Inverse Filtering Research Articles

A Comprehensive Analysis of Traditional and Advanced Approaches of Image Restoration Techniques

Introduction: Tremendous developments in multimedia technology have promoted a massive amount of research in image and video processing. As imaging technologies are rapidly increasing, it is becoming essential to use images in almost every application in our day-today life. Materials and Method: This paper presents a comparative analysis of various image restoration approaches, ranging from fundamental methods to advanced techniques. These approaches aim to improve the quality of images that have been degraded during acquisition or transmission. A brief overview of the image restoration approaches is mentioned in the paper, which are as follows: Wiener Filter: The Wiener filter is a classical approach used for image restoration. It is a linear filter that minimizes the mean square error between the original image and the restored image. Inverse Filter: The inverse filter is another traditional restoration technique. It attempts to invert the degradation process to recover the original image. However, inverse filtering is highly sensitive to noise and tends to amplify noise artifacts. Linear and Nonlinear Filtering: These methods involve applying linear or nonlinear filters to the degraded image to enhance its quality. Linear filters, such as Gaussian filters, can effectively reduce noise but may blur the image. Nonlinear filters, such as median filters, can preserve edges while reducing noise. Compressive Sensing (CS) Restoration Approaches: Compressive sensing is a signal processing technique that exploits the sparsity of signals or images to reconstruct them from fewer measurements. CS-based restoration methods aim to recover high-quality images from compressed or incomplete measurements. Neural Networks Approaches: With the advancements in deep learning, neural networks have been widely used for image restoration tasks. Convolutional neural networks (CNNs) and generative adversarial networks (GANs) have shown promising results in restoring degraded images by learning from large datasets. Result: The paper likely provides a detailed analysis and comparison of these approaches, highlighting their strengths, weaknesses, and performance in different scenarios. Conclusion: This paper aims to improve the quality of restored images. Several image restoration approaches have been compared, and they have exhibited enhanced performance compared to several existing image restoration approaches.

Нестационарный инверсный фильтр

In this paper, an approach is proposed to solve the problem of critical components in the signal spectrum while synthesizing an inverse filter using the basis correction method. By using correction, it is possible to avoid the "division by zero" error when processing signals with critical components in their spectrum. It has been shown that the resulting inverse filter has the property of non-stationarity in such cases.

Theory of classical and modified spaceborne synthetic aperture radar imaging

This paper presents a novel method for spaceborne synthetic aperture radar (SAR) imaging, achieved through the statistical solution of the inverse optimization problem of image reconstruction, specifically focusing on radar cross-section (RCS). The proposed method improves upon traditional techniques by incorporating signal decorrelation, implemented via inverse filtering. A comparative analysis is provided between the modified method and the classical aperture synthesis approach, both of which are grounded in the optimization problem solution within the theory of the maximum likelihood method.

Design of inverse filter using <i>H</i><sub>∞</sub> control theory for sound field reproduction with shoulder-mounted wearable-speakers

Design of inverse filter using <i>H</i><sub>∞</sub> control theory for sound field reproduction with shoulder-mounted wearable-speakers

FDS-MOMEDA: optimization-blind deconvolution in finite high-dimensional spaces for extracting pulse signal in rolling bearing fault diagnosis

Rolling bearing fault diagnosis is crucial for ensuring the safe and reliable operation of mechanical equipment. Detecting faults directly from measurement signals is challenging due to severe noise and interference. Blind deconvolution (BD), as a preferred method, effectively recovers periodic pulses from the measured vibration signals of faulty bearings. This study introduces a simulated annealing-based BD approach to enhance the pulse signal components reflecting faults in vibration signals measured on rolling bearings. This method iteratively searches for the optimal coordinates in a high-dimensional orthogonal optimization space, where the optimal coordinates reflect the combination of the inverse filter coefficients. Compared to the generalized spherical optimization space used in the ‘Optimization-Blind Deconvolution’ method in previous works, the proposed finite high-dimensional optimization space helps overcome the problem of inverse filter coefficient convergence, allowing for the design of inverse filters without limit of its shape. To better accommodate the cyclostationarity characteristics of bearing signal measured in reality, the proposed method employs a target vector that allows for uncertainty in pulse occurrence instants, thus overcomes challenges introduced by pseudo-periodic phenomena resulting from bearing slippage. Numerical simulations and experimental results on real bearing vibration signals confirm that the proposed method can design more flexible filters to enhance pulse-like patterns in signals, effectively utilize limited filter resources. Its capacity to tolerate inaccurate fault period estimates, high background noise, and pulse randomness enables it to effectively address vibration measurement signals in real-world scenarios.

