Search Results (272)

Accurate nanoscale displacement readout is essential for optical inertial sensors, precision positioning, and micro-vibration characterization. In this work, we develop a free-space optical heterodyne interferometric readout system for low-frequency nanoscale displacement sensing and establish an SNR-guided adaptive demodulation framework. Two complementary demodulation strategies are integrated: Bessel-function-based frequency-domain sideband extraction for small-amplitude low-SNR motion and IQ quadrature phase tracking for larger-amplitude displacement. The experimentally demonstrated framework maps the applicability regimes of the two methods and enables wavelength-referenced displacement readout over a range from sub-nanometer narrowband detection to 250 nm under the present experimental conditions. The implemented system achieves a repeated-measurement repeatability of 0.40 nm under a 10 Hz excitation condition, and spectral SNR analysis is consistent with time-domain statistical evaluation. Finally, the readout system is applied to a quartz pendulum inertial structure, demonstrating its potential for photonic displacement sensing and optical inertial sensor characterization. Full article

Magnetic anomaly detection is vulnerable to environmental noise and insufficient prior target information, making non-periodic anomaly signals difficult to detect at low-signal-to-noise-ratio (SNR) conditions. This paper proposes a detection method based on a multi-parameter-constrained mirror dual-branch biased monostable stochastic resonance (SR) system. Nonlinear odd-order bias terms are introduced into the conventional biased monostable potential function to build a multi-parameter-controllable SR model. This improves regulation of potential-well width, depth, and wall morphology, enhancing noise-energy utilization and responses to non-periodic features. Considering peak-type, valley-type, and bipolar anomaly morphologies, a mirror dual-branch SR structure is developed to cooperatively detect features with different polarities. To preserve temporal waveforms and time–frequency structures during parameter optimization, a composite metric combining the correlation coefficient and wavelet-domain image structural similarity index is constructed. Multi-fidelity robust Bayesian optimization is used to obtain a unified robust parameter set for the magnetic anomaly signal family. Experiments with simulated colored noise and measured geomagnetic noise show that the proposed method effectively recovers magnetic anomaly features under strong noise. At −19 dB SNR, its detection probability remains above 80%. Compared with orthogonal basis function decomposition, empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise, the method achieves better noise suppression, feature preservation, and detection performance under low-SNR conditions. Full article

As the modulation rate in high-speed optical communication systems continues to increase and modulation formats become increasingly complex, conventional electrical-domain sampling techniques, limited by the “electronic bottleneck,” are unable to meet the time-domain analysis requirements of optical vector signals with bandwidths exceeding 100 GHz. In this paper, a system based on linear optical sampling (LOS) is implemented for time-domain analysis of high-speed polarization-division-multiplexed (PDM) optical vector signals. An unbalanced input method is proposed to ensure the integrity of the sampling clock when the power of the signal under test is zero; a resampling method combined with soft integration is proposed to replace the conventional peak detection method, improving the accuracy of sampling point position and amplitude information extraction; and an adaptive frequency offset estimation algorithm is proposed to compensate for the continuously varying frequency offset caused by the use of low-repetition-rate sampling pulses. We constructed a signal acquisition system for optical vector signal measurement based on LOS. Using the above methods, the eye diagrams and constellation diagrams of 50 Gbaud PDM-QPSK (quadrature phase-shift keying), PDM-16QAM (quadrature amplitude modulation), and PDM-32QAM signals are successfully measured, and related parameters, including error vector magnitude (EVM) and signal-to-noise ratio (SNR), are calculated. The experimental results show that the proposed system achieves quasi-real-time measurement of 500 Gbps optical vector signals, and the measured performance parameters are on the same order of magnitude as those obtained from a commercial high-speed oscilloscope. Full article

