Search Results (57)

Road noise has become a dominant interior noise source in electrified vehicles, especially at low and medium speeds. In practical active road noise control (ARNC) systems, the error microphones capture not only the road noise component correlated with the reference sensors but also non-coherent disturbances such as wind noise, engine harmonics, and heating, ventilation and air conditioning (HVAC) noise. These disturbances degrade the convergence stability and steady-state attenuation of the conventional filtered-x least mean square (FxLMS) algorithm. This study analyzes FxLMS under non-coherent interference and develops two robustness optimization methods. Under the small-step-size assumption, a statistical convergence model is derived for stationary random inputs, together with the corresponding convergence region and steady-state error expressions. Based on this analysis, a multichannel cascaded controller (MCC) and a bounded variable-step-size (VSS) FxLMS algorithm are proposed. Offline simulations and dSPACE-based experiments are conducted on a single-channel HVAC duct ANC test platform and a vehicle test bench. The vehicle-bench tests use controlled tonal excitations and should be interpreted as an intermediate validation step before real-driving broadband tests. Average noise reduction (ANR) and the norm of the adaptive-filter coefficients are used to evaluate robustness. Both MCC and VSS improve attenuation and reduce coefficient fluctuations under non-coherent interference. Relative to fixed-step FxLMS, the maximum ANR improvement reaches 15.8 dB in simulation and 14.2 dB in the real-time duct experiment. Within the controlled tonal and tonal-plus-white-noise conditions tested here, VSS achieves robustness improvements close to those of MCC with much lower computational cost; therefore, it is a practical candidate for further onboard ARNC evaluation rather than a completed validation under real-driving broadband road noise. Full article

Detectability of auditory signals in built environments is a critical issue in architectural acoustics, particularly in public spaces where notification sounds must be perceived reliably under background noise. This study investigated reaction times (RTs) to amplitude-modulated pure tones under silent, white noise, and bandpass-noise conditions. Twenty young and twenty elderly participants responded to 1 and 2 kHz tones with flat, gentle, and steep onset envelopes. To describe perceptual detection in physically interpretable terms, a time-integrated sound-exposure level model, $L_{AE}\left(t\right)$, was applied. RT was defined as the moment when cumulative acoustic energy exceeded a criterion value relative to the hearing threshold. In silent conditions, RTs were accurately predicted by $L_{AE}\left(t\right)$, with onset-envelope shape influencing early energy accumulation. In noise conditions, RTs increased systematically with spectral proximity between target and masker, consistent with auditory filter theory. When spectral separation exceeded approximately four ERB numbers, masking effects were minimal, and RT approached silent-condition values. These findings demonstrate that perceptual detection timing is governed by cumulative acoustic energy and spectral masking rather than instantaneous sound pressure level. The $L_{AE}\left(t\right)$ model provides a detection-oriented metric that complements conventional room-acoustic parameters and may support evidence-based design of perceptually robust auditory signals in architectural environments. Full article

Chaotic signals commonly exhibit nonlinear and nonstationary characteristics, while noise contamination reduces signal interpretability and degrades subsequent feature extraction and dynamical analysis. To improve the stability of mode-boundary determination and mitigate reconstruction distortion, this paper proposes a hybrid denoising framework that integrates feature mode decomposition (FMD), amplitude-aware permutation entropy (AAPE), dual-tree complex wavelet transform (DTCWT), and Savitzky–Golay (SG) filtering. First, the noisy signal is decomposed into multiple mode components using FMD. Then, the AAPE of each mode is calculated to adaptively distinguish high-frequency noise-dominant modes from non-high-frequency modes. For the high-frequency noise-dominant modes, improved logarithmic threshold shrinkage is applied to the magnitudes of DTCWT complex coefficients to suppress random noise and reduce threshold-induced bias. For the non-high-frequency modes, SG filtering is employed to further attenuate residual noise while preserving local waveform structures. Finally, the processed modes are reconstructed to obtain the denoised signal. Experiments on a simulated Lorenz chaotic signal and a real-world sunspot time series demonstrate that, across different noise levels, AAPE provides more stable mode partitioning than ApEn, CC, and CMSE. Moreover, under Gaussian white noise, Poisson noise, and uniform noise, the proposed method generally achieves a higher output signal-to-noise ratio (SNR) and a lower root mean square error (RMSE) than WT, CEEMD, EEMD, CEEMDAN+LMS, and VMD, while also yielding better performance in phase-space reconstruction and temporal-detail recovery. These results verify the effectiveness and practical applicability of the proposed method for chaotic signal denoising. Full article

