Search Results (78)

Accurate localization of underwater targets in shallow water is challenging because nonlinear sonar geometry, range-amplified angular errors, uncertain measurement noise, and environmental constraints jointly degrade state estimation. This paper proposes a variational Bayesian constrained extended Kalman filter (VB-C-EKF) for active-sonar-based underwater target tracking. A weak-maneuver motion model and an active-sonar range–bearing–elevation–Doppler measurement model are adopted, while bathymetric depth, speed, and reachable-region constraints are incorporated through sequential local Mahalanobis projection with a conservatively regularized covariance correction. To address unknown and time-varying measurement noise, the measurement-noise covariance is recursively estimated using a variational Bayesian scheme with an inverse-Wishart prior and a forgetting mechanism. In Monte Carlo experiments, the proposed method achieved an overall three-dimensional position RMSE of 7.57 m with a 95% confidence-interval half-width of 0.25 m, while maintaining zero depth/speed violations. Its mean normalized innovation squared and normalized estimation error squared were 4.04 and 6.79, respectively, and its average runtime was 0.225 ms per update. These results show that jointly adapting measurement uncertainty and enforcing physical constraints improves accuracy, feasibility, and covariance consistency under the simulated shallow-water conditions. Full article

In this paper, an optical color zero-watermarking scheme based on robust bimodal biometric keys and phase-shifting digital holography is proposed. The color watermark is first encrypted into three amplitude ciphertexts through an optical encryption framework combining grating modulation, Fresnel-domain double random phase encoding (DRPE), and phase-shifting digital holography, where the phase masks are generated from biometric keys derived from the iris and three-dimensional (3D) face features of the encryption user. These high-level biometric features are extracted by a bimodal biometric high-order feature extraction network (BBHEN), including an iris high-order data extraction network and a 3D face high-order data extraction network. The extracted features of the color host image are then XORed with the corresponding ciphertexts, and the results are merged to construct a single zero-watermark image containing both host and watermark information. During extraction, biometric authentication is first performed to verify the identity of the decryption user. Only authorized users can recover the original watermark through zero-watermark reconstruction and extraction; otherwise, the process is terminated. Numerical simulations demonstrate the effectiveness, security, and robustness of the proposed scheme, particularly the strong protection capability of the bimodal biometric keys. Full article

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

Rough volatility and jump clustering are empirically important features of equity dynamics, yet their joint treatment in American-option pricing remains computationally demanding. We develop a Volterra–Hawkes stochastic-volatility model in which a regularized weakly singular fractional kernel governs both rough diffusive memory and variance-jump propagation. Regularizing the kernel at an explicit resolution scale preserves complete monotonicity and a nonnegative Bernstein representation while replacing the unresolved zero-lag jump response by a finite plateau. A positive exponential-sum approximation then yields a finite-dimensional Ornstein–Uhlenbeck Markovian lift; we identify and correct a rank-one covariance defect in the naive shared-shock simulation of the lifted factors and combine the corrected scheme with least-squares Monte Carlo valuation for American puts. We assess the model by an ablation over a two-branch nested design—rough-Heston diffusion as the common base, a price-jump Hawkes channel and a variance-jump Hawkes channel as two parallel single-channel extensions, and the full model combining both—calibrated and evaluated out of sample on short-maturity puts for five underlyings (NVDA, TSLA, META, AAPL, MSFT). Pooled across assets, the full model attains the lowest per-date vega-weighted RMSE on $77.8\%$ of out-of-sample dates and the lowest pooled error on four of five underlyings, with META the exception. The improvement is not claimed to be uniform, and the two jump channels are complementary rather than individually sufficient. The evidence in this short-maturity sample supports the presence of both channels through their baseline intensities; the identification of their self-exciting feedback is left to a longer-maturity panel. Full article

