Band Pass Research Articles

Identification Of Shear Strain On The Surface Ground Of Wangi-Wangi Island, Southeast Sulawesi, Indonesia, Using Nakamura’s Technique and The Possibility Of Its Impacts

This research was conducted to determine the possible impact of an earthquake on the mainland of Wangi-Wangi Island based on the presence of shear strain on the surface ground (γ). The size of γ is obtained by multiplying the ground susceptibility index and the acceleration of basement ground or PGA using Nakamura’s technique. The data used are microtremor data and earthquake data from 1920 to 2020 sourced from the USGS. Microtremor data are obtained from the results of filtering ground vibration signals using a Band Pass Filter in the frequency range between 0.5 to 25 Hz. Ground vibration signals were recorded at 47 measurement points spread over the surface of Wangi-Wangi Island within 29.25 to 48.16 minutes. Furthermore, the microtremor data were processed using the HVSR (Horizontal to Vertical Spectral Ratio) method. The use of earthquake data must meet the requirements for a surface magnitude (MS) ≥ 5.0 SR and an earthquake epicenter depth (h) ≤ 45 km. The results obtained are the γ sizes of Wangi-Wangi Island in the order of 10-06 to 10-03. Based on the size distribution, it is known that the majority of the Wangi-Wangi Island area has the potential to experience cracks and land subsidence due to settlements if an earthquake occurs, and only a portion of the area is vibrating. In addition, it is also known that the mainland of Wangi-Wangi Island is not prone to landslides and liquefaction because γ<10-2.

Pass bands formation in YIG film with periodic metal grating

Experimental results on the magnetostatic surface wave (MSSW) propagation in an yttrium-iron garnet film with a periodical array of metal stripes on the surface are presented. An effect of the pass bands formation in the MSSW transmission characteristics contrasting to the known Bragg stop bands inherent in a periodical structure is reported and discussed. Our findings provide one more way to affect the spin wave propagation and realize a control in magnonic devices.

A novel miniaturized waveguide all pole band pass filter using confined multi‐layered frequency selective surfaces

AbstractThis letter presents a novel technique for realizing waveguide all pole band pass filters (BPFs). To the best of the authors’ knowledge this work is the first of its kind to confine the frequency selective surface (FSS) within a waveguide to realize highly miniatured high‐performance BPF. For the proof of the concept, a sixth‐order all pole BPF is realized at 10 GHz with a fractional bandwidth of 10%. The measured peak insertion loss of the filter is better than 1.2 dB and return loss is better than 15 dB. The proposed concept yields a remarkable miniaturization with an internal volume reduction of 30%. Space mapping technique has been adopted to systematically design the filter.

A configuration to double the gain of pyramidal horn antenna using single layer frequency selective surface

AbstractA gain enhancement technique in pyramidal horn antenna using a slot type band pass frequency selective surface (FSS) is presented. The horn antenna without FSS has achieved a measured gain of 10.5 dBi at 10 GHz, while the antenna with the single layer slotted FSS yields an enhanced measured gain of 14.2 dBi (an improvement of 3.7 dB or 134%). Conventionally, such a gain enhancement would 5 times increase in its dimensions (flaring). However, the authors achieve the same with only 2 times increase in dimensions. Another interesting value addition of the proposed idea is that an FSS sheet can be added to the existing horn antenna to increase its gain without altering the antenna itself. Such a scheme is highly beneficial in strategic applications. The proposed gain enhanced horn antenna can be recommended to be utilised in several X‐band applications, such as radar systems (weather, surveillance, traffic control and military), satellite communications and point–point microwave links that require high gain and small form factor.

