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The production mechanism of fast radio bursts (FRBs)—mysterious, bright, millisecond-duration radio flashes from cosmological distances—remains unknown. Understanding potential correlations between burst occurrence times and various burst properties may offer important clues about their origins. Among these properties, the spectral peak frequency of an individual burst (the frequency at which its emission is strongest) is particularly important because it may encode direct information about the physical conditions and environment at the emission site. Analyzing over 4000 bursts from the three most active sources—FRB 20121102A, FRB 20201124A, and FRB 20220912A—we measure the two-point correlation function ξ(Δt, Δνpeak) in the two-dimensional space of time separation Δt and peak frequency shift Δνpeak between burst pairs. We find a universal trend of asymmetry about Δνpeak at high statistical significance; ξ(Δνpeak) decreases as Δνpeak increases from negative to positive values in the region of short time separation (Δt ≲ 0.3 s), where physically correlated aftershock events produce a strong time correlation signal. This indicates that aftershocks tend to exhibit systematically lower peak frequencies than mainshocks, with this tendency becoming stronger at shorter Δt. We argue that the “sad trombone effect”—the downward frequency drift observed among subpulses within a single event—is not confined within a single event but manifests as a statistical nature that extends continuously to independent yet physically correlated aftershocks with time separations up to Δt ∼ 0.3 s. This discovery provides new insights into underlying physical processes of repeater FRBs.

The Chang’e-4 lander successfully landed in the Von Kármán crater on the farside of the Moon in 2019, collecting a large amount of scientific data to analyze the surface material and subsurface structures of the Von Kármán crater. In this study, we processed the high-frequency radar data from the first 25 lunar days collected by the Lunar Penetrating Radar along the route of the Yutu-2 rover. Using the peak frequency shift method, we calculated the loss tangent of the shallow regolith layer ranged from 3×10 −3 to 5.5×10 −3 . The estimated TiO 2 and FeO content in the regolith is 11.2 wt% - 14.7 wt%, revealing a heterogeneous distribution of iron and titanium content along the CE-4 landing site.

Objectives To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness. Methods A phantom with clinical-relevant densities was imaged on seven DECT scanners with the same voxel size using typical abdominal-pelvis examination protocols. On one DECT scanner, raw data were reconstructed using both conventional IR (adaptive statistical iterative reconstruction-V, ASIR-V) and DLIR. Nine sets of corresponding images were generated on other six DECT scanners using scanner-equipped conventional IR. Regions of interest were delineated through rigid registrations. Image quality was compared. Pyradiomics platform was used for radiomics feature extraction. Test-retest repeatability was assessed by Bland-Altman analysis for repeated scans. Inter-reconstruction algorithm reproducibility between conventional IR and DLIR was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Robust features were identified. Results DLIR significantly improved image quality. Ninety-four radiomics features were extracted and nine features were considered as robust. 93.87% features were repeatable between repeated scans. ASIR-V images showed higher reproducibility to other conventional IR than DLIR (ICC mean, 0.603 vs 0.558, p = 0.001; CCC mean, 0.554 vs 0.510, p = 0.004). 7.45% and 26.83% features were reproducible among scanners evaluated by CV and QCD, respectively. Conclusions DLIR improves quality of DECT images but may alter radiomics features compared to conventional IR. Nine robust DECT radiomics features were identified. Key Points • DLIR improves DECT image quality in terms of signal-to-noise ratio and contrast-to-noise ratio compared with ASIR-V and showed the highest noise reduction rate and lowest peak frequency shift. • Most of radiomics features are repeatable between repeated DECT scans, while inter-reconstruction algorithm reproducibility between conventional IR and DLIR, and inter-scanner reproducibility, are low. • Although DLIR may alter radiomics features compared to IR algorithms, nine radiomics features survived repeatability and reproducibility analysis among DECT scanners and reconstruction algorithms, which allows further validation and clinical-relevant analysis.