Peri-urban rural public spaces are increasingly involved in territorial development, rural-regeneration management, and built-environment upgrading, yet existing research often treats rural regeneration as program implementation rather than as a spatial-development and public-space management problem. This study develops a built-environment governance framework from the 2025 Hsinchu City Community Rural Regeneration Facilitation Program in Taiwan. Using a reflective document-based case-study design, the analysis integrates administrative reports, training records, plan-review materials, spatial-planning documents, built-environment case evidence, agricultural net-zero policy materials, and Taiwanese rural-regeneration literature. Four descriptive readiness ratios are used to profile the governance process: SIE (urban–rural interface exposure), PPC (training participation), RAC (review conversion), and ATR (approval-track readiness). These indicators clarify administrative and governance readiness, but they are not treated as direct measurements of spatial quality, carbon reduction, or user wellbeing. The results identify a four-dimensional management framework for peri-urban rural public-space regeneration: wellbeing-oriented public spaces, low-carbon ecological infrastructure, cultural-landscape continuity, and community-based implementation capacity. The study further proposes standardized ordinal scoring rules, a policy-to-space translation model, and a future post-occupancy and spatial-quality validation scheme. The theoretical contribution lies in reframing rural regeneration as a built-environment governance interface for territorial development, while the practical contribution lies in providing a reviewable framework for managing public-space proposals, construction priorities, maintenance readiness, and future validation in peri-urban rural areas. Full article

Accurate photovoltaic (PV) power forecasting is important for grid dispatch, energy-storage management, and electricity-market trading. This paper presents a PV power forecasting framework that combines multi-model ensemble learning with multi-perspective interpretability analysis. A 22-dimensional feature set was constructed from irradiance, meteorological, temporal, and lagged-power variables. Five supervised regressors, a zero-shot Chronos-Bolt-Small time-series foundation model, and six additional forecasting baselines were evaluated at two PV plants. The three ensemble schemes used only the five supervised regressors and were compared on the final 20% of the 2019 development data; the complete 2020 period was used only for final evaluation. The selected methods achieved normalized RMSE values of 0.0350 and 0.0376 at Sites 1 and 2, respectively. SHAP, PDP/ICE, LIME, and permutation importance were applied to the ensemble model at each site. Recent power-history features dominated this one-step-ahead forecasting task with a 15 min horizon, while irradiance features provided additional site-dependent information. Full article

Image encryption schemes based on chaotic maps offer strong statistical properties but are vulnerable to quantum attacks, and their integration with post-quantum cryptography has not been sufficiently explored. This paper presents a post-quantum secure image encryption framework integrating ML-KEM (FIPS 203), standardized by NIST in 2024, with a two-dimensional Sinh-Logistic chaotic map, HKDF-SHA256 nonce-based key derivation, feedback diffusion, and a novel Seismic Wave Permutation (SWP). The scheme derives channel-specific encryption keys from ML-KEM shared secrets using random, channel-specific nonces via HKDF-SHA256, ensuring plaintext independence and avoiding metadata-based leakage. The proposed SWP effectively breaks spatial correlations by displacing pixels according to a chaotic SWP model. RGB images are processed with independent ML-KEM encapsulation and HKDF-derived key material per channel, enabling multi-channel encryption without cross-channel leakage. Experiments on 512 × 512 test images have demonstrated Shannon entropy exceeding 7.999 bits per pixel across all channels, NPCR of at least 99.59%, UACI between 33.41% and 33.53%, and near-zero pixel correlations, further validated across 14 standard SIPI test images. An IND-CPA game simulation using four independent distinguishers, including a learned classifier trained via chosen-plaintext oracle access, over 5000 rounds per image, showed a maximum adversary advantage of 0.0186, consistent with random prediction. ML-KEM encapsulation contributes between 3.9% (ML-KEM-512) and 8.0% (ML-KEM-1024) of total encryption latency at 512 × 512 resolution, remaining a minority cost across all security levels while keeping the total encryption time within a narrow 227–258 ms range. The proposed architecture bridges standardized post-quantum cryptography with chaos-based image security for privacy-preserving image transmission. Full article

Diesel engine emissions remain a concern because of their environmental and health impacts. Homogeneous charge compression ignition experiments on low-speed two-stroke marine diesel engines are costly, risky, and limited by scarce transient data. To address this issue, a one-dimensional/zero-dimensional AVL BOOST-ANSYS CHEMKIN coupled framework was established for an MAN B&W 6S50MC low-speed two-stroke marine diesel engine, providing a reasonable approach under data-limited conditions. The framework provided key initial conditions for detailed chemical-kinetic analysis and was used to examine methyl decanoate (MD)/di-n-butyl ether (DBE) blends with 0–20% DBE. The results indicate that DBE addition alters the balance between aromatic growth and oxidative removal and enhances low-temperature chain branching, while the increased peak temperature raises nitrogen oxides (NO_X) emissions. To relate these mechanistic results to engineering evaluation, the weighting scheme of the IMO NO_X Technical Code 2008 test cycle was introduced. Pyrene and its isomers and NO_X were treated by weighted normalization, followed by Pareto analysis and TOPSIS methods. MD90 (90 vol% MD and 10 vol% DBE) showed the best emissions trade-off over a wide range of weighting settings, which may provide useful guidance for optimizing oxygenated fuel blending ratios. Full article

