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Rotor imbalances in wind turbines present safety risks and lead to economic losses, and a method to diagnose rotor imbalances is urgently needed. A diagnostic method for rotor imbalances is proposed in this paper. First, a signal reconstruction method is proposed, and a novel index is used to determine the number of components used in signal decomposition in order to effectively address the interference by noise on the sensor. Second, an entropy calculation method is proposed, and the Gaussian kernel function is used to replace the fuzzy functions. The results indicate significant differences for different types of rotor imbalances. Moreover, it exhibits good noise robustness and a low dependence on the data length. Third, a support vector machine with multiscale kernels is proposed, and kernel functions with various characteristics and scales are combined. It has a well-distributed hyperplane and better classification performance, and it is robust to wind conditions. Finally, the method is tested and verified with varying levels of noise and turbulence. The results demonstrate satisfactory performance because the proposed method can effectively identify rotor imbalances under different noise and wind conditions. [ABSTRACT FROM AUTHOR]

This article presents an improved and extended modeling approach for acoustic wave propagation in rigid porous materials, focusing on examples, such as plastic foams used for noise reduction in automotive applications. We demonstrate that the classical model (Johnson-Champoux-Allard) in the asymptotic high-frequency limit, widely employed in the literature, fails to accurately reconstruct the transmitted acoustic signal through high absorbent porous materials characterized by significant wave attenuation. The study focuses on the airborne ultrasonic frequency range (30–200 kHz). To address this limitation, we introduce new non-acoustic parameters Σ and V for viscous effects, and Σ ′ and V ′ for thermal effects, with surface and volumetric dimensions, respectively, allowing for the reconstruction of the transmitted signal and accurate modeling of the pronounced acoustic attenuation within the material. These parameters are incorporated into the expansion on skin depths of the dynamic tortuosity α(ω) and thermal tortuosity α ′ (ω) response functions, which describe the inertial-viscous and thermal interactions between the fluid and the solid, respectively. This novel modeling approach enables a more comprehensive study of high attenuating porous media, which are crucial for effective noise reduction. Additionally, it opens up new possibilities for characterization beyond the capabilities of current models. [ABSTRACT FROM AUTHOR]

Direction-of-arrival estimation is difficult for signals spatially undersampled by more than half the wavelength. Frequency-difference beamforming [Abadi, Song, and Dowling (2012). J. Acoust. Soc. Am. 132, 3018–3029] offers an alternative approach to avoid such spatial aliasing by using multifrequency signals and processing them at a lower frequency, the difference-frequency. As with the conventional beamforming method, lowering the processing frequency sacrifices spatial resolution due to a beam broadening. Thus, unconventional beamforming is detrimental to the ability to distinguish between closely spaced targets. To overcome spatial resolution deterioration, we propose a simple yet effective method by formulating the frequency-difference beamforming as a sparse signal reconstruction problem. Similar to compressive beamforming, the improvement (compressive frequency-difference beamforming) promotes sparse nonzero elements to obtain a sharp estimate of the spatial direction-of-arrival spectrum. Analysis of the resolution limit demonstrates that the proposed method outperforms the conventional frequency-difference beamforming in terms of separation if the signal-to-noise ratio exceeds 4 dB. Ocean data from the FAF06 experiment support the validity. [ABSTRACT FROM AUTHOR]

The "compressed sensing" theory is the foundation for the compressed sampling system's design. In addition to the sparse representation and observation matrix, more studies in compressed sensing theory focus on signal reconstruction and recovery. Only a small number of research studies estimate the original signal parameter information using the compressed sample data. In this research, we propose a linear frequency modulation (LFM) signal parameter estimation approach based on the Gabor transform for the enhanced Modulated Wideband Converter (MWC) system, which can directly estimate the parameters of an LFM signal utilizing compressed sampling data. Based on the MWC system prototype, a better MWC system based on Gabor transform is created by fusing the Gabor transform with compressed sensing theory, and the system's structure, function, parameter settings, and viability are all examined. Finally, we show through a simulation experiment that the proposed method is capable of accurately estimating the parameter information of a huge broadband LFM signal by merely restoring Gabor coefficients. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring is emerging as a low-cost, non-invasive methodology for automated species-level population surveys. However, systems for automating the detection and classification of vocalizations in complex soundscapes are significantly hindered by the overlap of calls and environmental noise. We propose addressing this challenge by utilizing an acoustic vector sensor to separate contributions from different sound sources. More specifically, we describe and implement an analytical pipeline consisting of (1) calculating direction-of-arrival, (2) decomposing the azimuth estimates into angular distributions for individual sources, and (3) numerically reconstructing source signals. Using both simulation and experimental recordings, we evaluate the accuracy of direction-of-arrival estimation through the active intensity method (AIM) against the baselines of white noise gain constraint beamforming (WNC) and multiple signal classification (MUSIC). Additionally, we demonstrate and compare source signal reconstruction with simple angular thresholding and a wrapped Gaussian mixture model. Overall, we show that AIM achieves higher performance than WNC and MUSIC, with a mean angular error of about 5°, robustness to environmental noise, flexible representation of multiple sources, and high fidelity in source signal reconstructions. [ABSTRACT FROM AUTHOR]