Smart controllable wave dispersion in acoustic metamaterials using magnetorheological elastomers

The success in the flexible design of smart acoustic metamaterials is crucially contingent upon the degree of control over the parameters that uniquely define the spectrum of band structure and morphology of dispersion surfaces. In this work, we have studied the driving physical mechanisms, the control of which makes possible operating, in real-time, on the set of band gaps formed in 3D metamaterials based on magneto elastomers. In such acoustic structures, the stiffness of the medium in which unit cells are immersed, as well as the stiffness of the shells surrounding multi-particle cores depend on the induction, B(t), of an external magnetic field. The results obtained are systematized through the qualitative analysis of diversе scenarios for the evolution of the frequency characteristics of the metamaterial and summarized in the following complete physical picture of their dynamics. Variation of the stiffness of the medium/shells changes the wavelength of the shell's surface waves at characteristic frequencies of the core vibrations and, as a result, the level of coupling between the vibration modes of the flexible shells and cores. Consequently, with an increase/decrease in the stiffnesses of the shells/medium, the dispersion surfaces of the entire acoustic system shift up/down along the frequency axis with noticeably different ‘mobilities’ that reversibly lead either to the formation of the band gaps in initially dense frequency spectrum or to the transformation of the band gaps formed into pass bands. The tuning of the set of dispersion surfaces depending on the range of changes in the magnetic field induction can be carried out in dynamic, quasi-stationary, and over-critical regimes when some of the dispersion surfaces degenerate into planes. In anisotropic metamaterials, simultaneously with creating full band gaps, it is possible to create tunable directional band gaps with adjustable frequency and angular widths. The results obtained, within the framework of the idealized discrete mass-spring model, are in good agreement with the data presented in the available experimental works and can be useful in the design of acoustic systems with desirable properties.

Serial Analog-Digital QAM Modems Based on Complex Band-Pass Filters with LF Butterworth Prototypes

The article is devoted to the development and implementation of quadrature amplitude modulation algorithms based on an analog complex band-pass filter with Butterworth low-frequency prototypes. The use of complex Butterworth band pass filters makes it possible to obtain carrier signals with practically non-overlapping spectra. But the analog complex Butterworth filter has a non-linear phase-frequency response and, accordingly, the impulse responses of which are infinite and asymmetric. An overview of the development and implementation of a serial QAM modem based on an analog Butterworth complex band-pass filter and an inverse digital FIR filter. The procedure for obtaining the expression for the impulse response of analog complex band-pass Butterworth filters is considered. With the help of circuit simulation, the frequency response and impulse response of such a filter are determined. The choice of the center frequency is carried out by setting two coefficients. A block diagram of a serial data transmission system with quadrature amplitude modulation based on an analog complex Butterworth bandpass filter and an inverse digital FIR filter is proposed. The results of its circuit simulation in the Micro-Cap environment are presented.

An advanced median filter for improving the signal-to-noise ratio of seismological datasets

Strong noise usually disturbs the recorded seismic waves, resulting in low-quality seismic data sometimes with an extremely low signal-to-noise ratio (S/N), which negatively impacts the subsequent seismological processes, e.g., imaging, inversion, and interpretation. Suppressing undesirable noise is a meaningful procedure to increase the S/N of seismological datasets. This issue can be partially addressed using the recently proposed structure-oriented space-varying median filter (SOSVMF) by filtering out the unwanted noise from noise-corrupted signals. However, the SOSVMF approach has limitations when the raw data is corrupted by various sources of noise which is more common in practice. To conquer the difficulties in improving the S/N in the face of diverse types of strong noise, we introduce MATamf, an open-source MATLAB code package based on a novel advanced median filter (AMF). The proposed AMF workflow takes advantage of multiple denoising operators, i.e., the bandpass (BP) filter, SOSVMF, dip filter in the frequency–wavenumber (FK) domain, Curvelet, and local orthogonalization (LO), all in the same framework to simultaneously attenuate all types of noise and improve S/N. To demonstrate the usefulness of the MATamf package and its advantage over conventional denoising methods, we introduce the principles behind the proposed AMF framework and present results from a variety of seismological problems, including seismic reflection, distributed acoustic sensing (DAS), as well as global seismological tasks of receiver function and SS-precursors imaging. All results show that the proposed AMF workflow can be conveniently utilized to improve the S/N of a wide spectrum of seismological applications.

