Search Results (181)

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article

This paper systematically analyzes the propagation, transformation, and accumulation mechanisms of multi-source noise and device non-idealities within the complete signal chain from the transmitter through the channel to the receiver, focusing on wireless optical coherent communication systems from a signal propagation perspective. It establishes the stepwise propagation process of signals and noise from the transmitter through the atmospheric turbulence channel to the coherent receiver, clarifying the coupling mechanisms and accumulation patterns of various noise sources within the propagation chain. From a signal propagation viewpoint, the study focuses on analyzing the impact mechanisms of factors, such as Mach–Zehnder modulator nonlinear distortion, atmospheric turbulence effects, 90° mixer optical splitting ratio imbalance, and dual-balanced detector responsivity mismatch, on system bit error rate performance and constellation diagrams under conditions of coexisting multiple noises. Simultaneously, by introducing differential and common-mode processes, the propagation and suppression characteristics of additive noise at the receiver end within the balanced detection structure were analyzed, revealing the dominant properties of different noise components under varying optical power conditions. Simulation results indicate that within the range of weak turbulence and engineering parameters, the impact of modulator nonlinearity on system bit error rate is relatively minor compared to channel noise. Atmospheric turbulence dominates system performance degradation through the combined effects of amplitude fading and phase perturbation, causing significant constellation spreading. Imbalanced optical splitting ratios and mismatched responsivity at the receiver weaken common-mode noise suppression, leading to variations in effective signal gain and constellation stretching/distortion. Under different signal light power and local oscillator light power conditions, the system noise exhibits distinct dominant characteristics. Full article

Aiming at the core issues of the traditional YOLO11n model in rail surface defect detection—fine-grained feature loss of small defects, insufficient micro-target recognition accuracy, and the mismatch of existing downsampling/fusion methods for micro-defect feature extraction—this paper proposes an improved YOLO11n algorithm with two-dimensional network structure innovations. First, the Adaptive Downsampling (ADown) module is introduced into the backbone network for the first time, retaining global features via 2D average pooling and extracting local details through channel-split multi-path convolution/max pooling to avoid fine texture loss. Second, the original SOEP-RFPN-MFM neck network is designed, integrating SNI, GSConvE and MFM modules to achieve dynamic weighted fusion of multi-scale features and break the bottleneck of inefficient small-target feature aggregation. Trained and verified on a 4020-image rail dataset covering four defect types (Spalling, Squat, Wheel Burns, Corrugation), the improved algorithm achieves 93.7% detection accuracy, 92.4% recall and 95.6% mAP, realizing incremental improvements of 1.2, 2.6 and 0.8 percentage points, respectively, compared with the original YOLO11n, which is particularly optimized for rail micro-defect detection scenarios. This study provides a new deep learning method for rail transit micro-defect detection and a reference for scenario-specific improvement of lightweight YOLO11n models. Full article

Reducing urban–rural income inequality is a central objective of sustainable development and a critical challenge for inclusive growth in emerging economies. This study examines the impact of the digital economy on urban–rural income inequality in China. Using panel data across 279 cities in China and Chinese Household Income Project (CHIP) data, we find that the digital economy significantly widens urban–rural income inequality. Specifically, digital industries and innovations are the main drivers, contrasting with inclusive infrastructure. Although we observe a positive association between the digital economy and educational attainment among rural residents, such improvements do not appear to be fully translated into higher income. One possible explanation is that the digital economy is more effective in alleviating skill mismatch among urban residents than among their rural counterparts. Finally, our results suggest that Public Employment Services may help mitigate this negative effect through channels such as fiscal subsidies and improving skills training accessibility. Our findings highlight the need for caution regarding the inequality implications of digital economy development in emerging economies and underscore the crucial role of institutional arrangements in promoting socially sustainable and inclusive development. Full article

As an actuation mechanism for achieving precision attitude control in aircraft, the electromechanical actuator (EMA) plays a critical role in ensuring flight safety and stability. However, the EMA is subject to unmeasurable unknown disturbances that act through mismatched channels relative to the system’s control input. To address this, this paper employs feedback linearization to transform the existing model. The transformed model effectively recasts the unknown disturbance into the same channel as the control input, thereby enabling active disturbance rejection via control law design. Furthermore, to overcome the challenge of immeasurable disturbances, an extended state observer (ESO) is designed to estimate the unknown disturbance; the estimated value is then directly utilized in the control law synthesis. Subsequently, a fuzzy logic system (FLS) is developed to perform real-time online adaptation and optimization of the controller parameters. Finally, extensive simulation results are provided to validate the effectiveness of the proposed algorithm. Full article

