Asymmetric Filter Research Articles

Improving Real-Time Trend Estimates Using Local Parametrization of Polynomial Regression Filters

This paper examines and compares real-time estimates of the trend-cycle component using moving averages constructed with local polynomial regression. It enables the reproduction of Henderson’s symmetric and Musgrave’s asymmetric filters used in the X-13ARIMA-SEATS seasonal adjustment algorithm. This paper proposes two extensions of local polynomial filters for real-time trend-cycle estimates: first including a timeliness criterion to minimize the phase shift; second with procedure for parametrizing asymmetric filters locally while they are generally parametrized globally, which can be suboptimal around turning points. An empirical comparison, based on simulated and real data, shows that modeling polynomial trends that are too complex introduces more revisions without reducing the phase shift, and that local parametrization reduces the delay in detecting turning points and reduces revisions. The results are reproducible and all the methods can be easily applied using the R package rjd3filters.

A novel asymmetric superwetting filter prepared by easily mass-produced strategy for high-efficient and switchable water/oil filtration

All-purpose materials with on-demand oil/water separation property have received broad attention recently. Herein, a filter paper (FP)-based filter with asymmetric wettability was designed and fabricated via simple combination of liquid phase reduction route with symmetry adhesion method. The fabricated filter exhibits underoil superhydrophobic (UOSHP) and underwater superoleophobic (UWSOP) property. Especially, the good underliquids lyophobicity is still maintained after tape peeling and corrosive, organic solvents treatments. By fixing the hydrophobic (HO) side of filter facing up, the water/light oil mixtures and oil-in-water (O-in-W) emulsions can be highly efficient separated, and the water/heavy oil mixtures and water-in-oil (W-in-O) emulsions are able to be separated with the HI side facing up. The separation efficiencies for water/oil mixtures and oil/water emulsions are higher than 98% and 99.1%, respectively. The study offers a new idea for the preparation of asymmetric superwetting materials with good stability and on-demand water/oil separation capability.

Effect of ash-bridge deposition in asymmetric diesel particulate filter channels on the pressure drop and particulate matter trapping characteristics

A diesel particulate filter (DPF) is commonly used to trap particulate matter (PM). The collapse and sintering of carbon and ash deposits during exhaust gas flow and DPF regeneration frequently result in the formation of ash bridges, which are buildups of ash and PM that clog the filter channels. To investigate the impact of an ash bridge formed by accumulated ash on the pressure drop and PM trapping characteristics of asymmetric DPFs, an asymmetric DPF channel with an ash bridge was constructed. The analysis employed computational fluid dynamics to examine how the ash bridge affects the pressure drop and PM trapping characteristics in an asymmetric DPF. This study reveals that the asymmetric DPF design effectively enhances the ash-holding capacity, thereby mitigating the risk of channel blockage. The presence of ash bridges in DPF channels has a more substantial impact on the pressure drop through radial congestion than through axial congestion. The application of asymmetric thin-wall DPFs can counteract the increase in exhaust backpressure caused by ash bridges. An ash bridge located in the middle of a DPF channel intensifies the uneven deposition of PM. However, implementing an asymmetric DPF structure enhances the uniformity of PM deposition.

Ultra‐Power‐Efficient, Electrically Programmable, Multi‐State Photonic Flash Memory on a Heterogeneous III‐V/Si Platform

AbstractNon‐volatile charge‐trap flash memory (CTM) co‐located with heterogeneous III‐V/Si photonics is demonstrated. The wafer‐bonded III‐V/Si CTM cell facilitates non‐volatile optical functionality for a variety of devices such as Mach–Zehnder Interferometers (MZIs), asymmetric MZI lattice filters, and ring resonator filters. The MZI CTM exhibits full write/erase operation (100 cycles with 500 states) with wavelength shifts of Δλnon‐volatile = 1.16 nm (Δneff,non‐volatile ≈ 2.5 × 10−4) and a dynamic power consumption <20 pW (limited by measurement). Multi‐bit write operation (2 bits) is also demonstrated and verified over a time duration of 24 h and most likely beyond. The cascaded second order ring resonator CTM filter exhibited an improved ER of ≈7.11 dB compared to the MZI and wavelength shifts of Δλnon‐volatile = 0.041 nm (Δneff, non‐volatile = 1.5 × 10−4) with similar pW‐level dynamic power consumption as the MZI CTM. The ability to co‐locate photonic computing elements and non‐volatile memory provides an attractive path toward eliminating the von‐Neumann bottleneck.