It is difficult to effectively distinguish the key information of non-stationary dynamic signals in many engineering applications, such as fault detection, geological exploration, and logistics transportation. To deal with this problem, a classification and recognition algorithm based on variational mode decomposition (VMD) and the Support Vector Machine (SVM) optimized by the Whale Optimization Algorithm (WOA) optimization model is first proposed in this study. The algorithm first applies VMD to decompose the non-stationary time-domain signals into multiple variational intrinsic mode functions (VIMFs). Then, it calculates the correlation coefficient between each mode and the original signals and conducts signal reconstruction by sorting the VIMFs. On the base of this, it performs modal filtering on the non-stationary signals according to the correlation coefficients between the reconstructed signal and the original signal. Subsequently, the WOA is used to optimize two key parameters of the SVM. Finally, the optimization model is exploited to classify and recognize the impact and vibration of non-stationary signals. A series of simulations and experiments for the algorithm is carried out and analyzed deeply. The comparative test results indicate that the classification and recognition method for non-stationary signals based on VMD and WOA-SVM (VMD-WOA-SVM) proposed in this paper converges faster and recognizes the key information of non-stationary dynamic signals more accurately with a recognition precision of 96.66%. [ABSTRACT FROM AUTHOR]

The Doppler-signal processing method based on two integral convolutions with respect to approximate Doppler phase, φ(t), being a variable is introduced. While reducing the noise, these transforms produce the first Doppler harmonic, S 1 ‖ (t) , and its quadrature complement, S 1 ⊥ (t). The complex harmonic, s 1 (t) = S 1 ‖ (t) + i S 1 ⊥ (t) , makes it possible to find a new approximation of both Doppler signal frequency ωD(t) and phase φ(t), as well as the rms frequency error, χ ω D (t) , used to regularize the solution. The iterative procedure to estimate frequency ωD(t), which converges in the mean to χ ω D (t) -vicinity of Doppler frequency, ΩD(t), is proposed. Kernels of transforms are defined on the phase interval [−πn; +πn] for n ≥ 2. Method resolution is Rrs = n/2 beats periods, and for n = 2, it equals the Doppler period, while the error is χ ω D (t) ∼ n − 3 / 2 . Example velocity profiles are reconstructed from two experimental Doppler signals. [ABSTRACT FROM AUTHOR]

In the process of block compressed sensing (CS) applied to the rolling bearing fault signal, the reconstruction accuracy of the signal is low due to the large difference in sparsity between blocks and the unreasonable components of reconstruction support set, which affects the overall reconstruction effect of the signal. To improve the signal reconstruction results, forward and backward stagewise orthogonal matching pursuit (FBStOMP) based on the adaptive block method is proposed. First, to equalize the sparsity of each block signal, the fault signal is divided into blocks according to the adaptive block length, which is obtained by the short-time autocorrelation algorithm. Then, the K-singular value decomposition algorithm is used to train the sparse dictionary to obtain a better sparse effect. Finally, the FBStOMP algorithm is proposed. The atom backtracking and screening process is added in the reconstruction process to improve the possibility that all the effective atoms can be selected into the support set. The experimental analysis of the simulation signal and bearing fault signal show that, compared with the traditional CS reconstruction algorithm, the adaptive block-FBStOMP algorithm proposed in the paper can effectively improve the reconstruction accuracy of the bearing fault signal. [ABSTRACT FROM AUTHOR]

