Search Results (30)

Pixelated electromagnetic representations enable topology-free numerical exploration of microwave layouts without enforcing a predefined circuit template. This work presents a simulation-based electromagnetic modeling and analysis framework for pixelated microstrip low-pass filters within a physical footprint comparable to that of a classical stepped-impedance reference. A binary metallization encoding is embedded in a fixed microstrip domain and explored through full-wave electromagnetic optimization under consistent material, excitation, and specification conditions. The objective is not extreme miniaturization or hardware prototyping, but to assess whether topology-free pixelated layouts can produce useful low-pass responses within the same general physical scale and to analyze the structural properties of the resulting feasible layouts. Across 20 independent optimization runs, 12 specification-compliant and geometrically unique metallization layouts were identified. Aggregate analysis of 360 single-pixel perturbations reveals topology-dependent sensitivity, while finite-width regularization of ideal corner contacts preserves modeled specification compliance after refined-mesh verification, with the suitable bridge width depending on the topology. The scope is deliberately limited to full-wave EM modeling and numerical analysis; experimental fabrication and measurement are left as a subsequent validation stage. These findings support topology-free pixelated EM modeling and analysis as a route for investigating non-obvious low-pass filter layouts under comparable footprint constraints. Full article

Entropy has evolved from a secondary thermodynamic property (Clausius, 1865) to a potentially fundamental organizing structure of physical reality, particularly through its gravitational manifestation in the Bekenstein–Hawking formula. This article systematically reviews four theoretical developments (2015–2024) that test this hypothesis using analytical methods from quantum information theory, holographic duality, and quantum gravity. First, we examine how the quantum island formula (Equation (¹)) resolves the black hole information paradox by demonstrating that fine-grained entropy depends on global causal structure rather than local degrees of freedom. Second, we analyze the Complexity = Volume and Complexity = Action conjectures, showing that computational complexity encodes post-thermalization dynamics on exponentially long timescales, with predicted maximum complexity $C_{max}\sim e^{S_{\mathrm{BH}}}$ testable in SYK simulations. Third, we examine the scope and limitations of three gravity frameworks (AdS/CFT holography, emergent gravity, loop quantum gravity) in addressing entropy’s role in initial conditions and extract their distinct observational signatures for 2025–2035 experiments. Fourth, we explore entropy–motion duality through mixed metric signatures; while the correspondence $\beta \leftrightarrow it$ is suggestive, its full physical interpretation remains conjectural outside semiclassical and toy-model contexts. We articulate quantum entropic relationalism as an epistemological framework wherein entropy constitutes an objective relational structural property encoding physical relations without substantial reducibility. This synthesis suggests spacetime emerges from quantum entanglement substrates, with testability prospects via gravitational interferometry (LISA, Einstein Telescope), quantum simulators, and cosmological observations (Cosmic Microwave Background (CMB)-S4, LiteBIRD) anticipated by 2035. Full article

The counter-diabatic shortcut to adiabaticity (CDSTA) has been explored in implementing qudit quantum Fourier transforms (QFTs) via stimulated Raman adiabatic passage (STIRAP) to suppress non-adiabatic leakage. While direct CD driving may introduce unwanted global microwave couplings, seeking an approach without explicit CD fields is highly desirable. By employing rotating-frame absorption, we implement an all-optical CDSTA strategy to synthesize the QFT. Direct CD driving (STIRSAP) removes the adiabatic time floor of STIRAP and sets the acceleration upper bound; the all-optical scheme reproduces this acceleration with a peak-amplitude overhead below $5\%$ in the working regime while eliminating the microwave coupling. The quartit encoding further reduces the integrated energy by a factor of 3 relative to the two-qubit decomposition. Extending to open quantum systems, the all-optical strategy shows improved robustness against amplitude noise and spontaneous emission. Full article

This paper proposes a novel multi-frequency extended Deep Learning (DL) model for electromagnetic image reconstruction under Transverse Magnetic (TM) wave incidence in layered media, inspired by conventional microwave imaging techniques that combine nonlinear inversion algorithms with neural networks to improve reconstruction performance. The proposed framework adopts a two-stage neural network architecture. In the first stage, a Deep Residual Convolutional Neural Network (DRCNN) is employed to extrapolate multi-frequency scattered fields from single-frequency input data, thereby enriching the frequency-dependent scattering information available for reconstruction. Subsequently, the extrapolated multi-frequency scattered fields are fed into a Deep Convolutional Encoder–Decoder (DCED) network to reconstruct an accurate dielectric constant distribution within the imaging domain. To validate the effectiveness of the proposed approach, two representative comparison methods are considered: (1) a hybrid framework combining the Back-Propagation Scheme (BPS) with a Convolutional Neural Network (CNN), and (2) a framework integrating the Dominant Current Scheme (DCS) with a CNN. In both approaches, conventional inversion algorithms are first utilized to generate coarse initial reconstructions, which are subsequently refined by the neural network. Numerical simulations and experimental results show that the proposed multi-frequency extension model achieves lower reconstruction error and higher structural similarity than the reference methods. These results confirm the effectiveness and potential of the proposed framework for advanced electromagnetic imaging applications. Full article

