Cascade Of Filters Research Articles

ExceRpt: A Comprehensive Analytic Platform for Extracellular RNA Profiling.

Small RNA sequencing has been widely adopted to study the diversity of extracellular RNAs (exRNAs) in biofluids; however, the analysis of exRNA samples can be challenging: they are vulnerable to contamination and artifacts from different isolation techniques, present in lower concentrations than cellular RNA, and occasionally of exogenous origin. To address these challenges, we present exceRpt, the exRNA-processing toolkit of the NIH Extracellular RNA Communication Consortium (ERCC). exceRpt is structured as a cascade of filters and quantifications prioritized based on one's confidence in a given set of annotated RNAs. It generates quality control reports and abundance estimates for RNA biotypes. It is also capable of characterizing mappings to exogenous genomes, which, in turn, can be used to generate phylogenetic trees. exceRpt has been used to uniformly process all ∼3,500 exRNA-seq datasets in the public exRNA Atlas and is available from genboree.org and github.gersteinlab.org/exceRpt.

Distribution of heavy metals in various compartments of the constructed wetland system in Przywidz

In the last decade constructed wetlands have become a very popular technology for removal of contaminants from domestic sewage. They are also assesed from the point of view of their capacity for removal of toxic heavy metals and organic substances resistant to degradation. Constructed wetland in Przywidz localized about 60 km from Gdansk is a pilot wastewater treatment plant (WWTP) designed for 150 PE (person equivalent). The system consists of two sections: vegetated submerged bed (VSB) with horizontal flow of sewage and a cascade filter situated on a slope of a hill. Domestic sewage after a conventional pretreatment ( consists of an Imhoff tank and a trickling filter) is pumped to the VSB filter located on slope of the hill. Total area of constructed wetland is about 870 m2. In the period 1995-98 the measurments of several heavy metals (Cd, Cu, Pb) were carried out. These measurments were carried out in inflowing and outflowing sewage and as well in samples collected from each section of constructed wetland system. In particular analysis of sediment collected in ditches of the cascade filter, filling material of the dykes, VSB filter and plants were carried out. It was found out that content of heavy metals in suspended soil decreased along the course of treatment, starting from VSB filter, through the first ditch to the last ditch. Measurable concentration of dissolved heavy metals were found in sewage collected from several subsequent ditches. The main mechanism of removal on particular matter in subsequent ditches was sorption.

The effect of reverberation on listening effort

The current study investigates whether long reverberation increases listening effort during speech recognition. Listening effort during word recognition in multi-talker babble noise was assessed with or without high levels of reverberation. A dual-task paradigm was adopted, in which the primary task was word recognition in noise at individually selected signal-to-noise ratio (SNR) that yielded an average performance level of 50% correct, and the secondary task was a visual-tracking task with individually adjusted difficulty level to yield an average performance level of 85% correct. In each 30-s trial, seven monosyllabic words were presented sequentially at a rate of four seconds per word. Young normal-hearing listeners were instructed to verbally repeat each word while performing the secondary task. In the reverberant condition, a cascade of all-pass filters was used to achieve a reverberation time of 1 s without altering the original spectrum of the speech. For the primary task in isolation, the reverberant condition required 10 dB or more in SNR to achieve the 50% target performance level. When the listeners performed the two tasks simultaneously, no consistent adverse effect of reverberation was found on the performance of the primary or secondary task compared to the no-reverberation condition.The current study investigates whether long reverberation increases listening effort during speech recognition. Listening effort during word recognition in multi-talker babble noise was assessed with or without high levels of reverberation. A dual-task paradigm was adopted, in which the primary task was word recognition in noise at individually selected signal-to-noise ratio (SNR) that yielded an average performance level of 50% correct, and the secondary task was a visual-tracking task with individually adjusted difficulty level to yield an average performance level of 85% correct. In each 30-s trial, seven monosyllabic words were presented sequentially at a rate of four seconds per word. Young normal-hearing listeners were instructed to verbally repeat each word while performing the secondary task. In the reverberant condition, a cascade of all-pass filters was used to achieve a reverberation time of 1 s without altering the original spectrum of the speech. For the primary task in isolation, the reverbe...

