Coherent Signals Research Articles

Deep learning-enhanced atomic norm minimization for DOA estimation of coherent and incoherent sources using coprime array

Abstract In the field of array signal processing, accurate direction of arrival (DOA) estimation is crucial for handling both coherent and incoherent mixed signal sources. This paper proposes a deep learning-enhanced atomic norm minimization (DLEANM) algorithm, designed to improve DOA estimation for coprime arrays. The algorithm first models the mixed signal scenarios realistically and employs interpolation to transform coprime arrays into virtual uniform linear arrays, which facilitates the formulation of the atomic norm minimization problem. Solving this problem reconstructs an augmented Hermitian Toeplitz covariance matrix. To further enhance the accuracy of DOA estimation, we developed a multi-scale convolutional neural network with an attention mechanism, which uses the covariance matrix as input to better capture signal variations across different frequencies and spatial distributions. By processing multi-scale inputs in parallel, the model improves adaptability and robustness in estimating mixed signal sources. Simulation results show that, under equivalent SNR and snapshot conditions, the DLEANM algorithm achieves lower estimation errors, more accurate multi-target DOA estimation, and relatively faster computation speed compared to conventional methods. Field experiments further confirm the effectiveness of the proposed algorithm. Therefore, after training, the algorithm is able to accurately identify the directions of unknown signals received by sensor arrays.

Seasonal macro‐demography of North American bird populations revealed through participatory science

Avian population sizes fluctuate and change over vast spatial scales, but the mechanistic underpinnings remain poorly understood. A key question is whether spatial and annual variation in avian population dynamics is driven primarily by variation in breeding season recruitment or by variation in overwinter survival. We present a method using large‐scale volunteer‐collected data from project eBird to develop species‐specific indices of net population change as proxies for survival and recruitment, based on twice‐annual, rangewide snapshots of relative abundance in spring and fall. We demonstrate the use of these indices by examining spatially explicit annual variation in survival and recruitment in two well‐surveyed nonmigratory North American species, Carolina wren Thryothorus ludovicianus and northern cardinal Cardinalis cardinalis. We show that, while interannual variation in both survival and recruitment is slight for northern cardinal, eBird abundance data reveal strong and geographically coherent signals of interannual variation in the overwinter survival of Carolina wren. As predicted, variation in wintertime survival dominates overall interannual population fluctuations of wrens and is correlated with winter temperature and snowfall in the northeastern United States, but not the southern United States. This study demonstrates the potential of participatory science (also known as citizen science) datasets like eBird for inferring variation in demographic rates and introduces a new complementary approach towards illuminating the macrodemography of North American birds at comprehensive continental extents.

Deep learned processing of coherent diffractive correlators signals

Deep learned processing of coherent diffractive correlators signals

A novel damage localization method of Circular Phased Array using Minimum Variance Distortionless Response Beamforming with Autocorrelation Matrix Diagonal Loading

Damage localization methods based on Acoustic Emission (AE) can be classified into time-based and waveform-based. However, the former requires a large number of sensors while the latter is limited to 2D plane localization. In order to address the challenge of achieving more accurate 3D localization using a reduced number of sensors, this paper proposes a Circular Phased Array using Minimum Variance Distortionless Response (MVDR) Beamforming with Autocorrelation Matrix Diagonal Loading (AMDL) method. Firstly, a sparse circular array is utilized to form multiple beamforming for coherent shear wave signals, decomposing the original 3D localization problem into Direction Of Arrival (DOA) estimation. Secondly, azimuth angle, elevation angle and autocorrelation matrix diagonal loading methods are introduced, working in conjunction with the MVDR beamforming algorithm. Finally, spatial integration is performed through matrix decomposition to solve geometric overdetermined equations. The effectiveness of the proposed method is validated through numerical simulations and experimental verifications under various damage conditions. Results indicate that estimation errors for azimuth and elevation angles are both less than 2 %, while 3D damage source localization errors remain within a range of less than 3 %. This proposed method extends beamforming technology from 2D plane localization to 3D localization, significantly reducing the complexity of sensor arrangement and lowering the cost of structural health monitoring systems by utilizing a small number of sensors.

