Search Results (3,213)

Accurate pattern transfer in the lithography process demands extreme positioning accuracy. However, various external disturbances acting on the wafer stage can lead to positioning errors. To address this issue, this paper proposes a pole-placement-based Variable-Gain Extended State Observer (VGESO). First, the trade-off between disturbance rejection and noise attenuation faced by conventional Extended State Observers (ESOs) in precision motion systems is analyzed. Then, a modified ESO structure is introduced, in which two pole-related parameters are employed to adaptively adjust the observer gains. These parameters enable effective suppression of both low-frequency disturbances and high-frequency measurement noise within their designated ranges. Finally, simulation results verify the effectiveness and superior performance of the proposed method. Full article

This paper presents an all-digital fractional-N phase-locked loop (ADPLL) operating in the 2.86–3.2 GHz range, optimized for IoT and high-frequency RF transceiver applications demanding stringent phase noise performance, fast settling time, and high integration capability. The key innovation lies in the introduction of a bandpass delta-sigma time-to-digital converter (BPDSTDC) that achieves high-resolution phase detection, an extended detection range of ±2$\pi$, and superior noise-shaping characteristics, completely eliminating the complex calibration procedures typically required in conventional TDC designs. The proposed architecture synergistically combines the BPDSTDC with digital down-conversion blocks to extract phase error at baseband, a divider chain integrated with phase interpolators achieving 1/4 fractional resolution to suppress in-band quantization noise, and a wide-bandwidth digital loop filter (>1 MHz) ensuring fast dynamic response and robust stability. The bandpass delta-sigma modulator is implemented with compact resonator structures and a flash quantizer, achieving an optimal balance among resolution, power consumption, and silicon area. The incorporation of highly linear phase interpolators extends fractional frequency synthesis capability without requiring complex digital-to-time converters (DTCs), significantly reducing design complexity and calibration overhead. Fabricated in a 180-nm CMOS technology, the proposed chip demonstrates robust measured performance. The band-pass delta-sigma TDC achieves a low integrated rms timing noise of 183 fs within a 1-MHz bandwidth. Leveraging this low TDC noise, the complete ADPLL exhibits a measured in-band phase noise of −120 dBc/Hz at a 1-MHz offset for a 3.2-GHz output frequency while operating with a loop bandwidth exceeding 1 MHz. This corresponds to a normalized phase noise of −216 dBc/Hz. The system operates from a 1.8-V supply and consumes 10 mW, achieving competitive performance compared with prior noise-shaping TDC-based all-digital PLLs. Full article

The emergence of radio frequency machine learning has significantly propelled the application of deep learning (DL) methods in automatic modulation classification (AMC). However, under non-cooperative scenarios, the performance of DL-based AMC suffers severe performance degradation due to scarce labeled samples and noise interference. To enhance noise robustness in few-shot AMC, this paper proposes a complex-domain autoencoder-based method where a complex-valued noise reduction network (CNRN) is embedded into the AMC framework, jointly extracting complex-valued and temporal features from noisy signals to achieve signal–noise separation. Our framework executes four sequential operations: high-signal-to-noise-ratio (high-SNR) samples are first isolated from limited raw data via unsupervised classification; rotation and cyclic time-shifting operations then augment the sample space; the CNRN is subsequently trained on augmented data; and final AMC classification is implemented through DL-based classifiers. Experimental validation on RML 2016.10a dataset demonstrates: (1) for −20 dB signals, denoising achieves 20.18 dB SNR improvement with 87.74% mean squared error reduction; (2) across the −20 dB to 18 dB range, denoised signals exhibit accuracy improvements of 21.57% under DL-based classifiers. Physical validation further confirms that the proposed method exhibits enhanced noise robustness, demonstrating its practical utility in real-world scenarios. Full article