Designing of CCII-Based Fractional Order Inverse Chebyshev Filters Using Gray Wolf Optimization Approach

The paper introduces a new approach for designing fractional-order inverse Chebyshev low-pass filters (FOICLF), with an emphasis on filters with an order of 1+α. The coefficients for these filters are determined through Gray Wolf Optimization by approximating filter response with the ideal response of same order FOICLF. Stability tests are conducted, and filters are implemented using Multiple Input Single Output topology with commercial ICs. Comparative analysis reveals close proximity between the ideal response of FOICLF and the designed filters, featuring maximum and mean square errors of –22.48 dB and –61.07 dB, respectively, for 1.7 order. Results are benchmarked against existing literature, highlighting superior performance in stopband and passband magnitude errors and stopband attenuation. Process, Voltage, and Temperature analysis assesses the impact of the parameter variations on the –3 dB frequency, assuring the robustness of designed filters with relative standard deviations ranging from 0.003% to 3.013% for the 1.7 order filter.

Modeling and Sentiment Analysis of Social Relationships in Elderly Smart Homes Based on Graph Neural Networks

With the expansion of Speech Emotion Recognition in the consumer domain, several devices, particularly those designed for managing smart home personal assistants for the elderly, have been widely available on the market. The increasing processing power and connection, together with the growing need to facilitate longer residency through technological interventions, highlight the potential benefits of smart home assistants. Enabling these assistants to recognize human emotions would greatly improve user-assistant communication, allowing the assistant to deliver more constructive and customized feedback to the user. In this research work, Modeling and Sentiment Analysis of Social Relationships in Elderly Smart Homes Based on Graph Neural Networks (SASR-MBHNN-BBOA) is proposed. The input data are collected from Social Recommendation Dataset. Then, input data are pre-processed utilizing Inverse Optimal Safety Filters (IOSF) for cleaning the data and removing the background noise. Then the pre-processed data are given to Memristive Bi-neuron Hopfield Neural Network (MBHNN) for predicting the sentiments like positive, negative and neutral. In general, MBHNN doesn’t express some adoption of optimization approaches for determining optimal parameters to predicting the sentiments accurately. Hence BBOA is proposed to optimize MBHNN classifier which precisely predicts the sentiments in elderly smart home. The proposed SASR-MBHNN-BBOA method is implemented in Python, and it assessed with numerous performance metrics such as accuracy, precision, recall, F1-score, ROC. The outcomes show SASR-MBHNN-BBOA attains 20.8%, 19.5%, and 29.6% higher Accuracy, 28.8%, 22.5%, and 32.6% higher Precision, 15.5%, 27.4%, and 18.2% higher Recall are analysed with existing methods such as, Emotional speech analysis in real time for smart home assistants.(SASR-CNN-SHA), Machine Learning to Investigate Elderly Care Requirements in China via the Lens of Family Caregivers (SASR-ML-IECR),Identifying User Emotions via Audio Conversations with Smart Assistants (SASR-DNN-EASA) methods respectively.

Continuous-time model identification of the subglottal system

Mathematical models that accurately simulate the physiological systems of the human body serve as cornerstone instruments for advancing medical science and facilitating innovative clinical interventions. One application is the modeling of the subglottal tract and neck skin properties for its use in the ambulatory assessment of vocal function, by enabling non-invasive monitoring of glottal airflow via a neck surface accelerometer. For the technique to be effective, the development of an accurate building block model for the subglottal tract is required. Such a model is expected to utilize glottal volume velocity as the input parameter and yield neck skin acceleration as the corresponding output. In contrast to preceding efforts that employed frequency-domain methods, the present paper leverages system identification techniques to derive a parsimonious continuous-time model of the subglottal tract using time-domain data samples. Additionally, an examination of the model order is conducted through the application of various information criteria. Once a low-order model is successfully fitted, an inverse filter based on a Kalman smoother is utilized for the estimation of glottal volume velocity and related aerodynamic metrics, thereby constituting the most efficient execution of these estimates thus far. Anticipated reductions in computational time and complexity due to the lower order of the subglottal model hold particular relevance for real-time monitoring. Simultaneously, the methodology proves efficient in generating a spectrum of aerodynamic features essential for ambulatory vocal function assessment.

