Frequency Domain Techniques Research Articles

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.

Super-resolution from unregistered aliased astronomical images

Images captured from astronomical telescopes always have aliasing artifacts due to atmospheric turbulence, which typically conveys additional high-frequency content containing extra details of the astronomical scene. Super-resolution (SR) reconstruction can exploit the information existing in the aliasing to create a higher-resolution astronomical image, where the precise registration of input observations is generally regarded as a necessary premise. We consider SR reconstruction as a multichannel sampling scheme with uncertain deviations mathematically and present a frequency domain technique to achieve accurate alignment of aliased astronomical images by utilizing their low-frequency and aliasing-free part of the spectrum. Moreover, a planar motion model including three parameters is allowed to describe the relative uncontrolled motions between the acquisition system and the astronomical scene. Finally, a sharp high-resolution astronomical image can be reconstructed by utilizing the adaptive normalized convolution algorithm. The technique proposed is tested in simulations and practical experiments and shows very excellent SR reconstruction capabilities in the case of aliased astronomical images.

Soft-Output Detection Methods for Sparse Millimeter-Wave MIMO Systems With Low-Precision ADCs

The use of low-precision analog-to-digital converters (ADCs) is a low-cost and power-efficient solution for a millimeter wave (mmWave) multiple-input multiple-output (MIMO) system operating at sampling rates higher than a few Gsample/sec. This solution, however, can make significant frame-error-rates (FERs) degradation due to inter-subcarrier interference when applying conventional frequency-domain equalization techniques. In this paper, we propose computationally-efficient yet near-optimal soft-output detection methods for the coded mmWave MIMO systems with low-precision ADCs. The underlying idea of the proposed methods is to construct an extremely sparse inter-symbol-interference (ISI) channel model by jointly exploiting the delay-domain sparsity in mmWave channels and a high quantization noise caused by low-precision ADCs. Then we harness this sparse channel model to create a trellis diagram with a reduced number of states or a factor graph with very sparse edge connections. Using the reduced trellis diagram, we present a soft-output detection method that computes the log-likelihood ratios (LLRs) of coded bits by optimally combining the quantized received signals obtained from multiple receive antennas using a forward-and-backward algorithm. To reduce the computational complexity further, we also present a low-complexity detection method using the sparse factor graph to compute the LLRs in an iterative fashion based on a belief propagation algorithm. Simulations results demonstrate that the proposed soft-output detection methods provide significant FER gains compared to the existing frequency-domain equalization techniques in a coded mmWave MIMO system using one- or two-bit ADCs.

Toward Microwave S- and X-Parameter Approaches for the Characterization of Ferroelectrics for Applications in FeFETs and NCFETs

Ferroelectric and negative-capacitance field-effect transistors (FeFETs and NCFETs) have recently garnered great attention as devices for applications in memory and low-power logic, respectively. As these technologies are pursued, it is critical to have a variety of measurement approaches, including methods familiar to the electron-device and microwave communities that can aid in fully understanding the behavior of ferroelectrics in FeFETs and NCFETs. In this paper, we propose and show the viability of using frequency-domain electrical measurement techniques employing the well-known microwave S-parameters, and their large-signal generalization and X-parameters. Our methods provide the means to trace the intrinsic polarization versus electric-field curve of the ferroelectric, i.e., with the parasitics de-embedded, thereby showing the innate ferroelectric response, which cannot be done using conventional techniques. These methods also enable extraction of all the parameters of the Landau–Khalatnikov equation, which is commonly used to model ferroelectric behavior in FeFETs and NCFETs. This paper hence takes a useful step toward methods familiar to the electron-device community that can help to better understand and optimize FeFET and NCFET technologies.

