Coherent Signals Research Articles

Diffraction Neural Network for Multi‐Source Information of Arrival Sensing

AbstractAll‐optical diffractive neural networks (DNNs) based on passive structures have been shown to perform complicated functions by optical acceleration, thus reforming in situ applications requiring digital computing processing. Its stable, compact, and passive features make it suitable for many conventional applications in complex electromagnetic environments. Here, a DNN based on a multilayer passive metasurface array is presented to estimate the information of arrival (IOA) over a wide range of frequency bands and incident angles. Unlike traditional methods, multiple correlated or coherent electromagnetic signals interfere with each other, resulting in difficult separation which is a fundamental obstacle in multi‐source IOA detection. Here, the proposed diffraction system creates separate virtual channels to isolate and process different incoming waves. It recognizes and classifies the beams, focuses them on the output plane divided by the arrival angle and working frequency, and displays the IOA and number of sources in real time at the speed of light. Furthermore, the massive‐parallelism and data‐post‐processing‐free strategy allows for broader applications in harsh environments, such as seismic detection and underwater circumstances.

Can the Clocks Tick Together Despite the Noise? Stochastic Simulations and Analysis

The suprachiasmatic nucleus (SCN), also known as the circadian master clock, consists of a large population of oscillator neurons. Together, these neurons produce a coherent signal that drives the body’s circadian rhythms. What properties of the cell-to-cell communication allow the synchronization of these neurons, despite a wide range of environmental challenges such as fluctuations in photoperiods? To answer that question, we present a mean-field description of globally coupled neurons modeled as Goodwin oscillators with standard Gaussian noise. Provided that the initial conditions of all neurons are independent and identically distributed, any finite number of neurons becomes independent and has the same probability distribution in the mean-field limit, a phenomenon called propagation of chaos. This probability distribution is a solution to a Vlasov–Fokker–Planck type equation, which can be obtained from the stochastic particle model. We study, using the macroscopic description, how the interaction between external noise and intercellular coupling affects the dynamics of the collective rhythm, and we provide a numerical description of the bifurcations resulting from the noise-induced transitions. Our numerical simulations show a noise-induced rhythm generation at low noise intensities, while the SCN clock is arrhythmic in the high noise setting. Notably, coupling induces resonance-like behavior at low noise intensities, and varying coupling strength can cause period locking and variance dissipation even in the presence of noise.

Ultra-long-distance distribution of low-phase-noise reference lightwave for optical communications.

The remote delivery of optical reference with highly stable oscillation frequency and carrier phase can eliminate the need of digital signal processing for the estimation of these parameters in optical communication. The distribution distance of the optical reference has been limited, however. In this paper, an optical reference distribution over 12,600 km is achieved while maintaining low-noise characteristics, using an ultra-narrow-linewidth laser as a reference source and a fiber Bragg grating filter for noise removal. The distributed optical reference enables 10 GBaud, 5 wavelength-division-multiplexed dual-polarization 64QAM data transmission without using carrier phase estimation, which significantly reduces off-line signal processing time. In the future, this method can enable all coherent optical signals in the network to be synchronized to a common reference ideally, thereby improving overall energy efficiency and cost.

Effect of synchronization system errors on the reception noise immunity of amplitude-phase shift keyed signals

Objectives. An urgent task in the context of modern radio and television systems is to improve the quality and quantity of transmitted information. For example, the use of multiple amplitude-phase shift keyed (APSK) signals—16-APSK and 32-APSK—in digital satellite television systems of the Digital Video Broadcasting-Satellite2 (DVB-S2) standard made it possible to transmit 30% more data in the same frequency bands in comparison with the previous DVB-S standard. Such increases in information transmission rates impose more stringent requirements on hardware. An important role in the reception of APSK signals, as well as the signals of other coherent signal processing systems, is played by the stability of synchronization systems. The presence of operational errors can significantly reduce the quality of information reception. The aim of the present work was to analyze the effect of phase and clock synchronization errors on the reception noise immunity of APSK signals with a ring signal constellation structure.Methods. The study used statistical radio engineering methods informed by optimal signal reception theory.Results. The effect of phase and clock synchronization errors on the reception noise immunity of APSK signals having a signal constellation ring structure is analyzed. The dependencies of the bit error probability on the magnitude of the phase shift and the clock offset were characterized. The effect of synchronization errors on reception quality were compared with the known results for quadrature amplitude modulation (QAM) signals.Conclusions. At an acceptable energy loss of no more than 1 dB, the critical phase error can be considered as 2°-3°, while the critical clock error is 3-4%. A coherent receiver of APSK signals is more sensitive to the phase error of reference oscillations than a similar receiver of QAM signals, whereas clock errors have the same effect on the reception quality of these signals.