Design of a wideband, highly efficient FPA using an asymmetrical open-circuit coupled-lines-based filtering matching network

This study introduces a novel approach to designing a broadband, highly efficient filtering power amplifier (FPA) characterized by unique integrated filtering capabilities. Central to the proposed technique is the utilization of an asymmetrical open-circuit coupled-lines bandpass filter (BPF), serving dual functions as both an input matching network and an output matching network. Notably, this filter design not only enhances the bandpass filtering response but also significantly improves second harmonic rejection performance. To validate this methodology, a broadband FPA is implemented, employing the Cree 10 W GaN HEMT. The proposed FPA, operating between 2.0–3.0 GHz, achieves a peak drain efficiency of 45.9 %-73.26 %, corresponding to a peak output power of 38.5–41.95 dBm at saturation. This marks a notable efficiency enhancement compared to recent research findings. It is believed that the FPA solution is custom-tailored to meet the demanding requirements of modern base stations, striking the balance between improved power-efficiency and good filtering performance.

Viewing zone enlargement method for holographic displays based on the slanted pixel arrangement on a spatial light modulator.

This study proposes a method for enlarging the viewing zone of holographic displays using the slanted arrangement of pixels on a spatial light modulator (SLM). The pixel arrangement equivalently reduces the horizontal pixel pitch, which enlarges the horizontal viewing zone of displays. Computer-generated holograms (CGHs) were calculated using an asymmetric band-limit filter corresponding to the asymmetric bandwidth of the SLM with slanted pixels. The proposed methods were evaluated through an optical reconstruction experiment using static holograms with a pixel size of 1×1µm, fabricated via electron-beam lithography. The enlarged horizontal viewing zone angle was found to be 41.6°.

Deep-feature-based asymmetrical background-aware correlation filter for object tracking

Correlation filter-based video object-tracking algorithms have gained widespread attention due to their efficiency and excellent tracking performance. However, traditional correlation filtering tracking algorithms possess several limitations. (1) They extract image features only by using rule sampling, which ignores the shape information of the target, resulting in insufficient discriminative power of the features. (2) They focus only on the difference between the background and the object, ignoring the effects of more challenging intra-class interference. (3) They do not further evaluate the reliability of the optimal candidate samples, resulting in easy failure in occlusion scenarios. This paper proposes an asymmetric background-aware correlation filter method for object tracking with deep features to solve the above limitations. First, an asymmetrical background-aware sampling method based on the shape information of the object is proposed. This sampling method significantly differs from the symmetrical sampling method in the traditional correlation filter framework. By exploring the shape information of the object, improving the otherness between the background and object samples is easy, thus suppressing the intra-class interferences. Second, deep neural networks are introduced in the correlation filter framework to extract the deep object features and a spatio-temporal regularization factor is adaptively assigned to suppress the boundary effects, intra-class distractors and aberrance between frames. Finally, a multi-modal object pool is constructed to evaluate the optimal candidate sample in each frame. This template pool fully exploits object diversity and solves the tracking drift and failure caused by invalid appearance changes in scenes such as occlusion and vigorous motion scenarios. To validate the effectiveness of the proposed method, it was compared with the state-of-the-art methods on public datasets. The experimental results show that the tracking performance of the proposed method is competitive.

Forced Convection Heat Transfer Characteristics of Developed Laminar Flow in the Octagonal Channels of Octo-Square Asymmetric Particulate Filters

Forced Convection Heat Transfer Characteristics of Developed Laminar Flow in the Octagonal Channels of Octo-Square Asymmetric Particulate Filters

Asymmetric morphological filter for roughness evaluation of multifunctional surfaces

