Search Results (113)

This article develops a boundary-adaptive nonparametric methodology for estimating the geometric conditional quantiles of a multivariate response when the conditioning covariate is supported on the simplex—an important case, as it is the natural domain of compositional data. The statistical difficulty addressed here is twofold. First, geometric conditional quantiles for multivariate responses must be defined and estimated through a genuinely directional and convex framework rather than through any scalar ordering. Second, when the covariate is compositional or otherwise simplex-constrained, conventional symmetric kernel procedures suffer from intrinsic support mismatch and severe boundary distortion, thereby compromising both estimation accuracy and inferential validity near faces and edges of the simplex. The method proposed in this paper is designed precisely to overcome this combined obstacle. Our main innovation consists in embedding the spatial quantile formalism of Chaudhuri within a Dirichlet–Kernel smoothing scheme whose shape parameters depend deterministically on the evaluation point. This produces a convex M-estimator that respects the simplex geometry exactly, automatically adapts its local shape to the position of the target point, and removes the need for artificial boundary corrections. To the best of our knowledge, this is the first contribution to provide a complete asymptotic treatment of geometric conditional quantile estimation under simplex-supported covariates with location-adaptive asymmetric kernels. We establish a Bahadur-type linear representation with an explicit negligible remainder, from which we derive refined asymptotic bias and variance expansions. The variance analysis reveals a distinctive geometric phenomenon: each coordinate direction approaching the simplex boundary induces an additional $b^{-1/2}$ inflation factor, so that the variance at a face of codimension $\left|\mathcal{J}\right|$ scales as $n^{-1}{b}^{-(s+|\mathcal{J}\left|\right)/2}$. We further obtain the asymptotic mean squared error, an explicit optimal bandwidth rate, asymptotic normality under the nonstandard normalization $n^{1/2}{b}^{-s/4}$, and consistent plug-in covariance estimators yielding valid confidence ellipsoids. Numerical experiments and a real-data illustration based on the GEMAS data confirm the practical merit of the approach, especially in boundary regions where classical methods are known to deteriorate. Full article

Visible light communication (VLC)-based Li-Fi systems suffer from limitations in transmission capacity expansion due to the restricted modulation bandwidth of LEDs. In this study, a latent representation-based NRZ-OOK Li-Fi transmission framework that exploits the statistical feature distribution of the latent space is proposed to improve transmission efficiency without expanding the physical bandwidth. An autoencoder is employed to transform input images into low-dimensional latent vectors, which are then quantized and modulated for transmission. At the receiver, hard decision and inverse quantization are performed, and the image is reconstructed through a trained decoder by leveraging the distribution characteristics of the latent representation. The effective transmission capacity gain $G_{\mathrm{c}\mathrm{a}\mathrm{p}}$ is defined to quantify the amount of representable information relative to the original data under the same physical link resources according to the latent dimension, achieving up to a 49-fold data representation efficiency. The experimental results over practical optical links (0.5–1.5 m) showed that, in short-range conditions, larger latent dimensions maintained higher reconstruction PSNR, whereas under channel degradation conditions, smaller latent dimensions exhibited higher robustness, demonstrating a performance inversion phenomenon. Furthermore, it was confirmed that the dominant factor governing reconstruction performance shifts from the representational capability of the data to error accumulation characteristics depending on the channel condition. These results suggest that the latent representation-based transmission framework is an effective Li-Fi strategy that can simultaneously consider transmission efficiency and channel robustness through information representation optimization in bandwidth-limited environments. Full article

Line-field spectral-domain optical coherence tomography (LF-SD-OCT) offers high-speed parallel imaging, but lateral beam expansion limits the photon budget per spatial channel, compromising sensitivity. Here, we demonstrate a high-speed LF-SD-OCT system driven by a gain-managed nonlinear (GMN) all-fiber source operating at a central wavelength of 1063.2 nm. Delivering 269 mW of average power with a smooth 98 nm (3 dB) bandwidth, the GMN source effectively fulfills the stringent photon budget and stability requirements of parallel detection. The system achieves a 5.68 μm axial resolution and a ~1.2 mm effective imaging range. Ex vivo porcine myocardial tissue ablation experiments validate its capability for high-contrast cross-sectional visualization of ablation crater morphology, showing excellent agreement with optical microscopy. These results establish GMN-enabled LF-SD-OCT as a robust solution for the precise intraoperative monitoring of laser-induced tissue damage. Full article