Accurate retrieval of cloud liquid water content (LWC) and ice water content (IWC) vertical profiles remains limited by strong vertical variability and nonlinear dependencies among observed variables. Ground-based cloud radar reflectivity and microwave radiometer-derived thermodynamic profiles provide complementary constraints, but their joint use requires consistent time–height matching and bias-controlled predictors. This study develops a vertically structured machine-learning framework that explicitly represents profile-level dependencies by constructing vertical-structure-enhanced features to encode local gradients and contextual information, integrating multiple tree-based learners with heterogeneous configurations through a profile-aware stacking strategy, and introducing a profile-level refinement step to suppress layer-to-layer inconsistencies. The framework is evaluated using year-round Cloudnet observations from the Lindenberg site, where IWC RMSE decreases from 0.0152 g m⁻³ to 0.0092 g m⁻³ with R² increasing from 0.412 to 0.784, and LWC RMSE decreases from 0.0786 g m⁻³ to 0.0591 g m⁻³ with R² increasing from 0.303 to 0.606. Additional boundary-region evaluation shows that the improvement is particularly evident near radar-derived cloud boundaries, where cloud structure and hydrometeor content may vary rapidly with height. These results indicate that treating cloud retrieval as a vertically structured learning problem reduces inconsistencies inherent in pointwise models and establishes a data-driven baseline for incorporating vertical constraints into atmospheric profile retrieval. Full article

Translating Synthetic aperture radar (SAR) images into optical images is intrinsically ill-posed because microwave backscatter and optical reflectance describe different physical properties of the observed scene. Although frequency-domain modeling has been introduced into diffusion-based translation, existing methods mainly rely on independent weighting of individual Fourier coefficients and provide limited modeling of interactions among neighboring frequencies and feature channels. To address this limitation, we propose dualCDM, a conditional diffusion model that jointly exploits spatial- and frequency-domain representations. In the diffusion backbone, a spatial-frequency hybrid residual block (SFHRB) combines a spatial convolution branch with complex-valued convolution in the Fourier domain. The complex convolution aggregates neighboring Fourier coefficients across all input feature channels, enabling local cross-frequency and cross-channel modeling, while its response is modulated by the diffusion timestep. In the SAR conditional encoder, an adaptive frequency-domain feature recalibration block (AFFRB) predicts input-dependent real-valued gains from magnitude and trigonometric phase representations of intermediate GRD features. These gains adaptively recalibrate the complex frequency responses without introducing an additional phase shift, while the residual connection preserves the original conditional information. A dual-domain objective further constrains both the predicted diffusion noise and the one-step optical reconstruction in the spatial and frequency domains. We also construct the S1S2 dataset using 16-bit Sentinel-2 reflectance data, retaining the original 0–10,000 value range and including the near-infrared band. Experiments on SEN1-2 and S1S2 show that dualCDM improves radiometric accuracy, spectral consistency, and structural preservation over six representative methods. Paired statistical tests further confirm significant improvements over the strongest competing method across all six evaluation metrics on both datasets. Full article

With the wide application of generative models in the field of SAR image inpainting, inadequate reconstruction quality of scattering characteristics and insufficient global coherence of semantic logic remain the core challenges of such tasks. To address these issues, this paper proposes a Feature-Encoding Diffusion Model (FEDM). Guided by local valid regions, the proposed model accurately learns the microwave backscattering distribution law of ground features through a SAR-specific Variational Auto-Encoder (SAR-VAE), thus improving the reconstruction accuracy of backscattering statistics. Meanwhile, it integrates semantic embedding and cross-attention mechanism to strengthen the semantic constraints of SAR scenes, ensuring the logical rationality of the ground feature layout. With progressive diffusion generation and sliding window strategy, the model achieves high-quality reconstruction with coherent semantics and consistent global spatial structure for large-scale missing regions. Experiments on public datasets including OSdataset, SEN1-2, SRSDD-v1.0 and MRSSC show that the proposed method achieves excellent performance in terms of scattering characteristic reconstruction quality and globally coherent generation of semantic logic, and realizes high-quality SAR image inpainting. Full article

