Cascade Of Filters Research Articles

Sliding mode controlled DC microgrid system with enhanced response

Renewable energy sources are being incorporated into power networks to guarantee dependable and inexpensive electricity for the urban and industrial sectors. Recent research approximates microgrids powered by renewable energy and controlled by smart grids may provide more reliable and efficient energy systems economically. Its natural interaction among renewable energy sources, electric demands, and energy storage devices makes DC microgrid a contemporary electrical grid technology. In this paper, a nonlinear control approach with outstanding precision, robustness, and simplicity of tuning and implementation, is made to deal with the control of the DC microgrid system to bring out the efficacy of the system at higher points. A closed-loop continuous time sliding mode controller is proposed for a boost converter with a cascade filter. DC microgrid system with sliding mode controller is simulated and the analysis has been carried outs with motor load to check the adequacy of the proposed system.

Fiber-bundle-based 2D Raman and Rayleigh imaging for major species and temperature measurement in laminar flames.

In this Letter, we introduce a novel fiber-bundle-based 2D Raman and Rayleigh imaging system for 2D measurements of major species mole fractions and temperature in flames. A cascade of dichroic mirrors and bandpass filters separate the Raman signal associated with each major species and direct it to a custom-built four-inputs/one-output fiber bundle. The single output of the fiber bundle allows simultaneous detection of four species by using a single back-illuminated CCD camera. The novel imaging system combined with a pulse-burst laser operated at 10 kHz and a 10 kHz optical shutter is characterized from measurements in a Mckenna burner and then applied to a flame stabilized over a Santoro burner. The work is, to the best of our knowledge, the first demonstration of 2D measurements of temperature and major species (i.e., N2, O2, H2, and H2O) in H2-air diffusion flames at atmospheric pressure.

Investigation of automatic spindle detection in sleep EEG signals contaminated with noise and artifact sources

AbstractElectroencephalography (EEG) serves as the gold standard for noninvasive diagnosis of different types of sleep disorders such as sleep apnea, insomnia, narcolepsy, restless leg syndrome, and parasomnias. In this study, a novel automated cascade filter is introduced as a preprocessing tool for suppressing all noise and artifact interferences from sleep EEG signals before detecting sleep spindles. The multi-stage filter employs the Multi-Kernel Normalized Least Mean Square with Coherence-based Sparsification (MKNLMS-CS) algorithm in the first step to remove all artifact interferences while applying the 1-D patch-based Non-Local Means (NLM) algorithm in the subsequent step to remove all noise components. Three state-of-the-art automated spindle detection algorithms, namely Mc-Sleep, Spinky, and Spindler, are examined in EEG signals contaminated with noise and artifact components individually and concurrently. The spindle detection performance is investigated with real EEG data taken from the well-known DREAMS database, and the experimental results demonstrate the importance of the proposed multi-stage filter in enhancing the performance of spindle detection using the three spindle detection algorithms. This elucidates the robustness of the suggested multi-stage filter in providing high-resolution sleep EEG data from noisy EEG recordings. Also, experimental results reveal that Spinky algorithm outperforms Mc-Sleep and Spindler methods in detecting spindles for filtered EEG signals using several evaluation metrics, including accuracy (94.8% versus 92.0% and 94.6%), precision (53.4% versus 36.4% and 47.5%), specificity (97.3% versus 93.9% and 96.1%) and F1-score (58.2% versus 41.3% and 50.9%), respectively. This shows that combining the proposed multi-stage filter with Spinky algorithm outperforms the other two methods in detecting spindles in EEG signals, and it represents an efficient automated spindle detection system that achieves high diagnosis performance in terms of accuracy (94.8%), specificity (97.3%), and F1-score (58.2%).

How Stimulus Statistics Affect the Receptive Fields of Cells in Primary Visual Cortex.

