Signal Reconstruction Performance Research Articles

Supraharmonics Reconstruction Method Based on Blackman Window and Compressed Sensing

This paper proposes a method that combines window functions with compressed sensing for the detection of ultra-high harmonics in the frequency range of 2–150 kHz. By analyzing the sparsity of the signal, the Bruckmann window function, which is most suitable for the compressed sensing reconstruction process and the characteristics of ultra-high harmonics, is selected. Simulations indicate that, compared to existing methods, the proposed algorithm based on the fusion of the Bruckmann window and compressed sensing achieves a sparser post-observation signal with reduced fluctuations. The robustness and anti-interference capabilities are enhanced, while the harmonic detection accuracy and signal reconstruction performance are significantly improved. The reconstruction error reaches 4.15 × 10−6, the mean squared error percentage (MSE) reaches 4.13 × 10−6, and the signal-to-noise ratio (SNR) is as high as 97.69 dB, marking an increase of 54.11%. This study provides a new theoretical and methodological approach for the analysis and processing of ultra-high harmonics caused by a high proportion of power electronic devices.

An efficient algorithm with fast convergence rate for sparse graph signal reconstruction

In this paper, we consider the graph signals are sparse in the graph Fourier domain and propose an iterative threshold compressed sensing reconstruction (ITCSR) algorithm to reconstruct sparse graph signals in the graph Fourier domain. The proposed ITCSR algorithm derives from the well-known compressed sensing by considering a threshold for sparsity-promoting reconstruction of the underlying graph signals. The proposed ITCSR algorithm enhances the performance of sparse graph signal reconstruction by introducing a threshold function to determine a suitable threshold. Furthermore, we demonstrate that the suitable parameters for the threshold can be automatically determined by leveraging the sparrow search algorithm. Moreover, we analytically prove the convergence property of the proposed ITCSR algorithm. In the experimental, numerical tests with synthetic as well as 3D point cloud data demonstrate the merits of the proposed ITCSR algorithm relative to the baseline algorithms.

Bridge progressive damage detection using unsupervised learning and self-attention mechanism

This paper presents a novel unsupervised method for detecting progressive damage in bridge structures. Traditional supervised learning approaches require a large number of damaged samples to train a high-performing detection model, which poses challenges when applied to normal in-service bridges. Since in-service bridges usually lack data from damage scenarios and existing methods fail to achieve real-time detection, unsupervised learning has gained popularity due to its ability to work without damaged samples and offer better real-time performance. In light of this, this paper proposes a novel unsupervised damage detection method that utilizes an unsupervised attention-convolutional auto-encoder to reconstruct real-time vibration signals from bridge. Two damage indicators are employed to evaluate the effectiveness of the reconstruction process, aiming to capture potential changes in the progressive damage of bridges. The results demonstrate that: the attention-convolutional auto-encoder achieves excellent reconstruction performance for vibration signals from intact structures, which reconstruction error converging to 0 and structural similarity approaching 1. However, the reconstruction performance is less satisfactory for vibration signals in damage scenarios, deteriorating further as the damage level increases. By analyzing the variation patterns of vibration signals in attention-convolutional auto-encoders, it was confirmed that the reliability of the proposed evaluation effects by the two different types of damage indicators. Finally, the trend of progressive damage was extracted through these indicators, showcasing the effectiveness of the method in detecting progressive damage. This approach facilitates the identification of potential progressive changes in the normal operation process of bridges, thus enhancing real-time damage detection capabilities and contributing to the knowledge of bridge monitoring.

