Dynamic polarization fusion network (DPFN) for imaging in different scattering systems.

Deep learning has broad applications in imaging through scattering media. Polarization, as a distinctive characteristic of light, exhibits superior stability compared to light intensity within scattering media. Consequently, the de-scattering network trained using polarization is expected to achieve enhanced performance and generalization. For getting optimal outcomes in diverse scattering conditions, it makes sense to train expert networks tailored for each corresponding condition. Nonetheless, it is often unfeasible to acquire the corresponding data for every possible condition. And, due to the uniqueness of polarization, different polarization information representation methods have different sensitivity to different environments. As another of the most direct approaches, a generalist network can be trained with a range of polarization data from various scattering situations, however, it requires a larger network to capture the diversity of the data and a larger training set to prevent overfitting. Here, in order to achieve flexible adaptation to diverse environmental conditions and facilitate the selection of optimal polarization characteristics, we introduce a dynamic learning framework. This framework dynamically adjusts the weights assigned to different polarization components, thus effectively accommodating a wide range of scattering conditions. The proposed architecture incorporates a Gating Network (GTN) that efficiently integrates multiple polarization features and dynamically determines the suitable polarization information for various scenarios. Experimental result demonstrates that the network exhibits robust generalization capabilities across continuous scattering conditions.

Polarization motivating high-performance weak targets' imaging based on a dual-discriminator GAN.

High-level detection of weak targets under bright light has always been an important yet challenging task. In this paper, a method of effectively fusing intensity and polarization information has been proposed to tackle this issue. Specifically, an attention-guided dual-discriminator generative adversarial network (GAN) has been designed for image fusion of these two sources, in which the fusion results can maintain rich background information in intensity images while significantly completing target information from polarization images. The framework consists of a generator and two discriminators, which retain the texture and salient information as much as possible from the source images. Furthermore, attention mechanism is introduced to focus on contextual semantic information and enhance long-term dependency. For preserving salient information, a suitable loss function has been introduced to constrain the pixel-level distribution between the result and the original image. Moreover, the real scene dataset of weak targets under bright light has been built and the effects of fusion between polarization and intensity information on different weak targets have been investigated and discussed. The results demonstrate that the proposed method outperforms other methods both in subjective evaluations and objective indexes, which prove the effectiveness of achieving accurate detection of weak targets in bright light background.

Dynamically manipulating long-wave infrared polarized thermal radiation by a vanadium dioxide metasurface.

Dynamically manipulating the spectra and polarization properties of thermal radiation is the key to counter an infrared polarization imaging system (IPIS) under the different background environments. In this Letter, we propose a phase-change metasurface thermal emitter (PCMTE) composed of vanadium dioxide (VO2) dipole antenna arrays to dynamically manipulate polarized radiation spectra in the long-wave infrared (LWIR) region of 8-14 µm. During the thermally induced and reversible insulator-to-metal transition (IMT) in VO2, by simulating the LWIR images at different polarization angles for the PCMTE and background plates, the PCMTE can realize dynamically tunable LWIR camouflage; then, their degree of linear polarization (DoLP) can be calculated, which can demonstrate that the PCMTE can also achieve dynamically tunable LWIR polarization camouflage at the specific radiation angles and backgrounds. Our proposed PCMTE provides an effective scheme for adaptive IR polarization camouflage.

Robust orbital-angular-momentum-based underwater acoustic communication with dynamic modal decomposition method.

Recently, acoustic communication employing orbital angular momentum (OAM) opens another avenue for efficient data transmission in aquatic environments. Current topological charge (TC) detection of OAM beams relies on the orthogonality among different-order OAM beams. However, such strategy requires measurements of the complete azimuthal acoustic pressure, which inevitably reduces the efficiency and increases the bit error rate (BER). To address these challenges, this study proposes a modified dynamic modal decomposition (DMD) method by partially sampling the acoustic field for precise TC detection. Numerical simulations confirm the accuracy of this approach in extracting single or multiple TCs magnitudes within a partially sampled acoustic field. We theoretically compare the performance of the modified DMD approach with conventional orthogonal decoding method. Simulation results indicate that our modified DMD scheme exhibits lower BER under the same noise interference and is more robust to the array misalignment. This research introduces an efficient demodulation solution for acoustic OAM communication, offering potential benefits for simplifying receiver array design and enhancing long-distance underwater data transmission.

