Search Results (406)

With the widespread deployment of automotive millimeter-wave radars, mutual interference and broadband noise severely degrade the signal-to-noise ratio (SNR) of range–Doppler (RD) maps, leading to the loss of weak targets. Existing deep learning methods rely on difficult-to-obtain paired training samples and often cause excessive target smoothing due to a lack of physical constraints. To address these challenges, this paper proposes RIME-Net, a physics-guided unpaired learning framework designed to jointly achieve radar interference mitigation and weak target enhancement. First, based on a cycle-consistent adversarial architecture, we designed the Interference Mitigation Network (IM-Net). IM-Net integrates spectral consistency loss and identity mapping constraints, learning a robust mapping from the interference domain to the clean domain without paired supervision, effectively suppressing low-rank interference and preserving signal integrity. Second, to recover target details attenuated during denoising, we propose the saliency-aware Target Enhancement Network (TE-Net). TE-Net combines multi-scale residual blocks and channel-spatial attention mechanisms, selectively enhancing weak target features based on saliency priors. Extensive experiments on diverse datasets show that RIME-Net significantly outperforms existing supervised and model-driven methods in terms of SINR, recall, and structural similarity, providing a robust solution for reliable radar perception in complex electromagnetic environments. Full article

The rapid expansion of smart city infrastructures and Internet of Things (IoT) networks has led to extremely dense wireless deployments, driving unsustainable energy consumption and exacerbating environmental concerns. To improve sustainability in the long term, future wireless systems must fundamentally prioritize energy-efficient and autonomous operation. Integrated sensing and communication (ISAC) is emerging as a key enabler for next-generation systems by jointly supporting sensing and communication through shared spectrum, hardware, and signal processing resources. In IoT systems, sensing of target parameters, e.g., range, angle, velocity and identity, etc., form the basis of autonomous and environment-aware applications. However, this integration increases overall power consumption due to the added coordination overhead and the workload placed on shared hardware components. To this end, backscatter communication provides a low-power alternative that enables passive data transmission through energy harvesting and sharply reduces the need for active radio circuits. However, the coexistence of sensing and backscatter functions introduces mutual interference, which often requires large multiple-input multiple-output (MIMO) arrays for effective mitigation. Furthermore, sensing performance depends heavily on line-of-sight conditions, while backscatter links operate only over short ranges. Although increasing array size or transmit power can extend coverage, it imposes substantial energy and hardware costs and undermines sustainability goals. To address these limitations, cell-free MIMO is emerging as a promising candidate technology for next-generation systems. Cell-free MIMO relies on a dense deployment of distributed access points that cooperate to serve devices across a wide area. This cooperation enables effective beamforming and interference management, providing spatial diversity comparable to large, centralized antenna arrays without incurring their associated hardware or power costs. They also enable aggregation of weak double-hop reflections, reduced effective-illumination distances, multi-view sensing, and robustness to blockage, which is invaluable to backscatter communication. This perspective article introduces the foundations, challenges, and architectural considerations of cell-free backscatter-aided integrated sensing and communication (CF-BISAC) systems. By leveraging the advantages of battery-less backscatter IoT devices and the distributed nature of cell-free MIMO, CF-ISABC aims to maximize sensing and communication performance under strict energy constraints, contributing toward energy-aware ISAC systems capable of supporting high-density, low-power wireless applications. Full article

