Search Results (4,283)

To address Global Navigation Satellite System (GNSS) spoofing threats to Inertial Navigation System (INS)/GNSS integrated navigation systems, this paper analyzes the internal error propagation mechanisms and quantifies perturbation patterns within the Kalman filter (KF) architecture. Mathematical models for step-type, linear ramp, and nonlinear smooth ramp spoofing are established, and the Anomaly Signal-to-Noise Ratio (ASNR) is adopted to quantify disturbances across four core filtering dimensions based on real-world vehicular test data. The results demonstrate that filtering innovations at the forefront of information fusion respond most directly and sensitively (peaking at an ASNR of 341.4181) with a standard zero-mean Gaussian baseline, serving as the optimal metric for spoofing detection; in contrast, error states exhibit marked amplitude attenuation (maximum ASNR of 116.3291), while filter gains and state covariance show negligible variations (maximum ASNRs of 22.6023 and 19.3014, respectively). Further evaluation of Inertial Measurement Unit (IMU) accuracy constraints reveals that under step spoofing, position innovations remain robust (ASNR: 260–510), whereas velocity innovation ASNR drops by approximately 50% with IMU degradation; under linear ramp spoofing, velocity innovations dominate the response (ASNR: 41.16–70.46) while position innovations decay markedly; and under nonlinear smooth ramp spoofing, overall innovations are suppressed, and low-grade IMUs suffer severe noise masking (peak horizontal ASNRs dropping below 10), significantly enhancing attack stealthiness. The findings provide quantitative empirical evidence and theoretical guidance for anti-spoofing design in integrated navigation. Full article

High-frequency broadband acoustics can provide fine spatial resolution for near-range fish measurements, but the performance and limitations of beam-position-resolved backscatter measurements require careful evaluation. This proof-of-concept study examined one commercially sourced, dead, previously frozen specimen of each of three cultured fishes: golden pompano (Trachinotus ovatus; 24.4 cm), mandarin fish (Siniperca chuatsi; 29.1 cm), and large yellow croaker (Larimichthys crocea; 31.2 cm). A 650–750 kHz narrow-beam system was referenced to a 10.3 mm tungsten-carbide sphere, and matched-filter pulse compression and 1° stepwise scanning were used to estimate a beam-position-resolved backscatter metric along each body. At broadside incidence, the section-summed backscatter indices were −33.61, −22.45, and −33.07 dB for the T. ovatus, S. chuatsi, and L. crocea specimens, respectively. The section-summed abdominal backscatter index, in the region occupied by the swimbladder in the post-thaw X-ray images, exceeded the arithmetic mean of the head and tail group indices by 9.71 ± 1.84 dB (range: 8.27–11.86 dB). Tailward beam positions fell below the noise floor at approximately 12.5% of the expected scan positions for S. chuatsi and 22.2% for L. crocea. A 15° departure from broadside reduced the section-summed index by 3.73–11.43 dB. Kirchhoff-ray-mode (KRM) simulations based on post-thaw dual-view X-ray geometry were broadly consistent with the specimen-level contrast observed among the preserved specimens, but were 0.92–2.74 dB lower than the corresponding broadside section-summed measurement indices at 700 kHz. These differences are not a quantitative validation because the measured and modeled estimators, frequency weighting, geometry, and tissue parameters were not equivalent. These results demonstrate the feasibility of a calibrated beam-position workflow for preserved specimens, while not establishing live-fish target strength, species benchmarks, or biomass-estimation performance. Full article

Vision-based traffic accident anticipation is critical for active vehicle safety systems, yet existing architectures frequently conflate performance gains with heavy parameter scaling optimized from scratch on compact domains. Consequently, during real-time inference, these frameworks suffer from severe prediction volatility and early triggering biases that induce dangerous control instability. To address these limitations, this paper shifts the research focus away from network modifications toward a highly controlled, strategy-driven training pipeline executed under a completely invariant spatial–temporal neural backbone. Our proposed paradigm establishes a robust framework through three decoupled milestones. First, an out-of-domain initialization strategy transferred generalized driving kinetics from a large-scale sequence domain (Mapillary) to serve as a stable temporal anchor. Second, a target-domain generative enrichment step injected synthetic nighttime scenes to decouple hazard features from low-light ambient noise. Third, progressive temporal supervision paradigm scaling targeted labels monotonically to align with continuous kinetic risk accumulation. Overall evaluations on the Car Crash Dataset (CCD) benchmark demonstrate that the fully integrated configuration (C4) pipeline achieves 69.89% in frame-level Mean Average Precision (mAP), which is an improvement of +22.81 percentage points over the baseline configuration. Continuous temporal measurements prove that our framework can adapt to tracking volatility, compressing Temporal Confidence Variance to 0.00328, and dropping the Prediction Instability Count to 0.66. While hyper-sensitive baselines report early raw latency averages driven by premature trigger noise, our model purposefully filters this early-frame variability to deliver a secure warning profile, achieving an absolute zero false alarm rate (FAR = 0.00%) across evaluated non-hazardous driving sequences, establishing the sequence-level trustworthiness required for practical autonomous deployment. Full article

