Search Results (8,024)

Reliable calibration that converts transduced signals into physical displacement is essential for quantitative studies and applications of nanoelectromechanical resonators, including nonlinear dynamics, precision sensing, and optomechanical and electromechanical coupling. Existing harmonic calibration based on nonlinear optical transduction is generally restricted to systems with optically thin suspended layers and highly reflective substrates. Their weak higher-order harmonic signals are also susceptible to noise, drift, and inconsistencies between separately acquired frequency sweeps. Here, we generalize this approach to hexagonal boron nitride/graphene (h-BN/Gra) heterostructure resonators without local metallic reflectors. Multilayer thin-film interference calculations show that a branch-local phase correction enables the effective two-beam inversion to recover vibration amplitude and local static gap within acceptable error bounds. Experimentally, we use multi-demodulator lock-in detection to acquire the ω, 2ω, and 3ω optical responses simultaneously at each frequency point. Ratios among these harmonics then yield frequency-resolved vibration amplitude and local static gap. Repeated frequency sweeps at a constant gate bias simultaneously track the resonance characteristics and local static gap, revealing a time-dependent relaxation of approximately 24 nm in the local device configuration. This work provides a practical in situ route for simultaneously resolving resonance characteristics and configurational evolution across a broader range of nanomechanical resonator architectures. Full article

This paper proposes a location error-tolerant data embedding technique for display-to-camera (D2C) communication systems. The method is designed to enable robust data transmission while maintaining high image fidelity, facilitating simultaneous digital content display and data communication. To address the common issue of alignment and localization inaccuracies in D2C systems, the proposed approach defines specific regions of interest to ensure robustness against object detection model location errors. The architecture employs an expanding and contracting network structure for the encoder to achieve seamless data integration, while the decoder utilizes a computationally efficient “thin” structure for rapid data extraction. To improve performance in diverse environments, various distortion models were integrated into the system training. The system’s effectiveness was evaluated by measuring the bit error rate under conditions of Gaussian noise, blur, simulated localization errors, and real-world distortions. Image quality was validated using peak signal-to-noise ratio and the structural similarity index measure. The results indicate that the proposed technique maintains superior image quality and achieves reliable data recovery even in the presence of significant localization errors. These findings suggest that the approach provides a stable and effective solution for practical mobile-based D2C communication. Full article

The sensitivity of classical electrochemical biosensors is constrained by noise processes at the electrode–electrolyte interface: low-frequency 1/f noise, thermal noise and capacitance fluctuations degrade the signal-to-noise ratio in ways that circuit-level mitigation reduces but does not remove. Chirality-Induced Spin Selectivity (CISS) has been proposed as a route past that limit, by shifting transduction from the scalar quantity of charge to the vector property of electron spin. Spin polarizations of up to approximately 60% have been reported at room temperature for double-stranded DNA monolayers in spin-resolved photoemission, while spin-dependent electrochemistry on smaller chiral adsorbates typically yields values in the range of about 5–30%; the reported magnitude is therefore system-, geometry-, technique- and analysis-dependent rather than a universal property of biological helices. Analyte binding modulates this efficiency through changes in helical pitch, dipole and structural integrity. This review unites the physics of CISS with the surface chemistry of spin-selective sensor layers, compares the competing mechanistic accounts of the effect, and then examines a persistent quantitative gap: the polarizations obtained from first-principles transport calculations on isolated chiral molecules remain well below the measured values. Non-relativistic, spin-restricted calculations on closed-shell helices in vacuum yield no polarization by construction, and although spin-polarized and relativistic implementations that treat spin–orbit coupling explicitly are available, they typically still underestimate experiments by orders of magnitude. We argue that a substantial part of this deficit is attributable to the widespread use of static, vacuum-based or implicitly solvated models, and that multiscale quantum mechanics/molecular mechanics (QM/MM) frameworks with explicit solvents are one necessary—though probably not sufficient—correction. Full article