Prognostic Model Development with Missing Labels

Condition-based maintenance (CBM) has emerged as a proactive strategy for determining the best time for maintenance activities. In this paper, a case of a milling process with imperfect maintenance at a German automotive manufacturer is considered. Its major challenge is that only data with missing labels are available, which does not provide a sufficient basis for classical prognostic maintenance models. To overcome this shortcoming, a data science study is carried out that combines several analytical methods, especially from the field of machine learning (ML). These include time-domain and time–frequency domain techniques for feature extraction, agglomerative hierarchical clustering and time series clustering for unsupervised pattern detection, as well as a recurrent neural network for prognostic model training. With the approach developed, it is possible to replace decisions that were made based on subjective criteria with data-driven decisions to increase the tool life of the milling machines. The solution can be employed beyond the presented case to similar maintenance scenarios as the basis for decision support and prognostic model development. Moreover, it helps to further close the gap between ML research and the practical implementation of CBM.

Photoacoustic F-Mode imaging for scale specific contrast in biological systems

In photoacoustic (PA) imaging, time domain reconstruction techniques are the current gold standard for image formation. While these techniques provide high-resolution spatial maps of optical absorption, they neglect the structural information encoded in the frequency domain of the broadband PA signals. In this work, we introduce a frequency domain technique for PA image formation, termed F-Mode. By leveraging information contained in the frequency content of PA signals, F-Mode can be used to generate images with scale-specific contrast. To demonstrate the robustness of our technique, we apply F-Mode to datasets acquired using both PA tomography and PA microscopy systems, utilizing linear array and single-element transducers with central frequencies ranging from 40–400 MHz. Here we show that the technique can be used to: differentiate between vessels and microspheres of different size in phantoms, enhance visualization of organelles in cultured cells, and selectively display single blood vessels in vivo in zebrafish larvae.

Model based analysis and identification of multiple fault parameters in coupled rotor systems with offset discs in the presence of angular misalignment and integrated with an active magnetic bearing

In the present paper, the modelling, analysis and identification of various system faults in the presence of angular misalignment in coupled rotor-train systems integrated with an auxiliary AMB support has been presented. The method discussed in the paper quantifies the effect of misalignment by way of estimation of additive coupling stiffness (ACS). The novelty of the present paper lies in the modelling of a rotor-train-AMB system with two 4-dof simple rotors, each with an offset disc to have gyroscopic effects. With the combination of flexible coupling and weight dominance assumption the dynamic behaviour of rotor-bearing-coupling-AMB system has been characterized by the eight generalized coordinates of the two rotors. The coupling's angular stiffness has been modelled as the sum of direct stiffness and a time varying ACS, whose magnitude is affected by the amount of angular misalignment. A steering function has been selected such that the forced reponse due to coupling misalignment yields both odd and even harmonics in full spectrum. An identification algorithm based on least-squares regression technique in frequency domain has been developed using the forward and backward harmonics of rotor vibration and AMB current for the estimation of coupling direct stiffness, coupling additive stiffness, unbalance magnitude and its phase, equivalent viscous damping in each rotor, current and displacement constants of AMB. The assessment of severity of angular misalignment from the magnitude of estimated ACS is a novel deviation that has potential for practical application.

Discrete Adjoint Approach for Nonlinear Unsteady Aeroelastic Design Optimization

Presented is a discrete adjoint approach for computing nonlinear unsteady aeroelastic geometric design sensitivities, which can subsequently be used for nonlinear unsteady aeroelastic geometric design optimization. The methodology is applicable to compressible Reynolds-averaged Navier–Stokes computational-fluid-dynamics solvers and is based on a harmonic balance nonlinear frequency-domain technique for modeling nonlinear aerodynamics in the frequency domain. Automatic differentiation is used to derive the computer code representing the adjoint gradient of the harmonic balance computational-fluid-dynamics solver. Discrete adjoint method airfoil geometric design nonlinear unsteady aerodynamic sensitivities are first computed for a benchmark NLR 7301 airfoil transonic aeroelastic configuration, and results are compared with finite difference computations to demonstrate the accuracy of the methodology. The airfoil surface is mathematically curve-fit using an airfoil parameterization technique, and the discrete adjoint methodology provides the gradient of the flutter onset or limit-cycle oscillation reduced velocity with respect to changes in each of the parameters of the airfoil curve-fit methodology for the cost of a single adjoint solution. This gradient information is supplied to a limited memory Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization solver, which then determines values for the curve-fit parameters that maximize flutter onset or limit-cycle oscillation reduced velocity. The discrete adjoint nonlinear unsteady aeroelastic geometric design sensitivity computation methodology is then used to redesign the shape of the benchmark NLR 7301 airfoil aeroelastic configuration to optimize the aeroelastic flutter onset reduced velocity or the reduced velocity for a specified limit-cycle oscillation response amplitude.

