Search Results (144)

The extraction of ocean wave wavelengths from optical imagery via Fast Fourier Transform (FFT) exhibits significant potential for Wave-Derived Bathymetry (WDB). However, in practical applications, this method frequently produces anomalously large wavelength estimates. To date, there has been insufficient exploration into the mechanisms underlying image spectral leakage to low wavenumbers and its suppression strategies. This study investigates three plausible mechanisms contributing to spectral leakage in optical images and proposes a subimage-based preprocessing framework: prior to executing two-dimensional FFT, the remote sensing subimages employed for wavelength inversion undergo three sequential steps: (1) truncation of distorted pixel values using a Gaussian mixture model; (2) application of a polynomial detrending surface; (3) incorporation of a two-dimensional Hann window. Subsequently, the dominant wavenumber peak is localized in the power spectrum and converted to wavelength values. Water depth is then inverted using the linear dispersion equation, combined with wave periods derived from ERA5. Taking 2 m-resolution WorldView-2 imagery of Sanya Bay, China as a case study, 1024 m subimages are utilized, with validation conducted against chart-sounding data. Results demonstrate that the proportion of subimages with anomalous wavelengths is reduced from 18.9% to 3.3% (in contrast to 14.0%, 7.8%, and 16.6% when the three preprocessing steps are applied individually). Within the 0–20 m depth range, the water depth retrieval accuracy achieves a Mean Absolute Error (MAE) of 1.79 m; for the 20–40 m range, the MAE is 6.38 m. A sensitivity analysis of subimage sizes (512/1024/2048 m) reveals that the 1024 m subimage offers an optimal balance between accuracy and coverage. However, residual anomalous wavelengths persist in near-shore subimages, and errors still increase with increasing water depth. This method is both concise and effective, rendering it suitable for application in shallow-water WDB scenarios. Full article

In order to achieve 0.1 m resolution and fully polarimetric observation capabilities for airborne SAR systems, the adoption of stepped-frequency modulation waveform combined with the polarization time-division transmit/receive (T/R) technique proves to be an effective technical approach. Considering the issue of range resolution degradation and paired echoes caused by multichannel amplitude–phase mismatch in fully polarimetric airborne SAR with 0.1 m resolution, an amplitude–phase error estimation algorithm based on echo data is proposed in this paper. Firstly, the subband amplitude spectrum correction curve is obtained by the statistical average of the subband amplitude spectrum. Secondly, the paired-echo broadening function is obtained by selecting high-quality sample points after single-band imaging and the nonlinear phase error within the subbands is estimated via Sinusoidal Frequency Modulation Fourier Transform (SMFT). Thirdly, based on the minimum entropy criterion of the synthesized compressed pulse image, residual linear phase errors between subbands are quickly acquired. Finally, two-dimensional cross-correlation of the image slice is utilized to estimate the positional deviation between polarization channels. This method only requires high-quality data samples from the echo data, then rapidly estimates both intra-band and inter-band amplitude/phase errors by using SMFT and the minimum entropy criterion, respectively, with the characteristics of low computational complexity and fast convergence speed. The effectiveness of this method is verified by the imaging results of the experimental data. Full article

In high-frequency surface wave radar (HFSWR) systems, the retrieval of the directional wave spectrum has remained challenging, especially in the case of echoes from long ranges with a low signal-to-noise ratio (SNR). Therefore, a quadratic programming algorithm based on the regularization technique is proposed with an empirical criterion for estimating the optimal regularization parameter, which minimizes the effect of noise to obtain more accurate inversion results. The reliability of the inversion method is preliminarily verified using simulated Doppler spectra under different wind speeds, wind directions, and SNRs. The directional wave spectra inverted from a radar network with two multiple-input multiple-output (MIMO) systems are basically consistent with those from the ERA5 data, while there is a limitation for the very concentrated directional distribution due to the truncated second order in the Fourier series. Further, in the field experiment during a storm that lasted three days, the wave parameters are calculated from the inverted directional spectra and compared with the ERA5 data. The results are shown to be in reasonable agreement at four typical locations in the core detection area. In addition, reasonable performance is also obtained under the condition of low SNRs, which further verifies the effectiveness of the proposed inversion algorithm. Full article