Weighted sum-rate (WSR) maximization in downlink massive multi-user multiple-input (MU-MIMO) with per-antenna power constraints (PAPCs) and imperfect channel state information (CSI) is computationally challenging. Classical weighted minimum mean-square error (WMMSE) algorithms, in particular, have per-iteration costs that scale cubically with the number of base-station antennas. This article proposes a robust low-complexity WMMSE-based precoding framework (RLC-WMMSE) tailored for massive MU-MIMO downlink under PAPCs and stochastic CSI mismatch. The algorithm retains the standard WMMSE structure but incorporates three key enhancements: a diagonal dual-regularization scheme that enforces PAPCs via a lightweight projected dual ascent with row-wise safety projection; a Woodbury-based transmit update that replaces the dominant $M\times M$ inversion with an $\left(NK\right)\times \left(NK\right)$ symmetric positive-definite solve, greatly reducing the per-iteration complexity; and a hybrid switching mechanism with adaptive damping that blends classical and low-complexity updates to improve robustness and convergence under channel estimation errors. We also analyze computational complexity and signaling overhead for both TDD and FDD deployments. Simulation results over i.i.d. and spatially correlated channels show that the proposed RLC-WMMSE scheme achieves WSR performance close to benchmark WMMSE-PAPCs designs while providing substantial runtime savings and strictly satisfying the per-antenna power limits. These properties make RLC-WMMSE a practical and scalable precoding solution for large-scale MU-MIMO systems in future wireless sensor and communication networks. Full article

In open wireless communication channels, the combined effects of random pulse jamming and multipath-induced time-varying fading significantly degrade the reliability and efficiency of information transmission. Particularly in highly dynamic scenarios such as unmanned aerial vehicle (UAV) communications, existing Q-learning-based anti-jamming methods often rely on idealized channel assumptions, leading to mismatched “transmit/silence” decisions under fading conditions. To address this issue, this paper proposes a Q-learning and time-varying fading channel-aware anti-jamming method against random pulse jamming. In the proposed framework, a fading channel model is incorporated into Q-learning, where the state space jointly represents timeslot position, jamming history, and channel sensing results. Furthermore, a reward function is designed by jointly considering jamming power and channel quality, enabling dynamic strategy adaptation under rapidly varying channels. A moving average process is applied to smooth simulation fluctuations. The results demonstrate that the proposed method effectively suppresses jamming collisions, enhances the successful transmission rate, and improves communication robustness in fast-fading environments, showing strong potential for deployment in practical open-channel applications. Full article

Gallium nitride (GaN)-based high electron mobility transistors (HEMTs) are pivotal for next-generation power-switching applications, but their reliability under high electric fields remains constrained by lattice mismatches and high dislocation densities in heterogeneous substrates. Herein, we systematically investigate the electrical performance and reliability of GaN-on-GaN HEMTs in comparison to conventional GaN-on-SiC HEMTs via DC characterization, reverse gate step stress, off-state drain step stress, and on-state electrical stress tests. Notably, the homogeneous epitaxial structure of GaN-on-GaN devices reduces dislocation density by 83.3% and minimizes initial tensile stress, which is obtained through HRXRD and Raman spectroscopy. The GaN-on-GaN HEMTs exhibit a record BFOM of 950 MW/cm², enabled by a low specific on-resistance (R_ON-SP) of 0.6 mΩ·cm² and a high breakdown voltage (BV) of 755 V. They withstand gate voltages up to −200 V and drain voltages beyond 200 V without significant degradation, whereas GaN-on-SiC HEMTs fail at −95 V (reverse gate stress) and 150 V (off-state drain stress). The reduced dislocation density suppresses leakage channels and defect-induced degradation, as confirmed by post-stress Schottky/transfer characteristics and Frenkel–Poole emission analysis. These findings establish GaN-on-GaN technology as a transformative solution for power electronics, offering a unique combination of high efficiency and long-term stability for demanding high-voltage applications. Full article

We present a parallel DNA molecular analysis platform based on an array of plano-concave Fabry–Pérot (PC-FP) microcavity lasers that enables the simultaneous, sequence-specific detection of multiple DNA targets. Each PC-FP cavity is functionalized with a distinct probe DNA and integrated within a microfluidic channel, allowing localized hybridization and lasing emission upon optical pumping. When Cy3-labeled complementary targets were introduced, distinct lasing peaks emerged from corresponding cavities at ~607 nm, whereas single-base-mismatched sequences produced no measurable signal. The lasing threshold was approximately 0.6 µJ/mm², confirming highly efficient optical feedback and cavity-enhanced signal amplification. The parallel operation of three PC-FP cavities demonstrated independent, multiplexed detection without optical crosstalk. The plano-concave geometry provides mode stability, compact alignment tolerance, and a tenfold reduction in threshold compared to flat FP mirrors. These results highlight the potential of PC-FP microcavity laser arrays as a scalable alternative to fluorescence-based assays, offering rapid, high-throughput DNA hybridization and melting analysis within a miniaturized solid-state architecture. Full article