Wide-Range Tunable Coherent Dual-Frequency Microwave Signal Generation With Low Spurious Components in Optoelectronic Oscillator

Wide-Range Tunable Coherent Dual-Frequency Microwave Signal Generation With Low Spurious Components in Optoelectronic Oscillator

A randomized study on the effect of a wearable device using 0.75 Hz transcranial electrical stimulation on sleep onset insomnia.

The normal transition to sleep is characterized by a reduction in higher frequency activity and an increase in lower frequency activity in frontal brain regions. In sleep onset insomnia these changes in activity are weaker and may prolong the transition to sleep. Using a wearable device, we compared 30min of short duration repetitive transcranial electric stimulation (SDR-tES) at 0.75Hz, prior to going to bed, with an active control at 25Hz in the same individuals. Treatment with 0.75Hz significantly reduced sleep onset latency (SOL) by 53% when compared with pre-treatment baselines and was also significantly more effective than stimulation with 25Hz which reduced SOL by 30%. Reductions in SOL with 25Hz stimulation displayed order effects suggesting the possibility of placebo. No order effects were observed with 0.75Hz stimulation. The decrease in SOL with 0.75Hz treatment was proportional to an individual's baseline wherein those suffering from the longest pre-treated SOLs realized the greatest benefits. Changes in SOL were correlated with left/right frontal EEG signal coherence around the stimulation frequency, providing a possible mechanism and target for more focused treatment. Stimulation at both frequencies also decreased perceptions of insomnia symptoms measured with the Insomnia Severity Index, and comorbid anxiety measured with the State Trait Anxiety Index. Our study identifies a new potential treatment for sleep onset insomnia that is comparably effective to current state-of-practice options including pharmacotherapy and cognitive behavioral therapy and is safe, effective, and can be delivered in the home.

Reservoir computing for equalization in a self-coherent receiver scheme.

Short-reach optical networks, the backbone of data centers, face a significant challenge: transmitting high data rates at low cost and low energy consumption. While coherent signals can carry high data rates, coherent receivers are expensive and complex. Also, to equalize channel dispersion, they rely on digital signal processing modules, which consume large amounts of power and introduce more latency. Photonic reservoirs emerged as a way to process these signals in the analog optical domain, alleviating the power consumption and latency issues in state-of-the-art receivers. In this work, we show in simulations that a photonic reservoir combined with a self-coherent photonic receiver achieves a BER of 3.8 × 10-3 for a 32 Gbaud 16-QAM signal and an 80 km link, requiring a low CSPR of 3 dB compared to state-of-the-art self-coherent receivers.

Research on the fusion imaging method of sign coherence and time reversal for Lamb wave sparse array

Time-reversal imaging struggles to detect plate-like structures due to interference from Lamb wave mode conversion and the processing demands, leading to less effective outcomes. This paper proposes a sign coherence factor and time reversal fusion (SCF-TR) imaging method based on amplitude and phase estimation. This method removes the coherence of array signals during signal reversal and refocusing. It reintroduces the sign coherence component to reduce interference from non-target scattered waves and partially overcome the constraints imposed by the Rayleigh criterion. The method allows imaging at a resolution smaller than the wavelength of Lamb and enhances the quality of the resulting images. In addition, a sparse array design utilizing the White Shark Optimisation Algorithm (WSO) is proposed to streamline the SCF-TR calculation process. This design utilizes sparse full matrix data to improve imaging efficiency. The experimental results show that for single blind hole defects, the SCF-TR method improves the array performance metrics and signal-to-noise ratio by 22.46% and 42.50%, respectively, compared to the TR method. For multiple asymmetric blind hole defects, when the defect size exceeds the resolution threshold, SCF-TR accurately reflects the position and morphology of defects smaller than the wavelength. When the defect size is below the resolution threshold, SCF-TR achieves super-resolution imaging. The sparse array designed using the White Shark Optimization algorithm demonstrates good sidelobe characteristics, effectively reducing sidelobe noise without reducing the array aperture. Moreover, the SCF-TR imaging time is reduced by approximately half while maintaining imaging accuracy.