A gain–phase-calibrated four-channel in-phase/quadrature (I/Q) fusion and recursive beat-frequency estimation framework is developed for a square He–Ne ring laser gyroscope. Calibration-derived channel parameters enable ordinary least-squares (OLS) fusion, while residual covariance estimation provides a generalized least-squares (GLS) extension. Known-ground-truth simulations verify the theoretical fusion gain, covariance propagation, and consistency of the recursive estimation framework. A chronologically partitioned experimental record is used for calibration, parameter tuning, and held-out validation. The proposed fusion improves spectral signal-to-noise ratio compared with individual channels, and the independently tuned extended and unscented Kalman filters achieve millihertz-level frequency estimation accuracy. Innovation analysis further reveals remaining carrier-synchronous temporal correlations, indicating the importance of more complete residual modeling for future stochastic refinement. Full article

Non-contact vital sign monitoring using millimeter-wave radar has emerged as a promising alternative to contact-based devices for continuous healthcare and elderly care applications. However, accurate heart rate estimation remains challenging because the weak cardiac-induced chest displacement is approximately an order of magnitude smaller than respiratory motion, and its fundamental frequency is frequently masked by higher-order respiratory harmonics. Here we propose a signal processing framework that addresses this challenge through three integrated stages: a slow-time phase correlation method that enhances the signal-to-noise ratio by coherently aggregating vital sign energy from adjacent range bins; an adaptive harmonic matching filtering approach based on complementary ensemble empirical mode decomposition that isolates and suppresses respiratory harmonic interference; and autocorrelation-based heart rate estimation. Experimental results obtained with a 77 GHz FMCW radar demonstrate that the proposed method achieves heart rate estimates within 5% error of reference wearable sensors in the presence of respiratory harmonics, with robustness confirmed through long-duration testing. This framework provides a practical solution for reliable radar-based heart rate monitoring without requiring subject-specific calibration or specialized hardware modifications. Full article

To address the limitations of conventional discrete ammonia-monitoring schemes, this study proposes a closed-loop sensing–inference architecture that integrates a distributed chemical-sensing network with a physics-informed neural network (PINN) to achieve high-fidelity three-dimensional dynamic reconstruction of gas diffusion in confined environments. A distributed sensor array based on a PPy/Graphene/WO₃ ternary nanocomposite provides real-time wireless monitoring and reliable observational data streams owing to its sub-ppm detection limit and high signal-to-noise ratio. Fick’s second law of diffusion and Neumann no-flux boundary conditions are embedded in a mesh-free PINN, and rolling horizon data assimilation (RHDA) is introduced to dynamically fine-tune the network weights with high-frequency real-time observations, thereby establishing a real-time closed-loop correction between theoretical inference and the monitored environment. The proposed method achieves accurate three-dimensional concentration-field reconstruction under steady-state conditions, markedly suppresses errors and maintains robustness under unknown abrupt concentration disturbances, and mitigates the temporal divergence of conventional data-driven models during long-term purely physical extrapolation without data support. Thus, the framework combines hardware sensitivity, computational efficiency, and macroscopic physical generalization. Full article

Conventional image acquisition requires an electrically powered image sensor to be placed directly behind the camera lens, constraining camera placement. To overcome this issue, we introduce M3-RGB as an incoherent-light fiber imaging system in which a multicore, multimode optical fiber passively relays lens images to a remotely located image sensor. Unlike conventional approaches, M3-RGB is designed to operate directly on incoherent light and requires no electrical power or active components at the sensing interface. Because propagation through the fiber yields spatially scrambled patterns, a neural network is used to reconstruct the original scene by exploiting the spatial locality preserved by the multicore structure. In a controlled optical bench setup, where a liquid crystal display monitor displays road-scene images, we construct a paired dataset of scrambled and ground-truth images and quantitatively evaluate reconstruction performance across different fiber core counts, fiber lengths, and calibration settings, utilizing the peak signal-to-noise ratio and structural similarity index measure as performance metrics. By decoupling imaging electronics from the sensing point, this passive remote image relay approach may expand sensor placement options for potential applications such as all-around perception for mobile robots and autonomous vehicles, surveillance, and inspection in confined spaces. Evaluations in real outdoor environments constitute future work. Full article

