High-efficiency static speckle-suppression method based on a combination of beam splitting cavity and liquid-core fiber.

Current static speckle suppression methods have an extremely large system size and unsatisfactory performance. This study proposes a device called beam-splitting cavity (BSC) and establishes a model of speckle suppression based on the combination of BSC and a liquid-core fiber. Subsequently, a passive static speckle suppression system is constructed and the key factors affecting the speckle contrast are studied. Consequently, the speckle contrast was reduced from 30.2% to 3.1%, which is below the human-eye speckle-discrimination limit (<4%). The scheme consists entirely of passive optical elements, which are more applicable to projectors than the traditional static and dynamic speckle-suppression methods.

Data augmentation using continuous conditional generative adversarial networks for regression and its application to improved spectral sensing.

Machine learning-assisted spectroscopy analysis faces a prominent constraint in the form of insufficient spectral samples, which hinders its effectiveness. Meanwhile, there is a lack of effective algorithms to simulate synthetic spectra from limited samples of real spectra for regression models in continuous scenarios. In this study, we introduced a continuous conditional generative adversarial network (CcGAN) to autonomously generate synthetic spectra. The labels employed for generating the spectral data can be arbitrarily selected from within the range of labels associated with the real spectral data. Our approach effectively produced spectra using a small spectral dataset obtained from a self-interference microring resonator (SIMRR)-based sensor. The generated synthetic spectra were subjected to evaluation using principal component analysis, revealing an inability to discern them from the real spectra. Finally, to enhance the DNN regression model, these synthetic spectra are incorporated into the original training dataset as an augmentation technique. The results demonstrate that the synthetic spectra generated by CcGAN exhibit exceptional quality and significantly enhance the predictive performance of the DNN model. In conclusion, CcGAN exhibits promising potential in generating high-quality synthetic spectra and delivers a superior data augmentation effect for regression tasks.

Laser speckle reduction using polymer-stabilized liquid crystals doped with Ag nanowires.

Miniaturized and pure static devices are expected to be used in laser-imaging systems for speckle reduction. In this study, a pure static device based on polymer-stabilized liquid crystal (PSLC) doped with Ag nanowires was developed to effectively suppress laser speckles. The concentrations of the polymer and Ag nanowires in the PSLC were optimized, and then the PSLC devices were fabricated. A measurement system was set up to characterize the electro-optical properties of the fabricated PSLC devices. Subsequently, a laser projection system was built to demonstrate the speckle-reduction performance. Moreover, the degree of scattering and response time of the developed PSLC devices were investigated and discussed. A PSLC doped with 0.02 wt% Ag nanowires and 3 wt% polymer having a device size of 2 × 2 × 0.1 cm3 was demonstrated to produce a speckle-reduction efficiency of 51.4 % under very low driving voltages. The experimental results verified effectiveness and superiority of the developed speckle reduction method based on PSLC doped with Ag nanowires.

The Effect of Nitridation on Sputtering AlN on Composited Patterned Sapphire Substrate.

Here, we report on the epitaxial growth of GaN on patterned SiO2-covered cone-shaped patterned sapphire surfaces (PSS). Physical vapor deposition (PVD) AlN films were used as buffers deposited on the SiO2-PSS substrates. The gallium nitride (GaN) growth on these substrates at different alternating radio frequency (RF) power and nitridation times was monitored with sequences of scanning electron microscopy (SEM) and atomic force microscopy (AFM) imaging results. The SEM and AFM show the detail of the crystalline process from different angles. Our findings show that the growth mode varies according to the deposition condition of the AlN films. We demonstrate a particular case where a low critical alternating current (AC) power is just able to break SiO2 covalent bonds, enabling the growth of GaN on the sides of the patterns. Furthermore, we show that by using the appropriate nitridation condition, the photoluminescence (PL) integral and peak intensities of the blue light epi-layer were enhanced by more than 5% and 15%, respectively. It means the external quantum efficiency (EQE) of epitaxial structures is promoted. The screw dislocation density was reduced by 65% according to the X-ray diffraction (XRD) spectra.

Anti-Fatigue Glasses Based on Microprisms for Preventing Eyestrain.