Aiming at the problems of strong model uncertainty, unmodeled perturbation disturbances and unknown maneuvering of space non-cooperative targets in orbital rendezvous missions, an orbit prediction and control method combining active disturbance rejection and model predictive control (ADRC-MPC) is proposed in this paper. Firstly, the relative motion dynamic equations of chaser–target are established under a geocentric inertial coordinate system, and the lumped disturbance including space perturbation and unknown evasive maneuvers of the target is expanded into extended state variable. Secondly, an extended state observer (ESO) is designed to achieve real-time accurate estimation and feedforward compensation of time-varying lumped disturbances, which mitigates adverse influences induced by J2 zonal harmonics, atmospheric drag, solar radiation pressure, and target evasive maneuvers. Thirdly, a constrained MPC strategy incorporating thrust saturation, approach corridor, and collision safety zone constraints is developed. Sufficient conditions for closed-loop input-to-state stability (ISS) are theoretically derived via the small-gain theorem under the given assumptions, which ensures the uniform ultimate boundedness (UUB) of tracking errors. Finally, numerical simulations, comparative benchmarks against conventional MPC and proportional-derivative (PD) control, and Monte Carlo robustness tests are performed. Simulation results demonstrate that the proposed ADRC-MPC framework delivers superior rendezvous precision, stronger disturbance rejection, reduced propellant consumption, and improved robustness against initial state offsets and measurement noises, laying solid theoretical and technical foundations for autonomous orbital rendezvous with space non-cooperative targets. Full article

Industrial IoT sensor nodes face a triple burden—sampling, compression, and security—under severe resource constraints; yet, the question of which signals can actually benefit from compressed sensing (CS) remains largely implicit in the literature. SCCS answers this question by unifying compression, chaotic obfuscation, and authentication within a single CS measurement and deriving an empirical deployability rule consisting of the PCA energy concentration ratio $\rho$. When $\rho$ exceeds 80%, signals reconstruct at high fidelity; when $\rho$ falls below 50%, they are intrinsically incompressible; and in the intermediate 50–80% band, reconstruction is uncertain and may fail outright rather than degrading gracefully (as shown on CWRU). This empirical deployability rule is supported by evaluation on three real datasets: high-fidelity reconstruction is confirmed on CBM ($\rho =99.9\%$), while CWRU ($\rho =65.9\%$) and CCPP ($\rho =15.4\%$) establish the applicability boundaries and validate the $\rho$-based screening criterion. The enabling system integrates a block-circulant chaotic measurement matrix (BCCM, from a two-dimensional sine-logistic iteration mapping (2D-SLIM) map) that compresses and obfuscates in one operation (online measurement seed 0.84 KB, down from a 512 KB dense matrix; the full reference implementation requires 185 KB Flash, including a 160 KB decoder dictionary); an offline principal component analysis (PCA) dictionary that lifts reconstruction signal-to-noise ratio (SNR) from 5.36 to 33.52 dB at CR = 4 (+28.16 dB over the fixed-basis configuration; Wilcoxon $p<0.001$, 30 independent trials); and a dual-layer authentication scheme combining always-on hash-based message authentication code (HMAC) with adaptive reconstruction-based implicit authentication (RBIA), the latter providing zero-overhead tamper pre-screening that reuses the decoder’s reconstruction residual and automatically falls back to HMAC-only under channel noise. Security boundaries are explicitly disclosed: the chaotic measurement resists known-plaintext attacks but is vulnerable to chosen-plaintext recovery (N plaintexts recover the linear matrix), and 1.13 bits of amplitude side-channel leakage exist. The SCCS framework demonstrates that the three functions need not be separate serial stages, provided the target signals satisfy the $\rho$ screening rule. Full article