Enhancing SONAR performance through statistically-informed sub-array processing

Ocean fluctuations affect acoustic propagation and reduce the performance of SONAR arrays. In particular, internal waves can lead to a loss of coherence in incident signals, reducing the efficiency of classical processing. Precise knowledge of the environment is necessary to predict the degradation it causes. However, it is impossible to know the exact state of the ocean, it is therefore necessary to use statistical methods to study the ocean and acoustic propagation. In this work, statistical method, canonical correlation analysis (CCA), is used to find linear relationships between acoustic variables of interest and oceanographic measurements. In particular, the coherence radius is inferred from empirical modes of temperature using the model learned by CCA. A sub-array processing scheme can then be informed by defining the length of the sub-arrays from the coherence radius. To compensate for the loss of angular resolution inherent in sub-arrays, we introduce the use of a Bayesian algorithm exploiting l0 norm regularization. We show on experimental data from the ALMA 2017 campaign that the proposed processing improves detection when signal coherence is low.

Data Analysis of Dynamics in Protein Solutions Using Quasi-Elastic Neutron Scattering─Important Insights from Polarized Neutrons.

Protein dynamics play a vital role in biology. Quasi elastic neutron scattering (QENS) is an ideal method to access these dynamics. To isolate protein dynamics, it is important to separate the signal of the buffer and the protein. Normally data analysis is performed based on the assumption that the scattering spectrum is incoherent. To observe the full range of protein dynamics, it is necessary to perform the experiments in solution. This solution is usually a fully deuterated buffer, while the protein remains protonated. It is generally assumed that subtracting the buffer contribution removes all coherent signal from the measured spectrum, and the rest can be considered as purely incoherent. Up until recently, there was no way to experimentally verify this assumption. Polarized QENS experiments allow for the coherent and incoherent contributions to be separated. By comparing the results from the polarized QENS experiment and the standard analysis method from unpolarized QENS, we are thus able to check this assumption experimentally. We show that the pure incoherent spectrum obtained from polarization analysis does not match the results for unpolarized QENS. We discuss the implications of this for data analysis and possible solutions to the problem, as well as mitigation techniques for standard QENS.

Role of the inverse Čerenkov effect in the formation of ultrashort Raman solitons in silica microspheres.

We theoretically demonstrate a new regime, to the best of our knowledge, of the formation of ultrashort optical solitons in spherical silica microresonators with whispering gallery modes. The solitons are driven by a coherent CW pump at the frequency in the range of normal dispersion, and the energy is transferred from this pump to the solitons via two channels: the Raman amplification and inverse Čerenkov effect. We discuss three different regimes of soliton propagation and we also show that these Raman solitons can be controlled by weak coherent CW signals.

Discovery of SRGA J144459.2−604207 with the SRG/ART-XC telescope: A well-tempered bursting accreting millisecond X-ray pulsar

