Ideal Filter Research Articles

Impact of blue filtering on effective modulation bandwidth and wide-angle operation in white LED-based VLC systems

This work studies the impact of employing blue filters on the resulting effective modulation bandwidth of visible light communication (VLC) systems using phosphor-coated white light emitting diodes (LEDs) under wide-angle operation. Effective modulation bandwidths are assessed and compared for VLC systems with ideal and non-ideal transmission blue filters at the receiver. Angle tolerance capabilities of the employed filters have also been investigated in the study. Commercially available phosphor-coated white LED and photodetector (PD) models are utilized to obtain realistic results. We show that the choice of the blue filter structure, combined with the white LED and PD, affects the system’s effective modulation bandwidth. We also show that there exists a trade-off between obtaining a relatively large modulation bandwidth and preserving the wide-angle operation of the system. A figure of merit (FOM) is also introduced to represent such a trade-off. Moreover, a simple estimation of the expected effective modulation bandwidth of a VLC system using phosphor-coated white LED is developed as a result of the weighted average of the blue and yellow light responsivity of the filter. A numerical example is also included in which the optical signal to noise ratio (OSNR) is computed in VLC systems with and without the addition of a blue filter. The OSNR shows enhancement when a blue filter with a wide rejection region is inserted in the studied VLC system.

Deep Color Guided Coarse-to-Fine Convolutional Network Cascade for Depth Image Super-Resolution.

Depth image super-resolution is a significant yet challenging task. In this paper, we introduce a novel deep color guided coarse-to-fine convolutional neural network (CNN) framework to address this problem. First, we present a datadriven filter method to approximate the ideal filter for depth image super-resolution instead of hand-designed filters. Based on large data samples, the filter learned is more accurate and stable for upsampling depth image. Second, we introduce a coarse-to-fine CNN to learn different sizes of filter kernels. In coarse stage, larger filter kernels are learned by CNN to achieve crude high-resolution depth image. As to fine stage, the crude high-resolution depth image is used as the input so that smaller filter kernels are learned to gain more accurate results. Benefit from this network, we can progressively recover the high frequency details. Third, we construct a color guidance strategy that fuses color difference and spatial distance for depth image upsampling. We revise the interpolated high-resolution depth image according to the corresponding pixels in highresolution color maps. Guided by color information, the depth of high-resolution image obtained can alleviate texture copying artifacts and preserve edge details effectively. Quantitative and qualitative experimental results demonstrate our state-of-the-art performance for depth map super-resolution.

Ligand-Free Nano-Au Catalysts on Nitrogen-Doped Graphene Filter for Continuous Flow Catalysis.

In this study, the authors rationally designed a high-performance catalytic filter for continuous flow catalysis. The catalytic filter consisted of ligand-free nanoscale gold (nano-Au) catalysts and nitrogen-doped graphene (N-rGO). The Au catalyst was fabricated in situ onto a pre-formed N-rGO support by the NaBH4 reduction of the Au precursor, and the size of the nano-Au was fine-tuned. A hydrothermal pretreatment of graphene oxide enriched nitrogen-containing species on the surface of two-dimensional graphene supports and enhanced the affinity of Au precursors onto the support via electrocatalytic attraction. The nano-Au catalysts acted as high-performance catalysts, and the N-rGO acted as ideal filter materials to anchor the catalysts. The catalytic activity of the as-designed catalytic filter was evaluated using 4-nitrophenol (4-NP) hydrogenation as a model catalytic reaction. The catalytic filters demonstrated superior catalytic activity and excellent stability, where a complete 4-nitrophenol conversion was readily achieved via a single pass through the catalytic filter. The as-fabricated catalytic filter outperformed the conventional batch reactors due to evidently improved mass transport. Some key operational parameters impacting the catalytic performance were identified and optimized. A similar catalytic performance was also observed for three 4-nitrophenol spiked real water samples (e.g., surface water, tap water, and industrial dyeing wastewater). The excellent catalytic activity of the nano-Au catalysts combined with the two-dimensional and mechanically stable graphene allowed for the rational design of various continuous flow catalytic membranes for potential industrial applications.

A Hybrid Multilayer Filtering Approach for Thyroid Nodule Segmentation on Ultrasound Images.