Diversity-Based Cascade Filters for JPEG Steganalysis

Steganalysis is a technique for detecting the existence of secret information hidden in digital media. In this paper, we propose a novel scheme for JPEG steganalysis. In this scheme, we first design the diverse base filters which are able to obtain the image residuals from various directions. Then, we propose a cascade filter generation strategy to construct a set of high order cascade filters from the base filters. We further select the cascade filters with the maximum diversity. The selected filters are convolved with the decompressed JPEG image to obtain residuals which capture the subtle embedding traces. The residuals, termed as the maximum diversity cascade filter residual, are eventually used to extract features to train an ensemble classifier for classification. The experiments are carried out on the detection of stego-images generated using common JPEG steganographic schemes, the results of which demonstrate the effectiveness of the proposed scheme for JPEG steganalysis.

DeepVoCoder: A CNN Model for Compression and Coding of Narrow Band Speech

This paper proposes a convolutional neural network (CNN)-based encoder model to compress and code speech signal directly from raw input speech. Although the model can synthesize wideband speech by implicit bandwidth extension, narrowband is preferred for IP telephony and telecommunications purposes. The model takes time domain speech samples as inputs and encodes them using a cascade of convolutional filters in multiple layers, where pooling is applied after some layers to downsample the encoded speech by half. The final bottleneck layer of the CNN encoder provides an abstract and compact representation of the speech signal. In this paper, it is demonstrated that this compact representation is sufficient to reconstruct the original speech signal in high quality using the CNN decoder. This paper also discusses the theoretical background of why and how CNN may be used for end-to-end speech compression and coding. The complexity, delay, memory requirements, and bit rate versus quality are discussed in the experimental results.

An analytic physically motivated model of the mammalian cochlea.

In this paper, an analytic model of the mammalian cochlea is developed. A mixed physical-phenomenological approach by utilizing existing work on the physics of classical box-representations of the cochlea and behavior of recent data-derived wavenumber estimates is used. Spatial variation is incorporated through a single independent variable that combines space and frequency. This paper arrives at closed-form expressions for the organ of Corti velocity, its impedance, the pressure difference across the organ of Corti, and its wavenumber. Model tests using real and imaginary parts of chinchilla data from multiple locations and for multiple variables are performed. The model also predicts impedances that are qualitatively consistent with current literature. For implementation, the model can leverage existing efforts for both filter bank or filter cascade models that target improved algorithmic or analog circuit efficiencies. The simplicity of the cochlear model, its small number of model constants, its ability to capture the variation of tuning, its closed-form expressions for physically-interrelated variables, and the form of these expressions that allows for easily determining one variable from another make the model appropriate for analytic and digital auditory filter implementations as discussed here, as well as for extracting macromechanical insights regarding how the cochlea works.

Multi-stage discrete time and randomized dynamic average consensus

In this paper we propose a novel local interaction protocol which solves the discrete time dynamic average consensus problem, i.e., the consensus problem on the average value of a set of time-varying input signals in an undirected graph. The proposed interaction protocol is based on a multi-stage cascade of dynamic consensus filters which tracks the average value of the inputs with small and bounded error. We characterize its convergence properties for time-varying discrete-time inputs with bounded variations. The main novelty of the proposed algorithm is that, with respect to other dynamic average consensus protocols, we obtain the next unique set of advantages: i) The protocol, inspired by proportional dynamic consensus, does not exploit integral control actions or input derivatives, thus exhibits robustness to re-initialization errors, changes in the network size and noise in the input signals; ii) The proposed design allows to trade-off the quantity of information locally exchanged by the agents, i.e., the number of stages, with steady-state error, tracking error and convergence time; iii) The protocol can be implemented with randomized and asynchronous local state updates and keep in expectation the performance of the discrete-time version. Numerical examples are given to corroborate the theoretical findings, including the case where a new agent joins and leaves the network during the algorithm execution to show robustness to re-initialization errors during runtime.