A single-pixel camera is an interesting alternative to modern digital cameras featuring millions of pixels. A single-pixel camera is a method that produces images by exploring the object features with a series of spatially resolved patterns of light field while measuring the correlated intensity on a single detector. Nowadays, single-pixel cameras are used on those applications where multi-pixel detectors are not available because the wavelength is not in visible range or light intensity is extremely low. The spatial light modulator is an essential part of any single-pixel camera systems. They are, unfortunately, very expensive. We describe a low-cost version of single pixel camera that can be used in undergraduate physics laboratories. We show that with this camera setup students can easily demonstrate basic characteristics of computational ghost imaging and traditional raster and basis scan. Finally, we explain how to perform compressive sampling of images where the number of measurements is well below the actual pixel number. Compressive sampling is a rapidly expanding method to perform image or signal reconstructions in many field of research. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) aims at decreasing sampling rate to reduce the needed number of samples. On the other hand, the one-bit CS is proposed to reduce the quantization bit. In this paper, we proposed a one-bit CS system aiming at acquiring the multiband sparse signal which is a very popular signal model in wireless communication, especially in cognitive radio. This proposed system, called direct one-bit sampler (DOS), is simple in hardware implementation, and it consists of only a comparator working at Nyquist rate. In the stage of signal reconstruction, it can be equivalent to a special multicoset sampler which is a popular scheme in CS. Moreover, we propose an enhanced binary iterative hard thresholding (BIHT), a popular one-bit recovery algorithm, to deal with the multiple measurement vectors in the one-bit CS framework. Both the theoretical model and experimental results demonstrate that the proposed DOS, with the help of the enhanced BIHT, can not only accurately recover the positions of active subbands of the multiband sparse signal but also roughly estimate the power of each active subband. [ABSTRACT FROM AUTHOR]

The magnetic flux leakage (MFL) evaluation is often used for the overhauling of oil extracting operation in the oil field to realize the real-time damage assessment of the pipeline. Since the MFL signal is affected by various noise sources in the field, this paper introduces the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On the basis of this, a particle swarm optimization wavelet threshold (PSO-WT) method is proposed, and the signal reconstruction option is improved to extract the leakage magnetic flux signal of tubing defects. First, CEEMDAN is used to add pairs of positive and negative white noise to the MFL signal, and then the signal is decomposed into several intrinsic mode functions (IMFs). Second, the correlation coefficient selection limit is defined. Taking into account the characteristics of the decomposed signal, the useless IMFs and useful IMFs are selected from the IMF components, where some of the useful IMF components contain less noise. Third, the PSO-WT algorithm is combined to further filter the noisy and useful IMF components. Finally, the filtered IMF components and the pure useful IMF components are selected to reconstruct the signal. In the experiment, the ensemble empirical mode decomposition (EEMD) method and CEEMDAN are used to decompose the noisy MFL signals ensemble in the field. The MFL signal is reconstructed under the correlation coefficient selection. It can be seen from the comparison of EEMD that the MFL signal is reconstructed under the same conditions after CEEMDAN decomposition, and its signal-to-noise ratio is increased by 8%. At the same time, after CEEMDAN decomposition, the selected noisy useful IMFs are further filtered by the wavelet threshold (WT) method and the PSO-WT method. Also, it indicates that the reconstructed signal processed by PSO-WT is 17% higher than the reconstructed signal after WT processing. [ABSTRACT FROM AUTHOR]

A sub-Nyquist coprime sampling system for sparse signals is implemented in this article. The proposed system is composed of coprime sampling hardware and a multicoset signal reconstruction algorithm. A pair of uniform samplers is utilized in the hardware to sample a wideband spare analog signal with an uncertain difference in start times. A time difference acquisition module embedded into a field-programmable gate array and a pulse-expanding circuit are then used to measure the difference in start times. Owing to the different frequencies of the two samplers, the coprime sample sets obtained are nonuniform. Before they are used as input to the multicoset signal reconstruction algorithm, these coprime sample sets need to be regrouped into multicoset sample sets according to the sample pattern. The results of experiments indicate that the signals can be reconstructed at an equivalent rate of the order of gigahertz from sub-Nyquist samples acquired by the designed coprime acquisition system. [ABSTRACT FROM AUTHOR]

This paper focuses on the reconstruction of digital signals in multirate digital signal processing. We study the discrete time Weyl-Heisenberg (DTWH) system for oversampling schemes and describe the frame operator of DTWH frames as the composition of the sampling operator and interpolation operator. Using discrete time Zak transform, we characterize the dual discrete time Weyl-Heisenberg tight frames, DTWH frames, and tight DTWH frames based on oversampling schemes. In addition, we discuss the applications of DTWH frames to the reconstruction of Weyl-Heisenberg systems in periodic space L2([0, 1)). Finally, we give Balian-Low theorem for an orthonormal basis formed by DTWH systems and weak Balian-Low theorem for exact DTWH frames in sequence space ℓ 2 (Z). [ABSTRACT FROM AUTHOR]