Wearable near-infrared spectroscopy (NIRS) enables non-invasive hemodynamic monitoring, yet conventional CW-NIRS systems suffer from limited temporal resolution, scalp-only measurement, and mandatory manual tuning to compensate for optical heterogeneity across subjects and sites. This work develops NIRSLINK, a modular cascaded wearable NIRS system for high-speed multi-site hemodynamic acquisition. Its flexible probes adopt spring-floating optics with a standardized 30 mm optode separation, integrating dual 735/850 nm LEDs, silicon photodiodes, and a two-stage closed-loop tuning algorithm. A single probe achieves a peak sampling rate of 3 kHz, and up to eight cascaded probes form 52 valid channels, with a signal-to-noise ratio (SNR) of 78.61 ± 7.03 dB and an optical dynamic range (DR) of 101.32 ± 12.41 dB. Phantom experiments verify its millisecond temporal resolution and high sensitivity to blood flow and hemoglobin variations. In vivo trials, including the Valsalva maneuver, forearm occlusion, and two-back cognitive tasks, demonstrate simultaneous recording of hemoglobin concentration shifts, pulse waveforms, and beat-to-beat pulse transit times (PTTs). NIRSLINK supports hemodynamic measurements across multiple anatomical locations, including the forehead, forearm, upper arm, and thigh, covering both cranial and peripheral body regions. The embedded auto-tuning module stabilizes signals from the forehead, forearm, and other regions within the optimal ADC range without manual adjustment, preventing signal saturation and SNR degradation. This scalable adaptive platform overcomes critical drawbacks of traditional wearable NIRS, applicable to cognitive neuroscience, non-invasive cardiovascular assessment, and ambulatory physiological monitoring, and provides design references for multi-site optical sensors. Full article

Short-term multivariate forecasting of hydrological variables remains challenging because river systems exhibit nonlinear and time-dependent dynamics, complex relationships among water level, flow, and precipitation, and uncertainty arising from measurement errors and external disturbances. Although neural network models can learn nonlinear relationships among hydrological variables, their predictive performance often deteriorates in the presence of noise. Moreover, existing approaches rarely integrate the learning of long-term temporal dependencies and cross-variable relationships with a data assimilation mechanism capable of recursively updating state estimates and reducing forecast uncertainty. This limitation reveals the need for a robust forecasting framework that combines both capabilities. Therefore, this study aimed to develop and evaluate a hybrid Long Short-Term Memory–Ensemble Kalman Filter (LSTM–EnKF) model for short-term multivariate water-level forecasting under noisy conditions. The proposed framework extends a previously developed NARX–EnKF approach by replacing the NARX network with an LSTM architecture capable of learning nonlinear temporal patterns and relationships among water level, flow, and precipitation. The model was implemented using data from two hydrological stations located along the Atrato River in Colombia and configured to generate water-level forecasts with a two-day prediction horizon. The LSTM network generated the initial forecasts, whereas the EnKF assimilated the available observations to recursively update the estimated states and reduce forecast uncertainty. Model robustness was examined by introducing Gaussian white noise with variance levels of 0.001, 0.05, 0.10, and 0.20 to represent measurement uncertainty and external disturbances. Performance was evaluated using the root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). Across all evaluated noise levels, the LSTM–EnKF model outperformed the standalone LSTM model. Its total RMSE ranged from 0.1009 to 0.1929 m, compared with 0.1740 to 0.2356 m for the standalone LSTM, representing reductions of approximately 18.1–42%. The hybrid model achieved NSE values ranging from 0.9760 to 0.9992, whereas the standalone LSTM produced values between 0.9200 and 0.9776. Furthermore, the LSTM–EnKF reduced the MAE by approximately 51.9–56.2% across both outputs. These results indicate that integrating LSTM-based temporal learning with EnKF-based data assimilation improves short-term forecasting accuracy and robustness under noisy conditions. The developed framework provides a promising tool for supporting flood early-warning systems, flood-risk management, and the protection of riverine communities. Full article

In this paper, an adaptive estimation algorithm for heterogeneous group targets considering uncertain multiplicative and additive noises is proposed. Firstly, a state-space model for heterogeneous group targets with composite multiplicative and additive noise is established. Since the coupling effect of multiplicative noise renders the marginal likelihood analytically intractable and induces heavy-tailed characteristics, a tailored hierarchical Gaussian–Gamma model is introduced for robust approximation. Second, a joint posterior probability density function incorporating the target kinematic state, extended morphology, and noise parameters is constructed. Within the variational Bayesian framework, approximate posterior distributions of these variables are derived, and fixed-point iteration is employed to compute the system state and noise statistics. Simulation results demonstrate that, under environments corrupted by unknown and time-varying multiplicative and additive noises, the proposed algorithm adaptively estimates a unified measurement noise covariance, achieving superior estimation performance compared to the random matrix model and the VB-EOT-SN method. Full article