Background: Cardiac-respiratory dual gating in SPECT (DG-SPECT) is an emergent technique for alleviating motion blurring artifacts in myocardial perfusion imaging (MPI) due to both cardiac and respiratory motions. Moreover, the attenuation artifact may arise from the spatial mismatch between the sequential SPECT and CT attenuation scans due to the dual gating of SPECT data and non-gating CT images. Objectives: This study adapts a four-dimensional (4D) cardiac SPECT reconstruction with post-reconstruction respiratory motion correction (4D-RMC) for dual-gated SPECT. In theory, a respiratory motion-matched attenuation correction (MAC) method is expected to yield more accurate reconstruction results than the conventional motion-averaged attenuation correction (AAC) method. However, its potential benefit is not clear in the presence of practical imaging artifacts in DG-SPECT. In this study, we aim to quantitatively investigate these two attenuation methods for SPECT MPI: 4D-RMC (MAC) and 4D-RMC (AAC). Methods: DG-SPECT imaging (eight cardiac gates and eight respiratory gates) of the NCAT phantom was simulated using SIMIND Monte Carlo simulation, with a lesion (20% reduction in uptake) introduced at four different locations of the left ventricular wall: anterior, lateral, septal, and inferior. For each respiratory gate, a joint cardiac motion-compensated 4D reconstruction was used. Then, the respiratory motion was estimated for post-reconstruction respiratory motion-compensated smoothing for all respiratory gates. The attenuation map averaged over eight respiratory gates was used for each respiratory gate in 4D-RMC (AAC) and the matched attenuation map was used for each respiratory gate in 4D-RMC (MAC). The relative root mean squared error (RMSE), structural similarity index measurement (SSIM), and a Channelized Hotelling Observer (CHO) study were employed to quantitatively evaluate different reconstruction and attenuation correction strategies. Results: Our results show that the 4D-RMC (MAC) method improves the average relative RMSE by as high as 5.42% and the average SSIM value by as high as 1.28% compared to the 4D-RMC (AAC) method. Compared to traditional 4D reconstruction without RMC (“4D (MAC)”), these metrics were improved by as high as 11.23% and 27.96%, respectively. The 4D-RMC methods outperformed 4D (without RMC) on the CHO study with the largest improvement for the anterior lesion. However, the image intensity profiles, the CHO assessment, and reconstruction images are very similar between 4D-RMC (MAC) and 4D-RMC (AAC). Conclusions: Our results indicate that the improvement of 4D-RMC (MAC) over 4D-RMC (AAC) is marginal in terms of lesion detectability and visual quality, which may be attributed to the simple NCAT phantom simulation, but otherwise suggest that AAC may be sufficient for clinical use. However, further evaluation of the MAC technique using more physiologically realistic digital phantoms that incorporate diverse patient anatomies and irregular respiratory motion is warranted to determine its potential clinical advantages for specific patient populations undergoing dual-gated SPECT myocardial perfusion imaging. Full article

The growing demand for traceable, high-precision attenuation measurements in electromagnetic compatibility (EMC) testing and low-frequency wireless communication systems has driven the development of a primary attenuation standard covering 1 kHz to 10 MHz. The system employs a dual channel null-detection method using an inductive voltage divider (IVD) as a reference, ensuring the highest accuracy and traceability while eliminating sensitivity to detector nonlinearity. Attenuation at 1 kHz, 9 kHz, and 10 kHz is measured directly against the IVD ratio, while higher-frequency measurements (100 kHz–10 MHz) are performed via heterodyne detection, down-converting signals to 1 kHz for comparison. To ensure comparable accuracy at higher attenuation levels, a double-step method is applied at 9 kHz and 10 kHz to mitigate the increased IVD uncertainty above 1 kHz. Linearity is ensured by suppressing common-mode currents with toroidal ferrite chokes and minimizing inter-channel coupling. Type B (non-statistical) measurement uncertainties are evaluated, with major contributions from the IVD reference, system errors, and mismatch. The expanded uncertainties are 2.2 × 10⁻³ dB at 20 dB, 3.0 × 10⁻³ dB at 40 dB, and 4.0 × 10⁻³ dB at 60 dB attenuation. To facilitate wider dissemination and extend the calibration range, a resistive step attenuator with 10 dB pads is evaluated as a practical transfer standard, providing a simple and robust solution for traceable attenuation calibration in this frequency range. Full article