The scope of multifunctional surfaces in mechanical engineering and instrumentation is constantly expanding. Examples are products with multilayer coatings, products made of composite materials or using additive manufacturing technologies. A feature of multifunctional surfaces is two levels of texture with different parameter values. Various types of filters are used to decomposition and then analyze texture components. Traditionally, for such problems, a robust Gaussian regression filter and a morphological filter are used. The advantage of the morphological filter lies in the lower computational complexity and the ease of removing the shape component from the profile. This research presents generalized analysis of various types of an asymmetric morphological filter based on simulation. The asymmetric filter differs from the standard morphological filter by using different nesting indexes for combinations of morphological opening and closing operations. We have designed a compositional model of profile, which is superposition of two Gaussian distributions with different parameters and waviness. Relative filtering error of the arithmetic mean heights Ra was chosen as evaluation parameter. Based on the Monte Carlo experiments technique, the relative errors of the filters are determined. The main result of the research is algorithm for choosing nesting indexes depending on the type of primary profile. Thus, we give scientifically justified choice of the filter type and its task-specific parameters for applied use. The choice of filter type and two different nesting indexes in accordance with the developed algorithm provide relative error with a coverage interval less 3 % for 95 % coverage probability. The asymmetric morphological filter is effective for scale space analysis of surfaces with significant shape errors and waviness. The results obtained can be used to analyze the tribological properties of multifunctional surfaces of products.

Acoustic diode realized by asymmetric filter

Devices of one-way transport for acoustic waves are called acoustic diodes. They are able to promote the advancement of noise isolation, acoustic communication, and acoustic signal processing. A lot of designs of acoustic diodes based on various mechanisms have been given. However, most designs have problems of one kind or another, such as low efficiency, instability, bulky volume, complex structure, frequency change, waveform distortion, and so on. An asymmetric acoustic filter with only three layers is proposed in this work. The total length of the acoustic diode is less than half the wavelength. Its backward transmission is almost completely stopped. For the forward transmission, the amplitude of the transmitted wave is almost proportional to the driving voltage. This characteristic is better than its electronic counterpart which is often annoyed by the unavoidable nonlinearity at high driving voltage. A simple, compact, stable, broadband, frequency-preserved, highly efficient, linear acoustic diode is realized.

A novel tool path smoothing algorithm of 6R manipulator considering pose-dependent dynamics by designing asymmetrical FIR filters

The joint dynamic constraints of robot manipulators are normally set to conservative constant values to meet the dynamic tolerances at different poses, which leads to incomplete utilization of the joint drive capability. To fully utilize the drive capability of joint drives, this paper proposes an asymmetrical finite impulse response (FIR) filter-based tool path smoothing algorithm, which considers the pose-dependent dynamics of the robot with 6 rotational (6R) joints. To analyze the pose-dependent motor drive capabilities, a simplified dynamics model is established by considering both the tangential and joint dynamic constraints. An asymmetrical FIR filter-based tool path smoothing algorithm is also proposed to realize different constraints of the acceleration and the deceleration limits at different positions of the linear segments. The tool tip position and the tool orientation commands are respectively smoothed in the Cartesian coordinate system and spherical coordinate system, with the constraints of the tool path deviations in the task frame. The synchronization of the position and the orientation is realized by adjusting the parameters of FIR filters. The simulation and experimental results show that the proposed algorithm guarantees the corner error tolerances of the tool tip position and the tool orientation. Moreover, the proposed method improves the robot motion efficiency by more than 10 % through the full utilization of the joint drive capability compared with the traditional tool path smoothing algorithm based on symmetrical FIR filters.

Size-resolved particulate matter filtration efficiency of macroporous asymmetric SiC ceramic filters

Controlling particulate matter (PM) emissions to the atmosphere in industrial processes is critical for a safe and healthy environment. Because of its high strength and thermal stability, silicon carbide (SiC) is among the best materials for producing hot gas filters to be coupled to sugarcane bagasse-fired boilers in Brazil and India. In this study, we evaluated the performance of asymmetric SiC-based filters of different features for removing fine PM with particle sizes ranging from 90 to 3000 nm. The collection efficiency of the asymmetric filter made of SiC/mullite support with no pore former, porosity of 31–33 %, and permeability of (0.17–0.30) × 10−11 m2 with a coating thickness of 109–138 μm, effective pore size of 19–26 μm, and effective collector diameter of 64–86 μm was higher than 99 % for PM with size greater than 0.60 μm. The SiC formulation with mullite and pore former also presented promising performance. The resulting pressure drop remained within the range reported for commercial hot gas filters. Over 99 % of the mass of soot and fly ash particles generated during sugarcane bagasse combustion exceeds the MPPS range found for filters, which was between 0.16 and 0.36 μm. Therefore, the SiC asymmetric filters presenting mullite in their formulation are anticipated to meet current international emission standards.

