Search Results (3,865)

Background: Multi-center resting-state functional magnetic resonance imaging (rs-fMRI) is important for neurodevelopmental disorder diagnosis, but cross-site differences in repetition time (TR) can cause temporal feature misalignment. In addition, blood-oxygen-level-dependent (BOLD) signals are non-stationary, so disease-related information may be distributed across multiple time scales. Existing methods usually do not explicitly model physical sampling intervals or coordinate temporal and spectral information across scales, which may limit cross-site generalization in heterogeneous multi-center settings. Methods: We propose Spec-RWKV, a spectrum-guided linear recurrent framework for multi-site rs-fMRI diagnosis. It includes three components: PrismTimeMix, which models temporal dynamics using decay rates derived from physical half-lives and converts them adaptively across TRs; a TR-adaptive continuous wavelet transform, which aligns spectral representations across sites by adjusting frequency coverage; and spectrum-guided adaptive temporal aggregation, which uses spectral context to weight temporal features. Results: On ABIDE-I and ADHD-200, Spec-RWKV achieved AUCs of 75.86% and 76.31%, respectively. Under leave-one-site-out validation, it achieved the best mean AUC on ABIDE-I and the best mean accuracy and AUC on ADHD-200. Conclusions: Spec-RWKV explicitly models sampling-rate differences and multi-scale spectral structure, with results supporting strong cross-site generalizability. Full article

This paper presents a time-domain closed-loop resistive temperature sensor architecture. The design employs a frequency-locked loop (FLL)-based oscillator as the sensing element, generating a monotonic frequency response to temperature variations. The output frequency is digitized on-chip and converted into a temperature code. Within the oscillator core, a switched-capacitor technique converts frequency to voltage for closed-loop control, reducing charging/discharging voltage swings and significantly lowering dynamic power consumption. The FLL topology enhances frequency stability, minimizes distortion, and suppresses power supply sensitivity. Fabricated in a 180 nm CMOS process with a core area of 0.12 mm², the sensor achieves a peak-to-peak inaccuracy of +1.2 °C/−0.9 °C from −40 °C to 125 °C. Operating at 1 V, the circuit consumes only 10 μW with a resolution of 51 mK within 12 ms. Full article

Bidirectional power converters generate significant heat losses during high-frequency operation, posing a severe challenge to the performance of heat dissipation systems. Traditional thermal design methods often struggle to balance multiple objectives, such as cooling efficiency, cost, weight, and size, thereby limiting the reliability and safety of the system. To address these challenges, this paper proposes a novel Multi-Objective Exponential Distribution Optimizer algorithm based on the Exponential Distribution Optimizer. Subsequently, key design variables of the heat dissipation system are selected. Next, the Optimal Latin Hypercube Sampling method is employed to generate sample points, and a second-order response surface surrogate model for the heat pipe radiator’s volume and temperature is developed. Lastly, by integrating elite non-dominated sorting, crowding distance mechanisms, and an information feedback mechanism, the multi-objective challenge is decomposed into subproblems, thereby enhancing optimization efficiency. Through comparative simulation experiments on benchmark functions, the Wilcoxon signed-rank test results for the MOEDO algorithm on the majority of the three metrics are denoted as ‘+’, indicating statistically significant advantages over the compared algorithms, thereby demonstrating its superior performance in addressing multi-objective optimization problems. The study further conducts simulation verification of the heat pipe heat dissipation system before and after optimization using ANSYS Icepak. The simulation results demonstrate that, compared with the conventional design, the maximum Insulated Gate Bipolar Transistor (IGBT) temperature is reduced by 17.12% and the heat sink volume is reduced by 14.61%. Full article

Synchronous Alternating Current (AC) power systems are increasingly supported by embedded High-Voltage Direct Current (HVDC) links to enhance operational flexibility and ensure security of supply. However, the loss of High-Voltage Alternating Current (HVAC) interconnections in these synchronous areas may lead to transmission network splitting, posing serious challenges to frequency stability due to the reduction in overall system inertia and stiffness. In this paper, a supplementary control layer is proposed to enable embedded HVDC systems, particularly those based on modern Voltage Source Converters (VSCs), to support frequency stability under post-splitting conditions. The proposed control strategy combines Angle-Difference Control (ADC), Frequency-Difference Control (FDC), and feedforward action, enabling fast and coordinated active-power modulation. A single-bus, dynamic multi-area Load Frequency Control (LFC) model is developed, combining the regulation of thermal units, Renewable Energy Sources’ (RESs’) Fast Frequency Response (FFR) with Synthetic Inertia (SI), and VSC-HVDC modulation. The effectiveness of the proposed control layer is demonstrated by applying it to the East Tyrrhenian Link (ETL), an embedded VSC-HVDC interconnection connecting Sicily with the mainland of Italy, under a post-splitting low-inertia condition in which Sicily operates as an islanded synchronous system, i.e., after losing synchronism with the mainland of Italy, in a 2030 scenario condition. The simulation results demonstrate that the proposed controller enables embedded VSC-HVDC systems to actively participate in post-splitting frequency containment and damping, as well as coordinated active power reallocation, thereby enhancing overall system stability and resilience. Full article

