Burst Signals Research Articles

Phase-difference carrier frequency estimation of signal bursts with phase shift keying

Due to the rapid development of communication systems with time division multiple access (TDMA) there is a need to estimate the carrier frequency of signal bursts with phase shift keying. This paper presents the carrier frequency estimation of signal bursts with phase shift keying based on previously developed methods of optimal weighting of the difference-phase statistics without significant computational costs. Algorithms of computationally efficient methods for estimating the carrier frequency of packet signals with Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK), as well as with quadrature amplitude digital modulation, using optimal weighting of the difference-phase statistics are presented. A comparative analysis of computationally efficient methods for estimating the carrier frequency of signal bursts with QPSK is performed for various methods using optimal weighting of the difference-phase statistics, based on the results of experimental observations of packet communication signals of an aircraft under various reception conditions. Statistical simulation of estimation algorithms is performed. Estimation results for different signal-to-noise ratios are illustrated. The proposed algorithms for computationally efficient estimation of the carrier frequency of signal bursts with digital modulation using optimal weighting of phase-difference statistics allow to increase the accuracy of measuring the carrier frequency of these signals for different dynamics of the observed aircraft motion, which is of great importance when receiving signals from low earth orbit (LEO) spacecraft. The proposed algorithms for estimating the carrier frequency of signal bursts with phase shift keying are characterized by high computational efficiency and allow to increase the accuracy of estimating the carrier frequency of signal bursts with phase shift keying under different reception conditions at moderate signal-to-noise ratios, which makes it advisable to use the presented algorithms in the equipment of multi-satellite communication systems based on LEO such as Starlink and OneWeb.

Burst-mode digital signal processing for passive optical networks based on discrete multi-tone.

Driven by the ever-increasing capacity demands, digital signal processing (DSP) has been first applied to improve the performance of a 50G passive optical network (PON). The main challenge is implementing the burst-mode DSP to deal with the upstream burst signal. This paper proposes a burst-mode DSP using preambles designed for the 50G PON based on entropy-loading discrete multi-tone (DMT). The burst-mode DSP mainly includes the burst-mode timing recovery with the initial timing offset and the burst-mode frequency-domain equalizer with the initial tap coefficients. The experimental results of the 50G DMT PON demonstrate that the burst-mode DSP can achieve fast convergence based on the ∼27ns preamble with 800 samples. The proposed burst-mode DSP makes the DMT PON highly feasible to support non-residential point-to-multi-point applications such as passive optical local area networks.

Near Real-time Leak Location by Inverse Analysis Integrating Measurement Uncertainty

This paper presents a novel model-based method for near real-time pipe burst location in water distribution networks by integrating measurement uncertainty into inverse analysis. The method accounts for expected errors between measured and computed values, providing a pipe burst location area whose size varies according to the expected error level and the burst size. The proposed method is demonstrated and compared with the traditional inverse approach using a real case study with artificial bursts of different sizes and with different pressure signal noise levels. The performance of both methods is also assessed and discussed considering the effect of seasonal water demands. The traditional inverse analysis fails to accurately locate the pipe burst events, and depending on the expected error level and pipe burst size, the obtained locations may be significantly further away from the real burst location. Conversely, the proposed method does not point to the exact burst location but provides an approximated area in which step-testing can be carried out to pinpoint the exact burst location; the size of this area can be larger or smaller depending on the burst flow rate and signal uncertainty.

Self-Assembled Perylene Diimide (PDI) Nanowire Sensitized In2O3@MgIn2S4 S-Scheme Heterojunction as Photoelectrochemical Biosensing Platform for the Detection of CA15-3.

Inorganic/organic heterojunctions show promising applications as high-performance sensing platforms for photoelectrochemical (PEC) immunosensors. This work reports constructing a PEC biosensor for CA15-3 based on a self-assembled perylene diimide (PDI) nanowire sensitized In2O3@MgIn2S4 S-scheme heterojunction platform. P-type semiconductor Cu2O nanoparticles were designed as a signal burst source and were used as immunoassay labels. The carboxyl group on self-assembled PDI nanowires eliminates the step of additional surface functionalization for covalent immobilization of the capture elements. The π-π stacking of PDI enhances electron generation efficiency, while the carboxylic acid groups on PDI promote electron transfer. The performance of the constructed sensor was validated using CA15-3 as a model. The experimental results showed that the sensor based on In2O3@MgIn2S4/PDI has excellent selectivity, stability, and reproducibility, and can sensitively detect CA15-3 in the range of 0.001-100 U·mL-1 with the detection limit of 0.00056 U·mL-1. The sensor has a broad application prospect. It is hoped that this research work based on the unique advantages of the organic compound PDI will inspire other researchers to design light-responsive materials and promote the development of the field of photoelectrochemical sensing.

