Butler matrix enabled multi-beam optical phased array for two-dimensional beam-steering and ranging

Based on the wavelength transparency of the Butler matrix(BM)beamforming network,we demonstrate a multibeam optical phased array(MOPA)with an emitting aperture composed of grating couplers at a 1.55μm pitch for wavelength-assisted two-dimensional beam-steering.The device is capable of simultaneous multi-beam operation in a field of view(FOV)of 60°×8°in the phased-array scanning axis and the wavelength-tuning scanning axis,respectively.The typical beam divergence is about 4°on both axes.Using multiple linearly chirped lasers,multibeam frequency-modulated continuous wave(FMCW)ranging is realized with an average ranging error of 4 cm.A C-shaped target is imaged for proof-of-concept 2D scanning and ranging.

A Network-Level Connected Autonomous Driving Evaluation Platform Implementing C-V2X Technology

With the emerging connected autonomous driving paradigm,more advanced applications leveraging vehicular communications are drawing tremendous attentions.In order to analyze the feasibility and performance of these applications,it is necessary to build an evaluation platform that jointly considers vehicular communication,road traffic and vehicle dynamics.This article describes our recent progress on network-level autonomous driving simulator based on the Cellular-Vehicle-to-Everything(C-V2X)protocol,and a joint platform combined with SUMO and CARLA simulators for evaluating road traffic and vehicle dynamics.To demonstrate its effectiveness,this article implements a hybrid multi-intersection scheduling scheme on the platform,and shows the advantages of the scheme in terms of traffic efficiency and fault tolerance.A remote driving application based on CARLA,wherein the interplay between communication and computation is also investigated.

Distortion Function for Emoji Image Steganography

Nowadays,emoji image is widely used in social networks.To achieve covert communication in emoji images,this paper proposes a distortion function for emoji images steganography.The profile of image content,the intra-and inter-frame correlation are taken into account in the proposed distortion function to fit the unique properties of emoji image.The three parts are combined together to measure the risks of detection due to the modification on the cover data.With the popular syndrome trellis coding(STC),the distortion of stego emoji image is minimized using the proposed distortion function.As a result,less detectable artifacts could be found in the stego images.Experimental results show that the proposed distortion function performs much higher undetectability than current state-of-the-art distortion function HILL which is designed for natural image.

Observation of 550 MHz passively harmonic mode-locked pulses at L-band in an Er-doped fiber laser using carbon nanotubes film

We demonstrate a passively harmonic mode-locked（PHML） fiber laser operating at the L-band using carbon nanotubes polyvinyl alcohol（CNTs-PVA） film. Under suitable pump power and an appropriate setting of the polarization controller（PC）, the 54^（th） harmonic pulses at the L-band are generated with the side mode suppression ratio（SMSR） better than 44 dB and a repetition frequency of 503.37 MHz. Further increasing the pump power leads to a higher frequency of 550 MHz with compromised stability of 38.5 dB SMSR. To the best of our knowledge, this is the first demonstration on the generation of L-band PHML pulses from an Er-doped fiber laser based on CNTs.

Research on red tide occurrence forecast method based on deep learning

As a marine disaster,red tides have a serious impact on marine fisheries,ecology,economy,human production and life.Red tides have been widely concerned by researchers for a long time.However,due to its complex formation mechanism,red tide forecasting is extremely challenging.Aiming at addressing problem of red tide forecasting,this paper collects the marine monitoring data before and after the occurrence of red tide in Xiamen sea area,and analyzes the correlation between multiple environmental factors and the red tide occurrence by combining the methods of Pearson correlation coefficient,Scatter matrix,and multiple correlation coefficient.The fusion method of LSTM and CNN based on deep learning are applied to mine the temporal dependence of environmental factors and find the local features of sequence data,then predict the occurrence of red tides.In the Xiamen No.1 and Xiamen No.2 datasets,the RMSE and MAE errors of this method are reaching 0.5218 and 0.5043,respectively.The forecast probability of red tide occurrence was further determined through the collaborative comparison model.The final forecast accuracy of the two datasets is 67.58%and 63.49%,respectively.This study provides exploratory experience for the analysis and forecasting of red tides,which proves the feasibility of applying deep learning methods to red tide forecasting.