Electromechanical interactions between cell membrane and nuclear envelope: Beyond the standard Schwan’s model of biological cells

We investigate little-appreciated features of the hierarchical core–shell (CS) models of the electrical, mechanical, and electromechanical interactions between the cell membrane (CM) and nuclear envelope (NE). We first consider a simple model of an individual cell based on a coupled resistor–capacitor (Schwan model (SM)) network and show that the CM, when exposed to ac electric fields, acts as a low pass filter while the NE acts as a wide and asymmetric bandpass filter. We provide a simplified calculation for characteristic time associated with the capacitive charging of the NE and parameterize its range of behavior. We furthermore observe several new features dealing with mechanical analogs of the SM based on elementary spring–damper combinations. The chief merit of these models is that they can predict creep compliance responses of an individual cell under static stress and their effective retardation time constants. Next, we use an alternative and a more accurate CS physical model solved by finite element simulations for which geometrical cell reshaping under electromechanical stress (electrodeformation (ED)) is included in a continuum approach with spatial resolution. We show that under an electric field excitation, the elongated nucleus scales differently compared to the electrodeformed cell.

Predicting virus filter performance using an advanced membrane structural model

Virus filters are essential in bioprocessing for safe therapeutic protein production. Understanding the correlation between virus filter structure and performance is important for efficient process design. By applying a multilayer structural model comprised of theoretical layers having pore size distributions, to published visualization data, we demonstrate in the present study that virus removal performance and filtration behavior can be quantitatively calculated. Virus filter structure can be classified into asymmetric high porosity, symmetric high density and laminated structure types. All types exhibit sufficient virus removal properties when used in normal processes. Laminated structure filters show extremely high virus particle removal performance, achieving the most robust virus removal even in filtration with process pauses, although flux decay tends to occur during filtration of solutions containing even very small amounts of aggregate. Conversely, asymmetric high porosity filters may show lower virus removal in processes involving multiple pauses that exceed practical conditions but shows stable filtration behavior with lower increase in pressure and lower flux decay even for the filtration of solutions containing aggregates. Such filtration behavior and virus removal properties can be quantitatively expressed and predicted by numerical calculations using a model based on the theoretical multilayer structure.

AUTOMATIC MODULATION CLASSIFICATION USING DEEP LEARNING POLAR FEATURE

The automatic modulation classification of signals is of great importance in modern communications, especially on cognitive radio. Several methods have been used in this field, the most important of which is the classification of modulation automatically using Deep Learning, where the methods depend on the convolution neural network, which is one of the Deep Learning networks, achieved high accuracy in classifying the modulation, so the proposed network depends on the type of deep learning CNN consisting of four blocks, each block contains a set of symmetric and asymmetric filters. The network also contains Max Pool. In this paper, the features extracted in phase-squaring and polar have been combined for the input, which helps in extending the input, that is, an increase in the features inside the network. It also contributes to improving the accuracy of classifying the higher-order modulation through the Polar plane. The dataset RadioML 2018.01A was adopted, which is used in the most recent research, where 11 types of modulation normal-class: (FM, GMSK, QPSK, BPSK, 0QPSK, AM-SSB-SC, 4ASK, AM-DSB-SC, 16QAM, 8PSK,00K) were taken. A simulation of which can be found in Matlab 2021. The proposed network achieved 100% classification accuracy when the signal-to-noise ratio is greater or equal to 2 dB for 11 types of modulation. The results of the paper were compared with modern networks Baseline network, Visual Geometry Group network, and Residual Neural network. The comparison showed the superiority of the proposed network over these networks, as the proposed network achieved an accuracy equal to 100% at SNR 2 dB while BL achieved an accuracy equal to 72% at SNR 2 dB, RN, and VGG almost reach 93% at SNR 2 dB.

Automatic modulation classification based deep learning with mixed feature

<span lang="EN-US">The automatic modulation classification (AMC) plays an important and necessary role in the truncated wireless signal, which is used in modern communications. The proposed convolution neural network (CNN) for AMC is based on a method of feature expansion by integrating I/Q (time form) with r/Ɵ (polar form) in order to take advantage of two things: first, feature expansion helps to increase features; the second is that converting to polar form helps to increase classification accuracy for higher order modulation due to diversity in polar form. CNN consists of six blocks. Each block contains symmetric and asymmetric filters, as well as max and average pooling filters. This paper uses DeepSig: RadioML which is a dataset of 24 modulation classes. The proposed network has outperformed many recent papers in terms of classification accuracy for 24 modulation types, with a classification accuracy of up to 96.06 at an SNR=20 dB.</span>