Although microprisms have become an important medical means of strabismus treatment, related research concerning the design, fabrication, and testing of microprismatic glasses for preventing eyestrain has rarely been reported. In this study, the structure of microprismatic glasses for preventing eyestrain related to using electronic monitors, including computers and mobile phones, is introduced. A designing theory of anti-fatigue glasses with microprisms is developed. The fabrication technique and the process are described, and the performances of the fabricated microprisms are characterized. Finally, a compact testing system for the measurement of prismatic diopter is designed and constructed. This measuring system can be used not only for Fresnel microprisms, but also for other types of prisms. The measured results agree with our calculations. Although this study is focused on optimizing the objective prismatic diopter for anti-fatigue microprismatic glasses, 2.0-3.0 prismatic diopters (Δ) for each eye in the anti-fatigue glasses are suggested according to our experience on strabismus treatments. The clinical research for patients using the developed anti-fatigue glasses will be fully implemented in our further research to confirm the optimal subjective prismatic value.

Static laser speckle suppression using liquid light guides.

Static laser speckle suppression using multimode fibers has practical limitations as the technique requires an extremely long fiber to achieve an acceptable speckle contrast. An effective method based on liquid light guides was developed in this study to suppress laser speckle. In this study, a speckle simulation model of the liquid light guide was established for numerically calculating the speckle contrast without solving the analytical solution of the photon diffusion equation. The obtained simulation results were compared with the experimental results for the dependence of speckle contrast on the required length and numerical aperture with different liquid core types of liquid light guides. A speckle contrast of 12% and a speckle suppression efficiency of 5 was achieved at the end of a 2.4 m long liquid light guide. For the same fiber length, liquid light guides were found to suppress speckle more efficiently when compared to multimode fibers.

Expert recommendations based on link prediction during the COVID-19 outbreak.

Since the emergence of COVID-19, the number of infections has significantly increased. As of April 7, 8:00 am, the total number of global infections has already reached 1,338,415, with the number of deaths being 74,556. Medical experts from various countries have conducted relevant researches in their own fields and countries, and the development of an effective vaccine has been expected soon. Although some progress has been made in the development of therapeutic drugs and vaccines, interdisciplinary and cooperative studies are scarce. However, it is easy to form information islands and conduct repeated scientific research. To date, no therapeutic drug or vaccine for COVID-19 has been officially approved yet for marketing. In this article, the features of experts in cooperation networks, such as graph structure, context attribute, sequential co-occurrence probability, weight features and auxiliary features, are comprehensively analyzed. Based on this, a novel graph neural network + long short-term memory + generative adversarial network (GNN + LSTM + GAN) expert recommendation model based on link prediction is constructed to encourage cooperation among relevant experts in research social networks. Finding experts in related fields, establishing cooperative relations with them and achieving multinational and cross-field expert cooperation are significant to promote the development of therapeutic drugs and vaccines.

Real-Time Multiobject Tracking Based on Multiway Concurrency.

This paper explored a pragmatic approach to research the real-time performance of a multiway concurrent multiobject tracking (MOT) system. At present, most research has focused on the tracking of single-image sequences, but in practical applications, multiway video streams need to be processed in parallel by MOT systems. There have been few studies on the real-time performance of multiway concurrent MOT systems. In this paper, we proposed a new MOT framework to solve multiway concurrency scenario based on a tracking-by-detection (TBD) model. The new framework mainly focuses on concurrency and real-time based on limited computing and storage resources, while considering the algorithm performance. For the former, three aspects were studied: (1) Expanded width and depth of tracking-by-detection model. In terms of width, the MOT system can support the process of multiway video sequence at the same time; in terms of depth, image collectors and bounding box collectors were introduced to support batch processing. (2) Considering the real-time performance and multiway concurrency ability, we proposed one kind of real-time MOT algorithm based on directly driven detection. (3) Optimization of system level-we also utilized the inference optimization features of NVIDIA TensorRT to accelerate the deep neural network (DNN) in the tracking algorithm. To trade off the performance of the algorithm, a negative sample (false detection sample) filter was designed to ensure tracking accuracy. Meanwhile, the factors that affect the system real-time performance and concurrency were studied. The experiment results showed that our method has a good performance in processing multiple concurrent real-time video streams.

Seven-Layer Model in Complex Networks Link Prediction: A Survey.

Link prediction is the most basic and essential problem in complex networks. This study analyzes the observed topological, time, attributive, label, weight, directional, and symbolic features and auxiliary information to find the lack of connection and predict the future possible connection. For discussion and analysis of the evolution of the network, the network model is of great significance. In the past two decades, link prediction has attracted extensive attention from experts in various fields, who have published numerous high-level papers, but few combine interdisciplinary characteristics. This survey analyzes and discusses the existing link prediction methods. The idea of stratification is introduced into the classification system of link prediction for the first time and proposes the design idea of a seven-layer model, namely the network, metadata, feature classification, selection input, processing, selection, and output layers. Among them, the processing layer divides link prediction methods into similarity-based, probabilistic, likelihood, supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning methods. The input features, evaluation metrics, complex analysis, experimental comparisons, relative merits, common dataset and open-source implementations for each link prediction method are then discussed in detail. Through analysis and comparison, we found that the link prediction method based on graph structure features has better prediction performance. Finally, the future development direction of link prediction in complex networks is discussed.