Robust Visual Tracking via Dirac-Weighted Cascading Correlation Filters

Correlation filter-based trackers (CFTs) have recently raised considerable attention in visual tracking and achieved competitive performance. Nevertheless, conventional structures of such CFTs build a shallow architecture with a single correlation filter, which cannot comprehensively depict the target appearance. Hence, these trackers lack strong discriminative ability and easily drift when the target suffers drastic appearance variations. To address the limitations, we propose Dirac-weighted cascading correlation filters (DWCCF) for visual tracking. It incorporates cascading characteristics of multiple filters to construct the target appearance model, and dynamically learns Dirac weights for each filter, which is accordingly robust to appearance variations. Besides, we design a boundary penalization strategy to adaptively reduce the boundary effects, which efficiently improves the detection precision for tracking. Qualitative and quantitative evaluations on OTB-2013 and OTB-2015 datasets demonstrate that the proposed DWCCF significantly outperforms other state-of-the-art methods.

Analysis and Comparison of Notch Filter and Capacitor Voltage Feedforward Active Damping Techniques for LCL Grid-Connected Converters

The use of LCL filters is a well-accepted solution to attenuate the harmonics created by the pulsewidth modulation. However, inherently LCL filters have a resonance region where the unwanted harmonics are amplified, which can compromise stability. Several techniques have been developed in order to tackle this issue. At first the use of passive damping, by intentionally increasing the resistance of the LCL filter components, is a simple, robust, and straightforward solution; however, it decreases the overall efficiency of the system, and hence in general is unwanted. Alternatively, active damping strategies, where the resonance damping is provided by the current controller, are of major interest. This paper analyzes the robustness of the closed-loop dynamics when different active damping techniques are implemented. The analyzed active damping techniques, which have been selected because of their readiness and simplicity, are: 1) filtered capacitor voltage feedforward and 2) second-order filters in cascade with the main current controller. The impedance/admittance stability formulation is used to model the system, which has been proven to be very convenient for the assessment of robustness. Experimental tests are provided in order to show the accuracy of the analysis and verify the findings. This paper proves that filtered capacitor voltage feedforward is a more robust and reliable solution than implementations based on cascade notch filters.

Determination of Pull Directions for a Freeform Surface Using Cascade Filters of Visibility Maps

Determination of pull directions for a freeform surface is a challenging problem because the number of normal vectors is unlimited for such type of surfaces. Consequently, researchers generally convert a freeform surface into planar faces to limit the number of normal vectors. This paper presents an approach using cascade filters of visibility maps to determine feasible pull directions for freeform surfaces such that the required accuracy is retained. Pull direction candidates are first generated using a modified regular placement method. Normal vectors are then extracted by a sampling box, and the number of normal vectors is optimized through a numerical approach. Significant normal vectors are then selected by a modified Barycentric method. A cascade filter of the visibility map is built using these vectors and used for the selection of feasible pull directions. A minimum number of sliders and cavities in molding are finally achieved using a clustering algorithm based on maximum intersections of visibility maps. A real-life part is created using commercial CAD software for performance evaluation of the proposed algorithm.

Design of delayed fractional state variable filter for parameter estimation of fractional nonlinear models

This paper presents a novel direct parameter estimation method for continuous-time fractional nonlinear models. This is achieved by adapting a filter-based approach that uses the delayed fractional state variable filter for estimating the nonlinear model parameters directly from the measured sampled input–output data. A class of fractional nonlinear ordinary differential equation models is considered, where the nonlinear terms are linear with respect to the parameters. The nonlinear model equations are reformulated such that it allows a linear estimator to be used for estimating the model parameters. The required fractional time derivatives of measured input–output data are computed by a proposed delayed fractional state variable filter. The filter comprises of a cascade of all-pass filters and a fractional Butterworth filter, which forms the core part of the proposed parameter estimation method. The presented approaches for designing the fractional Butterworth filter are the so-called, square root base and compartmental fractional Butterworth design. According to the results, the parameters of the fractional-order nonlinear ordinary differential model converge to the true values and the estimator performs efficiently for the output error noise structure.