Predicting visual acuity with machine learning in treated ocular trauma patients

AIM:To predict best-corrected visual acuity(BCVA)by machine learning in patients with ocular trauma who were treated for at least 6mo.METHODS:The internal dataset consisted of 850 patients with 1589 eyes and an average age of 44.29y.The initial visual acuity was 0.99 log MAR.The test dataset consisted of 60 patients with 100 eyes collected while the model was optimized.Four different machine-learning algorithms(Extreme Gradient Boosting,support vector regression,Bayesian ridge,and random forest regressor)were used to predict BCVA,and four algorithms(Extreme Gradient Boosting,support vector machine,logistic regression,and random forest classifier)were used to classify BCVA in patients with ocular trauma after treatment for 6mo or longer.Clinical features were obtained from outpatient records,and ocular parameters were extracted from optical coherence tomography images and fundus photographs.These features were put into different machine-learning models,and the obtained predicted values were compared with the actual BCVA values.The best-performing model and the best variable selected were further evaluated in the test dataset.RESULTS:There was a significant correlation between the predicted and actual values[all Pearson correlation coefficient(PCC)>0.6].Considering only the data from the traumatic group(group A)into account,the lowest mean absolute error(MAE)and root mean square error(RMSE)were 0.30 and 0.40 log MAR,respectively.In the traumatic and healthy groups(group B),the lowest MAE and RMSE were 0.20 and 0.33 log MAR,respectively.The sensitivity was always higher than the specificity in group A,in contrast to the results in group B.The classification accuracy and precision were above 0.80 in both groups.The MAE,RMSE,and PCC of the test dataset were 0.20,0.29,and 0.96,respectively.The sensitivity,precision,specificity,and accuracy of the test dataset were 0.83,0.92,0.95,and 0.90,respectively.CONCLUSION:Predicting BCVA using machine-learning models in patients with treated ocular trauma is accurate and helpful in the identification of visual dysfunction.

Advanced modulation formats for underwater visible light communications [Invited]

In this paper, we present a detailed comparison of applying three advanced modulation formats including carrierless amplitude and phase modulation(CAP), orthogonal frequency division multiplexing(OFDM), and discrete Fourier transform spread orthogonal frequency division multiplexing(DFT-S OFDM) in underwater visible light communication(UVLC) systems. Cascaded post-equalization schemes are suggested to compensate the system impairments. For the first time, a two-level post-equalizer is presented to mitigate the nonlinear effect and improve the system performance of UVLC. The first post-equalization is based on a novel recursive least square Volterra. These modulation formats are all experimentally demonstrated with corresponding digital signal processing(DSP) algorithms. The experimental results show that single carrier modulations including CAP and DFT-S OFDM can outperform OFDM. Our experiment results show that up to 3 Gb/s over a 1.2 m underwater visible light transmission can be achieved by using DFT-S OFDM 64 QAM and CAP-64. The measured bit error rate is well under the hard decision-forward error correction(HD-FEC) threshold of 3.8 × 10^(-3).

Visible light communication for Vehicle to Everything beyond 1 Gb/s based on an LED car headlight and a 2 × 2 PIN array

Visible light communication(VLC) shows great potential in Internet of Vehicle applications. A single-input multi-output VLC system for Vehicle to Everything is proposed and demonstrated. A commercial car headlight is used as transmitter. With a self-designed 2 × 2 positive-intrinsic-negative(PIN) array, four independent signals are received and equalized by deep-neural-network post-equalizers, respectively. Maximum-ratio combining brings high signal-to-noise ratio and data rate gain. The transmission data rate reaches 1.25 Gb/s at 1 m and exceeds 1 Gb/s at 4 m. To the best of our knowledge, it is the first-time demonstration of beyond 1 Gb/s employing a commercial car headlight.