Research Progress on Router Devices for the OAM Optical Communication.

Vortex beams carrying orbital angular momentum (OAM) provide a new degree of freedom for light waves in addition to the traditional degrees of freedom, such as intensity, phase, frequency, time, and polarization. Due to the theoretically unlimited orthogonal states, the physical dimension of OAM is capable of addressing the problem of low information capacity. With the advancement of the OAM optical communication technology, OAM router devices (OAM-RDs) have played a key role in significantly improving the flexibility and practicability of communication systems. In this review, major breakthroughs in the OAM-RDs are summarized, and the latest technological standing is examined. Additionally, a detailed account of the recent works published on techniques related to the OAM-RDs has been categorized into five areas: channel multicasting, channel switching, channel filtering, channel hopping, and channel adding/extracting. Meanwhile, the principles, research methods, advantages, and disadvantages are discussed and summarized in depth while analyzing the future development trends and prospects of the OAM-RDs.

Self-attention module in a multi-scale improved U-net (SAM-MIU-net) motivating high-performance polarization scattering imaging.

Polarization imaging has outstanding advantages in the field of scattering imaging, which still encounters great challenges in heavy scattering media systems even though there are helps from deep learning technology. In this paper, we propose a self-attention module (SAM) in multi-scale improved U-net (SAM-MIU-net) for the polarization scattering imaging, which can extract a new combination of multidimensional information from targets effectively. The proposed SAM-MIU-net can focus on the stable feature carried by polarization characteristics of the target, so as to enhance the expression of the available features, and make it easier to extract polarization features which help to recover the detail of targets for the polarization scattering imaging. Meanwhile, the SAM's effectiveness has been verified in a series of experiments. Based on proposed SAM-MIU-net, we have investigated the generalization abilities for the targets' structures and materials, and the imaging distances between the targets and the ground glass. Experimental results demonstrate that our proposed SAM-MIU-net can achieve high-precision reconstruction of target information under incoherent light conditions for the polarization scattering imaging.

Transmission characteristics of vortex frozen waves in different obstacle channels.

The obstacle in a channel is a typical scenario for free-space optical (FSO) communications, however, it will destroy the information in channels, especially for the orbital angular momentum (OAM) multiplexing systems and cause performance degradation. Motivated by the feature of predefining intensity profile, here we propose to use frozen wave (FW) carrying OAM for the FSO communications to mitigate the influence of obstacles on the beam propagation. The key idea is to design the longitudinal intensity profile of FW to distribute the beam energy of the location where the obstacle exists over a large region and focus again on the central region after the obstacle for propagation. By analyzing the cases under different sizes, positions, and shapes of the obstacles with on-axis and off-axis scenarios, it has been demonstrated that the detection probability of OAM mode carried by FW can be improved by 0.35 and 0.15 in short-distance and long-distance transmission scenarios, respectively, when compared to that carried by Bessel-Gaussian beam. It demonstrates the FWs have great potential in the OAM-based FSO communications, especially for the obstacle channels.

TSMPN-PSI: high-performance polarization scattering imaging based on three-stage multi-pipeline networks.

Polarization imaging, which provides multidimensional information beyond traditional intensity imaging, has prominent advantages for complex imaging tasks, particularly in scattering environments. By introducing deep learning (DL) into computational imaging and sensing, polarization scattering imaging (PSI) has obtained impressive progresses, however, it remains a challenging but long-standing puzzle due to the fact that scattering medium can result in significant degradation of the object information. Herein, we explore the relationship between multiple polarization feature learning strategy and the PSI performances, and propose a new multi-polarization driven multi-pipeline (MPDMP) framework to extract rich hierarchical representations from multiple independent polarization feature maps. Based on the MPDMP framework, we introduce a well-designed three-stage multi-pipeline networks (TSMPN) architecture to achieve the PSI, named TSMPN-PSI. The proposed TSMPN-PSI comprises three stages: pre-processing polarization image for de-speckling, multiple polarization feature learning, and target information reconstruction. Furthermore, we establish a real-world polarization scattering imaging system under active light illumination to acquire a dataset of real-life scenarios for training the model. Both qualitative and quantitative experimental results show that the proposed TSMPN-PSI achieves higher generalization performance than other methods on three testing data sets refer to imaging distances, target structures, and target materials and their background materials. We believe that our work presents a new framework for the PSI and paves the way to its pragmatic applications.