Gaussian and Gaussian-pulsed-like Fermi velocity graphene structures

Gaussian and Gaussian-related structures are quite attractive due to its versatility to modulate the electronic transport, including its possibility as electron filters. Here, we show that these non-conventional profiles are not the exception when dealing with Fermi velocity barriers in monolayer graphene. In particular, we show that Gaussian Fermi velocity graphene barriers (G-FVGBs) and Gaussian-pulsed-like Fermi velocity graphene superlattices (GPL-FVGSLs) can serve as electron band-pass filters and oscillating conductance structures. We reach this conclusion by theoretically studying the transmission and transport properties of the mentioned structures. The study is based on the continuum model, the transfer matrix method and the Landauer–Büttiker formalism. We find nearly flat transmission bands or pass bands for G-FVGBs modulable through the system parameters. The pass bands improve as the maximum ratio of Fermi velocities () increases, however its omnidirectional range is reduced. These characteristics result in a decaying conductance (integrated transmission) with . The integrated transmission remains practically unaltered with the size of the system due to the saturation of the electron pass band filtering. In the case of GPL-FVGSLs the GPL profile results in regions of high transmission probability that can merge as flat transmission minibands if the pulse fraction and the superlattice parameters are appropriately tuned. The GPL profile also results in conductance (integrated transmission) oscillations that can be multiplied or reduced in number by adjusting the pulse fraction as well as the superlattice parameters.

Computing dispersion relations with modal analysis methods

Dispersion diagrams depicting dispersion relations are usually computed from a periodic structure single unit cell using either analytical methods, such as the plane wave expansion (PWE) method, or numerical methods, such as the wave finite element (WFE) method, by applying Bloch-Floquet periodic boundary conditions. However, the latter is still not available in most commercial FE codes. In this work, we propose a method to compute the dispersion diagram using standard modal analysis tools available in FE commercial codes currently used for structural analysis. We show that if a metastructure consisting of a few cells is "periodized" by "wrapping around" the degrees of freedom at its ends, its natural frequencies will be points on the dispersion curve pass bands. The wavenumbers corresponding to these natural frequencies can be obtained from the corresponding vibration modes using Prony's method. These natural frequencies are real for Hermitian structures and complex for non-Hermitian structures. Besides providing a useful tool for wave dispersion analysis using standard FE codes, the proposed method provides interesting insights into the dynamics of periodic structures. The proposed method is applied to simple passive and active periodic rod structures.

Study of short-wavelength pass dichroic laser mirror coatings with hafnia–silica mixture layers

Hafnia/silica (HfO2/SiO2) short-wavelength pass dichroic laser mirror (DLM) coatings are widely utilized in high-power laser systems. However, the half-wave hole effect resulting from the refractive index inhomogeneity of HfO2 severely restricts the spectra and potential applications of these coatings. To address this issue, an investigation was conducted on HfO2–SiO2 mixture coatings with different proportions, which were produced by electron beam co-evaporation. Compared to the inhomogeneous growth characteristics observed in pure HfO2 coatings, the introduction of 10 % or higher SiO2 admixing in HfO2–SiO2 mixture coatings results in an amorphous structure characterized by excellent refractive index homogeneity and improved surface smoothness. Leveraging this, different DLM coatings with the objective of achieving high transmittance across the 400 to 900 nm range were designed and prepared, while ensuring high reflectance at 1064 nm. The incorporation of HfO2–SiO2 mixture layers instead of pure HfO2 layers in the mirror coatings led to a fundamental inhibition of the half-wave hole effect throughout the entire broad transmission band, resulting in a substantial improvement in the optical performance of the coatings. Moreover, by incorporating several types of HfO2–SiO2 mixture layers with different proportions in the coating stacks, the transmittance of DLM coatings in the pass band can be further increased, and the transmission fluctuations at different wavelengths can be further compressed. Additionally, compared to traditional DLM coatings with HfO2 layers, DLM coatings incorporating HfO2–SiO2 mixture layers exhibit lower roughness, higher laser-induced damage threshold (LIDT) at 532 nm, and similar LIDT at 1064 nm. These results indicate a promising strategy for fabricating DLM coatings tailored for high-power laser systems.