Time-Domain Blind ICI Compensation in Coherent Optical FBMC/OQAM System.

A blind discrete-cosine-transform-based phase noise compensation (BD-PNC) is proposed to compensate the inter-carrier-interference (ICI) in the coherent optical offset-quadrature amplitude modulation (OQAM)-based filter-bank multicarrier (CO-FBMC/OQAM) transmission system. Since the phase noise sample can be approximated by an expansion of the discrete cosine transform (DCT) in the time-domain, a time-domain compensation model is built for the transmission system. According to the model, phase noise compensation (PNC) depends only on its DCT coefficients. The common phase error (CPE) compensation is firstly performed for the received signal. After that, a pre-decision is made on a part of compensated signals with low decision error probability, and the pre-decision results are used as the estimated values of transmitted signals to calculate the DCT coefficients. Such a partial pre-decision process reduces not only decision error but also the complexity of the BD-PNC method while keeping almost the same performance as in the case of the pre-decision of all compensated signals. Numerical simulations are performed to evaluate the performance of the proposed scheme for a 30 GBaud CO-FBMC/OQAM system. The simulation results show that its bit error rate (BER) performance is improved by more than one order of magnitude through the mitigation of the ICI in comparison with the traditional blind PNC scheme only aiming for CPE compensation.

Study on the Moving Target Tracking Based on Vision DSP.

The embedded visual tracking system has higher requirements for real-time performance and system resources, and this is a challenge for visual tracking systems with available hardware resources. The major focus of this study is evaluating the results of hardware optimization methods. These optimization techniques provide efficient utilization based on limited hardware resources. This paper also uses a pragmatic approach to investigate the real-time performance effect by implementing and optimizing a kernel correlation filter (KCF) tracking algorithm based on a vision digital signal processor (vision DSP). We examine and analyze the impact factors of the tracking system, which include DP (data parallelism), IP (instruction parallelism), and the characteristics of parallel processing of the DSP core and iDMA (integrated direct memory access). Moreover, we utilize a time-sharing strategy to increase the system runtime speed. These research results are also applicable to other machine vision algorithms. In addition, we introduced a scale filter to overcome the disadvantages of KCF for scale transformation. The experimental results demonstrate that the use of system resources and real-time tracking speed also satisfies the expected requirements, and the tracking algorithm with a scale filter can realize almost the same accuracy as the DSST (discriminative scale space tracking) algorithm under a vision DSP environment.

Prediction of Soil Organic Carbon in a New Target Area by Near-Infrared Spectroscopy: Comparison of the Effects of Spiking in Different Scale Soil Spectral Libraries.

Near-infrared (NIR) spectroscopy is widely used to predict soil organic carbon (SOC) because it is rapid and accurate under proper calibration. However, the prediction accuracy of the calibration model may be greatly reduced if the soil characteristics of some new target areas are different from the existing soil spectral library (SSL), which greatly limits the application potential of the technology. We attempted to solve the problem by building a large-scale SSL or using the spiking method. A total of 983 soil samples were collected from Zhejiang Province, and three SSLs were built according to geographic scope, representing the provincial, municipal, and district scales. The partial least squares (PLS) algorithm was applied to establish the calibration models based on the three SSLs, and the models were used to predict the SOC of two target areas in Zhejiang Province. The results show that the prediction accuracy of each model was relatively poor regardless of the scale of the SSL (residual predictive deviation (RPD) < 2.5). Then, the Kennard-Stone (KS) algorithm was applied to select 5 or 10 spiking samples from each target area. According to different SSLs and numbers of spiking samples, different spiked models were established by the PLS. The results show that the predictive ability of each model was improved by the spiking method, and the improvement effect was inversely proportional to the scale of the SSL. The spiked models built by combining the district scale SSL and a few spiking samples achieved good prediction of the SOC of two target areas (RPD = 2.72 and 3.13). Therefore, it is possible to accurately measure the SOC of new target areas by building a small-scale SSL with a few spiking samples.

Investigation of speckle suppression beyond human eye sensitivity by using a passive multimode fiber and a multimode fiber bundle.

