Signal Processing Reconstruction Research Articles

Research on signal enhancement and flow rate calculation methods of multi-channel ultrasonic flowmeter

Abstract In the field of drilling, accurate measurement of mud flow helps detect potential leaks or abnormal situations, thereby improving overall safety and preventing accidents from occurring. Traditional contact flow meters require breaking the pipeline before installation, making them difficult to install in underground environments. In addition, the complex composition of underground fluids and their corrosive nature can also lead to unsatisfactory measurement results. Therefore, as a non-invasive flow measurement tool, ultrasonic flowmeter is a better choice. However, the ultrasonic echo signals of current ultrasonic flow meters are susceptible to external interference in actual testing, resulting in a decrease in signal quality and making it impossible to perform accurate detection. This article focuses on the research of ultrasound echo signal processing model, designs a four channel ultrasound transceiver model, improves the wavelet denoising algorithm, designs signal reconstruction and baseline correction processing methods, and proposes a pipeline flow calculation algorithm based on Gaussian numerical integration. This method can greatly improve the quality of ultrasound echo signals and the average accuracy of flow measurement. Finally, relevant testing experiments were conducted, and the results of the final performance measurement showed that the method proposed in this paper improved the average accuracy of the measurement by 5.07%, and the relative error remained at around 2%, with a significant improvement in error rate. Therefore, the method proposed in this article has extremely high accuracy in mud flow measurement.

Fractional synchrosqueezing transform for enhanced multicomponent signal separation

The precise separation of multicomponent signals encounters numerous challenges due to the complexity of signals and widespread interference. Synchrosqueezing Transform (SST) is one of the important technologies for improving the accurate separation of multicomponent signals, but it faces challenges in terms of the difficulty and effectiveness of squeezing. This paper introduces a multicomponent signal separation method based on innovative Fractional Synchrosqueezing Transform (FrSST). FrSST rearranges along the fractional frequency axis, improving the accuracy of time–frequency ridges and, consequently, enhancing the precision of multicomponent signal separation. In the signal reconstruction process, chirp multiplication and energy rearrangement compensate for chirp bases’ effects, boosting energy concentration and reconstruction potential. Utilizing improved ridges from FrSST ensures effective signal reconstruction. Simulation comparisons demonstrate that, with varying SNRs from − 5 to 15 dB, the reconstructed components based on FrSST exhibit favorable approximation to the original signal components. Furthermore, as the sample size increases, the proposed algorithm shows satisfactory computational efficiency.

Pre-processing the Photoplethysmography Signals for Enhancing the Cardiovascular Diseases Detection for Wrist Pulse Analysis in Nadi Ayurveda

INTRODUCTION: In recent years, Photoplethysmography (PPG) signal has played a vital role in detecting Cardiovascular Diseases (CVDs) in case of wrist pulse analysis emulating the Nadi Ayurveda. The PPG signals acquired from the sensor measurement are severely distorted by various artifacts, which significantly impact the accuracy of disease detection and hamper the disease diagnosis process. OBJECTIVES: Removing the noises is essential before detecting CVDs from the signals and thus, developing a simple and effective noise reduction method for enhancing the PPG signal quality constitutes a challenging research problem, particularly with prominent artifacts. METHODS: This paper designs an effective pre-processing technique that improves denoising methods to enhance the PPG signal quality. The design of pre-processing technique contains two major phases: Primary denoising-based artifact removal and secondary denoising-based Premature Ventricular Contraction (PVC) detection and Power-Line Interference (PLI) noise removal. The primary denoising method involves coarse and fine-grained filtering. The coarse-grained filtering removes the major artifacts, such as Baseline Wander (BLW) and Motion Artifacts (MA), by developing the Two-Stage Adaptive Noise Canceller (TANC) method. The fine-grained filtering process utilizes a detrended filter to filter the refined signal obtained from the TANC method. For the signals filtered from the primary denoising method, the secondary denoising method targets to detect the PVC-induced PPG signals from the decomposed high-frequency signals and removes high-frequency noise, such as PLI from noisy signals, by adopting the Wavelet Transform (WT) method. RESULTS: During the signal reconstruction process in the WT method, the research work reconstructs the denoised PPG signals along with the PVC-induced PPG signals. The experimental results of the noise removal methodology illustrated significant improvements in PPG signal quality. CONCLUSION: The designed pre-processing technique effectively denoises PPG signals, leading to enhanced signal quality which can further aid in accurate disease detection.