Speckle noise is the main factor that degrades ultrasound image contrast and segmentation failure. Determining an effective filter can reduce speckle noise and improve segmentation performances. The aim of this study was to define a useful filter to improve the segmentation outcome. Twelve filters, including median, hybrid median (Hmed), Fourier Butterworth, Fourier ideal, wavelet (Wlet), homomorphic Fourier Butterworth, homomorphic Fourier ideal, homomorphic wavelet (Hmp_Wlet), frost, anisotropic diffusion, probabilistic patch-based (PPB), and homogeneous area filters, were used to find the best filter(s) to prepare thyroid nodule segmentation. A receiver operating characteristic (ROC) analysis was used for filter evaluation in the nodule segmentation process. Accordingly, 10 morphologic parameters were measured from segmented regions to find the best parameters that predict the segmentation performance. The best segmentation performance was reached by using 4 hybrid filters that mainly contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters. The area under the ROC curve for these filters ranged from 0.900 to 0.943 in comparison with the original image, with an area under the curve of 0.685. From 10 morphologic parameters, the area, convex area, equivalent diameter, solidity, and extent can evaluate segmentation performance. Hybrid filters that contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters have a high potential to provide good conditions for thyroid nodule segmentation in ultrasound images. In addition to an ROC analysis, morphometry of a segmented region can be used to evaluate segmentation performances.

Alias-Free Products of Signals Near Nyquist Rate

Products of time-series signals have found wide-spread application in many fields of signal processing. For example, they are often used in modeling and compensating distortions generated by analog and mixed-signal components. Without excess bandwidth, the products of time-series signals will produce aliased artifacts that do not represent the physical phenomenology of the components being modeled. To ameliorate the effects of these potentially unwanted, aliased distortion products, we compare two multidimensional filters: the first is a simple one-dimensional (1-D) polyphase filter that is used to generate excess bandwidth; the second is a multidimensional (M-D) filter that is convolved with the time-series product. We demonstrate that the 1-D polyphase filter, although computationally simple, still produces unwanted aliasing when applied to signals near Nyquist rate, which can adversely affect modeling and/or compensation performance. By contrast, the M-D filter is computationally expensive, but is capable of most closely matching the ideal antialias filter response.

Parametric Synthesis of Feed Drives for CNC Machine Tools by Polynomial Approximation of Ideal Low-Frequency Filters

The relations between the dynamic characteristics of a supply drive and its design parameters and the control system based on standard polynomials approximating an ideal low-frequency filter are described. The possibility of implementing those polynomials is analyzed. The operational characteristics and design parameters of drives obtained on that basis are presented and their applications are considered.

Composite Lowpass Filter Realized by Image Parameter method and Integrated with Defected Ground Structures

An asymmetric DGS consisting of two square headed slots connected transversely with a rectangular slot is etched in the ground plane underneath a high-low impedance microstrip line. It provides a band-reject filtering characteristics with sharp transition. Thus, it may be modeled as m-derived filter section. Accordingly, T-type LC equivalent circuit is proposed and LC parameters are extracted. A planar composite lowpass filter is designed by image parameter method and implemented by different DGS units. A composite filter requires at least three filter sections, of which two m-derived sections and one constant k-section. In proposed scheme, one such m-derived section is directly replaced by proposed DGS unit, whose cut-off frequency is same as that of designed m-derived section. The other m-derived section implemented by proposed DGS unit divided into equivalent two L-sections which will act as a termination for impedance matching. A constant k-section has been realized by a dumbbell shaped DGS having same cutoff frequency. Here lumped LC parameters are directly replaced by the equivalent LC values of the DGSs and therefore, produce almost ideal filter characteristics by overcoming the limitations of microstrip technology.

Digital compensation of lowpass filters imperfection in the Modulated Wideband Converter compressed sensing scheme for radio frequency monitoring

This paper focuses on non-ideal filters in a Modulated Wideband Converter (MWC) scheme. The MWC is a system that can sample a sparse wideband signal at sub-Nyquist rate. Generally, the output of the ideal MWC components will ensure a perfect reconstruction. In practice, the reconstruction should be based on the output of non-ideal components, especially filters. The impact of non-ideal filters will trigger to a bad reconstruction. In this paper, a detailed study on non-ideal lowpass filters imperfection used in compressed sensing MWC scheme is synthesized. A digital post-treatment scheme with amplitude and phase compensation is proposed after real lowpass filtering step in order to have the filtered output as close as the ideal lowpass filter output. At last, reconstruction spectra obtained from different simulated lowpass filters are compared with different parameters of MWC.