The accurate monitoring and dynamic analysis of metal ions are of considerable practical significance in environmental toxicology and life sciences. Colorimetric analysis and surface-enhanced Raman scattering (SERS) sensing technologies, utilizing the aggregation effect of gold and silver nanoparticles (Au/Ag NPs), have emerged as prominent methods for rapid metal ion detection. While sharing a common plasmonic basis, these two techniques serve distinct yet complementary analytical roles: colorimetric assays offer rapid, instrument-free visual screening ideal for point-of-care testing (POCT), whereas SERS provides superior sensitivity and structural fingerprinting for precise quantification in complex matrices. Furthermore, the synergistic integration of these modalities facilitates the development of dual-mode sensing platforms, enabling mutual signal verification for enhanced reliability. This article evaluates contemporary optical sensing methodologies utilizing aggregation effects and their advancements in the detection of diverse metal ions. It comprehensively outlines methodological advancements from nanomaterial fabrication to signal transduction, encompassing approaches such as biomass-mediated green synthesis and functionalization, targeted surface ligand engineering, digital readout systems utilizing intelligent algorithms, and multimodal synergistic sensing. Recent studies demonstrate that these techniques have attained trace-level identification of target ions regarding analytical efficacy, with detection limits generally conforming to or beyond applicable environmental and health safety regulations. Moreover, pertinent research has enhanced detection linear ranges, anti-interference properties, and adaptability for POCT, validating the usefulness and developmental prospects of this technology for analysis in complicated matrices. Full article

Image-Based Overlay (IBO) equipment leverages optical reflection imaging principles, combined with focusing and alignment strategies to measure overlay marks. Among all measurement steps, the focal plane measurement of marks exerts the most critical impact on overlay accuracy, while the time consumed by focal plane detection directly determines the overall measurement throughput. To address the trade-off between accuracy and efficiency in advanced process nodes, this paper proposes an integrated optimization strategy encompassing optical hardware design and software algorithms. The hardware solution adopts a dual-wavelength, dual-detector architecture: optimal imaging wavelengths are selected independently for the previous-layer and current-layer marks, ensuring each layer achieves ideal imaging conditions without mutual interference. The software strategy employs a deep learning framework to simultaneously predict and adjust the horizontal position (alignment) and vertical defocus number of measured marks in real time with high precision, thereby securing the optimal imaging posture. By synergizing hardware-based optimal imaging conditions and software-based posture adjustment, this method effectively mitigates the impact of background noise and system aberrations, ultimately improving both the accuracy and efficiency of overlay measurement. Full article

The high peak-to-average power ratio (PAPR) in classical high-speed digital data transmission systems with orthogonal frequency division multiplexing (OFDM) limits energy efficiency and communication range. This paper proposes a method for randomizing OFDM signals via frequency coding using synthesized pseudorandom sequences with improved autocorrelation properties, obtained through machine learning, to minimize PAPR in complex, non-stationary hydroacoustic channels for communicating with underwater robotic systems. A neural network architecture was developed and trained to generate codes of up to 150 elements long based on an analysis of patterns in previously found best short sequences. The obtained class of OFDM signals does not require regular and accurate estimation of channel parameters while remaining resistant to various types of impulse noise, Doppler shifts, and significant multipath interference typical of the underwater environment. The attained spectral efficiency values (up to 0.5 bits/s/Hz) are relatively high for existing hydroacoustic communication systems. It has been shown that the peak power of such multi-frequency information transmission systems can be effectively reduced by an average of 5–10 dB, which allows for an increase in the communication range compared to classical OFDM methods in non-stationary hydrological conditions at acceptable bit error rates (from 10⁻² to 10⁻³ and less). The effectiveness of the proposed methods of randomization with synthesized codes and frequency coding for OFDM signals was confirmed by field experiments at sea on the shelf, over distances of up to 4.2 km, with sea waves of up to 2–3 Beaufort units and mutual movement of the transmitter and receiver. Full article

Single-frequency ground penetrating radar (GPR) systems are fundamentally constrained by a trade-off between penetration depth and resolution, alongside issues like narrow bandwidth and ringing interference. To break this limitation, we have developed a multi-frequency data fusion technique grounded in convolutional sparse representation (CSR). The proposed methodology involves spatially registering multi-frequency GPR signals and fusing them via a CSR framework, where the convolutional dictionaries are derived from simulated high-definition GPR data. Extensive evaluation using information entropy, average gradient, mutual information, and visual information fidelity demonstrates the superiority of our method over traditional fusion approaches (e.g., weighted average, PCA, 2D wavelets). Tests on simulated and real data confirm that our CSR-based fusion successfully synergizes the deep penetration of low frequencies with the fine resolution of high frequencies, leading to substantial gains in GPR image clarity and interpretability. Full article

