Time-domain Filtering Research Articles

A novel spatial–temporal detection method of dim infrared moving small target

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial–temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time–space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

Design of high-Q polystyrene nonlinear cavity for ultrafast all-optical switching in mid-infrared photonic crystal slabs with cavity-waveguide structure

In this study, we design a nonlinear cavity with ultrafast response speed material in photonic crystal slabs for all-optical switching. We consider triangular lattice photonic crystal slab made from air holes in anisotropic Tellurium background which is on top of Teflon substrate. The cavity itself is then created by enlarging one of the air holes and infiltrating it with polystyrene. Optimization of structural parameters yields a single mode cavity with quality factor of Q=2.5×104, by using the three-dimensional finite-difference time-domain (FDTD) simulation and filter diagonalization approach. This shows great enhancement compared with previous studies in which organic polymer materials have been used. In order to study the coupling characteristic of cavity mode and waveguides, the nonlinear cavity is placed between two waveguides, symmetrically. At the end, we used the FDTD method to investigate shift magnitude of cavity mode resonance frequency under pump light. The designed structure can be helpful to achieve extremely fast response speed in all-optical switching devices with high efficiency in the mid-infrared wavelength range.

Pitch modification of speech signal using harmonic model with time-varying parameters

The paper presents a solution to the problem of pitch modification of speech. The problem occurs in different speech processing applications such as voice conversion, accent correction, hiding speaker's personality and other. Developed processing scheme for voiced part of speech is based on the harmonic model with nonstationary (time-varying) parameters. In order to improve frequency resolution of the model parameters are extracted using narrow-band filtering in warped time domain aligned to instantaneous pitch frequency. Using subjective listening tests it is shown that developed system provides high naturalness and intelligibility of reconstructed speech and can be applied to wideband and narrowband communication channels with various coding standards (including G.711 and GSM).

Substance identification based on transmission THz spectra using library search

Over recent years terahertz spectroscopy has become a new tool for the characterization of solid materials, in particular for investigating polymorphism and crystallinity in pharmaceutics. Search strategies have been tested for THz spectra of various organic compounds with their spectra taken from the Riken database (http://www.riken.jp), using the GRAMS spectroscopy software. A subset of the entire database was used, which had been processed by removal of atmospheric water vapour lines and smoothing applied based on Savitzky-Golay convolution or time domain filtering. The spectral range available for all library substances was restricted to an interval from 0.9 to 4.5 THz (30–150cm−1). The number of vibrational bands within this spectral range is much reduced compared with mid-infrared or Raman spectra. The appropriateness of spectral pre-treatment is demonstrated with regard to reliability and robustness of the search methods. In particular, time-domain filters for smoothing and pre-treatment by the removal of water lines and etalon effects have been successfully tested in combination with least squares and correlation methods. With these insights, applications for substance identification, especially for the pharmaceutical industry, may be enlarged.

Energy-time entanglement generation in optical fibers under CW pumping

In this paper, the energy-time entangled photon-pairs at 1.5 μm are generated by the spontaneous four wave mixing (SFWM) in optical fibers under continuous wave (CW) pumping. The energy-time entanglement property is demonstrated experimentally through an experiment of Franson-type interference. Although the generation rates of the noise photons are one order of magnitude higher than that of the photon-pairs under CW pumping, the impact of noise photons can be highly suppressed in the measurement by a narrow time domain filter supported by superconducting nanowire single photon detectors with low timing jitters and time correlated single photon counting (TCSPC) module with high time resolution. The experiment results show that the SFWM in optical fibers under CW pumping provides a simple and practical way to generate energy-time entanglement at 1.5 μm, which has great potential for long-distance quantum information applications over optical fibers.