Vision transformers motivating superior OAM mode recognition in optical communications.

Orbital angular momentum (OAM) has recently obtained tremendous research interest in free-space optical communications (FSO). During signal transmission within the free-space link, atmospheric turbulence (AT) poses a significant challenge as it diminishes the signal strength and introduce intermodal crosstalk, significantly reducing OAM mode detection accuracy. This issue directly impacts the performance of OAM-based communication systems and leads to a reduction in received information. To address this critical bottleneck of low mode recognition accuracy in OAM-based FSO-communications, a deep learning method based on vision transformers (ViT) is proposed for what we believe is for the first time. Designed carefully by numerous experts, the advanced self-attention mechanism of ViT captures more global information from the input image. To train the model, pretraining on a large dataset, named IMAGENET is conducted. Subsequently, we performed fine-tuning on our specific dataset, consisting of OAM beams that have undergone varying AT strengths. The computer simulation shows that based on ViT method, the multiple OAM modes can be recognized with a high accuracy (nearly 100%) under weak-to-moderate turbulence and with almost 98% accuracy even under long transmission distance with strong turbulence (C N2=1×10-14). Our findings highlight that leveraging ViT enables robust detection of complex OAM beams, mitigating the adverse effects caused by atmospheric turbulence.

I-DNAN6mA: Accurate Identification of DNA N6-Methyladenine Sites Using the Base-Pairing Map and Deep Learning.

The recent discovery of numerous DNA N6-methyladenine (6mA) sites has transformed our perception about the roles of 6mA in living organisms. However, our ability to understand them is hampered by our inability to identify 6mA sites rapidly and cost-efficiently by existing experimental methods. Developing a novel method to quickly and accurately identify 6mA sites is critical for speeding up the progress of its function detection and understanding. In this study, we propose a novel computational method, called I-DNAN6mA, to identify 6mA sites and complement experimental methods well, by leveraging the base-pairing rules and a well-designed three-stage deep learning model with pairwise inputs. The performance of our proposed method is benchmarked and evaluated on four species, i.e., Arabidopsis thaliana, Drosophila melanogaster, Rice, and Rosaceae. The experimental results demonstrate that I-DNAN6mA achieves area under the receiver operating characteristic curve values of 0.967, 0.963, 0.947, 0.976, and 0.990, accuracies of 91.5, 92.7, 88.2, 0.938, and 96.2%, and Mathew's correlation coefficient values of 0.855, 0.831, 0.763, 0.877, and 0.924 on five benchmark data sets, respectively, and outperforms several existing state-of-the-art methods. To our knowledge, I-DNAN6mA is the first approach to identify 6mA sites using a novel image-like representation of DNA sequences and a deep learning model with pairwise inputs. I-DNAN6mA is expected to be useful for locating functional regions of DNA.

A tunable infrared emitter based on phase-changing material GST for visible-infrared compatible camouflage with thermal management.

Visible-infrared compatible camouflage is important to increase the counter-detection ability of a target due to the fast development of detection systems. However, most of the previously reported visible-infrared compatible camouflage structures are not suitable when the temperature of targets and type of background environment change. In this paper, we propose a tunable infrared emitter composed of ZnS/Ge/Ag/Ge2Sb2Te5/Ag films and numerically demonstrate visible-infrared compatible camouflage and radiation heat dissipation. Firstly, the proposed infrared emitter can produce different structural colors as the thickness of the ZnS film changes, which can be applied to visible camouflage. Secondly, the crystallization fraction of the Ge2Sb2Te5 (GST) layer could help to engineer the average emissivity of the proposed infrared emitter, achieving tunable mid-infrared (MIR) camouflage, radiation heat dissipation, and long-infrared (LIR) camouflage in wavelength ranges of 3-5 µm, 5-8 µm, and 8-14 µm, respectively. Finally, we numerically demonstrate the visible camouflage and infrared camouflage for different application scenarios by using the simulated visible and infrared images. This work has promising application potential in visible-infrared compatible camouflage technology.

Compact all-dielectric metasurface for full polarization detection at the long-wavelength infrared region.