This study examines the effect of financial mismatch on corporate ESG performance in the context of China’s developmental strategy and its dual-carbon goals. Using panel data for Chinese A-share firms spanning 2009–2023 and employing fixed-effects regression models, we find that financial mismatch significantly weakens ESG performance. Further analysis reveals that this negative effect mainly operates through three channels: increased financing constraints, weakened internal control quality, and reduced innovation capability. The results remain robust across a series of alternative specifications and sensitivity tests. This study contributes to the literature by identifying financial mismatch as a key determinant of ESG outcomes and by clarifying the mechanisms through which it exerts influence. From a practical perspective, the findings suggest that alleviating financial mismatch by fostering patient capital, improving internal governance structures, and supporting firms’ green and sustainable investments is essential for enhancing corporate ESG performance and achieving China’s dual-carbon targets. Full article

The detection and extraction of weak signals are crucial in various engineering and scientific fields, yet current acoustic sensing technologies are restricted by fundamental pressure detection methods. This paper proposes gradient-equivalent medium-coupled metamaterials (GEMCMs) utilizing strong wave compression and an equivalent medium mechanism to capture weak signals in complex environments and enhance target acoustic signals. Overcoming shape and impedance mismatch limitations of traditional gradient structures, GEMCMs significantly improve control performance. Experimental and numerical simulations indicate that GEMCMs can effectively enhance specific frequency components in acoustic signals, outperforming traditional gradient structures. This enhancement of specific frequency components relies on the resonance effect of the unit cell structure. By introducing acoustic resonance within a spatially wound acoustic channel, a significant amplification of weak acoustic signals is achieved. This provides a new research direction for acoustic wave manipulation and enhancement, and holds significant importance in fields such as mechanical fault diagnosis and medical diagnostics. Full article

Global Navigation Satellite System (GNSS) spoofing, which manipulates PVT solutions through false measurements, increasingly threatens GNSS reliability and user safety. However, most existing simulator-based spoofers, constrained by their inability to access real-time ionospheric information (e.g., Global Ionosphere Maps, GIMs) from external sources, struggle to replicate authentic total electron content (TEC) along each signal propagation path accurately and in a timely manner. In contrast, widespread dual-frequency (DF) receivers with access to the internet can validate local TEC measurements against external references, establishing a pivotal spoofing detection distinction. Here, we propose an Ionospheric Information-Assisted Spoofing Detection Technique (IIA-SDT), exploiting the inherent consistency between TEC values derived from DF pseudo-range measurements and external references in spoofing-free scenarios. Spoofing probably disrupts this consistency: in simulator-based full-channel spoofing where all channels are spoofed, the inaccuracies of the offline ionospheric model used by the spoofer inevitably cause TEC mismatches; in partial-channel spoofing where the spoofer fails to control all channels, an unintended PVT deviation is induced, which also causes TEC deviations due to the spatial variation of the ionosphere. Basic principles and theoretical analysis of the proposed IIA-SDT are elaborated in the paper. Simulations using ionospheric data collected from 2023 to 2024 at a typical mid-latitude location are conducted to evaluate IIA-SDT performance under various parameter configurations. With a window length of 5 s and satellite number of 8, the annual average detection probability approximates 75% at a false alarm rate of $1\times {10}^{-3}$, with observable temporal variations. Field experiments across multiple scenarios further validate the spoofing detection capability of the proposed method. Full article

Intracortical microelectrode arrays (MEAs) are tools for recording and stimulating neural activity, with potential applications in prosthetic control and treatment of neurological disorders. However, when chronically implanted, the long-term functionality of MEAs is hindered by the foreign body response (FBR), characterized by gliosis, neuronal loss, and the formation of a glial scar encapsulating layer. This response begins immediately after implantation and is exacerbated by factors such as brain micromotion and the mechanical mismatch between stiff electrodes and soft brain tissue, leading to signal degradation. Despite progress in mitigating these issues, the underlying mechanisms of the brain’s response to MEA implantation remain unclear, particularly regarding how cells sense and respond to the associated mechanical forces. Mechanosensitive ion channels, such as the Piezo family, are key mediators of cellular responses to mechanical stimuli. In this study, silicon-based NeuroNexus MEAs consisting of four shanks were implanted in the rat somatosensory cortex for sixteen weeks. Weekly neural recordings were conducted to assess signal quality over time, revealing a decline in active electrode yield and signal amplitude. Immunohistochemical analysis showed an increase in GFAP intensity and decreased neuronal density near the implant site. Furthermore, Piezo1—but not Piezo2—was strongly expressed in GFAP-positive astrocytes within 25 µm of the implant. Piezo2 expression appeared relatively uniform within each brain slice, both in and around the MEA implantation site across cortical layers. Our study builds on previous work by demonstrating a potential role of Piezo1 in the chronic FBR induced by MEA implantation over a 16-week period. Our findings highlight Piezo1 as the primary mechanosensitive channel driving chronic FBR, suggesting it may be a target for improving MEA design and long-term functionality. Full article