We report the discovery of the new accreting millisecond X-ray pulsar SRGA J144459.2−604207 using data of the SRG/ART-XC. The source was observed twice in February 2024 during the declining phase of the outburst. The timing analysis revealed a coherent signal near 447.9 Hz modulated by the Doppler effect due to the orbital motion. The derived parameters for the binary system are consistent with a circular orbit with a period of ∼5.2 h. The pulse profiles of the persistent emission, showing a sine-like part during half a period with a plateau in between, can be well modeled by emission from two circular spots that are partially eclipsed by the accretion disk. Additionally, during our observations with an exposure of 133 ks, we detected 19 thermonuclear X-ray bursts. All bursts have similar shapes and energetics, and none show any signs of an expanding photospheric radius. The burst recurrence times decreases linearly from ∼1.6 h at the beginning of observations to ∼2.2 h at the end and anticorrelate with the persistent flux. The spectral evolution during the bursts is consistent with the models of the neutron star atmospheres that are heated by accretion and implies a neutron star radius of 11–12 km and a distance to the source of 8–9 kpc. We also detected coherent pulsations during the bursts and showed that the pulse profiles differ substantially from those observed in the persistent emission. However, we could not find a simple physical model explaining the pulse profiles detected during the bursts.

DOA estimation of noncircular signals with direction-dependent mutual coupling

In this paper, a reweighted sparse recovery algorithm based on the optimal weighted subspace fitting (WSF) for non-circular signals in direction-dependent mutual coupling (MC) is proposed. Firstly, a new augmented model is constructed by leveraging the characteristics of non-circular signals. Next, a new direction matrix without mutual coupling coefficients is obtained by a novel transformation method. Then, two sparse recovery models are constructed by utilizing the WSF technique, and the sparsity of the solution is increased by constructing a weighted matrix. Finally, the direction of arrival (DOA) is achieved by a sparse recovery approach. For both coherent and incoherent signals, the developed approach can achieve precise DOA estimation in the case of direction-dependent MC. The robustness and advantage of the developed approach are testified by various experiments.

Unimodular Multi-Input Multi-Output Waveform and Mismatch Filter Design for Saturated Forward Jamming Suppression.

Forward jammers replicate and retransmit radar signals back to generate coherent jamming signals and false targets, making anti-jamming an urgent issue in electronic warfare. Jamming transmitters work at saturation to maximize the retransmission power such that only the phase information of the angular waveform at the designated direction of arrival (DOA) is retained. Therefore, amplitude modulation of MIMO radar angular waveforms offers an advantage in combating forward jamming. We address both the design of unimodular MIMO waveforms and their associated mismatch filters to confront mainlobe jamming in this paper. Firstly, we design the MIMO waveforms to maximize the discrepancy between the retransmitted jamming and the spatially synthesized radar signal. We formulate the problem as unconstrained non-linear optimization and solve it using the conjugate gradient method. Particularly, we introduce fast Fourier transform (FFT) to accelerate the numeric calculation of both the objection function and its gradient. Secondly, we design a mismatch filter to further suppress the filtered jamming through convex optimization in polynomial time. The simulation results show that for an eight-element MIMO radar, we are able to reduce the correlation between the angular waveform and saturated forward jamming to -6.8 dB. Exploiting this difference, the mismatch filter can suppress the jamming peak by 19 dB at the cost of an SNR loss of less than 2 dB.

REWARE: a seismic processing algorithm to retrieve geological information from the water column

SUMMARY When interpreting marine very high-resolution (VHR) single-channel seismic reflection data, the signal in the water column is generally considered as noise and is often eliminated by a water-mute application to focus on geological information under the seafloor. Alternatively, the signal in the water column can be used to study ocean currents or gas/fluid emissions. To provide images of the sedimentary formations and tectonic structures beneath the seafloor in shallow water regions, such as continental shelves and lakes, marine seismic reflection profiles are often acquired using a single-channel streamer and sparker-type source, providing VHR data, with limited penetration depth. To exploit the full potential of these single-channel data, we propose a simple algorithm, called REWARE (Recovery of Water-column Acoustic Reflectors). This algorithm allows to extract further geological information from the water-column data using open-source codes (Seismic Un*x), adding the coherent signal from the previous shots, recorded in the water column, to the previous traces. The record length becomes longer while maintaining a very high trace-to-trace consistency. To demonstrate its efficiency, we present two examples of the REWARE processing in two different geological contexts: the East Sardinia shelf (Italy) and the North Evia Gulf (Greece). This method provides deeper images than with original data for seismic data acquired across steep slopes, such as canyons or continental shelf breaks. Thus, depending on the seafloor geometry and subseafloor structures, it is possible to image or map sediment layers and tectonic structures at depth, keeping a very high structural resolution.