Axion string source modeling

In this paper, we investigate the effect of the string radius of curvature RGaussian on the massive and massless scalar radiation emitted from collisions of traveling waves propagating along an axion (global cosmic) string. We construct initial conditions for two colliding Gaussians and perform parameter scans over their amplitude A and standard deviation σd. We show that these collisions emit isotropic bursts of massless radiation and that the energy emitted via this channel obeys a power law ∼∝Aγ, where the coefficient γ depends on the regime of A/δ and δ is the string width. Massive radiation is exponentially suppressed ∼∝e−ζRGaussian in the quasilinear regime σd≫δ and exhibits power-law decay ∼∝RGaussian−γ′ in the nonlinear regime σd≲2δ, with different ζ and γ′ in different regimes of RGaussian. In this nonlinear regime, massive particle radiation can comprise up to 50% of the total energy emitted. Drawing on a known parallel between axion radiation from global strings and gravitational radiation from Abelian-Higgs strings, this suggests that massive particle radiation may become significant with respect to the massless (gravitational) channel for such nonlinear burst signals. For all configurations studied, we obtain a spectral index q≳1 for the axion radiation, where q→1 as A increases; i.e., a higher proportion of radiation is emitted in high-frequency modes. Published by the American Physical Society 2024

Correlation-enhanced short-time analysis of coherent structures for acoustic bursts in subsonic jets

A correlation-enhanced event-driven decomposition (CEED) method is developed to analyze the coherent structure during the acoustic burst in subsonic jets. To sample the acoustic burst signal for the CEED, a conditional filter is proposed. The correlation between the acoustic burst signal and the signal from the flow field is introduced into the decomposition, to deal with the low signal-to-noise ratio issue between these two signals. Suitability for the subsonic jets is demonstrated for the CEED, based on validated large eddy simulation results. Then, the short-time evolution of the coherent structure is analyzed by the CEED. The results show that the sampled acoustic burst signal is intermittent. Its spectrum appears the same shape as that of the original signal. Thus, the acoustic burst is dominant in the sound radiation. The correlation is found to accelerate the convergence of the CEED. The leading mode of the CEED is shown able to represent the short-time evolution of the coherent structure clearly. During the acoustic burst, the leading mode appears as a train of puffs, and it presents three stages: (1) Two in-phase puffs enclose an anti-phase puff. These puffs move downstream with their size growing. (2) The anti-phase puff vanishes, while the two in-phase puffs merge into a large puff. The large puff forms a wavefront. (3) Three puffs form waves in sequence. Together, these waves compose a train of waves. The merging accelerates the puff into a supersonic state intermittently; therefore, the merging is responsible for the acoustic burst.

An Experimental Study of the Acoustic Signal Characteristics of Locked-Segment Damage Evolution in a Landslide Model.

Three-section landslides are renowned for their immense size, concealed development process, and devastating impact. This study conducted physical model tests to simulate one special geological structure called a three-section-within landslide. The failure process and precursory characteristics of the tested samples were meticulously analyzed using video imagery, micro-seismic (MS) signals, and acoustic emission (AE) signals, with a focus on event activity, intensity, and frequency. A novel classification method based on AE waveform characteristics was proposed, categorizing AE signals into burst signals and continuous signals. The findings reveal distinct differences in the evolution of these signals. Burst signals appeared exclusively during the crack propagation and failure stages. During these stages, the cumulative AE hits of burst signals increased gradually, with amplitude rising and then declining. High-amplitude burst signals were predominantly distributed in the middle- and high-frequency bands. In contrast, cumulative AE hits of continuous signals escalated rapidly, with amplitude monotonously increasing, and high-amplitude continuous signals were primarily distributed in the low-frequency band. The emergence of burst signals and high-frequency AE signals indicated the generation of microcracks, serving as early-warning indicators. Notably, the early-warning points of AE signals were detected earlier than those of video imagery and MS signals. Furthermore, the early-warning point of burst signals occurred earlier than those of continuous signals, and the early-warning point of the classification method preceded that of overall AE signals.