In all-digital transmitters (ADTx), the in-band quantization noise generated by pulse coding provides only limited clean bandwidth (CBW), significantly increasing the difficulty of analog filter design. To address the constrained CBW of RF pulse sequences in ADTx, this paper proposes an optimization strategy for suppressing noise across a broader frequency domain. Distinguished from traditional schemes with limited noise suppression range, the expansion of CBW is innovatively achieved by setting multiple groups of frequency observation points near the carrier frequency, enabling more comprehensive constraints of in-band noise. Meanwhile, aiming at the problems of large look-up table scale and slow query speed, a partitioned look-up strategy is proposed. During a look-up, traversal is confined only to the partition containing the input point, eliminating the need to scan all elements. This strategy substantially reduces the number of error calculations and comparisons, significantly improving the real-time performance of mapping look-up and lowering the computational demands on digital processing devices. Through the collaborative optimization of noise suppression and query efficiency, this study highlights its breakthrough contributions and provides technical support for the optimization of RF pulse sequences in ADTx. Full article

Phononic band gap crystals are characterized by periodic scatterers embedded within a matrix, which enable precise modulation of acoustic or elastic waves. Conventional optimization prioritizes bandwidth maximization, yet practical engineering often requires band gaps at specified frequencies. This requirement creates a significant design challenge. To this end, we develop a topology optimization strategy capable of maximizing elastic wave band gaps around prescribed target frequencies. The approach utilizes Material-Field Series Expansion (MFSE) for unit cell representation and a gradient-free Kriging-based algorithm to tackle the complex optimization problems. This strategy is systematically applied to optimize the band gaps of out-of-plane, in-plane, and complete wave modes, and is further extended to more complex scenarios involving dual-target frequencies. A variety of numerical results demonstrate the method’s effectiveness in engineering phononic crystals for bespoke frequency specifications. Full article

The rapid global expansion of offshore wind energy (OWE) has established it as a critical component of the renewable energy transition; however, this development concurrently introduces significant underwater noise pollution into marine ecosystems. This paper provides a comprehensive review of the acoustic footprint of OWE across its entire lifecycle, rigorously distinguishing between the high-intensity, acute impulsive noise generated during pile-driving construction and the chronic, low-frequency continuous noise associated with decades-long turbine operation. We critically evaluate the engineering capabilities and limitations of current underwater acoustic monitoring architectures, including buoy-based real-time monitoring nodes, cabled high-bandwidth systems (e.g., cabled hydrophone arrays with DAQ/DSP and fiber-optic distributed acoustic sensing, DAS), and autonomous seabed archival recorders (PAM deployment). Furthermore, documented biological impacts are synthesized across diverse taxa, ranging from auditory masking and threshold shifts in marine mammals to the often-overlooked sensitivity of invertebrates and fish to particle motion—a key metric frequently missing from standard pressure-based assessments. Our analysis identifies a fundamental gap in current governance paradigms, which disproportionately prioritize the mitigation of short-term acute impacts while neglecting the cumulative ecological risks of long-term operational noise. This review synthesizes recent evidence on chronic operational noise and outlines a conceptual pathway from event-based compliance monitoring toward long-term, adaptive soundscape management. We propose the implementation of integrated, adaptive acoustic monitoring networks capable of quantifying cumulative noise exposure and informing real-time mitigation strategies. Such a paradigm shift is essential for optimizing mitigation technologies and ensuring the sustainable coexistence of marine renewable energy development and marine biodiversity. Full article

The current-mode logic (CML) driver has evolved alongside integrated circuit (IC) technology. Its typical structure contains a tail current source, differential amplifying transistors, and load resistors. It is widely used in modern optical transceivers and other serial link transceivers, and is compatible with various processes, including CMOS, SiGe BiCMOS, and InP DHBT. The basic performance indicators of CML driver include gain, bandwidth, power, and total harmonic distortion (THD). For different application scenarios, different tail currents and load resistance are required. Nowadays, as the performance requirements for drivers in various applications continue to increase, more techniques need to be employed to balance high speed, high output amplitude, high linearity, and low power, such as bandwidth expansion techniques, linearity improvement techniques, and gain control techniques. In this review, the electrical characteristics of basic CML circuits are highlighted and compared with other interface level standards. The advancement of CML drivers is summarized. Emerging CML structures and performance enhancement technologies are introduced and analyzed. Design considerations are concluded in terms of the challenges faced by high-speed drivers. The review provides comparative study and comprehensive reference for designers. Full article

