Search Results (54)

Differential wavefront sensing (DWS) is widely adopted for high-precision angular detection in interferometric systems, yet its measurement range is constrained by the nonlinear implicit phase–angle relationship. This paper proposes a strategy-enhanced differential evolution algorithm, termed Bi-inheritance and Tournament-Selection-based Differential Evolution (BiTsDE), to suppress nonlinear angular errors. The method introduces fitness-guided inheritance of mutation and crossover factors and tournament-based elite parent selection, enabling adaptive balance between global exploration and local exploitation. Unlike conventional DE variants that mainly tune control parameters, BiTsDE optimizes the evolutionary search strategy, enhancing early-stage diversity and late-stage convergence stability. Simulations demonstrate angular resolution better than 0.06 nrad within ±1 mrad. Experiments show that up to 600 μrad, BiTsDE reduces demodulation error by 99.9% compared with linear DWS, achieving 17.9 nrad precision and 42% faster convergence. These results validate BiTsDE as an effective solution for nonlinear error suppression in DWS-based high-precision optical metrology, particularly for space-based gravitational wave detection. Full article

TMR-based magnetic encoders provide sensitive SIN/COS signals, but practical accuracy is degraded by channel mismatch and decoder dynamics. This study evaluates an end-to-end embedded implementation on a PMSM (Permanent Magnet Synchronous Motor) bench. We consider amplitude mismatch, quadrature non-orthogonality, and harmonic/noise disturbances in the measured differential channels. We implement a lightweight compensation chain, including fixed-window moving-average filtering, min–max amplitude normalization, and correlation-based quadrature identification with sample-shift correction. We then compare four demodulation configurations under identical sampling and reference alignment to a 24-bit encoder: (A0) conventional second-order PLL (phase locked loop), (A1) compensation + open-loop atan2, (A2) compensation + fixed-${\omega }_n$ third-order PLL, and (A3) compensation + adaptive-${\omega }_n$ third-order PLL. Experiments with a TMR3081 sensor and an STM32 controller show clear differences among A0–A3. In steady operation, A3 removes the DC bias observed with A0 and keeps the angle error within approximately ±0.3° in the evaluated steady window. During commutation and ramp-like segments, PLL-based tracking (A0/A2/A3) is more robust than open-loop atan2 (A1), and bandwidth adaptation in A3 improves the acquisition–noise trade-off within the preset ${\omega }_n$ bounds. These results are reported for this prototype and the tested parameter settings. Full article

As a crucial long-range positioning, navigation, and timing (PNT) system, eLoran will utilize the ninth pulse to broadcast differential data. However, conventional demodulation methods are ill-suited to the unique modulation characteristics of the ninth pulse and suffer from poor noise resistance, necessitating a more efficient demodulation solution. To address this, this paper proposes a lightweight Multilayer Perceptron (MLP)-based demodulation framework designed explicitly for the eLoran ninth pulse. The approach begins with a preprocessing stage that extracts a 1600-point key segment from each received frame, which is then fed into a compact MLP architecture with a 1600-dimensional input layer, a 512-neuron hidden layer, and a 32-class output layer trained using the Adam optimizer. Experimental results demonstrate that the proposed model achieves 99.97% accuracy on the validation set and maintains over 90% demodulation accuracy even at an SNR of −10 dB, whereas the improved EPD algorithm yields only about 70% demodulation accuracy. Notably, although the improved EPD algorithm itself exhibits a clear performance advantage over the basic correlation method and the peak-position detection method—both of which still present non-zero error rates even at an SNR of 20 dB—it remains significantly inferior to the proposed MLP-based scheme in the low-SNR regime. In addition, CNN-based and LSTM-based demodulation models show very poor performance under severe noise conditions, with symbol error rates rising to around 0.8 at −10 dB, despite being able to reach an error-free state when the SNR increases to approximately 2 dB. By adopting an end-to-end learning strategy, the method effectively avoids performance degradation caused by inter-module error propagation, while combining high precision with strong noise immunity. These features meet the requirements for real-time differential data reception and highlight the promising engineering potential of neural-network-based demodulation for high-reliability PNT applications in complex electromagnetic environments. Full article

