Separation Of Signal Components Research Articles

ECG signal decomposition using Fourier analysis

This paper explores the Fourier decomposition method to approximate the decomposition of electrocardiogram (ECG) signals into their component waveforms, such as the QRS-complex and T-wave. We compute expansion coefficients using the ℓ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _1$$\\end{document} Fourier transform and the traditional ℓ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _2$$\\end{document} Fourier transform. Numerical examples are presented, and the analysis focuses on ECG signals as a real-world application, comparing the performance of the ℓ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _1$$\\end{document} and ℓ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _2$$\\end{document} Fourier transforms. Our results demonstrate that the ℓ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _1$$\\end{document} Fourier transform significantly enhances the separation of ECG signal components, such as the QRS-complex and T-wave. This improvement is attributed to a notable reduction in the Gibbs phenomenon introduced by the Fourier-series expansion when using the ℓ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _1$$\\end{document} Fourier transform, as opposed to the traditional ℓ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _2$$\\end{document} Fourier transform.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs.

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

A dual-ratiometric strategy to design the NIR-IIb reference-based activatable nanoprobe for accurate nitric oxide detection

Fluorescence imaging offers a real-time, convenient method for detection nitric oxide (NO) in vivo, especially using the near-infrared IIb (NIR-IIb, 1500–1700 nm) region. However, traditional single-ratiometric fluorescent probes for NO detection suffer from signal distortion due to inherent limitations such as signal crosstalk and component separation. Herein, we proposed a dual-ratiometric strategy to ensure detection accuracy. The detection result was considered to be reliable upon the auxiliary ratio and practical ratio exhibiting a synchronous change. Based on this strategy, we fabricated a NO-responsive probe, comprising a conjugated polymer P-NH2 and a rare-earth doped nanocrystal NaYF4:Yb,Er,Ce@NaYF4:Yb,Nd@NaYF4. When interaction with NO, the probe generated two ratiometric signals: FNIR-I/FNIR-IIb and FNIR-II’/FNIR-IIb (NIR-I, 700–900 nm; NIR-II’, 1000–1400 nm), where the latter acted as the practical ratio for its better imaging clarity. With an acetaminophen-induced liver injury model, the probe demonstrated the potential for precise ratiometric imaging of NO distribution in vivo. Particularly, ratio imaging revealed the intricate vascular structure of the abdomen, demonstrating the significant advantages of the NIR-IIb region. Our study offers an effective approach to reduce detection errors in the bioimaging applications and provides a powerful tool for basic biomedical research.

An image processing mechanism for aerial inspection robots to detect submillimeter-width concrete cracks in social infrastructures

Inspection robots for early detection of potential risks in severe environments require a high accuracy like human experts. A fine mechanism is crucial for extracting target components from noisy signals. We have proposed a detection system for submillimeter-width cracks in concrete surfaces of social infrastructures, such as bridges, by using morphological component analysis (MCA), for aerial image-inspection robots. Traditional schemes like PCA have relied on linear decomposition for the separation of target signal and noise components. Recent advancement in signal decomposition focuses on the enhancement of linearity in the separation by introducing a set of nonlinear basis functions to represent the raw signal even when multiple factors are mixed in a nonlinear manner. In this sense, MCA is a core technique to be able to isolate target components to represent submillimeter-width cracks from others. We proposed a proper pre- and post-processing operations to attach MCA, which demonstrated a high accuracy yet coarse and fine image components have to be integrated redundantly. In the present study, we successfully found a simpler mechanism to set the single basis function to extract the target by introducing a new thresholding mechanism. It suggests a high potential of MCA for inspection robots for various purposes.