This paper presents a novel cochlear implant (CI) sound coding strategy called Spectral Feature Extraction (SFE). The SFE is a novel Fast Fourier Transform (FFT)-based Continuous Interleaved Sampling (CIS) strategy that provides less-smeared spectral cues to CI patients compared to Crystalis, a predecessor strategy used in Oticon Medical devices. The study also explores how the SFE can be enhanced into a Temporal Fine Structure (TFS)-based strategy named Spectral Event Extraction (SEE), combining spectral sharpness with temporal cues. Background/Objectives: Many CI recipients understand speech in quiet settings but struggle with music and complex environments, increasing cognitive effort. De-smearing the power spectrum and extracting spectral peak features can reduce this load. The SFE targets feature extraction from spectral peaks, while the SEE enhances TFS-based coding by tracking these features across frames. Methods: The SFE strategy extracts spectral peaks and models them with synthetic pure tone spectra characterized by instantaneous frequency, phase, energy, and peak resemblance. This deblurs input peaks by estimating their center frequency. In SEE, synthetic peaks are tracked across frames to yield reliable temporal cues (e.g., zero-crossings) aligned with stimulation pulses. Strategy characteristics are analyzed using electrodograms. Results: A flexible Frequency Allocation Map (FAM) can be applied to both SFE and SEE strategies without being limited by FFT bandwidth constraints. Electrodograms of Crystalis and SFE strategies showed that SFE reduces spectral blurring and provides detailed temporal information of harmonics in speech and music. Conclusions: SFE and SEE are expected to enhance speech understanding, lower listening effort, and improve temporal feature coding. These strategies could benefit CI users, especially in challenging acoustic environments. Full article

Stylolites, common dissolution surfaces in carbonate rocks, form due to localized stress-induced pressure-solution during burial compaction or tectonic contraction. Their morphology and growth are influenced by dissolution kinetics, rock heterogeneity, clay content, burial depth, stress evolution, diagenesis, and pore fluid availability. This study applies the stylolite roughness inversion technique (SRIT), a proven paleopizometer that quantifies the principal vertical stress (σ_v = σ₁) prevailing in strata in the last moments of bedding-parallel stylolites (BPS) formation, to the Late Cretaceous Bagh Group carbonates in the Narmada Basin, India, to estimate their burial paleo-depth. Using the Fourier Power Spectrum (FPS), we obtained 18 σ₁ values from a collection of 30 samples, enabling us to estimate paleo-burial depths for the Bagh Group ranging from 660 to 1320 m. As the Bagh Group burial history is unknown, but as there is no subsequent sedimentary deposition above it, we relate this ca. 1.3 km burial depth to the now eroded thickness of the deposits related to Deccan volcanism at the end of the Cretaceous time, implying a quasi-instantaneous development of the BPS population in the strata. This research highlights the robustness of SRIT for reconstructing burial histories in carbonate sequences and that it can be a reliable way to reconstruct the thickness of eroded deposits in well-constrained geological history. Full article

Passive wireless surface acoustic wave (SAW) sensors are widely adopted for monitoring the safety status of industrial equipment due to their compact size and maintenance-free operation. Replacing traditional discrete-component interrogators with software-defined radio (SDR) architectures offers lower cost and greater flexibility. However, conventional frequency estimation methods often rely on iterative algorithms with high computational complexity, limiting their real-time applicability. This paper presents an SAW sensing system based on an SDR platform and a non-iterative spectrum-fitting method for SAW frequency measurement. The feasibility of the proposed method is theoretically analyzed, and its performance under different window functions and length of fast Fourier transform (FFT) configurations is evaluated through simulations and experimental measurements. The results demonstrate a favorable trade-off between time efficiency and SAW frequency measurement accuracy. Compared to traditional approaches, the proposed method reduces complexity while maintaining ± 3$\mathrm{k}$$\mathrm{Hz}$ peak-to-peak accuracy with only 4096-point FFT length according to experimental results. Full article