Quantum stochastic processes are widely used in describing open quantum systems and in the context of quantum foundations. Physically relevant quantum stochastic processes driven by multiplicative colored noise are generically non-Markovian and analytically intractable. Further, their Markovian limits are generically inequivalent when using either the Ito or Stratonovich conventions for the same quantum stochastic processes. We introduce a quantum noise homogenization scheme that temporally coarse-grains non-Markovian, colored-noise-driven quantum stochastic processes and connects them to their effective white-noise (Markovian) limits. Our approach uses a novel phase-space augmentation that maps the non-Markovian dynamics into a higher-dimensional Markovian system and then applies a controlled perturbative coarse-graining scheme in the characteristic time scales of the noise. This allows an explicit analytical algorithm to derive effective Markovian generators with renormalized coefficients and enables various physical constraints, such as causality, to be imposed on them. We thus resolve the Ito–Stratonovich ambiguity for multiplicative colored-noise-driven quantum stochastic processes, wherein we show that their consistent Markovian limit corresponds to the Stratonovich convention with renormalized coefficients as well as correction terms in Ito’s convention. By assuming their Markovian limit unravels causal, completely positive and trace-preserving dynamics, we further characterize a physically relevant family of non-Markovian quantum stochastic processes driven by multiplicative colored noise. Full article

Underwater images often suffer from color casts, insufficient contrast, and blurred details due to wavelength-dependent absorption and scattering, which limits their use in marine observation and underwater target recognition. Therefore, it remains challenging to jointly improve color fidelity, contrast, and detail visibility under varying water types and illumination. To address these issues, this paper proposes an underwater image enhancement method that integrates RGB Space color-cast correction with Lab Space luminance-chrominance synergistic enhancement. First, channel compensation is performed in the RGB space according to color-cast types, together with a Gray-World white-balance strategy, to suppress severe color bias and recover more natural tones. Subsequently, synergistic enhancement is conducted in the Lab space as follows: an improved Auto-MSR is applied on the L channel for contrast restoration while suppressing noise amplification, and an edge-aware detail enhancement module is incorporated to reinforce structural textures. Finally, chrominance balancing is applied to A and B channels to further improve color consistency and rendition. Experiments on the UIEB and UIQS datasets demonstrate that the proposed method achieves the best performance among the methods considered on AG, EI, and CCF. Specifically, it attains AG = 10.716, EI = 107.197, and CCF = 46.935 on UIEB, as well as AG = 10.136, EI = 103.780, and CCF = 39.433 on UIQS. These results indicate clear advantages in image clarity, edge detail preservation, and color rendition. Future work will focus on extending the proposed method to real-time underwater video enhancement. Full article

We present a new computable geometry-based upper bound on the capacity of peak-power-limited and band-limited signal over the Additive White Gaussian Noise Channel. The peak limit applies at continuous time. The bound is a function of the volume and shape of the transmitted signal set, namely the body, in the space of Nyquist-rate samples, comprising all of the points the transmitted signal can reach. At a high SNR, the bound is tight, better than previously known upper bounds and, together with a known lower bound, provides the capacity at an asymptotically high SNR. We found, using a numerical evaluation, the high-SNR capacity of signals with the structure used in Cyclic Prefix assisted Frequency Domain Equalization (CP-FDE) and OFDM for sequence length of up to 100 Nyquist intervals, and we present a conjecture that this result is correct for any sequence length and does not depend on the CPA-FDE structure. This paper extends the methodology developed in previous works. The penalty in power efficiency at a high SNR due to the peak power constraint relative to an average power constraint is about 7.5 dB in the low-pass case and about 5.4 dB in the band-pass case. Full article

Natural rubber quality assessment traditionally relies on subjective visual inspection, leading to inconsistent grading and processing inefficiencies. This study presents a colorimetric imaging system integrating 48-megapixel image acquisition with automated colorimetric analysis for objective rubber classification. Five rubber grades—white crepe, STR5, STR5L, RSS3, and RSS5—were analyzed using standardized 25 × 25 mm² specimens under controlled environmental conditions (25 ± 2 °C, 50 ± 5% relative humidity, 3200 K illumination). The image processing pipeline employed color space transformations from RGB through CIE1931 $XYZ$ to CIELAB coordinates, with yellowness index calculation following ASTM E313-20 standards. The classification algorithm achieved 100% accuracy across 100 validation specimens under controlled laboratory conditions, with a processing time of 1.01 ± 0.09 s per specimen. Statistical validation via one-way ANOVA confirmed measurement reliability (p > 0.05) with yellowness index values ranging from 8.52 ± 0.52 for white crepe to 72.15 ± 7.47 for RSS3. Image quality metrics demonstrated a signal-to-noise ratio exceeding 35 dB and a spatial uniformity coefficient of variation below 5%. The system provides 12-fold throughput improvement over manual inspection, offering objective quality assessment suitable for industrial implementation, though field validation under diverse conditions remains necessary. Full article

