Apply Frequency Domain Research Articles

Robust crop row detection using discrete Fourier transform (DFT) for vision-based in-field navigation

Accurate row detection is crucial for autonomous navigation systems in agricultural applications. Since the crops are typically planted in evenly spaced, parallel rows, crop rows mathematically correlate to a specific spectral signature within the frequency domain. In this study, we propose a robust frequency domain-based image processing pipeline for accurately locating crop rows within images. Using the two-dimensional discrete Fourier transform (DFT), we identify the spacing and direction of crop rows from significant frequency components, and their positions from the phase. To enhance precision, we apply frequency domain interpolation, a Hanning window, and discrete-time Fourier transform (DTFT) to accurately locate peak responses and corresponding phases in the spectrum. Extensive experiments were conducted to validate the accuracy and efficiency of our algorithm. The algorithm accurately detected crop rows in images and estimated lateral position and heading deviations relative to row centerlines, across different plant growth stages and illumination conditions. Additionally, by integrating a linear quadratic Gaussian (LQG) navigation controller with this vision-based crop row detection algorithm, we successfully enabled a robot to follow crop row centerlines with a mean absolute tracking error of 3.74 cm. These results highlight the potential of our frequency domain-based approach for enhancing precision agriculture through robust crop row detection and navigation.

Research of laser width variability in line laser infrared thermal wave inspection of CFRP laminates and defect reconstruction method for mobile detection

The line laser scanning infrared thermography method is a rapid and comprehensive approach for defect detection. This study investigates the impact of line laser width on detecting Carbon fiber reinforced polymers (CFRP) defects, revealing that narrower line lasers are more effective for shallow subsurface defects, while wider line lasers provide greater stability for deeper subsurface defects. To address issues like target loss, misdetection, and leakage in moving detection, a quasi-static reconstruction method based on Discrete Fourier Transform and Principal Component Analysis (DFT-PCA) is proposed. This method tracks and localizes moving defects, converting the image sequence into a quasi-static format and applying frequency domain denoising to achieve clear defect boundaries and reduced background noise. The study further explores the optimal line laser width by analyzing the relationship between the defect diameter-to-depth ratio and the signal-to-noise ratio of the reconstructed images. The findings indicate that for defects with a diameter-to-depth ratio greater than 6, a line laser with a width smaller than 4 mm offers better detection results, whereas for a diameter-to-depth ratio smaller than 6, a line laser with a width larger than 4 mm is more effective. These conclusions confirm previous research on the varying effects of line laser width on detection performance.

Advancements in the Theory and Practice of Flight Vehicle System Identification

System identification methods have played an essential role in the research and industry projects at the Institute of Flight System Dynamics of the Technical University of Munich. Besides the application of the established system identification approaches to multiple aircraft, novel methods have been developed at the institute to deal with the challenges associated with fixed- and rotary-wing aircraft system identification in the time and frequency domains. This paper provides an overview of these new developments, as well as practical application examples from the past and ongoing projects at the institute. After a brief introduction to the work at the institute, the paper describes the new advancements in the fields of time domain system identification and optimal input design by introducing optimal control methods. It continues with an alternative problem formulation for applying frequency domain system identification and parameter estimation methods to a novel flight control design and tuning approach. Additional topics from the past and ongoing research at the institute such as novel methods and practical findings in rotorcraft system identification and flight path reconstruction for different applications will be discussed and referenced.

A slotted plus-shaped antenna with a DGS for 5G Sub-6 GHz/WiMAX applications

A slotted plus-shaped patch antenna (PSPA) with defected ground structure (DGS) is modelled and proposed for 5G Sub-6 GHz and WiMAX applications by using computer simulation technology (CST) MWS suite. The PSPA incorporates a rectangular slotted plus-shaped metal patch and a DGS. The PSPA is designed on a Rogers RT5880 (lossy) substrate with a compact dimension of 20 × 35 × 0.79 mm3. Its reflection coefficient is -52.06 dB resonating at 3.12 GHz and operates over a wider bandwidth of 2.56 GHz (2.67–5.23 GHz) to accommodate suitable Sub-6 GHz bands. The PSPA has a good gain (2.44 dB), directivity (2.53 dBi), and VSWR (1.005) at 3.12 GHz with omnidirectional radiation characteristics. The maximum efficiency of the proposed PSPA is about 98% for almost loss free power radiation. The apex of estimated gain and directivity are 4.65 dB and 4.95 dBi. The impact of different physical parameters on the antenna performance has also been studied and analysed in this paper. Initially, the proposed PSPA has been investigated by using time domain (TD) solver of CST then again it is buttressed by applying frequency domain (FD) solver of CST. Furthermore, the design has also been verified by high-frequency structure simulator (HFSS) as well as FEKO (a computational electromagnetics software). All the simulators show a very good agreement in results.