Simulation Study on Constraining Gravitational Wave Propagation Speed by Gravitational Wave and Gamma-ray Burst Joint Observation on Binary Neutron Star Mergers

Theories of modified gravity suggest that the propagation speed of gravitational waves (GW) v g may deviate from the speed of light c. A constraint can be placed on the difference between c and v g with a simple method that uses the arrival time delay between GW and electromagnetic wave simultaneously emitted from a burst event. We simulated the joint observation of GW and short gamma-ray burst signals from binary neutron star merger events in different observation campaigns, involving advanced LIGO (aLIGO) in design sensitivity and Einstein Telescope (ET) joint-detected with Fermi/GBM. As a result, the relative precision of constraint on v g can reach ∼10−17 (aLIGO) and ∼10−18 (ET), which are one and two orders of magnitude better than that from GW170817, respectively. We continue to obtain the bound of graviton mass m g ≤ 7.1(3.2) × 10−20 eV with aLIGO (ET). Applying the Standard-Model Extension test framework, the constraint on v g allows us to study the Lorentz violation in the nondispersive, nonbirefringent limit of the gravitational sector. We obtain the constraints of the dimensionless isotropic coefficients s¯00(4) at mass dimension d = 4, which are −1×10−15<s¯00(4)<9×10−17 for aLIGO and −4×10−16<s¯00(4)<8×10−18 for ET.

Identification and prediction method for acoustic emission and electromagnetic radiation signals of rock burst based on deep learning

With the deep development of underground rock engineering, the threat of rock burst disasters is increasing. At present, the identification and prediction of rock burst mostly rely on the experience of field staff to determine the critical value and development trend, and there is a lack of efficient and intelligent methods for the utilization of massive data. Therefore, this paper constructs a rock burst signal recognition and prediction model based on deep learning methods to solve the above problems. In this paper, the acoustic emission (AE) and electromagnetic radiation (EMR) data of the site are first marked and input into the long-short-term memory-fully connected neural network model to realize the identification of rock burst danger signals. Then, the graph data of the AE and EMR sensor monitoring networks are constructed and input into the spatiotemporal graph convolutional network signal prediction model to predict future monitoring data. Finally, this paper uses the same dataset to compare and analyze several other commonly used deep learning models. The results show that the model constructed in this paper has the best performance in the identification and prediction of AE and EMR signals with rockburst risk. This study can provide theoretical reference for intelligent monitoring and early warning of rock burst in underground rock engineering.

Towards an optimization of automatic defect detection by artificial neural network using Lamb waves

This paper presents a damage detection method based on the inverse pattern recognition technique by artificial neural network (ANN) using ultrasonic waves. Lamb waves are guided elastic waves, are widely employed in nondestructive testing thanks to their attractive properties such as their sensitivity to the small defects. In this work, finite element method was conducted by Abaqus to study Lamb modes propagation. A data collection is performed by the signals recorded from the sensor of 300 models: healthy and damaged plates excited by a tone burst signal with the frequencies: 100 kHz, 125 kHz, 150 kHz, 175 kHz, 200 kHz, and 225 kHz. The captured signals in undamaged plat are the baseline, whereas the signals measured in damaged plates are recorded for various positions of external rectangular defects. To reduce the amount of training data, only two peaks of measured signals are required to be the input of the model. Continuous wavelet transform (CWT) was adopted to calculate the key features of the signal in the time domain. The feed forward neural network is implemented using MATLAB program. The data are divided as follows: 70% for training the model, 25% for the validation, and 5% for the test. The proposed model is accurate estimating the position of the defect with an accuracy of 99.98%.

Design of Fast Radio Burst Signal Recognition System based on Deep Learning

In order to identify fast radio burst signals from the original observation data of FAST radio telescopes, this paper designs a fast radio burst signal recognition system based on deep learning object detection algorithm. The system uses the incoherent achromatization algorithm and the YOLO series target recognition algorithm to realize the recognition of fast radio burst signals, and provides users with a friendly graphical system interface. In view of the different performance of users' computers, the system has the function of selecting different algorithm models. Experiments have proved that the system achieves 86% recall and 83% accuracy in the FRB20201124A real-world data test set.

Strengthening effect of cyclic load on siltstone and its macro–micro fracture mechanism

AbstractThe purpose of this research is to study the strengthening effect of cyclic load on the weakly cemented rock and reveal its microscopic mechanism. In this study, a series of cyclic‐to‐monotonic loading tests with different cycle numbers were conducted on siltstone. Results show that the strengthening and weakening effects of siltstone under cyclic loading depend on the upper limit of cyclic load. The microfracture mode that was changed by cyclic load is the essential reason for the strengthening effect, according to the study of acoustic emission b‐value, sources, and microscopic fracture morphology. Under the cyclic load test, the skeleton area of siltstone can bear the axial load increased. However, the burst of acoustic emission signals is delayed, and the crack scale is small due to the strengthening effect. The difficulty of geotechnical engineering disaster prediction and prevention has increased significantly, and attention should be paid to similar geotechnical engineering.