P048 CARDIOBREATH APPLICATION: SLOW BREATH TO DECREASE BLOOD PRESSURE AND IMPROVE AUTONOMIC MODULATION

CardioBreath is an application of slow deep breath for increasing cardiovascular performance in health and sports. Biological signals like spontaneous respiratory rate (SRR) are employed to prescribe slow respiratory exercises. Heart rate (HR) collected by the app is employed to track results. It aims to increase vagal modulation and decrease arterial pressure. The aim of this study is to verify the accuracy of CardioBreath data collection of SRR and HR. Following this, the evaluation of the cardiac vagal modulation evoked by the audiovisual lessons of slow breath of CardioBreath. Methods: Coherence of biological signals: Healthy individuals have been assessed using CardioBreath data collection of HR to compare their values simultanealy with those from Polar V 800, (ten sets each participant. SRR was collected by CardioBreath compared to visual counting (five sets). Pair wise t-test comparisons were performed in order to detect diferences between evaluations. Cardiac vagal modulation has been collected by Finometer and analyzed by Cardioseries, before (baseline evaluation) and after randomized audiovisual lessons of the CardioBreath App. These data will be analyzed by GEE (generalized estimation equation). All data are demonstrated as Mean (M) ± Standard deviation (SD) (p< 0,05) Partial Results: 50 healthy individuals participated of the first phase of the research, aged from 21 to 57 years old generating 460 analyzed pairs: HRApp 71.66 ± 11.68 bpm versus HRPolar 72.02 ± 13.24 bpm (p = 0.66). A total of 225 pairs have been analyzed for SRR: SRRApp 13.88 ± 4.63 cpm versus SRRvisual 13.82 ± 4.48 com (p = 0.44). Conclusions: The comparison of biological signals of CardioBreath App demonstrate that either HR and SRR are in coherence with data obtained by the Polar V 800 (HR) and visual method (SRR). New data of the autonomic responses of lessons of the CardioBreath App will be available in September. More on CardioBreath App at www.cardiobreath.com (only in Portuguese)

Demodulation algorithm of low coherent heterodyne interference signal for axial clearance measurement of rotating machinery

The time-domain low coherent heterodyne interference axial clearance measurement method has the challenges of small range and low precision in the clearance measurement of rotating machinery. An axial clearance demodulation algorithm based on the windowed power spectrum density transform (WPSD) is proposed. Based on the characteristics of low coherent heterodyne interference signal, the relationship model between window width and rotating structure motion parameters (amplitude of axial clearance change, rotor speed) is constructed, and the optimal window width mathematical model is obtained. The axial clearance measurement experiments are completed under different axial clearance measurement distances based on an experimental clearance platform, different rotor speeds and different axial clearance variation amplitudes. The experimental results show that the absolute error of the axial clearance reconstruction signal is less than 17 μm and the relative error is less than 4.8 % when the axial clearance measurement distance is 2.1 mm ∼ 11.3 mm, the rotor speed is in the range of 6000 rad/min ∼ 12000 rad/min, and the variation of axial clearance is in the range of 150 μm ∼ 350 μm. The experimental and simulation results are basically consistent.

