Signal Singularity Research Articles

Integrated OPECT and Smartphone Colorimetry Dual‐Mode Detection of Okadaic Acid Based on Ce‐MOF Modified MXene@SnO2 Z‐Scheme Heterostructure

AbstractThe organic photoelectrochemical transistor (OPECT) biosensing relies solely on a singular signal readout inherently, which restrains the precision and dependability nestled within pertinent biological measurements. Herein, a high‐precision magnetic assisted OPECT and smartphone colorimetric (SCL) dual‐mode biosensing platform is first established for detecting harmful algal toxin okadaic acid (OA) by biocatalytic reaction. MXene@SnO2‐Ce‐MOF (MXSnO/Ce‐MOF) Z‐scheme heterojunctions with abundant oxygen vacancies are prepared as photoactive materials. Initially, in the presence of OA, the coupling of trigger DNA (tDNA) to magnetic beads (MBs) via anchor DNA (aDNA) is released through the interaction of the target analyte with the aptamer. Subsequently, the carried tDNA triggers HCR between the two hairpin sequences, producing long double helix chains to capture glucose oxidase (GOx). The obtained GOx supernatant catalyzes glucose to produce H2O2, which can oxidize Ce‐MOF, leading to the alteration of electrode color and a significant decrease in the overall photocurrent of MXSnO/Ce‐MOF. Crucially, the novel OPECT‐SCL biosensor exhibits excellent sensitivity and precision, boasting detection thresholds as low as 42.9 pM and 1.2 nM, respectively, and accomplishes the automated detection of OA within real samples. The proposed OPECT‐SCL dual‐signal measurement model constitutes a sensitive, portable, and precise platform for the quantification of marine toxins.

Optimal signal quality index for remote photoplethysmogram sensing

Remote photoplethysmography (rPPG) enables non-invasive monitoring of circulatory signals using mobile devices, a crucial advancement in biosensing. Despite its potential, ensuring signal quality amidst noise and artifacts remains a significant challenge, particularly in healthcare applications. Addressing this, our study focuses on a singular signal quality index (SQI) for rPPG, aimed at simplifying high-quality video capture for heart rate detection and cardiac assessment. We introduce a practical threshold for this SQI, specifically the signal-to-noise ratio index (NSQI), optimized for straightforward implementation on portable devices for real-time video analysis. Employing (NSQI < 0.293) as our threshold, our methodology successfully identifies high-quality cardiac information in video frames, effectively mitigating the influence of noise and artifacts. Validated on publicly available datasets with advanced machine learning algorithms and leave-one-out cross-validation, our approach significantly reduces computational complexity. This innovation not only enhances efficiency in health monitoring applications but also offers a pragmatic solution for remote biosensing. Our findings constitute a notable advancement in rPPG signal quality assessment, marking a critical step forward in the development of remote cardiac monitoring technologies with extensive healthcare implications.

Tapping process fault identification by LSTM neural network based on torque signal singularity feature

Tapping process fault identification by LSTM neural network based on torque signal singularity feature

Suppression effect of auxiliary laser on stimulated Raman scattering effect of high-power Yb-doped fiber laser amplifier

&lt;sec&gt;A novel technique to suppress the stimulated Raman scattering (SRS) effect in high-power ytterbium-doped fiber amplifier is proposed and theoretically investigated by introducing an auxiliary laser to manipulate the gain distribution in the amplifier.&lt;/sec&gt;&lt;sec&gt;By injecting an auxiliary laser with shorter wavelength than the signal into the amplifier, the auxiliary laser, owing to its larger stimulated emission cross-section, initially extracts a significant portion of the laser gain. At this point, the gain of the longer-wavelength signal laser is suppressed to a certain extent. As the pump power is depleted in the rear segment of the gain fiber, the amplified auxiliary laser, which has larger absorption cross-section than the signal, is gradually absorbed by the active fiber and transfers its power to the signal laser. This process enhances the gain of the long-wavelength signal laser, enabling it to be rapidly amplified at the end of the amplifier. Compared with the amplification of the singular signal laser, the introduction of an extra auxiliary laser shifts the high-gain region of the signal laser to the rear portion of the amplifier, thereby reducing the effective length and alleviating the interaction strength between the signal laser and Stokes wave, in order to obtain a higher SRS threshold.&lt;/sec&gt;&lt;sec&gt;The SRS threshold of a 20 μm/400 μm fiber amplifier is investigated by using numerical simulation under different wavelengths of the auxiliary laser and different power ratios of the signal laser to auxiliary laser. The results indicate that incorporating an auxiliary laser with an appropriate wavelength and power level can significantly reduce the interaction strength between the signal and Stokes wave, thereby enhancing the SRS threshold of the amplifier efficiently. Specifically, in a 1080 nm fiber amplifier utilizing a 20 μm/400 μm ytterbium-doped large mode area fiber, if the total power of the 1080 nm signal and 1040 nm auxiliary laser is set to 200 W, while with a power ratio of 1:25, the SRS threshold increasing from 3.14 kW (singular signal laser) to 8.42 kW can be anticipated. Moreover, based on the auxiliary laser amplification technique that suppresses the SRS effect, the output power enhancement of fiber lasers with the structure of master oscillator power amplifier (MOPA) is also analyzed. This technical solution is relatively straightforward to implement and can be seamlessly integrated with other techniques aimed at reducing the SRS effect, which is promising to promote further power scaling of all-fiber amplifier.&lt;/sec&gt;