We studied the changes that neuronal receptive field (RF) models undergo when the statistics of the stimulus are changed from those of white Gaussian noise (WGN) to those of natural scenes (NSs), by fitting the models to multielectrode data recorded from primary visual cortex (V1) of female cats. This allowed the estimation of both a cascade of linear filters on the stimulus, as well as the static nonlinearities that map the output of the filters to the neuronal spike rates. We found that cells respond differently to these two classes of stimuli, with mostly higher spike rates and shorter response latencies to NSs than to WGN. The most striking finding was that NSs resulted in RFs that had additional uncovered filters compared with WGN. This finding was not an artifact of the higher spike rates observed for NSs relative to WGN, but rather was related to a change in coding. Our results reveal a greater extent of nonlinear processing in V1 neurons when stimulated using NSs compared with WGN. Our findings indicate the existence of nonlinear mechanisms that endow V1 neurons with context-dependent transmission of visual information.SIGNIFICANCE STATEMENT This study addresses a fundamental question about the concept of the receptive field (RF): does the encoding of information depend on the context or statistical regularities of the stimulus type? We applied state-of-the-art RF modeling techniques to data collected from multielectrode recordings from cat visual cortex in response to two statistically distinct stimulus types: white Gaussian noise and natural scenes. We find significant differences between the RFs that emerge from our data-driven modeling. Natural scenes result in far more complex RFs that combine multiple features in the visual input. Our findings reveal that different regimes or modes of operation are at work in visual cortical processing depending on the information present in the visual input. The complexity of V1 neural coding appears to be dependent on the complexity of the stimulus. We believe this new finding will have interesting implications for our understanding of the efficient transmission of information in sensory systems, which is an integral assumption of many computational theories (e.g., efficient and predictive coding of sensory processing in the brain).

Novel cascade filter design of improved sparse low-rank matrix estimation and kernel adaptive filtering for ECG denoising and artifacts cancellation

ElectroCardioGram (ECG) signals are highly vulnerable to disturbances caused by noise and artifact sources which can degrade the ECG signal quality and increase the difficulty in obtaining reliable and accurate clinical interpretations for heart conditions. This paper introduces, for the first time, the Improved Sparse Low-Rank (ISLR) algorithm for suppressing white/colored noises, and the Kernel Recursive Least Squares with Approximate Linear Dependency (ALDKRLS) algorithm for eliminating various artifact sources. A novel automated multi-stage filter is introduced for suppressing artifact components in the first stage using ALDKRLS and eliminating noise sources in the subsequent stage using ISLR. The robustness of the suggested multi-stage filter is demonstrated by eliminating noise and artifact components individually and when both present concurrently using real ECG data. Experimental results elucidate the outstanding accuracy of the suggested framework in eliminating interference sources and keeping the essential and important characteristics of the original ECG data. Also, the application of the suggested framework in practical systems is examined by investigating a new efficient ECG multi-class classification system before and after suppressing noise and artifact interferences. Results show that the suggested framework manages not only to eliminate effectively noise and artifact components, but also to achieve very accurate ECG diagnosis results by maintaining the essential characteristics of the ECG signal that differentiate different heart disorders. This elucidates the usefulness of the proposed multi-stage filter as a promising preprocessing tool for obtaining high-resolution ECG data and consequently enhancing the diagnosis performance of several heart diseases.

Microplastic sampling from wastewater treatment plant effluents: Best-practices and synergies between thermoanalytical and spectroscopic analysis

Wastewater treatment plants (WWTPs) may represent point sources for microplastic discharge into the environment. Quantification of microplastic in effluents of WWTPs has been targeted by several studies although standardized methods are missing to enable a comparability of results. This study discusses theoretical and practical perspectives on best practices for microplastic sampling campaigns of WWTPs. One focus of the study was the potential for synergies between thermoanalytical and spectroscopic analysis to gain more representative sampling using the complementary information provided by the different analytical techniques. Samples were obtained before and after sand filtration from two WWTPs in Germany using cascade filtration with size classes of 5,000 – 100 µm, 100 – 50 µm, and 50 – 10 µm. For spectroscopic methods samples were treated by a Fenton process to remove natural organic matter, whereas TED-GC-MS required only sample extraction from the filter cascade. µFTIR spectroscopy was used for the 100 µm and 50 µm basket filters and µRaman spectroscopy was applied to analyze particles on the smallest basket filter (10 µm). TED-GC-MS was used for all size classes as it is size independent. All techniques showed a similar trend, where PE was consistently the most prominent polymer in WWTP effluents. Based on this insight, PE was chosen as surrogate polymer to investigate whether it can describe the total polymer removal efficiency of tertiary sand filters. The results revealed no significant difference (ANOVA) between retention efficiencies of tertiary sand filtration obtained using only PE and by analyzing all possible polymers with µFTIR and µRaman spectroscopy. Findings from this study provide valuable insights on advantages and limitations of cascade filtration, the benefit of complementary analyses, a suitable design for future experimental approaches, and recommendations for future investigations.