Solar Radiation Tracking Design Based on Photoelectric Sensor for Tracing Signal Reconstruction Algorithm

Due to the high precision, low power requirements, and maintenance-free nature, the four-quadrant photoelectric sensor (FQPS) and the laser displacement sensor are employed in the development of the two-axis solar tracker. Among them, when designing the optical path of the FQPS, a cavity-wandering optical path is introduced to control the spot size of the direct sunlight falling on the sensor. The sensor interface has five data transmission lines, of which four positive poles correspond to the four quadrants of the planar two-dimensional coordinate system. AD7793 is used as the signal collector and a differential amplifier with small clutter is integrated. A laser displacement sensor is installed in the solar radiation tracker to realize the self-correction of movement error. The main control circuit adopts DSP chip TMS320F28335 for the whole dual-axis sun tracker. In the experiment, Code Composer Studio was used for development, and SPA algorithm was used to measure the position. A number of error correction variables, such as year, month, day, time, minute, second, ΔT, time zone, longitude, latitude, etc., are added to the algorithm. After debugging the hardware and software, the sunrise and sunset times of a certain place on the solar terms such as spring equinox, summer solstice, autumn equinox and winter solstice in 2022 are tested, and compared with those released by the Meteorological Bureau. The tracking signal is rebuilt using proximal gradient descent technology. The results demonstrate that the signal-to-noise ratio under the reconstruction algorithm is continuously enhanced and tends to stabilize with an increase in sampling rate without noise, and it exhibits good signal reconstruction performance in the presence of noise and growing noise.

FECG compressed sensing mode based on joint block sparsity

The development of medical Internet of Things technology has enabled the rise of telemedicine. For medical signals, real-time transmission, accuracy of signals and portability of equipment are required to be strict. The telemedicine system based on Compressed Sensing (CS) technology can avoid sampling a large number of redundant information, and reduce the complexity of data processing and computing time. However, the performance of existing Compressed Sensing algorithms in processing and reconstructing medical signals is poor. It is difficult to meet the strict requirements. In this paper, the sparse representation method which is most suitable for Fetal Electrocardiogram (FECG) signals is obtained by comparison experiment. And a Logistics-Tent Sine Bernoulli Measurement Matrix (LTSBM) construction algorithm is proposed for more convenient hardware applications. Based on the characteristics of FECG signals, a Joint Block Multiple Orthogonal Least Squares (JBMOLS) algorithm is proposed to improve the reconstruction performance in this paper. Combined with the above algorithms, the FECG compressed sensing mode is constituted, which is used to improve the compression, sampling and reconstruction performance of FECG signals. The experimental results show that, compared with traditional compressed sensing technology, the FECG compressed sensing mode improves the overall signal reconstruction performance, the running time is greatly reduced, and the practicability is stronger.

Joint optimization methods for Gaussian random measurement matrix based on column coherence in compressed sensing

In compressed sensing, a measurement matrix Φ having low coherence with sparse dictionary Ψ can achieve better signal reconstruction performance. To improve the signal reconstruction performance, this paper proposes two joint optimization algorithms for the Gaussian random measurement matrix to minimize the coherence between the measurement matrix Φ and the sparse dictionary Ψ. First, a joint optimization algorithm is proposed that can simultaneously reduce the average mutual coherence μg and the mutual coherence μ based on an alternating projection strategy. Then, to further decrease the coherence between Φ and Ψ, an improved shrinkage method based on K-order cumulative coherence μK is proposed. Furthermore, another joint optimization algorithm is proposed by fusing this improved shrinkage method, which can simultaneously decrease the average mutual coherence μg and the K-order cumulative coherence μK. Simulation results show that the two proposed joint optimization algorithms outperform existing algorithms in reducing coherence and improving reconstruction performance.

Low-Cost and Confidential ECG Acquisition Framework Using Compressed Sensing and Chaotic Systems for Wireless Body Area Network.

Recent years have witnessed an increasing popularity of wireless body area network (WBAN), with which continuous collection of physiological signals can be conveniently performed for healthcare monitoring. Energy consumption is a critical issue because it directly affects the duration of the equipped sensors. In this article, we propose a low-cost and confidential electrocardiogram (ECG) acquisition approach for WBAN. The compressed sensing (CS) is employed for low-cost signal acquisition, and its cryptographic features are exploited for promoting the framework's confidentiality. In particular, the RIPless measurement matrix is used to give CS the resistance against plaintext attack, while the first-order Σ∆ quantizer is employed to embed the cryptographic diffusion feature into the whole system. Two chaotic systems are employed for generating the required secret elements for the acquisition and encryption. Experiment results well demonstrate the signal reconstruction and security performance of the proposed framework.