A wavelet based data coupling method for spatial damage detection in beam-type structures.

Spatial damage identification is of great significance in mechanical, aerospace, and civil engineering. In this study, a data coupling method based on continuous wavelet transform (CWT) is proposed to identify the spatial damage location of beam-type structures. The singularity of the wavelet coefficient can be used to identify the signal singularity, and data coupling method calculates the spatial location of the damage. Numerical simulations and experimental analyses of different type of beams with transfixion damage are carried out to evaluate the accuracy of the method. The results show that the wavelet based data coupling method (W-DCM) can identify the minimum 4.9% damage severity of fixed beam and continuous beam, and can also identify the damage of non-free end of cantilever beam. However, the 9.7% damage severity of the free end of the cantilever beam cannot be identified. It is also found that the W-DCM can effectively circumvent the problem of wavelet coefficients edge effect. This method and wavelet singularity are used to provide a solution to the problem of structural edge damage identification.

Retracted: Singular Signal Measurement Based on Bidirectional Recursive Complex-Valued Wavelet Algorithm

Retracted: Singular Signal Measurement Based on Bidirectional Recursive Complex-Valued Wavelet Algorithm

Ultrasonic Detection of Aliased Signal Separation Based on Adaptive Feature Dictionary and K–SVD Algorithm for Protective Coatings of Assembled Steel Structure

When using ultrasound to detect the thickness of protective coatings on assembled steel structures, the coatings are extremely thin, which can cause echo signals to overlap and impair the detection accuracy. Therefore, the study of the separation of the superimposed signals is essential for the precise measurement of the thickness of thinner coatings. A method for signal time domain feature extraction based on an adaptive feature dictionary and K–SVD is investigated. First, the wavelet transform, which is sensitive to singular signal values, is used to identify the extreme values of the signal and use them as the new signal to be processed. Then, the feature signal extracted by wavelet transform is transformed into Hankel matrix form, and the initial feature dictionary is constructed by period segmentation and random extraction. The optimized feature dictionary is subsequently obtained by enhancing the K–SVD algorithm. Finally, the time domain signal is reconstructed using the optimized feature dictionary. Simulations and experiments demonstrate that the method is more accurate in separating mixed signals and extracting signal time domain feature information than the conventional wavelet transform and Gabor dictionary-based MP algorithm, and that it is more advantageous in detecting the thickness of protective coatings.

Accurate Detection of Concrete Pavement Thickness Based on Ultrasonic Array

Thickness detection of concrete pavement is a critical step in construction completion acceptance and serves as an important metric for subsequent pavement performance evaluations. The crux of thickness evaluation lies in determining the interface reflection echo propagation sound time. Based on the acoustic impedance difference between the surface layer and contact layer, pavement can be classified into two types: large-difference and small-difference. By examining the singularity of the detection signal, we employed the improved correlation coefficient method and the wavelet transform maximum value method to identify the interface reflection echo sound time. This study refines the traditional correlation coefficient method by using the surface direct wave as the reference signal, simplifying the signal analysis process. In actual detection, for the type of concrete pavement with large differences in acoustic impedance, the average relative error between the improved correlation coefficient method and the core drilling method can be reduced to 1.7%; for the type with small differences, the average relative error of the detection results obtained using the wavelet transform modulus maximum method can be reduced to 1.9%, showcasing high accuracy.

Design and Applications of Genetically-Encoded Voltage-Dependent Calcium Channel Inhibitors.