An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter

Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.

Development of a transportable single-use biomanufacturing system for therapeutic phage preparations

Background: The worldwide antibiotic crisis has led to a renewed interest in phage therapy. Lytic phages against bacterial pathogens can be isolated from the environment or selected from a collection in a matter of days. Product discovery costs are very low, but conventional biopharmaceutical production of phages is lengthy, complex and costly. In addition, phage treatment in low-income countries with poor infrastructure, battlefields, healthcare emergency situations (e.g. the 2012 foodborne E. coli 0104:H4 outbreak in Germany) demands a fast, global and decentralized access to phages. To address this demand, we developed a single-use, pre-sterilised, portable and closed biomanufacturing platform for therapeutic phage preparations. Methods & Materials: The basic components of this platform are as follows: a stacked bioreactor vessel with an adaptable number of chambers, in which the host bacterial strain, phage seed, culture medium (soft agar overlay technique) and extraction buffer are injected, is connected to (a) a cascade of clarification, purification and sterilisation filters, (b) phage vent filters, (c) tubings and connectors and (d) a filling system to dispense the final phage suspension into recipients. The following devices are necessary: a peristaltic pump, a fridge, a shaker and an incubator. Twenty-four different prototype assemblies for the production of Pseudomonas aeruginosa phages were tested. The following parameters were measured: pressure in function of volumetric throughput, phage yield and titre, endotoxin level, pH and assembly integrity (leaks). Results: The most suitable of the 24 tested prototype assemblies (presented at the conference) allowed for the production of 1-200 L (per bioreactor chamber) of phage suspension at a concentration of 107 pfu/ml with endotoxin levels and pH compatible with intravenous (IV) application, and this in 24 to 36 h. Conclusion: We developed a small-scale, portable and disposable biomanufacturing platform that allows for bacterial infection treatment at point-of-care, in short time frames, and at low cost, especially useful in remote locations with limited infrastructure since it does not necessarily require a cleanroom environment. MM and HVR contributed equally to this work. Funding: Grant HFM 15-5 of the Royal Higher Institute for Defense.

Learning From the Optical Spectrum: Failure Detection and Identification

The availability of coarse-resolution cost-effective optical spectrum analyzers (OSAs) allows their widespread deployment in operators’ networks. In this paper, we explore several machine learning approaches for soft-failure detection, identification, and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures: filter shift and tight filtering, which noticeably deform the expected shape of the optical spectrum. However, filter cascading is a key challenge as it affects the shape of the optical spectrum similarly to the tight filtering; the approaches are specifically designed to avoid the misclassification of properly operating signals when normal filter cascading effects are present. The proposed solutions are as follows: First, the multi-classifier approach, which uses features extracted directly from the optical spectrum; second, the single-classifier approach, which uses preprocessed features to compensate for filter cascading; and third, the residual-based approach, which uses a residual signal computed from subtracting the signal acquired by OSAs from an expected signal synthetically generated. Extensive numerical results are ultimately presented to compare the performance of the proposed approaches in terms of accuracy and robustness.

A Multiresolution Taylor–Kalman Approach for Broken Rotor Bar Detection in Cage Induction Motors

Broken rotor bar (BRB) fault is common in cage rotor induction motors. Motor current signature analysis (MCSA) has been a popular method to detect BRB faults. In the MCSA, the fault signature frequencies for BRB are close to the fundamental frequency. This spectral nearness leads to the requirement of larger observation windows, which inherently increases BRB fault detection time. This paper presents an alternative algorithm to detect BRB faults in induction motors from MCSA using two Taylor–Kalman (TK) filters in cascade with a subsampling scheme. The proposed BRB fault detection approach allows to use the TK filter to estimate lower frequencies with less computational burden in comparison to conventional TK analysis. Experimental analysis shows that nearly accurate estimates and competitive detection time can be achieved by the proposed BRB detection method. The performance of the proposed algorithm has been compared with classical spectral techniques using numerical simulations and records acquired from induction motors under real operating conditions.