Physiological signal emulators support the calibration, validation, and stress testing of cardiovascular monitoring devices. However, many existing systems generate electrocardiography (ECG) or photoplethysmography (PPG) independently and offer limited control over arrhythmia detection and ECG–PPG coupling. This study presents a programmable physiological signal emulator that integrates a unified event-driven ECG–PPG model with synchronized multichannel hardware. The model represents atrial pacing, atrioventricular conduction, and ventricular activation as separate functional blocks, enabling normal sinus rhythm, first-degree atrioventricular block, second-degree atrioventricular block Mobitz I, complete atrioventricular block, atrial tachycardia, and ventricular tachycardia. A Gaussian-based ECG is generated from the atrial and ventricular event sequences, while a multi-Gaussian PPG waveform is derived from ventricular activation using a beat-class-dependent electromechanical delay. The same processing architecture supports playback of recorded 12-lead clinical ECG data through an inverse lead transformation. The hardware uses an STM32F407VET6 microcontroller and MCP4921 digital-to-analog converters (DACs) to generate 10 synchronized analog outputs, comprising 09 ECG electrodes and 01 PPG channel. Validation covered physiological timing, analog-chain performance, and end-to-end signal reproduction. PR interval errors relative to a commercial electrocardiograph were 1.23 ms for normal sinus rhythm and 1.66 ms for first-degree atrioventricular block. The measured beat-to-beat PR increment during Mobitz I conduction was $40.02\pm 0.04$ ms for a programmed value of 40 ms. At commanded amplitudes of at least 800 mV, both output channels achieved absolute amplitude errors below 0.60%, total harmonic distortion below 1%, and signal-to-noise ratios (SNRs) above 30 dB. Inter-channel R-peak skew remained below the 2 ms sampling interval, and all monitored metrics varied by less than 1.5% during 60 min of continuous operation. Reproduction of a clinical 12-lead recording yielded per-lead $R^2$ values of 0.967–0.986 and a cycle-to-cycle correlation of 0.996. The emulator also reproduced amplitude-dependent bias in automated interval measurements and interpretation labels. These results demonstrate a low-cost, open-source platform for reproducible device calibration, algorithm stress testing, medical training, and physiological signal processing research. Full article

Radar active jamming recognition is an essential component of radar anti-jamming processing and cognitive radar decision making. As jamming categories proliferate and electromagnetic environments become more complex, collecting sufficient labeled samples for every possible jamming condition can be challenging in practical scenarios. Thus, it becomes necessary to exploit the recognition capability under few-shot conditions, especially in the face of varying jamming-to-noise ratio (JNR) and compound interference. Existing methods commonly rely on a single signal representation or treat jamming categories as discrete labels, which may underuse the complementary evidence available across signal domains and the semantic knowledge associated with jamming categories. This paper proposes FuseCLIP, a semantic-guided multidomain fusion framework for few-shot radar active jamming recognition. FuseCLIP jointly encodes time-domain sequences, frequency-domain sequences, and short-time Fourier transform (STFT) spectrograms to capture waveform, spectral, and time-varying spectral characteristics. The resulting features are adaptively fused and mapped into a unified representation space, where they are matched with fixed Contrastive Language–Image Pre-training (CLIP) text prototypes generated from task-related jamming descriptions. By combining multidomain physical information with category-level semantic priors, the proposed framework is intended to make more effective use of limited labeled data. Experiments on a simulated radar active jamming dataset under different shot numbers and JNR levels demonstrate the effectiveness of FuseCLIP relative to representative baselines. Ablation experiments further indicate the complementary contributions of the time-domain, frequency-domain, and time-frequency inputs. Full article