Techniques for Accelerometer Reading Processing on Railway Transport Using Wavelet Transform

Introduction. Safety issues of railway transport are inevitably connected with the condition of railway tracks and railway wheels. Various defects, such as irregularities of the railway track, may lead to emergencies and incidents. Therefore, it is important to measure and calculate short and impulse irregularities. A joint analysis of vibration acceleration signals is needed in order to study the types and size of railway track irregularities.Aim. Development of an algorithm for irregularity search based on accelerometer readings with the vertical measurement axis.Materials and methods. The research encompassed wavelet transformation and wavelet-based signal processing including discrete-time signal processing and continuous wavelet processing. In addition, time-frequency analysis based on Fourier transform and continuous wavelet scalogram was used. These methods provide for time-frequency localization for irregularity detection and measurement.Results. Algorithms for vibrational signal processing using the continuous and discrete-time wavelet transform are proposed. The results show that the discrete wavelet transform is effective for multiresolution and multiband analysis, and continuous wavelet transform and wavelet scalogram allows extraction of irregularities and determination of their parameters. The relative error for irregularity depth was improved by 18 %, and the absolute error for irregularity length determinations was reduced by 7 times.Conclusion. Application of the discrete Fourier transform and Fourier spectrogram provides for fine resolution in the frequency domain. However, separation of signal components in the time-frequency domain is impeded. The continuous-time wavelet transform ensures sufficient resolution in the low-frequency domain for component localization and visualization of irregularities.

Analysis of complex mixtures with benchtop nuclear magnetic resonance: Solvent suppression with T2 and diffusion filters.

Benchtop nuclear magnetic resonance (NMR) spectrometers are being employed in a wide variety of applications from undergraduate teaching and research in academia to quality control and process monitoring in industrial settings. Incorporating benchtop NMR in some of these applications presents opportunities for new practical uses of the technology and challenges that truly test the capabilities of compact NMR spectrometers. For instance, the use of protonated solvents in manufacturing or process monitoring requires separating and quantitating the analyte signals of interest from the strong (overwhelming) response from the solvents. Furthermore, due to the lower field strength available with permanent magnet spectrometers, the NMR spectra of complex mixtures can be more difficult to analyze due to partial or complete signal overlap. To address some of these challenges and to extend the range of applications of benchtop NMR, we investigate NMR techniques that enable quantitative analysis of different components in mixtures. These pulse sequences can be used to suppress one or multiple solvent peaks, to filter out signals by spin-spin relaxation time (T2), or to separate signal components by a molecule's diffusion coefficient (NMR diffusometry). In this paper, we discuss quantitative analysis of excipients in buffers for therapeutic proteins to highlight the usefulness of these NMR pulse sequences in the analysis of complex samples with benchtop NMR spectrometers.

Synchro-Squeezed Adaptive Wavelet Transform-Based Optimized Multiple Analytical Mode Decomposition: Parameter Identification of Cable-Stayed Bridge under Earthquake Input

Deriving critical parametric information from recorded signals for system identification is critical in structural health monitoring and damage detection, while the time-varying nature of most signals often requires significant processing efforts due to structural nonlinearity. In this study, synchro-squeezed adaptive wavelet transform-based optimized multiple analytical mode decomposition (SSAWT-oMAMD) is proposed. The SSAWT algorithm acts as the preprocessing algorithm for clear signal component separation, high temporal and frequency resolution, and accurate time–frequency representation. Optimized MAMD is then utilized for signal denoising, decomposition, and identification, with the help of AWT for bisecting frequency determination. The SSAWT-oMAMD is first verified by the analytical model of two Duffing systems, where clear separation of the two signals is presented and identification of complex time-varying stiffness is achieved with errors less than 2.9%. The algorithm is then applied to system identification of a cable-stayed bridge model subjected to earthquake loading. Based on both numerical and experimental results, the proposed method is effective in identifying the structural state and viscous damping coefficient.

Application of oscillatory time frequency manifold for extraction of rolling element bearing fault signature

Abstract To overcome the problem that traditional feature extraction algorithms are sensitive to noise, a bearing fault signature extraction scheme is proposed in this paper with the help of oscillation-based signal decomposition and time frequency manifold (TFM) learning. Firstly, an oscillation-based signal component separation method based on tunable Q factor wavelet transform (TQWT) is utilized to separate the low oscillatory component from vibration signals. Then, concept of TFM is utilized on the separated low oscillatory component to generate the low oscillatory time frequency manifold signature. The proposed method is termed as oscillatory time frequency manifold (OTFM). Compared to that of traditional short time Fourier transform (STFT) and original TFM algorithm, results of experiment show that the proposed algorithm has better time frequency characterization ability for bearing fault signature.