Parametric Synthesis of Supply Drives for CNC Metalworking Machines

Parametric synthesis of a two-mass supply drive is considered, in the case where one mass is the moment of inertia of the drive’s rotating part, while the other is the reduced moment of inertia of the drive’s linearly moving part. The elastic coupling between the masses determines the axial rigidity of the drive. In synthesis, the parameters of the electromechanical system are determined by setting the coefficients of the characteristic polynomial equal to the coefficients of the polynomial approximating an ideal low-frequency filter.

A-Optimal Sampling and Robust Reconstruction for Graph Signals via Truncated Neumann Series

Graph signal processing (GSP) studies signals that live on irregular data kernels described by graphs. One fundamental problem in GSP is sampling---from which subset of graph nodes to collect samples in order to reconstruct a bandlimited graph signal in high fidelity. In this paper, we seek a sampling strategy that minimizes the mean square error (MSE) of the reconstructed bandlimited graph signals assuming an independent and identically distributed (iid) noise model---leading naturally to the A-optimal design criterion. To avoid matrix inversion, we first prove that the inverse of the information matrix in the A-optimal criterion is equivalent to a Neumann matrix series. We then transform the truncated Neumann series based sampling problem into an equivalent expression that replaces eigenvectors of the Laplacian operator with a sub-matrix of an ideal low-pass graph filter. Finally, we approximate the ideal filter using a Chebyshev matrix polynomial. We design a greedy algorithm to iteratively minimize the simplified objective. For signal reconstruction, we propose an accompanied signal reconstruction strategy that reuses the approximated filter sub-matrix and is provably more robust than conventional least square recovery. Simulation results show that our sampling strategy outperforms two previous strategies in MSE performance at comparable complexity.

The role of horizontal facial structure on the N170 and N250

Recent studies have shown that horizontal facial structure is important for face identification (Dakin and Watt, 2009; Goffaux and Dakin, 2010). Also, sensitivity to horizontal structure is associated with the size of the face inversion effect (Pachai et al., 2013). However, it is unclear how the N170 and N250, two components of visual event-related potentials ERPs that have been implicated in face perception, are modulated by oriented facial structure in an upright face identification task. Here, we recorded ERPs and behavioural accuracy from adult observers performing a 1-of-6 face identification task in conditions that parametrically manipulated the orientation structure of upright faces. Faces were filtered with ideal orientation filters centred on either 0 (horizontal) or 90 deg (vertical). Filter bandwidth was varied across conditions from ±45 to ±90 deg in steps of ±9 deg. As has been reported previously, response accuracy was significantly higher for faces that contained horizontal structure than vertical structure, and the horizontal-vertical difference was correlated with accuracy for unfiltered faces. In addition, the N170 and N250 were affected by the manipulation of horizontal facial structure. Furthermore, for the N250, but not the N170, the relative sensitivity to horizontal compared to vertical facial structure was significantly correlated with identification accuracy for unfiltered faces. We suggest that in a face identification task, the N250 but not the N170 is modulated by the amount of diagnostic information conveyed by horizontal structure.

Allpass decorrelating filter design and evaluation

By breaking up the phase coherence of a signal broadcast from multiple loudspeakers, it is possible to control the perceived spatial extent and location of a sound source. This so-called signal decorrelation process is commonly achieved using a set of linear filters, and finds applications in audio upmixing, spatialization, and auralization. It is important that any individual decorrelating filter does not perceivably alter the sound of its input, i.e., that it does not color the signal spectrum or smear transients over time. In that way, the process will only affect the spatial properties of the sound. Allpass filters make ideal decorrelation filters since they have unit magnitude spectra, and therefore can be perceptually transparent. By manipulating their phase, allpass filters can be leveraged to achieve a degree of decorrelation. Here, we present a method for designing allpass decorrelation filters by specifying group delay trajectories in a way that allows for control of the amount of correlation as a function of frequency. This design is efficiently implemented as a cascade of biquad allpass filters. We present statistical and perceptual methods for evaluating the amount of decorrelation and audible distortion.