The development of a universal method for real-time gas leak localisation imaging is crucial for preventing substantial financial losses and hazardous incidents. To achieve this objective, this study integrates array signal processing and electronic techniques to construct an ultrasonic sensor array for gas leak detection and localisation. A digital microelectromechanical system microphone array is used to capture spatial ultrasonic information. By processing the array signals using beamforming algorithms, an acoustic spatial power spectrum is obtained, which facilitates the estimation of the locations of potential gas leak sources. In the pre-processing of beamforming, the Hilbert transform is employed instead of the fast Fourier transform to save computational resources. Subsequently, the spatial power spectrum is fused with visible-light images to generate acoustic localisation images, which enables the visualisation of gas leak sources. Experimental validation demonstrates that the system detects minor and multiple gas leaks in real time, meeting the sensitivity and accuracy requirements of embedded industrial applications. These findings contribute to the development of practical, cost-effective, and scalable gas leak detection systems for industrial and environmental safety applications. Full article

A flash smelting furnace operation is based on the exothermic reduction of copper concentrates in the combustion shaft, and these reactions occur at high temperatures (1250–1350 °C), where flame control is fundamental to optimizing copper reduction. Furthermore, inherent physicochemical reactions of the reduction process have been shown to emit spectral lines in the visible-near infrared spectrum (250–900 nm). Thus, an optoelectronic sensor prototype is proposed and developed for flame measurements of an industrial copper concentrate flash smelting furnace. The sensor system is composed of a high-temperature optical fiber probe, which functions as a waveguide to capture the emitted flame radiation and a visible-near infrared spectrometer. From the measured radiation, flame temperature and flame dynamics are analyzed. Flame temperature is estimated using the two-wavelength temperature estimation method, and flame dynamics are defined as variations in the total emissive power, which are studied in the time and frequency domain via the Fourier Transform method. These combustion dynamics are then used to create a flame instability index, which is used to characterize the flame combustion quality. The combination of this index and sensor platform provides a powerful tool to aid in proper flame control. Full article

Cortisol, known as the “stress hormone”, is vital for stress response, metabolism regulation, and immune function, and salivary cortisone reflects serum cortisol levels. The measurement of salivary cortisone levels has been proposed as an effective alternative method for estimating serum cortisol levels. Objective: This study aimed to evaluate the use of Fourier Transform Infrared Spectroscopy (FTIR) for salivary cortisone identification and quantification and to assess the impact of adding the surfactant TWEEN 80 to the analysis. Methods: Initially, cortisone was diluted in chloroform and methanol (5,000,000 µg/dL). FTIR spectra were obtained, and absorbance characteristics and peaks were identified. The spectrum of this initial dilution was processed using the Savitzky-Golay filter to evaluate peak heights at 1655 cm⁻¹ and 1700 cm⁻¹, and the effect of signal processing on these peaks was assessed. Additionally, two series of dilutions were performed by adding the surfactant TWEEN 80 at two different concentrations, and the effect of the surfactant on the cortisone spectra was evaluated to reduce noise and enhance the signal. Results: The spectra obtained from the cortisone solution were similar to those found in the literature for solid samples. The peak corresponding to the wavenumber range of 1600–1680 cm⁻¹, related to the stretching bands of C=C, was found to be reliable for use in cortisone quantification studies. The standard deviation between the spectra of the same sample was less than 0.01. It was not possible to detect cortisone when TWEEN 80 was added; however, with signal processing, TWEEN 80 could be detected in quantities as low as 0.0033% of the solution. Conclusions: FTIR demonstrates potential as a non-invasive method for cortisone analysis. While Tween 80 aids in the dilution of cortisone in water, it obscures its spectrum. Full article