To address the degradation of recovery accuracy caused by amplitude fluctuation, phase distortion, delay distortion, and noise amplification in single-carrier signal measurement chains, this paper investigates direct inverse and regularized inverse de-embedding compensation methods. Based on a linear time-invariant system model, single-carrier signal de-embedding is formulated as an ill-conditioned inverse problem that is sensitive to weak-response frequency points and observation noise. A unified frequency-domain compensation framework is then established, including the Direct method, Tikhonov regularized inverse compensation, Wiener-type inverse compensation, and truncated inverse compensation. To evaluate the applicability of these methods, a narrowband single-carrier signal and four measurement-chain models are constructed, including a smooth reference chain, a passband-edge attenuation chain, a multiple local-fading ill-conditioned chain, and a measured S-parameter-based chain. The simulation results show that the compensation gain is closely related to the magnitude response of the measurement chain. The Direct, Tikhonov, and Truncated methods produce similar results when the chain response is relatively flat or when the regularization constraint is weak, whereas the Wiener-type method achieves better NMSE performance under the tested conditions. Parameter-sweep and SNR experiments further show that the effectiveness of regularized inverse compensation depends on the ill-conditioning degree of the measurement chain, the noise level, and the parameter settings. Measured single-carrier signal experiments verify the feasibility of the proposed framework. Frequency-domain de-embedding compensation based on the measured $S_{21}$ improves the NMSE from −18.7808 dB before compensation to −37.9458 dB after compensation. The measured results also show that, when the measurement-chain response is relatively flat, the additional improvement of Tikhonov and Truncated methods over the Direct method is limited, while the Wiener-type method provides a slight NMSE improvement. Overall, the proposed framework provides a practical approach for single-carrier signal recovery and clarifies the applicability of different inverse compensation methods under different measurement-chain and noise conditions. Full article

Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft. Full article

This paper proposes a two-stage hybrid framework for biosignal quality validation that produces beat-level or segment-level labels for real-time filtering and offline dataset curation. The framework is quantitatively validated exclusively on ECG data. Its modular architecture is designed to extend to further non-stationary periodic biomedical time-series signals including photoplethysmography (PPG), impedance cardiography (ICG), phonocardiography (PCG), electromyography (EMG), and electroencephalography (EEG) through modality-specific parameter adaptation; however, this broader applicability currently reflects architectural extensibility rather than experimentally validated performance. A prerequisite is synchronized acquisition of the primary biosignal together with inertial motion sensing (IMU/accelerometer) and electrode impedance or lead-off status, with the IMU positioned near the sensing electrodes. The first stage performs sensor-integrity gating to reject intervals corrupted by motion or poor electrode contact. The second stage applies software signal quality indices to the remaining beats, including physiological plausibility constraints (R to R peaks analysis), DTW-based morphological consistency against adaptive templates, frequency domain SNR estimation, and baseline wander quantification. This study systematically evaluates and compares the classification performance of six complementary sensor-level and software-based signal quality assessment methods. When integrated within the proposed hybrid framework, validation against expert-annotated ECG quality labels from 20 healthy participants demonstrates high methodological classification accuracy (98.1%), achieving approximately a 98% F1-score, 99% sensitivity, and 97% specificity. Prospective validation on patient populations with cardiovascular pathology is identified as a necessary step toward clinical deployment. This modular approach improves the reliability of downstream analysis by preventing corrupted data from entering feature extraction and model training pipelines, enabling more stable physiological monitoring in free-living conditions, reducing false alarms in continuous monitoring applications, and generating higher-quality datasets for AI-based diagnostic systems. Full article