The weepy cry – short neural signal bursts in intraoperative neuromonitoring

PurposeThis study aimed to establish an in-vitro alternative to existing in-vivo systems to analyze nerve dysfunction using continuous neuromonitoring (C-IONM).MethodsThree hundred sixty-three recurrent laryngeal nerves (RLN) (N(pigs) = 304, N(cattle) = 59) from food industry cadavers were exposed by microsurgical dissection following euthanasia. After rinsing with Ringer's lactate, they were tempered at 22 °C. Signal evaluation using C-IONM was performed for 10 min at 2 min intervals, and traction forces of up to 2N were applied for a median time of 60 s. Based on their post-traumatic electrophysiological response, RLNs were classified into four groups: Group A: Amplitude ≥ 100%, Group B: loss of function (LOS) 0–25%, Group C: ≥ 25–50%, and Group D: > 50%.ResultsA viable in-vitro neuromonitoring system was established. The median post-traumatic amplitudes were 112%, 88%, 59%, and 9% in groups A, B, C, and D, respectively. A time-dependent further dynamic LOS was observed during the 10 min after cessation of strain. Surprisingly, following initial post-traumatic hyperconductivity, complete LOS occurred in up to 20% of the nerves in group A. The critical threshold for triggering LOS was 2N in all four groups, resulting in immediate paralysis of up to 51.4% of the nerves studied.ConclusionConsistent with in-vivo studies, RLN exhibit significant intrinsic electrophysiological variability in response to tensile forces. Moreover, nerve damage progresses even after the complete cessation of strain. Up to 20% of nerves with transiently increased post-traumatic amplitudes above 100% developed complete LOS, which we termed the "weepy cry." This time-delayed response must be considered during the interpretation of C-IONM signals.

Magnetic printing characteristics of burst signals by using double magnet master media

In this study, the magnetic printing characteristics of burst signals with double magnet master (DMM) media, which consists of hard and soft magnet patterns corresponding to the burst signals, onto energy-assisted magnetic recording (EAMR) media was investigated by the micromagnetic simulation. For comparison, the magnetic printing characteristics with the conventional master media, which includes only soft magnet patterns, was also calculated. The magnetic film patterns of each master media transfer automatic-gain-control (AGC) and burst parts to the recording medium. The burst signals was amplitude AB-burst patterns, which consists of the repeated pattern region and blank area without burst patterns. Two possible master media structures of DMM media were considered, which are herein called positive- and negative- DMM media. By utilizing the positive-DMM, which spatially divides soft magnets, the AGC and the burst signals can be clearly printed, comparing with the conventional master media. While the negative-DMM is insufficient to print burst signals because the blank area without burst patterns has some non-recorded clusters. Thus, the positive-DMM are feasible for the magnetic printing of burst signals. The clear printing onto EAMR media is expected to be obtained by the optimum non-magnetic separator thickness and the optimum printing field. Therefore, the magnetic printing with positive DMM master is promising onto EAMR media.

Leak detection in an operational underground water distribution network using active acoustics

Leaks present in the water distribution networks (WDNs) lead to an enormous loss of a valuable resource, not only affecting the economic efficiency of water utilities but also posing significant potential safety hazards, and an increased burden on infrastructure maintenance. In the past, several studies have focused on passive acoustic-based leak detection by primarily sensing and analyzing the acoustic signals emitted from the leak source. In this study, we installed a low-frequency transducer in the water column of an operational WDN near Los Angeles, California, and excited the system using steady-state sinusoidal and sine burst signals. The acoustic pressure signals inside the pipe network were measured at multiple locations using state-of-the-art hydrophone-enabled devices retrofitted to fire hydrants. The experiments were conducted in the presence of a simulated leak in the network. The leak acts as an impedance discontinuity for the acoustic wave propagation, and therefore, the excited signals undergo partial reflection at the leak location. Based on signal processing techniques, we attempt to detect and localize the leaks in the WDN. The goal of using the active acoustics is to detect small leaks and increase the range of sensing.

