Inverse Filtering Research Articles

Application of pseudo-3D Chirp sub-bottom profiler survey: a case study of ancient wooden shipwreck site, west coast of Korea

Chirp sub-bottom profilers (SBP) provide centi-to-decimetre resolution, seismic data with applications for various geophysical and geological purposes. To verify the field application of imaging of a buried target with a cost-effective and easy-to-apply pseudo-3D Chirp SBP survey, we explored the buried site of an ancient wooden shipwreck off the west coast of Korea before underwater excavations. The survey was conducted using a commercial 2D Chirp SBP system with a newly devised recording system that preserved the true polarity of the chirp signal. To produce high-resolution 3D Chirp SBP data from 2D Chirp SBP datasets recorded by the novel system, an optimal data processing sequence, consisting of a first phase of 2D data processing and a second phase of 3D data processing was designed. The first, 2D phase, included the estimation of a source sweep signature, cross-correlation, and deconvolution using an inverse filter. The resulting resolution of the 2D Chirp SBP data was better than that of the enveloped data provided by the commercial acquisition system. The second phase of 3D data processing included gathering 3D datasets, redistributing of ping positions, and static correction. To improve the consistency of the seismic events and reduce the repetitive corrections (swell, tidal, tie, and residual corrections), a static correction was based on multi-beam echo sounder data. The amplitude variation near the shipwreck was clearly apparent in the time slice from the final pseudo-3D Chirp SBP dataset with a bin size of 2.0 m (crossline) × 0.6 m (inline). Through 3D rendering, the buried ancient shipwreck with dimensions of 5 m (width) × 12 m (length) × 2 m (depth) was imaged successfully.

Compensation Techniques for Artifacts Arising Due to Non-ideal Components in Acoustic Reflectometry System

One of the applications of the acoustic reflectometry technique is to study the layered media structures such as soil, snow. The issues of acoustic reflectometry system to study the layered media structures related with using non-ideal components are given in this paper. It has been demonstrated that there are several artifacts present in the acoustic reflectometry analysis in practice. The paper presents the signal processing algorithms to compensate the effects of non-ideal components of an acoustic reflectometry system. We have developed the signal processing techniques using inverse filtering to compensate for the artifacts due to the non-ideal nature of the transmit–receive chain. The effectiveness of the inverse filter to compensate the artifacts is also tested. The application of the acoustic reflectometry system after artifacts compensation to study the layered structures has been demonstrated using experiments conducted in a full anechoic room.

SHADE: Absorption spectroscopy enhancement with ambient light estimation and narrow-band detection

Ambient light (AML) limits the usage of absorption spectroscopy to strictly-controlled environments due to its impact on the quality of the signals. In medical applications for Optical instruments that require high reliability and validity results, it is indispensable to control AML impact. In this work, we present a multi-wavelength optical acquisition method, here called SHADE. The technique increases the quality of the optical signals, dynamically assesses AML, and enables mitigating the AML influence on the data. Our proposed technique involves four primary functions: (1) Multiplexing/demultiplexing of target wavelengths using a frequency-division method; (2) Robust signal recovery using inverse notch filters; (3) Concurrent AML intensity estimation; (4) A simple post-processing (offline) suppression of AML interference. We introduce the mathematical framework of SHADE to demonstrate the theoretical scope and limitations. SHADE was also experimentally tested by using a digital signal processing board under different conditions. The results confirmed the performance of the AML reconstruction and the potential of such a method for further improvements of the signal quality.

Discrete wavelet transform-based RI adaptive algorithm for system identification

In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCTLMS), discrete-wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