Long propagation delays, severe multipaths, and narrow bandwidths make feedback-based link adaptation impractical in UWA channels at kilometer ranges, so we replace the feedback step with a prediction step. The transmitter runs a two-layer coded OFDM link in which Polar codes handle bit errors, and Raptor fountain codes handle packet erasures, with the Raptor overhead (OH) as the only real-time knob. The OH is picked from a lookup table indexed by three quantities the receiver can estimate online: SNR, RMS delay spread, and Doppler frequency. Two CSI predictors feed that table: Temporal Multiple Sparse Bayesian Learning (TMSBL), which exploits delay-domain sparsity, and the Square-Root Unscented Kalman Filter (SRUKF), which tracks per-subcarrier variations. We evaluate the system in five channel environments (AWGN, Rayleigh, K-distribution, Bellhop ray-tracing, and synthetic proxies parameterized from the KAM11 and WATERMARK sea-trial statistics). Across the nine Bellhop scenarios, the adaptive link’s throughput gain over a fixed-OH ($\mathrm{OH}=1.5$) baseline at SNR $=4$ dB spans roughly $-4\%$ to $+30\%$, with the largest benefit in the marginal short-range cases (shallow 500 m, $+30\%$) where the fixed baseline is most over-provisioned and near-parity elsewhere. The scheme’s principal benefit is collapse prevention, tracking the Oracle within the safety margin and avoiding the throughput collapse the fixed baseline suffers at low SNRs. This effect is specific to the physically structured Bellhop channels; in the homogeneous Rayleigh and K-distribution channels, both schemes enter deep outage at very low SNRs, so it is not a universal guarantee. A 1000-trial high-resolution Rayleigh campaign sharpens the head-to-head between predictors: at SNR $=4$ dB, SRUKF + OH reaches PER $0.048$ (95% Wilson CI $[0.036, 0.063]$) and TMSBL + OH reaches $0.071$ ($[0.057, 0.089]$), and at SNR $=12$ dB, their throughputs ($0.748$ and $0.746$) are statistically indistinguishable from each other (95% Wilson halfwidth $\sim \pm$0.014) and lie close to the Oracle’s $0.768$ (within $\sim 0.02$). The two predictors therefore occupy overlapping operating regions once the safety margin is matched, and a sparsity-dependent tendency (TMSBL in sparse multipath, SRUKF in dense multipath) appears only in physically structured channels and only at the $n=100$ screening level, where it is not statistically resolved and would benefit from higher-trial confirmation. A finite-blocklength check confirms that CA-SCL-decoded Polar codes at $N=128$ stay within $0.5$ dB of the Polyanskiy normal approximation, which makes Polar a sensible inner code at UWA block lengths. Full article

This paper proposes a hybrid time–frequency domain identification method for second-order plus dead time models with an additional process zero (SOPDT+Z). A dual-relay experiment combined with step response data provides six independent equations for five model parameters, whose mathematical well-posedness is established through Jacobian rank analysis. A cascaded initialization strategy (Sundaresan → SIMC → Jin → proposed) guarantees monotonically improving accuracy. An Internal Model Control (IMC) framework yields equivalent PID parameters with a single tuning parameter λ, supported by a formal robust stability theorem. Simulation studies on five benchmark systems demonstrate 60–100% reduction in open-loop IAE compared to existing SOPDT methods, 36% faster settling, and 100% closed-loop stability under ±20% Monte Carlo perturbation (N = 200). Noise robustness analysis under SNR = 20–40 dB and additional performance metrics (ITAE, ISE) further validate the method. Full article

Index-modulated orthogonal frequency division multiplexing (OFDM-IM) has been recognized as a promising multicarrier transmission scheme due to its flexibility and favorable bit error rate (BER) performance. However, for future wireless communication systems requiring high reliability, high spectral efficiency, and low complexity, existing OFDM-IM schemes still face challenges in simultaneously improving spectral efficiency, maintaining diversity gain, and controlling detection complexity at the receiver. To address these issues, this paper proposes a joint-mode and repeated-index modulation-based coordinate interleaved OFDM scheme (MRIM-CI-OFDM). Building upon the shared subcarrier activation pattern (SAP) and coordinate interleaving structure, the proposed scheme introduces cross-cluster mode-pair indexing, enabling information bits to be jointly carried by the SAP domain, mode domain, and constellation symbol domain. This design enhances spectral efficiency while preserving the diversity advantages of coordinate interleaving. Furthermore, a rotated multi-mode constellation construction method based on inter-constellation minimum product distance is developed to improve mode separability. By exploiting the equivalent real-valued orthogonal structure introduced by coordinate interleaving, low-complexity maximum likelihood (ML) and three-stage Max-Log detectors are constructed. Simulation results demonstrate that the proposed low-complexity detectors achieve near-ML detection performance. Additionally, at a spectral efficiency of 1.25 bps/Hz, MRIM-CI-OFDM achieves approximately 3 dB SNR gain over the coordinate-interleaved/repeated-index benchmarks and more than 5 dB gain over conventional OFDM-IM. Full article