Fourth‐order inverse filter configuration using current differencing buffered amplifier

AbstractThis article presents three different higher‐order inverse filter (IF) configurations using current differencing buffered amplifier (CDBA) as an active component and a few passive elements. The topology can be used to synthesize fourth‐order inverse low pass filter (FO‐ILPF), inverse band pass filter (FO‐IBPF), and inverse all‐pass filter (FO‐IAPF) using suitable admittance combinations. PSPICE simulations verify the functionality of the proposed IFs with the CMOS‐based CDBA using 180 nm technology. The theoretical analysis and simulated results are also carried out, which shows that they are in close agreement. The passive sensitivity analysis, non‐ideality analysis, Monte Carlo simulation, temperature analysis, percentage of total harmonic distortion (%THD), and noise analysis of the proposed filters are also performed. The proposed design has also been implemented using the current feedback operational amplifier (CFOA) as IC AD844AN to verify the functionality.

Bench studies of the process of transporting an inverse gravel filter of block type along the well

The purpose of the study is to develop a technology for fresh water treatment in deep hydrogeological wells with productive horizons represented by medium-grained, fine-grained, and silty sands by equipping their intake part with block-type inverse gravel filters. Experimental studies were carried out using provisions of the general theory of scientific experiment and the theory of random processes. A theory concerning the possibility of equipping the water intake part of hydrogeological wells, being more than 200 m deep, with block-type inverse gravel filters using the proposed technology was further developed. For the first time, the dependence of the maximum depth of transporting block-type inverse gravel filters on the length of their section, the mass concentration of a binding agent, and the ambient temperature was established. A scope of the proposed technology was substantiated. A range of depths for equipping with block-type inverse gravel filters depending on the mass concentration of a binding agent in the polymer-gravel filter element and the length of its section was substantiated.

Development of a universal, machine tool independent dynamometer for accurate cutting force estimation in milling

When integrating a dynamometer into a machining system, it is necessary to identify the dynamic relationship between the effective input forces and the measured output signals (i.e., its transmissibility) through dedicated experimental modal analysis. Subsequently, a filter can be derived and applied to reconstruct the effective input forces from the measured signals. Unfortunately this identification phase can be complex, posing challenges to the device’s applicability in both laboratory and industrial conditions. Here this challenge is addressed by introducing a novel dynamometer concept based on both load cells and accelerometers, along with a Universal Inverse Filter. Notably, this filter is independent of the dynamic behavior of the mechanical system where the device is installed. A single calibration suffices, ideally conducted by the device manufacturer or by an expert, allowing the dynamometer’s integration by a non-expert user into any machining system without the need for repeating the identification phase and the filter generation. Furthermore, this new concept offers another significant advantage: it attenuates all inertial disturbances affecting the measured signals, including those arising from the cutting process and those originating from exogenous sources such as spindle rotation, linear axes’ movements, and other vibrations propagating through the machine tool structure. To illustrate, a simplified model is introduced initially, followed by an overview of the novel dynamometer design, innovative identification phase, and filter construction algorithm. The outstanding performance of the novel (non-parametric) Universal Inverse Filter – about 5 kHz of usable frequency bandwidth along direct directions and 4.5 kHz along cross dir. – was experimentally assessed through modal analysis and actual cutting tests, compared against state of the art filters. The efficacy of the new filter, which is even simpler than its predecessors, was successfully demonstrated for both commercial and taylor-made dynamometers, thus showing its great versatility.

Extending the spatial extent of steady state broadband noise signals using time reversal

Exciting structures with broadband noise can be used to assess structural health by how it responds to the noise. Here time reversal (TR) is used to focus high-amplitude acoustic energy with the goal to use it to excite a structure. Equalization methods help achieve a desired spectral shape at the location of the TR focus. It is desired to increase the spatial extent of the focusing to excite larger regions. This study explores methods to expand the spatial extent first through the superposition of focusing to multiple locations and second by using a spatial inverse filter. These methods widen the spatial extent of the focusing but can come at the cost of the desired spectrum and/or overall level. The overall goal is to see if TR excitation yields higher amplitude equalized noise than the standard broadcast of it.