ADD: Frequency Attention and Multi-View Based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images

Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to significant performance degradation with low-quality compressed deepfake images. Because of the limited information in low-quality images, detecting low-quality deepfake remains an important challenge. In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. We explore transfer learning capability in KD to enable a student network to learn discriminative features from low-quality images effectively. In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher’s and student’s tensors under different views to transfer the teacher tensor’s distribution to the student more efficiently. Our extensive experimental results demonstrate that our approach outperforms state-of-the-art baselines in detecting low-quality compressed deepfake images.

Modelling diurnal variation magnetic fields due to ionospheric currents

SUMMARY Accurate models of the spatial structure of ionospheric magnetic fields in the diurnal variation (DV) band (periods of a few hours to a day) would enable use of magneto-variational methods for 3-D imaging of upper mantle and transition zone electrical conductivity. Constraints on conductivity at these depths, below what is typically possible with magnetotellurics, would in turn provide valuable constraints on mantle hydration and Earths deep water cycle. As a step towards this objective, we present here a novel approach to empirical modelling of global DV magnetic fields. First, we apply frequency domain (FD) principal components analysis (PCA) to ground-based geomagnetic data, to define the dominant spatial and temporal modes of source variability. Spatial modes are restricted to the available data sites, but corresponding temporal modes are effectively continuous in time. Secondly, we apply FD PCA to gridded surface magnetic fields derived from outputs of the physics-based Thermosphere–Ionosphere–Electrodynamics General Circulation Model (TIEGCM), to determine the dominant modes of spatial variability. The TIEGCM spatial modes are then used as basis functions, to fit (or interpolate) the sparsely sampled data spatial modes. Combining the two steps, we have a FD model of DV band global magnetic fields that is continuous in both space and time. We show that the FD model can easily be transformed back to the time domain (TD) to directly fit time-series data, allowing the use of satellite, as well as ground-based, data in the empirical modelling scheme. As an illustration of the methodology we construct global FD and TD models of DV band source fields for 1997–2018. So far, the model uses only ground-based data, from 127 geomagnetic observatories. We show that the model accurately reproduces surface magnetic fields in both active and quiet times, including those at sites not used for model construction. This empirical model, especially with future enhancements, will have many applications: improved imaging of electrical conductivity, ionospheric studies and improved external field corrections for core and crustal studies.

Interannual variability of wave climate in the Caribbean Sea

Low-frequency interannual climatic oscillations have been reported to influence the precipitation regimes, river discharges, winds, and sea level in the Caribbean Sea. Here, we analyzed the presence and relevance of low-frequency oscillations in the waves’ climate of the Caribbean Sea using data from WAVEWATCH III (NOAA) reanalysis and applying frequency domain wavelet transform technique. Results show a clear sectorization of the Caribbean Sea according to the presence and relevance of interannual oscillations in wave climate, where the southwestern region (Colombian basin) exhibits a larger number of statistically significant oscillations (at 95% confidence levels) with recurrence periods of 2–4, 4–8, and 8–12 years. Only some of these frequencies are observed in the rest of Caribbean basin. Two climatic events occurring in the periods 1997–1998 and 2010–2011 stand out in the study zone for their high energy in wavelet analysis. These events have been associated with the occurrence of the warm and cold phases of the El Nino Southern Oscillation (ENSO), respectively, and are well known due to their strong global effects on hydroclimatology. Thus, an additional analysis was performed in the time domain to investigate the effect of the two events on Caribbean Sea waves. The results revealed an influence of ENSO on significant wave height and peak period comparing with multi-year average conditions, producing highly variable spatial and temporal wave patterns, with more evident effects during the cold phase of ENSO (La Nina). These results confirm the sectorization of the Caribbean Sea regarding the energy and interannual variability of waves.