In actual industrial applications, the defect detection performance of deep learning models mainly depends on the size and quality of training samples. However, defective samples are difficult to obtain, which greatly limits the application of deep learning-based surface defect detection methods to industrial manufacturing processes. Aiming at solving the problem of insufficient defective samples, a surface defect detection method based on Frequency shift-Convolutional Autoencoder (Fs-CAE) network and Statistical Process Control (SPC) thresholding was proposed. The Fs-CAE network was established by adding frequency shift operation on the basis of the CAE network. The loss of high-frequency information can be prevented through the Fs-CAE network, thereby lowering the interference to defect detection during image reconstruction. The incremental SPC thresholding was introduced to detect defects automatically. The proposed method only needs samples without defects for model training and does not require labels, thus reducing manual labeling time. The surface defect detection method was tested on the air rudder image sets from the image acquisition platform and data augmentation methods. The experimental results indicated that the detection performance of the method proposed in this paper was superior to other surface defect detection methods based on image reconstruction and object detection algorithms. The new method exhibits low false positive rate (FP rate, 0%), low false negative rate (FN rate, 10%), high accuracy (95.19%) and short detection time (0.35 s per image), which shows great potential in practical industrial applications.

A novel, high surface encoding capacity compact planar multiresonator tailored for Chipless RFID tag applications is discussed in this article. The tag consists of three hexagonal open loop resonators that are etched on the ground plane of a 50Ω microstrip transmission line. It operates within the frequency range of 2.12 GHz to 5.45 GHz, with a bandwidth of 3.33 GHz. Frequency Shift Coding is employed to record the tag's identification in the spectral domain. A maximum of 343 distinct code words can be generated utilizing three resonators. A notable feature of this tag is its capability to achieve distinct resonating frequencies by adjusting the overall dimensions of the slot. The tag prototype is designed and fabricated on an RT5880 lossy substrate, characterized by loss tangent of 0.0009 and dielectric constant of 2.2. Experimental data from actual prototypes are presented to verify the dependability of the suggested design.

Recent advances in high-field (≥7 T) MRI have made it possible to study the fine structure of the human brain at the level of fiber bundles and cortical layers. In particular, techniques aimed at detecting MRI resonance frequency shifts originating from local variation in magnetic susceptibility and other sources have greatly improved the visualization of these structures. A recent theoretical study [He X, Yablonskiy DA (2009) Proc Natl Acad Sci USA 106:13558–13563] suggests that MRI resonance frequency may report not only on tissue composition, but also on microscopic compartmentalization of susceptibility inclusions and their orientation relative to the magnetic field. The proposed sensitivity to tissue structure may greatly expand the information available with conventional MRI techniques. To investigate this possibility, we studied postmortem tissue samples from human corpus callosum with an experimental design that allowed separation of microstructural effects from confounding macrostructural effects. The results show that MRI resonance frequency does depend on microstructural orientation. Furthermore, the spatial distribution of the resonance frequency shift suggests an origin related to anisotropic susceptibility effects rather than microscopic compartmentalization. This anisotropy, which has been shown to depend on molecular ordering, may provide valuable information about tissue molecular structure.

In this paper, a blind channel and Doppler frequency shift (DFS) estimation method based on cyclostationarity is proposed for orthogonal frequency division multiplexing (OFDM) transmission under impulse noise environment. The proposed method begins with a compressing transform (CT) applied to the received signal to mitigate the adverse effects of impulse noise on estimation performance. Through analysis, it is found that the energy distributions of the cyclic correlation functions of the OFDM signal and Gaussian white noise (AWGN) before and after CT remain unchanged. Furthermore, it is shown that the energy of the cyclic correlation function of the impulse noise after CT only exists at zero cyclic frequency and delay variables. Based on these properties, we simplify the cyclic spectral functions and construct Toeplitz matrices by carefully selecting cycle frequency and delay variables. This selection is guided by the distinct energy distributions of the cyclic correlation functions corresponding to the OFDM signal, impulse noise, and AWGN, respectively. Thus, a matrix equation based on cyclic spectral functions is further constructed. Based on these analyses, DFS and channel estimation values are derived by solving this equation. The simulation results show that the proposed method has lower mean square errors (MSE).