A distributed beamforming interference minimization algorithm based on node selection

Distributed beamforming uses nodes in a wireless sensor network to transmit signals in different phases with controllable delay, to obtain coherent output signals with a gain after superposition. However, a wireless sensor network has a large topological area and wide distribution range, and it is difficult for distributed beamforming to obtain a highly directional beam as centralized beamforming does, which will cause interference to the non-target base stations. To solve this problem, a discrete adaptive dual-population cooperative differential evolution (DPCDE) algorithm is proposed, which can effectively reduce the interference by selecting nodes suitable for participating in distributed beamforming in a wireless sensor network. Simulation results show that the proposed algorithm can optimize the node set participating in distributed beamforming to minimize the interference of the wireless sensor network to the non-target base stations, and the effect is better than other classic intelligent optimization algorithms.

Rapid monitoring of cleaning efficiency of fouled hollow fiber membrane module via non-invasive NMR diffraction technique

Early fouling warning is important for the economical operation of membrane separation systems. In parallel multi-channel flow systems, flow re-distribution between channels due to fouling is often associated with maloperation. In the current research we use low magnetic field NMR to monitor multi-fiber hollow fiber membrane modules undergoing a fouling-cleaning cycle and show that rapid detection of fouling is possible by detecting the loss of signal coherence associated with flow re-distribution within the 401 hollow fiber membrane module. This effect is demonstrated to be both reproducible, and reversible via membrane cleaning. The results demonstrate a strong correlation between the coherence signal magnitude and the number of fibers fouled. This may be used in practice for high sensitivity early warning, and to monitor the efficiency of cleaning. This approach may also be particularly useful in the case of detecting residual fouling after cleaning, evidenced in this research by significant flow re-distribution between the before fouling and after cleaning signal coherence.

Coherent dual-frequency signal generation in an optoelectronic oscillator.

Coherent dual-frequency microwave signal generation using an optoelectronic oscillator (OEO) is presented and demonstrated. In the proposed OEO, a dual-band bandpass filter (DB-BPF) is utilized to select two oscillation modes. An external signal is injected into the OEO loop with its frequency equaling the frequency interval of the two oscillation modes. Owing to the modulation nonlinearity of the Mach-Zehnder modulator, the two oscillation frequencies interact with the injection frequency. When the phase and gain conditions are satisfied within the loop, injection locking between the two oscillation signals will be established, and their phases will be synchronized. The effect of gain competition in the OEO loop, which leads to single-frequency oscillation, is suppressed. An experiment is carried out, and two frequencies, of 16.083 GHz and 9.998 GHz, are generated at the same time. The phase noise values are -140.1 and -141.0 dBc/Hz @ 10 kHz, respectively. The coherence between the generated signals and sidemode suppression performance are evaluated.

Noise analysis in a seeded four-wave mixing process generated in a photonic crystal fiber pumped by a chirped pulse.

Four-wave mixing is investigated when chirped pump and signal pulses are injected in a photonic crystal fiber. The shot-to-shot stability of the amplified coherent signal was measured by using the dispersive Fourier transform method and compared with numerical simulations. We highlight that the signal-to-noise ratio (SNR) of the pulsed signal increases with the injected power and show that it is not deteriorated through the amplification when the fiber optical parametric amplifier is strongly saturated. The SNR of the signal remains nearly constant after the amplifier.