Temporal large-eddy simulation based on direct deconvolution

In this work, we propose an approach for Temporal Large-Eddy Simulation (TLES) with direct deconvolution. In contrast to previous approaches such as the Temporal Approximate Deconvolution Model (TADM) by Pruett et al. [“A temporal approximate deconvolution model for large-eddy simulation,” Phys. Fluids 18, 028104 (2006)], the non-filtered velocity field is recovered using the differential form of the filter operation rather than from a truncated series expansion of the inverse filter operator. This direct deconvolution is used to obtain formal closure of an analytic evolution equation of the temporal residual-stress tensor. Thus, the Temporal Direct Deconvolution Model (TDDM) has advantages relative to the TADM in being both more accurate and requiring less computational effort. As for the TADM, a secondary regularization term based on selective frequency damping is employed. The TDDM was implemented in the spectral element code Nek5000 (Argonne National Laboratory, NEK5000 Version 17.0, 2019, https://nek5000.mcs.anl.gov) to simulate two canonical incompressible flows as three test cases: an a priori test case of Homogeneous Isotropic Turbulence (HIT) at Reλ = 50, a greatly coarsened a posteriori HIT case at Reλ = 190, and an a posteriori highly anisotropic turbulent channel flow at Reτ = 180. Analyses of the energy spectrum, the mean flow, the root-mean-square of the velocity fluctuations, and the Reynolds stresses are presented. The results demonstrate a significant improvement compared to no-model solutions regarding the mean flow in the turbulent channel and the energy spectrum in the HIT case, while the computational cost is reduced dramatically compared to the direct numerical simulation.

Observer-Based Deconvolution of Deterministic Input in Coprime Multichannel Systems With Its Application to Noninvasive Central Blood Pressure Monitoring.

Estimating central aortic blood pressure (BP) is important for cardiovascular (CV) health and risk prediction purposes. CV system is a multichannel dynamical system that yields multiple BPs at various body sites in response to central aortic BP. This paper concerns the development and analysis of an observer-based approach to deconvolution of unknown input in a class of coprime multichannel systems applicable to noninvasive estimation of central aortic BP. A multichannel system yields multiple outputs in response to a common input. Hence, the relationship between any pair of two outputs constitutes a hypothetical input-output system with unknown input embedded as a state. The central idea underlying our approach is to derive the unknown input by designing an observer for the hypothetical input-output system. In this paper, we developed an unknown input observer (UIO) for input deconvolution in coprime multichannel systems. We provided a universal design algorithm as well as meaningful physical insights and inherent performance limitations associated with the algorithm. The validity and potential of our approach were illustrated using a case study of estimating central aortic BP waveform from two noninvasively acquired peripheral arterial pulse waveforms. The UIO could reduce the root-mean-squared error (RMSE) associated with the central aortic BP by up to 27.5% and 28.8% against conventional inverse filtering (IF) and peripheral arterial pulse scaling techniques.

Objective Characterization of Phonation Type Using Amplitude of Flow Glottogram Pulse and of Voice Source Fundamental

Phonation type, a phonatory dimension ranging from hypofunctional/breathy to hyperfunctional/pressed, is important both from a clinical and acoustical point of view; hyperfunctional voice can lead to voice disorders and hypofunctional voice reduces text intelligibility. Five male singers sang diminuendo sequences of the syllable /pae/ and three of them also produced speech or singing samples with different phonation types which were rated for phonatory pressedness by a panel of voice experts. The sequences were analyzed by inverse filtering and the associated subglottal pressures were estimated as the oral pressures during the /p/ occlusion. The results showed strong quasi-linear correlations between the peak-to-peak amplitude of the flow glottogram (henceforth pulse amplitude) and mean subglottal pressure, mean airflow, and the level difference between the first and second voice source partials L1-L2. These correlations were found to vary systematically with phonation type. Regardless of phonation type, the correlation between the pulse amplitude and the amplitude of the voice source fundamental frequency was very close to 1.0. The level difference between the first and second spectrum partials L1-L2 in narrow-band long-term-average spectra of speech and singing was found to vary systematically with phonation type in a manner related to voice experts' ratings of perceived degree of pressedness. The findings support the assumption that the combination of subglottal pressure and level of the voice source fundamental is useful for an objective measure of phonation type.