Ca2+ influx through high-voltage-gated Ca2+ channels (HVGCCs; CaV1/CaV2) is an exceptionally powerful and versatile signal that controls numerous cell and physiological functions including neurotransmission, muscle contraction, and regulation of gene expression. The impressive ability of a singular signal, Ca2+ influx, to have such a plethora of functional outcomes is enabled by: molecular diversity of HVGCC pore-forming α1 and auxiliary subunits; organization of HVGCCs with extrinsic modulatory and effector protein to form discrete macromolecular complexes with unique properties; distinctive distribution of HVGCCs into separate subcellular compartments; and varying expression profiles of HVGCC isoforms among different tissues and organs. The capacity to block HVGCCs with selectivity and specificity with respect to the different levels oftheir organization is critical for fully understanding the scope of functional consequences of Ca2+ influx through them, and is also important for realizing their full potential as therapeutic targets. In this review, we discuss the gaps in the current landscape of small-molecule HVGCC blockers and how these may be addressed with designer genetically-encoded Ca2+ channel inhibitors (GECCIs) that draw inspiration from physiological protein inhibitors of HVGCCs.

Selective posttranslational inhibition of CaVβ1-associated voltage-dependent calcium channels with a functionalized nanobody.

Ca2+ influx through high-voltage-activated calcium channels (HVACCs) controls diverse cellular functions. A critical feature enabling a singular signal, Ca2+ influx, to mediate disparate functions is diversity of HVACC pore-forming α1 and auxiliary CaVβ1-CaVβ4 subunits. Selective CaVα1 blockers have enabled deciphering their unique physiological roles. By contrast, the capacity to post-translationally inhibit HVACCs based on CaVβ isoform is non-existent. Conventional gene knockout/shRNA approaches do not adequately address this deficit owing to subunit reshuffling and partially overlapping functions of CaVβ isoforms. Here, we identify a nanobody (nb.E8) that selectively binds CaVβ1 SH3 domain and inhibits CaVβ1-associated HVACCs by reducing channel surface density, decreasing open probability, and speeding inactivation. Functionalizing nb.E8 with Nedd4L HECT domain yielded Chisel-1 which eliminated current through CaVβ1-reconstituted CaV1/CaV2 and native CaV1.1 channels in skeletal muscle, strongly suppressed depolarization-evoked Ca2+ influx and excitation-transcription coupling in hippocampal neurons, but was inert against CaVβ2-associated CaV1.2 in cardiomyocytes. The results introduce an original method for probing distinctive functions of ion channel auxiliary subunit isoforms, reveal additional dimensions of CaVβ1 signaling in neurons, and describe a genetically-encoded HVACC inhibitor with unique properties.

Adaptive sliding mode fault-tolerant control for satellite attitude tracking system

In this paper, an adaptive modified sliding mode control approach is developed for attitude tracking of a nano-satellite with three magnetorquers and one reaction wheel. A sliding variable is chosen based on finite-time convergence of the nano-satellite attitude tracking error and avoiding the singularity of the control signal. The control gain of the proposed method is developed adaptively to reduce the tracking error and improve the closed-loop control performance. The sliding variable and adaptive parameter are also employed in the reaching phase of the control law to decrease the chattering phenomenon. In addition, the finite-time convergence of attitude variables in the presence of actuator faults, inertia uncertainty, and external disturbances is proved using the extended Lyapunov theorem. The simulations are conducted to evaluate the performance of the proposed method according to different evaluation criteria. Monte Carlo simulations are also used to survey the reliability of the system in the presence of the mentioned condition.

Railway bridge damage detection based on extraction of instantaneous frequency by Wavelet Synchrosqueezed Transform

In bridge structural health monitoring, typically the dynamic response of the system is used to assess the health condition of the bridge. However, the dynamic interaction between a bridge and a passing vehicle imposes non-stationarity on the system response, whereby the bridge modal parameters become time-dependent and detecting damage, for example, based on the bridge modal parameters, becomes challenging. Dynamic vehicle-bridge interaction (VBI) responses have mainly been investigated for damage detection through identifying signal singularities and abrupt changes. The singularities are usually associated with high-frequency components (relative to the bridge natural frequencies), and it is demanding to isolate the damage-induced singularities from those caused by either an operational condition, i.e., track irregularities, or noise. Unlike the high-frequency range, the influence of damage on the resonance frequency of the coupled system has not been fully explored. The present study proposes the shape of the bridge instantaneous frequency as a damage sensitive feature in which the influence of the vehicle dynamics can be excluded. This study demonstrates the feasibility of a damage detection approach based on the bridge instantaneous frequency by applying Wavelet Synchrosqueezed Transform (WSST). In this approach the bridge instantaneous frequency variation induced by damage is distinguished from the bridge instantaneous frequency variation induced by the vehicle. Several damage scenarios that are implemented numerically are analyzed to verify the method’s performance. The results demonstrate that a high resolution instantaneous frequency extracted from the VBI dynamic response outperforms the resonance frequency in determining the local disruption, leading to detecting the damage. A Damage Index (DI) is also proposed as an attempt to quantify the damage severity.