Perceptual and Model-Based Evaluation of Ideal Time-Frequency Noise Reduction in Hearing-Impaired Listeners

State-of-the-art hearing aids (HAs) try to overcome the deficit of poor speech intelligibility (SI) in noisy listening environments using digital noise reduction (NR) techniques. The application of time-frequency masks to the noisy sound input is a common NR technique to increase SI. The binary mask with its binary weights and the Wiener filter with continuous weights are representatives of a hard- and a soft-decision approach for time-frequency masking. In normal-hearing listeners, the ideal Wiener filter (IWF) outperforms the ideal binary mask (IBM) in terms of SI and speech quality with perfect SI even at very low signal-to-noise ratios. In this paper, both approaches were investigated for hearing-impaired (HI) listeners. Perceptual and auditory model-based measures were used for the evaluation. The IWF outperformed the IBM in terms of SI. Quality-wise, there was no overall difference between the NR algorithms perceived. Additionally, the processed signals were evaluated based on an auditory nerve model using the neurogram similarity metric (NSIM). The mean NSIM values were significantly different for intelligible and unintelligible sentences. The results suggest that a soft-mask seems to be promising for application in HAs.

Fluid-based spectrally selective filters for direct immersed PVT solar systems in building applications

Liquid filters applied in hybrid photovoltaic-thermal (PVT) solar systems report potential benefits as they allow for selecting the wavelengths at which the solar cell operates with higher efficiency. Among the rest, low frequency photons are absorbed by the liquid, therefore not warming the cell up. In addition, direct immersion of photovoltaic cells in liquids enhances temperature control by cooling the cells under almost negligible contact resistance between the liquid and the cell. The characteristics of the liquids for direct immersed PVT application are described, defining an indicator (ideal filter window) to select the best liquid depending on the PV technology and the incident irradiance. Several liquids are assessed based on the required properties for the present application. As a conclusion, regarding optical, thermal, electrical and operational aspects, a mixture of deionized water and isopropyl alcohol results as proper candidate satisfying well all of them.

Design of an Ideal Low Pass Microstrip Line Filter Using the Defected Ground Structure

A design and simulation for low pass microstrip line filter with defected ground structure has been researched, the main purpose is with the simplest method to design an ideal low pass filter. In this paper, simulated soft (Ansoft HFSS V.6.0) used to be simulated the frequency response under different geometric shape of DGS. The results show good performance of a low pass filter with DGS. Final, a low pass filter with DGS design and fabricated, The properties are shown as flow: center-frequency: 7.28G, S21:-47dB, cut-off frequency: 5.88GHz.

New Models for the Calibration of Four-Channel Time-Interleaved ADCs Using Filter Banks

New linear models to calibrate four-channel time-interleaved analog-to-digital converters are proposed and investigated. The ideal four-periodic correction filters, which cancel distortions, are computed as a function of the error filters that model the analog transfer function of each channel, including the sampling time. These correction filters are then approximated as a linear combination of base filters and new accurate models with a limited number of free parameters are proposed. Calibration is performed using the recursive least squares algorithm to estimate the coefficients of the linear combination (and the offset term). The resulting algorithms are tested for accuracy, convergence speed, and stability in a fixed-point implementation, and are compared with previously published linear background calibration techniques. The proposed filter bank significantly improves the accuracy/complexity tradeoff with respect to previously published techniques.

Characteristics of particle removals in a compressible media filter system

AbstractThe ability to adjust the properties of the filter medium by altering the degree of the medium compression is important because the different porosities in media affect the ideal filter performance according to the properties of influent wastewater. This study therefore focused on the particle size distribution (PSD) of compressible media filter (CMF) effluents depending on the compression ratio (0–30%) and the filter bed depth (Layers 1–5) in order to evaluate the CMF performance. The results showed that the removal efficiency for particles larger than 10 µm was more than 90% while that for particles smaller than 10 µm was less than 85% of removal efficiency. From the power law fit, substantial decreases in the number of large‐sized particles were observed in the PSD. Particle removal efficiencies increased along with the average compression ratio increase. Furthermore, the results demonstrated that each layer in the bed had a different role in the filter performance, and it was found that a filter bed of about 0.45–0.60 m would be needed for the production of the desired effluent quality in this study. The detachment phenomenon was observed in particles larger than 5 µm, especially in 5–15 µm particles.