Analysis and comparison of direct inversion and Kalman filter methods for self-powered neutron detector compensation

Many light-water reactors use self-powered neutron detectors (SPNDs) to monitor neutron flux. Some SPNDs suffer from delayed response. To compensate for this inherent delay, many methods such as the direct inversion method (DIM) and the Kalman filter method can be used. In the current investigation, these two methods were applied to the most common SPNDs, rhodium SPND (Rh-SPND) and vanadium SPND (V-SPND). The DIM, a leading compensation method, is used for V-SPND and Rh-SPND response compensation. The Kalman filter, which is a closed-loop compensation method, has also been developed for both SPNDs. The performance of the DIM and Kalman method for V-SPND and Rh-SPND was computed and surveyed with various sampling times. To evaluate the sensitivity of the compensator algorithm to miscalculations of the prompt fraction of the detector signal, computations were also conducted with various prompt fractions. Results show that the Kalman method is much more stable with different prompt fractions. Both methods improve the response time of V-SPNDs and Rh-SPNDs, and thus would allow them to be used for reactor protection, reactor control functions, reactor core monitoring, and for fulfilling surveillance requirements.

Extending the piezoelectric transducer bandwidth of an optical interferometer by suppressing resonance using a high dimensional IIR filter implemented on an FPGA.

This paper considers the application of Field Programmable Gate Array (FPGA)-based infinite impulse response (IIR) filtering to increase the usable bandwidth of a piezoelectric transducer used in optical phase locking. We experimentally perform system identification of the interferometer with the cross-correlation method integrated on the controller hardware. Our model is then used to implement an inverse filter designed to suppress the low frequency resonant modes of the piezoelectric transducer. This filter is realized as a 24th-order IIR filter on the FPGA, while the total input-output delay is kept at 350 ns. The combination of the inverse filter and the piezoelectric transducer works as a nearly flat response position actuator, allowing us to use a proportional-integral (PI) control in order to achieve stability of the closed-loop system with significant improvements over a non-filtered PI control. Finally, because this controller is completely digital, it is straightforward to reproduce. Our control scheme is suitable for many experiments that require highly accurate control of flexible structures.

The Vocal Tract in Loud Twang-Like Singing While Producing High and Low Pitches

This study investigates the possible contribution of the lower and upper vocal tract configurations during loud twang-like singing on high and low pitches in a real subject. One male contemporary commercial music singer produced a sustained vowel [a:] in his habitual speaking pitch (B2) and loudness. The same vowel was also produced in a loud twang-like singing voice on high (G4) and low pitches (B2). Computerized tomography, acoustic analysis, inverse filtering, and audio-perceptual assessments were performed. Both loud twang-like voices showed a megaphone-like shape of the vocal tract, being more notable on the low pitch. Also, low-frequency spectral changes, a peak of sound energy around 3 kHz and increased vocal fold adduction were found. Results agreed with audio-perceptual evaluation. Loud twang-like phonation seems to be mainly related to low-frequency spectral changes (under 2 kHz) and a more compact formant structure. Twang-like qualities seem to require different degrees of twang-related vocal tract adjustments while phonating in different pitches. A wider mouth opening, pharyngeal constriction, and epilaryngeal tube narrowing may be helpful strategies for maximum power transfer and improved vocal economy in loud contemporary commercial music singing and potentially in loud speech. Further studies should focus on vocal efficiency and vocal economy measurements using modeling and simulation, based on real-singers' data.

An intelligent personalized web blog searching technique using fuzzy-based feedback recurrent neural network

In web information retrieval, every single user has a unique contextual objective when searching for information on web blogs. Based on the given query, the task of the web search engine is to fetch the most related information from the collection of web blogs. In order to enhance the searching ability, efficient semantic matching models, richer training, and evaluation resources are required. The conventional keyword-based search algorithms have a minimum efficiency in knowing users’ intentions compared to machine learning algorithms. Recently, neural networks are well-recognized in information retrieval due to the ability of vector representation learning. This paper proposes an adaptive fuzzy feedback recurrent neural network-based web blog searching technique which follows inverse filtering (IF) algorithm using Word2Vec representation. Initially, the user query is pre-processed, and then given to the IF for accelerating the search process. Inside the IF, the web blog content is labelled according to their blog information and stored in the hash table, and then relevant contents are extracted by TF–IDF; this result is given to the similarity estimation to obtain the similarity score. Finally, the proposed technique considers the feedback got from users and performs re-ranking by fuzzy-based RNN to achieve rich user intention satisfaction. The implementation is performed in Python, and statistical measures such as accuracy, precision, and recall are utilized for performance evaluation. The results demonstrate that the proposed technique has enhanced accuracy (94%) compared to conventional techniques, namely deep auto-encoder, deep neural networks, and artificial neural networks.