We propose a vector bending sensor based on a tapered few-mode multi-core fiber (FM-MCF). A seven-core six-mode fiber is tapered with an optimized taper ratio, enabling bending sensing through power monitoring. When the tapered FM-MCF bends, coupling occurs between the central core and side cores in the tapered region. By monitoring the power of all cores and employing a power differential method, the bending direction and curvature can be reconstructed. The results show that within a curvature range of 2.5 m⁻¹ to 10 m⁻¹, the sensitivity of the ratio of the side core’s power to the middle core’s power with respect to curvature is not less than 0.14/m⁻¹. A deep fully connected feedforward neural network (DNN) is used to demodulate all power information and predict the bending shape of the optical fiber. The algorithm predicts the bending radius and rotation angle with mean absolute errors less than 0.038 m and 3.087°, respectively. This method is expected to achieve low-cost, high-sensitivity bending measurement applications with vector direction perception, providing an effective solution for scenarios with small curvatures that are challenging to detect using conventional sensing methods. Full article

Orthogonal time–frequency space (OTFS) modulation has emerged as a promising technology to alleviate the effects of the Doppler shifts in high-mobility environments. As a prerequisite to demodulation and signal processing, automatic modulation classification (AMC) is essential for OTFS systems. However, a very limited number of works have focused on this issue. In this paper, we propose a novel AMC approach for OTFS systems. We build a dual-stream convolutional neural network (CNN) model to simultaneously capture multi-domain signal features, which substantially enhances recognition accuracy. Moreover, we propose a differentiated embedded pilot structure that incorporates information about distinct modulation schemes to further improve the separability of modulation types. The results of the extensive experiments carried out show that the proposed approach can achieve high classification accuracy even under low signal-to-noise ratio (SNR) conditions and outperform the state-of-the-art baselines. Full article

In fiber Bragg grating (FBG) sensing demodulation systems, high-precision peak detection is a core requirement for demodulation algorithms. However, practical spectral signals are often susceptible to environmental noise interference, which leads to significant degradation in the accuracy of traditional demodulation methods. This study proposes a self-adaptive K-SVD (SAK-SVD) denoising algorithm based on adaptive window parameter optimization, establishing a closed-loop iterative feedback mechanism through dual iterations between dictionary learning and parameter adjustment. This approach achieves a synergistic enhancement of noise suppression and signal fidelity. First, a dictionary learning framework based on K-SVD is constructed for initial denoising, and the peak feature region is extracted by differentiating the denoised signals. By constructing statistics on the number of sign changes, an adaptive adjustment model for the window size is established. This model dynamically tunes the window parameters in dictionary learning for iterative denoising, establishing a closed-loop architecture that integrates denoising evaluation with parameter optimization. The performance of SAK-SVD is evaluated through three experimental scenarios, demonstrating that SAK-SVD overcomes the rigid parameter limitations of traditional K-SVD in FBG spectral processing, enhances denoising performance, and thereby improves wavelength demodulation accuracy. For denoising undistorted waveforms, the optimal mean absolute error (MAE) decreases to 0.300 pm, representing a 25% reduction compared to the next-best method. For distorted waveforms, the optimal MAE drops to 3.9 pm, achieving a 63.38% reduction compared to the next-best method. This study provides both theoretical and technical support for high-precision fiber-optic sensing under complex working conditions. Crucially, the SAK-SVD framework establishes a universal, adaptive denoising paradigm for fiber Bragg grating (FBG) sensing. This paradigm has direct applicability to Raman spectroscopy, industrial ultrasound-based non-destructive testing, and biomedical signal enhancement (e.g., ECG artefact removal), thereby advancing high-precision measurement capabilities across photonics and engineering domains. Full article

This study proposes a derivative-based, carrierless pulse position modulation (PPM) scheme utilizing a voltage-controlled oscillator (VCO) and a monostable multivibrator. In contrast to conventional PPM systems that rely on reference carriers or complex demodulation methods, the proposed architecture simplifies signal generation by directly modulating the time derivative of the message signal. The modulated signal, when processed through standard analog demodulators, inherently yields the derivative of the original message. This behavior is first established through theoretical derivations and then confirmed by simulations and circuit-level experiments. The proposed method includes a differentiator feeding into a VCO, followed by a monostable multivibrator to generate a carrierless PPM waveform. Experimental validation confirms that, under all tested demodulation approaches—integrator-based, PLL-based, and quasi-FM—the recovered output aligns with the differentiated message signal. The integration of this output to retrieve the original message was not performed to maintain focus on verifying the modulation principle. Additionally, the study aimed to ensure the consistency of derivative recovery. Signal-to-noise ratio (SNR) expressions for each demodulator type are presented and discussed in the context of their relevance to the proposed system. Limitations and directions for further study are also identified. Full article

