Search Results (2,714)

This review addresses the challenges of obtaining high-quality quantitative data in the optical imaging of membrane voltage and calcium dynamics. The paper provides a comprehensive overview and systematization of recent studies that analyze factors limiting signal fidelity and propose strategies to enhance data quality. The primary sources of signal degradation in biological optical imaging, with an emphasis on membrane voltage and calcium imaging, are systematically explored across four major indicator classes: voltage-sensitive dyes (VSDs), genetically encoded voltage indicators (GEVIs), calcium-sensitive dyes (CSDs), and genetically encoded calcium indicators (GECIs). Common mechanisms that compromise data quality are classified into three main categories: fundamental photon shot noise, device-related errors, and sample-related measurement errors. For each class of limitation, its physical or biological origin and characteristic manifestations are described, which are followed by an analysis of available mitigation strategies, including hardware optimization, choice of sensors, sample preparation and experimental design, post-processing and computational correction methods. Full article

Weak-signal fluorescence channels (e.g., 4′,6-diamidino-2-phenylindole (DAPI)) often fail to provide reliable structural details due to low signal-to-noise ratio (SNR) and insufficient high-frequency information, limiting the ability of single-channel super-resolution methods to restore edge continuity and texture. This study proposes a multi-channel guided super-resolution method based on SwinIR, utilizing the high-SNR fluorescein isothiocyanate (FITC) channel as a structural reference. Dual-channel adaptation is implemented at the model input layer, enabling the window attention mechanism to fuse cross-channel correlation information and enhance the structural recovery capability of weak-signal channels. To address the loss of high-frequency information in weak-signal imaging, we introduce a frequency-domain consistency loss: this mechanism constrains spectral consistency between the predicted and true images in the Fourier domain, improving the clarity of fine-structure reconstruction. Experimental results on the DAPI channel demonstrate significant improvements: PSNR increases from 27.05 dB to 44.98 dB, and SSIM rises from 0.763 to 0.960. Visual analysis indicates that this method restores more continuous nuclear edges and weak textural details while suppressing background noise; frequency-domain results reduce the minimum resolvable feature size from approximately 1.5 μm to 0.8 μm. In summary, multi-channel structural information provides an effective and physically interpretable deep learning approach for super-resolution reconstruction of weak-signal fluorescence images. Full article

This study tackles real-time state estimation for autonomous underwater vehicle (AUV)–mobile docking station (MDS) cooperation over low-bandwidth, high-latency, jitter-dominated acoustic links, with the goal of turning delayed/out-of-sequence measurements (OOSM) into consistent and informative constraints without sacrificing online operation. We propose an integrated scheme centered on a delay-compensated extended Kalman filter (DC-EKF): a ring buffer enables backward updates and forward replay so that OOSM are absorbed strictly at their physical timestamps; a data-driven delay threshold is learned from “effective information gain” combined with normalized estimation error squared (NEES) filtering; and dynamic confidence, derived from innovation statistics, delay, and signal-to-noise ratio (SNR) proxies, scales the measurement noise to adapt fusion weights. Simulations show the learned delay threshold converges to about 6.4 s (final 6.35 s), error spikes are suppressed, and the overall position root-mean-square error (RMSE) is 5.751 m; across the full data stream, 1067 station measurements were accepted and 30 rejected, and the fusion weights shifted smoothly from inertial measurement unit (IMU)-dominant to station-dominant (≈0.16/0.84) over time. On this basis, a cooperative augmented EKF (Co-Aug-EKF) is added as a lightweight upper layer for unified-frame cooperative estimation, further improving relative consistency. The results indicate that the framework reliably maps delayed acoustic measurements into closed-loop useful information, significantly enhancing estimator stability and docking readiness, while remaining practical to deploy and readily extensible. Full article