Data-driven separation of MRI signal components for tissue characterization

PurposeMRI can be utilized for quantitative characterization of tissue. To assess e.g. water fractions or diffusion coefficients for compartments in the brain, a decomposition of the signal is necessary. Imposing standard models carries the risk of estimating biased parameters if model assumptions are violated. This work introduces a data-driven multicomponent analysis, the monotonous slope non-negative matrix factorization (msNMF), tailored to extract data features expected in MR signals. MethodsThe msNMF was implemented by extending the standard NMF with monotonicity constraints on the signal profiles and their first derivatives. The method was validated using simulated data, and subsequently applied to both ex vivo DWI data and in vivo relaxometry data. Reproducibility of the method was tested using the latter. ResultsThe msNMF recovered the multi-exponential signals in the simulated data and showed superiority to standard NMF (based on the explained variance, area under the ROC curve, and coefficient of variation). Diffusion components extracted from the DWI data reflected the cell density of the underlying tissue. The relaxometry analysis resulted in estimates of edema water fractions (EWF) highly correlated with published results, and demonstrated acceptable reproducibility. ConclusionThe msNMF can robustly separate MR signals into components with relation to the underlying tissue composition, and may potentially be useful for e.g. tumor tissue characterization.

Accurate near-field millimeter-wave imaging of concave objects using circular polarizations.

Millimeter-wave (MMW) imaging is becoming an important option in many sensing applications. However, the resulting images are often plagued with artifacts caused by complex target scenarios such as concave structures, hampering applications where precise recognition is emphasized. It has been shown that existing imaging techniques can effectively resolve this issue by considering the multi-reflection propagation process in the forward model of the inverse problem. But the accuracy of such method still depends on the precise separation of reflected signals exhibiting different number of interactions with the target surfaces. In this article, an improved imaging technique based on circular polarizations is proposed for accurate imaging of concave objects. By utilizing circular polarized measurements, the received signal can be divided into odd and even number of reflection times. Then, an iterative reconstruction technique is introduced to automatically separate signal components and reconstruct precise contours of the concave surfaces. Furthermore, a strict observation angle boundary model is derived based on methods of the stationary phase to correct the image deformation of edges existing in previous algorithms. Both numerical and experimental results synthesized from 6∼18 GHz dual-polarized measurements are used to demonstrate the improved accuracy and automation of the proposed method.

Order spectrum analysis enhanced by surrogate test and Vold-Kalman filtering for rotating machinery fault diagnosis under time-varying speed conditions

Rotating machinery signals are usually dominated by rotating frequency harmonics, and the presence of these frequency components or change in their magnitudes indicate health condition. Order spectrum is widely applied in rotating machinery fault feature extraction, because of its capability in intuitive spectral representation of rotating frequency harmonics in order domain. However, some speed non-synchronous components are often present. They show wide-band features in order spectra, and hinder accurate identification of rotating frequency harmonic orders. To address this issue, a scheme is proposed to identify and remove speed non-synchronous components of either constant or time-varying frequency. To this end, surrogate test is generalized through new candidate construction method and nonstationarity based indicator for test criterion, to identify true nonstationary signal components adaptively and eliminate subjective influences. Vold-Kalman filter is used to separate signal components by exploiting its capability in mono-component decomposition of complex nonstationary signals. The proposed method is validated through analyses of both induction motor stator current signals and hydraulic turbine rotor vibration signal. The results demonstrate its advantages over conventional computed order spectrum analysis.