The operating frequency of a resonant oscillator is determined by the coupled behavior of the resonator, sustaining electronics, package, power supply, mounting conditions, and operating environment. Frequency stability is critical in sensor systems, where oscillators provide references for sampling, synchronization, phase-sensitive measurements, sensor fusion, and distributed sensing. This review examines how these factors affect the short- and long-term stability of quartz oscillators (XOs, TCXOs, and OCXOs) and microelectromechanical systems (MEMS) oscillators. Organized by physical cause rather than device type, the review covers temperature, including gradients and hysteresis, aging, acceleration, vibration, shock, pressure, humidity, power-supply and load variations, electric and magnetic fields, electromagnetic interference, and ionizing radiation. For each factor, the dominant degradation mechanisms and compensation methods are compared across both technologies. The comparison indicates that OCXOs retain an advantage in low-noise timekeeping over long averaging times, whereas advanced MEMS oscillators can approach quartz performance in selected operating regimes while offering smaller size, monolithic integration, and, in ruggedized products, 0.01 ppb/g acceleration sensitivity and 20,000 g shock ratings. In the literature reviewed, appropriate measurement methods exist but are applied inconsistently across technologies. Oscillator selection should therefore be based on a comprehensive error budget covering all relevant environmental and system-level factors. Full article

The efficient operation of lithium-ion battery management systems (BMSs) depends on accurate state-of-charge (SOC) estimation. However, the performance of conventional model-based SOC estimation methods may progressively worsen owing to parameter uncertainty and nonlinear battery dynamics. This study proposes a hybrid SOC estimation framework termed DO-EKF_Res, comprising two sequential stages. In the first stage, the process and measurement-noise covariance matrices are optimized offline using a data-driven strategy. In the second stage, a Bidirectional Long Short-Term Memory (BiLSTM) residual learning network is employed to compensate for the remaining SOC estimation errors. The proposed framework was evaluated using two complementary validation protocols: a synthetic Monte Carlo experiment and a Leave-One-Battery-Out (LOBO) cross-validation framework based on the NASA Prognostics Center of Excellence (PCoE) lithium-ion battery dataset. In the synthetic validation, DO-EKF_Res achieved an RMSE of 0.803%, corresponding to reductions of 48.83% and 26.84% relative to the EKF and DO-EKF, respectively. In the NASA LOBO evaluation, the proposed framework achieved a macro-averaged RMSE of 11.534%, corresponding to reductions of 49.25% and 9.68% relative to the EKF and DO-EKF, respectively. These results demonstrate that integrating offline covariance optimization with BiLSTM-based residual learning improves estimation accuracy, robustness, and cross-battery generalization, providing a practical solution for lithium-ion battery SOC estimation in battery management systems. Full article

Objective: Recent advances in dry electrode EEG have enabled rapid setup and recording in unconventional scenarios. However, past developments were primarily driven by brain–computer interfaces (BCI), leaving their comparability to wet electrodes in clinical and daily life applications an open question. Here, we developed a new dry EEG system and systematically benchmarked its performance against a commercial wet EEG system across various tasks. Methods: Participants (n = 19) underwent simultaneous recording using both devices. We first collected resting-state EEG under both eyes-closed and eyes-open conditions, followed by a steady-state visual evoked potential (SSVEP) task at different flicker frequencies and a motor imagery (MI) task. System performance was evaluated using power spectral density (PSD), signal to noise ratio (SNR), event-related spectral perturbation (ERSP), and single-trial classification accuracy. Results: The two systems performed similarly across different tasks. During the resting state, no statistically significant differences were observed between the two systems in the PSD of the five frequency bands (p > 0.05 in all cases). Similarly, SNR in the SSVEP task showed no significant differences at 8 Hz, 10 Hz, and 12 Hz after correction. For cognitive tasks, classification accuracies were comparable (SSVEP: dry 80.08% ± 7.1% vs. wet 81.10% ± 6.5%; MI: dry 72.46% ± 3.89% vs. wet 70.7% ± 2.37%). Conclusions: The developed dry EEG system can effectively record electrophysiological measurements commonly employed in research and clinical settings, with quality comparable to that of traditional wet EEG systems. Full article