Secure cancelable face recognition system based on inverse filter

Secure cancelable face recognition system based on inverse filter

Analysis of continuous-variable quantum teleportation enhanced by measurement-based noiseless quantum amplification.

Continuous-variable quantum teleportation enables deterministic teleportation of quantum states of optical modes. However, the state transfer is imperfect and limited by the amount of squeezing in the shared two-mode entangled state. Recently, it has been proposed and experimentally demonstrated that the performance of continuous-variable teleportation can be conditionally improved using a measurement-based noiseless quantum amplification [J. Zhao et al., Nat. Commun.14, 4745 (2023)10.1038/s41467-023-40438-z]. An inverse Gaussian filter with sufficiently high cut-off is applied to outcomes of the continuous-variable Bell measurement, which can increase the fidelity of state teleportation and the cost of making the protocol probabilistic. Here we provide a detailed theoretical analysis of this protocol and discuss its effects and limitations. We focus on teleportation of classes of Gaussian states with fixed covariance matrix and variable displacement. The measurement-based noiseless amplification conditionally improves the precision of estimation of the coherent displacement of the teleported state from the outcomes of continuous-variable Bell measurement. Therefore, more information about the teleported state is revealed and unity-gain teleportation becomes possible with a lower added thermal noise as compared to deterministic teleportation.

Roundness Profile Measurement Using a Combination Method of Three-Point Method for Roundness Profile Measurement and Integration Method for Straightness Profile Measurement

A three-point method has been used to separate the roundness profile of a workpiece and radial motions of a turntable. First, weighted addition is used to extract the roundness profile, and then, inverse filtering is used to recover the original roundness profile. The three-point method works well in the low spatial frequency domain. However, in the high spatial frequency domain, the setting angle error of the sensor causes a large deflection of the transfer function. Therefore, a combination method of the three-point method for roundness profile measurement and an integration method for the straightness profile measurement is proposed. The three-point method for roundness profile measurement is used to estimate low spatial frequency domain and the integration method for straightness profile measurement is used to estimate the high spatial frequency domain. Experimental results showed that the standard deviation of the combination method was smaller than that of the three-point method for most positions.

Determination of the thermal state of a block gravel filter during its transportation along the borehole

Purpose. Development of a methodology for determining the thermal state of block inverse gravel filters manufactured using low-temperature technology during their transportation in a well based on computer and mathematical modeling. Methods. The study of hydrodynamic processes occurring during the transportation of the filter in the borehole, as well as the calculation of thermal fields in the filter body, was performed using the Ansys Fluent software package. To determine the effective thermophysical characteristics of the filter, the approaches of the heat transfer theory in porous media were applied. To investigate the thermal conditions of the filter at heat exchange with well fluid, the use of analytical solution of the heat conduction problem in a cylindrical shell, taking into account the properties of porous dispersed water-saturated medium, is proposed. Findings. The methodology for calculating the thermal state of a gravel filter during its operation in a well has been developed. For estimation of the filter surface temperature, the expression obtained on the basis of the analytical solution of the heat conduction equation, taking into account the characteristics of the porous filter medium, is proposed. Hydrodynamic and thermal fields in the borehole and the filter body during the filter transportation process in the borehole have been obtained. Originality. For the first time, the problem of heat exchange of a gravel inversion filter, manufactured by low-temperature technology in a well is considered. The influence of hydrothermal conditions in the well on the process of filter heating during its transportation in the well is shown. The hydrodynamic fields during the flow of the drilling mud around the inverse gravel filter are determined. Practical implications. The proposed approaches and results of the study allow to determine and can be used in the development of technological regulations for the use of block gravel filters produced by low-temperature technology for the equipment of hydrogeological wells. The methodology for modelling the process of two-phase inversion transition of aggregate state of the binding agent during transportation of inverted block-type gravel filter during well construction has been developed.