Design and fabrication of cascaded dichromate gelatin holographic filters for spectrum-splitting PV systems

The technique of designing, optimizing, and fabricating broadband volume transmission holograms using dichromate gelatin (DCG) is summarized for solar spectrum-splitting applications. The spectrum-splitting photovoltaic (PV) system uses a series of single-bandgap PV cells that have different spectral conversion efficiency properties to more fully utilize the solar spectrum. In such a system, one or more high-performance optical filters are usually required to split the solar spectrum and efficiently send them to the corresponding PV cells. An ideal spectral filter should have a rectangular shape with sharp transition wavelengths. A methodology of designing and modeling a transmission DCG hologram using coupled wave analysis for different PV bandgap combinations is described. To achieve a broad diffraction bandwidth and sharp cutoff wavelength, a cascaded structure of multiple thick holograms is described. A search algorithm is then developed to optimize both single- and two-layer cascaded holographic spectrum-splitting elements for the best bandgap combinations of two- and three-junction spectrum-splitting photovoltaic (SSPV) systems illuminated under the AM1.5 solar spectrum. The power conversion efficiencies of the optimized systems are found to be 42.56% and 48.41%, respectively, using the detailed balance method, and show an improvement compared with a tandem multijunction system. A fabrication method for cascaded DCG holographic filters is also described and used to prototype the optimized filter for the three-junction SSPV system.

FPGA based Synthesize of PSO Algorithm and its Area-Performance Analysis

Digital filters are the most significant part of signal processing that are used in enormous applications such as speech recognition, acoustic, adaptive equalization, and noise and interference reduction. It would be of great benefit to implement adaptive FIR filter because of self-optimization property, linearity and frequency stability. Designing FIR filter involves multi-modal optimization problems whereas conservative gradient optimization technique is not useful to design the filter. Hence, Particle Swarm Optimization (PSO) algorithm is more flexible and optimization technique based on population of particles in search space and alternative approach for linear phase FIR filter design. PSO improves the solution characteristic by giving a novel method for updating swarm’s position and velocity vector. Set of optimized filter coefficients will be generated by PSO algorithm. In this paper, PSO based FIR Low pass filter is efficiently designed in MATLAB and further Xilinx System Generator tool is used to efficiently design, synthesize and implement FIR filter in FPGA using SPARTEN 3E kit. For an example specifications, output of PSO algorithm is obtained that is set of optimized coefficients whose response is approximating to the ideal response. Hence, functional verification of the proposed algorithm has been performed and the error between obtained filter and ideal filter is minimized successfully. This work demonstrates the effectiveness of the PSO algorithms in parallel processing environment as compared to the Remez Exchange algorithm.

Noise stability of signal reception algorithms with multipulse pulse-position modulation

An algorithm for estimating time positions and amplitudes of a periodic pulse sequence from a small number of samples was proposed. The number of these samples was determined only by the number of pulses. The performance of this algorithm was considered on the assumption that the spectrum of the original signal is limited with an ideal low-pass filter or the Nyquist filter, and conditions for the conversion from one filter to the other were determined. The efficiency of the proposed algorithm was investigated through analyzing in which way the dispersion of estimates of time positions and amplitudes depends on the signal-to-noise ratio and on the number of pulses in the sequence. It was shown that, from this point of view, the efficiency of the algorithm decreases with increasing number of sequence pulses. Besides, the efficiency of the proposed algorithm decreases with decreasing signal-to-noise ratio. It was found that, unlike the classical maximum likelihood algorithm, the proposed algorithm does not require a search for the maximum of a multivariable function, meanwhile characteristics of the estimates are practically the same for both these methods. Also, it was shown that the estimation accuracy of the proposed algorithm can be increased by an insignificant increase in the number of signal samples. The results obtained may be used in the practical design of laser communication systems, in which the multipulse pulse-position modulation is used for message transmission.