Neuro-adaptive fixed-time control with novel command filter design for nonlinear systems with input dead-zone

This paper designs novel command filters (CFs) and a singularity-free controller with fixed-time performance for strict feedback nonlinear systems with input dead-zone. Compared with traditional filter designs, such as dynamic surface control and CFs, the novel CFs designed in this paper can guarantee fixed-time convergence of filter errors and compensating signals. Moreover, the controller design excludes the singularities of control signals, which might result in severe oscillation and usually appear in the conventional fixed-time controller design. To compensate the input dead-zone, a compensation filter is constructed, where controller design and stability analysis can be simplified. In the end, a simulation example is given to verify the effectiveness of the theoretical results.

Multimedia Image Data Compression Based on Wavelet Analysis

In order to improve the compression technology of multimedia image data, a compression method of multimedia image data based on wavelet analysis was proposed. By combining Fourier transform and wavelet packet multithreshold denoising method, the image data was denoised and compressed through the transform of wavelet analysis and multithreshold processing, so as to realize the compression of image data with high quality. The experimental results showed that it was effective to analyze signal singularity, singularity position, and singularity degree by using wavelet analysis. And the signal mutation position near t = 0 could be seen clearly from the wavelet coefficient diagram, while the Fourier transform diagram did not provide any information about when the frequency mutation occurred. It was concluded that the method of multimedia image data compression based on wavelet analysis could promote the development of multimedia image data compression technology effectively.

Singular Signal Measurement Based on Bidirectional Recursive Complex-Valued Wavelet Algorithm

The normal operation fault of the power system is usually caused by a short-circuit fault. At this time, the system changes drastically from one state to another, accompanied by complex transient phenomena. Therefore, the measured signal contains a large number of transient components. How to effectively analyze such signals, extract their characteristics, and develop new protection devices has always been an important research field in power system protection technology. The protection of the power system is to achieve the purpose of correct action and elimination of faults by quickly detecting and locating faults. At present, the power signal analysis tools used in microcomputer protection include FFT, Kalman filter, and finite impulse response filter. They are efficient for the analysis of stationary signals, but have their limitations in analyzing nonstationary signals; especially it is difficult to identify nonlinear faults, such as the detection of high-impedance nonlinear short-circuit faults, which is a long-term unsolved problem in power systems. Based on wavelet transform, this paper selects complex-valued wavelet algorithm, analyzes a real-time recursive wavelet algorithm, and deduces the realization process of the algorithm in detail. The algorithm greatly reduces the computational complexity of the existing two-way recursive algorithm, can be used for real-time detection of fault signals in various fields of power system, and can be extended to realize other fast recursive algorithms of wavelet functions. Based on the sensitivity of complex-valued wavelet transform phase information to singularity, a method for real-time monitoring of power system fault mutation signals using the phase information of complex-valued wavelet fast recursion algorithm to assist amplitude information is proposed. The validity and practicability of this complex-valued wavelet and its real-time recursive algorithm for fault detection are demonstrated by an example.

Influence of wavelet characteristics on single-phase grounding fault line selection

It is difficult to select the fault line in distribution network because of the small single-phase grounding fault current. Wavelet transform is especially suitable for fault transient analysis because of its unique time-frequency localization performance. However, the characteristics of different wavelets have a direct impact on the line selection results. Improper selection of wavelets will directly lead to misjudgment. Firstly, the transient characteristics of single-phase grounding fault and the relationship between wavelet transform modulus maxima and singularity are analysed, and the line selection criterion of wavelet modulus maxima is given. Then, the influence of wavelet vanishing moment order, support length, symmetry, and orthogonality on the detection effect of signal singularity is analysed, and four principles for selecting wavelet in fault line selection are proposed. The larger the order of vanishing moment and support length, the more conducive to detection. The influence of vanishing moment order is much greater than that of support length. The 10kV system simulation model is built, five representative wavelets are selected to decompose the zero- sequence current of the fault line in four scales respectively, and the different modulus maxima corresponding to the sudden change point are compared. The experimental results prove the reliability of the wavelet selection principles proposed in this paper.

