Stack Filters Research Articles

Two-wavelength non-dispersive infrared sensing systems using stacked electrically controllable photonic-crystal band-pass filters

We present a scheme of non-dispersive infrared (NDIR) sensing utilizing stacked, two-wavelength electrically controllable bandpass filters that integrate multiple quantum wells (MQWs) and photonic crystals. By appropriately designing the MQWs and photonic crystals, we realize electrically controllable filters that exhibit symmetric, narrow-band transmittance spectra with high transmittance at two target wavenumbers (1035 cm-1 and 1085 cm-1) and demonstrate independent modulation of the transmittance at each target wavenumber when the two filters are stacked. In addition, by using the proposed stacked filters, we construct two-wavelength mechanical-chopper-free NDIR systems with a single detector and demonstrate its ability to robustly measure the gas concentration even when the absolute optical power incident on the detector is varied.

A Novel Stacked Ceramic-Monoblock Filter With Highly Flexible Transmission Zero Pair

In this letter, a novel five-pole monoblock dielectricresonator (MDR) bandpass filter with two transmission zeroes (TZs) based on the compact stacked configuration is proposed. The TZ pair is introduced with great flexibility and can be readily allocated at either or both sides of the passband with only a few filter dimension adjustments. The proposed filter has a simple fabrication process with tuning flexibility since there is no need to weld or assemble cavities. A 2.6-GHz filter with a symmetric TZ pair and 150-MHz bandwidth is fabricated. Measurement results show good agreement with simulated responses for validating the design theory.

Reflectivity enhancement of full color tri-layer electrowetting display with polymer cholesteric liquid crystal films

Electronic paper display has received increasing attention due to its outstanding properties of wide viewing angle, bistable state and lower energy consumption. Among various electronic paper display technologies, electrowetting e-paper (EWD) is the most promising as it can achieve full-color display by using stacked layers or RGB color filter. However, the EWD is faced with several challenges, including poor color brightness, low contrast ratio and small color gamut. To improve the performance of the EWD, we proposed a new configuration of EWD consisting of three independent PCLC reflection films based on the cholesteric liquid crystal materials. We designed and experimentally fabricated three types of reflection films, and then added the PCLC films to the tri-layered EWD device. We experimentally characterized the reflectivity and color gamut of the sample in the dark room. It is found that the experimental results and simulation results match with each other. It is demonstrated that with the PCLC films, the reflectivity is improved by 20%, while the color gamut is improved by 80%, which is sufficient for the daily display demand of E-paper. The proposed EWD device containing PCLC reflection films provide a new strategy to improve the brightness and color gamut of current EWD device, and is promising for realizing the full-color E-paper display.

Effect of Batch Dissimilarity on Permeability of Stacked Ceramic Foam Filters and Incompressible Fluid Flow: Experimental and Numerical Investigation

Ceramic foam filters (CFFs) are used to remove inclusions and/or solid particles from molten metal. In general, the molten metal poured on the top of a CFF should reach a certain height to form the pressure (metal head) required to prime the filter. For estimating the required metal head and obtaining the permeability coefficients of the CFFs, permeability experiments are essential. Recently, electromagnetic priming and filtration of molten aluminum with low and high grades of CFF, i.e., 30, 50 and 80 pore per inch (PPI) CFFs, have been introduced. Since then, there has been interest in exploring the possibility of obtaining further inclusion entrapment and aluminum refinement by using electromagnetic force to prime and filter with stacked CFFs. The successful execution of such trials requires a profound understanding concerning the permeability parameters of the stacked filters. Such data were deemed not to exist prior to this study. As a result, this study presents experimental findings of permeability measurements for stacks of three 30, three 50 and three 80 PPI commercial alumina CFFs from different industrial batches and compares the findings to numerically modelled data as well as previous research works. Both experimental and numerical findings showed a good agreement with previous results. The deviation between the experimentally and numerically obtained data lies in the range of 0.4 to 6.3%.

Effect on simultaneous removal of ammonia, nitrate, and phosphorus via advanced stacked assembly biological filter for rural domestic sewage treatment.