Visible–near-infrared (VNIR) hyperspectral imaging provides a non-contact approach for cultural heritage examination. This study presents the design and calibration of a compact finite-conjugate VNIR pushbroom hyperspectral camera for close-range mural imaging. Operating over 400–1000 nm at a nominal working distance of 404 mm, the system provides a mean spectral sampling interval of 4.85 nm and an object-space sampling interval of approximately 82.4 μm/pixel. An integrated calibration workflow was established for wavelength assignment, spectral response characterization, geometric correction, radiometric calibration, and scan synchronization. The experimental results yielded a modulation transfer function (MTF) of 0.34 at the effective detector Nyquist frequency, a mean spectral response function full width at half maximum (FWHM) of 6.3 nm, a maximum absolute wavelength residual below 0.90 nm, residual smile and keystone errors below 0.3 pixels, a residual radiometric nonuniformity of 0.71%, and a mean signal-to-noise ratio (SNR) of 339. Measurements of Potala Palace mural samples demonstrate the acquisition of spatially detailed, radiometrically corrected hyperspectral data under close-range conditions. Full article

Computational imaging shifts part of the aberration correction from optical hardware to algorithms, offering a viable path toward compact, simplified systems. However, overexposed regions—often caused by phenomena such as water-body reflections—can readily induce severe ringing artifacts in reconstructed images. To address this problem, we propose a Ringing-perceptive Cooperative Reconstruction Network (RPCR-Net). This network integrates a learned Wiener filter and a field-of-view shared kernel prediction network (FOV-KPN) for feature extraction and innovatively incorporates a combined regularization mechanism that leverages a Local Maximum Gradient Prior and a multi-scale ringing measurement model within its loss function to suppress artifacts while preserving details. Validated on a constructed overexposed image dataset, RPCR-Net improves the Peak Signal-to-Noise Ratio (PSNR) from 29.08 dB to 37.06 dB and the Structural Similarity Index Measure (SSIM) from 0.8795 to 0.9549. Experiments on real-world scenes further confirm its capability to suppress ringing artifacts while maintaining visual quality. The proposed method can generate high-quality images such as image reconstruction and robustness improvement in optical systems. Full article

This paper proposes a novel probabilistic approach to Active Disturbance Rejection Control (ADRC) based on particle filtering (PF) for extended-state estimation. Unlike the classical Extended State Observer (ESO), the proposed method represents the system state and total disturbance using weighted particles, enabling the direct incorporation of non-Gaussian noise and model uncertainty. The PF-based estimator is integrated within the ADRC framework and compared with conventional ESO- and Kalman filter-based approaches. In addition to the standard PF-ADRC scheme based on the posterior mean, a risk-aware control mechanism is introduced. The particle-wise state and disturbance realizations are used to construct a distribution of candidate control actions, which is subsequently employed to account for unfavorable posterior realizations and reduce excessive control activity and actuator saturation risk. Simulation studies conducted for nonlinear mechanical systems, including a preliminary validation on a higher-order nonlinear underactuated plant, illustrate the feasibility of the proposed framework under non-Gaussian noise, measurement outliers, nonlinearities, and actuator constraints. The results characterize the potential benefits, limitations and performance trade-offs of posterior-based action selection. Full article

Internet of Vehicles (IoV) security mechanisms often classify isolated messages or assign node-level trust scores, yet these decisions do not explain whether a malicious but authenticated event has distorted the intended evolution of traffic. This paper proposes IntentProv-IoV, a causally grounded provenance framework for traffic-intent preservation in V2X environments. Traffic intent is modeled as the short-horizon collective state expected under non-adversarial conditions, and deviation is measured between predicted and observed traffic states. The framework constructs temporal provenance graphs linking vehicles, roadside units (RSUs), cooperative perception outputs, prediction nodes, and traffic-control decisions. To remove the ambiguity of marginal contribution, node contribution is formalized as an interventional effect in a structural causal model and estimated through Monte Carlo counterfactual edge-weight attenuation, with a linear sensitivity fallback for real-time edge deployment. A calibrated composite score integrates anomaly evidence, traffic-intent deviation, trust risk, and provenance contribution. The evaluation design compares IntentProv-IoV with detection, trust, blockchain trust, graph anomaly, Granger causal, structural causal, and counterfactual GNN baselines and includes predictor sensitivity, adaptive adversaries, prediction noise, packet loss, trajectory-only real-data validation, and edge overhead. Simulation-scale results indicate improved attribution precision, stronger traffic-intent deviation reduction, and edge-suitable latency. By shifting V2X security from message-level detection to causally explainable traffic-intent assurance, IntentProv-IoV provides a more accountable security objective for cooperative vehicular systems. Full article