Metasurfaces have been extensively demonstrated in engineering and detection of polarization of light from the visible to terahertz regions. However, most of the previous metasurfaces for polarization detection are spatially divided into different parts, and each of the parts focuses on different polarization components, resulting in large metasurface size and hindering their integration development. In this paper, a compact all-dielectric metasurface is proposed and numerically demonstrated to achieve full polarization detection at the long-wavelength infrared region (LIR). First, we design the metasurface at a wavelength of 10 µm, which can converge incident beams to specific positions corresponding to different polarization states. In this design, the metasurface is based on an oblique alternant double-phase modulation method, which arranges meta-atoms with the ability to control as many as possible different polarizations in a limited region, ensuring the high efficiency of polarization detection while giving more freedom and flexibility to the metasurface. Second, the intensity distributions of the electric field of different polarization components are simulated at wavelengths of 9.4 µm and 10.5 µm, verifying the broadband performance of the proposed metasurface. The proposed method has potential applications in integrated multifunctional devices and multispectral polarization imaging.

Optimized sinusoidal patterns for high-performance computational ghost imaging.

Computational ghost imaging (CGI) can reconstruct scene images by two-order correlation between sampling patterns and detected intensities from a bucket detector. By increasing the sampling rates (SRs), imaging quality of CGI can be improved, but it will result in an increasing imaging time. Herein, in order to achieve high-quality CGI under an insufficient SR, we propose two types of novel sampling methods for CGI, to the best of our knowledge, cyclic sinusoidal-pattern-based CGI (CSP-CGI) and half-cyclic sinusoidal-pattern-based CGI (HCSP-CGI), in which CSP-CGI is realized by optimizing the ordered sinusoidal patterns through "cyclic sampling patterns," and HCSP-CGI just uses half of the sinusoidal pattern types of CSP-CGI. Target information mainly exists in the low-frequency region, and high-quality target scenes can be recovered even at an extreme SR of 5%. The proposed methods can significantly reduce the sampling number and real-time ghost imaging possible. The experiments demonstrate the superiority of our method over state-of-the-art methods both qualitatively and quantitatively.

Photonic spin Hall effect driven broadband multi-focus dielectric metalens.

The multi-focus metalens can couple the light into multiple channels in optical interconnections, which is beneficial to the development of planar, miniaturized, and integrated components. We propose broadband photonic spin Hall effect (PSHE) driven multi-focus metalenses, in which each nanobrick plays a positive role for all focal points. Three PSHE driven metalenses with four, six, and eight focal points have been designed and investigated, respectively. Under the incidences of left-/right-handed circularly polarized (LCP/RCP) light, these metalenses can generate regularly distributed two, three, and four RCP/LCP focal points, respectively. The uniformity of the focusing intensity has been investigated in detail by designing an additional four six-focus metalenses with different focus distributions. The uniqueness of these metalenses makes this design philosophy very attractive for applications in spin photonics, compact polarization detection, multi-imaging systems, and information processing systems.

High-performance scanning-mode polarization based computational ghost imaging (SPCGI).

Computational ghost imaging (CGI) uses preset patterns and single-pixel detection, breaking through the traditional form of point-to-point imaging. In this paper, based on the Monte Carlo model, a reflective polarization based CGI (PCGI) system has been proposed and constructed under the foggy environments. And the imaging performances of the PCGI at different optical distances have been investigated and analyzed quantitatively. When the targets and the background have a small difference in reflectivity, the difference of polarization characteristics between the targets and the background can help the CGI to remove the interference of scattering light and improve the imaging contrast. Besides, in order to further improve imaging efficiency, a scanning-mode polarization based CGI (SPCGI) has also been proposed, in which the combination of polarization characteristics and the scanning-mode plays an important role to improve the CGI's imaging efficiency and imaging quality.

Optically reconfigurable higher-order valley photonic crystals based on enhanced Kerr effect.

The reconfigurable higher-order topological states are realized in valley photonic crystals with enhanced optical Kerr nonlinearity. The inversion symmetry of the designed valley photonic crystal is broken due to the difference in optical responses between adjacent elements rather than their geometry structures. Therefore, by constructing photonic crystals with distinct topological phases, valley-dependent topological states can be realized, and their reconfigurability is demonstrated based on the Kerr effect. The investigated higher-order topological photonic crystals exhibit great robustness against the structural defects and inferior quality of pump introduced around the corner. Our work provides a new, to the best of our knowledge, platform for studying optical field manipulation and optical devices fabrication in the context of nonlinear higher-order topology.

Comparison of monoplanar and polyaxial screw fixation systems in percutaneous intermediate fixation for thoracolumbar fractures.