Precision Medicine Approach for Cardiometabolic Risk Factors in Therapeutic Apheresis.

Lipoprotein apheresis (LA) is currently the most powerful intervention possible to reach a maximal reduction of lipids in patients with familial hypercholesterolemia and lipoprotein(a) hyperlipidemia. Although LA is an invasive method, it has few side effects and the best results in preventing further major cardiovascular events. It has been suggested that the highly significant reduction of cardiovascular complications in patients with severe lipid disorders achieved by LA is mediated not only by the potent reduction of lipid levels but also by the removal of other proinflammatory and proatherogenic factors. Here we performed a comprehensive proteomic analysis of patients on LA treatment using intra-individually a set of differently sized apheresis filters with the INUSpheresis system. This study revealed that proteomic analysis correlates well with routine clinical chemistry in these patients. The method is eminently suited to discover new biomarkers and risk factors for cardiovascular disease in these patients. Different filters achieve reduction and removal of proatherogenic proteins in different quantities. This includes not only apolipoproteins, C-reactive protein, fibrinogen, and plasminogen but also proteins like complement factor B (CFAB), protein AMBP, afamin, and the low affinity immunoglobulin gamma Fc region receptor III-A (FcγRIIIa) among others that have been described as atherosclerosis and metabolic vascular diseases promoting factors. We therefore conclude that future trials should be designed to develop an individualized therapy approach for patients on LA based on their metabolic and vascular risk profile. Furthermore, the power of such cascade filter treatment protocols may improve the prevention of cardiometabolic disease and its complications.

Aerodynamic noise separation of helicopter main and tail rotors using a cascade filter with Vold-Kalman filter and cyclic Wiener filter

The noise issue has become an important factor limiting the massive application of helicopters as the requirements for helicopter reliability and comfort continue to increase. As the primary noise source of helicopters, the main and tail rotor aerodynamic noise should be separated for individual noise reduction investigations. According to the frequency domain characteristics, the rotor aerodynamic noise can be divided into tonal noise and broadband noise. A cascade filtering method with Vold-Kalman filter and cyclic Wiener filter is proposed in this paper to separate the tonal and modulated broadband noise of the main and tail rotors, respectively. The classical method only considers the periodic characteristics of the main and tail rotor aerodynamic noise for harmonic separation. The broadband noise is not considered within the separation of interest due to its randomness, which leads to the inaccuracy of the conventional method for the aerodynamic noise separation of the main and tail rotors. The broadband noise exhibits a second-order cyclostationarity due to the periodic modulation of the rotational motion of the rotor. According to its cyclostationarity, while considering the periodicity of tonal noise for harmonic separation, the proposed cascade filtering method also performs the separation of broadband noise. More precise separations of the main and tail rotor can be obtained by this method. The efficacy of the proposed cascade filtering method is demonstrated in both the simulation and wind tunnel noise measurement.