High-resolution three-dimensional (3D) radar imaging of non-coherent point target clusters faces significant challenges, particularly severe angular glint induced by the simultaneous presence of dual targets or co-channel interference (CCI) within the antenna mainlobe. Conventional monopulse systems often struggle to resolve such overlapping sources, particularly under conditions of high power disparity between signal components. To overcome the Rayleigh resolution limit, this paper proposes a polarimetric 3D imaging framework for three-channel monopulse radar by leveraging joint polarimetric-angular diversity. By exploiting the intrinsic instability of spatial parameter estimates induced by snapshot-to-snapshot echo envelope fluctuations, a cost function based on fluctuation minimization is constructed. Furthermore, an optimized oblique projection (OP) strategy is developed to decouple overlapped echoes in the joint domain, thereby effectively extracting stable angular features of non-coherent sources under various stochastic scattering scenarios (e.g., Swerling models). Extensive simulations demonstrate that, compared with traditional MPV, Seung, and Blair methods, the proposed approach consistently achieves superior estimation precision and robustness, especially in challenging scenarios characterized by low signal-to-noise ratios (SNR), limited snapshots, and restricted polarimetric diversity. Moreover, experimental validation using real-world data from a 45-m civilian vessel and an active non-cooperative radio frequency (RF) source confirms the practical effectiveness of the algorithm in resolving extended targets in the presence of strong non-coherent background emissions. This work provides a reliable solution for high-fidelity 3D imaging of point target clusters in environments characterized by dense targets and complex electromagnetic interference. Full article

With the widespread adoption of multiple-input–multiple-output (MIMO) technology, channel state information (CSI) prediction has become a crucial technique for enhancing the performance of wireless communication systems. Traditional channel prediction methods face performance bottlenecks under high-speed mobility and complex channel conditions, making it difficult to meet the requirements of modern communication systems. To address this issue, this paper proposes a fully complex-valued cross-domain modeling framework, termed a complex-valued multi-scale transformer with time–frequency cross-attention network (CMTF-Net), for MIMO CSI prediction. CMTF-Net integrates a learnable multi-scale short-time Fourier transform (LMS-STFT), complex-valued multi-head self-attention (C-MHSA), and bidirectional cross-domain attention for complex-valued sequences (BCDA-CVS). These modules are designed to preserve amplitude–phase consistency, adapt time–frequency representations to CSI evolution, and enable information interaction between temporal and spectral features. On the simulated Overall Test set, CMTF-Net achieves the lowest MAE of 0.000032 and the highest Corr. ($\rho$) of 0.8230 among the compared methods, while maintaining competitive SE and BER values of 0.4240 and 0.2411 at SNR = 10 dB. On the DICHASUS measured datasets, CMTF-Net also shows favorable Test-ID and Test-OOD performance. For example, on DICHASUS-2186, it obtains Corr. ($\rho$)/SE/BER values of 0.8367/0.4935/0.2243 on Test-ID and 0.8061/0.4697/0.2351 on Test-OOD. These results indicate that CMTF-Net provides a balanced performance profile across prediction accuracy, spatial alignment, and communication-oriented evaluation. Full article

Early-Warning Radar (EWR) is an advanced detection system capable of monitoring aerial targets over long distances with high precision, providing critical information support for defense security. However, EWR faces challenges such as a limited number of pulses, low coherent integration gain, small target Radar Cross Section (RCS), and complex clutter and electromagnetic interference environments. Conventional Constant False Alarm Rate (CFAR) detection algorithms struggle to effectively detect weak targets while maintaining an acceptable false alarm rate. To address these issues, this paper introduces a deep learning approach. A high target-clutter/interference/noise discriminative feature spectrum is obtained through phase difference transformation, upon which a dual-branch collaborative architecture network is constructed. In this architecture, the main network focuses on extracting spatiotemporal amplitude–phase characteristics, while the auxiliary branch implicitly mines the target’s physical boundary features from frequency-domain echoes. Through a self-attention mechanism, the features from both branches are semantically aligned and fused. This method significantly enhances the weak target detection capability of EWR under the constraint of a controlled false alarm rate. Test results show that under the false alarm rate ranging from ${10}^{-3}$ to ${10}^{-4}$, the SNR gain of the proposed algorithm is about 2∼5 dB, which is equivalent to increasing the radar detection range by 10∼30%. Full article