In this paper, we investigate the nonlinear dynamic behavior of a cart–double pendulum system with single time delay feedback control. Based on the center manifold theorem and stochastic averaging method, a reduced-order dynamic model of the system is established, with a focus on analyzing the influence of time delay parameters and stochastic excitation on the system’s Hopf bifurcation characteristics. By introducing stochastic differential equation theory, the system is transformed into the form of an Itô equation, revealing bifurcation phenomena in the parameter space. Numerical simulation results demonstrate that decreasing the time delay and increasing the time delay feedback gain can effectively enhance system stability, whereas increasing the time delay and decreasing the time delay feedback gain significantly improves dynamic performance. Additionally, it is observed that Gaussian white noise intensity modulates the bifurcation threshold. This study provides a novel theoretical framework for the stochastic stability analysis of time delay-controlled multibody systems and offers a theoretical basis for subsequent research. Full article

This study investigates the complex dynamic behavior of three-tailed helical microrobots operating in confined spaces. A stochastic dynamic model has been developed to analyze the effects of input angular velocity, current, fluid viscosity, and channel width on their motion trajectories, velocity, mean squared displacement (MSD), and wobbling rate. The results indicate that Gaussian white noise exerts a dispersive driving effect on the motion characteristics of the microrobots, leading to a 49% reduction in their velocity compared to deterministic conditions. Additionally, the time required for microrobots to traverse from the initial position to the bifurcation point decreases by 65% when the current is increased and by 39% when the fluid viscosity is reduced. These findings underscore the importance of optimizing control parameters to effectively mitigate noise impacts, enhancing the practical performance of the microrobots in real-world applications. This research offers solid theoretical support and guidance for the deployment of microrobots in complex environments. Full article

Orthogonal time frequency space (OTFS) modulation is a promising waveform for future wireless communication, offering robustness against time-varying channels and Doppler effects in high-mobility scenarios. However, the anti-jamming capabilities of OTFS systems remain underexplored, leaving gaps in understanding their potential in jamming-prone environments. In this work, we mainly focus on the jamming detection and suppression methods for OTFS systems in additive an white Gaussian noise (AWGN) channel. A frequency-domain anti-jamming technique is presented, employing a time-domain overlapping window approach to reduce jamming sidelobes while mitigating the signal-to-noise ratio (SNR) degradation caused by the window function. Additionally, a smoothing window method is introduced to provide stable samples for jamming detection, effectively reducing the false alarm rate. Furthermore, a low-complexity improved forward consecutive mean excision (FCME) algorithm is proposed, enhancing its practicality and making it more suitable for real-world applications. Finally, the effectiveness of the proposed anti-jamming methods is validated through simulation analyses under single-tone and partial-band jamming scenarios. Full article

Commonly used methods for identifying modal parameters under environmental excitations assume that the unknown environmental input is a stationary white noise sequence. For large-scale civil structures, actual environmental excitations, such as wind gusts and impact loads, cannot usually meet this condition, and exhibit obvious non-stationary and non-white-noise characteristics. The theoretical basis of the stochastic subspace method is the state-space equation in the time domain, while the state-space equation of the system is only applicable to linear systems. Therefore, under non-smooth excitation, this paper proposes a stochastic subspace method based on RDT. Firstly, this paper uses the random decrement technique of non-stationary excitation to obtain the free attenuation response of the response signal, and then uses the stochastic subspace identification (SSI) method to identify the modal parameters. This not only improves the signal-to-noise ratio of the signal, but also improves the computational efficiency significantly. A non-stationary excitation is applied to the spatial grid structure model, and the RDT-SSI method is used to identify the modal parameters. The identification results show that the proposed method can solve the problem of identifying structural modal parameters under non-stationary excitation. This method is applied to the actual health monitoring of stadium grids, and can also obtain better identification results in frequency, damping ratio, and vibration mode, while also significantly improving computational efficiency. Full article

