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Cough is a key symptom reflecting respiratory health, with its frequency and pattern providing valuable insights into disease progression and clinical management. Objective and reliable cough detection systems are therefore of broad significance for healthcare and remote monitoring. However, existing algorithms often struggle to jointly model spatial and temporal information, limiting their robustness in real-world applications. To address this issue, we propose a cough recognition framework based on frequency-modulated continuous-wave (FMCW) radar, integrating a deep convolutional neural network (CNN) with a Self-Attention mechanism. The CNN extracts spatial features from range-Doppler maps, while Self-Attention captures temporal dependencies, and effective data augmentation strategies enhance generalization by simulating position variations and masking local dependencies. To rigorously evaluate practicality, we collected a large-scale radar dataset covering diverse positions, orientations, and activities. Experimental results demonstrate that, under subject-independent five-fold cross-validation, the proposed model achieved a mean F1-score of 0.974±0.016 and an accuracy of 99.05±0.55 %, further supported by high precision of 98.77±1.05 %, recall of 96.07±2.16 %, and specificity of 99.73±0.23 %. These results confirm that our method is not only robust in realistic scenarios but also provides a practical pathway toward continuous, non-invasive, and privacy-preserving respiratory health monitoring in both clinical and telehealth applications.

Bolton & Schlegel presented a promising deconvolution method to extract one-dimensional (1D) spectra from a two-dimensional (2D) optical fiber spectral CCD (charge-coupled device) image. The method could eliminate the PSF (point-spread function) difference between fibers, extract spectra to the photo noise level, as well as improve the resolution. But the method is limited by its huge computation requirement and thus can not be implemented in actual data reduction. In this article, we develop a practical computation method to solve the computation problem. The new computation method can deconvolve a 2D fiber spectral image of any size with actual PSFs, which may vary with positions. Our method does not require large amounts of memory and can extract a 4 k × 4 k noise-free CCD image with 250 fibers in 2 hr. To make our method more practical, we further consider the influence of noise, which is thought to be an intrinsic ill-posed problem in deconvolution algorithms. We modify our method with a Tikhonov regularization item to depress the method induced noise. We do a series of simulations to test how our method performs under more real situations with Poisson noise and extreme cross talk. Compared with the results of traditional extraction methods, i.e., the Aperture Extraction Method and the Profile Fitting Method, our method has the least residual and influence by cross talk. For the noise-added image, the computation speed does not depend very much on fiber distance, the signal-to-noise ratio converges in 2-4 iterations, and the computation times are about 3.5 hr for the extreme fiber distance and about 2 hr for nonextreme cases. A better balance between the computation time and result precision could be achieved by setting the precision threshold similar to the noise level. Finally, we apply our method to real LAMOST (Large sky Area Multi-Object fiber Spectroscopic Telescope; a.k.a. Guo Shou Jing Telescope) data. We find that the 1D spectrum extracted by our method has both higher signal-to-noise ratio and resolution than the traditional methods, but there are still some suspicious weak features, possibly caused by the method around the strong emission lines. As we have demonstrated, our deconvolution method has solved the computation problem and progressed in dealing with the noise influence. Multifiber spectra extracted by our method will have higher resolution and signal-to-noise ratio, and thus will provide more accurate information (such as higher radial velocity and metallicity measurement accuracy in stellar physics) to astronomers than traditional methods.

Heartbeat inter-beat interval (IBI) extraction is a crucial technology for unobtrusive vital sign monitoring, yet its precision and robustness remain challenging. A promising approach is fusing heartbeat signals from different types of unobtrusive sensors. This paper introduces HSF-IBI, a novel and universal framework for unobtrusive IBI extraction using heterogeneous sensor fusion. Specifically, harmonic summation (HarSum) is employed for calculating the average heart rate, which in turn guides the selection of the optimal band selection (OBS), the basic sequential algorithmic scheme (BSAS)-based template group extraction, and the template matching (TM) procedure. The optimal IBIs are determined by evaluating the signal quality index (SQI) for each heartbeat. The algorithm is morphology-independent and can be adapted to different sensors. The proposed algorithm framework is evaluated on a self-collected dataset including 19 healthy participants and an open-source dataset including 34 healthy participants, both containing heterogeneous sensors. The experimental results demonstrate that (1) the proposed framework successfully integrates data from heterogeneous sensors, leading to detection rate enhancements of 6.25 % and 5.21 % on two datasets, and (2) the proposed framework achieves superior accuracy over existing IBI extraction methods, with mean absolute errors (MAEs) of 5.25 ms and 4.56 ms on two datasets.

Pulse signals can serve as important indicators of one’s cardiovascular condition. However, capturing signals with stable morphology using radar under varying measurement periods remains a significant challenge. This paper reports a non-contact arterial pulse measurement method based on mmWave radar, with stable signals achieved through a range–angle focusing algorithm. A total of six subjects participated in the experiment, and the results showed that, under different measurement times, the pulse morphology of the same body part for each subject had good consistency, reaching a correlation of over 0.84, and four selected pulse signs remained stable. This is a quantitative assessment revealing a high correlation in pulse morphology measured by radar over different periods. In addition, the influence of array size and measurement distance was analyzed, providing a reference of array selection for research work with different requirements. This work offers an effective reference framework for long-term pulse measurement using radar technology.