This paper systematically reviews the key spectral information extraction methods in Brillouin microscopy, aiming to address the core challenge of accurately extracting material mechanical parameters from raw spectra. Based on technical principles, the methods are categorized into three types for elaboration: Spontaneous Brillouin Scattering (SpBS) is characterized by low signal-to-noise ratio (SNR) and strong background interference, and its processing relies on high-precision spectrometers and complex preprocessing procedures to mitigate noise and background effects; Stimulated Brillouin Scattering (SBS) operates on the mechanism of optical gain/loss, which achieves significantly improved data SNR and thereby enables more robust and accurate Lorentzian fitting for spectral analysis; Impulsive Stimulated Brillouin Scattering (ISBS) retrieves the frequency spectrum by inverting time-domain oscillating signals, and the core of its processing lies in super-resolution algorithms such as Fast Fourier Transform (FFT) and the Matrix Pencil Method, which are tailored to match its high-speed data acquisition capability. The paper further compares the advantages and disadvantages of various methods, outlines future development trends of intelligent processing technologies such as deep learning and multi-modal data fusion, and provides a clear guide for selecting the optimal data processing strategy in different application scenarios. Full article

Automatic modulation classification (AMC) under low signal-to-noise ratio (SNR) and complex channel conditions remains a significant challenge due to the trade-off between robustness and efficiency. This study proposes a lightweight temporal convolutional network (TCN) and Mamba fusion architecture designed to enhance modulation recognition performance. In the modulation signal denoising stage, a non-local adaptive thresholding denoising module (NATM) is introduced to explicitly improve the effective signal-to-noise ratio. In the parallel feature extraction stage, TCN captures local symbol-level dependencies, while Mamba models long-range temporal relationships. In the output stage, their outputs are integrated through additive layer-wise fusion, which prevents parameter explosion. Experiments were conducted on the RadioML 2016.10A, 2016.10B, and 2018.01A datasets with leakage-controlled partitioning strategies including GroupKFold and Leave-One-SNR-Out cross-validation. The proposed method achieves up to a 3.8 dB gain in the required signal-to-noise ratio at 90 percent accuracy compared with state-of-the-art baselines, while maintaining a substantially lower parameter count and reduced inference latency. The denoising module provides clear robustness improvements under low signal-to-noise ratio conditions, particularly below −8 dB. The results show that the proposed network strikes a balance between accuracy and efficiency, highlighting its application potential in real-time wireless receivers under resource constraints. Full article

Due to the high mobility, flexibility, and low cost, unmanned aerial vehicles (UAVs) can provide an efficient way for provisioning data communication and computing offloading services for massive Internet of Things (IoT) devices, especially in remote areas with limited infrastructure. However, current transmission schemes for unmanned aerial vehicle-assisted Internet of Things (UAV-IoT) predominantly employ polling scheduling, thus not fully exploiting the potential multiuser diversity gains offered by a vast number of IoT nodes. Furthermore, conventional opportunistic scheduling (OS) or opportunistic beamforming techniques are predominantly designed for downlink transmission scenarios. When applied directly to uplink IoT data transmission, these methods can incur excessive uplink training overhead. To address these issues, this paper first proposes a low-overhead multi-UAV uplink OS framework based on channel reciprocity. To avoid explicit massive uplink channel estimation, two scheduling criteria are designed: minimum downlink interference (MDI) and the maximum downlink signal-to-interference-plus-noise ratio (MD-SINR). Second, for a dual-UAV deployment scenario over Rayleigh block fading channels, we derive closed-form expressions for both the average sum rate and the asymptotic sum rate based on the MDI criterion. A degrees-of-freedom (DoF) analysis demonstrates that when the number of sensors, K, scales as ${\rho }^{\alpha }$, the system can achieve a total of $2\alpha$ DoF, where $\alpha \in \left[0,1\right]$ is the user-scaling factor and $\rho$ is the transmitted signal-to-noise ratio (SNR). Third, for a three-UAV deployment scenario, the Gamma distribution is employed to approximate the uplink interference, thereby yielding a tractable expression for the average sum rate. Simulations confirm the accuracy of the performance analysis for both dual- and three-UAV deployments. The normalized error between theoretical and simulation results falls below 1% for K > 30. Furthermore, the impact of fading severity on the system’s sum rate and DoF performance is systematically evaluated via simulations under Nakagami-m fading channels. The results indicate that more severe fading (a smaller m) yields greater multiuser diversity gain. Both the theoretical and simulation results consistently show that within the medium-to-high SNR regime, the dual-UAV deployment outperforms both the single-UAV and three-UAV schemes in both Rayleigh and Nakagami-m channels. This study provides a theoretical foundation for the adaptive deployment and scheduling design of UAV-assisted IoT uplink systems under various fading environments. Full article