This paper presents a low-power 60 GHz low-noise amplifier (LNA) designed for Gbit/s applications using 28 nm CMOS technology. The LNA exploits a single-stage pseudo-differential architecture with integrated input transformer for both electrostatic discharge (ESD) protection and simultaneous noise/impedance matching. An effective power-constrained design strategy is adopted to pursue the lowest current consumption at the minimum noise figure (NF), with the best tradeoff between gain and frequency bandwidth. The LNA, which has been designed to drive an on–off keying (OOK) demodulator, is operated at a supply voltage as low as 0.9 V and achieves a voltage gain of about 21 dB with a 3 dB bandwidth of 2 GHz around 60 GHz. Thanks to the proper impedance transformation at the 60 GHz input, the amplifier exhibits an NF of 6.3 dB, also including the input transformer loss with a very low power consumption of about 5 mW. The adoption of a single-stage topology also allows an excellent input 1 dB compression point (IP_1dB) of −4.7 dBm. The input transformer guarantees up to 2 kV human body model (HBM) ESD protection. Full article

High Spectral Spatial Frequency Domain Imaging (HSFDI) combines high spectral imaging and spatial frequency domain imaging techniques, offering advantages such as wide spectral range, non-contact, and differentiated imaging depth, making it well-suited for measuring the optical properties of agricultural products. The diffuse reflectance spectra of the samples at spatial frequencies of 0 ${\mathrm{mm}}^{-1}$ ($R_{d0}$) and 0.2 ${\mathrm{mm}}^{-1}$ ($R_{d0}$) were obtained using the three-phase demodulation algorithm. The pixel-by-pixel inversion was performed to obtain the absorption coefficient (${\mu }_a$) spectra and the reduced scattering coefficient (${\mu }_s^{\prime }$) spectra of the pears. For predicting the SSC and firmness of the pears, these optical properties and their specific combinations were used as inputs for partial least squares regression (PLSR) modeling by combining them with the wavelength selection algorithm of competitive adaptive reweighting sampling (CARS). The results showed that ${\mu }_a$ had a stronger correlation with SSC, whereas ${\mu }_s^{\prime }$ exhibited a stronger correlation with firmness. Taking the plane diffuse reflectance $R_{d0}$ as the comparison object, the prediction results of SSC based on both ${\mu }_a$ and the combination of diffuse reflectance at two spatial frequencies ($R_d$) were superior (the best $R_p^2$ of 0.90 and $RMS{E}_P$ of 0.41%). Similarly, in the prediction of firmness, the results of ${\mu }_s^{\prime }$, ${\mu }_a\times {\mu }_s^{\prime }$, and $R_{d1}$ were better than that of $R_{d0}$ (the best $R_p^2$ of 0.80 and $RMS{E}_P$ of 3.25%). The findings of this research indicate that the optical properties represented by HSFDI technology and their combinations can accurately predict the internal quality of pears, providing a novel technical approach for the non-destructive internal quality evaluation of agricultural products. Full article

The electromagnetic eddy current non-destructive testing system enables the non-destructive analysis of surface defect information on tested materials. Based on the principles of eddy current detection, this paper presents a digital eddy current detection method using high-speed sampling based on STM32. A differential eddy current coil is used as the detection probe, and the combination of a differential bridge and a differential amplifier circuit helps to reduce common-mode noise interference. The detection signal is collected via an STM32-based acquisition circuit and transmitted to the host computer through Ethernet for digital demodulation processing. The host computer performs operations such as smoothing averaging, sinusoidal fitting, and outlier removal to extract the amplitude and phase of the detection signal. The system also visually displays the condition of the tested object’s surface in real time through graphical visualization. Testing showed that this system can operate at frequencies up to 8.84 MHz and clearly identify defects as narrow as 1 mm on the surface of the tested steel plate. Full article