Wireless CardioS framework for continuous ECG acquisition

A first-level textile-based electrocardiogram (ECG) monitoring system referred to as “CardioS” (cardiac sensor) for continuous health monitoring applications is proposed in this study to address the demand for resource-constrained environments. and the signal quality assessment of a wireless CardioS was studied. The CardioS consists of a Lead-I ECG signal recorded wirelessly using silver-plated nylon woven (Ag-NyW) dry textile electrodes to compare the results of wired wearable Ag-NyW textile electrode-based ECG acquisition system and CardioS. The effect of prolonged usage of Ag-NyW dry electrodes on electrode impedance was tested in the current work. In addition, electrode half-cell potential was measured to validate the range of Ag-NyW dry electrodes for ECG signal acquisition. Further, the quality of signals recorded by the proposed wireless CardioS framework was evaluated and compared with clinical disposable (Ag–AgCl Gel) electrodes. The signal quality was assessed in terms of mean magnitude coherence spectra, signal cross-correlation, signal-to-noise-band ratio (S band/N band), crest factor, low and high band powers and power spectral density. The experimental results showed that the impedance was increased by 2.5–54.6% after six weeks of continuous usage. This increased impedance was less than 1 MΩ/cm2, as reported in the literature. The half-cell potential of the Ag-NyW textile electrode obtained was 80 mV, sufficient to acquire the ECG signal from the human body. All the fidelity parameters measured by Ag-NyW textile electrodes were correlated with standard disposable electrodes. The cardiologists validated all the measurements and confirmed that the proposed framework exhibited good performance for ECG signal acquisition from the five healthy subjects. As a result of its low-cost architecture, the proposed CardioS framework can be used in resource-constrained environments for ECG monitoring.

Improved imaging technique for nondestructive evaluation using arbitrary virtual array source aperture (AVASA)

In this paper, we propose a new phased array imaging technique called Arbitrary Virtual Array Source Aperture (AVASA) to image deeper defects with an improved SNR with fewer transmissions. The approach is to transmit the ultrasound waves by electronic beamforming at several arbitrary virtual source positions to achieve higher focal depth to increase the SNR of the received A-scans. Backscattered signals are recorded with all the array elements. A high-resolution image is obtained on reception by virtually focusing on every point in the region of interest by signal coherence summation. In this paper, the proposed AVASA and TFM methods are employed for scanning the larger thickness structure with an unknown defect nature to contrast the defect SNR and the number of defect imaging. Compared with TFM imaging, the AVASA method shows a significantly increasing defect-detecting range with higher amplitude. To further improve the imaging quality and reduce the reconstruction time, the influence of the virtual source parameters on the AVASA imaging and a scanning strategy is demonstrated. A good agreement between the AVASA and TFM is observed, and the number of transmissions is required to inspect the test specimen using AVASA reduced by a factor of four to eight.

Using High-Order Particle Velocity Gradient Polarization Characteristics to Detect Underwater Coherent Interference

Detection of underwater coherent interference is necessary as its presence affects the estimation of the desired target. In this work, a method based on high-order particle velocity gradient polarization characteristics is proposed to distinguish the presence of coherent interference. Characteristics including ellipse ratio and inclination angles of the high-order particle velocity gradient are chosen as effective detection features because they remain unchanged at different orders of the desired target but changed at different orders of coherent signals when there is coherent interference. Based on such altered properties, a quick and effective marine detection system can be established. Both simulation and experimental results validate the proposed detection method.

3DFlex: determining structure and motion of flexible proteins from cryo-EM

Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental questions in structural biology. We introduce Three-Dimensional Flexible Refinement (3DFlex), a motion-based neural network model for continuous molecular heterogeneity for cryo-EM data. 3DFlex exploits knowledge that conformational variability of a protein is often the result of physical processes that transport density over space and tend to preserve local geometry. From two-dimensional image data, 3DFlex enables the determination of high-resolution 3D density, and provides an explicit model of a flexible protein’s motion over its conformational landscape. Experimentally, for large molecular machines (tri-snRNP spliceosome complex, translocating ribosome) and small flexible proteins (TRPV1 ion channel, αVβ8 integrin, SARS-CoV-2 spike), 3DFlex learns nonrigid molecular motions while resolving details of moving secondary structure elements. 3DFlex can improve 3D density resolution beyond the limits of existing methods because particle images contribute coherent signal over the conformational landscape.

Identifying disappearance of a white dwarf binary with LISA

ABSTRACT We discuss the prospect of identifying a white dwarf binary merger by monitoring disappearance of its nearly monochromatic gravitational wave. For a 10 yr operation of the laser interferometer space antenna (LISA), the chance probability of observing such an event is roughly estimated to be 20 per cent. By simply using short-term coherent signal integrations, we might determine the merger time with an accuracy of ∼3–10 d. Also considering its expected sky localizability ∼0.1–0.01 deg2, LISA might make an interesting contribution to the multimessenger study on a merger event.

