Transform-based Method Research Articles

Damage detection technique using ultrasonic guided waves and outlier detection: Application to interface delamination diagnosis of integrated circuit package

This paper presents the development of a robust data-driven damage diagnosis technique to accurately detect sealant delamination in an integrated circuit (IC) package. A sealant is used to bond the integrated heat spreader (IHS) to the substrate of IC packages for effective thermal dissipation. Delamination of the sealant causes significant reduction in heat management capability of the package, which leads to degradation of processor performance and component reliability. In this work, non-destructive evaluation (NDE) using ultrasonic guided wave (UGW) is performed to interrogate the integrity of the IC package. The advantage of using UGW is its large area inspection capability and small defect detection sensitivity. Ultrasonic tests using a pitch-catch active sensing method with removable piezoelectric (PZT) tranducers are performed with a wide range of excitation frequencies to classify various delamination scenarios in the IC package. Data preprocessing and Hilbert transform-based feature extraction method are also performed on the collected UGWs to extract dominant UGW features. Pattern analysis using a density-based spatial clustering of applications with noise (DBSCAN) algorithm is conducted on the UGW feature space to classify the damage indicators. These damage indicators (outliers) are then used to identify the IC packages with sealant delamination. The proposed technique is validated by comparing it to dye-and-pry tests, delamination metrics, and detection method using a multivariate Gaussian model. The results demonstrate that the proposed diagnosis technique has the potential to be a part of the industrial quality assurance process for IC packages for its capability to detect manufacturing defects with high accuracy.

Contrast Enhancement of Digital Images Using an Improved Type-II Fuzzy Set-Based Algorithm

Contrast is a distinctive image feature that tells if it has adequate visual quality or not. On many occasions, images are captured with low-contrast due to inevitable obstacles. Therefore, an improved type-II fuzzy set-based algorithm is developed to enhance the contrast of various color and grayscale images properly while preserving the brightness and providing natural colors. The proposed algorithm utilizes new upper and lower ranges, amended Hamacher t-conorm, and a transform-based gamma correction method to provide the enhanced images. The proposed algorithm is assessed with artificial and real contrast distorted images, compared with twelve specialized methods, and the outcomes are evaluated using four advanced metrics. From the obtained results of experiments and comparisons, the developed algorithm demonstrated the ability to process various color and grayscale images, performed the best among the comparative methods, and scored the best in all four quality evaluation metrics. The findings of this study are significant because the proposed algorithm has low-complexity and can adjust the contrast of different images expeditiously, which enables it to be used with different imaging modalities especially those with limited hardware resources or produce high-resolution images.

Single-Fourier transform based full-bandwidth Fresnel diffraction

In physical optics, Fresnel diffraction matters. The single Fourier transform method has been widely used in Fresnel diffraction calculation and can perform the direct computation of the output field with high efficiency. Based on a phase-space analysis, we find that the conventional single Fourier transform method cannot correctly deal with the full bandwidth of the input field. Aliasing occurs with high-frequency components because the bandwidth transfer capacity of the conventional method is insufficient, which greatly deteriorates the calculation accuracy. To address this serious problem, we propose a single Fourier transform-based full-bandwidth Fresnel diffraction calculation method. By rearrangement of the sampling resources, all the frequency components can be correctly transferred for accurate, efficient, and flexible Fresnel diffraction calculation.

Estimation of Displacement Response in Steel Plate Girder Bridge Using a Single MEMS Accelerometer

Live load-induced deformation is a significant evaluation index for bridge maintenance. To measure a structural displacement response, a method using several MEMS (Micro Electro Mechanical Systems) accelerometers has previously been developed. However, this method requires several sensors, and for improved practicality, a reduction in the number of sensors is desired. Therefore, in this article, a simple displacement monitoring method is proposed that uses only one MEMS accelerometer, which is installed at the longitudinal center of the target bridge. A wavelet transform-based method is applied to the acceleration records to identify the integration range for displacement estimation. The accuracy was evaluated on an actual in-service bridge. The result indicates high accuracy of displacement measurements, with only 5% error when compared with the values recorded by a contact displacement gauge.

Time Reversal and Fractional Fourier Transform-Based Method for LFM Signal Detection in Underwater Multi-Path Channel

Fractional Fourier transform (FrFT) is a useful tool to detect linear frequency modulated (LFM) signal. However, the detection performance of the FrFT-based method will deteriorate drastically in underwater multi-path environment. This paper proposes a novel method based on time-reversal and fractional Fourier transform (TR-FrFT) to solve this problem. We make use of the focusing ability of time-reversal to mitigate the influence of multi-path, and then improve the detection performance of FrFT. Simulated results show that, compared to FrFT, the difference between peak value and maximum pseudo-peak value of the signal processed by TR-FrFT is improved by 8.75 dB. Lake experiments results indicate that, the difference between peak value and maximum pseudo-peak value of the signal processed by TR-FrFT is improved by 7.6 dB. The detection performance curves of FrFT and TR-FrFT detectors with simulated data and lake experiments data verify the effectiveness of proposed method.