To address the challenges faced by loquat detection algorithms in orchard settings—including complex backgrounds, severe branch and leaf occlusion, and inaccurate identification of densely clustered fruits—which lead to high computational complexity, insufficient real-time performance, and limited recognition accuracy, this study proposed a lightweight detection model based on the YOLO-MCS architecture. First, to address fruit occlusion by branches and leaves, the backbone network adopts the lightweight EfficientNet-b0 architecture. Leveraging its composite model scaling feature, this significantly reduces computational costs while balancing speed and accuracy. Second, to deal with inaccurate recognition of densely clustered fruits, the C2f module is enhanced. Spatial Channel Reconstruction Convolution (SCConv) optimizes and reconstructs the bottleneck structure of the C2f module, accelerating inference while improving the model’s multi-scale feature extraction capabilities. Finally, to overcome interference from complex natural backgrounds in loquat fruit detection, this study introduces the SimAm module during the initial detection phase. Its feature recalibration strategy enhances the model’s ability to focus on target regions. According to the experimental results, the improved YOLO-MCS model outperformed the original YOLOv8 model in terms of Precision (P) and mean Average Precision (mAP) by 1.3% and 2.2%, respectively. Additionally, the model reduced GFLOPs computation by 34.1% and Params by 43.3%. Furthermore, in tests under complex weather conditions and with interference factors such as leaf occlusion, branch occlusion, and fruit mutual occlusion, the YOLO-MCS model demonstrated significant robustness, achieving mAP of 89.9% in the loquat recognition task. The exceptional performance serves as a robust technical base on the development and research of intelligent systems for harvesting loquats. Full article

Quantum communication presents new opportunities for overcoming the limitations of classical wireless systems, particularly those associated with noise, fading, and interference. Building upon the principles of classical orthogonal frequency division multi-plexing (OFDM), this work proposes a quantum OFDM architecture tailored for video transmission. In the proposed system, video sequences are first compressed using the versatile video coding (VVC) standard with different group of pictures (GOP) sizes. Each GOP size is processed through a channel encoder and mapped to multi-qubit states with various qubit configurations. The quantum-encoded data is converted from serial-to-parallel form and passed through the quantum Fourier transform (QFT) to generate mutually orthogonal quantum subcarriers. Following reserialization, a cyclic prefix is appended to mitigate inter-symbol interference within the quantum channel. At the receiver, the cyclic prefix is removed, and the signal is restored to parallel before the inverse QFT (IQFT) recovers the original quantum subcarriers. Quantum decoding, classical channel decoding, and VVC reconstruction are then employed to recover the videos. Experimental evaluations across different GOP sizes and channel conditions demonstrate that quantum OFDM provides superior resilience to channel noise and improved perceptual quality compared to classical OFDM, achieving peak signal-to-noise ratio (PSNR) up to 47.60 dB, structural similarity index measure (SSIM) up to 0.9987, and video multi-method assessment fusion (VMAF) up to 96.40. Notably, the eight-qubit encoding scheme consistently achieves the highest SNR gains across all channels, underscoring the potential of quantum OFDM as a foundation for future high-quality video transmission. Full article

When considering the adsorption effect, traditional experimental methods have faced significant challenges in obtaining the solute transport parameters for composite pollutants. Based on the adsorption test data of three types of composite pollutants collected from the Web of Science and China National Knowledge Infrastructure databases from 2014 to 2024, this study employed four commonly used machine learning models, that is, Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Decision Tree (DT) models, to establish adsorption isotherms of pollutants with liquid-phase equilibrium concentration as the horizontal coordinate and solid-phase adsorption capacity as the vertical coordinate, and systematically investigated the adsorption characteristics of combined pollutants in the porous aquifer. Subsequently, the Mean Square Errors (MSEs) and coefficients of determination, two commonly used evaluation metrics for regression models in machine learning, were chosen to estimate the prediction effect of datasets. Combined with the convection–diffusion equation, the adsorption kinetic parameters under the mutual interference of composite pollutants, namely, the retardation factor, were solved. The results show that for the adsorption isotherms of heavy metal composite pollutants, organic composite pollutants, and heavy metal and organic combined composite pollutants, SVM, BPNN, and RF models have the best prediction effect, respectively, and their MSEs are 0.032, 0.001, and 0.018. The adsorption isotherm fitting results indicate that the heavy metal composite pollutants and organic composite pollutants conform to the Freundlich model. The retardation factor of organic composite pollutants is significantly higher than that of heavy metal composite pollutants. Full article