Spatial resolution is a critical parameter for optical frequency domain reflectometry (OFDR). Phase-sensitive OFDR (Φ-OFDR) measures strain by detecting phase variations between adjacent sampling points, having the potential to achieve the theoretical limitation of spatial resolution. However, the results of Φ-OFDR suffer from large fluctuations due to multiple types of noise, including coherent fading and system noise. This work presents an OFDR-based strain sensing method that combines phase demodulation with cross-correlation analysis to achieve high spatial resolution. In the phase demodulation, the frequency-shift averaging (FSAV) and rotating vector summation (RVS) algorithms are first employed to suppress coherent fading noise and achieve accurate strain localization. Then the cross-correlation approach with an adaptive window is proposed. Guided by the accurate strain boundary obtained from phase demodulation, the length and position of the cross-correlation window are automatically adjusted to fit for continuous and uniform strain regions. As a result, an accurate and complete strain distribution along the entire fiber is finally obtained. The experimental results show that, within a strain range of 100–700 με, the method achieves a spatial resolution of 0.27 mm for the strain boundary, with a root-mean-square error approaching 0.94%. The processing time reaches approximately 0.035 s, with a demodulation length of 1.6 m. The proposed approach offers precise spatial localization of the strain boundary and stable strain measurement, demonstrating its potential for high-resolution OFDR-based sensing applications. Full article

Automatic Music Transcription (AMT) plays a fundamental role in Music Information Retrieval (MIR) by converting raw audio signals into symbolic representations such as MIDI or musical scores. Despite advances in deep learning, accurately transcribing piano performances remains challenging due to dense polyphony, wide dynamic range, sustain pedal effects, and harmonic interactions between simultaneous notes. Existing approaches using convolutional and recurrent architectures, or autoregressive models, often fail to capture long-range temporal dependencies and global harmonic structures, while conventional Vision Transformers overlook the anisotropic characteristics of audio spectrograms, leading to harmonic neglect. In this work, we propose SpectTrans, a novel piano transcription framework that integrates a Spectral Gating Network with a multi-head self-attention Transformer to jointly model spectral and temporal dependencies. Latent CNN features are projected into the frequency domain via a Real Fast Fourier Transform, enabling adaptive filtering of overlapping harmonics and suppression of non-stationary noise, while deeper layers capture long-term melodic and chordal relationships. Experimental evaluation on polyphonic piano datasets demonstrates that this architecture produces acoustically coherent representations, improving the robustness and precision of transcription under complex performance conditions. These results suggest that combining frequency-domain refinement with global temporal modeling provides an effective strategy for high-fidelity AMT. Full article

Millimeter-wave technology helps achieve antenna miniaturization and high gain, but it is limited by factors such as short wavelength, high transmission loss, and high signal-to-noise ratio, which put higher requirements on the accuracy and computing speed of signal processing methods. The weak signal detection method based on the Duffing oscillator is suitable for detecting and estimating the parameters of such signals, but its intermittent chaotic state brings difficulties in phase determination and limited frequency detection accuracy. This article proposes a Heterodyne Duffing equation, which analyzes system properties through bifurcation diagrams, timing diagrams, and phase diagrams. Based on this, signal detection and frequency estimation models are designed, and frequency detection accuracy and calculation time are discussed. The analysis and simulation results show that the phase state discrimination speed and accuracy of the Heterodyne Duffing oscillator (HDO) are superior to the traditional Duffing equation-based intermittent chaotic state method. It has adjustable frequency resolution, overcomes the inherent 0.03ω frequency detection error limitation of the traditional Duffing oscillator, and has a significant advantage in phase state discrimination speed. The frequency estimation method based on the proposed HDO can better meet the frequency resolution and real-time requirements of millimeter-wave sensor signals. Full article

High-spectral-resolution retrievals of nitrogen dioxide (NO₂) provide detailed atmospheric absorption information, but they usually involve large data volume, low computational efficiency, and complex instrument requirements. To address these limitations, we employ a low-spectral-information retrieval strategy for fast atmospheric monitoring. In this study, the Discrete-Wavelength Differential Optical Absorption Spectroscopy (DWDOAS) technique is applied by selecting 14 representative wavelength samples in the 420–450 nm window. Multiple wavelength–resolution configurations are constructed and quantitatively assessed using an entropy-weighting scheme to identify the optimal setup. Using TROPOspheric Monitoring Instrument (TROPOMI) and Environmental Trace Gases Monitoring Instrument (EMI-II) measurements as case studies, we show that at a spectral resolution of ~2 nm, DWDOAS-derived NO₂ vertical column density (VCD) are highly consistent with those from conventional DOAS retrievals (correlation coefficient R > 0.7) and exhibit relative differences of approximately ±30%. Monte Carlo simulations further demonstrate method robustness, yielding mean uncertainties below 2 × 10¹⁴ molecules·cm⁻². The results indicate that DWDOAS effectively suppresses high-frequency spectral noise while preserving key differential absorption structures, thereby achieving a favorable trade-off between information retention and noise robustness. Nevertheless, increased retrieval uncertainty is observed under low-NO₂ background conditions or strong aerosol loading, which reduces sensitivity to weak absorption features. Overall, this study confirms that reliable NO₂ retrieval performance can be maintained while substantially reducing spectral information requirements, offering practical implications for low-resolution spectrometer design, onboard data compression, and rapid, wide-area atmospheric trace-gas monitoring. Full article