An analytical study of reaction diffusion, (3 + 1)-dimensional diffusion equations using Caputo Fabrizio fractional differential operator

Motivated by the memory features and property to portray substance heterogeneities and arrangement with various sizes of Caputo Fabrizio operator of fractional order to investigate hidden dynamics of several nonlinear differential systems. In the present work, we conduct analytical study and obtain numerical simulations of one, two and (3+1)-dimensional Caputo-Fabrizio reaction-diffusion equations. Hybrid Laplace transform-based iterative method is constructed to find approximate solutions of diffusion equations involving Caputo–Fabrizio derivative with the exponential kernel. We have also carried out comparative analysis between Caputo and Caputo Fabrizio fractional differential operator. Moreover, we have obtained absolute error between exact and approximate solutions. 2D and 3D plots are obtained to demonstrate the efficiency of method graphically.

Scale-Invariant Feature Transform-Based Heterogeneous Image Registration Method

Scale-Invariant Feature Transform-Based Heterogeneous Image Registration Method

Coexisting Parallelogram Method to Handle Jump Point on Hough Transform-based Clock Skew Measurement

In this paper, we improve the robustness of the Hough transform-based clock skew measurement on the occurrence of a jump point. The current Hough transform-based skew method uses angle (θ), thickness (ω), and region (β), to create a parallelogram that covers the densest part of an offset-set. However, the assumption that all offsets are considered to line up roughly in only one direction restricts the ability of the current method when handling an offset-set in which its densest part is located separately, the jump point condition. By acquiring the parallelogram from coexisting angle-region tuples at the beginning and the ending parts of the offset-set, we completed the ability of the Hough transform-based method to handle the jump point. When handling the jump point problem, the proposed coexisting parallelogram method could reach 0.35 ppm accuracy compared with tens ppm by the current methods.

Continuous wavelet transform-based method for enhancing estimation of wind turbine blade natural frequencies and damping for machine learning purposes

In the current study, an operational modal analysis is performed on a wind turbine blade with pull-and-release excitation at the tip. The experiments were carried out in large-scale facility of Risoe campus in Technical University of Denmark. Two different blade configurations are compared – blade on the block and a configuration for fatigue testing comprising of blade on the block with mass resonance exciter and seismic masses. A continuous wavelet transform is employed for estimation of modal parameters of the fundamental flapwise bending mode of the blade from acceleration responses. Statistical analysis along with an outlier removal from the extracted modal parameters is carried out to provide clear estimates of the extracted values. These results are compared to the values extracted with other algorithms. The proposed method for modal parameter extraction aims at extraction of numerous observations allowing for statistical decision making and machine learning capabilities.

A new autoregressive moving average modeling of H/V spectral ratios to estimate the ground resonance frequency

We propose a new method to estimate the horizontal-to-vertical (H/V) spectral ratio using microtremor measurements. The technique is based on modeling the H/V transfer function by means of an AutoRegressive Moving Average (ARMA) filter. As compared with the conventional, Fourier-based spectra processing routines, this method is efficient in extracting the fundamental resonant frequency with a higher spectral resolution. We demonstrate its usefulness by an application to several sites in northeastern Iberian Peninsula. For the data sets examined, our approach shows improvement over the Fourier transform-based method under adverse experimental conditions. We conclude that the new technique provides robust estimates of the fundamental resonance frequency and can be effective to characterize empirically the unconsolidated sediment thickness and bedrock geometry.

An analytical study of reaction diffusion, (3 + 1)-dimensional diffusion equations using Caputo Fabrizio fractional differential operator

Motivated by the memory features and property to portray substance heterogeneities and arrangement with various sizes of Caputo Fabrizio operator of fractional order to investigate hidden dynamics of several nonlinear differential systems. In the present work, we conduct analytical study and obtain numerical simulations of one, two and (3+1)-dimensional Caputo-Fabrizio reaction-diffusion equations. Hybrid Laplace transform-based iterative method is constructed to find approximate solutions of diffusion equations involving Caputo–Fabrizio derivative with the exponential kernel. We have also carried out comparative analysis between Caputo and Caputo Fabrizio fractional differential operator. Moreover, we have obtained absolute error between exact and approximate solutions. 2D and 3D plots are obtained to demonstrate the efficiency of method graphically.

