The experiments are carried out using NMCPC-CLP database collected from 41 speakers with CLP and 32 speakers without CLP. The experimental results show that, SFFB-LTAS features performed better compared to state-of-art spectral and prosody features. The proposed systems for the detection and assessment of hypernasality have shown classification accuracy of 89% and 82.1%, respectively.

The filtering of optical signals and the transformation of information using stimulated echo-holography is examined in the case where it is excited by an information-carrying object laser pulse and a pulse that serves as a frequency filter. It is shown that the shape of the response of the stimulated echo hologram depends on the duration, amplitude, area, and temporal structure of the exciting pulses. This can be used for logical operations with signals.

A new zonal two-dimensional method for wavefront retrieval from a surface under test using lateral shearing interferometry is presented. A modified Saunders method and phase shifting techniques are combined to generate a method for wavefront reconstruction. The result is a wavefront with an error below 0.7 λ and without any global high frequency filtering. A zonal analysis over square cells along the surfaces is made, obtaining a polynomial expression for the wavefront deformations over each cell. The main advantage of this method over previously published methods is that a global filtering of high spatial frequencies is not present. Thus, a global smoothing of the wavefront deformations is avoided, allowing the detection of deformations with relatively small extensions, that is, with high spatial frequencies. Additionally, local curvature and low order aberration coefficients are obtained in each cell.

This paper examines the profitability of a pair-trading strategy in the Taiwan stock market while considering alternative frequency-distance filters, thresholds for opening a pair trade relationship, reinvestment mechanisms, different lengths of trading period, industry boundary, and the short-selling restriction. In contrast with the recent literature showing that pair-trading strategy returns are insignificant and negative in the Taiwan stock market, we find that the profitabilities of pair trades developed by positions in the Taiwan 50 Index during 1990/1–2016/3 present significant annualized mean returns of 1.84%–3.04%. Moreover, thresholds with different stringent degrees, industry boundary, and alternative reinvestment mechanisms are unable to help pick out more profitable pair-trading portfolios. The distance filter and shorter trading-day setting are more reliable for pair trading. Finally, the deregulation on short selling results in more pair-trading activities, which obviously diminish the profitability of a pair-trading strategy.

This paper presents a new filter-based algorithm to estimate the fundamental phasor of fault currents, using the proposed complex frequency filter. The presence of decaying dc component (ddc) in fault currents causes large error in discrete Fourier transform (DFT)-based phasor estimation methods. The proposed filter discriminates the ddc from dc and sinusoidal signals. In this algorithm, first, the ddc is filtered out by the proposed filter. Then, the fundamental phasor can be precisely extracted by applying DFT. Second and fourth order of the complex frequency filter and their design method are presented. To evaluate the performance of the proposed algorithm, several test signals are used. The algorithm is also compared with recent DFT-based and mimic-DFT methods, using four performance indices. The simulation results demonstrate the robustness of the proposed algorithm against harmonics, noise, multiple ddc components, and off-nominal frequency. It also offers faster convergence speed. The proposed complex frequency filter can also be used for the phasor estimation of harmonic components in the presence of ddc with no need to readjust its parameters.

In order to meet the requirements of shake tables, frequency filtration of seismic input is usually performed before tests. Aiming to study the error of seismic analysis caused by filtering, the artificial seismic waves before and after filtering with four different waveforms were synthesized in this paper. Based on a spring-friction isolation system, the effects of filtering on seismic responses with various spring constants, friction coefficients and envelope functions were investigated. The results show that the maximum acceleration of the pure friction system is not affected by filtering while the error of maximum relative displacement caused by filtering can reach 60% and decreases with the increment of the friction coefficient. For the spring-friction system, the friction coefficient corresponding to a smaller structural seismic response is exactly the most unfavorable friction coefficient for filtering error and hence needs more attention during shaking table tests. Filtering errors based on different waveforms have the same tendency despite some slight differences owing to the different responses.

This paper considers the problem of filter design for two-dimensional (2-D) continuous nonlinear systems in Takagi–Sugeno (T–S) form. Disdomain. The so-called FF L2 gain is defined for 2-D continuous systems which extends the standard L2 gain. Our attention is focused on designing filters that guarantee the bounded-input-bounded-output (BIBO) stability and the FF L2 gain performance of the filtering error system. The existence conditions of fuzzy filters are obtained in terms of solving linear matrix inequalities (LMIs). An example is given to validate the proposed methods.

The author introduces news sentiment as a variable that can explain and predict subsequent changes in the USD/EUR exchange rate and therefore close a gap in the foreign exchange literature. By applying the concept of frequency filtering from the domain of electrical engineering, the author shows an innovative way of filtering for noise not only in news sentiment, but also in price momentum. The author finds that news sentiment is not correlated to price momentum, and that trading strategies based on news sentiment achieve around twice as high information ratios (up to 0.9) than with trading strategies based on price momentum.

In this paper we propose a method for extracting the fundamental frequency fo from degraded speech signals using single frequency filtering SFF approach. The SFF of frequency-shifted speech signal gives high signal-to-noise ratio SNR segments at some frequencies and hence the SFF approach can be exploited for fo extraction using autocorrelation function of those segments. Since the fo is computed from the envelope of a single frequency component of the signal, the vocal tract resonances do not affect the fo extraction. The use of the high SNR frequency component in a given segment helps in overcoming the effects of degradations in the speech signal, without explicitly estimating the characteristics of noise. The proposed method of fo extraction is shown to give better performance for several types of real and simulated degradations, in comparison with some of the methods reported recently in the literature.