Containment control of heterogeneous fractional-order multi-agent systems

Fractional-order calculus has been studied deeply because many networked systems can only be described with fractional-order dynamics in complex environments. When different agents of networked systems show diverse individual features, fractional-order dynamics with heterogeneous characters will be used to illustrate the multi-agent systems (MAS). Based on the distinguishing behaviors of agents, a compounded fractional-order multi-agent systems(FOMAS) is presented with diverse dynamical equations. Suppose multiple leader agents existing in FOMAS, containment consensus control of FOMAS with directed weighted topologies is studied. By applying frequency domain analysis theory of the fractional-order operator, an upper bound of delays is obtained to ensure containment controls of heterogenous FOMAS with communication delays. The consensus results of delayed fractional-order dynamics in this paper can be expanded to the integer-order models. Finally, the results are verified by simulation examples.

Reconstruction Enhancement by Denoising the Magnetic Particle Imaging System Matrix Using Frequency Domain Filter

Magnetic particle imaging is a new modality, which allows the determination of the spatial distribution of magnetic nanoparticles in-vivo. A standard approach for magnetic particle image reconstruction employs a so-called system matrix. The system matrix is typically acquired by a calibration measurement, whereby the system response at numerous positions in the field-of-view is measured. Due to the measurement process, the system matrix contains noise, which affects the reconstruction of the particle distribution. In this paper, the special structure of the system matrix is exploited for noise reduction by applying frequency domain filters. It is shown that image reconstruction with the denoised system matrix yields an improved resolution and a better signal-to-noise ratio.

Quantitative analysis of insulation condition of oil-paper insulation based on frequency domain spectroscopy

In order to distinguish the influences of moisture and aging on the frequency domain dielectric response of oil-paper insulation and better apply frequency domain spectroscopy (FDS) to assess the insulation condition of power transformers, the oil-paper insulation samples with different moisture contents and different aging states were prepared in the laboratory. The FDS of the samples were tested and a group of characteristic parameters were extracted from dissipation factor (tanδ) curves which could be used to assess the moisture content and aging states of oil-paper insulation respectively. The quantitative relationship among characteristic parameters, degree of polymerization (DP) and moisture contents (Km.c) was accurately established. The observations show that the proposed characteristic parameters are sensitive to the moisture in 10-3-102Hz, while the aging states influence the characteristic parameters in 10-3-10-1Hz. Meanwhile, an exponential relationship equation which could be used to assess the oil-paper insulation condition was established among the characteristic parameters, DP and the moisture content. Finally, the evaluation technique proposed in this paper was used to diagnose the insulation condition of several field transformers in this way, and its validity was preliminary and reasonably verified.

Seismic characterization of hydrates in faulted, fine‐grained sediments of Krishna‐Godavari Basin: Full waveform inversion

In fine‐grained, faulted sediments, both stratigraphic and fault‐induced structural variations can simultaneously determine the gas hydrate distribution. Insights into hydrate distribution can be obtained from P wave velocity (VP) and attenuation (QP−1) character of the gas hydrate stability zone (GHSZ). In this paper, we apply frequency domain full‐waveform inversion (FWI) to surface‐towed 2D multichannel seismic data from the Krishna‐Godavari (KG) Basin, India, to image the fine‐scale (100 × 30 m) VP and QP−1 variations within the GHSZ. We validate the inverted VP model by reconciling it with a sonic log from a nearby (∼250 m) well. The VPmodel shows a patchy distribution of hydrate. Away from the faults‐dominated parts of the profile, hydrates demonstrate stratigraphic control which appears to be permeability driven. The QP−1model suggests that attenuation is relatively suppressed in hydrates‐bearing sediments. Elevated attenuation in non‐hydrate‐bearing sediments could be driven by the apparent pore fluid immiscibility at seismic wavelengths. The VP and the QP−1models also suggest that fault zones within the GHSZ can be hydrate‐ or free‐gas‐rich depending on the relative supply of free gas and water from below the GHSZ.