Estimating Model Parameters Using FFT

A Fast Fourier transform-based method for estimation of parameters of a stochastic process from a historical time series is proposed. The main advantage of this method is that allows to fit distributions that are defined in terms of characteristic function, instead of more commonly used density-based estimation. In turn more exotic models can be calibrated to the past data, thereby extending these exotic models to applications in risk management.

Detection of changes in the circadian rhythm of cattle in relation to disease, stress, and reproductive events.

Disease and stress can disrupt the circadian rhythm of activity in animals. Sensor technologies can automatically detect variations in daily activity, but it remains difficult to detect exactly when the circadian rhythm disruption starts. Here we report a mathematical Fourier-Based Approximation with Thresholding (FBAT) method designed to detect changes in the circadian activity rhythm of cows whatever the cause of change (typically disease, stress, oestrus). We used data from an indoor positioning system that provides the time per hour spent by each cow resting, in alleys, or eating. We calculated the hourly activity level of each cow by attributing a weight to each activity. We considered 36-h time series and used Fourier transform to model the variations in activity during the first and last 24h of these 36-h series. We then compared the Euclidian distance between the two models against a given threshold above which we considered that rhythm had changed. We tested the method on four datasets (giving a cumulative total of ~120000 cow*days) that included disease episodes (acidosis, lameness, mastitis or other infectious diseases), reproductive events (oestrus or calving) and external stimuli that can stress animals (e.g. relocation). The method obtained over 80% recall of normal days and detected 95% of abnormal rhythms due to health or reproductive events. FBAT could be implemented in precision livestock farming system monitoring tools to alert caretakers to individual animals needing specific care. The FBAT method also has the potential to detect anomalies in humans to guide healthcare intervention or in wild animals to detect disturbances. We anticipate that chronobiological studies could apply FBAT to help relate circadian rhythm anomalies to specific events.

Quantitative analysis of cell morphology based on the contourlet transform

Cellular morphology analysis has been widely used to detect abnormalities in biological processes. Clinicians have observed that lymphocytes become highly deformable under special conditions, particularly when graft rejection occurs. The characterisation of lymphocyte boundary deformation provides important quantitative parameters to assist clinical rejection diagnosis. To evaluate the dynamic features of the boundaries of target lymphocyte when a graft rejection occurs, a contourlet transform-based method is proposed to extract the characteristics of cell boundary variation. First, the lymphocyte is segmented and tracked to obtain their edge-to-centroid distance signals. Subsequently, a contourlet transform is performed on these signals, during which the edge-to-centroid distance signals of the lymphocyte is decomposed at multiple scales using the Laplacian pyramid; a multi-directional decomposition is then performed using a direction filter to merge the singularities distributed along the same direction and obtain the contourlet transform coefficients. Finally, statistical parameters of the cell dynamic boundaries are calculated, then fed into the support vector machine for classification of the cell deformation. Our findings demonstrate that contourlet transform has better performance in representing image features such as cell boundaries than wavelet transform for its prosperities of multi-scale and multi-directional decomposition for cell images.

A Fast Fourier Transform-Based Method for Power Swing Detection and Distance Relay Malfunction Prevention

Fourier transform has abundant applications in the field of power systems. One of the chief uses of this technique is differentiating between power swings and the faults occurring simultaneously with these swings in the power system. Despite many efforts to use Fourier transform for this purpose, the available methods have significant restrictions that affect their applicability. This paper introduces a new method based on Fourier transform for detecting power swings and various types of simultaneous faults that occur during the power swings. The strength of the proposed method in comparison with currently used schemes is the capability of detecting the unstable and multi-mode power swings and also detection of high-impedance faults. Since the performance of the proposed method depends on defining a threshold value, this paper also provides a quick and simple way to determine this threshold value for various power networks. DIgSILENT and MATLAB softwares have been used to test the proposed method on the standard IEEE 39-bus test system for different scenarios of power swing and simultaneous fault conditions.

Time-frequency analysis of Fourier transform mass spectrometry data by the hilbert transform-based time-domain method

Rich information possesses within the transient image current signal collected from a Fourier transform (FT) mass spectrometer. The central frequency corresponds to the mass to charge ratio of an ion, the amplitude decay profile corresponds to its collision cross sections (CCS). Conventionally, fast Fourier transform (FFT) was applied to transfer the time-domain transient data to frequency-domain. There are demanding needs to develop efficient data processing methods to improve the mass accuracy and resolution of the constructed mass spectrum, while shortening the length of the transient data for high-throughput analysis. In this work, a Hilbert transform based time domain method was developed, so that time-frequency analysis of ion transient data could be performed. This method could accurately extract the instantaneous ion motional frequency and amplitude, which could potentially improve the mass accuracy and mass resolution of a FT-based mass spectrometer. Furthermore, ion CCSs could also be acquired from the ion motion decay profiles.