A reliability study on automated defect assessment in optical pulsed thermography

Nowadays, the reliability of data analysis and decision-making based on deep learning (DL) remains a primary concern in promoting DL technology for industrial non-destructive testing and evaluation (NDT&E). This study focuses on the quantitative assessment of the reliability of various automated data analysis techniques in NDT&E. To achieve this, optical pulsed thermography was employed to inspect three non-planar carbon-fiber-reinforced polymer (CFRP) samples, each containing embedded Teflon to simulate debonding defects. After applying thermographic signal reconstruction and first-order derivation processing to the raw thermal data, automated analysis was performed using three image-processing-based segmentation methods and three sequential-signal-based DL algorithms. Additionally, an experienced inspector manually analyzed the data for comparison purposes. Subsequently, the probability of detection and false positive analyses were conducted to quantitatively evaluate and compare the reliability of the automated and human-based evaluations. The comparison results demonstrated that optimal DL classification and advanced image-processing-based segmentation techniques could achieve performance levels close to that of human inspectors in defect detection, even for challenging non-planar CFRP samples.

Gas Pipeline Leakage Detection Method Based on IUPLCD and GS-TBSVM

To improve the identification accuracy of gas pipeline leakage and reduce the false alarm rate, a pipeline leakage detection method based on improved uniform-phase local characteristic-scale decomposition (IUPLCD) and grid search algorithm-optimized twin-bounded support vector machine (GS-TBSVM) was proposed. First, the signal was decomposed into several intrinsic scale components (ISC) by the UPLCD algorithm. Then, the signal reconstruction process of UPLCD was optimized and improved according to the energy and standard deviation of the amplitude of each ISC, the ISC components dominated by the signal were selected for signal reconstruction, and the denoised signal was obtained. Finally, the TBSVM was optimized using a grid search algorithm, and a GS-TBSVM model for pipeline leakage identification was constructed. The input of the GS-TBSVM model was the data processed by the IUPLCD algorithm, and the output was the real-time working conditions of the gas pipeline. The experimental results show that IUPLCD can effectively filter the noise in the signal and GS-TBSVM can accurately judge the working conditions of the gas pipeline, with a maximum identification accuracy of 98.4%.

Combining CEEMDAN with PCA for effective cardiac artifact suppression from single-channel EEG

The large signal due to cardiac activity can easily distort the signals originating from the relatively weak electrical activity of the brain, commonly measured as an Electroencephalogram (EEG). The artifact due to cardiac activity in EEG is called cardiac artifact, which contaminates the EEG data and makes interpretation of the EEG difficult for clinicians. Hence it is crucial to remove the cardiac artifact from EEG data. To suppress the cardiac artifact, we propose a novel approach to effectively extract cardiac artifacts from single-channel contaminated EEG data without using reference Electrocardiogram (EKG) data. The proposed methodology uses Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose EEG data contaminated by cardiac activity into the Intrinsic Mode Functions (IMFs). Principal Component Analysis (PCA) is performed on these IMFs to obtain the principal components arranged in the order of decreasing variance. Effective cardiac artifact extraction is achieved by optimizing the signal reconstruction process so that only those principal components that capture the cardiac activity are retained with the constraint that distortion introduced in EEG data should be minimum. The comparison clearly shows that the proposed method outperforms conventionally employed methods like wavelet-based approach.

Experimental Study of Maritime Moving Target Detection Using Hitchhiking Bistatic Radar

Hitchhiking bistatic radar system takes the direct wave signal that is transmitted by the non-cooperative radar emitter as the reference to detect and analyze the target echo signal, so as to realize the positioning and tracking of the target. This radar system has the advantages of low cost and strong survivability. Aiming at the problem of passive radar to covert the detection of maritime targets, this paper develops a hitchhiking bistatic radar system for maritime target detection, which uses the shore-based radar as the non-cooperative radar emitter. By continuously collecting the direct wave and target echo data of the non-cooperative radar, the direct wave reference signal reconstruction, pulse compression, interference suppression and synchronization processing, non-coherent integration, MTI (moving target indication), clutter map processing, and adaptive CFAR (constant false alarm rate) detection are completed to obtain the azimuth, bistatic range, and Doppler frequency of the target, and finally realize the positioning of non-cooperative maritime targets. This paper first introduces and demonstrates the composition principle of the system, introduces the signal processing implementation method of the system in detail, and tests and analyzes the key algorithms. The experimental results show that the system can realize the passive coherent detection of maritime moving targets and locate multiple targets at the same time. The experiment obtains a very clear PPI (plane position indicator) display picture of the hitchhiking bistatic radar system, and the radar detection data of the experimental system is in good agreement with the AIS (automatic identification system) data.