Bearing fault diagnosis based on spatial filtering constraint feature extraction and deep network

As the core component of a gas turbine, the health condition of the main bearing has a crucial impact on the safe operation of the gas turbine. However, the distribution inconsistency problem existing in the time series data and the nonlinear coupling effect between the data will affect the extraction of characteristic fault features, leading to a decrease in diagnostic accuracy. To solve the above problems, this paper proposes a bearing fault diagnosis method based on frequency domain distribution filter and deep learning. Specifically, a sinusoidal Mel filter bank is designed to extract the fault features of low-frequency vibration signals based on the distribution principle of the traditional Mel filter bank. Then, considering that the fault information contained in other frequency bands of the vibration signals is also not easy to ignore, an inverse sinusoidal Mel filter bank is constructed to further mine the signature fault features of the vibration signals in the middle and high-frequency bands. Finally, the frequency domain distribution filter is proposed by combining the above two filters to reduce the impact of data distribution inconsistency on the accuracy of fault diagnosis. In addition, a deformable convolutional network is applied to further decouple the fault data and improve the spatial separability of the data features. Experiments with inconsistent data distribution are validated on two public datasets, and the diagnostic accuracy reaches 100%; the engineering reliability of the proposed method is verified on the main bearing of a gas turbine, and the accuracy reaches 99.86%.

Single-input multiple-output inverse filters designs with cascade capability

Enhancing a topology of the literature, single-input multiple-output first-order inverse filter topologies are presented in this work. They offer the features of minimized circuit complexity, grounded capacitors requirement and, the most important, the capability of cascade connection with other stages without extra buffers needed. This is achieved thanks to the properties of the Current Feedback Operational Amplifier terminals. The behavior of the structures with regards to the effect of parasitics is evaluated for establishing the suitable frequency range of their operation. The performance of the filters is evaluated using the OrCAD PSpice suite, as well as through experimental results.

Variational multi-harmonic duality mode pursuit method for extracting repetitive transient components from vibration signals

The repetitive transient component (RTC) in vibration signals is a typical symptom reflecting the localised defects of rotating machinery, which can be mathematically defined by feature parameters, such as the inter-arrival interval, maximum amplitude, resonance frequency, and attenuation ratio. In particular, the inter-arrival interval and maximum amplitude reveal the location and severity of failure and provide critical information for condition monitoring and fault diagnosis. However, there are some shortcomings of applicability, anti-noise, and fidelity, which are unfavourable for pursuing fault-induced RTC and enabling reliable recognition of key feature parameters. Hence, a novel method called variational multi-harmonic duality mode pursuit (VMHDMP) is proposed to extract the RTC accurately. A multi-harmonic duality mode function (MHDMF) that comprehensively depicts the critical feature parameters of the RTC was first defined in the frequency domain. Based on the defined MHDMF, the pursuit of RTC is formulated as a frequency-domain variational optimisation problem with a local compact time duration constraint, which optimally searches for the mode with an explicit MHDMF from the complex-valued frequency signal. A strategy involving the mutual conversion of real- and complex-valued signals was incorporated into the solution algorithm to satisfy the decomposability of complex-valued frequency signals. Finally, an inverse Fourier transform is performed on the analytical form of the extracted MHDMF to obtain a faithful RTC. Essentially, VMHDMP provides an optimal ‘transient-pass’ inverse filter bank with compact passage only near the occurrence moment of each transient in RTC. Thus, the vital parameters closely related to the faults are fully retained, whereas the frequency-overlap noise and large-amplitude interference occurring at different moments can be effectively eliminated. Simulated and experimental examples were used to test the effectiveness of VMHDMP.

Serial Analog-Digital QAM Modems Based on Complex Band-Pass Filters with LF Butterworth Prototypes

The article is devoted to the development and implementation of quadrature amplitude modulation algorithms based on an analog complex band-pass filter with Butterworth low-frequency prototypes. The use of complex Butterworth band pass filters makes it possible to obtain carrier signals with practically non-overlapping spectra. But the analog complex Butterworth filter has a non-linear phase-frequency response and, accordingly, the impulse responses of which are infinite and asymmetric. An overview of the development and implementation of a serial QAM modem based on an analog Butterworth complex band-pass filter and an inverse digital FIR filter. The procedure for obtaining the expression for the impulse response of analog complex band-pass Butterworth filters is considered. With the help of circuit simulation, the frequency response and impulse response of such a filter are determined. The choice of the center frequency is carried out by setting two coefficients. A block diagram of a serial data transmission system with quadrature amplitude modulation based on an analog complex Butterworth bandpass filter and an inverse digital FIR filter is proposed. The results of its circuit simulation in the Micro-Cap environment are presented.