SCBSS Signal De-Noising Method of Integrating EEMD and ESMD for Dynamic Deflection of Bridges Using GBSAR

Ground-based synthetic aperture radar (GBSAR) technology, as a new measurement technology, has the advantages of noncontact measurements, high precision, and all-weather measurement capability, and it has been widely used for bridge dynamic deflection measurements. In order to reduce the influence of noise in dynamic deflection of bridges obtained using GBSAR, this article proposes a single-channel blind source separation signal (SCBSS) de-noising method to obtain the denoised dynamic deflection of bridges. First, the extreme-point symmetric mode decomposition (ESMD) method and the ensemble empirical mode decomposition (EEMD) method are used to decompose the obtained dynamic deflection—as the original observation signal—into a series of intrinsic mode functions (IMFs) and a residual R. Second, the Spearman's Rho of each IMF with the original observation signal is calculated to remove the dominant IMFs of high-frequency noise. Third, the remaining IMFs and R decomposed by ESMD and EEMD are reconstructed into two sets of new signals, which form a new virtual multichannel data with the original observation signal. Finally, blind source separation is performed on the new virtual multichannel signal to obtain separated signal components. The separate signal components are converted in the frequency domain using the fast Fourier transform algorithm, and the noise signal components are identified using a spectrum analysis, to achieve further removal of noise information. The results of both simulated and on-site experiments show that the SCBSS signal de-noising method has a powerful signal de-noising ability.

Screening of cardiac disease based on integrated modeling of heart rate variability

Background:Cardiac diseases due to the disorder of the sympathetic nervous system and the parasympathetic nervous system are the leading causes for an estimated 17.9 million deaths worldwide each year. The existing cardiac disease detection approaches based on heart rate variability of electrocardiograph (ECG) signals limit their application in modern smart disease diagnosis due to complicated feature selection, unreliability and lack of pathological interpretation. Objectives:To better capture the abnormal activities caused by cardiac diseases and improve the performance of cardiac disease detection. Methods:This study proposes an integrated cardiac detection approach through identification of different physiological signal components. Specifically in the first stage, an improved empirical model decomposition and the independent component analysis are applied to separate physiological signal components from heart rate variability signals. Then, a heuristic component matching method is proposed to discriminate the associated physiological components generated from the first stage. Finally, a kernel distance based support vector multivariate control chart is further implemented to detect cardiac events. Results:A real clinical case is given to validate the proposed approach. The results show that the integrated model is effective to detect single cardiac events as well as multiple cardiac events for screening of cardiac diseases. Furthermore, the predicted event lists for various subjects are capable of reflecting the severity of those subjects. Conclusions:This study presents a novel cardiac disease detection scheme based on decomposition of sympathetic and parasympathetic system in light of analysis of heart rate variability of ECG signals. The effectiveness of the proposed approach has been successfully demonstrated through sleep apnea event detection. Furthermore, the signal-based diagnostic approach can be further applied to screen and detect other forms of cardiac diseases by combining relevant knowledge of pathology.

Mixed LFM Signal Estimation Based on Radon-Wigner Transform and Matching Pursuit

Parameter estimation of mixed signals is a key problem in electronic reconnaissance. Based on Radon-Wigner transform (RWT) and Matching Pursuit (MP) algorithm, a parameter estimation method for mixed LFM signals is proposed in this paper. The core of the method is to separate signal components from the mixed signal and estimate their parameters one by one. Firstly, a rough parameter estimation of the strongest signal is obtained by RWT. After that an optimized estimation based on MP algorithm is performed to fine-tune the estimation result. Then, the strongest signal component is reconstructed with the optimized estimation, and it is separated from the mixed signal. Therefore, by iteratively estimating and separating the stronger signal within the residual mixed signal, all of the signal components can be precisely estimated. Experimental results show that the proposed method is able to achieve satisfactory performance on a lower signal-to-noise ratio.

Design and Integration of a Wireless Stretchable Multimodal Sensor Network in a Composite Wing.