In this paper, we propose a reconstruction approach for a multiple-sinusoidal signal. The signal reconstruction requires a small number of samples and is based on a sub-Nyquist sampling scheme with dual rate channels. In the proposed sampling scheme, the samples are grouped into multiple cosets. To obtain enough different cosets to reconstruct a signal, the sampling rates of channels are required to be relative coprime. For each coset, the Whittaker–Shannon interpolation formula is employed to construct the relation between the sub-Nyquist samples and the original signal, which is used to construct the measurement matrix. Since the multiple-sinusoidal signal is sparse in the frequency domain, compressed sensing theory can be adopted to reconstruct the signal. Simulation results are reported to demonstrate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Reducing data volume and improving signal-to-noise ratio (SNR) is of great importance for echoes from submerged targets, affected by serious marine environment noise. The echo from a target is made of its response to the incident wave with the superposition of highlights (sub-echoes from main constituents of the target). Each of these highlights can be seen as a block, and the echo therefore has a block-sparse feature. This paper proposes a compressive sensing method to leverage prior information (CSPI), in which knowledge of the incident wave and the block-sparse feature are leveraged into the dictionary structure and signal reconstruction. CSPI is illustrated with simulations and field measurements of backscattering for a 1:20 model of the Benchmark Target Strength Simulation Submarine. For simulated signals with different noise levels, CSPI can reconstruct an almost invisible signal (original SNR = 0 dB), and improve SNR by up to 13 dB (for an original SNR of 4 dB) down to a still significant SNR of 7 dB (for an original SNR of 0 dB). For field measurements, CSPI can obtain the same SNR as the original signal using only 13% of the data, increasing the SNR to 15 dB using 30% data, and increasing with the compression ratio. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) based sampling techniques exhibit many advantages over other existing approaches for sparse signal spectrum sensing; they are also incorporated into non-uniform sampling signal reconstruction to improve the efficiency, such as random equivalent sampling (RES). However, in CS based RES, only one sample of each acquisition is considered in the signal reconstruction stage, and it will result in more acquisition runs and longer sampling time. In this paper, a sampling sequence is taken in each RES acquisition run, and the corresponding block measurement matrix is constructed using a Whittaker-Shannon interpolation formula. All the block matrices are combined into an equivalent measurement matrix with respect to all sampling sequences. We implemented the proposed approach with a multi-cores analog-to-digital converter (ADC), whose ADC cores are time interleaved. A prototype realization of this proposed CS based sequential random equivalent sampling method has been developed. It is able to capture an analog waveform at an equivalent sampling rate of 40 GHz while sampled at 1 GHz physically. Experiments indicate that, for a sparse signal, the proposed CS based sequential random equivalent sampling exhibits high efficiency. [ABSTRACT FROM AUTHOR]

Direction-of-arrival (DOA) estimation refers to the localization of sound sources on an angular grid from noisy measurements of the associated wavefield with an array of sensors. For accurate localization, the number of angular look-directions is much larger than the number of sensors, hence, the problem is underdetermined and requires regularization. Traditional methods use an ‘2-norm regularizer, which promotes minimum-power (smooth) solutions, while regularizing with ‘1-norm promotes sparsity. Sparse signal reconstruction improves the resolution in DOA estimation in the presence of a few point sources, but cannot capture spatially extended sources. The DOA estimation problem is formulated in a Bayesian framework where regularization is imposed through prior information on the source spatial distribution which is then reconstructed as the maximum a posteriori estimate. A composite prior is introduced, which simultaneously promotes a piecewise constant profile and sparsity in the solution. Simulations and experimental measurements show that this choice of regularization provides high-resolution DOA estimation in a general framework, i.e., in the presence of spatially extended sources. [ABSTRACT FROM AUTHOR]

Analysis of thermography spatial-transient patterns has considerable potential to enable automatic identification and quantification of defects in non-destructive testing and evaluation. This Letter proposes a non-negative pattern separation model for eddy current pulsed thermography to automatically extract important spatial and time patterns according to the transient thermal sequences without any pre-training or prior knowledge. In particular, the method is scale-invariant, such that large differences in surface emissivity, hot spots, and cool areas with dynamic range of thermal contrast can be extracted. Finally, an artificial slot in a steel sample with shining, black strip on the surface is tested to validate the proposed method. [ABSTRACT FROM AUTHOR]