On the propagation of gravity waves in the lower solar atmosphere in different magnetic configurations

Gravity waves are generated by turbulent subsurface convection overshooting or penetrating locally into a stably stratified medium. While propagating energy upwards, their characteristic negative phase shift over height is a well-recognized observational signature. Since their first detailed observational detection and estimates of energy content, a number of studies have explored their propagation characteristics and interaction with magnetic fields and other waves modes in the solar atmosphere. Here, we present a study of the atmospheric gravity wave dispersion diagrams utilizing intensity observations that cover photospheric to chromospheric heights over different magnetic configurations of quiet-Sun (magnetic network regions), a plage, and a sunspot as well as velocity observations within the photospheric layer over a quiet and a sunspot region. In order to investigate the propagation characteristics, we construct two–height intensity - intensity and velocity- velocity cross-spectra and study phase and coherence signals in the wavenumber - frequency dispersion diagrams and their association with background magnetic fields. We find signatures of association between magnetic fields and much reduced coherence and phase shifts over height from intensity-intensity and velocity-velocity phase and coherence diagrams, both indicating suppression/scattering of gravity waves by the magnetic fields. Our results are consistent with the earlier numerical simulations, which indicate that gravity waves are suppressed or scattered and reflected back into the lower solar atmosphere in the presence of magnetic fields.

NEUROIMAGING (RESTING-STATE fMRI) AND NEUROPSYCHOLOGICAL CHARACTERISTICS OF NON-CONVERTERS WITH CLINICAL HIGH RISK FOR PSYCHOSIS

Based on the concept of clinical high risk for psychosis, we aimed to reveal characteristics of brain functioning (resting-state fMRI) and neurocognition in 27 patients with non-psychotic mental disorders with attenuated schizophrenia symptoms who did not transit to psychosis for a long period of observation, in contrast to 24 patients with first-episode schizophrenia and 27 mentally healthy subjects. The main group was characterized by higher local coherence of BOLD signal in the right visual cortex and higher functional connectivity between the occipital component of the visual network and the right prefrontal component of the salience network (as compared to patients with schizophrenia). In both patient groups, a decreased productivity in verbal fluency tests was found. The neuroimaging and neuropsychological findings in the main group can be considered via the dichotomy of protective and pathological mechanisms in patients with high risk for psychosis.

Performance comparison of compact phased arrays and traditional seismic networks for microseismic monitoring at a CO2 sequestration test site

As carbon capture, utilization, and sequestration scales toward the gigatonnes level, the need for underground reservoir surveillance is driving efforts in advancing technologies for cost-effective passive seismic monitoring. Quantum Technology Sciences, in cooperation with Carbon Management Canada's Containment and Monitoring Institute (CaMI), installed a network of four permanent compact volumetric phased arrays (seismic and acoustic detection and ranging [SADAR] system) at CaMI's Field Research Station (FRS) to demonstrate the results that can be achieved through passive monitoring of microseismicity using this technology. Configured as a sparse network, the SADAR arrays provide passive, persistent, and permanent data acquisition and analysis for monitoring microseismicity in the earth volume of interest. Data from the phased arrays are processed to take advantage of the spatial coherence of the incident seismic signals to increase signal resolution while suppressing noise and clutter signals and providing signal attributes such as angle of incidence and phase velocity. The CaMI FRS has a network of 28 permanent surface stations that are deployed in an x-shaped geometry centered on the injection well. It has a downhole array of 24 geophones that are permanently deployed in an observation well. This provides a ready and unique opportunity to evaluate the detection and location performance of the different systems for passive seismic monitoring. We analyze observations of five example events selected from the microseismicity detected by the SADAR arrays with moment magnitudes (Mw) down to approximately −2. Signal-to-noise ratio (S/N) and location uncertainties are compared for the events acquired using SADAR arrays versus the surface sensors. The results demonstrate improved performance of networked SADAR arrays compared to traditional surface sensor deployment for detecting and locating microseismicity. Specifically, the results show that coherent processing of SADAR arrays achieves S/N gains up to about 20 dB and location errors down to 10 m.