Visible (400- to 700-nm) chirped-grating-coupled waveguide spectrometer.

An integrable on-chip spectrometer, based on a transversely-chirped-grating waveguide-coupler for the 400- to 700-nm visible spectral range is demonstrated. For a fixed angle of incidence, the coupling wavelength is dependent on the local grating period and the waveguide structure. The transversely-chirped-input grating is fabricated on a SiO2-Si3N4-SiO2 waveguide atop a Si substrate by interferometric lithography in two sections on a single silicon substrate. A uniform period grating, separated from the input coupler by a propagation region, is provided for out-coupling to a 2048 element CMOS detector array. The incident light with wavelength spanning 400- to 700-nm is coupled into waveguide at 33.5° through the chirped grating coupler. A resolution of ∼ 1.2 nm is demonstrated without any signal processing reconstruction.

Turbo Detection Aided Autoencoder for Multicarrier Wireless Systems: Integrating Deep Learning Into Channel Coded Systems

A variety of deep learning schemes have endeavoured to integrate deep neural networks (DNNs) into channel coded systems by jointly designing DNN and the channel coding scheme in specific channels. However, this leads to limitations concerning the choice of both the channel coding scheme and the channel paramters. We circumvent these impediments and conceive a turbo-style multi-carrier auto-encoder (MC-AE) for orthogonal frequency-division multiplexing (OFDM) systems, which is the first one that achieves the flexible integration of DNN into any given channel coded systems while achieving an iteration gain. More explicitly, first of all, we design the MC-AE independently of both the channel coding arrangement and of the channel model, where the output layer of the MC-AE decoder is designed for both accepting and producing reliable soft-bit decisions. Owing to the fact that bit-dependency is imposed by the MC-AE mapping, our bespoke MC-AE decoder becomes capable of achieving a beneficial iteration gain, when the extrinsic information is exchanged between the soft-decision MC-AE decoder and the soft-decision channel decoder. Secondly, in order to be able to interpret the performance advantages of our MC-AE over the conventional OFDM, we map the MC-AE’s input-output relationship to an equivalent model-based representation. The associated theoretical analysis verifies the fact that during the process of data-driven signal reconstruction across OFDM’s subcarriers, a beneficial frequency diversity gain is achieved by the proposed MC-AE design. Finally, our simulation results demonstrate that the MC-AE is capable of achieving substantial performance advantages over both conventional OFDM and OFDM based index modulation (OFDM-IM) in channel coded systems.

Diagnostic-Quality Guided Wave Signals Synthesized Using Generative Adversarial Neural Networks

Guided waves are a potent tool in structural health monitoring, with promising machine learning algorithm applications due to the complexity of their signals. However, these algorithms usually require copious amounts of data to be trained. Collecting the correct amount and distribution of data is costly and time-consuming, and sometimes even borderline impossible due to the necessity of introducing damage to vital machinery to collect signals for various damaged scenarios. This data scarcity problem is not unique to guided waves or structural health monitoring, and has been partly addressed in the field of computer vision using generative adversarial neural networks. These networks generate synthetic data samples based on the distribution of the data they were trained on. Though there are multiple researched methods for simulating guided wave signals, the problem is not yet solved. This work presents a generative adversarial network architecture for guided waves generation and showcases its capabilities when working with a series of pitch-catch experiments from the OpenGuidedWaves database. The network correctly generates random signals and can accurately reconstruct signals it has not seen during training. The potential of synthetic data to be used for training other algorithms was confirmed in a simple damage detection scenario, with the classifiers trained exclusively on synthetic data and evaluated on real signals. As a side effect of the signal reconstruction process, the network can also compress the signals by 98.44% while retaining the damage index information they carry.

On description of dual frames

One of key problems in signal reconstruction process with the use of frames is to find a dual frame. Typically, a canonical dual frame is used. However, there are many applications where this choice appears to be unfortunate. Due to that fact, it is necessary to develop a tool, which helps to find a suitable dual frame. In this paper we give a method to find every dual frames. The proposed method is based on Naimark's dilation theorem and the obtained description of dual frames involves parameters that characterize extension of a Parseval frame to an orthonormal basis. These formulas are simplified for frames in finite-dimensional spaces and for near-Riesz bases. In the latter case, the simplification is based on the extended and supplemented version of the Naimark theorem, which is proved in the last part of the paper.