The discharge of ammonia-nitrogen (NH3-N), total nitrogen (TN), chemical oxygen demand (COD), and total phosphorus (TP) in rural sewage usually exceeds the Pollutant Discharge Standard for Urban Sewage Treatment Plants (GB18918-2002). Efficient and cost-effective removal of these pollutants cannot be simultaneously realized using conventional rural sewage treatment methods. Thus, an assembled biological filter (D50 × W50 × H113 cm), including a phosphorus removal layer filled with solid polymeric ferric sulfate and alternating aerobic-anaerobic layers, is proposed herein. The aerobic (anerobic) layers were filled with zeolite (zeolite and composite soil) at different intervals. This system was used for the treatment of synthetic sewage having COD: 122.0-227.0mg/L; NH3-N: 29.1-47.0mg/L; TN: 28.0-58.0mg/L; and TP: 2.0-3.8mg/L. Based on optimal operation conditions (40L/h reflow rate, without artificial aeration, and 12-h operation cycle), the system showed NH3-N, TN, COD, and TP removal efficiencies of 87.1 ± 8.1, 83.4 ± 7.9, 91.0 ± 9.4, and 80.0 ± 6.4%, respectively. Further, in the pilot-scale test, under the same optimal parameters, the removal efficiencies of NH3-N, TN, COD, and TP were 78.9 ± 8.1, 75.4 ± 7.9, 82 ± 9.4, and 76 ± 6.4%, respectively. Furthermore, in the different functional units of the system, a large number of functional bacteria capable of efficiently facilitating the simultaneous removal of the different pollutants from sewage were identified. Therefore, this proposed system, which complies with current environmental discharge regulations, can be a more sustainable approach for the treatment of unattended rural sewage.

Stacked filters

We present Stacked Filters, a new probabilistic filter which is fast and robust similar to query-agnostic filters (such as Bloom and Cuckoo filters), and at the same time brings low false positive rates and sizes similar to classifier-based filters (such as Learned Filters). The core idea is that Stacked Filters incorporate workload knowledge about frequently queried non-existing values. Instead of learning, they structurally incorporate that knowledge using hashing and several sequenced filter layers, indexing both data and frequent negatives. Stacked Filters can also gather workload knowledge on-the-fly and adaptively build the filter. We show experimentally that for a given memory budget, Stacked Filters achieve end-to-end query throughput up to 130x better than the best alternative for a workload, either query-agnostic or classifier-based filters, and depending on where data is (SSD or HDD).

A Compact Modular Multilevel DC–DC Converter for High Step-Ratio MV and HV Use

In multi-terminal dc networks or future dc grids, there is an important role for high step-ratio dc–dc conversion to interface a high-voltage network to lower voltage infeeds or offtakes. The efficiency and controllability of dc–dc conversion will be expected to be similar to modular multilevel ac–dc converters. This paper presents a modular multilevel dc–dc converter with a high step-ratio for medium-voltage and high-voltage applications. Its topology on the high-voltage side is derived from the half-bridge single-phase inverter with stacks of sub-modules (SMs) replacing each of the switch positions. A near-square-wave current operation is proposed, which achieves near-constant instantaneous power for single-phase conversion, leading to reduced stack capacitor and filter volume and also increasing the power device utilization. A controller for energy balancing and current tracking is designed. The soft-switching operation on the low-voltage side is demonstrated. The high step-ratio is accomplished by combination of inherent half-bridge ratio, SM stack modulation, and transformer turns ratio, which also offers flexibility to satisfy wide-range conversion requirements. The theoretical analysis of this converter is verified by simulation of a full-scale 40 MW, 200 kV converter with 146 SMs and also through experimental testing of a down-scaled prototype at 4.5 kW, 1.5 kV with 18 SMs.

Hierarchical stack filtering: a bitplane-based algorithm for massively parallel processors

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition and then processes every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement.