Enhancement of Cognitive Radio Spectrum with Proposed Cascade Filter Model

The use of the frequency spectrum has become an urgent necessity, especially in the advanced generations of communications and various types of transmitted data. The radio spectrum is not being exploited effectively, primarily due to the current strict frequency allocation scheme. Only few bands of the spectrum are heavily utilized, such as those used for wireless base stations. Many bands are either not used at all or are only used infrequently. This problem has been solved through cognitive radio (CR) technology. CR technology faces many challenges in finding the unused frequency spectrum, especially the presence of noise in the spectrum analysis. This paper proposes a filter model with two-stages to enhance the cognitive radio performance and perform a comparison between proposed filter with the traditional cognitive radio techniques, by the means of QPSK modulation. The comparison result based on parameters such as the probability of detection as well as the probability of failure and the cumulative error rates. High probability of detection is required to achieve better network performance. A proposed filter model in the CR method has successfully passed all tests and provided better performance than other filters.

Strong pump rejection filter for polarization-diverse silicon platforms.

Integrated wavelength filters with high optical rejection are key components in several silicon photonics circuits, including quantum photon-pair sources and spectrometers. Non-coherent cascading of modal-engineered Bragg filters allows for remarkable optical rejections in structures that only support transverse-electric (TE) polarized modes such as uncladded 220-nm-thick silicon. However, the restriction to TE-only platforms limits the versatility of the non-coherent cascading approach. Here, we propose and experimentally demonstrate a new, to the best of our knowledge, approach for high-rejection filters in polarization-diverse platforms by combining non-coherent cascading of modal-engineered Bragg filters and anisotropy-engineered metamaterial bends. Bragg filters provide a high rejection of the TE mode, while the metamaterial bends remove any residual power propagating in the transverse-magnetic (TM) mode, without any penalty in terms of insertion loss or device footprint. Based on this strategy, we demonstrate optical rejection exceeding 60 dB in 300-nm-thick, cladded silicon waveguides.

Denoising electrocardiogram signals using multiband filter and its implementation on FPGA

The electrocardiogram (ECG) signal carries vital information related to cardiac activities. While measuring ECG using electrodes, the signal is contaminated with powerline interference (PLI) from harmonics, baseline wandering (BW), motion artefacts (MA) and high frequency (HF) noise. The extraction of the ECG signal, without the loss of useful information from the noisy environment, is required. Therefore, the selection and implementation of an efficient filter design is proposed. The Finite Impulse Response (FIR)-based multiband needs separate digital filters, such as Lowpass, Highpass, and Bandstop Filter in cascade. The coefficients of the FIR multiband filter are optimised using a least squares optimisation method and realised in a direct form symmetrical structure. The capability of the proposed filter is evaluated on a Physionet ECG ID database, having records of inherent noisy ECG signals. The performance is also verified by measuring the power spectrum of the noisy and filtered ECG waveform. Also, the feasibility of the proposed multiband filter is investigated on Xilinx ISE and the design is implemented on a field programmable gate array (FPGA) platform. A low order simple multiband filter structure is designed and implemented on the reconfigurable FPGA device.

Microplastic in Water and Sediments at the Confluence of the Elbe and Mulde Rivers in Germany

Accumulation of microplastics in aquatic environments is an issue of emerging concern. Initially, research focused on marine systems. However, recent studies also investigate the abundance of microplastics in freshwater environments. Rivers connect terrestrial with marine ecosystems and contribute a considerable share of macro- and microplastics to the oceans. A previous study found a large amount of micro-spheres in Dessau downstream the river mouth of the Mulde. Therefore, the objective of this research was to examine whether the Mulde river with its highly industrialized catchment contributes to the microplastic pollution of the Elbe. Sediment (Van Veen grab sampler) and water samples (filter cascade with the smallest mesh size 50 μm and nets with the smallest mesh size 150 μm) were taken from the Elbe river up- and downstream the confluence with the Mulde. After extensive sample preparation, we examined the samples under a digital microscope and determined polymer types by pyrolysis Gas Chromatography/Mass Spectrometry (pyr-GC-MS). The amount of primary microplastics increased in sediment and water samples just downstream the confluence. Those microplastics originate probably from the Mulde. We measured larger amounts and different shapes of microplastics in filter cascades that have a smaller mesh size compared to the nets.