Explosive synchronization of multi-layer complex networks based on star connection between layers with delay

Explosive synchronization(ES)is a kind of first-order jump phenomenon that exists in physical and biological systems.In recent years,researchers have focused on ES between single-layer and multi-layer networks.Most research on complex networks with delay has focused on single-layer or double-layer networks,multi-layer networks are seldom explored.In this paper,we propose a Kuramoto model of frequency weights in multi-layer complex networks with delay and star connections between layers.Through theoretical analysis and numerical verification,the factors affecting the backward critical coupling strength are analyzed.The results show that the interaction between layers and the average node degree has a direct effect on the backward critical coupling strength of each layer network.The location of the delay,the size of the delay,the number of network layers,the number of nodes,and the network topology are revealed to have no direct impact on the backward critical coupling strength of the network.Delay is introduced to explore the influence of delay and other related parameters on ES.

E®ect of laser acupuncture combined with electrical stimulation on recovery from exercise fatigue in mice

In sports events,the rapid recovery after high-intensity training or sport competition performance is very important for athletes'performance and health.The aim of this study is to evaluate the effect of laser acupuncture and electrical stimulation on the recovery from exercise fatigue,using mice with swimming fatigue as experimental model and the electromyography(EMG)and the Raman spectroscopy of blood as evaluation indicators.Root mean square(RMS)and mean power frequency(MPF)of EMG were analyzed after laser acupuncture and electrical stimulation.The amplitude frequency combined analysis(JASA)showed that the proportion of muscles in the fatigue recovery area of the control group,the laser acupuncture group,the multi-channel laser acupuncture group and the laser combined with electrical stimulation group were 34.78%,39.13%,39.13%and 43.48%,respectively.Raman spectroscopy of the mice blood during fatigue recovery showed there is a significant difference between the multi-channel laser acupuncture group and the laser combined with electric stimulation group compared with the recovery period and fatigue period(P<0:05)at the peak of 997 cm^(-1) and the laser combined electrical stimulation group had a statistical difference in the recovery period compared with the fatigue period(P<0.05)at the peak of 1561 cm^(-1).The results showed that laser acupuncture combined with electrical stimulation was beneficial to fatigue recovery in mice,and had the potential value in sports fatigue recovery.

Exceptional point enhanced nanoparticle detection in deformed Reuleaux‑triangle microcavity

In this paper,we propose a deformed Reuleaux-triangle resonator(RTR)to form exceptional point(EP)which results in the detection sensitivity enhancement of nanoparticle.After introducing single nanoparticle to the deformed RTR at EP,frequency splitting obtains an enhancement of more than 6 times compared with non-deformed RTR.In addition,EP induced a result that the far feld pattern of chiral mode responses signifcantly to external perturbation,corresponding to the change in internal chirality.Therefore,single nanoparticle with far distance of more than 4000 nm can be detected by measuring the variation of far feld directional emission.Compared to traditional frequency splitting,the far feld pattern produced in deformed RTR provides a cost-efective and convenient path to detect single nanoparticle at a long distance,without using tunable laser and external coupler.Our structure indicates great potential in high sensitivity sensor and label-free detector.

Simple real-time high-sensitivity heterodyne coherent optical transceiver at intraplane satellite communication

In this paper,we demonstrate a high-sensitivity and real-time heterodyne coherent optical transceiver for intraplane satellite communication,without digital-to-analog converter(DAC)devices and an optical phase lock loop(OPLL).Based on the scheme,a real-time sensitivity of-49 dBm is achieved at 5 Gbps QPSK.Because DAC is not needed at the transmitter,as well as OPLL at the receiver,this reduces the system cost.Furthermore,the least required Rx ADC bit-width is also discussed.Through theoretical analysis and experimental results,our cost-effective transceiver satisfies the scenario and could be a promising component for future application.