IGBT Gate Waveform Acquisition Based on Compressed Sensing Technology

As the core device of power electronic equipment, IGBT (Insulated Gate Bipolar Transistor) is related to the reliability of the system, so its online health monitoring is particularly important. Due to the large amount of data and high sampling rate for IGBT online health monitoring, data transmission has become a major problem, the method of effectively reconstructing the original signal has become one of the current research hotspots. Based on the theory of compressed sensing, the acquisition of IGBT gate waveforms is studied in this paper. First, the theoretical principle of compressed sensing is expounded and analyzed, and the evaluation criteria for signal reconstruction performance are given. Then, from the theory and simulation, the sparseness of IGBT gate drive signals under different sparse bases and different measurement matrices are discussed and analyzed. Next, aiming at the shortcomings of the stagewise weak orthogonal matching pursuit algorithm, a SWOMP algorithm based on the backtracking strategy of different Sigmoid functions is proposed. Simulation and experimental results show that the improved SWOMP4 algorithm has the best reconstruction effect. Finally, the SWOMP4 algorithm is applied to the process of IGBT gate drive signal reconstruction.This paper mainly collects and reconstructs the IGBT gate drive signal through the improved SWOMP4 algorithm. The designed algorithm has high reconstruction accuracy and can be used in the IGBT module online health status system.

Combining Gradient-Based and Thresholding Methods for Improved Signal Reconstruction Performance

Combining Gradient-Based and Thresholding Methods for Improved Signal Reconstruction Performance

Symmetric quadratic tetration interpolation using forward and backward operation combination

The existed interpolation method, based on the second-order tetration polynomial, has the asymmetric property. The interpolation results, for each considering region, give individual characteristics. Although the interpolation performance has been better than the conventional methods, the symmetric property for signal interpolation is also necessary. In this paper, we propose the symmetric interpolation formulas derived from the second-order tetration polynomial. The combination of the forward and backward operations was employed to construct two types of the symmetric interpolation. Several resolutions of the fundamental signals were used to evaluate the signal reconstruction performance. The results show that the proposed interpolations can be used to reconstruct the fundamental signal and its peak signal to noise ratio (PSNR) is superior to the conventional interpolation methods, except the cubic spline interpolation for the sine wave signal. However, the visual results show that it has a small difference. Moreover, our proposed interpolations converge to the steady-state faster than the cubic spline interpolation. In addition, the option number increasing will reinforce their sensitivity.

Compressed Sensing Framework for Heart Sound Acquisition in Internet of Medical Things

For continuous monitoring of cardiovascular diseases, this article presents a novel framework for heart sound acquisition. The proposed approach uses compressed sensing for signal sampling, and a two-stage reconstruction is developed for reconstruction. The first stage aims to give a tentative recovered signal, on which a peak detection technique is developed to identify whether there is a peak in current segment and, if so, its location. With such information, an adaptive dictionary is selected for the second round reconstruction. Because the selected dictionary is adaptive to the morphology of current frame, the signal reconstruction performance is consequently promoted. Experiment results indicate that a satisfactory performance can be obtained when the frame length is 256 and the signal morphology is divided into 16 categories. Furthermore, the proposed algorithm is compared with a series of counterparts, and the results well demonstrate the advantages of our proposal, especially at high compression ratios.