We propose a monolithically integrated optical frequency comb (OFC) generation platform on thin-film lithium niobate (TFLN), featuring cascaded dual-drive Mach–Zehnder modulators (DDMZM) and a ${\mathrm{S}\mathrm{i}}_3{\mathrm{N}}_4$-assisted spot size converter (SSC). To capture microscopic mode mismatches and spatial phase accumulation often overlooked in idealized scalar simulations, we establish a multi-physics co-simulation framework integrating finite-difference time-domain (FDTD) analysis with macroscopic transmission modeling. Based on this framework, the cascaded modulator architecture generates 25 highly stable comb lines with a dense 2 GHz spacing and an envelope flatness within 2 dB. Tolerance analysis indicates that the comb generation is highly resilient to typical manufacturing and environmental variations, including thermal bias drift, RF phase mismatch, and half-wave voltage ($V_{\pi }$) dispersion. Furthermore, physical-layer modeling shows that the integrated SSC reduces fiber-to-chip coupling loss to 0.55 dB per facet, preserving the necessary optical power budget. To validate the platform’s viability as a multi-wavelength continuous-wave source for spatial-division multiplexed (SDM) interconnects, a parallel transmission over a 20 km standard single-mode fiber is modeled. Using a digital signal processing (DSP)-free 10 Gb/s non-return-to-zero (NRZ) scheme, the 25-channel system maintains a worst-case bit error rate strictly below the forward error correction (FEC) threshold. This work offers a practical, physics-based evaluation framework for high-density co-packaged optics (CPO). Full article

Vertical-External-Cavity Surface-Emitting Lasers (VECSELs) have gained significant attention over the past two decades due to their versatility in a wide range of photonic applications. This review focuses on VECSEL configurations for dual-wavelength emission, highlighting their use in high-resolution spectroscopy, terahertz (THz) generation, and advanced optical communication. We explore recent developments in VECSEL designs, including systems utilizing birefringent crystals for polarization-based frequency separation and configurations with dual-VECSEL chips or dual-gain regions within a single cavity. These two-wavelength VECSELs enable diverse operation modes, including narrow-linewidth, pulsed, multimode, and frequency-converted emission, with high-brightness output, excellent beam quality, and tunable wavelengths. Additionally, the review discusses advancements in dual-frequency VECSELs, with applications in LIDAR systems for environmental monitoring, highly stable optical clocks, and fiber sensors. We examine improvements in cavity design, semiconductor structures, and power stabilization, which have enhanced frequency stability and spectral purity, making VECSELs suitable for precision metrology and sensing applications. Full article

Automatic modulation recognition (AMR) is increasingly relevant to communication-sensing front ends in robotic and human–robot collaborative systems, where reliable spectrum awareness and adaptive wireless reception are desired. However, existing methods often degrade sharply at low signal-to-noise ratios (SNRs), and large language models (LLMs) are not natively compatible with continuous I/Q signals due to the inherent modality gap. We propose BioLAMR, a GPT-2 adaptation framework for AMR inspired by the auditory system’s parallel time–frequency processing and cortical hierarchy. The framework combines bio-inspired dual-domain feature extraction with parameter-efficient LLM adaptation. BioLAMR includes three components. First, a lightweight dual-domain fusion (LDDF) module extracts complementary time- and frequency-domain features and fuses them through channel and spatial attention. Second, a convolutional embedding module converts continuous I/Q signals into GPT-2-compatible sequences without discrete tokenization. Third, a hierarchical fine-tuning strategy updates only 8.9% of parameters to preserve pretrained knowledge while adapting to modulation recognition. Experiments on the RadioML2016.10a and RadioML2016.10b benchmarks show that BioLAMR achieves overall accuracies of 64.99% and 67.43%, outperforming the strongest competing method by 2.60 and 2.47 percentage points, respectively. Under low-SNR conditions, it reaches 36.78% and 38.14%, the best results among the compared methods. Ablation studies verify the contribution of each component. These results demonstrate that combining dual-domain signal modeling with parameter-efficient GPT-2 adaptation is an effective route to robust AMR in challenging wireless environments. Full article