Online monitoring of partial discharge (PD) is a complex task traditionally requiring specialized expertise. However, recent advancements in signal processing and machine learning have facilitated the development of automated tools to identify and categorize PD patterns, aiding those without extensive experience. This paper aims to identify PD types and estimate the density distribution of frequency characteristics for three PD types, internal PD, surface PD, and corona PD, using verified PD data. The proposed method employs a findpeaks algorithm based on Fast Fourier Transform (FFT) to extract frequency key features, denoted as f₁ and f₂, from the frequency spectrum. These features are used to estimate model parameters for each PD type, enabling the representation of their frequency density distributions in a 2D map (f₁, f₂) via Gaussian Mixture Models (GMMs). The optimal number of Gaussian components, determined as five using the Bayesian Information Criterion (BIC), ensures accurate modeling. For PD identification, log-likelihood and softmax functions are applied, achieving an evaluation accuracy of 96.68%. The model also demonstrates robust performance in identifying unknown PD data, with accuracy ranging from 78.10% to 95.11%. This approach enhances the distinction between PD types based on their frequency characteristics, providing a reliable tool for PD signal analysis and identification. Full article

Variable marine environmental conditions, particularly at the sea surface, present considerable challenges to cross-media laser transmission. This study simulates uplink laser transmission through a seawater–sea surface–air channel via ray tracing and Monte Carlo methods, with an emphasis on the impacts of the sea surface channel. A spatial model of the sea surface is introduced, which uses a wave spectrum and fast Fourier transform technology, and the results are compared against those of a classical statistical model. The validity and applicability of six representative wind wave spectra are assessed for their effectiveness in characterizing the optical sea surface. Among these spectra, the Elfouhaily spectrum, which is refined for low-wind conditions, can most accurately represent the optical properties of the sea surface. The simulations reveal that the spatial model captures power fluctuations due to dynamic sea surface changes. At shorter underwater transmission distances, the spatial model may induce considerable drift, thereby degrading power estimates, where the difference is about 0.9 dB compared with the statistical model. Deeper underwater transmissions can mitigate beam distortions, resulting in a decrease in normalized peak power from −114 dB to −157 dB. Additionally, the laser centroid distribution tends to be elliptical because of the distribution of the sea surface azimuth. These findings underscore the importance of incorporating spatiotemporal dynamics in modeling sea surfaces and provide insights for optimizing underwater air laser transmission links in complex marine environments. Full article

Anchor bolts, such as rock bolts and concrete anchors, are widely used in civil, geotechnical, and mining engineering for anchorage and ground support. They are used in retaining walls, dry docks, dams, mines, and prestressed concrete structures. Evaluating the grouting condition of anchor bolts is essential to ensure the safety of these applications. Spectrum techniques have been used to develop non-destructive methods for estimating the grouting quality of grouted anchor bolts. The spectrum methods include fast Fourier transform, time–frequency analysis, wavelet transform analysis, and empirical mode decomposition. In this study, we introduce the parameter-optimized variational mode decomposition (VMD) method for the spectrum analysis of impact echo signals of anchor bolts. This method overcomes the difficulty of conventional spectrum methods that cannot separate highly coupled natural modes for advanced analysis. The parameter-optimized VMD method enables the generation of a new evaluation index for quantifying bolt grouting conditions, which has the potential to significantly enhance the quality evaluation of anchor bolts compared with conventional analysis of natural frequencies. This study uses impact response to establish a new benchmark for the integrity diagnosis of anchor bolts, paving the way for more accurate and reliable safety assessments. Full article