Does CPI Granger-cause WPI? New extensions from frequency domain approach in Pakistan

The present study significantly contributes to the economic literature by investigating the direction of causality between WPI and CPI by applying frequency domain causality approach developed by Lemmens et al. (2008) based on spectral approach. We use monthly frequency data covering the period of 1961–2010 in case of Pakistan. Our results provide evidence of cointegration between the variables. Furthermore, we find unidirectional causal relationship running from CPI to WPI that varies across frequencies i.e., CPI Granger-causes WPI at lower, medium as well as higher level of frequencies reflecting long-run, medium and short-run cycles. This implies that CPI should be a leading indicator for important policy decisions pertaining to monetary or fiscal policies in Pakistan.

High Range Resolution Ultrasonographic Vascular Imaging Using Frequency Domain Interferometry With the Capon Method

For high range resolution ultrasonographic vascular imaging, we apply frequency domain interferometry with the Capon method to a single frame of in-phase and quadrature (IQ) data acquired using a commercial ultrasonographic device with a 7.5 MHz linear array probe. In order to tailor the adaptive beam forming algorithm for ultrasonography we employ four techniques: frequency averaging, whitening, radio-frequency data oversampling, and the moving average. The proposed method had a range resolution of 0.05 mm in an ideal condition, and experimentally detected the boundary couple 0.17 mm apart, where the boundary couple was indistinguishable from a single boundary utilizing a B-mode image. Further, this algorithm could depict a swine femoral artery with a range beam width of 0.054 mm and an estimation error for the vessel wall thickness of 0.009 mm, whereas using a conventional method the range beam width and estimation error were 0.182 and 0.021 mm, respectively. The proposed method requires 7.7 s on a mobile PC with a single CPU for a 1×3 cm region of interest. These findings indicate the potential of the proposed method for the improvement of range resolution in ultrasonography without deterioration in temporal resolution, resulting in enhanced detection of vessel stenosis.

Correction of Records of Important Earthquakes of IRAN with Wavelet Transform and Filtering Method and Comparing between these Methods

During an earthquake, many stations record the ground motion, but only a few numbers of them could be corrected using conventional high-pass and low-pass filtering method and the others identified as highly contaminated by noise and as a result useless. There are two major problems associated to these noisy records. First, since signal to noise ratio (S/N) is low, it is not possible to discriminate between original signal and noise neither in frequency domain nor in time domain. The consequence is that it is not possible to cancel out the noise using conventional filtering methods. The second problem is about non-stationary characteristics of the noise. In other words, in many cases the characteristics of the noise are varying by time and in these situations, it is not possible to apply frequency domain correction schemes. For correction of acceleration signals contaminated with high-level non-stationary noise, there is an important question that is it possible to have estimation about the state of the noise in different bands of time and frequency? The wavelet multi-resolution analysis which decomposes a signal into different time-frequency components besides a suitable criterion for identification of the noise among each component provides the required mathematical tool for correction of highly noisy acceleration records. In this paper, the strong motion records of great earthquake events with Mw>5.5 in Iranian Plateau is corrected. For this purpose, first, signal to noise ratio is calculated for each record. If the value of this diagram is high enough, the signal is corrected with both methods and otherwise, the de-noising is applied using wavelet procedure. The effect of baseline correction of the final results is also investigated for each record. A comprehensive database containing the information of events, stations and correction parameters is also provided in this study.

Analysis of sympathetic neural discharge in rats and humans

Neural signals convey information through two different modalities: intensity and discharge pattern. The intensity code is based on the number of action potentials per unit time, which is then easily translated into neurotransmitter release. This kind of information may be assessed simply by counting the number of spikes or bursts over a time unit. However, the discharge pattern is a further, efficient means of neural information transfer. Rhythmic patterns (i.e. oscillations) can support highly structured, temporal codes based on correlation and synchronization. It is therefore clear that applying frequency domain analysis to sympathetic activity recorded in animals and humans may provide additional information about the neural control of the circulation. Over the last century, data obtained by the analysis of sympathetic activity in experimental animals, and recently also in humans, have provided fundamental contributions to our understanding of the physiological mechanisms involved in the neural control of circulation, as well as how these are altered in cardiovascular and non-cardiovascular diseases. The aim of this paper is to address some aspects related to the recording, analysis and interpretation of sympathetic activity in rats and humans, with special emphasis on analysis in the frequency domain.