Removal of Baseline Noise from Electrocardiography (ECG) Signal Based on time Domain Approach

Electrocardiographic (ECG) signals in measurements are often contaminated with different types of noises in which include baseline noise. In case of the frequency of baseline noise is greater or smaller than frequency of the ECG signal, it is easy to filter the baseline noise from ECG signal by using filtered methods in frequency domain. In contrast, if frequency of baseline noise and frequency of ECG signal are coincident, it is difficult to apply the frequency domain filters for baseline noise removal. In this paper, we introduce an approach to remove of baseline noise from ECG signal in time domain and evaluation the efficacy of the method based on Mean Square Error criteria. We have performed experiments with simulated ECG signal which including white noise, random and sinusoidal baseline noises. Throughout the experiment, we found that the errors of time domain filters depend on the amplitude of the base line noises.

Algorithms for Automatic, Real-time Tsunami Detection in Wind-wave Measurements: Using Strategies and Practical Aspects

The present paper concentrates on the actual use - in the framework of a Tsunami Early Warning System - of the algorithms to be implemented in the software of wind-wave gages in order to automatically perform the real-time detection of tsunamis within recorded signals. Focus is on the amplitude-discriminating algorithm, mainly based on an infinite impulse response time domain filter, proposed by the authors in a previous paper. In particular, the problems that may arise in filtering out ‘disturbances’ such as long tidal waves are addressed, and an original method to perform the full characterization of the waveform of an actually detected tsunami, automatically and in ‘quasi’ real-time is proposed. Tests were carried out using time-series resulting from the superposi- tion of actual tsunami signals recorded by some of the tsunamometers of the Deep-ocean Assessment and Reporting of Tsunamis program, and different wind-wave signals synthesized by means of the random-phase method. In particular, the tsunamometers’ records collected during the events triggered by the earthquakes occurred off the coast of Chile on February 27, 2010, and off the Pacific Coast of Tohoku (Japan) on March 11, 2011, were used. The results show that the overall detection and characterization of a tsunami can be effectively carried out, automatically and in real-time, using the proposed new algorithm.

A mechanical tuning method for superconducting filter in both frequency domain and time domain

ABSTRACTThis article presents a mechanical tuning method for high‐temperature superconducting (HTS) filter based on the frequency domain and the time domain using dielectric tuning rods and metallic tuning rods. The resonant frequency tuning effect with dielectric and metallic rods was analyzed using lumped‐element models. The simulation results showed that the resonant frequency of each resonator can be tuned separately. An eight‐pole HTS twin spiral filter was tuned with the proposed method in frequency domain and time domain simultaneously, as a demonstration. After tuning, the filter has a 16.82 dB return loss, a 0.26 dB insertion loss and a 0.14 dB in‐band ripple. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:217–223, 2014

Channel interpolation for LTE uplink systems with high mobility using Slepian sequences

Single Carrier Frequency Division Multiple Access (SC-FDMA) has been adopted for 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) uplink communication since it combines the low peak to average power ratio feature of single-carrier transmission systems with the multipath resistance and flexible frequency allocation of orthogonal frequency-division multiple access (OFDMA). Unfortunately, SC-FDMA transmission over time-varying multipath fading channels for very high speed environments the subcarrier orthogonality is destroyed giving rise to intercarrier interference (ICI) due to channel variation within an SC-FDMA symbol. This paper is, therefore, focused on the use of time-domain channel interpolator for LTE uplink systems in large Doppler spread scenarios to improve channel tracking performance. The resulting interpolation algorithm is based on the discrete prolate spheroidal wave functions (DPSWF) which is particularly well suited to represent the rapidly time-varying fading channel due to optimum finite time and frequency support feature. The variations of the channel taps are tracked first by a Kalman filter in time domain during training symbols. Then, a DPSWF based channel interpolator is applied to recover the time variation of channel taps between training symbols within two consecutive slots in a single subframe. The performance of the proposed interpolator is compared with a polynomial interpolator whose order is adjusted adaptively to achieve the best fit under particular user mobilities. Moreover, the analytical mean square error (MSE) performance of DPSWF is derived and compared with the simulations. The results show that the DPSWF represents the time-varying fading channel very effectively and the proposed algorithm has excellent channel estimation performance in high SNR even with a very small number of channel interpolation parameters employed in the algorithm.