Efficient Convolutional Networks for Robust Automatic Modulation Classification in OFDM-Based Wireless Systems

Orthogonal frequency-division multiplexing (OFDM) is commonly deployed in Internet of Things (IoT) systems to achieve high data rates with reasonable complexity, where noncooperative protocols encode signals with different modulations for multiple users. However, conventional modulation recognition is limited to single-carrier communication systems, and there is an urgent need to design an effective method to blindly identify the unknown modulation format of received signals. In this article, we propose an automatic modulation classification method for the OFDM systems with the presence of channel deterioration. Our method first leverages a data reconstruction mechanism to arrange signals into high-dimensional data arrays and then exploits an efficient convolutional network, namely OFDMsym-Net, to learn underlying radio characteristics. OFDMsym-Net is designed by two kinds of processing modules, which manipulate one-dimensional asymmetric convolution filters to extract the intracorrelation within an OFDM symbol and the intercorrelation between different symbols. Moreover, a sophisticated structure with addition and concatenation layers is developed inside every module to improve learning efficiency. Based on simulation results achieved on a synthetic dataset of OFDM signals, our proposed method shows the classification robustness under various channel impairments. It reveals to have an overall accuracy of 95.41% at 10-dB signal-to-noise ratio, being superior to several state-of-the-art approaches.

Optimization of Mismatched Filters With Asymmetric Side-Lobe Shape for Short-Range MIMO Radars

This paper presents the design of signal compression and separation filters with asymmetric responses for multiple-input multiple-output (MIMO) radars that use pulses longer than the maximum return time of the reflected signal. Such radars suffer from direct penetration of the transmitted signal to the receiver, appearing as a very strong stationary reflection in the zero range. After pulse compression, the far-side lobes of this crosstalk may compete with the reflections of distant weak targets and limit the maximum radar range. Hence, in addition to the maximum suppression of the direct transmitted signal penetration to the receiver, the far-side lobe reduction of this crosstalk with positive time lags is desirable. Asymmetric response filters allow optimization to be focused on the region where the maximum suppression of crosstalk between the transmitted signal and the receiver is desired. Extreme suppression was achieved by applying lower demand in other areas and other parameters. In this paper, the problem is described in more detail along with the mathematical apparatus by which asymmetric filter outputs can be achieved.

Reconfigurable optical filter based on microring resonator assisted by tunable Sagnac reflector

To meet the demands for various applications in optical filtering and microwave signal processing, integrated silicon photonic filters are required to be multifunctional, reconfigurable and tunable. In this work, an integrated multi-functional optical filter is proposed, which is designed based on a tunable Sagnac loop reflector and a microring resonator. The through port and drop port of an add-drop microring resonator are connected with the two ports of a tunable reflector. By controlling the thermal phase shifters in different scenarios, the device can be reconfigured into a reflective-type asymmetric Mach-Zehnder interferometer filter, a reflective-type all-pass microring resonator filter and self-interference microring resonator filters. An analytical model is established based on the transfer matrix. The simulation results show that the device can achieve the following functions: sinusoidal spectral filtering with four different free spectral ranges, Lorentzian spectral filtering toggling between band pass and band stop, and spectral reconfigurations of Fano resonance, electromagnetically induced transparency, and electromagnetically induced absorption. Each spectrum mentioned above can be tuned fast and widely. Reflection provides a new degree of freedom in design, breaks through the inherent footprint limit, and achieves a wide range of free spectral ranges. Our proposed tunable Sagnac loop reflector assisted microring resonator provides a new scheme for realizing flexible, tunable and multi-functional reconfigurable integrated photonic filters, and has broad applications in the integrated photonic analog signal processing and microwave photonics.

Engineering of Aberrated PSF by Asymmetric Apodization with the Complex Shaded Aperture

The point spread function (PSF) produced by a coherent optical system under the influence of defocus, coma, and primary spherical aberration (PSA) is examined in this work. This paper deals with asymmetric apodization and pupil engineering to control monochromatic aberrations. To reduce the influence of monochromatic aberrations on the diffracted PSF, this approach uses amplitude and phase apodization. Analytical investigations on intensity PSF are carried out with varying amounts of aberrations and degrees of amplitude and phase apodization. Computed central peak intensity and full width at half maxima (FWHM) and analyzed. The resolution of a diffraction-limited optical imaging system is improved by using an asymmetric optical filter that minimizes the effect of defocus.