Generating broadband cylindrical vector modes based on polarization-dependent acoustically induced fiber gratings using the dispersion turning point

Cylindrical vector beams(CVBs)with special polarization distribution have been extensively investigated due to the unique ways of interacting with matter.Although several configurations have been developed to generate CVBs,such as Q-plates and subwavelength gratings,the bandwidth of a single CVB is inherently narrow due to the phase geometry,which would limit its application for femtosecond lasers.Here,a broadband CVB mode converter based on an acoustically induced fiber grating(AIFG)and a tuning method of dispersion turning point(DTP)is demonstrated both theoretically and experimentally with the 3-dB bandwidth of 125 nm,which is more than 10 times that of conventional AIFGs.Not only can the DTP wavelength be tuned from the original 1500 nm to 1650 nm by thinning the fiber,but also the stable generation of a single broadband HEodd=even 21 mode can be controllably implemented by adjusting the polarization state of the incident light,owing to the larger beat length difference between HE21 and other CV modes.Additionally,the femtosecond CVBs and orbital angular momentum(OAM)modes are successfully generated and amplified by combining the broadband AIFG with a figure-9 mode-locked fiber laser.Meanwhile,it is verified by simulation that the choice of broadband CV mode and the tunability of DTP wavelength can be realized by designing ring-core fibers with different structures,which can furthermore improve the flexibility of generating high purity CVBs.This study provides a highly controllable technique for the generation of broadband CVBs and OAMs paving the way for high-capacity CVBs communication.

Subjective cognitive decline domain improves accuracy of plasma Aβ_(42)/Aβ_(40)for preclinical Alzheimer’s disease diagnosis:The SILCODE study

To the Editor:Accumulating evidence has shown that the preclinical stage of Alzheimer’s disease(AD)(i.e.,asymptomatic amyloidosis)lasts for decades before the onset of cognitive symptoms,providing a large window for early intervention.Amyloid pathology,the earliest pathological change associated with AD,can be detected in vivo with cerebrospinal fluid(CSF)analysis or positron emission tomography(PET),and its presence is necessary for the diagnosis of preclinical AD(pre-AD).However,both PET scans and CSF analyses are expensive,hampering their use in large-scale screening.Thus,blood-based biomarkers are desirable alternatives,as they are cost-effective and not invasive.

The Acquisition of Sand Vibration Information in Hinterland of Desert Based on Advanced Remote Sensing System and Network Technologies

The deep understanding on sand and sand dunes scale can be useful to reveal the formation mechanism of the sandstorm for early sandstorm forecast. The current sandstorm observation methods are mainly based on conventional meteorological station and satellites remote sensing, which are difficult to acquire sand scale information. A wireless sensing network is implemented in the hinterland of desert, which includes ad hoc network,sensor, global positioning system(GPS) and system integration technology. The wireless network is a three-layer architecture and daisy chain topology network, which consists of control station, master robots and slave robots.Every three robots including one master robot and its two slave robots forms an ad hoc network. Master robots directly communicate with radio base station. Information will be sent to remote information center. Data sensing system including different kinds of sensors and desert robots is developed. A desert robot is designed and implemented as unmanned probing movable nodes and sensors' carrier. A new optical fiber sensor is exploited to measure vibration of sand in particular. The whole system, which is delivered to the testing field in hinterland of desert(25 km far from base station), has been proved efficient for data acquisition.

High-performance meta-devices based on multilayer meta-atoms:interplay between the number of layers and phase coverage

Transmissive metasurfaces have provided an efficient platform to manipulate electromagnetic(EM)waves, but previously adopted multilayer meta-atoms are too thick and/or the design approach fully relies on brute-force simulations without physical understandings. Here, based on coupled-mode theory(CMT) analyses on multilayer meta-atoms of distinct types, it is found that meta-atoms of a specific type only allows the phase coverage over a particular range, thus suitable for polarization-control applications.However, combinations of meta-atoms with distinct types are necessary for building ultra-thin wavefront-control meta-devices requiring 360° phase coverage. Based on these physical understandings,high-efficiency meta-atoms are designed/fabricated, and used to construct three typical meta-devices,including quarter-and half-wave plates and a beam deflector. Our results elucidate the physics underlying the interplay between thicknesses and performances of transmissive metasurfaces, which can guide the realizations of miniaturized transmissive meta-devices in different frequency domains.