We study the cooperation for the joint unicast and multicast (JUMC) transmission system through a full-duplex (FD) decode-and-forward (DaF) mode relay and propose sub-optimal beamforming schemes for this cooperative JUMC FD relay channel. The beamforming vectors at the access point (AP) and at the full-duplex relay (FDR) are optimized with the metric based on the end-to-end information rate. The cooperation lets the user terminals (UT) outside of the direct coverage of the AP to be served by JUMC from the AP, and hence, the focus of this paper is on the sum rate resulting from the cooperation. As a reference scheme, a zero-forcing-based (ZF) beamforming algorithm is proposed, which suppresses the self-interference (SI) at the FDR perfectly. The SI at the FDR and the minimum operation for the signal-to-interference power ratios (SINR) at involved nodes of the DaF protocol are leveraged in designing and optimizing the second beamforming algorithm, which is the regularized beamforming scheme, since it allows an optimal amount of the SI at the FDR. This algorithm relies on the iterative applications of a quadratically constrained quadratic problem (QCQP) in its central part, while a few one-dimensional searches are running for the optimal SI levels for the individual rates. We consider three different scenarios depending on the existence of an FDR unicast message and the multiple UTs in the coverage of FDR for the application of the proposed algorithms, with some necessary modifications. Corroborating simulation results are presented to show the strengths and weaknesses of the proposed algorithms for the cooperative JUMC system. Full article

Take a finite subset S of an n-dimensional projective space. We study the Hilbert function of the multiples $mS$ of S, mainly when S is general or at least very general. We recall several classical conjectures on this problem, raise new open questions, and prove some particular cases. An open question is if all $mS$ have the expected Hilbert function. We find cases in which there are Zariski open subsets of sets S with maximal rank for all m and pairs $(n,\#S)$ for which no such open set exists. We start the study of the m-Terracini sets proving when the first one is nonempty for Veronese embeddings. Full article

Edge server placement (ESP) is a critical determinant of service quality in edge–cloud computing systems, yet existing solutions often neglect the inherent collaboration between edge and cloud, leading to suboptimal performance under dynamic workloads. To address this gap, this paper proposes a novel meta-heuristic edge server placement algorithm based on the Coati Optimization Algorithm (COA). We first formulate the ESP problem as a constrained binary nonlinear programming model that explicitly incorporates edge–cloud collaboration, aiming to minimize the average request processing delay. The proposed COA-based solver features a compact one-dimensional encoding scheme that simultaneously represents server placement and request offloading decisions, a tailored boundary correction mechanism to enforce coverage and atomicity constraints, and a balanced exploration–exploitation strategy inspired by coatis’ natural hunting and escape behaviors. Extensive simulations are conducted, comparing the proposed algorithm against ten representative heuristic and meta-heuristic algorithms, including GA, PSO, DE, GWO, and their variants. The experimental results demonstrate that our algorithm significantly outperforms all compared methods in terms of the mean, minimum, and standard deviation of the overall average processing delay. Specifically, it achieves a 98.2% reduction in the mean delay relative to suboptimal algorithms while maintaining near-zero variance, confirming its effectiveness, efficiency, and robustness. The proposed algorithm provides a promising solution for service providers to enhance quality of service through optimal edge server deployment and request offloading under edge–cloud collaboration. Full article

