Dissociable contributions of the hippocampus and orbitofrontal cortex to representing task space in a social context.

The hippocampus (HC) and the orbitofrontal cortex (OFC) jointly encode a map-like representation of a task space to guide behavior. It remains unclear how the OFC and HC interact in encoding this map-like representation, though previous studies indicated that both regions have different functions. We acquired the functional magnetic resonance imaging data under a social navigation task in which participants interacted with characters in a two-dimensional "social space." We calculate the social relationships between the participants and characters and used a drift-diffusion model to capture the inner process of social interaction. Then we used multivoxel pattern analysis to explore the brain-behavior relationship. We found that (i) both the HC and the OFC showed higher activations during the selective trial than the narrative trial; (ii) the neural pattern of the right HC was associated with evidence accumulation during social interaction, and the pattern of the right lateral OFC was associated with the social relationship; (iii) the neural pattern of the HC can decode the participants choices, while the neural pattern of the OFC can decode the task information about trials. The study provided evidence for distinct roles of the HC and the OFC in encoding different information when representing social space.

Neural substrates of affective temperaments: An intersubject representational similarity analysis to resting-state functional magnetic resonance imaging in nonclinical subjects.

Previous research has suggested that certain types of the affective temperament, including depressive, cyclothymic, hyperthymic, irritable, and anxious, are subclinical manifestations and precursors of mental disorders. However, the neural mechanisms that underlie these temperaments are not fully understood. The aim of this study was to identify the brain regions associated with different affective temperaments. We collected the resting-state functional magnetic resonance imaging (fMRI) data from 211 healthy adults and evaluated their affective temperaments using the Temperament Evaluation of Memphis, Pisa, Paris and San Diego Autoquestionnaire. We used intersubject representational similarity analysis to identify brain regions associated with each affective temperament. Brain regions associated with each affective temperament were detected. These regions included the prefrontal cortex, anterior cingulate cortex (ACC), precuneus, amygdala, thalami, hippocampus, and visual areas. The ACC, lingual gyri, and precuneus showed similar activity across several affective temperaments. The similarity in related brain regions was high among the cyclothymic, irritable, and anxious temperaments, and low between hyperthymic and the other affective temperaments. These findings may advance our understanding of the neural mechanisms underlying affective temperaments and their potential relationship to mental disorders and may have potential implications for personalized treatment strategies for mood disorders.

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.

Near-field coupling between topological corner states.

In this Letter, we investigate the near-field coupling between topological corner states. As a proof of concept, we build a higher-order topological photonic structure with a square lattice, based on the 2D Su-Schrieffer-Heeger (SSH) model. It is demonstrated that the topological corner state can be hosted at a corner via engineering its two boundaries, whereupon the near-field coupling between two corner states is investigated by bringing them close together. Numerical and theoretical results show that the near-field coupling between two corner states results in hybridized local resonances and significant enhancement of density of states, which are similar to the plasmonic resonances and Mie resonances. Moreover, the extraordinary advantage of the coupled corner states is verified via enhancing third-harmonic generation. Our results may provide insight into studying topological photonics with multimodes as well as an effective approach for manipulation of light.

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.

Enhanced river suspended sediment concentration identification via multimodal video image, optical flow, and water temperature data fusion.

Monitoring suspended sediment concentration (SSC) in rivers is pivotal for water quality management and sustainable river ecosystem development. However, achieving continuous and precise SSC monitoring is fraught with challenges, including low automation, lengthy measurement processes, and high cost. This study proposes an innovative approach for SSC identification in rivers using multimodal data fusion. We developed a robust model by harnessing colour features from video images, motion characteristics from the Lucas-Kanade (LK) optical flow method, and temperature data. By integrating ResNet with a mixed density network (MDN), our method fused the image and optical flow fields, and temperature data to enhance accuracy and reliability. Validated at a hydropower station in the Xinjiang Uygur Autonomous Region, China, the results demonstrated that while the image field alone offers a baseline level of SSC identification, it experiences local errors under specific conditions. The incorporation of optical flow and water temperature information enhanced model robustness, particularly when coupling the image and optical flow fields, yielding a Nash-Sutcliffe efficiency (NSE) of 0.91. Further enhancement was observed with the combined use of all three data types, attaining an NSE of 0.93. This integrated approach offers a more accurate SSC identification solution, enabling non-contact, low-cost measurements, facilitating remote online monitoring, and supporting water resource management and river water-sediment element monitoring.