Low-frequency noise, primarily generated by transformers and electrical machinery in substations, presents considerable environmental and health risks due to its strong penetration and minimal attenuation. Conventional noise control methods often fail to effectively absorb such low-frequency sounds. In response to this challenge, acoustic metamaterials featuring unique subwavelength structures have emerged as a promising solution for absorbing low-frequency and broadband noise. This study introduces a novel sound-absorbing metamaterial that integrates parallel-connected Helmholtz resonators with a helical cavity structure. To enhance its performance across a broad frequency range, the metamaterial is optimized using a genetic algorithm. Experimental validation, based on 3D-printed samples and impedance tube measurements, demonstrates high absorption efficiency at target frequencies (100 Hz, 300 Hz, and 500–1300 Hz), with absorption coefficients exceeding 0.9. The results confirm that the metamaterial effectively reduces low-frequency core noise. This work represents a significant advancement in noise control technologies for substations, with broader implications for urban noise mitigation and environmental protection. Full article

This paper examines precise timestamping of LoRaWAN messages (particularly beacons) to enable wide-area synchronization for end devices without GNSS. The need for accuracy demands hardware-level timestamping architectures, possibly using time-domain cross-correlation (matched filtering) against internally generated chirp references. Focusing on Time-of-Arrival (TOA) estimation from raw IQ samples, the authors analyze effects of non-idealities—additive white Gaussian noise (AWGN), Carrier Frequency Offset (CFO), Sampling Phase and Frequency Offset (SPO and SFO, respectively), and radio parameters such as spreading factor (SF) and sampling rate of the baseband signals. A MATLAB (R2020) simulation mimics preamble detection and Start-of-Frame Delimiter (SFD) timestamping while sweeping SF (7, 9, 12), sampling rates (0.25–10 MSa/s), SNR (−20 to +20 dB), and CFO/SFO offsets (−10–10 ppm frequency deviation). Errors are evaluated in terms of mean and dispersion, the latter represented by the P95–P5 range metric. Results show that oversampling not only improves temporal resolution, but sub-microsecond error dispersion can be achieved with high sampling rates in favorable SNR and SF cases. Indeed, SPO and SNR greatly contribute to error dispersion. On the other hand, higher SF values increase correlation robustness at the cost of longer chirps, making SFO a dominant error source; ±10 ppm SFO can induce roughly ±3 μs SFD bias for SF12. CFO largely cancels after up-/down-chirp averaging. As a concluding remark, matched-filter hardware timestamping can ensure sub-μs errors thanks to oversampling but requires SFO compensation for accurate real-world synchronization in practice. Full article

The high peak-to-average power ratio (PAPR) in classical high-speed digital data transmission systems with orthogonal frequency division multiplexing (OFDM) limits energy efficiency and communication range. This paper proposes a method for randomizing OFDM signals via frequency coding using synthesized pseudorandom sequences with improved autocorrelation properties, obtained through machine learning, to minimize PAPR in complex, non-stationary hydroacoustic channels for communicating with underwater robotic systems. A neural network architecture was developed and trained to generate codes of up to 150 elements long based on an analysis of patterns in previously found best short sequences. The obtained class of OFDM signals does not require regular and accurate estimation of channel parameters while remaining resistant to various types of impulse noise, Doppler shifts, and significant multipath interference typical of the underwater environment. The attained spectral efficiency values (up to 0.5 bits/s/Hz) are relatively high for existing hydroacoustic communication systems. It has been shown that the peak power of such multi-frequency information transmission systems can be effectively reduced by an average of 5–10 dB, which allows for an increase in the communication range compared to classical OFDM methods in non-stationary hydrological conditions at acceptable bit error rates (from 10⁻² to 10⁻³ and less). The effectiveness of the proposed methods of randomization with synthesized codes and frequency coding for OFDM signals was confirmed by field experiments at sea on the shelf, over distances of up to 4.2 km, with sea waves of up to 2–3 Beaufort units and mutual movement of the transmitter and receiver. Full article