A method of speckle suppression without any active device is expected for pico-projectors. The effectiveness of the passive method of speckle reduction using a single multimode fiber and a multimode fiber bundle was actually measured and theoretically analyzed. The dependences of the speckle contrast and speckle suppression coefficient on the parameters of multimode fiber and projection systems were investigated. Our results shown that the efficiency of speckle suppression was limited because only the radial direction of the objective lens aperture was used. An improvement using both of the radial and azimuthal directions of the objective lens aperture is required.

Multi-Parameter Sensing in a Multimode Self-Interference Micro-Ring Resonator by Machine Learning.

A universal multi-parameter sensing scheme based on a self-interference micro-ring resonator (SIMRR) is proposed. Benefit from the special intensity sensing mechanism, the SIMRR allows multimode sensing in a wide range of wavelengths but immune from frequency noise. To process the multiple mode spectra that are dependent on multiple parameters, we adopt the machine learning algorithm instead of massive asymptotic solutions of resonators. Employing the proposed multi-mode sensing approach, a two-parameter SIMRR sensor is designed. Assuming that two gases have different wavelength dependence of refractive indices, the feasibility and effectiveness of the two-parameter sensing strategy are verified numerically. Moreover, the dependence of parameter estimation accuracy on the laser intensity noises is also investigated. The numerical results indicate that our scheme of multi-parameter sensing in a multimode SIMRR holds great potential for practical high-sensitive sensing platforms compared with the single-mode sensing based on whispering gallery mode (WGM) resonators.

Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine.

Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900-1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE-ELM model performed better, having a correlation coefficient of prediction (rp), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE-ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization.

Nanostructured Amorphous Silicas Hydrophobized by Various Pathways.

Various nanostructured amorphous silicas [fumed silicas such as crude (A-300), hydro-compacted (cA-300, TS 100), and precipitated silica Syloid 244] were modified by different polydimethylsiloxanes such as PDMS5, PDMS100, PDMS200, PDMS1000, and PDMS12500 (the label numbers show the viscosity (η) values) using dimethyl carbonate (DMC) as a siloxane-bond-breaking reagent. In addition, hexamethyldisilazane was used to modify fumed silica cA-300. The nanocomposites were characterized using microscopy, infrared spectroscopy, thermodesorption, nitrogen adsorption-desorption, solid-state NMR spectroscopy, small-angle X-ray scattering, and zeta-potential methods. It was found that the morphological, textural, and structural characteristics of silicas grafted with PDMS depend strongly not only on the type and content of the polymers used but also on the organization of nonporous nanoparticles (NPNP) in secondary structures (aggregates of NPNP and agglomerated aggregates, ANPNP), as well on the reaction temperature (T r). Specifically, we determined that ANPNP with a macro/mesoporous character are favorable for the effective modification of the silicas studied with short polymers and no DMC addition but at higher temperatures or for a longer silicone polymer with the presence of DMC and at lower temperatures. In particular, the PDMS/DMC-modified silicas are of great interest from a practical point of view because they remain in a dispersed state with no strong compaction of the secondary structures after modification, and this corresponds to a better distribution of the modified nanoparticles in polymeric or other matrices.

Nanostructured Polymethylsiloxane/Fumed Silica Blends.

Polymethylsiloxane (PMS) and fumed silica, alone and in a blended form (1:1 w/w), differently pretreated, hydrated, and treated again, were studied using TEM and SEM, nitrogen adsorption-desorption, 1H MAS and 29Si CP/MAS NMR spectroscopy, infrared spectroscopy, and methods of quantum chemistry. Analysis of the effects of adding water (0-0.5 g of water per gram of solids) to the blends while they are undergoing different mechanical treatment (stirring with weak (~1-2 kg/cm2) and strong (~20 kg/cm2) loading) show that both dry and wetted PMS (as a soft material) can be grafted onto a silica surface, even with weak mechanical loading, and enhanced mechanical loading leads to enhanced homogenization of the blends. The main evidence of this effect is strong nonadditive changes in the textural characteristics, which are 2-3 times smaller than additive those expected. All PMS/nanosilica blends, demonstrating a good distribution of nanosilica nanoparticles and their small aggregates in the polymer matrix (according to TEM and SEM images), are rather meso/microporous, with the main pore-size distribution peaks at R > 10 nm in radius and average values of 18-25 nm. The contributions of nanopores (R < 1 nm), mesopores (1 nm < R < 25 nm), and macropores (25 nm < R < 100 nm), which are of importance for studied medical sorbents and drug carriers, depend strongly on the types of the materials and treatments, as well the amounts of water added. The developed technique (based on small additions of water and controlled mechanical loading) allows one to significantly change the morphological and textural characteristics of fumed silica (hydrocompaction), PMS (drying-wetting-drying), and PMS/A-300 blends (wetting-drying under mechanical loading), which is of importance from a practical point of view.