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.

Shipborne variable-FOV, dual-wavelength, polarized ocean lidar: design and measurements in the Western Pacific.

For the requirement of high-precision vertical profile of the polarization and optical properties of natural seawater, a ship-borne variable-FOV, dual-wavelength, polarized ocean lidar system is designed to obtain the volume linear depolarization ratio (VDR), color ratio and optical parameter profiles of seawater. With the high signal-to-noise ratio, which benefits from the high power (355 nm with 120 mJ, 532 nm with 200 mJ) solid-state laser and a photon counting recorder with a sampling rate of 1 GHz, the attenuated backscattered signal of seawater in the western Pacific campaign reaches to the depth of 50 m, where a plankton layer presents. The receiver of lidar is capable of switching to wide and narrow field of view (FOV), respectively, to obtain the lidar attenuation coefficient Klidar, which is in good agreement with the beam attenuation coefficient of seawater c with a narrow FOV and diffuse attenuation coefficient Kd with a wide FOV. Besides, the Klidar, and the VDR, at two wavelengths of 355 nm and 532 nm are compared to explore the possibility of multi-wavelength of laser application in the ocean lidar. The VDR and the color ratio profiles have a desirable correlation with the in-situ measurement of chlorophyll a (Chla) and chromophoric dissolved organic matter (CDOM) profiles, respectively. With the combination of the Klidar, the VDR and the color ratio profiles, measured in different regions and time periods during the campaign, the multi-wavelength and polarization lidar shows its potential to explore various ocean compositions, such as the ocean particles size shape, the species and vertical migration characteristics of planktons, and the profile distribution of the ocean compositions.

Ultra-thin 2-bit anisotropic Huygens coding metasurface for terahertz wave manipulation.

In this work, we design an ultrathin 2-bit anisotropic Huygens coding metasurface (AHCM) composed by bilayer metallic square-ring structures for flexible manipulation of the terahertz wave. Based on the polarized-dependent components of electric surface admittance and magnetic surface impedance, we confirm that both the electric and magnetic resonances on coding meta-atoms are excited, so as to provide a full phase coverage and significantly low reflection. By encoding the elements with distinct coding sequences, the x- and y-polarized incident waves are anomalously refracted into opposite directions. More uniquely, we also demonstrate that the designed AHCM can be utilized as a transmission-type quarter-wave plate. The proposed metasurface paves a new way toward multifunctional terahertz wavefront manipulation.

Tunable dual-band metamaterial absorber in the infrared range based on split-ring-groove array.

In this paper, we present a tunable dual-band perfect metamaterial absorber working in the infrared band by integrating a metallic split-ring-groove resonator array with a liquid crystal (LC) layer atop a metal substrate. By varying the height of the central nanodisks, the absorptivity of the dual-band absorption peaks can be simultaneously adjusted. The dual-band resonance frequencies of the proposed absorber exhibit continuous tunability by adjusting the refractive index of the LC, which can be controlled by applying external voltage. The mechanism of the perfect absorption is attributed to the gap plasmonic resonance coupling regime. The presented absorber exhibits good tolerance to incidence angles up to 60° and shows polarization dependent performance, which may offer promising applications in sensing, modulator, and optical absorption switching in the infrared regime.

All-dielectric bifunctional polarization converter with high transmission efficiency in near-infrared region.