Variability of the autoregulation index decreases after removing the effect of the very low frequency band

Dynamic cerebral autoregulation (dCA) estimates show large between and within subject variability. Sources of variability include low coherence and influence of CO2 in the very low frequency (VLF) band, where dCA is active. This may lead to unreliable transfer function and autoregulation index (ARI) estimates. We tested whether variability of the ARI could be decreased by suppressing the effect of the VLF band through filtering. We also evaluated whether filtering had any effect on mean group differences between healthy subjects and acute stroke patients.Data from a recent mobilization stroke study were re-analyzed. Middle cerebral artery cerebral blood flow velocity (MCA-CBFV), mean arterial blood pressure (MABP) and end tidal PCO2 (PetCO2) were obtained in 16 healthy subjects and 27 acute ischemic stroke patients in the supine position. The ARI index was calculated from the transfer function (TF) by using spontaneous BP fluctuations. Three different filtering strategies were compared; no filtering (NF), a high pass filter at 0.04Hz (Time Domain Filtering: TDF) and a high pass Transfer Function Filter (TFF) at 0.04Hz. In addition, a simulation study was done to obtain further insight into the effects of the applied filters.The variability of the ARI index decreased significantly only with TFF in healthy subjects (standard deviation (left vs. right) after NF 2.28 vs. 2.36, after TDF 2.13 vs. 2.31 after TFF 1.09 vs. 1.19, p<0.001).Variability was not significantly reduced in stroke patients. The mean ARI was significantly lower in stroke patients compared to healthy subjects after TFF (affected hemisphere 5.85±1.96 vs. 7.13±1.09, non-affected hemisphere 5.96±1.64 vs. 7.31±1.19, p<0.01 for both hemispheres), but not after NF or TDF. The simulation study showed that TFF results in an overestimation of the ARI index at low ARI levels (0–3), but in correct estimates at higher ARI levels.Removing the effect of the VLF band with TFF results in less ARI variability in healthy subjects, and in more pronounced group differences between stroke patients and healthy subjects. This will improve diagnostic properties when using TFA for ARI calculation.

A dual protection scheme for impulsive noise suppression in OFDM systems

Orthogonal frequency division multiplexing (OFDM) can be susceptible to impulsive noise arising from numerous sources in a noisy communications environment. Conventional Reed–Solomon (RS) codes are particularly useful for burst-error corrections and have been employed in OFDM systems to manage impulsive noise. The performance gains, however, have been somewhat limited given the sensitivity to other noise types typically present in a noisy channel. In this regard, a novel scheme utilizing a time-domain pre-processing mean filter in combination with RS coding is proposed for impulsive noise suppression in OFDM systems. This scheme is split into two stages. In the first stage, a proposed mean filter effectively detects and removes the impulsive noise using the measured statistics of the impulsive noise. In contrast to a conventional blanking type filter, the traditional mean replacement value is replaced by a composite comparison value (CCV). This principle creates a more accurate estimate of the original OFDM signal after impulsive noise removal. The residual impulsive noise is then managed by a RS decoder in the second stage. Our results show that this dual faceted approach improves OFDM performance when compared to filtering and coding techniques alone.

Sampling and Reconstruction of Spatial Fields Using Mobile Sensors

Spatial sampling is traditionally studied in a static setting where static sensors scattered around space take measurements of the spatial field at their locations. In this paper we study the emerging paradigm of sampling and reconstructing spatial fields using sensors that move through space. We show that mobile sensing offers some unique advantages over static sensing in sensing time-invariant bandlimited spatial fields. Since a moving sensor encounters such a spatial field along its path as a time-domain signal, a time-domain anti-aliasing filter can be employed prior to sampling the signal received at the sensor. Such a filtering procedure, when used by a configuration of sensors moving at constant speeds along equispaced parallel lines, leads to a complete suppression of spatial aliasing in the direction of motion of the sensors. We analytically quantify the advantage of using such a sampling scheme over a static sampling scheme by computing the reduction in sampling noise due to the filter. We also analyze the effects of non-uniform sensor speeds on the reconstruction accuracy. Using simulation examples we demonstrate the advantages of mobile sampling over static sampling in practical problems. We extend our analysis to sampling and reconstruction schemes for monitoring time-varying bandlimited fields using mobile sensors. We demonstrate that in some situations we require a lower density of sensors when using a mobile sensing scheme instead of the conventional static sensing scheme. The exact advantage is quantified for a problem of sampling and reconstructing an audio field.