Background/Objectives: N-3-[¹⁸F]fluoropropyl-2β-carbomethoxy-3β-4-iodophenyl nortropane ([¹⁸F]FP-CIT) positron emission tomography/computed tomography (PET/CT) is an effective imaging tool for diagnosing parkinsonism. This study evaluated the feasibility of deep learning (DL)-driven image restoration of short-duration [¹⁸F]FP-CIT images. Methods: List-mode data from 202 patients who underwent [¹⁸F]FP-CIT PET/CT were reconstructed into 30-s (30_sec), 1-min (1_min), and reference 10-min (10_min) acquisition durations. Patients were divided into training, validation, and test sets in a 6:2:2 ratio. The U-Net model was implemented to generate the DL images from 30_sec and 1_min data. The visual image quality was assessed using a three-point scale, visual interpretation of striatal dopamine transporter binding patterns, regional standardized uptake value ratios (SUVRs), and quantitative quality metrics including the peak signal-to-noise ratio, root mean squared error, and universal quality index among five series of images: 30_sec, 1_min, DL-30_sec, DL-1_min, and 10_min. Results: While 30_sec and 1_min low-count scans showed poor image quality, the DL-driven algorithm showed significant improvement; DL-1_min scans achieved excellent ratings in 95% of cases. Concordance with the reference images was 85.0% for visual image quality and 92.5% for visual interpretation in the DL-30_sec images, and 97.5% and 92.5%, respectively, in the DL-1_min images. Discordant interpretations occurred mainly in patients with atypical parkinsonism. Regional SUVR values and quantitative metrics for the DL-1_min images showed good agreement and smaller biases with respect to the reference images, compared with those of DL-30_sec images. Conclusions: The DL-driven method generated clinically acceptable [¹⁸F]FP-CIT images while reducing acquisition time, with better image quality in the DL-1_min than in the DL-30_sec images. Concordance in visual interpretation was reduced in the small subgroup of patients with atypical parkinsonism. Full article

Nighttime driving safety remains a critical challenge in modern transportation systems: insufficient ambient lighting significantly degrades visual perception quality, adversely affecting both human drivers and advanced driver-assistance systems (ADAS) and directly threatening road users’ safety. Traditional image enhancement methods often suffer from color distortion and visual artifacts, whereas existing deep learning approaches typically require paired training data and incur substantial computational overhead. To address these limitations, this paper presents BLEN (bio-inspired low-light enhancement network), a zero-reference deep learning framework that integrates biological vision principles with efficient convolutional architectures. Specifically, BLEN leverages Retinex theory for illumination–reflectance decomposition, is inspired by and functionally approximates lateral inhibition mechanisms for edge enhancement, and incorporates a Large-Kernel Convolution with Attention (LKCA) module that reduces the parameter count of the LKCA encoder block by 76% (0.56 M vs. 2.34 M for a standard 13 × 13 convolution) relative to standard large-kernel operations. Extensive experiments on the SICE and LOL benchmarks demonstrate that BLEN achieves state-of-the-art performance among real-time, edge-deployable zero-reference methods on the SICE benchmark, yielding a peak signal-to-noise ratio (PSNR) of 23.67 ± 0.14 dB and a structural similarity index measure (SSIM) of 0.891 ± 0.004 on SICE while maintaining 2.10 M parameters (2.1 MB in INT8, 8.4 MB in FP32). Furthermore, the proposed method enables real-time inference at 31 frames per second (FPS) on embedded platforms, including the HiSilicon SS928 and Jetson Nano, demonstrating that the proposed method is an efficient and effective front-end for camera-based ADAS perception on automotive-grade edge hardware. Full article

The accuracy and consistency of error-state Kalman filtering for SINS/GNSS integrated navigation depend critically on properly tuned process and measurement noise covariance matrices. In dynamic operation, these covariances can be non-stationary: inertial uncertainty changes with maneuver intensity, vibration, and sensor-bias instability, whereas GNSS measurement quality changes with satellite geometry, multipath, obstruction, and signal loss. Fixed-covariance and classical adaptive filters can therefore become overconfident or insufficiently responsive during abrupt maneuvers and degraded GNSS reception. We propose a physics-informed constrained neural covariance estimation (PC-NCE) framework that augments, rather than replaces, the error-state Kalman filter (ESKF) by estimating bounded process and measurement covariance-scale parameters online. The framework maps IMU-window sequences, GNSS-quality indicators, innovation statistics, and motion-state descriptors through a CNN-BiLSTM-attention network to filter-admissible Q_k and R_k parameterizations injected into a closed-loop ESKF. Training enforces positivity, bounds, temporal smoothness, and innovation–consistency regularization. In a reproducible filter-level MATLAB scenario suite, PC-NCE improved covariance-scale tracking and selected consistency ratios relative to fixed and unconstrained neural baselines, whereas position RMSE gains were scenario-dependent. These results provide a simulation-level proof of concept supplemented by an initial held-out measured-trajectory evaluation; broader validation using a full 15-state SINS/GNSS implementation and additional field datasets remains necessary. By treating neural networks as uncertainty-perception layers rather than black-box state estimators, PC-NCE retains the interpretability and engineering safeguards of classical Kalman filtering. Full article