The increasing integration of hybrid AC/DC networks in modern power systems introduces new challenges in fault detection and grounding scheme design, necessitating advanced techniques for stable and reliable operation. This paper investigates fault detection and grounding schemes in hybrid AC/DC networks using a machine learning (ML) approach to enhance accuracy, speed, and adaptability. Traditional methods often struggle with the dynamic and complex nature of hybrid systems, leading to delayed or incorrect fault identification. To address this, we propose a data-driven ML framework that leverages features such as voltage, current, and frequency characteristics for real-time detection and classification of faults. Additionally, the effectiveness of various grounding schemes is analyzed under different fault conditions to ensure system stability and safety. Simulation results on a hybrid AC/DC test network demonstrate the superior performance of the proposed ML-based fault detection method compared to conventional techniques, achieving high precision, recall, and robustness against noise and varying operating conditions. The findings highlight the potential of ML in improving fault management and grounding strategy optimization for future hybrid power grids. Full article

Building extraction from remote sensing imagery plays a pivotal role in applications such as smart cities, urban planning, and disaster assessment. Although deep learning has significantly advanced this task, existing methods still struggle to strike an effective balance among global semantic understanding, local detail recovery, and multi-scale contextual awareness—particularly when confronted with challenges including extreme scale variations, complex spatial distributions, occlusions, and ambiguous boundaries. To address these issues, we propose TriadFlow-Net, an efficient end-to-end network architecture. First, we introduce the Multi-scale Attention Feature Enhancement Module (MAFEM), which employs parallel attention branches with varying neighborhood radii to adaptively capture multi-scale contextual information, thereby alleviating the problem of imbalanced receptive field coverage. Second, to enhance robustness under severe occlusion scenarios, we innovatively integrate a Non-Causal State Space Model (NC-SSD) with a Densely Connected Dynamic Fusion (DCDF) mechanism, enabling linear-complexity modeling of global long-range dependencies. Finally, we incorporate a Multi-scale High-Frequency Detail Extractor (MHFE) along with a channel–spatial attention mechanism to precisely refine boundary details while suppressing noise. Extensive experiments conducted on three publicly available building segmentation benchmarks demonstrate that the proposed TriadFlow-Net achieves state-of-the-art performance across multiple evaluation metrics, while maintaining computational efficiency—offering a novel and effective solution for high-resolution remote sensing building extraction. Full article

To meet the demand for water-lubricated bearings (WLBs) with low vibration, low noise and high load-carrying capacity in propulsion systems, this study designed and tested a three-layer composite WLB consisting of an inner phenolic working layer, a middle rubber damping layer and a glass-fiber-reinforced composite layer. The lubrication, vibration and wear behaviors of three bearings with different groove structures, namely a non-grooved bushing, a fully straight-grooved bushing and a fully spiral-grooved bushing, were comparatively investigated under combined variations in rotational speed (20–400 r/min), specific pressure (0.18–0.8 MPa) and water flow rate (5–20 L/min). The results demonstrate that both specific pressure and flow rate strongly govern the transition from mixed lubrication to hydrodynamic lubrication and the associated vibration response. As the specific pressure and water flow rate increase, the transition speed and coefficient of friction of grooved bearings, particularly straight-grooved bearings, increase markedly. Non-grooved bearings consistently maintain the lowest levels, while spiral-grooved bearings exhibit lubrication performance intermediate between the above two types. Under low-speed and heavy-load conditions, non-grooved bearings show the smallest increase in vibration amplitude. Grooves amplify high-frequency vibrations and inject medium- and high-frequency energy as rotational speed increases. Considering lubrication, vibration control, and wear resistance simultaneously, spiral-grooved bearings exhibit the most robust overall performance under realistic operating conditions. The results provide experimental evidence and practical design guidance for groove-structure selection in multi-layer composite WLBs operating under low-speed and heavy-load conditions. Full article