Background: Hemorrhagic stroke is a life-threatening cerebrovascular disease, and early identification is crucial for timely clinical intervention. Microwave imaging is non-ionizing, portable, and low-cost, and thus has potential for pre-hospital and bedside screening; however, existing methods often suffer from limited reconstruction resolution, scarce data, and suboptimal information utilization when only a single modality is used. Methods: We propose a dual-channel, multi-input multimodal deep neural network for hemorrhagic stroke recognition, which jointly exploits complementary features from microwave images and time-domain waveforms and performs feature-level cross-modal fusion. A high-fidelity microwave brain simulation dataset is constructed for model training, and multiple temporal encoding strategies are systematically evaluated. Results: The proposed multimodal model achieves improved accuracy and stability compared with single-modality baselines and conventional approaches, demonstrating the benefit of cross-modal feature fusion for microwave-based hemorrhage recognition. Conclusions: Multimodal learning can enhance discrimination and robustness in microwave-based hemorrhage recognition, supporting its potential use for rapid, non-ionizing pre-hospital and bedside assessment. Full article

Chondroitin sulfate is a glycosaminoglycan polysaccharide, playing key roles in a plethora of physiopathological processes typical of higher animals. The position of sulfate groups within CS disaccharide subunits composing the polysaccharide chain is able to encode specific functional information. In order to expand such a “sulfation code”, access to non-natural CS variants and mimics thereof can be pursued. In this context, an interesting topic concerns phosphorylated analogs of CS polysaccharides, as the replacement of sulfate groups with phosphates can lead to unreported activities of phosphorylated CS. In light of this, the phosphorylation reaction of a microbial-sourced, unsulfated chondroitin polysaccharide with phosphoric acid is reported in the present study, testing different microwave irradiation conditions and comparing them with conventional heating procedures. The obtained products were subjected to a detailed characterization, in terms of chemical structure and hydrodynamic properties, by 1D- and 2D-NMR spectroscopy and HP-SEC-TDA analysis, respectively. The characterization study showed how different reaction conditions can not only influence the regioselectivity and degree of phosphorylation but also trigger the formation of phosphate diester functionalities acting as cross-linkers between polysaccharide chains. The results from the screening presented in this work could be interesting for any research devoted to the regioselective phosphorylation of a polysaccharide. Full article

Xinfeng County, Shaoguan City, Guangdong Province, China, is a typical vegetation-covered area that suffers from severe attenuation of rock and mineral spectral information in remote sensing images owing to dense vegetation. This situation limits the accuracy of traditional lithological mapping methods, making them unable to meet geological mapping demands under complex conditions, and thus necessitating a tailored lithological identification model. To address this issue, in this study, the penetration capability of microwave remote sensing (for extracting indirect textural features of lithology) was combined with the spectral superiority of hyperspectral remote sensing (for capturing lithological spectral features), resulting in a dual-branch deep-learning framework for lithological classification based on multisource remote sensing data. The framework independently extracts features from Sentinel-1 imagery and Gaofen-5 data, integrating three key modules: texture feature extraction, spatial–spectral feature extraction, and attention-based adaptive feature fusion, to realize deep and efficient fusion of heterogeneous remote sensing information. Ablation and comparative experiments were conducted to evaluate each module’s contribution. The results show that the dual-branch architecture effectively captures the complementary and discriminative characteristics of multimodal data, and that the encoder–decoder structure demonstrates strong robustness under complex conditions such as dense vegetation. The final model achieved 97.24% overall accuracy and 90.43% mean intersection-over-union score, verifying its effectiveness and generalizability in complex geological environments. The proposed multi-source remote sensing–based lithological classification model overcomes the limitations of single-source data by integrating indirect lithological texture features containing vegetation structural information with spectral features, thereby providing a viable approach for lithological mapping in vegetated regions. Full article

Quantum secure direct communication (QSDC) is convenient for the direct transmission of secure messages without requiring a prior key exchange by two participants, offering an elegant advantage in transmission security. The traditional implementations usually focus on the discrete-variable (DV) system, whereas its continuous-variable (CV) counterpart has attracted much attention due to its compatibility with existing optical infrastructure. In order to address its practical deployment in harsh environments, we propose a microwave-based scheme for the CV-QSDC that leverages entangled microwave quantum states through free-space channels in cryogenic environments. The two-step scheme is designed for the secure direct communication, where the classical messages can be encoded by using Gaussian modulation and then transmitted via displacement operations on microwave quantum states. The data processing procedures involve microwave entangled state generation, channel detection, parameter estimation, and so on. Simulation results demonstrate the feasibility of the microwave-based CV-QSDC, highlighting its potential for secure communication in integrated superconducting and solid-state quantum technologies. Full article