There are a number of characteristics that make metamaterial has wide applications and has been researched at present, such as negative phase, negative index of refraction, super resolution, and reverse of Doppler's effects. This paper investigates a hexagonal metamaterial structure with a split and a double concentric rings so-called split ring resonator hexagonal (SRR-H) for frequency filtering application. SRR-H is designed structurally and operated computationally for frequency range of 1 to 120 GHz. The simulation results showed that SRR-H size will affect the resonant frequency significantly. The resonant frequency tends to shift the lower frequency when the size of the structure is enlarged. The properties of the structure SRR-H, such as permeability, permittivity and the refractive index have a similarity because it is dominated by the properties of permeability. Metamaterial SRR-H successfully responded to double negative and agreed with Lorentz's model.

A novel microwave photonic filter (MPF) for frequency-tripled signals is proposed. The simulation results show that the proposed MPF successfully selects out frequency-tripled signals with reconfigurable center frequency and bandwidth. © 2020 The Author(s)

The SVD-based time and frequency domain Eigen image and Cadzow filters were compared and discussed in the previous chapter for random noise attenuation based on coherency of signal. The time domain frequency filtering of seismic traces using Singular Spectrum Analysis (SSA) method, a data adaptive time domain filtering mechanism, is presented in this chapter.

We report on the applicability of glasses and glass ceramics in the field of high frequency electronics. The main argument for applying such materials in high frequency electronics is their excellent homogeneity, which allows to make designs with kHz accuracy in the range of GHz frequencies. The range of dielectric properties which are covered by glasses and glass ceramics is outlined. Further on a number of examples are discussed were either glasses or glass ceramics are used with antenna functionalities or for high frequency filter applications.

Labile plasticity in a complex quantitative character is modeled, with multiple components contributing to net plasticity in the character. Each component has a specific development rate, norm of reaction, and cost of plasticity. For example, thermal adaptation in mammals includes seasonal fat deposition and fur growth, short-term shivering and sweating or panting, and movement between warm and cold sites. Norms of reaction do not reveal patterns of developmental integration, which must be investigated by studies of developmental dynamics in a changing environment. In a periodic environment, a labile character with a single component of plasticity is constrained by filtering environmental frequencies above the development rate and by the cost of plasticity. With multiple components of plasticity, some patterns of integration can alleviate these constraints to greatly improve fidelity of the mean phenotype tracking multiperiodic cycles in the optimum phenotype. This occurs by environmental signal amplification or inhibition through developmental integration among components and by an augmented development rate of net plasticity in the character that reduces environmental frequency filtering. When development of a component with high cost of plasticity is regulated partly by the norm of reaction of another component, evolution can diminish the reaction norm slope of the costly component without curtailing its development, thereby reducing the loss of fitness from its cost of plasticity. Apparent maladaptation in a component of plasticity may be an integral part of an adaptive pattern of developmental integration by mutual inhibition between components and compensatory evolution of a negative component reaction norm slope.

This paper proposes a new speech feature representation that improves the intelligibility assessment of dysarthric speech. The formulation of the feature set is motivated from the human auditory perception and high time-frequency resolution property of single frequency filtering (SFF) technique. The proposed features are named as perceptually enhanced single frequency cepstral coefficients (PE-SFCC). As a part of SFF technique implementation, speech signal passed through a single pole complex bandpass filter bank to obtain high-resolution time-frequency distribution. Then, the distribution is enhanced by using a set of auditory perceptual operators. Lastly, traditional homomorphic analysis has been carried out on the resulting signal to obtain PE-SFCC feature vector. The performance of proposed features in dysarthric speech detection and its intelligibility assessment has been reported on UASPEECH database. The PE-SFCC features outperformed the state-of-the-art features in dysarthric speech detection and intelligibility assessment.

This study aimed at demonstrating the AED derived feature component significantly outperforming MFCC features or log frequency filter parameters. This is effectively achieved without increasing the number of parameters in acoustic events detection under the speech and other audio sounds recognition. This has been made possible and suitable for the use of AdaBoost approach as discussed in the paper.

Nonstationary linear frequency modulated (FM) noisy signals are efficiently estimated by using recently developed optimal (Wiener) time-frequency filtering solution. It is developed based on the estimation of instantaneous frequency (IF) of the filtered noisy signal and on its correspondence to the filter’s region of support. Multiple-clock-cycle hardware design of this solution is firstly proposed, whereas the signal adaptive and the fully pipelined versions of the same solution, with the optimized time and hardware requirements and in their one-dimensional and two-dimensional forms, have also been recently developed. However, the non-liner FM signals cannot be ideally concentrated at their IFs and, hence, these signals cannot be efficiently estimated by using the IF estimation-based filtering solutions. Therefore, in this paper, challenges in estimation of the non-linear FM signals are analyzed in detail. Simultaneously, the consideration of the modified IF estimation-based algorithm suitable to provide high quality estimation of these signals is presented.

As wind power becomes an important part of power systems, more accurate forecast is critical to high-efficiency application of wind power. The paper proposes a novel forecast model of MHF-LSH-SVR based on morphological high-frequency filter (MHF) and local-sensitive hashing algorithms (LSH), focusing on short-term wind power forecast. To mitigate the effect of high volatility of the raw wind power time series on forecast, the MHF filter is used to extract the mean trend time series, which indicates the mean tendency. In order to perform local forecast, the mean trend time series is reconstructed into mean trend segments in the higher-dimension phase space. Afterwards, to obtain more accurate forecast result, the LSH algorithm is applied to pick out the similar segments of the forecast mean trend segment, which are then employed to perform local forecast by the algorithm of support vector regression (SVR). Simulation studies are carried out on the raw wind power time series collected from a real wind farm, and the final results demonstrate that the MHF-LSH-SVR model outperforms the models of persistence (Per.), SVR and MHF-SVR in terms of both accuracy and stability.