Physical-layer key generation (PLKG) is a technique that produces symmetric encryption keys by exploiting the inherent characteristics of wireless channels. It offers advantages including high physical-layer security, elimination of pre-shared keys, dynamic upgradability, and resistance to quantum attacks, making PLKG a promising security solution for next-generation (6G) networks. However, satellite communication channels exhibit high dynamics and long propagation delays. Characteristics such as large Doppler shifts, short coherence times, and orbital predictability pose severe challenges to PLKG, including reciprocity degradation, low key generation rate (KGR), and susceptibility to channel-prediction attacks. This work proposes a delay-Doppler domain time-hopping key generation scheme (KE-DD-TH) based on Orthogonal Time Frequency Space (OTFS) modulation for high-speed links between Low-Earth-Orbit (LEO)/Medium-Earth-Orbit (MEO) satellites and ground terminals in Ka/Ku bands. The scheme performs non-uniform sampling on the DD domain grid of OTFS symbols using an ephemeris-driven pseudo-random time-hopping sequence generated by cascaded linear feedback shift registers (LFSRs) and a nonlinear matrix transformation. Both legitimate parties estimate the channel only at time-hopping instants and multiply two adjacent estimates to construct an “equivalent channel” matrix, yielding a random source with high entropy, high reciprocity, and low predictability. The eavesdropper’s key disagreement rate (KDR) remains close to 0.5 under all signal-to-noise ratio (SNR) conditions, corresponding to the ideal random-guessing baseline. This indicates that Eve obtains negligible mutual information, i.e., $I(K_A;K_E)\approx 0$. By contrast, the conventional KE-DD scheme allows Eve’s KDR to degrade to 0.014 at 30 dB SNR, indicating near-complete key recovery. The generated keys pass all 12 randomness tests of the NIST SP 800-22 statistical test suite. Full article

Co-channel interference (CCI) remains a critical factor affecting link reliability in narrowband wireless systems, especially in scenarios with intensive frequency reuse, overlapping coverage, and dense terminal access. Existing interference detection methods are either computationally simple but insufficiently sensitive to short-term spectral variations, or highly accurate but dependent on labeled data and nontrivial inference resources. To address this issue, this paper proposes a lightweight CCI detection method in the FFT domain based on spectrum-jump analysis. The proposed method does not rely on absolute power growth as the primary interference indicator. Instead, it tracks the temporal inconsistency of dominant spectral-bin indices across consecutive FFT frames and converts recurrent peak-bin migration into an interference decision through a short-window counting mechanism. The method is computationally efficient, interpretable, and suitable for real-time deployment without offline model training. SDR-based measurements are combined with controlled repeated experiments to assess detector performance under varying signal-to-noise ratio (SNR), interference-to-signal ratio (ISR), carrier-frequency offset (CFO), multi-peak ambiguity, and two-path Rayleigh fading conditions. On the measured SDR record, the proposed method captures all interference-positive windows after the marked onset, while the controlled SNR/ISR experiments yield an overall detection probability of 96.0% over 250 CCI trials with no false alarms over 250 normal trials. ROC and precision–recall analyses further show that the selected threshold lies within a broad validation plateau. The results also reveal clear applicability boundaries: when the CFO approaches zero, when the interference is very weak, or when multiple stationary peaks have nearly equal power, dominant-bin migration may be weak or ambiguous. Therefore, the proposed approach is a low-complexity online detector for CCI cases that induce observable FFT-bin instability, and it can also serve as a front-end trigger for more advanced interference analysis modules. Full article

Automatic modulation classification (AMC) is a key component of spectrum awareness, cognitive radio, and signal intelligence, enabling receivers to identify modulation schemes from noisy in-phase and quadrature (IQ) observations. Traditional approaches rely on likelihood-based methods or handcrafted feature extraction, which often struggle under channel impairments and real-world variability. Recent advances in deep learning enable models to learn directly from multiple signal representations, including raw IQ samples, engineered features, and time–frequency or constellation-based encodings, improving adaptability across diverse signal conditions. This paper presents a structured review of deep learning approaches for AMC, including CNNs, RNN/LSTM models, and transformer-based architectures, with a focus on performance, robustness, and system-level trade-offs. We analyze how representation choices, dataset design, and evaluation protocols influence reported results, and highlight key challenges such as domain shift, low-SNR environments, and multi-signal interference. Finally, we outline future directions focused on improving generalization, integrating classical signal processing with learning-based methods, and enabling efficient deployment in real-world and resource-constrained systems. Full article