Attenuation of receiver ghosts in variable-depth streamer high-resolution seismic reflection data

Receiver ghosts attenuate marine seismic reflection data at harmonic frequencies that depend on the propagation angle and the streamer depth below the sea surface. The resulting loss of bandwidth is one of the major factors hampering seismic resolution. In near-surface and legacy multi-channel data, receivers depth is often unknown and may vary significantly both along the streamer length and during the survey, making frequency–slowness deghosting techniques unsuitable. In this work, we present a method for the attenuation of receiver ghost reflections in data with an arbitrary streamer depth profile varying during the survey. For each trace, a different deghosting operator is estimated and applied at different two-way-time windows, in order to account for depth-dependent changes in reflection angles. The ghost null frequencies are picked on the time-varying power spectrum via an automatic algorithm, guided by a user-dependent a priori function, and optimised to respect the harmonics’ periodicity. The power of the inverse filter is adjusted by adaptively damping abnormal amplitudes in the deghosted spectra. The algorithm is applied to high-resolution (GI-gun, 20–400 Hz) and ultra-high-resolution (Sparker, 0.2–3.0 kHz) multi-channel datasets, yielding an excellent bandwidth recovery and gain in resolution in the final stacks. Limited computing time and straightforward application make the method widely applicable and cost-effective.

Applying Fast Scanning Method Coupled with Digital Image Processing Technology as Standard Acquisition Mode for Scanning Electron Microscopy.

This study proposes an efficient and fast method of scanning (e.g., television (TV) scan) coupled with digital image processing technology to replace the conventional slow-scan mode as a standard model of acquisition for general-purpose scanning electron microscopy (SEM). SEM images obtained using the proposed method had the same quality in terms of sharpness and noise as slow-scan images, and it was able to suppress the adverse effects of charging in a full-vacuum condition, which is a challenging problem in this area. Two problems needed to be solved in designing the proposed method. One was suitable compensation in image quality using the inverse filter based on characteristics of the frequency of a TV-scan image, and the other to devise an accurate technique of image integration (noise suppression), the position alignment of which is robust against noise. This involved using the image montage technique and estimating the number of images needed for the integration. The final result of our TV-scan mode was compared with the slow-scan image as well as the conventional TV-scan image.

Application of a Digital Micromirror Device for Optical Encryption with Time Integration

The possibility of using a digital micromirror device (DMD) for implementing the optical encryption with time integration in quasimonochromatic spatially incoherent light is considered. An experimental setup for optical registration of image to be encrypted on a digital camera is presented. Images containing text and graphic information are encrypted and successfully decrypted using inverse filtering. The use of time integration in optical encryption makes it possible to reduce the noise caused by binarization of holograms display on DMD that contains encryption keys and to increase the diffraction efficiency of the encryption process.

Generation of Arbitrarily-Oriented Ripple Images using Circular-Sector-Type Smoothing Filter and Inverse Filter

A Non-photorealistic rendering method for generating ripple images from photographic images has been proposed using intensity gradient. Ripple patterns imitate a wave on the water surface and are composed of continuous lines with fluctuations. Ripple images are expressed by superimposing ripple patterns on photographic images. However, the conventional method can only generate vertical and horizontal ripple patterns. Therefore, in this paper, we develop a method that can generate ripple patterns in any orientation. Ripple images generated by the proposed method are called Arbitrarily-Oriented Ripple (AOR) images. The proposed method is executed by an iterative process using circular-sector-type smoothing filter and inverse filter. To verify the effectiveness of our method, we investigate the changes in AOR images by changing the values of the parameters.

Inverse Filtering for Frequency Identification of Bridges Using Smartphones in Passing Vehicles: Fundamental Developments and Laboratory Verifications.