Maintaining the Status Quo: Simultaneous Estimation and Elimination for Multiple Interference in Transform Domain Vehicular Communication

Wireless communication in vehicular networks is highly vulnerable to electromagnetic interference, which seriously undermines the communication effectiveness. Thus, interference estimation and elimination are essential in vehicular communications. To solve the multitasking problem of interference estimation and cancelation within the transformation domain of vehicular communications where they are divided into the consistency and inconsistency based on the different conditions of spectrum sensing, the present study proposes an iterative Bayesian sparse learning approach for simultaneous interference recovery and elimination which applies multiple Bayesian learning for measured signals, and the structured sparse learning method for simultaneous interference mitigation with signal recovery. Furthermore, the updating optimization based on Bayesian estimation framework is performed to facilitate the learning process and improve the performance of interference estimation and signal reconstruction. Simulation results show that the proposed algorithms achieved more rapid convergence and more accurate recovery (31.84% and 27.88% higher in spectrum sensing with consistency and inconsistency, respectively) than conventional algorithms. During all iterations, the proposed algorithms, because of updating optimization, were more robust than conventional ones, but the computational complexity remained largely the same. To explore the correlation between the parameters and the interference recovery performance, we performed experiments, which revealed linear, logarithmic, and exponential regression correlations for particular forms of interference. We also compared the demodulation performance of the proposed algorithm with existing methods, and it turned out the proposed algorithm also obtained higher gains on average in the bit error rate.

Optimization of Nonuniform Sensor Placement for Blade Tip Timing Based on Equiangular Tight Frame Theory

Abstract Blade tip timing (BTT) data are usually an under-sampled signal and are vulnerable to noise and sensor failures. In this paper, based on an arbitrary-angle compressed-sensing method and equiangular tight frame theory, combined with a niching microgenetic algorithm, a method for placing BTT sensors is proposed to ensure higher reconstruction accuracy and reliability. If the dimensions of the sensing matrix are moderate, the index range of arrangements with excellent performance in multifrequency signal reconstruction is determined by enumerating all the uniform-distribution extraction placements. A two-parameter search method is then proposed. Reconstruction of a mixed-signal is carried out to verify the asynchronous signal-reconstruction performance. Thus, to achieve a larger frequency multiplication recognition range and probe-installation flexibility, a method for optimizing the BTT sensor placement is proposed. Finally, a finite-element simulation of the signal from an aero-engine fan blade is used to verify the reconstruction ability of the proposed method. The results show that the placement determined by the optimization algorithm can achieve similar or even better performance than the optimal placement under uniform distribution extraction. The proposed sensor-placement optimization method has a high reconstruction success rate and the BTT system is robust. This approach has significant value for engineering applications.

A two-step channel mismatch estimation and compensation method for distributed MC SAR

ABSTRACT A distributed multichannel synthetic aperture radar system refers to a multichannel synthetic aperture radar system with channels distributed and installed at different locations on the platform. It is a promising system for high-resolution wide-swath imaging. The channels of the distributed multichannel synthetic aperture radar are sparsely distributed and have long cross-track baselines. The range-variant envelope migration and phase mismatch in the echoes between different channels are introduced by the long cross-track baselines and affect the performance of azimuth signal reconstruction. The sparsity of channels results in the poor correlation of data between channels, so the time-domain cross-correlation method typically used in azimuth multichannel synthetic aperture radar cannot be used in distributed multichannel synthetic aperture radar to estimate channel phase error directly. In this paper, the gain-phase error, sampling time delay and slant range difference introduced by cross-track baselines are modelled uniformly as channel mismatches. Firstly, a two-step channel mismatch estimation and compensation method for distributed multichannel synthetic aperture radar is proposed. Secondly, a range-variant envelope migration compensation method based on the chirp scaling principle is given. Thirdly, this paper also presents the processing of data rearrangement based on the distribution of spatial sampling points to make the time-domain cross-correlation method suitable for distributed multichannel synthetic aperture radar. Point target simulation and scene simulation are provided to show the effectiveness of the proposed methods.