An innovative FPGA-based ADC/DAC design using 1-bit adaptive-delta modulation

The Analog-to-Digital Converter (ADC), with its wide variety of applications in the electronics and communication domains, is the most crucial unit in every digital gadget. This paper discusses a smart way of converting an input analog signal to its digital counterpart using an innovative Adaptive-Delta modulator, followed by a signal reconstruction process to retrieve the original message. It has done the design in Matrix Laboratory (MATLAB) Simulink, and the hardware implementation is tested on the Xilinx Spartan-6 LX45 FPGA core. This design aims for high-accuracy conversion and optimization of hardware resources. The 1-bit adaptive model is superior in comparison to the traditional delta modulation (DM) scheme while tracking stiff analog input signals, producing a much lower mean square error. The implementation is quite simple as it uses the transmission of 1-bit digital data at a time. On the receiver side, digital-toanalog conversion (DAC) makes use of the same adaptive logic in reconstructing the original input signal. The designed prototype demonstrates its resistance to a wide range of input amplitude and frequency variations. The ADC-DAC design method in this paper is accurate, makes the best use of resources, and is easy to use.

Portable Ultrasound Research System for Use in Automated Bladder Monitoring with Machine-Learning-Based Segmentation.

We developed a new mobile ultrasound device for long-term and automated bladder monitoring without user interaction consisting of 32 transmit and receive electronics as well as a 32-element phased array 3 MHz transducer. The device architecture is based on data digitization and rapid transfer to a consumer electronics device (e.g., a tablet) for signal reconstruction (e.g., by means of plane wave compounding algorithms) and further image processing. All reconstruction algorithms are implemented in the GPU, allowing real-time reconstruction and imaging. The system and the beamforming algorithms were evaluated with respect to the imaging performance on standard sonographical phantoms (CIRS multipurpose ultrasound phantom) by analyzing the resolution, the SNR and the CNR. Furthermore, ML-based segmentation algorithms were developed and assessed with respect to their ability to reliably segment human bladders with different filling levels. A corresponding CNN was trained with 253 B-mode data sets and 20 B-mode images were evaluated. The quantitative and qualitative results of the bladder segmentation are presented and compared to the ground truth obtained by manual segmentation.

Tracking of an underwater source using sparse method

The sparse method or better known as compressed sensing (CS), is a method often used for the signal reconstruction process. This method had considered better than conventional methods because it can reconstruct a signal with a smaller amount of data. Many algorithms had used for signal reconstruction using the CS method, including l1-minimization and orthogonal matching pursuit (OMP). In this study, the two algorithms were used for signal reconstruction of underwater objects and then compared to find out which algorithm is better for the signal reconstruction of underwater objects. Comparing the two algorithms had based on parameters in the form of PSNR and RMSE against sparsity. Based on the simulations that had been doing, known that the l1-minimization algorithm can reconstruct signal up to 40% sparsity. Whereas the OMP algorithm can only reconstruct signals up to 30% sparsity. PSNR and RMSE generated from the l1-minimization algorithm show that this algorithm provides better reconstruction results than OMP for underwater object signals. The results obtained show that the best tracking process is at an angle of incidence of 90°.

FRI Sampling of Parametric Signals With Non-Ideal Sinc Kernel

Recent developed finite rate of innovation (FRI) theory provides an efficient way for sub-Nyquist sampling of wideband parametric signals in the fields of radar and communication. However, the existing hardware schemes of FRI sampling systems have not considered the non-ideal effects of physical components, which lead to low accuracy in the signal reconstruction process. In this brief, we propose a FRI sampling system for parametric signals with non-ideal sinc kernel, which significantly improves the reconstruction performance under non-ideal hardware environment. The proposed system consists of two parallel sampling channels. The first channel samples the parametric signal at a low speed after filtered with a LPF, which is used to obtain a part of Fourier coefficients. The other channel obtains a part of Fourier coefficients of its basis signal in the same way. To eliminate the non-ideal effects of physical components, we propose a parameters joint estimation algorithm by using the obtained Fourier coefficients. We also provide a hardware platform design scheme for implementing our system. According to simulation and hardware experiment results, the proposed system can eliminate non-ideal effects in the hardware process, and greatly improve the signal reconstruction accuracy.