In this paper, we demonstrate a polarization control device with different functions for oppositely propagating directions by a two-layer twisted silicon column array separated by a silica layer. The proposed structure can rotate the electric field directions of polarized linear wave backwards by 45º and serve as a linear-to-circular converter for the polarized linear wave forwards . The physical mechanism is discussed by the Jones matrix, and the numerical results show that the maximum transmissions ${ \gt }{0.9}$>0.9 for the two functions of the proposed structure are achieved in the near-infrared region. The high transmission originates from the all-dielectric materials, which is a major advance compared with previously reported bifunctional converters. The proposed simply shaped device with high transmission efficiency has potential applications in optical imaging, sensing, etc.

Silicon Metasurfaces for Third Harmonic Geometric Phase Manipulation and Multiplexed Holography.

Nonlinear wavefront control is a crucial requirement in realizing nonlinear optical applications with metasurfaces. Numerous aspects of nonlinear frequency conversion and wavefront control have been demonstrated for plasmonic metasurfaces. However, several disadvantages limit their applicability in nonlinear nanophotonics, including high dissipative loss and low optical damage threshold. In contrast, it has been shown that metasurfaces made of high-index dielectrics can provide strong nonlinear responses. Regardless of the recent progress in nonlinear optical processes using all-dielectric nanostructures and metasurfaces, much less advancement has been made in realizing a full wavefront control directly with the generation process. Here, we demonstrate the nonlinear wavefront control for the third-harmonic generation with a silicon metasurface. We use a Pancharatnam-Berry phase approach to encode phase gradients and holographic images on nanostructured silicon metasurfaces. We experimentally demonstrate the polarization-dependent wavefront control and the reconstruction of an encoded hologram at the third-harmonic wavelength with high fidelity. Further, we show that holographic multiplexing is possible by utilizing the polarization states of the third harmonic generation. Our approach eases design and fabrication processes and paves the way to an easy to use toolbox for nonlinear optical wavefront control with all-dielectric metasurfaces.

Aircraft wake vortex and turbulence measurement under near-ground effect using coherent Doppler lidar.

This paper evaluates the wake vortex characteristics using pulsed coherent Doppler lidar (PCDL) under near-ground effect (NGE). A wake vortex visualization demonstrator (V2D) is developed in order to visualize wake vortex in real-time. The combination of radial velocity distribution and FFT spectrum characterization are used to identify the core position of wake vortex. The velocity envelope and Burnham-Hallock model correction are used to retrieve the circulation of wake vortex under NGE. The circulation error, which is caused by PCDL scanning mode, is simulated and corrected. To investigate the dissipation rate's effect on wake vortex in real atmosphere, the cross wind and atmospheric turbulence are concurrently retrieved from the same measurement of wake vortex by using structure function. The statistics of wake vortex parameters are analyzed, based on the measurement campaign at Beijing Capital International Airport (BCIA) in 2017.

Start from Scratch: A Crowdsourcing-Based Data Fusion Approach to Support Location-Aware Applications.

As one of the most important breakthroughs for modern transportation, the indoor location-based technology has been gradually penetrating into our daily lives and underlines the foundation of the Internet of Things (IoT). To improve the positioning accuracy and efficiency, crowdsourcing has been widely applied in indoor localization in recent years. However, the crowdsourced data can hardly be fused easily to enable usable applications for the reason that the data are collected by different users, in different locations, at different times, with different noises and distortions. Although different data fusing methods have been implemented in different crowdsourcing services, we find that they may not fully leverage the data collected from multiple dimensions that can potentially lead to a better fusion results. In order to address this problem, we propose a more general solution, which can fuse the multi-dimensional crowdsourced data together and align them with the consistent time and location stamps, by using the features of the sensory data only, and thus build high quality crowdsourcing services from the raw data samplings collected from the environment. Finally, we conduct extensive evaluations and experiments using different commercial devices to validate the effectiveness of the method we proposed.

Enhance field water-color measurements with a Secchi disk and its implication for fusion of active and passive ocean-color remote sensing.