Tri-model classifiers for EEG based mental task classification: hybrid optimization assisted framework

The commercial adoption of BCI technologies for both clinical and non-clinical applications is drawing scientists to the creation of wearable devices for daily living. Emotions are essential to human existence and have a significant impact on thinking. Emotion is frequently linked to rational decision-making, perception, interpersonal interaction, and even basic human intellect. The requirement for trustworthy and implementable methods for the detection of individual emotional responses is needed with rising attention of the scientific community towards the establishment of some significant emotional connections among people and computers. This work introduces EEG recognition model, where the input signal is pre-processed using band pass filter. Then, the features like discrete wavelet transform (DWT), band power, spectral flatness, and improved Entropy are extracted. Further, for recognition, tri-classifiers like long short term memory (LSTM), improved deep belief network (DBN) and recurrent neural network (RNN) are used. Also to enhance tri-model classifier performance, the weights of LSTM, improved DBN, and RNN are tuned by model named as shark smell updated BES optimization (SSU-BES). Finally, the perfection of SSU-BES is demonstrated over diverse metrics.

Signal pre-processing techniques for fault signature enhancement in a bearing health monitoring system used in the automotive industry

Traditional internal combustion engine vehicles have low transmission bearing failure rates in their lifespans. However, the prolonged lifespan of electric and autonomous vehicles can surpass the reliable life of bearing designs, which poses a risk of bearing failure and loss of propulsion. Compared to replacing bearings on a fixed schedule to ensure reliability, a bearing health monitoring system is a more cost-effective solution. Despite extensive research on bearing condition monitoring, implementing well-known methods such as vibration spectrum analysis in vehicles can be challenging due to vibrations from vehicle components and the road. This paper explores and compares the effect of various pre-processing techniques on the spectrum of a faulty bearing with various fault levels. To achieve this objective, faults with the width size of 0.1 mm (mild), 0.5 mm (moderate) and 2 mm (severe) were injected into the inner race of a ball bearing. A bench setup was then used to capture the vibrations of multiple vehicle components including the faulty ball bearing under various speed/ load conditions. Phase domain transform, envelope and Fourier transform were used as the core signal processing steps, and advanced signal processing methods for removing discrete frequencies from other components and enhancing the fault signature were explored. 4 health indicators were then developed from the vibration spectrum of the vibration signals and calculated for the captured data. Next, for each fault level, the area under Receiver operating characteristic (ROC) curve was calculated and used as a metric to compare the performance of our health monitoring system for classification of faulty and healthy bearings. For our best health indicator, the results show that applying minimum entropy deconvolution, and spectral kurtosis-based band pass filtering increases the ROC area from 0.40, 0.99, 1.0 to 0.86, 1.0 and 1.0 for the mild, moderate, and severe inner race faults, respectively. This implies that although applying only phase domain transform, envelope and Fourier transform might be enough for moderate and severe faults, advanced signal processing is needed to enhance the fault signature for early detection of mild faults.

A Metamaterial Inspired Multiband Conformal Bandpass Filter with Improved Quality Factor for Sub-6 GHz Wireless Communication Applications

This paper deals with the design, simulation, and practical modeling of metamaterial-based multiband conformal bandpass filter (BPF) for various wireless communication applications with improved quality factors. The novel metamaterial in the form of a split ring resonator is loaded on the ground plane face of the proposed BPF. The overall dimension of the designed BPF is only [Formula: see text]. The proposed BPF is tuned initially for quality factor enhancement based on the thickness of the substrate, physical parameters of the f transmission line, ground plane, externally loaded elements, and the gap in the metamaterial loading. The suggested filter operates at triple band covering the frequency bands from 1.4 to 2.2, 3.6 to 3.9, and 4.8 to 5.9[Formula: see text]GHz, which are suitable for sub-6[Formula: see text]GHz 5G and other wireless applications. The insertion loss is observed as 1[Formula: see text]dB, which is suitable for the proposed BPF. The conformal behavior of the filter is judged through bending deformation analysis at various bending positions like (15[Formula: see text], 30[Formula: see text], 45[Formula: see text], 60[Formula: see text], and 90[Formula: see text]). The proposed BPF retains triple pass band characteristics at various bending deformations, which makes it suitable to be used in curved structures or flexible circuitry. The theory of equivalent circuits and quality factor [Formula: see text] of the designed BPF is discussed in this paper. The results are analyzed experimentally through ANRITSU-MS2037C combinational analyzer. The proposed BPF is suitable for sub-6 GHz 5G, WLAN, and Wi-Max applications.