Utilizing unmanned aerial vehicles (UAVs) to achieve accurate direction finding of radiation sources in hazardous and complex regions is an important means of information recon- naissance. However, the significant multipath effects of UAVs in complex environments cause serious signal coherence problems. Conventional signal decoherence techniques such as spatial smoothing (SS) and matrix reconstruction suffer from array aperture loss, which makes it difficult to meet the requirements of UAVs for high-resolution direction finding in severe multipath environments. Therefore, resolving the signal coherence problem has become a key bottleneck for high-resolution direction-of-arrival (DOA) estimation techniques in severe multipath environments. This paper proposes a joint high-precision DOA estimation method based on conjugate cross-correlation Toeplitz reconstruction and the Parallel Factor Analysis (PARAFAC) tensor model. First, we introduce the conjugate cross-correlation values of array element data collected by the UAV to conduct Toeplitz reconstruction without dimensionality-reduced reconstruction, achieving signal decoherence. Furthermore, we conduct cross-snapshot cross-correlation between the reconstruction matrix and the data of each array element collected by the UAV, which effectively suppresses noise accumulation and improves the signal-to-noise ratio (SNR). Finally, we stack the set of matrices into a three-dimensional tensor, employing PARAFAC tensor decomposition to enhance the UAV DOA estimation performance. Simulation results show that at low SNR, the proposed method can effectively improve estimation accuracy and solve the problem of signal correlation in strong multipath scenarios that limits traditional UAV lateral methods. Full article

Electroencephalography (EEG) signal acquisition is often affected by artifacts, challenging applications such as brain disease diagnosis and Brain-Computer Interfaces (BCIs). This paper proposes TF-Denoiser, a deep learning model using a joint time-frequency optimisation strategy for artifact removal. The proposed method first employs a position embedding module to process EEG data, enhancing temporal feature representation. Then, the EEG signals are transformed from the time domain to the complex frequency domain via Fourier transform, and the real and imaginary parts are denoised separately. The multi-attention denoising module (MA-denoise) is used to extract both local and global features of EEG signals. Finally, joint optimisation of time-frequency features is performed to improve artifact removal performance. Experimental results demonstrate that TF-Denoiser outperforms the compared methods in terms of correlation coefficient (CC), relative root mean square error (RRMSE), and signal-to-noise ratio (SNR) on electromyography (EMG) and electrooculography (EOG) datasets. It effectively reduces ocular and muscular artifacts and improves EEG denoising robustness and system stability. Full article

Frequency-Modulated Continuous Wave (FMCW) lidar for long-distance measurements face challenges in signal acquisition and frequency estimation due to the high sampling rates required, leading to increased processing load, cost, and power consumption. Although sub-Nyquist sampling can alleviate the burden of high sampling rates, it requires a complex reconstruction process that degrades real-time performance. In this study, we propose a frequency estimation algorithm based on multi-coset sampling (MCS) that not only reduces the sampling rate but also avoids reconstructing the original signal. This algorithm performs a preliminary frequency estimation by exploiting the relationship among the signal support set, the measured sequences by sampling spectrum, and the original signal spectrum, and then refines the spectrum to obtain an accurate frequency estimate. Since the algorithm relies solely on the sampled sequences for estimation, frequency ambiguity may occur during the calculation. We analyze the causes of ambiguity and propose a support set determination method to eliminate this issue. Simulation results demonstrate that the proposed algorithm attains the Cramér–Rao lower bound (CRLB) at low signal-to-noise ratios. It achieves a 10-fold improvement over Nyquist method and a 35 dB SNR reduction compared with the original MCS, while maintaining stable performance down to −20 dB. Full article