A 29Si, 1H, and 13C Solid-State NMR Study on the Surface Species of Various Depolymerized Organosiloxanes at Silica Surface.

Three poly(organosiloxanes) (hydromethyl-, dimethyl-, and epoxymethylsiloxane) of different chain lengths and pendant groups and their mixtures of dimethyl (DMC) or diethyl carbonates (DEC) were applied in the modification of fumed silica nanoparticles (FSNs). The resulting modified silicas were studied in depth using 29Si, 1H, and 13C solid-state NMR spectroscopy, elemental analysis, and nitrogen adsorption-desorption (BET) analysis. The obtained results reveal that the type of grafting, grafting density, and structure of the grafted species at the silica surface depend strongly on the length of organosiloxane polymer and on the nature of the "green" additive, DMC or DEC. The spectral changes observed by solid-state NMR spectroscopy suggest that the major products of the reaction of various organosiloxanes and their DMC or DEC mixtures with the surface are D (RR'Si(O0.5)2) and T (RSi(O0.5)3) organosiloxane units. It was found that shorter methylhydro (PMHS) and dimethylsiloxane (PDMS) and their mixtures with DMC or DEC form a denser coverage at the silica surface since SBET diminution is larger and grafting density is higher than the longest epoxymethylsiloxane (CPDMS) used for FSNs modification. Additionally, for FSNs modified with short organosiloxane PMHS/DEC and also medium organosiloxane PDMS/DMC, the dense coverage formation is accompanied by a greater reduction of isolated silanols, as shown by solid-state 29Si NMR spectroscopy, in contrast to reactions with neat organosiloxanes. The surface coverage at FSNs with the longest siloxane (CPDMS) greatly improves with the addition of DMC or DEC. The data on grafting density suggest that molecules in the attached layers of FSNs modified with short PMHS and its mixture of DMC or DEC and medium PDMS and its mixture of DMC form a "vertical" orientation of the grafted methylhydrosiloxane and dimethylsiloxane chains, in contrast to the reaction with PDMS/DEC and epoxide methylsiloxane in the presence of DMC or DEC, which indicates a "horizontal" chain orientation of the grafted methyl and epoxysiloxane molecules. This study highlights the major role of solid-state NMR spectroscopy for comprehensive characterization of solid surfaces.

Multidimensional vector quantization-based fast statistical estimation in compressed digitalized radio-over-fiber systems.

A multidimensional vector quantization-based fast statistical-estimation (VQ-FSE) algorithm is proposed to enhance data compression performance in digitalized radio over fiber (D-RoF) systems. The original samples with Gaussian distribution are first transformed into these with uniform distribution via companding transformation. After the companding transformation operation, the signal vector is constructed by grouping multiple samples in a certain way so that there is little correlation among them. The constructed signal vector may follow approximately multidimensional uniform distribution, and then multidimensional uniform quantization can be easily carried out, where the complex optimized process in nonuniform quantization is not required. For the proposed two-dimensional (2D) VQ-FSE algorithm, the proposed scheme is numerically verified in a 20 km D-RoF system with 2 Gbit/s RF wireless signal. Compared with the scalar-quantization-based FSE algorithm, its compression ratio is significantly enhanced. In comparison to the 2D k-means-clustering-based VQ algorithm, the proposed scheme shares a similar compression ratio and offers lower computational complexity. Therefore, the proposed algorithm has the ability to provide better compression and lower complexity for the digitized D-RoF system when the original sample follows Gaussian distribution.

Optical plasmon nanostrip probe as an effective ultrashort pulse delivery system.

In this paper, we analyze the ultrafast temporal and spectral responses of optical fields in tapered and metalized optical fibers (MOFs) and optical plasmon nanostrip probes (NPs). Computational experiment shows that output pulses of the NPs are virtually unchanged in shape and duration for input pulses with a duration of >1 fs and are not sensitive to changes in the parameters of the probe (such as convergence angle and taper length), while local enhancement of the electric field intensity reaches 300 times at the NP apex. Compared with the NPs, MOFs lead to significant output pulse distortions, even for input pulses with a duration of 10 fs. In addition, the temporal response at the MOF apex is critically sensitive to changes in MOF parameters and cannot provide any significant local enhancement of the electric field. These findings reveal the high potential of optical plasmon nanostrip probes as an ultrashort pulse delivery system to nanometer-size areas and indicate that its usage can be promising for a wide variety of techniques studying ultrafast processes in nanoscopic volumes.