Image stitching aims to construct wide field-of-view scenes from multiple narrow-FoV images, yet existing deep learning-based approaches may introduce substantial computational and parameter overhead, limiting their applicability in efficiency-sensitive scenarios. To address this issue, we propose a lightweight deep stitching framework that integrates multi-scale feature fusion with attention-enhanced matching. Specifically, a transformer-based channel attention (TCA) block improves the discriminative capability of fused features in low-texture regions and enhances global consistency. A coordinate-aware correlation module (CACM) combines correlation-based matching with position-sensitive coordinate attention to support registration under parallax, while GhostNet serves as the shared backbone. On UDIS-D, the complete model achieves a 25.19 dB peak signal-to-noise ratio (PSNR) and a structural similarity index (SSIM) of 0.833 under the overlap-region protocol, with a reported full-system complexity of 19.28 giga multiply-accumulate operations (GMACs) and 52.86 M parameters. These results demonstrate a favorable accuracy–efficiency trade-off under the stated GMAC, runtime, and memory protocol and the potential of the proposed framework for resource-conscious image stitching applications. Full article

This paper presents an FPGA (Field-Programmable Gate Array) implementation of a low-power VLSI (Very Large-Scale Integration) architecture for medical image scaling in portable diagnostic systems. The proposed architecture employs bilinear interpolation optimized through FSM (Finite-State-Machine)-based control, a clock-enable technique, and selective block activation to reduce switching activity and dynamic power consumption while preserving image quality. The architecture is described in Verilog HDL, synthesized using Vivado 2024.1, and implemented on the Xilinx Zynq-7000-based ZedBoard platform. A controlled post-implementation power analysis on the same FPGA platform demonstrates a reduction in estimated total on-chip power from 3.739 W for the unoptimized baseline architecture to 1.053 W for the optimized architecture, corresponding to an approximately 71.8% reduction. The system supports multiple operational modes, including original image display, grayscale conversion, Sobel X filtering, and Sobel Y filtering, providing enhanced diagnostic flexibility. Quantitative assessment of the exemplary X-ray Image 1 yielded PSNR (Peak Signal-to-Noise Ratio) of 42.97 dB and SSIM (Structural Similarity Index) of 0.9557, demonstrating satisfactory image-quality preservation after scaling. The proposed architecture demonstrates the feasibility of low-power FPGA-based medical image scaling for portable diagnostic and telemedicine imaging systems, offering an effective balance between energy efficiency and image fidelity. Full article

Symbiotic radio (SR) has emerged as a spectral-efficient paradigm for Internet-of-Things networks. However, simultaneously optimizing coexisting primary and secondary transmissions remains a challenge. To address this, a joint precoding and RIS phase shift design is proposed to maximize the sum-rate in the reconfigurable intelligent surface (RIS)-aided multiple-input single-output SR system. Specifically, based on an optimization problem that maximizes the sum-rate of primary and secondary data, an iterative algorithm that sequentially updates the precoder and the RIS phase shift matrix is developed. To address practical aspects, channel training overhead and minimum signal-to-interference-plus-noise ratio (SINR) constraints are also considered in the precoder design. Simulation results verify that the precoder design based on perfect successive interference cancellation achieves the highest sum-rate. The results also highlight the necessity of the proposed SINR-constrained precoding schemes in guaranteeing data rates of the primary and secondary links under various channel environments, proving the practical validity of our joint precoder and RIS phase shift design. Full article