Diagnosing water infiltration is imperative to assess the integrity and operation performance of sewer networks, which is challenging and costly due to the complex nature of these networks. This study proposes a simple approach to evaluate the extent of groundwater infiltration via a fluorescence spectroscopy method, i.e., the identification and quantification of the fluorescent signature components of the dissolved organic matter sewage. A newly built sewer network in Shantou, Southern China, was selected for the case study, and a mass balance method based on water quality characteristic factors (total phosphorus and NH4+-N) was applied in parallel for comparison. The results showed that the mass balance method was substantially influenced by fluctuations in sewage and external water concentrations, rendering it unreliable due to the extensive data and calculations required. Conversely, three-dimensional excitation–emission matrix–parallel factor analysis enabled the identification of terrestrial humus compounds as the signatures of underground water sources. The estimation indicates that the groundwater proportion across the four surveyed inspection wells along the pipeline network ranged from 10.8 ± 2.5% to 9.6 ± 3.5%, conforming to the allowable groundwater infiltration limits set for municipal sewage pipelines (10–15%). This study presents a simple method for the in-depth analysis of groundwater infiltration in urban sewage networks, providing valuable insights into maintaining water quality and network integrity.

Spectrograph slit is conventionally used to enhance the spectral resolution ~md manage how much light can be allowed to enter spectrograph. The narrow slit provides a higher resolution but sacrifices efficiency of spectrograph and results in a low signal to noise ratio （S/N） spectra product. We take GuoShouJing telescope as an example and carry out a series of experiments to study how its 2/3 slit mode affects the precision of stellar radial velocity measurement and atmosphere parameters estimate. By transforming the resolution and adding a Gaussian White Noise to the extremely high quality spectra from the Sloan Digital Sky Survey, we generate synthetic stellar spectra of various brightness with different S/Ns. Comparing the measurements on these noise added spectra with the original high quality ones, we summarize the influences of the 2/3 slit mode on the meas- urement accuracy of stellar radial velocity and atmospheric parameters.

All stellar-mass black holes have hitherto been identified by X-rays emitted from gas that is accreting onto the black hole from a companion star. These systems are all binaries with a black-hole mass that is less than 30 times that of the Sun 1 – 4 . Theory predicts, however, that X-ray-emitting systems form a minority of the total population of star–black-hole binaries 5 , 6 . When the black hole is not accreting gas, it can be found through radial-velocity measurements of the motion of the companion star. Here we report radial-velocity measurements taken over two years of the Galactic B-type star, LB-1. We find that the motion of the B star and an accompanying Hα emission line require the presence of a dark companion with a mass of 68 − 13 + 11 solar masses, which can only be a black hole. The long orbital period of 78.9 days shows that this is a wide binary system. Gravitational-wave experiments have detected black holes of similar mass, but the formation of such massive ones in a high-metallicity environment would be extremely challenging within current stellar evolution theories. Radial-velocity measurements of a Galactic B-type star show a dark companion that seems to be a black hole of about 68 solar masses, in a widely spaced binary system.

The double revolving fiber positioning unit (FPU) is one of the key technologies of The Large Sky Area Multi-Object Fiber Spectroscope Telescope (LAMOST). The positioning accuracy of the computer controlled FPU depends on robot accuracy as well as the initial parameters of FPU. These initial parameters may deteriorate with time when FPU is running in non-supervision mode, which would lead to bad fiber position accuracy and further efficiency degradation in the subsequent surveys. In this paper, we present an algorithm based on deep learning to detect the FPU’s initial angle using the front illuminated image of LAMOST focal plane. Preliminary test results show that the detection accuracy of the FPU initial angle is better than 2.°5, which is good enough to distinguish those obvious bad FPUs. Our results are further well verified by direct measurement of fiber position from the back illuminated image and the correlation analysis of the spectral flux in LAMOST survey data.

The near-Earth (within ∼100 pc) supernova explosions in the past several million years can cause the global deposition of radioactive elements (e.g., 60 Fe) on Earth. The remnants of such supernovae are too old to be easily identified. It is therefore of great interest to search for million-year-old near-Earth neutron stars or black holes, the products of supernovae. However, neutron stars and black holes are challenging to find even in our Solar neighbourhood if they are not radio pulsars or X-ray/ γ -ray emitters. Here we report the discovery of one of the nearest (127.7 ± 0.3 pc) neutron star candidates in a detached single-lined spectroscopic binary LAMOST J235456.73+335625.9 (hereafter J2354). Utilizing the time-resolved ground-based spectroscopy and space photometry, we find that J2354 hosts an unseen compact object with M inv being 1.4–1.6 M ⊙ . The follow-up Swift ultraviolet (UV) and X-ray observations suggest that the UV and X-ray emission is produced by the visible star rather than the compact object. Hence, J2354 probably harbours a neutron star rather than a hot ultramassive white dwarf. Two-hour exceptionally sensitive radio follow-up observations with Five-hundred-meter Aperture Spherical radio Telescope fail to reveal any pulsating radio signals at the 6 σ flux upper limit of 12.5 µJy. Therefore, the neutron star candidate in J2354 can only be revealed via our time-resolved observations. Interestingly, the distance between J2354 and our Earth can be as close as ∼ 50 pc around 2.5 million years (Myrs) ago, as revealed by the Gaia kinematics. Our discovery demonstrates a promising way to unveil the hidden near-Earth neutron stars in binaries by exploring the optical time domain, thereby facilitating understanding of the metal-enrichment history in our Solar neighbourhood.

This paper describes the 2-dimensional data reduction of the LAMOST pilot survey.