In this work, we propose a bipolar complementary pulse width modulation strategy based on the differential signaling system, and the modulation–demodulation methods are introduced in detail. The proposed modulation–demodulation strategy can effectively identify each symbol’s start and end time so that the transmitter and receiver can maintain correct bit synchronization. The system with differential signaling has the advantages of not requiring channel state information and reducing background radiation. To further reduce the noise in the system, a multi-bandpass spectrum noise reduction method is proposed according to the spectrum characteristics of the received modulation signals. The proposed modulation method has an error bit rate of 10⁻⁵ at a signal-to-noise ratio of 7 dB. The fabricated optical communication system can stably transfer voice and text over a distance of 5.6 km. Full article

Fiber-optic hydrophone (FOH) has significant potential in many applications of hydroacoustic sensing and underwater communication. A novel path-matched differential interferometer fiber optic hydrophone (PMDI-FOH) approach incorporating an integrated-optic component (IOC) is presented in this paper. It is presented to meet the demands for high-quality dynamic measurements, which solves the problems with the conventional homodyne detection system’s low modulation frequency. The IOC functions as a phase-generated carrier (PGC) component. The scheme is investigated both in theory and experiments. The theoretical and experimental results verify the effectiveness of the proposed scheme. It achieves a high SNR of up to 20.29 dB demodulations. The proposed system is cost-effective and has excellent potential in building next-generation underwater sensing and communication networks. Full article

Conventional eddy-current sensors have the advantages of being contactless and having high bandwidth and high sensitivity. They are widely used in micro-displacement measurement, micro-angle measurement, and rotational speed measurement. However, they are based on the principle of impedance measurement, so the influence of temperature drift on sensor accuracy is difficult to overcome. A differential digital demodulation eddy current sensor system was designed to reduce the influence of temperature drift on the output accuracy of the eddy current sensor. The differential sensor probe was used to eliminate common-mode interference caused by temperature, and the differential analog carrier signal was digitized by a high-speed ADC. In the FPGA, the amplitude information is resolved using the double correlation demodulation method. The main sources of system errors were determined, and a test device was designed using a laser autocollimator. Tests were conducted to measure various aspects of sensor performance. Testing showed the following metrics for the differential digital demodulation eddy current sensor: nonlinearity 0.68% in the range of ±2.5 mm, resolution 760 nm, maximum bandwidth 25 kHz, and significant suppression in the temperature drift compared to analog demodulation methods. The tests show that the sensor has high precision, low temperature drift and great flexibility, and it can instead of conventional sensors in applications with large temperature variability. Full article

Phase-sensitive optical time-domain reflectometry (φOTDR) is a technology for distributed vibration sensing, where vibration amplitudes are determined by recovering the phase of the backscattered light. Measurement noise induces phase errors, which degrades sensing performance. The phase errors, using a differentiation and cross-multiplication (DCM) algorithm, are investigated theoretically and experimentally in a φOTDR system based on a phase retrieval configuration consisting of an imbalanced Mach–Zehnder interferometer (IMZI) and a 3 × 3 coupler. Analysis shows that phase error is highly dependent on the AC component of the obtained signal, essentially being inversely proportional to the product of the power of the light backscattered from two locations. An analytical expression was derived to estimate the phase error and was confirmed by experiment. When applied to the same measurement data, the error is found to be slightly smaller than that obtained using in-phase/quadrature (I/Q) demodulation. The error, however, increases for longer measurement times. Full article

A new multi−carrier orthogonal double bit rate differential chaotic shift keying (MC−ODBR−DCSK) communication system is presented in this paper. With two composite signals generated by an orthogonal chaotic signal generator as reference signals, 2M bits of information data are transmitted on M−channel subcarriers, improving transmission speed and energy efficiency. In addition, the receiver does not require a radio frequency (RF) delay circuit to demodulate the received data, which makes the system easier to implement. This paper analyzes Data−energy−to−Bit−energy Ratio (DBR) of the system. The bit error rate performance of the system is simulated to verify the impact of parameters such as chaotic maps, semi-spread spectrum factor, and sub-carrier number. At the same time, the bit error rate performance of the MC−ODBR−DCSK system is compared with traditional DCSK systems in Rician fading and additive Gaussian white noise (AWGN) channels. Full article