For water dynamics investigation in unsaturated (vadose) zones, ground penetrating radar is a popular hydro-geophysical method because it is non-invasive for soil, has high resolution and the results have a direct link with water content. Soil water content and soil hydraulic properties are two key factors for describing the water dynamics in vadose zones. There has been tremendous progress in soil water content and soil hydraulic properties estimation with ground penetrating radar. The purpose of this paper is to provide an overview of the application of ground penetrating radar for soil water dynamics studies. This paper first summarizes various methods for the determination of soil water content. including traditional methods in the surveys of surface ground penetrating radar, borehole ground penetrating radar, and off-ground ground penetrating radar, as well as relatively new methods, such as full waveform inversion, the average envelope amplitude method, and the frequency shift method. This paper further provides a review for estimating soil hydraulic properties with GPR according to the types of ground penetrating radar data. We hope that this review can provide a reference for the application of ground penetrating radar in soil water dynamics studies in the future.

To establish ubiquitous and energy-efficient wireless sensor networks (WSNs), short-range Internet of Things (IoT) devices require Bluetooth low energy (BLE) technology, which functions at 2.4 GHz. This study presents a novel approach as follows: a fully integrated all-digital phase-locked loop (ADPLL)-based Gaussian frequency shift keying (GFSK) modulator incorporating two-point modulation (TPM). The modulator aims to enhance the efficiency of BLE communication in these networks. The design includes a time-to-digital converter (TDC) with the following three key features to improve linearity and time resolution: fast settling time, low dropout regulators (LDOs) that adapt to process, voltage, and temperature (PVT) variations, and interpolation assisted by an analog-to-digital converter (ADC). It features a digital controlled oscillator (DCO) with two key enhancements as follows: ΔΣ modulator dithering and hierarchical capacitive banks, which expand the frequency tuning range and improve linearity, and an integrated, fast-converging least-mean-square (LMS) algorithm for DCO gain calibration, which ensures compliance with BLE 5.0 stable modulation index (SMI) requirements. Implemented in a 28 nm CMOS process, occupying an active area of 0.33 mm2, the modulator demonstrates a wide frequency tuning range of from 2.21 to 2.58 GHz, in-band phase noise of −102.1 dBc/Hz, and FSK error of 1.42% while consuming 1.6 mW.

Auditory motion perception is one crucial capability to decode and discriminate the spatiotemporal information for neuromorphic auditory systems. Doppler frequency‐shift feature and interaural time difference (ITD) are two fundamental cues of auditory information processing. In this work, the functions of azimuth detection and velocity detection, as the typical auditory motion perception, are demonstrated in a WOx‐based memristive synapse. The WOx memristor presents both the volatile mode (M1) and semi‐nonvolatile mode (M2), which are capable of implementing the high‐pass filtering and processing the spike trains with a relative timing and frequency shift. In particular, the Doppler frequency‐shift information processing for velocity detection is emulated in the WOx memristor based auditory system for the first time, which relies on a scheme of triplet spike‐timing‐dependent‐plasticity in the memristor. These results provide new opportunities for the mimicry of auditory motion perception and enable the auditory sensory system to be applied in future neuromorphic sensing. A auditory sensory system with motion perception is demonstrated by using a WOx‐based memristive synapse. Due to the coexistence of the volatile mode and semi‐nonvolatile mode in the Ar‐plasma‐treated (APT) WOx memristor, the functions of azimuth detection and velocity detection are realized via implementing the high‐pass filtering and processing the spike trains with frequency shift in both modes, respectively.