Control charts are frequently used instruments for process quality monitoring. Another name for the NEEWMA control chart is the new extended exponentially weighted moving average (new extended EWMA) control chart. The lower control limit (LCL) and upper control limit (UCL) are equally spaced from the center line, giving it a symmetrical design. Because of its symmetry, the NEEWMA chart is very good at identifying even the tiniest changes in operation by detecting deviations from the target in both upward and downward directions. This study derives an explicit formula for the average run length (ARL) of the NEEWMA control chart based on the autoregressive (AR) model with exponential white noise. The focus is on the zero-state performance of the NEEWMA control chart, which is derived using explicit formulas. Banach’s fixed-point theorem was used to prove existence and uniqueness of this formula. The accuracy of this formula is validated by comparing it to the numerical integral equation (NIE) method using percentage accuracy (%Acc). The results show that the NEEWMA control chart is more efficient than the ARL evaluated by the NIE method, particularly regarding computation time. The performance of the NEEWMA control chart is compared with the EWMA and extended EWMA control charts by evaluating both the ARL and standard deviation run length (SDRL). The NEEWMA control chart outperforms the others in detection performance, followed by the extended EWMA and EWMA control charts. Further verification of its superior performance is provided through comparisons using the average extra quadratic loss (AEQL) and the performance comparison index (PCI), which confirm that it outperforms both the EWMA and extended EWMA control charts across various parameters and shift sizes. Finally, an illustrative example using real-life economic data demonstrates its efficiency. Full article

Several stochastic $H_{\infty }$ filters for estimating the attitude of a rigid body from line-of-sight measurements and rate gyro readings are developed. The measurements are corrupted by white noise with unknown variances. Our approach consists of estimating the quaternion while attenuating the transmission gain from the unknown variances and initial errors to the current estimation error. The time-varying $H_{\infty }$ gain is computed by solving algebraic and differential linear matrix inequalities for a given transmission threshold, which is iteratively lowered until feasibility fails. Thanks to the bilinear structure of the quaternion state-space model, the algorithm parameters are independent of the state. The case of a gyro drift is addressed, too. Extensive Monte-Carlo simulations show that the proposed stochastic $H_{\infty }$ quaternion filters perform well for a wide range of noise variances. The actual attenuation, which improves with the noise variance and is worst in the noise-free case, is better than the guaranteed attenuation by one order of magnitude. The proposed stochastic $H_{\infty }$ filter produces smaller biases than nonlinear Kalman or unscented filters and similar standard deviations at large noise levels. An essential advantage of this $H_{\infty }$ filter is that the gains are independent of the quaternion, which makes it insensitive to modeling errors. This desired feature is illustrated by comparing its performances against those of unmatched nonlinear optimal filters. When provided with too high or too low noise variances, the multiplicative Kalman filter and the unscented quaternion filter are outperformed by the $H_{\infty }$ filter, which essentially delivers identical error magnitudes. Full article

In our paper, we propose a generalized version of the Alternating Projections Digital Hard Successive Interference Cancellation (AP-HSIC) algorithm that is capable of decoding any order of constellation M in an M-Quadrature Amplitude Modulation (QAM) system. Our approach applies to Rayleigh deep-fading Multiple-Input Multiple-Output (MIMO) channels with high-level Additive White Gaussian Noise (AWGN). It can handle various destructive phenomena without restricting the number of antenna arrays in the transmitter/receiver. Importantly, it does not rely on closed-loop MIMO feedback or the need for Channel-State Information Transmission (CSIT). We have demonstrated the effectiveness of our approach and provided a Bit Error Rate (BER) analysis for 16-, 32-, and 64-QAM modulation systems. Real-time simulations showcase the differences and advantages of our proposed algorithm compared to the Multi-Group Space-Time Coding (MGSTC) decoding algorithm and the Lagrange Multipliers Hard Successive Interference Cancellation (LM-HSIC) algorithm, which we have also developed here. Additionally, our paper includes a mathematical analysis of the LM-HSIC algorithm. The AP-HSIC algorithm is not only effective and fast in decoding, including interference cancellation computational feedback, but it can also be integrated with any Linear Processing Complex Orthogonal Design (LPCOD) technique, including Complex Orthogonal Design (COD) schemes such as high-order Orthogonal Space–Time Block Code (OSTBC) with high-order QAM symbols. Full article

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method for classifying spatiotemporal processes based on entropy measurements. To test and validate the method, we selected five classes of similar processes related to the evolution of random patterns: (i) white noise; (ii) red noise; (iii) weak turbulence from reaction to diffusion; (iv) hydrodynamic fully developed turbulence; and (v) plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon permutation entropy ($S_H^p$) and a new approach based on Tsallis Spectral Permutation Entropy ($S_q^s$). Notably, our observations reveal the segregation of reaction terms in this $S_H^p\times S_q^s$ space, a result that identifies specific sectors for each class of dynamic process, and it can be used to train machine learning models for the automatic classification of complex spatiotemporal patterns. Full article