To achieve high-frequency inductance and impedance enhancement for effective electromagnetic interference (EMI) mitigation in power electronics, this paper presents an electromagnetic analysis and experimental study of laminated Mn-Zn toroidal ferrite cores. The electromagnetic field is analyzed using a 2D analytical solution based on a simplified Cartesian approximation. Although neglecting curvature, this approach enables efficient eigenfunction expansion and is rigorously validated against cylindrical finite difference (FDM) and 3D finite element (FEM) benchmarks. The results demonstrate that lamination effectively interrupts eddy current loops; notably, a four-layer structure increases the resonant frequency by approximately 2.8 times compared to a monolithic core. Experimental measurements confirm that this design significantly mitigates the skin effect and extends the stable frequency bandwidth. This study establishes a validated, computationally efficient methodology for optimizing core geometries to prevent impedance degradation. Full article

The rapid expansion of IoT devices represents significant progress in areas such as smart buildings and smart cities, but at the same time, the volume of data generated represents a challenge, which can lead to real bottlenecks in the data analysis process, thus resulting in increased waiting times for end users. The use of cloud-based solutions may prove inefficient in some cases, as the bandwidth required for transmitting data generated by IoT devices is limited. The integration with Edge computing mitigates this issue, bringing data processing closer to the resource that generates it. Edge computing plays a key role in improving cloud performance by offloading tasks closer to the data source, optimizing resource allocation. Achieving the desired performance requires a dynamic approach to resource management, where task execution can be prioritized based on current load conditions: either at the Edge node or the Cloud node. This paper proposes an approach based on the Seasonal Auto Regressive Integrated Moving Average (SARIMA) model for seamlessly switching between the Cloud and Edge nodes in the event of a loss of connection between the Cloud and Edge nodes. Thereby ensuring the command loop remains closed by transferring the task to the Edge node until the Cloud node becomes available. In this way, the prediction that could underlie a command is not jeopardized by the lack of connection to the cloud node. The method was evaluated using real-world resource utilization data and compared against a Simple Moving Average (SMA) baseline using standard metrics: RMSE, MAE, MAPE, and MSE. Experimental results demonstrate that SRIMA significantly improves prediction accuracy, achieving up to 64% improvement for CPU usage and 35% for RAM usage compared to SMA. These findings highlight the effectiveness of incorporating seasonality and autoregressive components in predictive models for edge computing, contributing to more efficient resource allocation and enhanced performance in smart city environments. Full article

Among the 15 beamlines in the first phase of the High-Energy Photon Source (HEPS) in China, the maximum peak data generation volume can reach 1 PB per day, with the maximum peak data generation rate reaching 3.2 Tb/s. This poses significant challenges to the underlying network system. To meet the storage, computing, and analysis needs of HEPS scientific data, this paper designed a high-performance and scalable network architecture based on RoCE (RDMA over Converged Ethernet). Test results demonstrate that the RoCE-based HEPS data center network system achieves high bandwidth and ultra-low latency, stably maintains reliable transmission performance during the interaction of scientific data storage, computing, and analysis, and exhibits excellent scalability to adapt to the future expansion of HEPS beamlines. Full article

We study the optical susceptibility of a CdSe-based semiconductor quantum dot with a cascade exciton–biexciton configuration, which is coupled via the Coulomb interaction to a gold spheroidal nanoparticle, in the presence of a nearly resonant strong pump field and a weak probe field. We take both fields’ polarization vectors to be parallel to the interparticle axis, derive the equations of motion for the density matrix, and proceed with a perturbative expansion approach to calculate the components of the density matrix associated with the effective optical susceptibility, which describes processes to first order in the probe field and to all orders in the pump field. We present spectra of the effective susceptibility and examine their dependence on the metal nanoparticle’s geometric characteristics for various interparticle distances and pump field detunings, under both one- and two-photon resonance conditions. The role of the biexciton energy shift is also studied. Lastly, we introduce a dressed-state picture to elucidate the origin of the observed spectral features. Our calculations reveal that reducing the interparticle distance and increasing the metal nanoparticle aspect ratio enhance the exciton–plasmon coupling, leading to pronounced resonance splitting, spectral shifts, and broadened gain regions. Prolate nanoparticles aligned with the field polarization exhibit the strongest coupling and the widest gain bandwidth, whereas oblate geometries produce nearly overlapping resonances. Under exact resonance, the probe displays zero absorption with a negative dispersion slope, indicating slow-light behavior. These results demonstrate the tunability of hybrid CdSe-Au nanostructures for designing nanoscale optimal amplifiers, modulators, and sensors. Full article