Traffic Clustering Algorithm of Urban Data Brain Based on a Hybrid-Augmented Architecture of Quantum Annealing and Brain-Inspired Cognitive Computing

In recent years,the urbanization process has brought modernity while also causing key issues,such as traffic congestion and parking conflicts.Therefore,cities need a more intelligent"brain"to form more intelligent and efficient transportation systems.At present,as a type of machine learning,the traditional clustering algorithm still has limitations.K-means algorithm is widely used to solve traffic clustering problems,but it has limitations,such as sensitivity to initial points and poor robustness.Therefore,based on the hybrid architecture of Quantum Annealing(QA)and brain-inspired cognitive computing,this study proposes QA and Brain-Inspired Clustering Algorithm(QABICA)to solve the problem of urban taxi-stand locations.Based on the traffic trajectory data of Xi’an and Chengdu provided by Didi Chuxing,the clustering results of our algorithm and K-means algorithm are compared.We find that the average taxi-stand location bias of the final result based on QABICA is smaller than that based on K-means,and the bias of our algorithm can effectively reduce the tradition K-means bias by approximately 42%,up to approximately 83%,with higher robustness.QA algorithm is able to jump out of the local suboptimal solutions and approach the global optimum,and brain-inspired cognitive computing provides search feedback and direction.Thus,we will further consider applying our algorithm to analyze urban traffic flow,and solve traffic congestion and other key problems in intelligent transportation.

Nonlinear adaptive filters for high-speed LED based underwater visible light communication [Invited]

Underwater visible light communication(UVLC)is expected to act as an alternative candidate in nextgeneration underwater 5G wireless optical communications.To realize high-speed UVLC,the challenge is the absorption,scattering,and turbulence of a water medium and the nonlinear response from imperfect optoelectronic devices that can bring large attenuations and a nonlinearity penalty.Nonlinear adaptive filters are commonly used in optical communication to compensate for nonlinearity.In this paper,we compare a recursive least square(RLS)-based Volterra filter,a least mean square(LMS)-based digital polynomial filter,and an LMS-based Volterra filter in terms of performance and computational complexity in underwater visible light communication.We experimentally demonstrate 2.325 Gb/s transmission through 1.2 m of water with a commercial blue light-emitting diode.Our goal is to assist the readers in refining the motivation,structure,performance,and cost of powerful nonlinear adaptive filters in the context of future underwater visible light communication in order to tap into hitherto unexplored applications and services.

Lamb waves topological imaging combining with Green’s function retrieval theory to detect near filed defects in isotropic plates

A method of combining Green’s function retrieval theory and ultrasonic array imaging using Lamb waves is presented to solve near filed defects in thin aluminum plates.The defects are close to the ultrasonic phased array and satisfy the near field calculation formula.Near field acoustic information of defects is obscured by the nonlinear effects of initial wave signal in a directly acquired response using the full matrix capture mode.A reconstructed full matrix of inter-element responses is produced from cross-correlation of directly received ultrasonic signals between sensor pairs.This new matrix eliminates the nonlinear interference and restores the near-field defect information.The topological imaging method that was developed in recent ultrasonic inspection is used for displaying the scatterers.The experiments are conducted on both thin aluminum plates containing two and four defects, respectively.The results show that these defects are clearly identified when using a reconstructed full matrix.The spatial resolution is equal to about one wavelength of the selectively excited mode and the identifiable defect is about one fifth of the wavelength.However, in a conventional directly captured image,the images of defects overlap together and cannot be distinguished.The proposed method reduces the background noise and allows for effective topological imaging of near field defects.

Recent development of flat supercontinuum generation in specialty optical fibers

Supercontinuum (SC) generation has attracted a significant scientific interest in the past decades due to its promising applications covering the fields of metrology, spectroscopy, defense, as well as medical treatments. To date, researchers are devoted to improving the spectral width and flatness of SC generation by using specialty optical fibers. The flatness of the spectrum is of importance because it can improve the accuracy of measurement in practical applications. This paper summarizes the theory of SC, the state of the art of flat SC generation using optical fiber including photonic crystal fibers, soft glass fibers as well as germania-doped fibers, and suggests the future research direction of flat SC light source.