Brain flexibility increases during the peri-ovulatory phase as compared to early follicular phase of the menstrual cycle

The brain operates in a flexible dynamic regime, generating complex patterns of activity (i.e. neuronal avalanches). This study aimed at describing how brain dynamics change according to menstrual cycle (MC) phases. Brain activation patterns were estimated from resting-state magnetoencephalography (MEG) scans, acquired from women at early follicular (T1), peri-ovulatory (T2) and mid-luteal (T3) phases of the MC. We investigated the functional repertoire (number of brain configurations based on fast high-amplitude bursts of the brain signals) and the region-specific influence on large-scale dynamics across the MC. Finally, we assessed the relationship between sex hormones and changes in brain dynamics. A significantly larger number of visited configurations in T2 as compared to T1 was specifically observed in the beta frequency band. No relationship between changes in brain dynamics and sex hormones was evident. Finally, we showed that the left posterior cingulate gyrus and the right insula were recruited more often in the functional repertoire during T2 as compared to T1, while the right pallidum was more often part of the functional repertoires during T1 as compared to T2. In summary, we showed hormone-independent increased flexibility of the brain dynamics during the ovulatory phase. Moreover, we demonstrated that several specific brain regions play a key role in determining this change.

Surface Acoustic Waves (SAW) Sensors: Tone-Burst Sensing for Lab-on-a-Chip Devices.

The article presents the design concept of a surface acoustic wave (SAW)-based lab-on-a-chip sensor with multifrequency and multidirectional sensitivity. The conventional SAW sensors use delay lines that suffer from multiple signal losses such as insertion, reflection, transmission losses, etc. Most delay lines are designed to transmit and receive continuous signal at a fixed frequency. Thus, the delay lines are limited to only a few features, like frequency shift and change in wave velocity, during the signal analysis. These facts lead to limited sensitivity and a lack of opportunity to utilize the multi-directional variability of the sensing platform at different frequencies. Motivated by these facts, a guided wave sensing platform that utilizes simultaneous tone burst-based excitation in multiple directions is proposed in this article. The design incorporates a five-count tone burst signal for the omnidirectional actuation. This helps the acquisition of sensitive long part of the coda wave (CW) signals from multiple directions, which is hypothesized to enhance sensitivity through improved signal analysis. In this article, the design methodology and implementation of unique tone burst interdigitated electrodes (TB-IDT) are presented. Sensing using TB-IDT enables accessing multiple frequencies simultaneously. This results in a wider frequency spectrum and allows better scope for the detection of different target analytes. The novel design process utilized guided wave analysis of the substrate, and selective directional focused interdigitated electrodes (F-IDT) were implemented. The article demonstrates computational simulation along with experimental results with validation of multifrequency and multidirectional sensing capability.

Nucleic acid aptamer based thermally oxidized porous silicon/zinc oxide microarray chip for detection of ochratoxin A in cereals

A nucleic acid aptamer based thermally oxidized porous silicon/zinc oxide microarray chip was constructed for the detection of ochratoxin A. The hybrid chains formed by aptamer and complementary chains labeled with fluorescent groups and fluorescent burst groups were used as recognition molecules, and the detection of toxins was accomplished on the chip by the principle of fluorescence signal burst and recovery. The modified QuEChERS method was used for sample pretreatment and the performance of the method was evaluated. The results showed that the linear range was 0.02 ∼ 200 ng/kg with the detection limit of 0.0196 ng/kg under the optimal detection conditions. The method was applied to different cereals with the recoveries of 90.30 ∼ 111.69 %. The developed microarray chip has the advantages of being cost-effective, easy to prepare, sensitive and specific, and can provide a new method for the detection of other toxins.

Direct spectrum division transmission adapter of burst signal for satellite communications

Direct spectrum division transmission adapter of burst signal for satellite communications

An adaptive cepstrum feature representation method with variable frame length and variable filter banks for acoustic emission signals

Acoustic emission technology is widely employed in defect detection, structural health monitoring, fault diagnosis, and other applications due to its benefits of non-contact, real-time, sensitivity, and flexibility. To implement efficient and timely intelligent monitoring, it is crucial to promptly and precisely gather the time-varying properties in the non-stationary acoustic emission signals. The current work proposes an adaptive cepstrum feature representation method that combines variable frame length and variable filter bank. The variable frame length is implemented based on the spectral distribution correlation, and the optimal frame length and frame rate are selected by iterative operations. The variable filter bank, on the other hand, characterizes the local measure features of variable frame length signals by setting dynamic weight distributions. The proposed adaptive cepstrum algorithm can be used as an auxiliary tool to initially analyze complex non-stationary signals, and thus effectively identify the time-varying and frequency components in acoustic emission signals. The superiority of the proposed method has been confirmed on simulation data. Furthermore, the practical performance of the proposed method is evaluated and illustrated based on the burst signal from laser shock peening processing and the mixed signal from pipeline leakage. The proposed method can be extended to other broadband signals and can be used for feature pre-processing before deep learning network modeling to improve recognition accuracy due to its high time–frequency resolution.