Reduced-sample numerical Laplace transform for transient and steady-state simulations: Application to networks involving power electronic converters

This paper further contributes to application of frequency-domain (FD) techniques in the analysis of modern power networks. It specifically presents a truncated-in-samples numerical Laplace transform (NLT) approach to calculate transient and steady-state of switched networks via a frequency-selective scheme. The FD samples selection is performed via magnitude truncation of an arbitrarily chosen solution spectrum. Truncation error analysis is presented and resultant Gibbs phenomenon, inherited by truncation, is alleviated via local and global data windows. The proposed approach avoids dimensions explosion by traditional harmonic domain (HD) based methods and offers computational savings compared to full-sample NLT simulations. Therefore, it becomes attractive for multiple runs applications, such as capacitor switching, among others. The proposed methodology is verified via case studies involving switching devices.

Application of Cole–Cole model to transformer oil‐paper insulation considering distributed dielectric relaxation

Researchers have been exploring dielectric testing techniques both in time and frequency domain for insulation condition assessment of oil-paper insulated transformers. In a practical dielectric system, dipoles are found to behave according to a distribution of elementary Debye relaxation properties. Suitable distribution density functions have been proposed to characterise such many-body interaction processes. Cole–Cole diagrams can be one of the methods for studying the nature of frequency dependency of dielectric materials of complex structure. Cole–Cole plots are commonly used for characterising different materials such as dielectric mixtures, ionic liquids, cable insulating oil, polar liquids etc. The scope of its application for assessing transformer oil-paper insulation considering distributed relaxation process has not been explored yet. The present contribution discusses mathematical formulations used for transforming the experimentally obtained time domain dielectric response test data to distribution domain and further to frequency domain for obtaining the Cole–Cole plots. Findings about the influence of various operating conditions and insulation status on the Cole–Cole diagram have been reported in this contribution. Results of tests on field transformers are also presented. This paper attempts to employ the features of Cole–Cole diagrams as potential indicators for analysing condition of the oil-paper insulation considering distributed relaxation process.

Advanced small‐signal‐based analytical approach to modelling high‐order power converters

This study devises an advanced small-signal based approach (SSA) to model high-order power converter system (HOPCS); in detail, a 3-Z-network converter which employs a single switch to achieve high voltage gain is utilised as a typical example of HOPCS. Therein, four operating modes are detailed in terms of the states of electric current flowing through the inductors. The proposed SSA is performed by means of the state-space averaging technique and small-signal equivalent circuits in frequency domain. Based on the proposed approach, the derived transfer functions for input voltage, output voltage and control input yield deep insight into the dynamic responses of the converter in each working mode. In order to well validate the effectiveness of the proposed approach, numerical results and experimental prototype are conducted and measured, the results agree well with the theoretical analyses.

Dynamic specification tests for dynamic factor models

SummaryWe derive computationally simple expressions for score tests of misspecification in parametric dynamic factor models using frequency domain techniques. We interpret those diagnostics as time domain moment tests which assess whether certain autocovariances of the smoothed latent variables match their theoretical values under the null of correct model specification. We also reinterpret reduced‐form residual tests as checking specific restrictions on structural parameters. Our Gaussian tests are robust to nonnormal, independent innovations. Monte Carlo exercises confirm the finite‐sample reliability and power of our proposals. Finally, we illustrate their empirical usefulness in an application that constructs a US coincident indicator.