Longitudinal wave propagation analysis in Entangled metallic wire materials using an improved wave and finite element method

This paper proposes a novel concept and methodology to study longitudinal wave propagation in entangled metallic wire material (EMWM), a porous structural network consisting of spatial helix wires. The numerical model of EMWM is constructed using representative volume elements (RVE) obtained through X-ray tomography and skeletonization of a standard specimen. By increasing the approximate periodic boundary, the improved wave and finite ele-ment method (WFE) is employed to model EMWM as approximately periodic structures, allowing for numerical simulations of wave propagation characteristics. Frequency bands and resonance modes of EMWM are calculated to analyze the characteristics of one-dimensional wave propagation. The numerical results show that EMWM exhibits unique frequency band characteristics compared to typical periodic porous materials. In the frequency range of 15 to n n 35 kHz, the first pass band corresponds to the tensile-compressive motion of the entire specimen, while the higher pass bands correspond to localized vibrations within the spatial wires. Additionally, the frequency bands can be adjusted across a wide range based on the relative density of EMWM specimens. The improved WFE method and obtained numerical data can facilitate the application of EMWM on high frequency vibration isolation.

Enhancing Speech Signal Quality through Noise Reduction for Improved Communication

Abstract: Speech is a fundamental and reliable mode of human communication, conveying not only linguistic content but also essential cues about the speaker, including language, emotion, gender, and identity. However, in contemporary telephone and audio-based communication systems, speech signals are highly susceptible to the deleterious effects of ambient and Gaussian noise. To detach this noise from the signal, many Digital Audio Filters can be employed, including the Audio Weighting Filter, low-pass and High-pass filter, Band Pass Filter, and Band Stop Filter. This paper implements and compares various digital filters for audio signal enhancement using MATLAB Simulink and compares the outcome to identify the best filter for this specific purpose.

Organic alginate encapsulated rGO-CdS millispheres for remarkable photocatalytic solar hydrogen production

Herein, hydrogen generation through photocatalytic water splitting is explored using a recyclable, environment-friendly organic alginate hydrogel-encapsulated rGO-CdS millisphere photocatalyst under solar irradiation (1 Sun) using a solar simulator. Effective incorporation of rGO-CdS nanoparticles into a network of organic polymer sodium alginate is achieved while conserving the pore structures. A remarkable (79.68 mmol g rGO-CdS−1 h−1) solar hydrogen production is observed for the as-developed photocatalysts, using full-band solar irradiation; an AQE of 19.51% is achieved using of 420 nm band pass filter. The extent of hydration (pre-adsorption and dynamic adsorption of water) on the alginate network strongly influences the rate and extent of hydrogen generation. Instead of the conventionally used powder photocatalyst, millisphere beads make the continuous operation of the photoreactor easier. A steady, constant rate of continuous, sustainable hydrogen generation is achieved by employing a laboratory-scale flow reactor. The toxic effects of CdS is minimised (evidenced from cell viability test) through encapsulation by sodium alginate and promoting performance by incorporating rGO.

Development of tuned hybrid fuzzy and BiLSTM-based epileptic seizure classification model with stacked 1D-CNN-fisher discriminant feature selection

ABSTRACT Electroencephalogram (EEG) is a signal which consists of different sinusoidal components with a dense frequency spectrum. The existing methods are time-consumingand includes inconsistencies in judgment among seizure classification. Therefore, it is very difficult to accurately detect epileptic seizures from the recorded EEG. Therefore, a new epileptic seizure detection model is developed with hybrid deep-structured technology. The EEG signal data obtained from the standard online resources which are given into the pre-processing phase with the help of band pass filtering and smoothing techniques. Then, the 5-level Discrete Wavelet Transform (5-level DWT) is used for signal decomposition to get the decomposed signals. 1-D stacked Convolutional Neural Network (1-D stacked CNN) is utilized for the extraction of features. After, the feature selection process is executed by utilizing the Fisher Discriminant Analysis (FDA). The selected features are subjected to the classification phase by Tuned Hybrid Fuzzy Bi-directional Long Short-Term Memory (THFBi-LSTM). Here, the parameter optimization takes place with hybridized optimization algorithm of Probability-based Dingo Coyote Optimization (P-DCO). In the overall simulation estimation, the offered approach achieves a 97% accuracy rate and also a 92% F1-score rate. Thus, the experimental results are reveled that it is better than the other baseline approaches.