This paper puts forward a novel methodology of employing inverse filtering technique to extract bridge features from acceleration signals recorded on passing vehicles using smartphones. Since the vibration of a vehicle moving on a bridge will be affected by various features related to the vehicle, such as suspension and speed, this study focuses on filtering out these effects to extract bridge frequencies. Hence, an inverse filter is designed by employing the spectrum of vibration data of the vehicle when moving off the bridge to form a filter that will remove the car-related frequency content. Later, when the same car is moving on the bridge, this filter is applied to the spectrum of recorded data to suppress the car-related frequencies and amplify the bridge-related frequencies. The effectiveness of the proposed methodology is evaluated with experiments using a custom-built robot car as the vehicle moving over a lab-scale simply supported bridge. Nine combinations of speed and suspension stiffness of the car have been considered to investigate the robustness of the proposed methodology against car features. The results demonstrate that the inverse filtering method offers significant promise for identifying the fundamental frequency of the bridge. Since this approach considers each data source separately and designs a unique filter for each data collection device within each car, it is robust against device and car features.

Image restoration and denoising using block-based singular-value derivative

ABSTRACTSingular-value decomposition (SVD) can be used to estimate the point spread function (PSF) of a blurred image to denoise the image, but it does not consider the local difference of the image; moreover, the selection of recombination order or rank is related to the estimation of the PSF and the recovery effect. An optical image restoration and denoising method with white Gaussian noise using the block-based singular-value derivative is proposed in this paper. First, PSF is estimated using the average energy spectrum of the singular-value vector of the ideal image, which is based on SVD properties, starting from the discrete degenerate model. Next, the inverse filtering is used to restore the image. Finally, the denoised image is obtained using block-based SVD filtering and combination, in which the singular-value recombination order is determined by the singular-value derivative. The experimental results demonstrate that the proposed method has a higher PSNR (peak signal-to-noise ratio) than other methods, and it can get a more clear image.

CMOS CDBA-Based Inverse Filter Structure

This paper presents a voltage-mode(VM) tunable multifunction inverse filter configuration employing current differencing buffered amplifiers (CDBA). The presented structure utilizes two CDBAs, two/three capacitors and four/five resistors to realize inverse low pass filter (ILPF), inverse high pass filter (IHPF), inverse band pass filter (IBPF), and inverse band reject filter(IBRF) from the same circuit topology by suitable selection(s) of the branch admittances(s). PSPICE simulations have been performed with 0.18μm TSMC CMOS technology to validate the theory. Some sample experimental results have also been provided using off-the-shelf IC AD844 based CDBA.

Lens aberration compensation in interference microscopy

Emergence of products that feature functional surfaces with complex geometries, such as freeform optics in consumer electronics and augmented reality and virtual reality, requires high-accuracy non-contact surface measurement. However, large discrepancies are often observed between the measurement results of optical methods and contact stylus methods, especially for complex surfaces. For interference microscopy, such as coherence scanning interferometry, the three-dimensional surface transfer function provides information about the instrument spatial frequency passband and about lens aberrations that can result in measurement errors. Characterisation and phase inversion of the instrument's three-dimensional surface transfer function yields an inverse filter that can be applied directly to the three-dimensional fringe data. The inverse filtering is shown to reduce measurement errors without using any data processing or requiring any a priori knowledge of the surface. We present an experimental verification of the characterisation and correction process for measurements of several freeform surfaces and an additive manufactured surface. Corrected coherence scanning interferometry measurements agree with traceable contact stylus measurements to the order of 10 nm.

CFOA-Based New Structure of Fractional Order Inverse Filters

This paper presents a CFOA-based fractional order inverse filter (FOIF) structure. Proposed structure utilizes two current feedback operational amplifiers (CFOAs), two fractional capacitors (FCs) and two resistors to design FOIFs to realize fractional order inverse low pass (FOILP), fractional order inverse high pass (FOIHP) and fractional order inverse band pass (FOIBP) responses. The proposed configuration of FOIFs is simulated in PSPICE using AD844 type CFOAs. Stability analysis and MATLAB simulations are also carried out to authenticate the theoretical propositions.