Multi-point fiber refractometer using Fresnel reflection and a coherent optical frequency-domain multiplexing technique.

We demonstrate a simple multi-point refractometer based on the coherent optical frequency-domain multiplexing technique. As local refractive index sensors, interferometers of different lengths formed between cleaved fiber tips and reference weak reflectors are employed. Referent reflectors were fabricated by splicing into lead SMF-28 fiber a very short section of hollow optical fiber using a standard fiber splicer and a cleaver with an optical microscope. Despite the very simple configuration and manufacturing of the sensor, the refractive index resolution of 6×10-4 was demonstrated during proof-of-concept experiments.

Real-Time Angular Velocity-Based Misfire Detection Using Artificial Neural Networks

In this paper, an artificial neural network (ANN) is introduced in order to detect the occurrence of misfire in an internal combustion (IC) engine by analyzing the crankshaft angular velocity. This study presents three reliable misfire detection procedures. In the first two methods, the fault features are extracted using both time domain and frequency domain techniques, and a multilayer perceptron (MLP) serves as the pattern recognition tool for detecting the misfiring cylinder. In the third method, a one-dimensional (1D) convolutional neural network (CNN) that combines feature extraction capability and pattern recognition is adopted for misfire detection. The experimental data are obtained by setting a six in-line diesel engine with different cylinder misfiring to work under representative operating conditions. Finally, all three diagnostic methods achieved satisfactory results, and the 1D CNN achieved the best performance. The current study provides a novel way to detect misfiring in IC engines.

Steganography Performance over AWGN Channel

Steganography can be performed using frequency domain or spatial domain. In spatial domain method, the least significant bits (LSB) is the mostly used method where the least significant bits of the image's pixels binary representation are used to carry the confidential data bits. On the other hand, secret data bits in the frequency domain technique are hidden using coefficients of the image frequency representation such as discrete cosine transform (DCT). Robustness against image attacks or channel's noise is a key requirement in steganography. In this paper, we study the performance of the steganography methods over a channel with Added White Gaussian Noise (AWGN). We use the bit error rate to evaluate the performance of each method over a channel with different noise levels. Simulation results show that the frequency domain technique is more robust and achieves better bit error rate in a noisy channel than the spatial domain method. Moreover, we enhanced the steganography system robustness by using convolution encoder and Viterbi decoder. The effect of the encoder’s parameters, such as rate and constraint length is evaluated.

Approximate Cloaking for Time-dependent Maxwell Equations via Transformation Optics

We study approximate cloaking using transformation optics for electromagnetic waves in the time domain. Our approach is based on estimates of the degree of visibility in the frequency domain for all frequencies in which the frequency dependence is explicit. The difficulty and the novelty analysis parts are in the low and high frequency regimes. To this end, we implement a variational technique in the low frequency domain and multiplier and duality techniques in the high frequency domain. Our approach is inspired by the work of Nguyen and Vogelius on the wave equation.

Development and Testing of a Combined Machine and Process Health Monitoring System