Efficient measurement matrix for speech compressive sampling

Signal sampling is an important concern for compressive sensing framework. The use of efficient sampling may enhance the overall performance by collecting informative samples. The work done in this paper is aimed to propose the efficient sampling matrix for speech compressive sensing by reviewing the reconstruction results obtained via conventionally used measurement matrices. Recently used measurement matrices are either randomly structured or deterministic in nature. Therefore, the prime objective of this work is to analyse speed and reconstruction performance of l1-norm minimization algorithm when samples are provided by the concerned sampling matrix. The speed and accuracy analysis is intended to propose efficient sampling matrix which can facilitate faithful signal reconstruction process for speech compressive sensing. The sampling matrices chosen for this work are Bernoulli random matrix, Gaussian random matrix, Hadamard matrix and Toeplitz matrix. The observed matrices are carefully adjusted to provide different range of sampling ratios for signal recovery process. In this work, the number of input samples are changed (from 10% to 40%) to search for the efficient sampling matrix which can survive the least possible number of samples. The performances of the sampling matrices are compared on the basis of Root Mean Squared Error (RMSE) values and reconstruction time (in seconds) is obtained via l1 minimization method.

Modified adaptive filter for digital subdivision of a four-frequency differential laser gyro.

This study represents a modified adaptive filter to suppress the beat noise of four-frequency differential laser gyro (FFDLG), which greatly affects the result of the eightfold digital subdivision. By constructing the demodulated signal model of FFDLG, the influence of beat noise to digital subdivision is analyzed. Based on the least mean square adaptive algorithm, a process of signal reconstruction and dead-zone operator of error are adopted in the modified adaptive algorithm. When implemented on a field-programmable gate array chip, the filter replaces the multiplication with 2:1 multiplexer to reduce the complexity of algorithm and resources in circuit. This circuit effectively suppresses the beat noise of the demodulated signal without changing the optical structure of the FFDLG and increases the signal-to-noise ratio from 20 dB to about 40 dB, which is conducive to improving the performance of the FFDLG.

Mitigation of PA Nonlinearity for IEEE 802.11ah Power-Efficient Uplink via Iterative Subcarrier Regularization

An orthogonal frequency division multiplexing (OFDM) transmitter for 802.11ah uplink may consume unnecessarily high power due to the malicious effect of a large peak-to-average power ratio (PAPR). This is particularly a problem for client devices (CDs) under the low-power wide-area (LPWA) technology in an Internet of thing (IoT) system, where high PAPR signals may drive the power amplifier (PA) to operate with large input back-off (IBO). This article focuses on receiver-side signal compensation (SC) techniques and introduces a novel scheme called iterative subcarrier regularization (ISR), which is based on the generalization of Papoulis-Gerchberg algorithm (GPGA). We claim that the proposed scheme is completely compatible with 802.11ah as it only exploits the prior information available in the standard operations and popular system-build-in functions in the iterative signal reconstruction process. Extensive numerical evaluations demonstrate that the proposed scheme can improve PA efficiency by 4-9 dB for uplink signaling.

Intrinsic photopeak efficiency measurement and simulation for pixelated CdZnTe detector

Intrinsic photopeak efficiency of pixelated CdZnTe detectors is an important property for quantitative measurements and analysis, especially for detection and characterization of special nuclear material (SNM). The intrinsic photopeak efficiency of a single 20 mm × 20 mm × 15 mm CdZnTe detector from 59 keV to 2614 keV are measured in experiment. However, compared to the simulated ideal intrinsic photopeak efficiency, the measured efficiency showed deficit. A detailed simulation model including gamma-ray interaction physics, readout electronic as well as signal reconstruction process is built to explain this observed efficiency deficit. The simulated efficiencies of the new model match with experimental results well after taking these factors into account. Efficiency loss due to the guard ring and anode side inaccurate reconstruction are the most important factors. Other miscellaneous efficiency loss mechanisms are discussed too.

Time-domain signal reconstruction of vehicle interior noise based on deep learning and compressed sensing techniques

During vehicle driving, the noise signal of passenger ear-sides is affected by many sound sources. Meanwhile, the composition and generation mechanism of these sound sources are complicated. The application of traditional data-driven technology in the noise signal reconstruction process of passenger ear-sides often requires complex signal processing and prior knowledge, thereby limiting its application in signal reconstruction. Thus, a multivariable-based vehicle interior noise time-domain signal reconstruction (MTSR) algorithm based on compressed sensing and deep learning is proposed to address such limitation. Raw data are compressed to acquire samples using the proposed algorithm to reduce the amount of data and realize the adaptive extraction of signal features. A deep neural network model for the noise signal reconstruction of passenger ear-sides is established on the basis of multisource signals of the compressed domain, which is pretrained by a restricted Boltzmann machine for improved reconstruction accuracy. The recovery compression signal method realizes the time-domain signal reconstruction of the passenger ear-sides. The effectiveness of the proposed MTSR algorithm is validated using noise signal sources collected from a vehicle. Compared with the different reconstruction models, the proposed algorithm is superior in reconstruction accuracy and time consumption.