A perspective on multichannel noise reduction in the time domain

Conventional multichannel noise reduction techniques are formulated by splitting the processed microphone observations into two terms: filtered noise-free speech and residual additive noise. The first term is treated as desired signal while the second is a nuisance. Then, the objective has typically been to reduce the nuisance while keeping the filtered speech as similar as possible to the clean speech. It turns out that this treatment of the overall filtered speech as the desired signal is inappropriate as will become clear soon. In this paper, we present a new study of the multichannel time-domain noise reduction filters. We decompose the noise-free microphone array observations into two components where the first is correlated with the target signal and perfectly coherent across the sensors while the second consists of residual interference. Then, well-known time-domain filters including the minimum variance distortionless response (MVDR), the space–time (ST) prediction, the maximum signal-to-noise ratio (SNR), the linearly constrained minimum variance (LCMV), the multichannel tradeoff, and Wiener filters are derived. Besides, the analytical performance evaluation of these time-domain filters is provided and new insights into their functioning are presented. Numerical results are finally given to corroborate our study.

Experimental study on dynamic performance of coriolis mass flow meter and compensation technology

The Coriolis mass flow meter, for its features of directly measuring mass flow and high precision, is widely utilized in the flow measuring fields like batch filling and commercial trade. However, the unsatisfactory dynamic performance is one major constraint to Coriolis mass flow meter from being even more widely used in batch filling industry. To solve the problem, an experimental system which generates refined flow step stimulus signals for Coriolis mass flow meter was designed and based on which experimental and theoretical study on dynamic performances of typical Coriolis mass flow meter was carried out. On top of this, a time-domain recursive digital filter was designed to provide dynamic compensation and the experiments showed this filter can reduce the step response time of Coriolis mass flow meter by about 50%.

Non-Causal Time-Domain Filters for Single-Channel Noise Reduction

In many existing time-domain filtering methods for noise reduction in, e.g., speech processing, the filters are causal. Such causal filters can be implemented directly in practice. However, it is possible to improve the performance of such noise reduction filtering methods in terms of both noise suppression and signal distortion by allowing the filters to be non-causal. Non-causal time-domain filters require knowledge of the future, and are therefore not directly implementable. If the observed signal is processed in blocks, however, the non-causal filters are implementable. In this paper, we propose such non-causal time-domain filters for noise reduction in speech applications. We also propose some performance measures that enable us to evaluate the performance of non-causal filters. Moreover, it is shown how some of the filters can be updated recursively. Using the recursive expressions, it is also shown that the output SNRs of the filters always increase as we increase the length of the filter when the desired signal is stationary. From both the theoretical and practical evaluations of the filters, it is clearly shown that the performance of time-domain filtering methods for noise reduction can be improved by introducing non-causality.

Artifact suppression from EEG signals using data adaptive time domain filtering

This paper presents a data adaptive filtering approach to separate the electrooculograph (EOG) artifact from the recorded electroencephalograph (EEG) signal. Empirical mode decomposition (EMD) technique is used to implement the time domain filter. Fractional Gaussian noise (fGn) is used here as the reference signal to detect the distinguish feature of EOG signal to be used to separate from EEG. EMD is applied to the raw EEG and fGn separately to produce a finite number band limited signals named intrinsic mode functions (IMFs). The energies of individual IMFs of fGn and that of raw EEG are compared to derive the energy based threshold for the suppression of EOG effects. The separation results using EMD based approach is also compared with wavelet thresholding technique. The experimental results show that the data adaptive filtering technique performs better than the wavelet based approach.