Real time aircraft fly-over noise discrimination

A method for measuring aircraft noise time history with automatic elimination of simultaneous urban noise is presented in this paper. A 3 m-long 12-microphone sparse array has been proven to give good performance in a wide range of urban placements. Nowadays, urban placements have to be avoided because their background noise has a great influence on the measurements made by sound level meters or single microphones. Because of the small device size and low number of microphones (that make it so easy to set up), the resolution of the device is not high enough to provide a clean aircraft noise time history by only applying frequency domain beamforming to the spatial cross-correlations of the microphones’ signals. Therefore, a new step to the processing algorithm has been added to eliminate this handicap.

Study on the fractional-order Liu chaotic system with circuit experiment and its control

In this paper, applying frequency domain techniques based on Bode diagram, fractional-order Liu chaotic system is studied. An electronic circuit unit of tree shape is designed to realize the fractional-order chaotic Liu system. Circuit experiment of 2.7-order Liu system, as well as simulation results of fractional-order Liu chaotic system with α=0.8—0.1 in step size of 0.1， verify the circuit unit's effectiveness, and prove that chaos actually exists in the fractional-order Liu chaotic system with order as low as 0.3. A simple, but effective linear feedback controller is designed to realize the chaotic control of fractional-order Liu chaotic system.

Application of wavelet multi-resolution analysis for correction of seismic acceleration records

During an earthquake, many stations record the ground motion, but only a few of them could be corrected using conventional high-pass and low-pass filtering methods and the others were identified as highly contaminated by noise and as a result useless. There are two major problems associated with these noisy records. First, since the signal to noise ratio (S/N) is low, it is not possible to discriminate between the original signal and noise either in the frequency domain or in the time domain. Consequently, it is not possible to cancel out noise using conventional filtering methods. The second problem is the non-stationary characteristics of the noise. In other words, in many cases the characteristics of the noise are varied over time and in these situations, it is not possible to apply frequency domain correction schemes. When correcting acceleration signals contaminated with high-level non-stationary noise, there is an important question whether it is possible to estimate the state of the noise in different bands of time and frequency. Wavelet multi-resolution analysis decomposes a signal into different time–frequency components, and besides introducing a suitable criterion for identification of the noise among each component, also provides the required mathematical tool for correction of highly noisy acceleration records. In this paper, the characteristics of the wavelet de-noising procedures are examined through the correction of selected real and synthetic acceleration time histories. It is concluded that this method provides a very flexible and efficient tool for the correction of very noisy and non-stationary records of ground acceleration. In addition, a two-step correction scheme is proposed for long period correction of the acceleration records. This method has the advantage of stable results in displacement time history and response spectrum.

Visualization and analysis of DNA sequences using DNA walks

Visual methods illustrate how DNA sequences are read along a single DNA strand from the 5′ end to the 3′ end and they provide the hopes of gaining an understanding of the underlying genomic language. By handling genomic sequence residues as elements of a discrete-time signal, digital signal processing techniques can be employed for the analysis of genomic information. Using these representations and applying frequency domain transformations, it is shown that structures, or seemingly nonrandom behavior, may be readily identified in nucleotide sequences. We review the basic method of DNA walks and we show how these representations can be used to extract useful knowledge from the genomic data; namely long-range correlation information, sequence periodicities, and other sequence characteristics. Further information is elucidated through wavelet transform analysis. This work finally relates a measure of sequence complexity to these visual findings and offers conclusions regarding quantifying DNA sequence behavior or structure.

Model Reduction Using Rational Approximation Techniques

An effective way to get a reduced order model for use in control system analysis and design is to apply frequency domain analysis and associated computational techniques of model reduction. This can he accomplished by direct experimentation in which spectral data (amplitude ratio and phase shift) is obtained by input/output measurements or by creating a file of frequency response data from a given analytical transfer function. It is then sufficient to obtain a rational transfer function fit to the data by Fourier series methods, after which one of the finite dimensional algorithms can he exploited to extract a reduced order model. Here we describe computationally effective user friendly software, based on MATLAB, for doing this frequency domain analysis and model reduction with associated H∞-norm (uniform norm on jℜ) error estimates. The H∞-norm error can often he decreased by increasing the order of the approximant so as to get models which are sufficient for robust controller designs.