Scattering transform and sparse linear classifiers for art authentication

Abstract Recently, a novel signal-processing tool was proposed, the scattering transform, which uses a cascade of wavelet filters and nonlinear (modulus) operations to build translation-invariant and deformation-stable representations. Despite being aimed at providing a theoretical understanding of deep neural networks, it also shows state-of-the-art performance in image classification. In this paper, we explore its performance for art authentication purposes. We analyze two databases of art objects (postimpressionist paintings and Renaissance drawings) with the goal of determining those authored by van Gogh and Raphael, respectively. To that end, we combine scattering coefficients with several linear classifiers, in particular sparse l1-regularized classifiers. Results show that these tools provide excellent performance, superior to state-of-the-art results. Further, they suggest the benefits of using sparse classifiers in combination with deep networks.

Investigation and performance evaluation of filter aided configurations for erbium doped fiber optical amplification system

Various possible configurations of EDFA’s namely Single Pass (SP) EDFA, Double Pass (DP) EDFA, Double Pass with Filter (DPF) EDFA, Triple Pass with Filter (TPF) EDFA and finally Quadruple Pass with Filter (QPF) EDFA are designed. DPF EDFA is an upgraded and enhanced version of DP EDFA configuration designed by using an optical filter in cascade. This filter has been designed with an optimal set of internal parameters namely center wavelength, bandwidth and depth. TPF and QPF EDFA configurations have also been designed. It is observed that in absence of upconversion, maximum Figure Of Merit (FOM) of about 40.35 dB is achieved in QPF EDFA configuration. With upconversion mechanism enabled the FOM of QPF EDFA reduces to only about 37.45 dB thus making QPF EDFA configuration suitable for practical applications. QPF and TPF EDFA had minimum gain ripple thus can be used in WDM applications, where flattened gain spectrum is an important requirement. The configurations offered here are less bulky with only one filter used instead of two as was the case in earlier configurations. QPF EDFA designed in this work has a reduced noise figure of about 4.85 dB at smaller lengths of EDFA of about 4.5 m and lower pump powers of about 90 mW. This configuration has highest sensitivity with the capacity to detect minimum signal power of −41.6 dBm. The QPF EDFA has an improved gain with reduction in noise figure, therefore QPF EDFA can be used as a preamplifier, to improve the sensitivity of receiver.

Design of Least-Squares and Minimax Composite Filters

We study a class of composite FIR filters (C-filters), each is composed of a prototype filter and a shaping filter in cascade, where the shaping filter is constructed by cascading several complementary comb filters. In particular, the problems of designing C-filters that are optimal in least-squares, equiripple passband and lease-squares stopband, and minimax sense are formulated, and three algorithms for designing such linear-phase FIR C-filters are proposed. The algorithms are based on an alternating optimization strategy in that the prototype and shaping filters are optimized in separate steps, which are coupled and carried out in a sequential manner to yield a satisfactory design. Design examples are presented to illustrate the algorithms and demonstrate the performance of the C-filters relative to their conventional FIR counterparts.

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.

Cascade and parallel combination (CPC) of adaptive filters for estimating heart rate during intensive physical exercise from photoplethysmographic signal.

Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable devices because of simplicity of construction and low cost of the sensor. The task becomes very difficult due to the presence of various motion artefacts. In this study, an algorithm based on cascade and parallel combination (CPC) of adaptive filters is proposed in order to reduce the effect of motion artefacts. First, preliminary noise reduction is performed by averaging two channel PPG signals. Next in order to reduce the effect of motion artefacts, a cascaded filter structure consisting of three cascaded adaptive filter blocks is developed where three-channel accelerometer signals are used as references to motion artefacts. To further reduce the affect of noise, a scheme based on convex combination of two such cascaded adaptive noise cancelers is introduced, where two widely used adaptive filters namely recursive least squares and least mean squares filters are employed. Heart rates are estimated from the noise reduced PPG signal in spectral domain. Finally, an efficient heart rate tracking algorithm is designed based on the nature of the heart rate variability. The performance of the proposed CPC method is tested on a widely used public database. It is found that the proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach.