Search Results (1,153)

Evaluation of generative models for RF (Radio Frequency) signals remains largely ad hoc, with existing approaches relying on uncalibrated metrics imported from computer vision without systematic justification or baseline establishment. We present a multi-dimensional, distribution-level evaluation framework comprising ten metrics across six layers, calibrated against real-real baselines. A real-real baseline is computed by splitting authentic signals into two independent subsets and measuring the same metric between them; the resulting value sets the achievable ceiling for that metric, against which generated-vs-real scores are normalized. The framework is grounded in four design principles: distribution-level aggregation, real-real baseline normalization, signal-to-noise ratio (SNR)-modulation stratification, and multi-dimensional coverage. Application of the framework reveals two previously unreported phenomena. First, linear Short-Time Fourier Transform preprocessing creates a gradient imbalance between frequency-domain and time-domain loss components that causes catastrophic generation failure for analog amplitude modulation; logarithmic compression resolves this. Second, the widely adopted noise-prediction training objective exhibits systematic gradient suppression for amplitude-modulated signals due to SNR-dependent implicit loss weighting; switching to signal-prediction substantially improves temporal structure fidelity for amplitude-modulated signals, with only modest trade-offs for digital communication signals. The framework and calibration methodology establish reproducible standards for comparative assessment of RF generative models. Full article

High-salinity waters, including seawater, brines, and saline radioactive effluents, contain recoverable uranium or require uranium removal, but their complex chemistry complicates both capture and regeneration. Under seawater-relevant conditions, U(VI) occurs mainly as uranyl carbonate complexes and ternary uranyl carbonate complexes containing Ca²⁺ or Mg²⁺ rather than as free UO₂²⁺. Selective separation therefore depends on coupled transport, hydration-shell reorganization, carbonate displacement, and interfacial coordination. Conventional sorbents largely optimize binding strength and adsorption capacity, often at the expense of harsh stripping and secondary waste. This review frames thermoresponsive uranium separation as a coupled aqueous-speciation, interfacial-state, and process-design problem. It examines how LCST and UCST transitions, polymer-brush reorganization, hydration-layer reconstruction, pore gating, and localized photothermal heating regulate access to binding sites and release pathways. Polymer brushes, hydrogels and microgels, membranes and nanochannels, ion-imprinted magnetic composites, and MXene-based hybrids are critically compared using cycle-level criteria, including U/V selectivity, switching time, regeneration demand, energy input, fouling resistance, material loss, synthesis reproducibility, and environmental performance. Particular emphasis is placed on distinguishing genuine structural gating from the generic effects of temperature on diffusion, ligand exchange, and adsorption equilibria. Current evidence supports the feasibility of programmable capture–release interfaces but remains limited by matrix-dependent transition windows, incomplete mechanistic attribution, scarce quantitative energy and temperature-gradient data, short cycling tests, and limited device-scale validation. Progress will require standardized testing in realistic saline matrices and complete capture–release mass and energy balances rather than evaluation by maximum adsorption capacity alone. Full article

The topology architecture of the communication network plays a critical role in ensuring the reliability and security of the Internet of Things. Connectivity reliability is regarded as a key metric for evaluating network topology performance. In this paper, a reliability analysis framework is established to quantitatively evaluate the effects of different deployment schemes and varying numbers of switches and terminal nodes on the connectivity reliability of the novel network, and a deployment scheme for improving network connectivity reliability is proposed. Experimental results demonstrate that, when the network is connected to ten terminal nodes, the maximum reliability achieved under different switch deployment schemes, switch quantities, and terminal node deployment schemes is 0.12%, 0.12%, and 0.14% higher than the corresponding minimum reliability, respectively. Moreover, under the 2-2 switch deployment structure, the network achieves a reliability improvement of up to 8.34% compared with the corresponding minimum reliability. The results further indicate that reducing the number of nodes can effectively improve network connectivity reliability. In multilayer cascading switch deployment architectures, fewer switches in the bottom layer lead to higher reliability, while terminal nodes deployed closer to the bottom layer also contribute to higher network reliability. The proposed deployment scheme provides theoretical support and practical guidance for the deployment optimization of the novel network topology architecture. Full article

Discrete-time sliding mode controllers that utilize saturation-based reaching laws require a gain that ensures contraction within the boundary layer in the presence of multiplicative gain uncertainty. The conventional fixed-gain approach does not maintain this property at moderate uncertainty levels. This work introduces a family of admissible reaching gains and identifies a unique optimal gain that guarantees a specified worst-case contraction. The proposed method offers a closed-form solution to the worst-case contraction problem over the gain-uncertainty interval and determines the optimal contraction factor for the saturation-based reaching law for any finite uncertainty ratio. The optimal gain is integrated into a discrete-time integral sliding-mode framework, thereby eliminating the reaching phase. Furthermore, a past-step disturbance estimator with a confidence factor is introduced to prevent error amplification, which reduces the quasi-sliding band from first to second order in the sampling period when the realized gain approximates its nominal value. The effectiveness of the proposed approach is validated through its application to a photovoltaic battery-charging system with a DC–DC boost converter, achieving robust inductor-current regulation across three battery banks under varying irradiance conditions in a switching-level model with parasitic elements. Full article

This paper presents a modular embedded edge-AI teaching platform built around the STM32N6 microcontroller, designed to meet demand for low-power, real-time, deployable edge intelligence in engineering education. The platform uses a heterogeneous architecture combining an ARM Cortex-M55 core with a dedicated Neural-ART NPU, enabling efficient on-device inference for both classroom projects and vision-based competition tasks. To improve stability across multi-peripheral setups, a multi-power-domain supply architecture combines switched-mode power supplies with low-noise LDO regulators, plus dual-input power switching, reverse-current and reverse-polarity protection, overcurrent limiting, and soft-start control. High-speed modular peripherals are integrated on board—MIPI-CSI camera input, RGB display output, high-speed NOR Flash, and SPI, I2C, UART, and TIMER expansion interfaces—with an 80-pin board-to-board connector for flexible extension. A four-layer PCB layout improves signal integrity for reliable high-speed operation. Deploying a custom lightweight vision algorithm (PEPoseNet), the prototype achieves an inference-only latency of 18.4 ms and 28.31 FPS/Watt energy efficiency within a sub-3 W power budget. These results confirm the platform’s reliability as an educational training platform for embedded edge-AI computing. Full article

To address the challenge of simultaneously achieving broadband absorption, multi-mechanism tunability, and angular stability in terahertz multifunctional devices, this paper proposes a MoS₂/VO₂ composite terahertz metamaterial absorber based on an asymmetric multi-nested C-shaped structure. The device adopts a three-layer configuration consisting of a MoS₂/VO₂ composite plane–SiO₂ dielectric–Au reflector layer. Unlike conventional symmetric structures, which are limited by selection rules and symmetry-protected dark modes that hinder the excitation of higher-order resonances, this design effectively breaks structural symmetry protection through geometric asymmetry. This induces strong mode hybridization between originally orthogonal dark and bright modes, enabling broadband high absorption exceeding 96.7% across the 1.88–3.52 THz frequency range (61% RBW). Notably, the device demonstrates synergistic tuning advantages: the macroscopic on/off switching of broadband absorption characteristics via the phase transition of VO₂, combined with fine blind-spot compensation and enhancement in absorption peaks using the electrical tunability of MoS₂. Furthermore, thanks to its sub-wavelength unit cell design, the structure maintains excellent performance stability over a wide incident angle range from 0° to 60°. This study reveals a synergistic enhancement mechanism combining the asymmetric unit cell and hybrid materials, providing a systematic physical solution for resolving the trade-off between bandwidth extension and dynamic reconfigurability. Full article

This study presents an adaptive sliding mode control approach for trajectory tracking of a quadrotor unmanned aerial vehicle functioning under external interference and kinematic unpredictability. Linear and rotational motion equations formulated in inactive local coordinate system frames are developed with the aid of a nonlinear six-degrees-of-freedom quadrotor dynamic model. Through mitigating excessive switching activity, reliability is improved. Here, the proposed controller integrates sliding mode control with bounded adaptive switching gain factors and boundary-layer smoothing. The operational design is applied within a sequential outer-loop/inner-loop structure for linear and orientation control. The conventional sliding mode control, alongside the proportional derivative control, which employs MATLAB/Simulink R2024a simulations while being interference-affected with an unknown performance set-up, is deployed in this work to relatively appraise the proposed ASM controller. The assessment involves three-dimensional trajectory, control input characteristics, tracking error analysis, adaptive gain growth, and chattering analysis with quantitative performance metrics. The computational output revealed that the proposed ASMC attained superior tracking performance with limited oscillation and level control action. The controller achieves a total RMSE of approximately 0.38 m and a lower aggregate tracking error when using the conventional SMC and PD controllers under equivalent conditions. Furthermore, the adaptive gain mechanism successfully lowers chattering while maintaining robustness against interferences, a large amount of ambiguity, and inertial imbalance with signal noise. The results validate that the proposed ASMC delivers a functional balance between robustness, control smoothness, and tracking accuracy alongside execution homogeneity for autonomous quadrotor UAV trajectory tracking in unsettled and unstable environments. Full article

Turbofan engines exhibit complex nonlinear dynamics across the entire flight envelope, which cannot be captured by explicit mathematical models, posing significant challenges for engine controller design. Traditional control designs often rely on multiple linearized models covering the entire operating range, which require designing multiple linear controllers and gain-scheduling strategy to switch these sub-controllers. This article aims to establish a global nonlinear affine model-based feedforward control method for turbofan engines, and the control input can be efficiently obtained through simple algebraic calculations with given reference signal. In this method, the nonlinear affine model via a multi-layer perceptron (MLP) combined with polynomial feature expansion is constructed based on engine model simulation data. With this affine model, the need for the cumbersome design process of multiple linear controllers and their sub-controller switching can be avoided. Additionally, a Proportional–Integral (PI) feedback controller is integrated with the feedforward controller to eliminate tracking errors caused by model mismatches and disturbances. Numerical simulation results verify that the proposed MLP model has high identification accuracy, and the composite control strategy can simplify the control design. During the acceleration/deceleration process between intermediate and idle state, the high-pressure rotor speed overshoot is less than 0.01%, and the settling time is less than 2.6 s, outperforming the 4.1 s of the gain-scheduled PI controllers. Full article

Switched reluctance motors (SRMs) are attractive for fault-tolerant drives because their rare-earth-free rotor and intrinsic phase isolation support continued operation after a converter fault. Realising this requires a post-fault control policy that preserves both torque tracking and per-phase thermal balance, with the latter being a safety-relevant design consideration motivated by—though not herein verified against—ISO 26262. This paper proposes and evaluates, by simulation, a three-layer fault-tolerant finite-control-set model predictive control (FCS-MPC) architecture for a four-phase 8/6 SRM under a single open-phase converter fault. The layers are (i) a vector-set reconfiguration from the eight healthy, active vectors to the twenty-six admissible post-fault vectors, which restores controllability of the reduced converter; (ii) soft commutation expressed as a position-dependent penalty inside the MPC cost; and (iii) asymmetry-aware balancing that evens out the accumulated thermal load across the three healthy phases. We additionally analyse an activated-on-demand max-penalty thermal limiter and show, both analytically and in simulation, that it shares its optimiser with the variance-based balancing term and therefore confers no measurable benefit over it; it is consequently retained only as an optional on-demand limiter rather than a separate layer. The architecture is benchmarked against a fault-blind baseline, a rule-based hard fault-tolerant reference (Hard-FT), and intermediate configurations through a deterministic ablation across three critical operating points, complemented by a robustness assessment under measurement noise and parameter mismatch. A six-criteria fault-tolerance scorecard is reported as a methodological observation on the transferability of healthy-mode SRM specifications to post-fault operation. Full article

Metasurfaces, as emerging functional optical coatings, enable precise wavefront manipulation at the subwavelength scale. In this work, we propose a polarization-encoded switchable structured light generator based on a single-layer holographic metasurface composed of silicon nanopillars. By combining Fresnel holography technology and the Pancharatnam–Berry phase modulation principle, two sets of holographic phase distributions for perfect vortex beams corresponding to orthogonal circularly polarized states are superimposed onto a single metasurface. The optimized nanopillar achieves a transmittance of approximately 85% and a polarization conversion efficiency of around 98% at the wavelength of 632.8 nm. By simply adjusting the incident polarization state, the metasurface generates a perfect vortex beam under right-handed circularly polarized light illumination and a cylindrical vector beam under horizontal linearly polarized light illumination, enabling on-demand switching between the two structured light modes. Furthermore, we design and validate a spatially multiplexed perfect vortex beam generator, in which the radius, topological charge, and focal plane position of each channel can be independently controlled. This flexible thin-film metasurface platform offers a promising route toward compact, multifunctional photonic devices for advanced optical integration. Full article

Fault-tolerant control systems deployed in manufacturing and process industries must maintain output regulation under actuator degradation and parametric drift while producing operator-readable intervention records. This paper proposes a three-layer cognitive supervisory control framework: a continuously synchronized nominal plant model serves as a lightweight digital twin, driving residual-based anomaly detection that responds to model-mismatch faults before tracking error accumulates; an event-triggered supervisory layer applies composite anomaly scoring, risk estimation, and dwell-time-constrained mode switching over a bank of pre-verified stable controllers; and an optional bounded LLM reasoning agent forming Layer 3 of the cognitive architecture produces real-time natural-language audit records at each supervisory event. The framework is functionally non-intrusive—the closed-loop response is numerically identical to standalone LQI in three of four benchmark scenarios and on an unstable plant variant, with zero supervisory interventions. Under abrupt 75% actuator loss, a single targeted intervention achieves lower overshoot and better tracking than all baselines, including MPC whose feasibility-degradation under reduced actuator authority is confirmed empirically. On near-undamped plants representative of compressor and pipeline dynamics, integral absolute error improves significantly relative to standalone LQI; on a distributed thermal process representative of laser welding and induction heating, IAE improves substantially under abrupt heater power loss. Parametric sensitivity analysis across fault severity, detection threshold, onset timing, measurement noise, score weights, and dwell-time confirms robustness of the detection architecture. The LLM layer’s contribution is regulatory rather than performative: its value is the causally connected audit record generated at the moment of each decision, not IAE improvement—a form of real-time explainability that after-the-fact attribution methods such as SHAP and LIME cannot provide by construction. Full article

Maritime unmanned aerial vehicle (UAV) missions such as ship inspection, search and rescue, environmental monitoring, and emergency response often involve multi-wave task releases, time-sensitive deadlines, constrained support vessel positions, and spatially heterogeneous risk. These factors couple task allocation with path planning and make fixed dispatching rules fragile under changing mission profiles. This study develops a hierarchical cooperative planning framework for multiple UAVs over a maritime risk field. A risk-cost A* layer generates feasible routes from support vessels to task points and estimates path length, risk exposure, and sortie duration. A rolling scheduler constructs feasible UAV task candidates, while a scenario-switching-aware LinUCB hyper-heuristic selects online among deadline-first, distance-first, risk-aware, and endurance-balancing rules. A forgetting-update, one-step look-ahead, scenario memory, and lightweight switching detection are used to improve adaptation to mission profile changes. Simulations on a 28 × 40 maritime grid with two support vessels, six UAVs, 40 tasks, and nine release waves show that the proposed framework achieves the highest average effective reward (370.18), the lowest average value regret (0.61), and a best reward ratio of 0.46 over 24 random scenarios. The results should be interpreted as evidence from an idealized simulation benchmark. The main benefit is improved reward robustness under non-stationary and high-risk profiles, rather than uniform gains across all metrics or direct field-deployment validation. Full article

Industrial Internet of Things (IIoT) networks require continuous, uninterrupted sensing operations despite the finite battery capacity of deployed IoT nodes. Conventional reactive energy management, where nodes switch to charging mode only after residual energy falls below a fixed threshold, cannot prevent depletion events and compromises network uptime. We propose a digital twin (DT)-enabled predictive scheduling framework in which a DT layer co-located with a multi-access edge computing (MEC) control center continuously mirrors the physical network state and generates H-slot look-ahead scheduling decisions before depletion can occur. The framework operates over a 5G network-sliced infrastructure with dedicated URLLC, eMBB, and mMTC slices. Two coupled integer programming problems are formulated, namely a predictive IoT node scheduling problem and a predictive energy transmitter scheduling problem. Optimal solutions are obtained via branch-and-bound with reliability branching (DT-PBB), and a low-complexity DT-Aware Greedy Priority Heuristic (DT-GPH) is also proposed. Evaluated against Earliest-Deadline-First (EDF-WPT), No-WPT (a baseline that disables wireless charging entirely), and Random baselines across three parameter configurations with K up to 200 nodes, DT-PBB achieves the highest sensing utility and the fewest energy depletion events in all scenarios. DT-GPH provides near-optimal depletion performance at substantially lower computation cost. EDF-WPT, the strongest reactive policy, incurs 2-4 times more depletion events than DT-PBB. Proactive DT-enabled look-ahead decisively outperforms reactive urgency-based scheduling, validating the zero-downtime paradigm for large-scale IIoT networks. Full article

This study addresses the need for dark-current control and stable current response in planar high-purity germanium (HPGe) detectors for low-energy X-ray detection. A device fabrication strategy based on the coupled optimization of near-surface treatment, N/P junction formation, and guard-ring electrode design is proposed. Unlike previous studies that mainly focused on contact-layer fabrication, segmented electrode structures, low-noise readout, or response simulation, this work investigates low-damage near-surface construction, N-type and P-type contact-layer formation, and edge-related leakage-current regulation as an interconnected processing route. The relationship among the near-surface state, junction quality, electrode configuration, and edge-related leakage current is emphasized. Chemical mechanical polishing (CMP) reduced the surface roughness Sa of the HPGe crystal to 6.68 nm, providing a low-damage near-surface foundation for subsequent junction fabrication. On this basis, the optimized Li thermal diffusion process, namely 0.5 Å s⁻¹, 325 °C, and 5 min, formed an N-type contact layer with preserved lattice ordering and favorable electrical properties. B ion implantation combined with rapid thermal processing (RTP) achieved acceptor activation and implantation-damage recovery, and the condition with Rp = 198.1 nm showed relatively better structural recovery and electrical characteristics. After introducing the guard-ring electrode, the dark current of the device at −20 V decreased from 6.5 × 10⁻⁹ A to 2.03 × 10⁻⁹ A, and a stable switching current response was obtained under 12 keV monochromatic synchrotron X-ray irradiation. Geant4 simulations were further used as an auxiliary analysis to evaluate the effect of the guard-ring structure on the simulated response spectra and full-energy peak efficiency (FEPE) for low-energy X-rays. Overall, this study provides experimental evidence for process optimization of planar HPGe detectors with low dark current and stable low-energy current response. Full article

Hafnium-zirconium oxide (HZO)-based ferroelectric field-effect transistors (FeFETs) are expected to become core devices for new embedded memory and compute-in-memory systems. However, existing simulations rely on finite-element-based TCAD tools, which are computationally intensive and time-consuming, and they struggle to account for the dynamic flipping of ferroelectric domains. This paper utilizes a time-domain simulation framework based on the nucleation-limited switching (NLS) model coupled with the surface potential of a MOSFET, enabling a self-consistent solution for polarization and electrical characteristics; a Monte Carlo method is employed to simulate device variability, and Shmoo plots are used to identify optimal programming and erasure process windows; an integrated solution is proposed for 22 nm FDSOI devices, addressing geometric scaling, modification of the Landau–Khalatnikov (L-K) dynamic model for ultrathin ferroelectric layers, and suppression of short-channel effects. Model validation is limited to selected operating metrics, and predictive accuracy outside the calibrated cases requires additional independent datasets. This method enables end-to-end simulation of FeFETs, from material polarization and device electrical characteristics to performance optimization, thereby providing model-based analytical and design support for the development of advanced, ultra-low-power FeFETs. Full article