Background: High-frequency ultrasonography (US) is increasingly used to guide closed reduction in nasal bone fractures, but near-field resolution over the curved nasal dorsum depends critically on the acoustic coupling medium. We aimed to determine whether a semi-solid standoff gel pad (PAD) provides superior image contrast and signal stability compared with a liquid gel barrier (LGB) during intraoperative nasal bone fracture sonography. Methods: In this prospective, single-center, within-subject crossover study, 30 adults with isolated nasal bone fractures underwent intraoperative high-frequency US of the nasal dorsum under two coupling conditions differing only by the medium used: a 7 mm hydrogel standoff pad (PAD) and a custom-made 7 mm liquid gel barrier (LGB). All scans were acquired on the same platform using fixed B-mode presets (10 MHz, 4.0 cm depth, single focal zone at the cortex). Rectangular regions of interest (ROIs) were placed on the cortical interface (bone ROI) and adjacent soft tissue (soft-tissue ROI) at matched depth. For each subject and condition, contrast-to-noise ratio (CNR) and two signal-to-noise ratios (SNR_bone, SNR_soft) were derived from ROI gray-level statistics and compared using paired t-tests. Results: The PAD yielded a significantly higher CNR at the cortical interface compared to the LGB (3.46 ± 0.17 vs. 2.50 ± 0.19; mean paired difference 0.96, 95% CI 0.88–1.04; p < 0.0001). SNR_bone was also higher with PAD (4.31 ± 0.35 vs. 3.63 ± 0.34; difference 0.68, 95% CI 0.52–0.83; p < 0.0001). Using the soft-tissue ROI as the noise reference (SNR_soft), PAD again outperformed LGB (7.64 ± 0.73 vs. 6.68 ± 0.78; difference 0.96, 95% CI 0.59–1.33; p = 0.000012). Conclusions: Compared with a liquid gel barrier of similar thickness, a semi-solid standoff gel pad provides higher near-field CNR and SNR at the nasal cortical interface under standardized intraoperative conditions. These quantitative differences support the use of a gel pad as a practical coupling medium for real-time ultrasound guidance during closed reduction in nasal bone fractures, although the impact on clinical outcomes remains to be determined. Full article

Frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) is a high-precision ranging and imaging system that has been widely used in various areas, such as self-driving vehicles and industrial inspection. However, during detection, the system is susceptible to noise interference. This interference results in a decrease in the signal-to-noise ratio (SNR) of mixed signals, which affects the ranging accuracy. In this study, a MATLAB r2021b simulation is used to generate LiDAR transmitted and echo signals, and Gaussian noise is introduced. After mixing, empirical mode decomposition (EMD) and wavelet transform (WT) are used to denoise mixed signals, and the denoising effects of different wavelet basis functions under different SNRs are analysed. Furthermore, an experimental FMCW LiDAR system is set up to collect practical target echo signals, and the simulation results are validated through experiments under various illumination conditions. The results also show that the noise in FMCW LiDAR signals is dominated by Gaussian noise and that the influence of environmental noise is minimal. The combined EMD-WT denoising algorithm and its wavelet basis optimisation strategy proposed in this study can be directly applied to practical scenarios with strict requirements for FMCW LiDAR signal quality, such as autonomous driving, aircraft navigation, and precision industrial measurement, providing theoretical basis and experimental support for wavelet basis selection and denoising strategies in different noise environments. Full article