This work presents the design and experimental validation of a 2 × 2 MIMO communication system assisted by a directly modulated analog radio-over-fiber (A-RoF) fronthaul, targeting low-complexity connectivity solutions for underserved/remote regions. The study details the complete end-to-end architecture, including a wireless access segment to complement the 20-km optical fronthaul link. The system is implemented on an software defined radio (SDR) platform using GNU Radio 3.7.11, running on Ubuntu 18.04 with kernel 4.15.0-213-generic. It also employs adaptive modulation driven by real-time signal-to-noise ratio (SNR) estimation to keep bit error rate (BER) close to zero while maximizing throughput. Performance is characterized over 20 km of single-mode fiber (SMF) using coarse wavelength division multiplexing (WDM) and assessed through root mean square error vector magnitude (${\mathrm{EVM}}_{\mathrm{RMS}}$), throughput, and spectral integrity. The results identify an optimum radio-frequency drive region around $-16$ dBm enabling high-order modulation (e.g., 256-QAM), whereas RF input powers above approximately $-10$ dBm increase ${\mathrm{EVM}}_{\mathrm{RMS}}$ due to nonlinearity in the RF front-end/low-noise amplifier (LNA) and direct modulation stage, forcing the adaptive scheme to reduce modulation order and throughput. Over the optical-power sweep, when the incident optical power exceeds approximately $-8$ dBm, the system reaches ∼130 Mbps (24-MHz channel) with ${\mathrm{EVM}}_{\mathrm{RMS}}$ approaching ∼1%, highlighting the need for careful joint tuning of RF drive, optical launch power, and wavelength allocation across transceivers. Finally, the integrated access link employs diplexers for transmitter/receiver separation in a 2 × 2 configuration with 2.8 m antenna separation and low channel correlation, demonstrating a 10 m proof-of-concept range and enabling end-to-end spectrum/EVM/throughput observations across the full communication chain. Full article

Targeting the thinly bedded and strongly heterogeneous tight sandstone gas reservoirs of the Shaximiao Formation in the Yingshan area of the Sichuan Basin, this study establishes an integrated workflow that combines high-fidelity 3D seismic processing with quantitative interpretation to address key challenges such as insufficient resolution of conventional seismic data under complex near-surface conditions and difficulty in depicting sand-body geometries. On the processing side, a 2D-3D integrated amplitude-preserving high-resolution strategy is applied. In contrast to conventional workflows that treat 2D and 3D datasets independently and often sacrifice true-amplitude characteristics during static correction and noise suppression, the proposed approach unifies first-break picking and static-correction parameters across 2D and 3D data while preserving relative amplitude fidelity. Techniques such as true-surface velocity modeling, coherent-noise suppression, and wavelet compression are introduced. As a result, the effective frequency bandwidth of the newly processed data is broadened by approximately 10–16 Hz relative to the legacy dataset, and the imaging of small faults and narrow river-channel boundaries is significantly enhanced. On the interpretation side, ten sublayers within the first member of the Shaximiao Formation are correlated with high precision, yielding the identification of 41 fourth-order local structural units and 122 stratigraphic traps. Through seismic forward modeling and attribute optimization, a set of sensitive attributes suitable for thin-sandstone detection is established. These attributes enable fine-scale characterization of sand-body distributions within the shallow-water delta system, where fluvial control is pronounced, leading to the identification of 364 multi-phase superimposed channels. Based on attribute fusion, rock-physics-constrained inversion, and integrated hydrocarbon-indicator analysis, 147 favorable “sweet spots” are predicted, and six well locations are proposed. The study builds a reservoir-forming model of “deep hydrocarbon generation–upward migration, fault-controlled charging, structural trapping, and microfacies-controlled enrichment,” achieving high-fidelity imaging and quantitative prediction of tight sandstone reservoirs in the Shaximiao Formation. The results provide robust technical support for favorable-zone evaluation and subsequent exploration deployment in the Yingshan area. Full article