A convex model and L1 minimization for musical noise reduction in blind source separation

Blind source separation (BSS) methods are useful tools to recover or enhance in- dividual speech sources from their mixtures in a multi-talker environment. A class of efficient BSS methods are based on the mutual exclusion hypothesis of the source signal Fourier spectra on the time- frequency (TF) domain, and subsequent data clustering and classification. Though such methodology is simple, the discontinuous decisions in the TF domain for classification often cause defects in the recovered signals in the time domain. The defects are perceived as unpleasant ringing sounds, the so called musical noise. Post-processing is desired for further quality enhancement. In this paper, an efficient musical noise reduction method is presented based on a convex model of time-domain sparse filters. The sparse filters are intended to cancel out the interference due to major sparse peaks in the mixing coefficients or physically the early arrival and high energy portion of the room impulse responses. This strategy is efficiently carried out by l1 regularization and the split Bregman method. Evaluations by both synthetic and room recorded speech and music data show that our method outperforms existing musical noise reduction methods in terms of objective and subjective measures. Our method can be used as a post-processing tool for more general and recent versions of TF domain BSS methods as well.

An Efficient Time-Varying Filter for Detrending and Bandwidth Limiting the Heart Rate Variability Tachogram without Resampling: MATLAB Open-Source Code and Internet Web-Based Implementation

The heart rate variability (HRV) signal derived from the ECG is a beat-to-beat record of RR intervals and is, as a time series, irregularly sampled. It is common engineering practice to resample this record, typically at 4 Hz, onto a regular time axis for analysis in advance of time domain filtering and spectral analysis based on the DFT. However, it is recognised that resampling introduces noise and frequency bias. The present work describes the implementation of a time-varying filter using a smoothing priors approach based on a Gaussian process model, which does not require data to be regular in time. Its output is directly compatible with the Lomb-Scargle algorithm for power density estimation. A web-based demonstration is available over the Internet for exemplar data. The MATLAB (MathWorks Inc.) code can be downloaded as open source.

Cooperative beamforming for OFDM-based amplify-and-forward relay networks

In this paper, we investigate the cooperative beamforming (BF) design problem for amplify-and-forward relay networks that are operating over frequency-selective channels using Orthogonal frequency division multiplexing (OFDM) signaling. We focus on the time-domain (TD) BF due to the lower implementation complexity and lower feedback requirement from the destination. Our aim for the BF design is maximizing the minimum signal-to-noise-ratio (SNR) over all subcarriers at the destination. We show that the BF designs lead to non-convex problems generally. Three approaches to approximate these problems into convex problems are proposed. In the first approach, semidefinite relaxation (SDR) techniques with randomization methods are applied. In the second approach, an existing iterative method for cooperative beamforming of multi-group multicasting (MGM) relay networks is extended to solve our problem. The third approach consists of improving the second approach by appropriately choosing the initial phase rotation. Simulation results demonstrate that the third approach always outperforms the other two approaches. Moreover, the performance of the second approach is close to that of the first approach when the filter length is relatively small. When a longer TD filter is employed, the second approach outperforms the first approach.

Interference of long-period seismic wavefield observed by the dense Hi-net array in Japan

[1] In order to recover small signals of long-period ground motions recorded by a short-period seismometer, we designed a time domain recursive filter that simulates seismic waves recorded with broadband seismometers. We applied the time domain filter to seismograms recorded by the dense Hi-net array, which consists of 800 stations at intervals of approximately 20 km. The effectiveness of the time domain filter is demonstrated through the comparison of broadband waveforms obtained by the filter and STS-2 true broadband seismograms from the F-net at the same site. Good agreement is observed between filtered and true broadband waveforms with high correlation coefficients for periods of up to 100 s for waveforms from the 2007 Sumatra earthquake (M7.9), which occurred approximately 5000–5800 km away from Japan. The applicability of the time domain filter to the recovery of long-period signals from the record of a short-period seismometer depends on the signal-to-noise ratio. As a result, a limited bandwidth of broadband signal is recovered for smaller earthquake. From the spatiotemporal variation of the long-period (T = 20–100 s) wavefield obtained by the time domain filter, we newly found an anomalous phase pattern of splitting of polarity along the wavefront, particularly for the relatively short period band of from 20–50 s. Array analysis of the dense broadband waveforms revealed multipathing of the Rayleigh waves, which propagated toward central Japan from two directions with slightly different slowness vectors. The interference of two Rayleigh waves caused such splitting of wavefield.