Computerized respiratory sound based diagnosis of pneumonia.

Globally, respiratory disorders are a great health burden, affecting as well as destroying human lives; pneumonia is one among them. Pneumonia stages can progress from mild stage to even towards deadly if it is misdiagnosed. Misdiagnosis happens as it exhibits the symptoms identical to other respiratory diseases. Respiratory sound (RS)-based detection of pneumonia could be the most perfect, convenient, as well as the economical solution to this serious problem. This paper presents a novel method to detect pneumonia based on RS. This study is carried out over 310 pneumonia RS and 318 healthy RS, recorded from a hospital. The noises from each RS are eliminated using the Butterworth band pass filter and sparsity-assisted signal smoothing algorithm. Approximate entropy, Shannon entropy, fractal dimension, and largest Lyapunov exponent are the nonlinear features, which are extracted from each denoised RS. The extracted features are inputted to support vector machine classifiers to distinguish pneumonia RS and healthy RS. This method discriminates against pneumonia and healthy RS with 99.8% classification accuracy, 99.8% sensitivity, 99.6% specificity, 99.6% positive predictive value, 99.6% F1-score, and area under curve value of 1.0. Future endeavours will be to examine the efficacy of the proposed algorithm to diagnose pneumonia from the real-time RS acquired from a pneumonia patient in a hospital. This proposed work could be a great support to clinicians in diagnosing pneumonia based on RS.

Detection of Epilepsy patients using coot optimization based feed forward multilayer neural network

ABSTRACT A familiar nervous system disorder characterised by seizures is called as Epilepsy. It is indeed hard to control the suitable type as an outcome of insufficient EEG information. In order to overcome these issues, a Multilayer Neural Network (MLNN)-based classifier is proposed to recognise if the patients are affected by epileptic disease or not. EEG signal is a contribution, and the input signal is preprocessed using antialiasing filter, finite impulse response, and band pass filter to eradicate unwanted noise present in the signal. After preprocessing, the features extracting process is done, and four extraction techniques are proposed in order to calculate the feature coefficient. The feature extraction outcome is fed into the MLNN classifier to predict the disease. MLNN performs with Coot-Optimization to reduce error and increase prediction accuracy. The future ideal applied in Matlab-software carried out numerous act metrics, and these parameters attained better performance such as accuracy of 96.5%, error of 0.03, precision of 98%, specificity is 97%, sensitivity is 95%, and so on. This displays the effectiveness of the future ideal than existing approaches such as ANN, SVM, KNN and NB. Based on this proposed classification, the epileptic disease prediction can be improved on this technique and can provide a living standard for patients.

A reconfigurable floating-gate-transistor-capacitor filter for analog signal processing

This paper introduces a fully reconfigurable floating-gate-transistor-capacitor (FGT-C) filter that exhibits compactness and power efficiency. The proposed filter incorporates a biquadratic section, enabling the generation of lowpass (LP) and bandpass (BP) outputs with adjustable filter parameters, including gains, natural frequency, quality factor, and DC levels for input and output signals. Moreover, the FGT-C filter topology demonstrates a modular structure, facilitating easy cascading and scalability of the biquadratic sections to implement high-order frequency responses. A prototype chip has been fabricated in the 0.35 μm CMOS process, where each biquadratic section occupies an area of 0.0313 mm2. The chip measurements were conducted with a supply voltage of 1.8 V and both output voltage levels programmed at 0.9 V. The biquadratic FGT-C filter exhibits a power consumption of 118.4 nW and achieves a spurious free dynamic range of 43 dB in a 10 kHz bandwidth setting. It is suitable for low-power analog signal processing in large-scale field programmable analog arrays.