Detection of trace gases with high sensitivity and weak excitation power is highly desired for long-range remote sensing. Here, we report the detection of the greenhouse gas nitrous oxide (N₂O) with the power of excitation light down to picowatts, by converting the mid-infrared laser to near-infrared photons through an intra-cavity-enhanced sum-frequency upconversion system. The intra-cavity-enhanced pumping power of 1064.0 nm reaches about 200.0 W, resulting in the conversion of the 4514.6 nm mid-infrared laser to 861.1 nm with an efficiency up to 73.4% under optimal conditions. The upconverted light is then detected by a single-photon avalanche detector, followed by a time-correlated single-photon counting module, which can measure the arrival time of each upconverted photon. By performing discrete Fourier transformations of the arrival time of the detected photons, the frequency spectrum can be determined. By using frequency modulation, this method can suppress background noise significantly. Consequently, the excitation power can be brought down to about 100 pW with the concentration of N₂O being 10 ppm. As a demonstration of application, the presented system is also used for N₂O sensing in an open-path geometry, highlighting the potential for stand-off leak detection. Our proposal offers promising applications to monitor trace gases over long distances with weak excitation powers. Full article

Bidirectional AC/DC converters in hybrid microgrids are prone to DC-bus voltage instability caused by source-side, grid-side, and load-side disturbances. Conventional linear active disturbance rejection control (LADRC) suffers from a trade-off between transient overshoot suppression and disturbance rejection capability, which limits its practical application. To address this issue, an improved LADRC strategy for bidirectional AC/DC converters is proposed in this paper. First, a linear tracking differentiator (LTD) is introduced to smooth the DC-bus voltage reference and suppress overshoot caused by abrupt command changes. Second, a proportional-derivative (PD) term is embedded into the linear extended state observer (LESO) to introduce phase lead compensation, thereby improving the observer phase characteristics without excessively increasing the observation bandwidth or amplifying high-frequency noise. Frequency domain analysis, MATLAB/Simulink simulations, and full-hardware prototype experiments are carried out to validate the proposed method. The simulation study covers grid voltage sag, photovoltaic-side source fluctuation, and DC-side load disturbance conditions. To further strengthen the experimental verification, hardware tests are conducted under grid voltage dip, PV-side voltage reduction, and DC-side load-switching conditions. The results consistently show that the proposed strategy can effectively reduce DC-bus voltage fluctuation and improve transient recovery performance compared with conventional LADRC. Therefore, the improved LADRC provides a practical and robust control solution for stabilizing bidirectional converters in AC/DC hybrid microgrids. Full article

During long-term operation, power electronic converters are jointly affected by component degradation and operational disturbances, leading to pronounced nonstationary and multi-scale characteristics in output-voltage signals, which pose challenges for fault prediction. To address the degradation forecasting problem of Boost converter output voltage, this paper proposes a multi-scale temporal modeling method that integrates multivariate variational mode decomposition, distribution entropy-based complexity features, and a temporal convolutional network. Multivariate variational mode decomposition is employed to achieve frequency-aligned decomposition of the voltage signal, enabling effective separation of dynamic components at different scales. Distribution entropy is then introduced to characterize the evolution of local structural complexity in each mode, and multi-channel complexity feature sequences are constructed accordingly. Based on these features, a temporal convolutional network is used to perform unified modeling of short-term fluctuations and long-term degradation trends. Experimental results demonstrate that the proposed approach achieves consistently high accuracy across multiple independent runs, with average RMSE ranging from 0.0111 to 0.0179 and average MAPE from 1.15% to 1.84%. The low standard deviations further confirm its robustness for degradation trend prediction under varying operating conditions. Full article

High renewable penetration reduces effective inertia and increases frequency variability in microgrids, thereby limiting the performance of purely reactive frequency regulation. This paper presents a two-timescale frequency-support strategy based on bidirectional electric vehicles. The main novelty lies in introducing a learning-assisted correction layer between forecast-based aggregate regulation and final EV-level dispatch. Rather than replacing the predictive controller with an end-to-end data-driven policy, this layer uses measured fleet-state information to correct the supervisory aggregate request online before a final feasibility-preserving dispatch stage converts it into executable vehicle-level commands under concurrent power, energy, plug-in, and departure constraints. A supervisory predictive layer determines the aggregate support action from forecasted photovoltaic and load disturbances, whereas a lower real-time dispatch layer redistributes that action across the available fleet. Feasibility is enforced through an explicit projection stage prior to actuation. The method is assessed in simulation using measured campus operating profiles of irradiance, temperature, demand, frequency, and electric-vehicle availability. Across four representative operating days, the proposed strategy reduced the mean cumulative frequency deviation by 30.3% relative to droop control and by 24.7% relative to predictive-only operation, while reducing the mean time outside the admissible frequency band by 22.2% and 20.0%, respectively. Zero post-projection constraint violations were observed in all evaluated cases. These gains were obtained at the expense of higher actuation usage, thereby making the regulation–usage trade-off explicit. Full article