Robust statistical methods for impulse noise suppressing of spread spectrum induced polarization data, with application to a mine site, Gansu province, China

In this paper, we investigated the robust processing of noisy spread spectrum induced polarization (SSIP) data. SSIP is a new frequency domain induced polarization method that transmits pseudo-random m-sequence as source current where m-sequence is a broadband signal. The potential information at multiple frequencies can be obtained through measurement. Removing the noise is a crucial problem for SSIP data processing. Considering that if the ordinary mean stack and digital filter are not capable of reducing the impulse noise effectively in SSIP data processing, the impact of impulse noise will remain in the complex resistivity spectrum that will affect the interpretation of profile anomalies. We implemented a robust statistical method to SSIP data processing. The robust least-squares regression is used to fit and remove the linear trend from the original data before stacking. The robust M estimate is used to stack the data of all periods. The robust smooth filter is used to suppress the residual noise for data after stacking. For robust statistical scheme, the most appropriate influence function and iterative algorithm are chosen by testing the simulated data to suppress the outliers' influence. We tested the benefits of the robust SSIP data processing using examples of SSIP data recorded in a test site beside a mine in Gansu province, China.

Calculating the output distribution of stack filters that are erosion-dilation cascades, in particular LU LU -filters

Two procedures to compute the output distribution φS of certain stack filters S (so called erosion-dilation cascades) are given. One rests on the disjunctive normal form of S and also yields the rank selection probabilities. The other is based on inclusion-exclusion and e.g. yields φS for some important LU LU -operators S. Properties of φS can be used to characterize smoothing properties of S. Also, in the same way as our polynomials φS are computed one could compute the reliability polynomial of a connected graph, or more generally the reliability polynomial w.r.t. any positive Boolean function.

High resolution upper mantle discontinuity images across the Pacific Ocean from SS precursors using local slant stack filters

SS precursor observations are a powerful tool to study the topography and character of transition zone discontinuities, especially in regions such as ocean basins where few seismic stations exist, precluding other high resolution approaches. Still, the available coverage is limited by the distribution of sources and stations, but also by the level of noise and by the fact that, in some distance ranges, interfering seismic phases mask the weak signal from the SS precursors. We introduce an array data processing tool, the local slant-stack filter, to address these challenges and clean up the otherwise noisy SS precursor record sections. We show that these filters are a powerful tool for extracting the weak yet coherent SS precursor signals while removing interfering seismic phases as well as random noise, yielding robust precursor traveltime measurements with spatial resolution higher than what can be achieved by the conventional common midpoint stacking method. The effectiveness of the filters are demonstrated by application to synthetic and real data. We systematically apply this filtering method to an SS precursor data set recorded by the U.S. Transportable Array that samples a vast region of the Pacific Ocean and its northwest margin, and present maps of 410 and 660 discontinuity topography. We discuss correlations observed between our discontinuity images and several fine-scale heterogeneities revealed by mantle shear wave tomography in the vicinity of Hawaii and the Pacific Superswell.

Particulate carbon and nitrogen determinations in tracer studies: The neglected variables

We address two issues in the determination of particulate carbon and nitrogen in suspended matter of aquatic environments. One is the adsorption of dissolved organic matter on filters, leading to overestimate particulate matter. The second is the material loss during filtration due to fragile algal cells breaking up. Examples from both laboratory cultures and natural samples are presented. We recommend using stacked filters in order to estimate the first and filtering different volumes of water in order to evaluate the second.

Elemental compositions of PM10–2.5 and PM2.5 aerosols of a Nigerian urban city using ion beam analytical techniques

The paucity of data on air quality studies in Nigeria prompted us to commence the sampling of particulate matter (PM10–2.5 and PM2.5) in Mushin Lagos, Nigeria. Both size-segregated fractions were collected using a double staged ‘Gent’ stack filter unit sampler. Elemental characterization was carried out by Particle Induced X-ray Emission (PIXE) and Proton Induced γ-ray Emission (PIGE) techniques using an external ion beam set-up. Twenty-four elements (Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Se, Br, Rb, Sr, Zr, Cs and Pb) were detected in both fractions and their concentrations were assessed. A study of their inter-elemental correlations indicated that some elements could have common source origins or similar chemical properties while enrichment factors (EF) displayed that most elements emanated from anthropogenic sources. Source apportionment studies are thus recommended.