Inversion of the total absorption (a) and backscattering coefficients of bulk water through a fusion of remote sensing reflectance (Rrs) and Secchi disk depth (ZSD) is developed. An application of such a system to a synthesized wide-range dataset shows a reduction of ∼3 folds in the uncertainties of inverted a(λ) (in a range of ∼0.01-6.8 m-1) from Rrs(λ) for the 350-560 nm range. Such a fusion is further proposed to process concurrent active (ocean LiDAR) and passive (ocean-color) measurements, which can lead to nearly "exact" analytical inversion of an Rrs spectrum. With such a fusion, it is found that the uncertainty in the inverted total a in the 350-560 nm range could be reduced to ∼2% for the synthesized data, which can thus significantly improve the derivation of a coefficients of other varying components. Although the inclusion of ZSD places an extra constraint in the inversion of Rrs, no apparent improvement over the quasi-analytical algorithm (QAA) was found when the fusion of ZSD and Rrs was applied to a field dataset, which calls for more accurate determination of the absorption coefficients from water samples.

Doppler lidar investigation of wind turbine wake characteristics and atmospheric turbulence under different surface roughness.

Four field experiments based on Pulsed Coherent Doppler Lidar with different surface roughness have been carried out in 2013-2015 to study the turbulent wind field in the vicinity of operating wind turbine in the onshore and offshore wind parks. The turbulence characteristics in ambient atmosphere and wake area was analyzed using transverse structure function based on Plane Position Indicator scanning mode. An automatic wake processing procedure was developed to determine the wake velocity deficit by considering the effect of ambient velocity disturbance and wake meandering with the mean wind direction. It is found that the turbine wake obviously enhances the atmospheric turbulence mixing, and the difference in the correlation of turbulence parameters under different surface roughness is significant. The dependence of wake parameters including the wake velocity deficit and wake length on wind velocity and turbulence intensity are analyzed and compared with other studies, which validates the empirical model and simulation of a turbine wake for various atmosphere conditions.

Wind turbine wake visualization and characteristics analysis by Doppler lidar.

Wind power generation is growing fast as one of the most promising renewable energy sources that can serve as an alternative to fossil fuel-generated electricity. When the wind turbine generator (WTG) extracts power from the wind, the wake evolves and leads to a considerable reduction in the efficiency of the actual power generation. Furthermore, the wake effect can lead to the increase of turbulence induced fatigue loads that reduce the life time of WTGs. In this work, a pulsed coherent Doppler lidar (PCDL) has been developed and deployed to visualize wind turbine wakes and to characterize the geometry and dynamics of wakes. As compared with the commercial off-the-shelf coherent lidars, the PCDL in this work has higher updating rate of 4 Hz and variable physical spatial resolution from 15 to 60 m, which improves its capability to observation the instantaneous turbulent wind field. The wind speed estimation method from the arc scan technique was evaluated in comparison with wind mast measurements. Field experiments were performed to study the turbulent wind field in the vicinity of operating WTGs in the onshore and offshore wind parks from 2013 to 2015. Techniques based on a single and a dual Doppler lidar were employed for elucidating main features of turbine wakes, including wind velocity deficit, wake dimension, velocity profile, 2D wind vector with resolution of 10 m, turbulence dissipation rate and turbulence intensity under different conditions of surface roughness. The paper shows that the PCDL is a practical tool for wind energy research and will provide a significant basis for wind farm site selection, design and optimization.

Full-color hologram using spatial multiplexing of dielectric metasurface.

In this Letter, we demonstrate theoretically a full-color hologram using spatial multiplexing of dielectric metasurface for three primary colors, capable of reconstructing arbitrary RGB images. The discrete phase maps for the red, green, and blue components of the target image are extracted through a classical Gerchberg-Saxton algorithm and reside in the corresponding subcells of each pixel. Silicon nanobars supporting narrow spectral response at the wavelengths of the three primary colors are employed as the basic meta-atoms to imprint the Pancharatnam-Berry phase while maintaining minimum crosstalk between different colors. The reconstructed holographic images agree well with the target images making it promising for colorful display.