Deterministic Construction of Compressed Sensing Measurement Matrix with Arbitrary Sizes via QC-LDPC and Arithmetic Sequence Sets

It is of great significance to construct deterministic measurement matrices with good practical characteristics in Compressed Sensing (CS), including good reconstruction performance, low memory cost and low computing resources. Low-density-parity check (LDPC) codes and CS codes can be closely related. This paper presents a method of constructing compressed sensing measurement matrices based on quasi-cyclic (QC) LDPC codes and arithmetic sequence sets. The cyclic shift factor in each submatrix of QC-LDPC is determined by arithmetic sequence sets. Compared with random matrices, the proposed method has great advantages because it is generated based on a cyclic shift matrix, which requires less storage memory and lower computing resources. Because the restricted isometric property (RIP) is difficult to verify, mutual coherence and girth are used as computationally tractable indicators to evaluate the measurement matrix reconstruction performance. Compared with several typical matrices, the proposed measurement matrix has the minimum mutual coherence and superior reconstruction capability of CS signal according to one-dimensional (1D) signals and two-dimensional (2D) image simulation results. When the sampling rate is 0.2, the maximum (minimum) gain of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) is up to 2.89 dB (0.33 dB) and 0.031 (0.016) while using 10 test images. Meanwhile, the reconstruction time is reduced by nearly half.

Tunable multi-outlet single photon quantum router in an optomechanical system

An experimentally accessible tunable single-photon multi-channel quantum router is investigated in a system consisting of an optomechanics cavity coulomb coupling to a nano-mechanical resonator. The router can extract a single-photon from the coherent input signal directly modulate into three different output channels, and the output probability is all close to 1. More important, the two output signal frequencies can be selected by adjusting Coulomb coupling strength. We also demonstrate the vacuum and thermal noise will be insignificant for the optical performance of the single-photon router at temperature of the order of 20 mK. Our proposal may have paved a new avenue towards multi-channel router and quantum network.

Pulse overlap artifacts and double quantum coherence spectroscopy.

The double quantum coherence (DQC) signal in nonlinear spectroscopy gives information about the many-body correlation effects not easily available by other methods. The signal is short-lived, consequently, a significant part of it is generated during the pulse overlap. Since the signal is at two times the laser frequency, one may intuitively expect that the pulse overlap-related artifacts are filtered out by the Fourier transform. Here, we show that this is not the case. We perform explicit calculations of phase-modulated two-pulse experiments of a two-level system where the DQC is impossible. Still, we obtain a significant signal at the modulation frequency, which corresponds to the DQC, while the Fourier transform over the pulse delay shows a double frequency. We repeat the calculations with a three-level system where the true DQC signal occurs. We conclude that with realistic dephasing times, the pulse-overlap artifact can be significantly stronger than the DQC signal. Our results call for great care when analyzing such experiments. As a rule of thumb, we recommend that only delays larger than 1.5 times the pulse length should be used.

Iterative deblending using unsupervised learning with double-deep neural networks

Simultaneous source acquisition technology can greatly improve seismic acquisition efficiency. However, due to continuous shooting and serious crosstalk noise of the adjacent sources in seismic data, simultaneous source data cannot be directly used in conventional data processing procedures. Therefore, simultaneous source data need to be deblended to obtain the conventional shot record. Under densely sampled sources, we have developed a novel unsupervised deep learning (UDL) method based on the double-deep neural networks for iterative inversion deblending of simultaneous source data. Our UDL, which is mainly composed of the residual neural network (R-net) and the U-net neural network, has excellent nonlinear optimization ability. The total loss function design can optimize our UDL in the correct direction and avoid the problem of overfitting. By minimizing the total loss function, the R-net and U-net branches of the UDL can extract the coherent effective signals of all sources and suppress the crosstalk noise. The most prominent advantage of our UDL method is that it does not require label data, and the training data set does not contain raw unblended data, thus solving the problem of missing training data sets. The examples with two synthetic and one field data set are used to prove the effectiveness of iterative inversion deblending of simultaneous source data based on our UDL method when sources are within a small distance of each other. By comparing our UDL method with the traditional curvelet-based and contourlet-based methods, the superiority of our method in the quality of separation results is demonstrated.