BACKGROUND: The newly developed monoplanar pedicle screws (MPPSs) can mobile in axial plane but fixed in the sagittal plane, which holds potential to combine ease of rod placement with sagittal plane strength theoretically. So far, few clinical studies focused on the outcomes of MPPSs for treatment of thoracolumbar fractures (TLFs). The aim of this study was to compare the efficacy of MPPSs to polyaxial pedicle screws (PAPSs) in percutaneous intermediate fixation of TLFs. METHODS: Seventy-eight patients who sustained TLFs without neurological deficits and underwent percutaneous intermediate fixation using MPPSs (40 patients) or PAPSs (38 patients) with a minimum 1-year follow-up were included in this study. The operation time, blood loss, local Cobb angle (LCA), vertebral wedge angle (VWA), anterior body height ratio (ABHR), visual analogue scale (VAS) and Oswestry Disability Index (ODI) were collected. RESULTS: No significant differences were observed in baseline demographics, clinical characteristics, operation time or blood loss between the two groups (P > 0.05). The postoperative LCA, VWA and ABHR were significantly corrected compared to these parameters preoperatively in both groups (#P < 0.05). The postoperative LCA, VWA and ABHR in the MPPS group were significantly better corrected than those in the PAPS group (*P < 0.05). Furthermore, the correction loss of LCA, VWA and ABHR in the MPPS group was significantly lower than that in the PAPS group (*P < 0.05). However, no significant difference in VAS and ODI scores was observed between the two groups. CONCLUSIONS: MPPSs showed similar efficiency as PAPSs in percutaneous intermediate fixation surgical procedures. More importantly, MPPSs achieved better radiological performance than PAPSs in the correction of TLFs and the prevention of correction loss.

Second harmonic generation enhancement and directional emission from topological corner state based on the quantum spin Hall effect.

Topological corner state has attracted much research interests since it does not obey the conventional bulk-edge correspondence and enables tightly confined light within small volumes. In this work, we demonstrate an enhanced second harmonic generation (SHG) from a topological corner state and its directional emission. To this end, we design an all-dielectric topological photonic crystal based on optical quantum spin Hall effect. In this framework, pseudospin states of photons, topological phase, and topological corner state are subsequently constructed by engineering the structures. It is shown that a high Q-factor of 3.66×1011 can be obtained at the corner state, showing strong confinement of light at the corner. Consequently, SHG is significantly boosted and manifests directional out-of-plane emission. More importantly, the enhanced SHG has robustness against a broad class of defects. These demonstrated properties offer practical advantages for integrated optical circuits.

Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion.

Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. The data set of laser welding process is often difficult to build and there is not enough experimental data, which hinder the applications of the data-driven laser welding defect detection method. In this paper, an intelligent welding defect diagnosis method based on auxiliary classifier generative adversarial networks (ACGAN) has been proposed. Firstly, a ten-class dataset consisting of 6467 samples, was constructed, which originate from the optical and thermal sensory parameters in the welding process. A new structured ACGAN network model is proposed to generate fake data similar to the true defect feature distributions. In addition, in order to make the difference between different defects categories more obvious after data expansion, a data filtering and data purification scheme was proposed based on ensemble learning and an SVM (support vector machine), which is used to filter the bad generated data. In the experiments, the classification accuracy can reach 96.83% and 85.13%, for the CNN (convolutional neural network) algorithm model and ACGAN model, respectively. However, the accuracy can further improve to 97.86% and 98.37% for the fusion models of ACGAN-CNN and ACGAN-SVM-CNN models, respectively. The results show that ACGAN can not only be used as an algorithm model for classification, but also be used to achieve superior real-time classification and recognition through data enhancement and multi-model fusion.

Multi-Beam Steering for 6G Communications Based on Graphene Metasurfaces.

As communication technology is entering the 6G era, a great demand for high-performance devices operating in the terahertz (THz) band has emerged. As an important part of 6G technology, indoor communication requires multi-beam steering and tracking to serve multi-users. In this paper, we have designed a graphene metasurface that can realize multi-beam steering for directional radiations. The designed metasurface consists of graphene ribbons, dielectric spacer, and metal substrate. By designing the graphene ribbons and controlling the applied voltage on them, we have obtained single-, double-, and triple-beam steering. In addition, we have also numerically calculated the far-field distributions of the steered multi-beam with a diffraction distance of 2 m. Our design has potential applications in future indoor directional 6G communications.