Partial discharge pattern recognition is a crucial task for assessing the insulation condition of Gas-Insulated Switchgear (GIS). However, the on-site environment presents challenges such as strong electromagnetic interference, leading to acquired signals with a low signal-to-noise ratio (SNR). Furthermore, traditional pattern recognition methods based on statistical parameters suffer from redundant and inefficient features that compromise classification accuracy, while existing artificial-intelligence-based classification methods lack the ability to quantify the uncertainty in defect classification. To address these issues, this paper proposes a novel GIS partial discharge pattern recognition method based on time–frequency energy grayscale maps and an improved variational Bayesian autoencoder. Firstly, a denoising-based approximate message passing algorithm is employed to sample and denoise the discharge signals, which enhances the SNR while simultaneously reducing the number of sampling points. Subsequently, a two-dimensional time–instantaneous frequency energy grayscale map of the discharge signal is constructed based on the Hilbert–Huang Transform and energy grayscale mapping, effectively extracting key time–frequency features. Finally, an improved variational Bayesian autoencoder is utilized for the unsupervised learning of the image features, establishing a GIS defect classification method with an associated confidence level by integrating probabilistic features. Validation based on measured data demonstrates the effectiveness of the proposed method. Full article

Accurate phase association and arrival time picking are pivotal for reliable microseismic event location and source characterization. However, the complexity of downhole microseismic wavefields, arising from heterogeneous subsurface structures, variable propagation paths, and ambient noise, poses significant challenges to conventional automatic picking methods, even when the signal-to-noise ratio (SNR) is moderate to high. Specifically, P-wave coda energy can obscure S-wave onsets analysis, and shear wave splitting can generate ambiguous arrivals. In this study, we propose a novel multi-channel arrival picking framework based on Constrained Dynamic Time Warping (CDTW) for phase identification and simultaneous P- and S-wave arrival estimation. The DTW algorithm aligns microseismic signals that may be out of sync due to differences in timing or wave velocity by warping the time axis to minimize cumulative distance. Time delay constraints are imposed to ensure physically plausible alignments and improve computational efficiency. Furthermore, we introduce a Multivariate CDTW approach to jointly process the three-component (3C) data, leveraging inter-component and inter-receiver arrival consistency across the entire downhole array. The method is validated against the Short-Term Average/Long-Term Average (STA/LTA) and waveform cross-correlation techniques using field data from a shale gas hydraulic fracturing. Results demonstrate that the proposed algorithm significantly enhances arrival time accuracy and inter-receiver consistency, particularly in scenarios involving P-wave coda interference and shear wave splitting. Full article

Using efficient voice alarms to ensure safe evacuation is important during emergencies, especially for the elderly. Factors that have important influence on speech perceptions have been investigated for several years. However, relatively few studies have specifically explored the key factors influencing perceptions of voice alarms in emergency situations. This study investigated the combined effects of speech rate (SR), signal-to-noise ratio (SNR), and reverberation time (RT) on older people’s perception of voice alarms. Thirty older adults were invited to evaluate speech intelligibility, listening difficulty, and perceived urgency after hearing 48 different voice alarm conditions. For comparison, 25 young adults were also recruited in the same experiment. The results for older adults showed that: (1) When SR increased, speech intelligibility significantly decreased, and listening difficulty significantly increased. Perceived urgency reached its maximum at the normal speech rate for older adults, in contrast to young adults, for whom urgency was greatest at the fast speech rate. (2) With the rising SNR, speech intelligibility and perceived urgency significantly increased, and listening difficulty significantly decreased. In contrast, with the rising RT, speech intelligibility and perceived urgency significantly decreased, while listening difficulty significantly increased. (3) RT exerted a relatively stronger independent influence on speech intelligibility and listening difficulty among older adults compared to young adults, which tended not to be substantially moderated by SR or SNR. The interactive effect of SR and RT on perceived urgency was significant for older people, but not significant for young people. These findings provide referential strategies for designing efficient voice alarms for the elderly. Full article

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article