This article presents the development of a stretchable sensor network with high signal-to-noise ratio and measurement accuracy for real-time distributed sensing and remote monitoring. The described sensor network was designed as an island-and-serpentine type network comprising a grid of sensor “islands” connected by interconnecting “serpentines.” A novel high-yield manufacturing process was developed to fabricate networks on recyclable 4-inch wafers at a low cost. The resulting stretched sensor network has 17 distributed and functionalized sensing nodes with low tolerance and high resolution. The sensor network includes Piezoelectric (PZT), Strain Gauge (SG), and Resistive Temperature Detector (RTD) sensors. The design and development of a flexible frame with signal conditioning, data acquisition, and wireless data transmission electronics for the stretchable sensor network are also presented. The primary purpose of the frame subsystem is to convert sensor signals into meaningful data, which are displayed in real-time for an end-user to view and analyze. The challenges and demonstrated successes in developing this new system are demonstrated, including (a) developing separate signal conditioning circuitry and components for all three sensor types (b) enabling simultaneous sampling for PZT sensors for impact detection and (c) configuration of firmware/software for correct system operation. The network was expanded with an in-house developed automated stretch machine to expand it to cover the desired area. The released and stretched network was laminated into an aerospace composite wing with edge-mount electronics for signal conditioning, processing, power, and wireless communication.

A novel approach to extracting useful information from noisy TFDs using 2D local entropy measures

The paper proposes a novel approach for extraction of useful information and blind source separation of signal components from noisy data in the time-frequency domain. The method is based on the local Rényi entropy calculated inside adaptive, data-driven 2D regions, the sizes of which are calculated utilizing the improved, relative intersection of confidence intervals (RICI) algorithm. One of the advantages of the proposed technique is that it does not require any prior knowledge on the signal, its components, or noise, but rather the processing is performed on the noisy signal mixtures. Also, it is shown that the method is robust to the selection of time-frequency distributions (TFDs). It has been tested for different signal-to-noise-ratios (SNRs), both for synthetic and real-life data. When compared to fixed TFD thresholding, adaptive TFD thresholding based on RICI rule and the 1D entropy-based approach, the proposed adaptive method significantly increases classification accuracy (by up to 11.53%) and F1 score (by up to 7.91%). Hence, this adaptive, data-driven, entropy-based technique is an efficient tool for extracting useful information from noisy data in the time-frequency domain.

Pulsed Waveforms and Intermittently Nonlinear Filtering in Synthesis of Low-SNR and Covert Communications

In traditional spread-spectrum techniques, a wideband transmit signal is obtained by modulating a wideband carrier by a narrowband signal containing a relatively low-rate message. In the receiver, the respective demodulation/despreading restores the information-carrying narrowband signal. In this paper, we introduce an alternative approach, where the low-rate information is encoded directly into a wideband waveform of a given bandwidth, without physical “spreading” of the carrier’s frequency. The main advantages of this approach lie in extended options for encoding the information, and in retaining a reversible control over the temporal and amplitude structures of the modulating wideband waveforms. Significant “excess bandwidth” (over that needed to carry the information) enables us to use allpass filters to manage statistical properties and time-domain appearances of these waveforms without changing their spectral composition. For example, a mixture of transmitted waveforms can be shaped as a low-crest-factor signal (e.g. to reduce the burden on the power amplifier), and/or made statistically indistinguishable from Gaussian noise (e.g. for covert transmissions and physical layer steganography), while the selected components of the received waveform can be transformed into high-crest-factor pulse trains suitable for multiplexing and/or low-SNR communications. Further, control over the temporal and amplitude structures of wideband waveforms carrying low-rate information enables effective use of nonlinear filtering techniques. Such techniques can be employed for robust real-time asynchronous extraction of the information, as well as for separation of wideband signal components with identical spectral content from each other. This can facilitate development of a large variety of low-SNR and covert communication configurations.

Direct sequence spread spectrum communication narrowband interference cancelation in compressed domain