Calculation of the permittivity of inhomogeneous media based on the TEDAWT method

In the field of nondestructive inspection, ground-penetrating radar (GPR) is a widely used investigation tool, usually to evaluate quality and safety in structural engineering. However, its development has been limited due to differences in the dielectric constant in inhomogeneous media. In this study, a time-energy density analysis of wavelet transform (TEDAWT) method for GPR signal singularity recognition was proposed. In addition, a biorthogonal wavelet basis with high similarity to GPR signal waveform was innovatively constructed and added into the wavelet toolbox. A model experiment was conducted to validate the feasibility of inverse calculation of the permittivity of wet clay by the TEDAWT method. Results showed that this method offered the advantages of signal denoising, resolution improvement, singularity recognition in GPR signal processing. Subsequently, a basic flow chart was established, which could be used to inversely calculate the permittivity of inhomogeneous media, and be applied to the quantitative identification of the thickness of the reinforcement protective layer in an expressway tunnel. The calculation results showed that the inverse calculation value of reinforcement protection layer thickness was 3.74% lower than the average relative theoretical error value. That improved detection accuracy provided a new method to quantitatively identify inhomogeneous media by GPR.

Design of optical system of photoelectric detection sensor and flare signal processing with array lens

AbstractBecause the flare signal formed by projectile explosion is affected by the external environment, it is difficult for multi‐acoustic sensor array to obtain accurate synchronization signal. In order to solve this problem, this paper proposes a design method of ultra‐wide‐angle photoelectric detection sensor by using circular curved surface array lens, analyze the detection ability of photoelectric detection sensor with array lens, and establish the output signal function calculation model. According to the output signal of flare detection circuit, the feature points detection algorithm of modulus maxima based on wavelet transform is studied. Based on this algorithm, the signal is processed on the hardware processor to output the actual flare trigger control signal. The processing and recognition algorithm of signal mainly uses the relationship among wavelet modulus maxima, scale, Lipschitz index, and signal singularity to select the appropriate wavelet basis to process the flare signal formed by projectile explosion. Thus, the feature points of the flare signal singularity can be obtained. When the detection conditions of the feature points of the signal singularity are satisfied, the hardware outputs pulse signals to control the acoustic sensor array. The experimental results show that the designed detection algorithm of wavelet filtering and modulus maxima feature points can effectively detect the flare signal.

On factorizable S-matrices, generalized TTbar, and the Hagedorn transition

We study solutions of the Thermodynamic Bethe Ansatz equations for relativistic theories defined by the factorizable S-matrix of an integrable QFT deformed by CDD factors. Such S-matrices appear under generalized TTbar deformations of integrable QFT by special irrelevant operators. The TBA equations, of course, determine the ground state energy E(R) of the finite-size system, with the spatial coordinate compactified on a circle of circumference R. We limit attention to theories involving just one kind of stable particles, and consider deformations of the trivial (free fermion or boson) S-matrix by CDD factors with two elementary poles and regular high energy asymptotics — the “2CDD model”. We find that for all values of the parameters (positions of the CDD poles) the TBA equations exhibit two real solutions at R greater than a certain parameter-dependent value R*, which we refer to as the primary and secondary branches. The primary branch is identified with the standard iterative solution, while the secondary one is unstable against iterations and needs to be accessed through an alternative numerical method known as pseudo-arc-length continuation. The two branches merge at the “turning point” R* (a square-root branching point). The singularity signals a Hagedorn behavior of the density of high energy states of the deformed theories, a feature incompatible with the Wilsonian notion of a local QFT originating from a UV fixed point, but typical for string theories. This behavior of E(R) is qualitatively the same as the one for standard TTbar deformations of local QFT.

Dynamic feature evaluation on streaming acoustic emission data for adhesively bonded joints for composite wind turbine blade

Damage mode identification and premature failure prevention for composite structures by acoustic emission have drawn a great deal of attention. Feature evaluation on streaming acoustic emission data is one of the significant issues in research of acoustic emission signal processing. This work conducts dynamic feature evaluation on 15 conventional acoustic emission features so as to seek a deeper insight into different features with damage accumulation. First, the procedure of dynamic feature evaluation is presented based on three basic algorithms. Second, the streaming acoustic emission data are collected from the adhesively bonded composite single-lap joint subjected to quasi-static tensile loads. Third, further efforts are made so as to explore the information contained as well as to interpret the effect of damage accumulation. It is found that different conventional acoustic emission features show distinctive functions, including damage mode identification, damage process indication, and both of them. Informative features for damage pattern recognition are independent on damage accumulation. Useful features for damage process description show sensitive dynamic characteristics with damage accumulation, especially before the complete failure of the specimen. Furthermore, dynamic feature evaluation can be used to detect singular signals.