Process monitoring has been shown to be capable of observing the quality of a machining operation through sensor signals and analysis in both the literature and in commercially available systems. Some of these systems provide an additional benefit of monitoring the health of a machine tool. However, the commercially available systems tend to utilise relatively simple analysis techniques for both the process and machine health, limiting their application and robustness. Industrial interest in systems that can profit from the current advances in machine tool digitalisation and data analytics has grown considerably. This is especially true for the capability of early-detection of quality issues in components, whilst also ensuring machine tools are in a condition that can achieve high quality production. The present research includes the development and testing of a fingerprint routine which can be run at regular intervals to detect potential failure modes or machine tool degradation through signal analysis. Machining trials were carried out with the objective of detecting known defects in a workpiece through signal analysis. For both cases, a combined monitoring system was developed for data capture during testing, and a number of failure modes and defects were physically simulated to test the possibility of detection in the acquired signals. Time domain, frequency domain, and time-frequency domain signal processing techniques were applied to the sensor data with various levels of success. Continuous wavelet transforms (CWT) were of particular interest, as they successfully captured signal changes between tests for the physically simulated failure modes of the machine tool and the component. Therefore, a comparative CWT analysis was developed which successfully emphasised some of the machine tool failure modes and part defects when compared to baseline signals. The output of the comparative analysis may be well-suited to automation through machine learning techniques.

Challenges in Multi-Layer Data Security for Video Steganography Revisited

The steganography is prone to number of attacks such as geomatical, salt & pepper, gaussian, median filtering, attacks. To overcome these problems, the cryptography and error correction codes are comes in the pictures and hybrid with steganography algorithms. The cryptography algorithms add one layer of security on steganography algorithms and error correction codes improves the robustness of steganography algorithms. On the other side, the hybridization of the algorithms, increase memory complexity and increase computation time for data embedding. Thus, in this paper a review on multi-layer algorithms for video steganography is done. This paper comprehensively reviews the steganography, spatial and frequency domain techniques hybrid with cryptography and error correction techniques. In-depth analysis on published literatures revealed that the spatial domain techniques require shorter computation time for data embedding and provide higher data hiding capacity than frequency domain methods. Performance of such techniques was evaluated in terms of visual quality and robustness parameters. Present challenges and future trends towards the improvement of the multi-layer data security in video steganography are discussed to provide the taxonomy for further navigation.

Non-parametric spectral estimation techniques for DNA sequence analysis and exon region prediction

Bioinformatics is the analysis of biological information using computers and statistical techniques. This paper presents non-parametric spectral estimation techniques based on the Discrete Fourier Transform (DFT) for the analysis of deoxyribonucleic acid (DNA) sequences. These techniques are efficient frequency-domain signal representation techniques, which improve the analysis of DNA sequences and enable the extraction of some desirable information that cannot be extracted from the time-domain representation of these sequences. The adopted techniques are the periodogram, average periodogram (Bartlett), modified average periodogram (Welch), and Blackman and Tukey spectral estimation techniques. The objective of these spectral estimation techniques is to investigate the locations of exons in DNA sequences for gene prediction. A comparison study is presented in this paper between the suggested spectral estimation techniques from the exon prediction perspective. The methods presented in this paper improve the detectability of peaks representing exon regions.

The effect of caffeine loading on cerebral autoregulation in preterm infants.

To evaluate cerebral autoregulation changes in preterm infants receiving a loading dose of caffeine base. In a cohort of 30 preterm infants, we extracted measures of cerebral autoregulation using time and frequency domain techniques to determine the correlation between mean arterial pressure (MAP) and tissue oxygenation index (TOI) signals. These measures included the cerebral oximetry index (COx), cross-correlation and coherence measures, and were extracted prior to caffeine loading and in the 2hours following administration of 10mg/kg caffeine base. We observed acute reductions in time domain correlation measures, including the cerebral oximetry index (linear mixed model coefficient -0.093, standard error 0.04; p=0.028) and the detrended cross-correlation coefficient (ρ5 coefficient -0.13, standard error 0.055; p=0.025). These reductions suggested an acute improvement in cerebral autoregulation. Features from detrended cross-correlation analysis also showed greater discriminative value than other methods in identifying changes prior to and following caffeine administration. We observed a reduced correlation between MAP and TOI from near-infrared spectroscopy following caffeine administration. These findings suggest an acute enhanced capacity for cerebral autoregulation following a loading dose of caffeine in preterm infants, contributing to our understanding of the physiological impact of caffeine therapy.