This study investigates the robustness of Frenet–Serret curvature ($\kappa$) and torsion ($\tau$) estimates derived from noisy discretely-sampled three-dimensional space curves, with emphasis on the comparative performance of cubic spline and cubic Hermite interpolation methods. Accurate estimation of these geometric invariants is essential for reliable analysis of curves arising in signal processing and shape reconstruction; yet, the higher-order derivatives required for their computation exhibit pronounced sensitivity to measurement noise. We examine curves constructed through a Hilbert transform-based parameterization of the form $\mathrm{r}\left(t\right)=\left(X\left(t\right),A\left(t\right)sin\varphi \left(t\right),g\left(t\right)\right),$ where discrete samples are contaminated with additive white Gaussian noise at varying signal-to-noise ratios. Reconstruction is performed using cubic spline interpolation, which ensures global $C^2$ continuity, as well as cubic Hermite spline interpolation, which provides $C^1$ continuity with local tangent control. Frenet frame computations are then applied via regularized finite difference schemes. To characterize noise amplification theoretically, we derive the Curvature Stability Index (CSI) and Torsion Stability Index (TSI) as first-order variance bounds under the delta method. While these indices formalize the derivative-order dependence of noise sensitivity, Monte Carlo simulations reveal that empirical variance exceeds theoretical predictions by factors of ${10}^4$ to ${10}^6$, indicating dominance of nonlinear error propagation. Nevertheless, the indices establish that torsion instability arises fundamentally from third-order derivative structure rather than ground-truth magnitude. Numerical experiments across three geometric regimes constant-invariant helices, variable-curvature helices, and planar curves with identically zero torsion demonstrate that the ratio of the torsion root mean square error to curvature root mean square error consistently ranges from $6.5$ to $9.8$. This disparity persists even in the degenerate planar case, where $\tau \equiv 0$ analytically, confirming that torsion sensitivity is an intrinsic property of the Frenet–Serret formulation. Across all configurations, cubic spline reconstruction yields lower Monte Carlo mean RMSE and reduced empirical variance compared to Hermite spline, providing superior stability for derivative-based invariant estimation. Full article

Modern power grids are increasingly vulnerable to strategic malicious attacks that can trigger large-scale cascading failures. Existing multi-step Markov game formulations often struggle to align with the instantaneous nature of cascading dynamics, potentially introducing estimation bias in multi-agent learning. To address this issue, we formulate the attack–defense interaction as a stage-wise zero-sum matrix game, enabling direct approximation of the underlying payoff structure without temporal credit assignment. Based on this formulation, we propose a Siamese Relational Nash Double Deep Q-Network (SR-Nash-DDQN), which incorporates a structured relational pooling mechanism to capture high-dimensional strategic dependencies. The framework further integrates physics-driven counterfactual experience replay for improved sample efficiency and adopts a two-timescale learning scheme to stabilize adversarial training. Extensive evaluations on the IEEE 9-bus, 39-bus, and 118-bus systems demonstrate that the proposed method consistently approximates Nash equilibria and maintains strategic diversity across independent trials. Moreover, zero-shot generalization across 100 unseen operating conditions shows that the learned policy effectively improves the security lower bound and reduces worst-case damage under severe uncertainties. Full article

With the rapid growth of digital image transmission, ensuring data security has become increasingly important. However, existing chaos-based image encryption algorithms often suffer from insufficient chaotic randomness and weak integration between chaotic dynamics and encryption mechanisms. To address these issues, a novel image encryption scheme based on a two-dimensional hyperbolic–exponential Sine–Logistic map (2D-HESLM) is proposed. A Sierpiński carpet-inspired scrambling strategy and a cascaded diffusion mechanism are designed to enhance permutation and diffusion performance based on the 2D-HESLM. The experimental results show that the information entropy value is 7.9980, while NPCR and UACI are approximately averaged 99.6147% and 33.4672%, respectively, with correlation coefficients close to zero. These results demonstrate the effectiveness and security of the proposed scheme. Full article

This paper proposes an advanced three-dimensional optical encryption technique based on double random phase encryption for the simultaneous encryption of two primary datasets. While conventional double random phase encryption offers high-speed encryption, it suffers from low data efficiency. To address this issue, the proposed method assigns the first primary dataset to the amplitude and the second to the phase. However, this approach faces a critical limitation: the phase information becomes undefined or lost when the amplitude is zero. Therefore, we introduce a biased amplitude encoding scheme for double random phase encryption to ensure the mathematical recoverability of the phase component. In the proposed method, a biased value $ϵ$ is added to the amplitude part during the double random phase encryption encryption process and subsequently subtracted from the decrypted data to recover the two primary datasets. To verify the effectiveness of our approach, we employ synthetic aperture integral imaging and volumetric computational reconstruction. The experimental results show that while the first dataset remains lossless, the lossy characteristics of the second dataset are significantly mitigated. Full article