Application of compressed sensing for image compression based on optimized Toeplitz sensing matrices

In compressed sensing, the Toeplitz sensing matrices are generated by randomly drawn entries and further optimizes them with suitable optimization methods. However, during an optimization process, state-of-the-art optimization methods tend to lose control over the structure of measurement matrices. In this paper, we proposed the novel approach for optimization of Toeplitz sensing matrices based on evolutionary algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) for compression of an image signal. Furthermore, we investigated the performance of Basis Pursuit (BP) and Orthogonal Matching Pursuit (OMP) algorithms for the reconstruction of the images. The proposed optimized Toeplitz sensing matrices based on evolutionary algorithms such as GA, SA, and PSO exhibit a significant reduction in the mutual coherence (μ) and thus improved the recovery performance of 2D images compared to state-of-the-art non-optimized Toeplitz sensing matrices. The result reveals that the optimized Toeplitz sensing matrices with Basis Pursuit (BP) achieved more accurate results with a robust and uniform reconstruction guarantee compared to the OMP algorithm. However, BP shows the slow reconstruction performance of the image signal. On the other hand, an optimized Toeplitz sensing matrix with OMP shows a fast reconstruction guarantee, but at the cost of a reduction in the PSNR. Furthermore, the proposed approach retains the structure of Toeplitz sensing matrices and improves the image recovery performance of compressed sensing. Finally, the experimental results validate the effectiveness of the proposed method based on evolutionary algorithms for image compression.

Proposal of a Geiger-geometry single-phase liquid xenon Time Projection Chamber as potential detector technique for dark matter direct search

Dual phase time projection chamber using liquid xenon as target material is one of most successful detectors for dark matter direct search, and has improved the sensitivities of searching for weakly interacting massive particles by almost five orders of magnitudes in past several decades. However, it still remains a great challenge for dual phase liquid xenon time projection chamber to be used as the detector in next-generation dark matter search experiments (∼ 50 tonne sensitive mass), in terms of reaching sufficiently high field strength for drifting electrons, and sufficiently low background rate. Here we propose a single phase liquid xenon time projection chamber with detector geometry similar to a Geiger counter, as a potential detector technique for future dark matter search, which trades off field uniformity for less isolated charge signals. Preliminary field simulation and signal reconstruction study have shown that such single phase time projection chamber is technically feasible and can have sufficiently good signal reconstruction performance for dark matter direct search.

Compressive sensing based on optical mixing using a spectral shaper with bipolar coding.

Photonic compressive sensing (CS) has attracted great research interest for its potentials in the acquisition of wideband sparse signals with relatively low sampling rate. The photonic CS scheme based on optical mixing using a spectral shaper can realize the mixing of a sparse signal with a high-speed pseudo-random bit sequence (PRBS), but avoids the use of high-speed electronics. In this approach, by utilizing the frequency-to-time mapping (FTTM) of chirped pulses, the spectral information on the spatial light modulator (SLM) within a spectral shaper can be projected into the time-domain waveform. However, the generated PRBS in the time domain is a unipolar sequence that alternates between 0 and 1, which leads to a nonzero-mean measurement matrix. This would result in a poorer performance of signal reconstruction compared to that with a zero-mean measurement matrix. Moreover, the length of PRBS that can be recorded in the SLM is also limited by the far-field condition. In this paper, we propose an optical mixer for photonic CS, which utilizes an SLM-based spectral shaper with complementary outputs as well as a balanced photodetector in order to generate bipolar PRBS. The performance of signal reconstruction can be significantly improved owing to the zero-mean measurement matrix induced by bipolar PRBS. In addition, the constraint on the length of PRBS can be greatly alleviated, since the obtained PRBS can still be kept zero-mean even if the PRBS is longer than that the far-field condition demands. Experimental and simulation results are presented to demonstrate the feasibility and advantage of the given approach.

Image Encryption Scheme with Compressed Sensing Based on a New Six-Dimensional Non-Degenerate Discrete Hyperchaotic System and Plaintext-Related Scrambling.