Wavefield decomposition in arbitrary direction and an imaging condition based on stratigraphic dip

In reverse time migration (RTM), wavefield decomposition can play an important role in addressing the issue of migration noise, especially low-frequency noise. The complete wavefield decomposition based on the Hilbert transform is a commonly used method in RTM, but it is accompanied by extra wavefield simulation and wavefield storage. We have developed three distinct methods. The first is a convenient method for wavefield decomposition, which is based on Poynting vectors. Only the unit vector in one direction is needed to realize the wavefield decomposition in an arbitrary direction by this method. It breaks through the limitation that the Hilbert transform-based method is applicable only to the up- and downgoing wave or left- and right-going wave decomposition, and the calculation cost is negligible compared with RTM. The second is a method based on the instantaneous wavenumber, which we developed for calculating the wave propagation direction. On the basis of wavefield decomposition, the imaging angle gather from the new method performs better than that of the Poynting vector method. Meanwhile, it also is used for generating the incident angle gather and dip angle gather. The latter expresses the dip angle of underground strata. More importantly, the above methods allow us to control the wavefield decomposition direction and three angles at any position underground. The third adopts a stratigraphic imaging condition method, and we briefly analyze the relationship between the new method and the inverse-scattering imaging condition. The stratigraphic imaging condition maps the results to the dip angle of the stratum through a spatial gradient wavefield, which can enhance the effective imaging information. The above three kinds of angle gathers also can be constructed by the stratigraphic imaging condition. Numerical experiments demonstrate that the imaging results and the angle gathers obtained by our proposed method have higher accuracy and resolution.

Relation and Application Method of Deep Learning Sea Target Detection and Segmentation Algorithm

Target detection and segmentation algorithms have long been one of the main research directions in the field of computer vision, especially in the study of sea surface image understanding, these two tasks often need to consider the collaborative work at the same time, which is very high for the computing processor performance requirements. This article aims to study the deep learning sea target detection and segmentation algorithm. This paper uses wavelet transform-based filtering method for speckle noise suppression, deep learning-based method for land masking, and the target detection part uses an improved CFAR cascade algorithm. Finally, the best separable features are selected to eliminate false alarms. In order to further illustrate the feasibility of the scheme, this paper uses measured data and simulation data to verify the scheme and discusses the effect of different signal-to-noise ratio, sea target type, and attitude on the algorithm performance. The research data show that the deep learning sea target detection and segmentation algorithm has good detection performance and is generally applicable to ship target detection of different types and different attitudes. The results show that the deep learning sea target detection and segmentation algorithm fully takes into account the irregular shape and texture of the interfering target detected in the optical remote sensing image so that the accuracy rate is 32.7% higher and the efficiency is increased by about 1.3 times. The deep learning sea target detection is compared with segmentation algorithm, and it has strong target characterization ability and can be applied to ship targets of different scales.

AUTOMATIC TUMULI DETECTION IN LIDAR BASED DIGITAL ELEVATION MAPS

Abstract. In this study, we aim to identify Iron Age tumuli with a probabilistic method, in digital terrain models extracted from airborne LiDAR measurements. Our task was particularly challenging due to the strong and uneven elevation of the ground where tumuli are located. In addition, the area is densely covered by vegetation, mainly by forests, which makes it difficult to create a surface model. The difficulty was exacerbated by the fact that the height and width of tumuli is very diverse but incomparably smaller than the extension of the area. In contrast to the various visualization techniques (openness, local relief model) used since decades for identifying tumuli, our method recognises these features automatically. Our approach is based on a Marked Point Process Model (MPP), which due to its inverse methodology, allows us to detect tumuli with high precision, especially in comparison with bottom-up methods. We quantitatively evaluated the method on six test data sets and we demonstrated its advantages compared to a traditional Hough transform-based method.

A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform

Fire accidents in the concrete structures affect the safety of human beings and structural components. Due to higher temperatures, thermal cracks are conceived in concrete elements. Crack identification, localization, and quantification are the three major components in the damage assessment system. This paper aims to detect thermal cracks of fire-affected concrete structures using ripplet transform-based computer vision method. Initially, the concrete images are decomposed with discrete ripplet transform (DRT). The low frequency sub-bands are approximated with column filtering-based gray level difference metric. It removes the uneven concrete background. Ripplet coefficient variance parameter (RCVP) is used to differentiate the crack pixels from noisy pixels which is used to eliminate the noises. Finally, in the reconstructed image, the major and minor thermal cracks are detected. The obtained thermal cracks are quantified with the crack properties of length, width, area, and perimeter. The novelty of the proposed method relays on the usage of ripplet transform for the detection of thermal cracks for different concrete grades and different durations of temperature profile. The experimental results are compared with four transform domain state-of-the-method crack detection methods. The proposed method yields better results in terms of the accuracy and average execution time.