AbstractDue to the broad bandwidth nature of the direct sequence spread spectrum (DSSS) signal, the application of narrowband interference (NBI) cancelation algorithms based on the Nyquist‐Shannon sampling theory are limited by the high sampling rate. To solve the sampling and processing difficulty in the traditional DSSS communication interference cancelation method, we used the compressive sensing (CS) to reduce the sampling rate of the DSSS communication system, and further proposed a compressed domain NBI elimination method in this study. We proved that NBI with a certain bandwidth and DSSS signal are both sparse in specific dictionaries and proposed the corresponding sparse dictionary construction method. Detailed analysis of the compressed domain features of NBI and the DSSS signal demonstrated that it is possible to extract the strong NBI components from the compressed measurements. Two compressed domain NBI cancelation algorithms were proposed based on this idea. The extracted NBI components were filtered out from the compressed measurements directly in the first algorithm, and then we achieved the DSSS signal demodulation in compressed domain using the separated DSSS signal components with the orthogonal matching pursuit (OMP) algorithm. A proposed extraction filter block sparse Bayesian learning (EFBSBL) framework algorithm was utilized to estimate the NBI from the compressed measurements in the second algorithm. The estimated NBI was eliminated from the received signal, and then we recompressed the DSSS signal after NBI cancelation and realized the DSSS signal demodulation in compressed domain with the OMP algorithm. Simulation results support our theoretical analysis results and reveal that the two proposed algorithms are both effective for NBI cancelation and significant interference cancelation performance are achieved.

Filtering the NPP-VIIRS Nighttime Light Data for Improved Detection of Settlements in Africa

Observing and understanding changes in Africa is a hotspot in global ecological environmental research since the early 1970s. As possible causes of environmental degradation, frequent droughts and human activities attracted wide attention. Remote sensing of nighttime light provides an effective way to map human activities and assess their intensity. To identify settlements more effectively, this study focused on nighttime light in the northern Equatorial Africa and Sahel settlements to propose a new method, namely, the patches filtering method (PFM) to identify nighttime lights related to settlements from the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) monthly nighttime light data by separating signal components induced by biomass burning, thereby generating a new annual image in 2016. The results show that PFM is useful for improving the quality of NPP-VIIRS monthly nighttime light data. Settlement lights were effectively separated from biomass burning lights, in addition to capturing the seasonality of biomass burning. We show that the new 2016 nighttime light image can very effectively identify even small settlements, notwithstanding their fragmentation and unstable power supply. We compared the image with earlier NPP-VIIRS annual nighttime light data from the National Oceanic and Atmospheric Administration (NOAA) National Center for Environmental Information (NCEI) for 2016 and the Sentinel-2 prototype Land Cover 20 m 2016 map of Africa released by the European Space Agency (ESA-S2-AFRICA-LC20). We found that the new annual nighttime light data performed best among the three datasets in capturing settlements, with a high recognition rate of 61.8%, and absolute superiority for settlements of 2.5 square kilometers or less. This shows that the method separates biomass burning signals very effectively, while retaining the relatively stable, although dim, lights of small settlements. The new 2016 annual image demonstrates good performance in identifying human settlements in sparsely populated areas toward a better understanding of human activities.

An Improved Second-Order Blind Identification (SOBI) Signal De-Noising Method for Dynamic Deflection Measurements of Bridges Using Ground-Based Synthetic Aperture Radar (GBSAR)

Ground-based synthetic aperture radar (GBSAR) technology has been widely used for bridge dynamic deflection measurements due to its advantages of non-contact measurements, high frequency, and high accuracy. To reduce the influence of noise in dynamic deflection measurements of bridges using GBSAR—especially for noise of the instantaneous vibrations of the instrument itself caused by passing vehicles—an improved second-order blind identification (SOBI) signal de-noising method is proposed to obtain the de-noised time-series displacement of bridges. First, the obtained time-series displacements of three adjacent monitoring points in the same time domain are selected as observation signals, and the second-order correlations among the three time-series displacements are removed using a whitening process. Second, a mixing matrix is calculated using the joint approximation diagonalization technique for covariance matrices and to further obtain three separate signal components. Finally, the three separate signal components are converted in the frequency domain using the fast Fourier transform (FFT) algorithm, and the noise signal components are identified using a spectrum analysis. A new, independent, separated signal component matrix is generated using a zeroing process for the noise signal components. This process is inversely reconstructed using a mixing matrix to recover the original amplitude of the de-noised time-series displacement of the middle monitoring point among three adjacent monitoring points. The results of both simulated and on-site experiments show that the improved SOBI method has a powerful signal de-noising ability.