Digital images can be large in size and contain sensitive information that needs protection. Compression using compressed sensing performs well, but the measurement matrix directly affects the signal compression and reconstruction performance. The good cryptographic characteristics of chaotic systems mean that using one to construct the measurement matrix has obvious advantages. However, existing low-dimensional chaotic systems have low complexity and generate sequences with poor randomness. Hence, a new six-dimensional non-degenerate discrete hyperchaotic system with six positive Lyapunov exponents is proposed in this paper. Using this chaotic system to design the measurement matrix can improve the performance of image compression and reconstruction. Because image encryption using compressed sensing cannot resist known- and chosen-plaintext attacks, the chaotic system proposed in this paper is introduced into the compressed sensing encryption framework. A scrambling algorithm and two-way diffusion algorithm for the plaintext are used to encrypt the measured value matrix. The security of the encryption system is further improved by generating the SHA-256 value of the original image to calculate the initial conditions of the chaotic map. A simulation and performance analysis shows that the proposed image compression-encryption scheme has high compression and reconstruction performance and the ability to resist known- and chosen-plaintext attacks.

Resonance based separation and energy based classification of lung sounds using tunable wavelet transform

Background and objectiveThe locations and occurrence pattern of adventitious sounds in the respiratory cycle have critical diagnostic information. In a lung sound sample, the crackles and wheezes may exist individually or they may coexist in a successive/overlapping manner superimposed onto the breath noise. The performance of the linear time-frequency representation based signal decomposition methods has been limited in the crackle/wheeze separation problem due to the common signal components that may arise in both time and frequency domain. However, the proposed resonance based decomposition can be used to isolate crackles and wheezes which behave oppositely in time domain even if they share common frequency bands. MethodsIn the proposed study, crackle and/or wheeze containing synthetic and recorded lung-sound signals were decomposed by using the resonance information which is produced by joint application of the Tunable Q-factor Wavelet Transform and Morphological Component Analysis. The crackle localization and signal reconstruction performance of the proposed approach was compared with the previously suggested Independent Component Analysis and Empirical Mode Decomposition methods in a quantitative and qualitative manner. Additionally, the decomposition ability of the proposed approach was also used to discriminate crackle and wheeze waveforms in an unsupervised way by employing signal energy. ResultsResults have shown that the proposed approach has significant superiority over its competitors in terms of the crackle localization and signal reconstruction ability. Moreover, the calculated energy values have revealed that the transient crackles and rhythmic wheezes can be successfully decomposed into low and high resonance channels by preserving the discriminative information. ConclusionsIt is concluded that previous works suffer from deforming the waveform of the crackles whose time domain parameters are vital in computerized diagnostic classification systems. Therefore, a method should provide automatic and simultaneous decomposition ability, with smaller root mean square error and higher accuracy as demonstrated by the proposed approach.

Clipping Noise Mitigation in Optical OFDM Using Decision-Directed Signal Reconstruction

In this paper, the performance of a new decision-directed signal reconstruction (DDSR) algorithm with optimal reconstruction thresholds for use with optical orthogonal frequency division multiplexing (OFDM) modulation is investigated. Clipping noise remains one of the main drawbacks in many practical optical OFDM transmission systems. It has been shown in previous studies that a time-domain based DDSR algorithm can be used to effectively reduce the clipping noise. However, in all existing works studying this DDSR algorithm, the decision threshold used to direct the time-domain signal reconstruction is fixed and equal to the level at which the transmitted signal is clipped. In this paper, it is shown that, due to the influences of the unavoidable noise at the receiver, fixing the DDSR threshold at the clipping level is not an optimal solution and can severely limit the advantages of using this algorithm. A detailed theoretical analysis is performed to investigate the key factors that influence the choices of the optimal DDSR thresholds. The presented simulation results are used to verify the accuracy of the theoretical analysis and also show that the performance of the DDSR algorithm is significantly improved by using the obtained optimal thresholds.