In this paper, a dual-band bandpass filter with a wide upper stopband is proposed and designed by integrating stepped-impedance resonators (SIRs) and a low-pass filter. The operating center frequencies of the designed dual-band filter are targeted at 2.5 GHz and 5.35 GHz, respectively, to meet the frequency requirements of typical wireless communication scenarios. Notably, the filter achieves a wide upper stopband ranging from 6.1 GHz to 25 GHz, which can effectively suppress unwanted high-frequency interference signals within this frequency range and avoid mutual interference with other high-frequency communication systems. And it exhibits insertion losses of 0.12 dB (2.5 GHz) and 0.6 dB (5.35 GHz) in its two passbands to ensure minimal useful signal attenuation. The simulation results agree well with the measured results. Full article

With the acceleration of urbanization, municipal landscape roads play a crucial role in urban public spaces. This study focuses on the distress detection and aging characteristics of asphalt pavements in municipal landscape roads. Firstly, a novel method is proposed based on the SpA-Former shadow removal network, which effectively addresses the interference caused by tree shadows and significantly improves the accuracy of automated distress identification. Distress detection results indicate that transverse cracks are the most common type of distress, primarily influenced by environmental factors such as asphalt material aging, temperature fluctuations, and freeze-thaw cycles—these factors induce asphalt embrittlement and a substantial decline in crack resistance. Subsequently, accelerated aging experiments were conducted to simulate the aging process of asphalt materials. It was found that as aging time extends, asphalt stiffness increases significantly; while this enhances deformation resistance, it also makes the material more prone to cracking under low-temperature conditions. Low-temperature crack resistance tests reveal that asphalt aged for more than six years exhibits a sharp deterioration in low-temperature crack resistance, showing distinct brittle characteristics. Furthermore, freeze-thaw cycle experiments demonstrate that the coupling effect of asphalt aging and freeze-thaw action significantly impairs its freeze-thaw resistance—particularly for asphalt aged over six years, which nearly loses its freeze-thaw resistance. In summary, the coupling effect of asphalt aging and environmental factors is the primary cause of pavement damage in municipal landscape roads. This study divides 2542 images into three mutually exclusive subsets: a training set of 2123 images, a validation set of 209 images, and a test set of 210 images. The research provides new theoretical references and technical support for the maintenance and management of landscape roads, especially demonstrating practical significance in distress detection and the analysis of material aging mechanisms. Full article

To extend the drilling limit of horizontal sections, the demand for utilizing multiple hydraulic oscillators at intervals has become increasingly prominent. However, current research on the operating range of multiple oscillators, their mutual interference characteristics, and the impact of oscillation on drill string fatigue damage remains scarce. The results indicate that when the vibration ranges of multiple oscillators overlap, the overlapping segment experiences negative effects under identical excitation frequencies. If the oscillators operate at different frequencies, the displacement envelope of the drill string tends to become irregular. Within the normal amplitude range of oscillators, no significant fatigue damage is generally observed in adjacent sections of the drill string. However, when minor initial cracks exist in the drill string, an increase in oscillator amplitude leads to accelerated crack propagation, significantly hastening the fatigue failure process. In field operations, the parameters of multiple oscillators should be optimized according to specific working conditions, and timely non-destructive inspection of the drill string sections within the effective range of the oscillators must be conducted. The study provides novel insights into vibration wave propagation and fatigue damage in drill strings under multi-point excitation. Full article