Microwave computational imaging (MCI) based on coded apertures does not rely on relative motion between the radar platform and the target, enabling forward-looking imaging. The performance of MCI depends on the computational methods and modulation of the coded aperture, particularly its design. However, current research methods treat the optimization of the coded aperture and computational imaging processing as independent tasks, with no unified framework to link these two aspects, limiting the potential for improving system performance. This paper proposes a novel deep learning-based MCI framework that jointly optimizes the coded aperture and image reconstruction process. Unlike traditional methods that decouple these two stages, our approach trains the sensing and reconstruction networks in an end-to-end fashion. The key novelty lies in constructing an end-to-end imaging network based on a convolutional neural network (CNN) where the coded aperture is modeled as a convolutional layer within the network. Physical constraints on the coded aperture are enforced by adding regularizers to the loss function. Simulation experiments demonstrate that under low signal-to-noise ratio (SNR) and low compression ratio conditions, the proposed method improves peak signal-to-noise ratio (PSNR) by 5 dB to 8 dB, enhances SSIM by 10% to 15%, and reduces relative imaging error by 0.5% to 1%. Full article

Rainfall intensity prediction based on commercial microwave link data has received significant attention in recent years due to the higher spatial resolution and lower energy consumption. However, the predictive performance is inferior to the model based on meteorological data by reason of the high noise in commercial microwave link data, further exacerbated by the imbalance in the number of samples across different rainfall intensities. Hence, a cost-sensitive rainfall intensity prediction model (CSRFP) is proposed to achieve better predictive performance in high-noise commercial microwave link data. First, the spatiotemporal scene information is encoded, and its weights are trained to provide the model with correlations between signal data from different stations, which helps the model to better capture potential patterns between the data and thus reduce the effect of noise. Next, the rainfall cross-entropy loss based on the rainfall distribution provides the model with the probability of different rainfall intensities occurring and back-calculates the signal attenuation at a specific rainfall intensity, assigning more reasonable weights to different samples considering signal attenuation, which makes the model cost-sensitive and can address the class imbalance problem. Extensive experiments are carried out on high-noise communication data and imbalanced rainfall data in Fuzhou. Compared to typical prediction methods such as RNN applied to rainfall and communication data, CSRFP improves Recall, Precision, AUC_ROC, AUC_PR and F₁ and Accuracy by approximately 19%, 37%, 8%, 22%, 30%, and 17%, respectively. Significantly, the model’s prediction accuracy for heavy rain with the smallest number of samples improves by about 13%. Full article

Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the estimation results. However, TC, as a typical chaotic system, often requires confidence intervals for intensity estimates in real-world emergency decision-making scenarios. Additionally, the use of multi-source image inputs contributes to the uncertainty of the model. Consequently, this study introduces a neural network (MTCIE) that utilizes multi-source satellite images to provide probabilistic estimates of TC intensity. The model utilizes infrared and microwave images from multiple satellites as inputs. It uses a dual-branch self-attention encoder to extract TC image features and provides uncertainty estimates for TC intensity. Furthermore, a dataset for estimating the intensity of multi-source TC remote sensing images (MTCID) is constructed through the registration of latitude, longitude, and time, along with data augmentation. The proposed method achieves a MAE of 7.42 kt in deterministic estimation, comparable to mainstream networks like TCIENet. In uncertain estimation, it outperforms methods like MC Dropout in the PICP metric, providing reliable probability estimates. This supports TC disaster emergency decision making, enhancing risk mitigation in real-world applications. Full article

Microwave imaging is emerging as an alternative modality to conventional medical diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced in the solution of the underlying inverse scattering problem, namely non-linearity and ill-posedness. In this paper, an innovative approach for a reliable and automated solution of the inverse scattering problem is presented, which combines a qualitative imaging technique and deep learning in a two-step framework. In the first step, the orthogonality sampling method is employed to process measurements of the scattered field into an image, which explicitly provides an estimate of the targets shapes and implicitly encodes information in their contrast values. In the second step, the images obtained in the previous step are fed into a neural network (U-Net), whose duty is retrieving the exact shape of the target and its contrast value. This task is cast as an image segmentation one, where each pixel is classified into a discrete set of permittivity values within a given range. The use of a reduced number of possible permittivities facilitates the training stage by limiting its scope. The approach was tested with synthetic data and validated with experimental data taken from the Fresnel database to allow a fair comparison with the literature. Finally, its potential for biomedical imaging is demonstrated with a numerical example related to microwave brain stroke diagnosis. Full article