Application of Stack Filter on Image Processing

This paper proposed of using stack filter technique to clears image noise. The key points of stack filter algorithm are threshold Decomposition and cascading features, and it is one of nonlinear filter based on the order statistics theory. Mat lab experiments show that stack filter can greatly reduce the time of clears image noise, and it performs better than median filters on noise reduction while retaining edges of an image.

Speckle reduction with adaptive stack filters

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into stacks of binary images according to a set of thresholds. Each binary image is then filtered by a Boolean function, which characterizes the filter. Adaptive stack filters can be computed by training using a prototype (ideal) image and its corrupted version, leading to optimized filters with respect to a loss function. In this work we propose the use of training with selected samples for the estimation of the optimal Boolean function. We study the performance of adaptive stack filters when they are applied to speckled imagery, in particular to Synthetic Aperture Radar (SAR) images. This is done by evaluating the quality of the filtered images through the use of suitable image quality indexes and by measuring the classification accuracy of the resulting images. We used SAR images as input, since they are affected by speckle noise that makes classification a difficult task.

Bounds on the average sensitivity of nested canalizing functions.

Nested canalizing Boolean functions (NCF) play an important role in biologically motivated regulatory networks and in signal processing, in particular describing stack filters. It has been conjectured that NCFs have a stabilizing effect on the network dynamics. It is well known that the average sensitivity plays a central role for the stability of (random) Boolean networks. Here we provide a tight upper bound on the average sensitivity of NCFs as a function of the number of relevant input variables. As conjectured in literature this bound is smaller than . This shows that a large number of functions appearing in biological networks belong to a class that has low average sensitivity, which is even close to a tight lower bound.

Computing the Output Distribution and Selection Probabilities of a Stack Filter from the DNF of Its Positive Boolean Function

Many nonlinear filters used in practise are stack filters. An algorithm is presented which calculates the output distribution of an arbitrary stack filter S from the disjunctive normal form (DNF) of its underlying positive Boolean function (PBF). Our algorithm avoids to enumerate the models of the PBF one by one, and thus is considerably more efficient than previous methods. The so called rank selection probabilities can be computed along the way.

PIXE characterization of PM10 and PM2.5 particulates sizes collected in Ikoyi Lagos, Nigeria

Ambient concentrations of PM10 (x ≤ 10 µm) and PM2.5 (x ≤ 2.5 µm) particulate fractions collected from Ikoyi Lagos, Nigeria, as well as their elemental compositions are presented in this study. Both size-segregated fractions were collected using a double staged ‘Gent’ stack filter unit sampler. Elemental characterizations of dust laden filters were carried out using proton-induced X-ray emission (PIXE) technique. Twenty-two elements (Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Pb, Br, Rb, Sr, Zr, Ag, Cd, and Ta) were detected as well as their concentrations and correlations were determined for both particulate size fractions. Their correlation matrix result indicates that some of the trace elements detected could have common source origins or similar chemical properties. The results were similar to the levels observed in moderately polluted urban areas and there is need for source identification and apportionment using receptor models in future studies.

Generalized Correlativity of Median Filtering Operator on Signals

The generalized correlativity of input signal and output signal of a stack filtering operator is defined and used for numerously measuring these filtering operators's behavior in removing noise in signals. We show that under the criterion of the generalized correlativity, of stack filtering operators the median filtering operator is optimal, which implies that this filtering operator possesses better filtering behavior than the others.

Ordered Hypothesis Machines

Stack Filters are a class of non-linear filter typically used for noise suppression. Advantages of Stack Filters are their generality and the existence of efficient optimization algorithms under mean absolute error (Wendt et al. in IEEE Trans. Acoust. Speech Signal Process. 34:898---910, 1986). In this paper we describe our recent efforts to use the class of Stack Filters for classification problems. This leads to a novel class of continuous domain classifiers which we call Ordered Hypothesis Machines (OHM). We develop convex optimization based learning algorithms for Ordered Hypothesis Machines and highlight their relationship to Support Vector Machines and Nearest Neighbor classifiers. We report on the performance on synthetic and real-world datasets including an application to change detection in remote sensing imagery. We conclude that OHM provides a novel way to reduce the number of exemplars used in Nearest Neighbor classifiers and achieves competitive performance to the more computationally expensive K-Nearest Neighbor method.