Fluid-rock interaction related to alpine subduction of Allalingabbro deduced from large-size μXRF element maps

Alpine subduction of a block of olivine gabbro produced a plethora of different assemblages and local structures typical of high-pressure and low-temperature metamorphism. However, gabbro with the locally well-preserved igneous assemblage olivine - augite - plagioclase occurs in some domains of a 2 km long outcrop (Allalinhorn, western Alps).A large variety of hydrate minerals including zoisite, glaucophane, talc, Mg-chloritoid, chlorite, paragonite and muscovite formed during Alpine subduction of the metagabbro. The three primary igneous minerals have been replaced by hydration reactions during Eocene subduction. Talc, omphacite, and zoisite + jadeite and are the main minerals in the local domains after olivine, augite and plagioclase respectively. The rocks preserved the coarse grained igneous texture.An excellent overview of the complex coarse textures of the eclogite facies rocks has been provided by element distribution maps produced from up to 25 cm large polished slabs of typical samples of Allalingabbro using a μXRF instrument. The RGB images suggest that the minerals formed at the high-pressure in the presence of an aqueous fluid. The pseudomorphing reactions progressed close to volume conservation. The hydration reactions required an H2O fluid containing dissolved solids. The mapped distribution of elements including trace elements provides important clues on the origin and mobility of these elements in the hydration fluid. The homogeneously distributed immobile Cr in omphacite portrays primary augite. The irregular patchy Sr distribution resulted from small-scale migration of components replacing plagioclase by Sr-bearing zoisite and jadeite. Coherent Ni signals are related to the presence of talc. The talc occurs as a late infill of structures produced by dissolving olivine. Mg-chloritoid, kyanite and mica coat the former grain-boundary of olivine and plagioclase suggesting that olivine dissolved at eclogite facies conditions. Talc formed at distinctly lower pressures, however, as implied by computed model reactions. The olivine replacement structures are often larger than the size thin section and can be excellently studied by the large size μXRF patterns used in this study.The fractured surroundings of the locations of olivine provided the necessary hydraulic conductivity for hydration reactions to progress. The fractures and small veinlets contain late Ni-bearing talc. Cl is only present in very late vein amphibole suggesting that hydration fluids in the subduction zone transported dissolved components as hydrous complexes (and their dissociated ions). There is no evidence for the presence of a Cl-brine during subduction. In addition to primary olivine as a local Ni source, the element may also have partly been imported with the hydration fluid and originate from dehydration of ophiolitic serpentinites in the slab.

Coherent Ultrafast Stimulated X-Ray Raman Spectroscopy of Dissipative Conical Intersections.

The quantum coherent dynamics of a vibronic wave packet in a molecule passing through a conical intersection can be revealed using attosecond transient coherent Raman spectroscopy. In particular, the time evolution of the electronic coherence can be monitored in the presence of vibrational dynamics. So far, the technique has been investigated without including environmental quantum noise. Here, we employ the numerically exact hierarchy equation of motion approach to show that the transient coherent Raman signals are robust and accessible on times of up to a few hundred femtoseconds with respect to electonic and vibrational dephasing.

Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution

Nonlinearity of dynamic systems plays a key role in neuromorphic computing, which is expected to reduce the ever-increasing power consumption of machine learning and artificial intelligence applications. For spin waves (magnons), nonlinearity combined with phase coherence is the basis of phenomena like Bose–Einstein condensation, frequency combs, and pattern recognition in neuromorphic computing. Yet, the broadband electrical detection of these phenomena with high-frequency resolution remains a challenge. Here, we demonstrate the generation and detection of phase-coherent nonlinear magnons in an all-electrical GHz probe station based on coplanar waveguides connected to a vector network analyzer which we operate in a frequency-offset mode. Making use of an unprecedented frequency resolution, we resolve the nonlocal emergence of a fine structure of propagating nonlinear magnons, which sensitively depends on both power and a magnetic field. These magnons are shown to maintain coherency with the microwave source while propagating over macroscopic distances. We propose a multi-band four-magnon scattering scheme that is in agreement with the field-dependent characteristics of coherent nonlocal signals in the nonlinear excitation regime. Our findings are key to enable the seamless integration of nonlinear magnon processes into high-speed microwave electronics and to advance phase-encoded information processing in magnonic neuronal networks.