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

Blind deconvolution (BD) is an effective algorithm for enhancing the impulsive signature of rolling bearings. As a convex optimization problem, the existing BDs have poor optimization performance and cannot effectively enhance the impulsive signature excited by weak faults. Moreover, the existing BDs require manual derivation of the calculation process, which brings great inconvenience to the researcher’s personalized design of the maximization criterion. A new BD algorithm based on backward automatic differentiation is proposed, which is named backward automatic differentiation blind deconvolution (BADBD). The calculation process does not require manual derivation so a general solution of BDs based on different maximization criteria is realized. BADBD constructs multiple cascaded filters to filter the raw vibration signal, which makes up for the deficiency of single filter performance. The filter coefficients are determined by Adam algorithm, which improves the optimization performance of the proposed BADBD. BADBD is compared with classic BDs by synthesized and real vibration signals. The results reveal superior capability of BADBD to enhance the impulsive signature and the fault diagnosis performance is significantly better than the classic BDs.

Highly sensitive and stable fiber-laser pressure-sensing system based on an unequal-arm Mach-Zehnder cascaded with a Sagnac structure

We propose, analyze, and experimentally demonstrate a highly sensitive and stable fiber-laser pressure-sensing system based on an unequal-arm Mach-Zehnder interferometer (MZI) merged with a Sagnac interferometer (SI). The unequal-arm MZI structure consists essentially of two single-mode fibers of different lengths, and the SI structure uses a section of polarization-maintaining fiber, which acts as a sensing head. Moreover, in the proposed sensing system, the MZI and SI structures both act as a cascade filter. Given the asymmetric MZI and cascade filter structure laser mode hopping is effectively suppressed, so minor variations in environmental parameters cause a stable shift of the interference spectrum. The experimental results show that the proposed system can be used to make accurate pressure measurements. The −3 dB linewidth of the reflection interference peak produced by the proposed system is less than 0.02 nm, and the signal-to-noise ratio (SNR) can exceed 45 dB. With a 1-m-long sensor head, the proposed system provides a pressure sensitivity of 29.275 nm/MPa, and the interference spectrum of the sensing system fluctuates less than ±0.02 nm over 1 h. The proposed sensing system thus offers the attractive characteristics of good sensing linearity and stability, high SNR, and high sensitivity.

Ladder-Based Synthesis and Design of Low-Frequency Buffer-Based CMOS Filters

Buffer-based CMOS filters are maximally simplified circuits containing as few transistors as possible. Their applications, among others, include nano to micro watt biomedical sensors that process physiological signals of frequencies from 0.01 Hz to about 3 kHz. The order of a buffer-based filter is not greater than two. Hence, to obtain higher-order filters, a cascade of second-order filters is constructed. In this paper, a more general method for buffer-based filter synthesis is developed and presented. The method uses RLC ladder prototypes to obtain filters of arbitrary orders. In addition, a set of novel circuit solutions with ultra-low voltage and power are proposed. The introduced circuits were synthesized and simulated using 180-nm CMOS technology of X-FAB. One of the designed circuits is a fourth-order, low-pass filter that features: 100-Hz passband, 0.4-V supply voltage, power consumption of less than 5 nW, and dynamic range above 60 dB. Moreover, the total capacitance of the proposed filter (31 pF) is 25% lower compared to the structure synthesized using a conventional cascade method (40 pF).

Design of filter coefficients for optimal factored truncated cascade FIR filter using optimization algorithm

Signal filtering acts as one of the basic requirement of communication networks for the removal of unwanted features from the signal. The design of appropriate digital filter requires the selection of optimal filter coefficients for the generation of desired frequency response with reduced hardware complexity. This paper proposes a hybrid optimization algorithm named as Brain Storm- Grey Wolf Optimizer (BSGWO) algorithm for the selection of filter coefficients in the design of factored truncated cascade FIR filter. The proposed algorithm is the hybridization of the optimization algorithms, namely Brain Storm Optimization (BSO) and Grey Wolf Optimizer (GWO). The input signal is interpolated initially for the formation of an intermediate signal using the FIR filter. Then, the factored truncated cascade filter is developed for the interpolation of the signal. After designing the filter coefficients, the optimal selection of the filter coefficients is performed using the proposed BSGWO algorithm. The original filter is developed with the use of the least square estimation and the new filter is developed using the proposed algorithm that tunes the filter coefficients. The performance of the proposed system is analyzed using the metrics, such as fitness, Mean Absolute Error (MAE), magnitude, and the number of components. The proposed method produces minimum fitness, MAE, magnitude and number of components of 0.05, 0.0155, − 96.0 dB and 3372, respectively that shows the effectiveness of the proposed method.