Microchannel plate gated framing camera is commonly used in inertial confinement fusion diagnostics. However, it is a vacuum electronic device with bulkiness and non-single-line-of-sight imaging. To reduce the size of the camera and achieve a single line of sight image, a CMOS image sensor composed of a pixel unit and a readout circuit is presented to form the framing camera. The CMOS image sensor has a 32 × 32 × 4 pixel array with ultrashort shutter-time and four-frame imaging. The pixel array and analog to digital converter (ADC) readout circuit are designed using a standard 0.18 μm CMOS process. The pixel array includes 5T structured pixel units, a voltage-controlled delay, a clock tree and the row decoding scan circuits. A temporal resolution of 65 ps for the pixel circuit is achieved. The ADC readout circuit is composed of a counter, a comparator, a ramp generator and a register, which operates at a sampling frequency of 24.41 kS/s. An effective number of bits of 11.3, a spurious free dynamic range (SFDR) of 73.4 dB, and a signal-to-noise ratio (SNR) of 70.0 dB for the ADC are achieved. The CMOS image sensor will provide a novel and important imaging method for the field of ultrafast science. Full article

While direct current (DC) ice-melting is currently adopted for some transmission lines, its application to 10 kV distribution transformers—often located in remote and rugged terrain—presents significant operational challenges. Disconnecting these transformers prior to ice-melting is a complex procedure that incurs substantial labor, material, and financial costs. Leaving transformers connected risks DC current flowing into idle windings, potentially causing damage. Furthermore, existing mobile DC ice-melting power supplies are bulky and impose stringent transportation requirements, rendering them unsuitable for use on mountain roads. To overcome these limitations, this paper proposes a compact, lightweight variable-frequency ice-melting device. The operating principle and output characteristics of the variable-frequency method are investigated in detail. Using Simulink, system modeling and simulation analyses are performed to obtain the voltage and current output characteristics, along with harmonic spectra. Simulation results demonstrate that the proposed device achieves significant miniaturization compared with conventional solutions: within the typical parameter range of conventional devices, the volume can be reduced by 44–58% and the weight by 43–52%. In addition, the selected LC filter parameters (L = 10.39 mH, C = 86.62 μF) represent an optimized compromise solution that effectively suppresses input harmonics while maintaining the output current total harmonic distortion (THD) within an acceptable limit of 3.6%. Experimental results further validate the feasibility of the variable-frequency ice-melting current. Based on a matrix converter topology, the proposed device enables flexible adjustment of the output melting voltage and frequency, exhibits excellent low-frequency performance and dynamic response, and maintains low output harmonic content—fully meeting the application requirements for variable-frequency ice-melting. The key novelty lies in a compact matrix-converter-based de-icing device with systematic low-frequency performance analysis, offering superior portability and adaptability over traditional DC solutions. Full article

Extreme fast charging (XFC) infrastructure is becoming increasingly necessary as the number of electric vehicles continues to grow. However, deploying such stations introduces several challenges related to power quality and compliance with regulatory standards. This work presents an alternative XFC station designed for charging multiple vehicles while ensuring low harmonic distortion in the grid currents, without the need for sinusoidal filters, by employing the Zero Harmonic Distortion (ZHD) converter. The proposed system offers galvanic isolation for each charging interface and supports additional functionalities, including the integration of Distributed Energy Resources (DERs) and the provision of ancillary services. These features are enabled through the combination of a bidirectional grid-connected active front-end operating at low switching frequency with high-frequency silicon carbide (SiC)-based dc/dc converters on the vehicle side. Hardware-in-the-loop (HIL) simulation results demonstrate a total demand distortion (TDD) of 1.12% for charging scenarios involving both 400 V and 800 V battery systems, remaining within the limits specified by IEEE 519-2022. Full article

Under the deformation potential model, the superconducting phenomenon in ABC-stacked multilayer graphene under a vertical electric field is investigated using linear combination operators and unitary transformation methods. Through the deformation potential model applied to a linear continuous medium, the effect of the external electric field is converted into the deformation potential energy of the crystal. Deformation potential phonons (LA phonons) act as propagators, generating electron–electron interactions. As the electric field increases, the ratio of the electric displacement vector to the dielectric function ($D/\epsilon$) rises, leading to an increase in the electron ground-state energy, the opening of the band gap, and an enhancement of the attractive electron–electron interaction. With further increases in the external electric field, the deformation potential constant of the crystal ($D_l$) increases. When the phonon vibration frequency ($\omega$) is around 8.5 THz, and the conditions are satisfied—where the wave vectors of different LA phonons are equal in magnitude and opposite in direction, and the electron spins are opposite—the attractive electron–electron interaction reaches its maximum ($H_{eff}$), resulting in the emergence of superconductivity. Our study also provides a new perspective for understanding the unique quantum properties—such as strong correlations, superconductivity, and ferromagnetism—in different stacking configurations like AB, ABC, and ABCA. Full article