Real-time monitoring of internal fracture evolution in fractured rock masses using fiber Bragg grating (FBG) technology can help mitigate geotechnical hazards. This study employed FBG, acoustic emission (AE), and digital image correlation (DIC) to analyze pre-cracked sandstone under uniaxial compression. During the failure of the pre-cracked specimens, the FBGs experienced non-uniform stresses. In the initial loading phase, the stress concentrations at the crack tips and the wing-crack development were dominated by tensile stresses, and the maximum tensile strain was 1.01%. After the initial yield strength was reached, the crack-propagation process transitioned to shear-stress dominance, and a maximum shear strain of 6.45% was exhibited. During multiple stress decreases (180–250 s), the FBG-measured local shear and tensile strains reflected stress variations that were associated with shear-locking effects and failure stages. Before the tensile-crack initiation, the FBG-detected principal-strain concentration zones exhibited prolonged incubation periods, whereas the shear-crack initiation was preceded by shorter incubation periods. The evolution curves of the damage variable, which was defined by the FBG coupling strength, could be categorized into three distinct stages: initial damage accumulation, damage acceleration, and final damage. When the initial yield strength was reached, the damage variable rapidly increased, particularly during the two stress decreases. Full article

The rapid expansion of communication networks and increasingly complex service demands have presented significant challenges to the intelligent management of network resources. To address these challenges, we have proposed a network self-optimization framework integrating the predictive capabilities of the Large Language Model (LLM) with the decision-making capabilities of multi-agent Reinforcement Learning (RL). Specifically, historical network traffic data are converted into structured inputs to forecast future traffic patterns using a GPT-2-based prediction module. Concurrently, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm leverages real-time sensor data—including link delay and packet loss rates collected by embedded network sensors—to dynamically optimize bandwidth allocation. This sensor-driven mechanism enables the system to perform real-time optimization of bandwidth allocation, ensuring accurate monitoring and proactive resource scheduling. We evaluate our framework in a heterogeneous network simulated using Mininet under diverse traffic scenarios. Experimental results show that the proposed method significantly reduces network latency and packet loss, as well as improves robustness and resource utilization, highlighting the effectiveness of integrating sensor-driven RL optimization with predictive insights from LLMs. Full article

The rapid expansion of broadband wireless communication systems, including 5G, satellite networks, and next-generation IoT platforms, has created a strong demand for antenna architectures capable of real-time beam control, compact integration, and broad frequency coverage. Traditional reflectarrays, while effective for narrowband applications, often face inherent limitations such as fixed beam direction, high insertion loss, and complex phase-shifting networks, making them less viable for modern adaptive and reconfigurable systems. Addressing these challenges, this work presents a novel wideband planar metasurface that operates as a polarization rotation reflective metasurface (PRRM), combining 90° polarization conversion with 1-bit reconfigurable phase modulation. The metasurface employs a mirror-symmetric unit cell structure, incorporating a cross-shaped patch with fan-shaped stub loading and integrated PIN diodes, connected through vertical interconnect accesses (VIAs). This design enables stable binary phase control with minimal loss across a significantly wide frequency range. Full-wave electromagnetic simulations confirm that the proposed unit cell maintains consistent cross-polarized reflection performance and phase switching from 3.83 GHz to 15.06 GHz, achieving a remarkable fractional bandwidth of 118.89%. To verify its applicability, the full-wave simulation analysis of a 16 × 16 array was conducted, demonstrating dynamic two-dimensional beam steering up to ±60° and maintaining a 3 dB gain bandwidth of 55.3%. These results establish the metasurface’s suitability for advanced beamforming, making it a strong candidate for compact, electronically reconfigurable antennas in high-speed wireless communication, radar imaging, and sensing systems. Full article

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article