A high performance grid synchronization method for renewable energy grid-connected applications

Aiming at the problem of insufficient harmonic suppression capability for distributed renewable energy grid-connected application equipments, an efficient grid synchronous phase-locked loop(PLL) which will suppress harmonics is proposed. Firstly, a novel high-order generalized integral (NHOGI) is presented in traditional αβ-frame, and the dual novel high-order generalized integral (DNHOGI) structure is presented. Secondly, to remove interference of harmonic on PLL, a moving average filter(MAF) is added to eliminate the multi-order harmonic components and improve the phase-locked accuracy. Finally, DNHOGI and MAF filters are combined into a cascade filter, and a phase-locked loop used the cascade filter is proposed. The feasibility of the new approach is tested by experimental comparison with traditional methods.

DATA-DRIVEN LEARNING OF GEOMETRIC SCATTERING MODULES FOR GNNS.

We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations. The incorporation of our LEGS-module in GNNs enables the learning of longer-range graph relations compared to many popular GNNs, which often rely on encoding graph structure via smoothness or similarity between neighbors. Further, its wavelet priors result in simplified architectures with significantly fewer learned parameters compared to competing GNNs. We demonstrate the predictive performance of LEGS-based networks on graph classification benchmarks, as well as the descriptive quality of their learned features in biochemical graph data exploration tasks.

Parameter Analysis on Backprojection for Super-Resolution (SR) Reconstruction Algorithm

In this paper, an image SR reconstruction scheme by using k-Singular Value Decomposition (k-SVD) with Orthogonal Matching Pursuit (OMP) as sparse coding method was proposed to obtain the High-Resolution (HR) image. The system conducted in this paper consists of two parts: image SR reconstruction algorithm and also the backprojection process. Since this paper is focused on analysing the effect of parameters in backprojection on the performance of final images produced at the end of the process, therefore, the flow of backprojection is discussed. Generally, the backprojection algorithm is added to the approach to improve the image quality by sharpening the edges of the HR image. However, the parameters used in backprojection could be the factor that caused decrease in the image quality. Thus, the main idea of this paper is to analyse these parameters throughout the flow of backprojection algorithm. The parameters include the predefined 2D filter and interpolation method. Based on the results, it can be concluded that the use of averaging filter (hsize = 3), Gaussian filter (hsize = 5, 7 and 9; σ = 1) and bicubic interpolation method or also known as the cubic kernel can be adopted to the backprojection algorithm since these parameters achieved the highest RMSE, PSNR and SSIM values of 13.87, 25.29dB and 0.83 respectively. The analyses done in this paper has brought a clear understanding on the backprojection algorithm and further implementation and analysis in backprojection such as by using cascaded filters for averaging and Gaussian filters can be considered in the future.

Cascade Configuration of Modal Filters for Power Bus Protection in Differential and Common Modes

The paper discusses the results of quasi-static simulation of two modal filter (MF) cascade configurations designed to attenuate an interference pulse in differential and common modes. The geometric parameters of the MF are optimized by heuristic search according to the amplitude minimization condition. The results of calculating the time responses to an ultrashort pulse (USP) and electrostatic discharge (ESD) are presented. The USP attenuation coefficient in the differential mode was 6.84 times for the horizontally placed cascades, and 6.94 times for the vertical configuration. In the common mode, the attenuation coefficient was 7.35 times for the horizontally placed cascades, and 7.57 times for the vertically placed cascades. For ESD, the attenuation coefficients were 1.51 times for the horizontally and 1.55 times for the vertically placed cascades in the differential and common modes. It was found that only the first spike is attenuated by the ESD excitation on the MF