This study introduces a radar-based model for estimating blood pressure (BP) in a touch-free manner. The model accurately detects cardiac activity, allowing for contactless and continuous BP monitoring. Cardiac motions are considered crucial components for estimating blood pressure. Unfortunately, because these movements are extremely subtle and can be readily obscured by breathing and background noise, accurately detecting these motions with a radar system remains challenging. Our approach to radar-based blood pressure monitoring in this research primarily focuses on cardiac feature extraction. Initially, an integrated-spectrum waveform is implemented. The method is derived from the short-time Fourier transform (STFT) and has the ability to capture and maintain minute cardiac activities. The integrated spectrum concentrates on energy changes brought about by short and high-frequency vibrations, in contrast to the pulse-wave signals used in previous works. Hence, the interference caused by respiration, random noise, and heart contractile activity can be effectively eliminated. Additionally, we present two approaches for estimating cardiac characteristics. These methods involve the application of a hidden semi-Markov model (HSMM) and a U-net model to extract features from the integrated spectrum. In our approach, the accuracy of extracted cardiac features is highlighted by the notable decreases in the root mean square error (RMSE) for the estimated interbeat intervals (IBIs), systolic time, and diastolic time, which were reduced by 87.5%, 88.7%, and 73.1%. We reached a comparable prediction accuracy even while our subject was breathing normally, despite previous studies requiring the subject to hold their breath. The diastolic BP (DBP) error of our model is $3.98\pm 5.81$ mmHg (mean absolute difference ± standard deviation), and the systolic BP (SBP) error is $6.52\pm 7.51$ mmHg. Full article

In the corner reflector jamming scenario, the ship target and the corner reflector array have different degrees of defocusing in the synthetic aperture radar (SAR) image due to their complex motions, which is unfavorable to the subsequent target recognition. In this manuscript, we propose an imaging method for marine targets based on time–frequency analysis with the modified Clean technique. Firstly, the motion models of the ship target and the corner reflector array are established, and the characteristics of their Doppler parameter distribution are analyzed. Then, the Chirp Rate–Quadratic Chirp Rate Distribution (CR-QCRD) algorithm is utilized to estimate the Doppler parameters. To address the challenges posed by the aggregated scattering points of the ship target and the overlapping Doppler histories of the corner reflector array, the Clean technique is modified by short-time Fourier transform (STFT) filtering and amplitude–phase distortion correction using fractional Fourier transform (FrFT) filtering. This modification aims to improve the accuracy and efficiency of extracting scattering point components. Thirdly, in response to the poor universality of the traditional Clean iterative termination condition, the kurtosis of the residual signal spectrum amplitude is adopted as the new iterative termination condition. Compared with the existing imaging methods, the proposed method can adapt to the different Doppler distribution characteristics of the ship target and the corner reflector array, thus realizing better robustness in obtaining a well-focused target image. Finally, simulation experiments verify the effectiveness of the algorithm. Full article

A novel method for the direction of arrival (DOA) estimation of coherent signals under a space–time–coding metasurface (STCM) is proposed in this paper. Noticeably, the STCM can replace multi-channel arrays with a single channel, which can be utilized to modulate incident electromagnetic waves and generate harmonics. However, coherent signals are overlapping in the frequency spectrum and cannot achieve DOA estimation through subspace methods. Therefore, the proposed method transforms the angle information in the time domain into amplitude and phase information at harmonics in the frequency domain by modulating incident coherent signals using the STCM and performing a fast Fourier transform (FFT) on these signals. Based on the harmonics in the frequency spectrum of the coherent signals, appropriate harmonics are selected. Finally, the ℓ₁ norm singular value decomposition (ℓ₁-SVD) algorithm is utilized for achieving high-precision DOA estimation. Simulation experiments are conducted to show the performance of the proposed method under the condition of different incident angles, harmonic numbers, signal-to-noise ratios (SNRs), etc. Compared to the traditional algorithms, the performance of the proposed algorithm can achieve more accurate DOA estimation under a low SNR. Full article