Small multi-rotor UAVs face endurance limitations during long-range missions due to high rotor energy consumption and limited battery capacity. This paper proposes a folding-wing range extender integrating a sliding-rotating two-degree-of-freedom folding wing—which, when deployed, quadruples the fuselage length yet folds within its profile—and a tail-thrust propeller. The device can be rapidly installed on host small multi-rotor UAVs. During cruise, it utilizes wing unloading and incoming horizontal airflow to reduce rotor power consumption, significantly extending range while minimally impacting portability, operational convenience, and maneuverability. To evaluate its performance, a 1-kg-class quadrotor test platform and matching folding-wing extender were developed. An energy consumption model was established using Blade Element Momentum Theory, followed by simulation analysis of three flight conditions. Results show that after installation, the required rotor power decreases substantially with increasing speed, while total system power growth slows noticeably. Although the added weight and drag increase low-speed power consumption, net range extension emerges near 15 m/s and intensifies with speed. Subsequent parametric sensitivity analysis and mission profile analysis indicate that weight reduction and aerodynamic optimization can effectively enhance the device’s performance. Furthermore, computational fluid dynamics (CFD) analysis confirms the effectiveness of the dihedral wing design in mitigating mutual interference between the rotor and the wing. Flight tests covering five conditions validated the extender’s effectiveness, demonstrating at 20 m/s cruise: 20% reduction in total power, 25% improvement in endurance/range, 34% lower specific power, and 52% higher equivalent lift-to-drag ratio compared to the baseline UAV. Full article

Abductive reasoning—the search for plausible explanations—has long been central to human inquiry, from forensics to medicine and scientific discovery. Yet formal approaches in AI have largely reduced abduction to eliminative search: hypotheses are treated as mutually exclusive, evaluated against consistency constraints or probability updates, and pruned until a single “best” explanation remains. This reductionist framing fails on two critical fronts. First, it overlooks how human reasoners naturally sustain multiple explanatory lines in suspension, navigate contradictions, and generate novel syntheses. Second, when applied to complex investigations in legal or scientific domains, it forces destructive competition between hypotheses that later prove compatible or even synergistic, as demonstrated by historical cases in physics, astronomy, and geology. This paper introduces quantum abduction, a non-classical paradigm that models hypotheses in superposition, allowing them to interfere constructively or destructively, and collapses only when coherence with evidence is reached. Grounded in quantum cognition and implemented with modern NLP embeddings and generative AI, the framework supports dynamic synthesis rather than premature elimination. For immediate decisions, it models expert cognitive processes; for extended investigations, it transforms competition into “co-opetition” where competing hypotheses strengthen each other. Case studies span historical mysteries (Ludwig II of Bavaria, the “Monster of Florence”), literary demonstrations (Murder on the Orient Express), medical diagnosis, and scientific theory change. Across these domains, quantum abduction proves more faithful to the constructive and multifaceted nature of human reasoning, while offering a pathway toward expressive and transparent AI reasoning systems. Full article

The task of tracking weak targets in low-altitude UAV scenarios requires high robustness and generalization ability of the model. Against this backdrop, this paper proposes a novel annotation and training mechanism based on SwinTrack. To improve the model’s tracking ability for weak targets, this paper proposes a pseudo-label consistency screening and background enhancement annotation strategy. This strategy enlarges the target box proportionally before training to obtain more effective background information. Furthermore, pseudo-labels are screened using a hybrid gating system of geometric overlap and confidence consistency to reduce the negative transfer interference of noise generated in different domains on the model. Since the data feature distribution varies significantly in tracking tasks, this paper introduces a mutual-teaching pseudo-label iterative training method into the field of weak target tracking. This aims to continuously transfer the model from the source domain to the target domain during iteration, thereby improving the model’s generalization ability. Experiments have shown that, when faced with a completely new dataset of weak target tracking, the proposed method improves upon recent strong baselines in single-target tracking by 0.05 in both P@20 and NP-AUC, and by 0.04 in SUS, demonstrating the enhanced tracking performance and generalization ability of the proposed method in the field of weak target tracking. Full article