Ultrafast dynamics of femtosecond laser-induced high spatial frequency periodic structures on silicon surfaces

Femtosecond laser-induced periodic surface structures(LIPSS)have been extensively studied over the past few decades.In particular,the period and groove width of high-spatial-frequency LIPSS(HSFL)is much smaller than the diffraction limit,making it a useful method for efficient nanomanufacturing.However,compared with the low-spatial-frequency LIPSS(LSFL),the structure size of the HSFL is smaller,and it is more easily submerged.Therefore,the formation mechanism of HSFL is complex and has always been a research hotspot in this field.In this study,regular LSFL with a period of 760 nm was fabricated in advance on a silicon surface with two-beam interference using an 800 nm,50 fs femtosecond laser.The ultrafast dynamics of HSFL formation on the silicon surface of prefabricated LSFL under single femtosecond laser pulse irradiation were observed and analyzed for the first time using collinear pump-probe imaging method.In general,the evolution of the surface structure undergoes five sequential stages:the LSFL begins to split,becomes uniform HSFL,degenerates into an irregular LSFL,undergoes secondary splitting into a weakly uniform HSFL,and evolves into an irregular LSFL or is submerged.The results indicate that the local enhancement of the submerged nanocavity,or the nanoplasma,in the prefabricated LSFL ridge led to the splitting of the LSFL,and the thermodynamic effect drove the homogenization of the splitting LSFL,which evolved into HSFL.

Numerical Simulation of Super-Resolution Structured Illumination Microscopy (SIM) Using Heintzmann-Cremer Algorithm with Non-Continuous Spatial Frequency Support

We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact of the diffraction limit of the underlying imaging system on the optimal SIM grating frequency that can be used to obtain the highest SR enhancement with non-continuous spatial frequency support. Besides confirming the previous theoretical and experimental work that SR-SIM can achieve an enhancement close to 3 times the diffraction limit with grating pattern illuminations, we also observe and report a series of more subtle effects of SR-SIM with non-continuous spatial frequency support. Our simulations show that when the SIM grating frequency exceeds twice that of the diffraction limit, the higher SIM grating frequency can help achieve a higher SR enhancement for the underlying imaging systems whose diffraction limit is low, though this enhancement is obtained at the cost of losing resolution at some lower resolution targets. Our simulations also show that, for underlying imaging systems with high diffraction limits, however, SR-SIM grating frequencies above twice the diffraction limits tend to bring no significant extra enhancement. Furthermore, we observed that there exists a limit grating frequency above which the SR enhancement effect is lost, and the reconstructed images essentially have the same resolution as the one obtained directly from the underlying imaging system without using the SIM process.

Combined spatial frequency spectroscopy analysis with visible resonance Raman for optical biopsy of human brain metastases of lung cancers

The purpose of this study is to examine optical spatial frequency spectroscopy analysis(SFSA)combined with visible resonance Raman(VRR)spectroscopic method,for thefirst time,to discriminate human brain metastases of lung cancers adenocarcinoma(ADC)and squamous cell carcinoma(SCC)from normal tissues.A total of 31 label-free micrographic images of three type of brain tissues were obtained using a confocal micro-Raman spectroscopic system.VRR spectra of the corresponding samples were synchronously collected using excitation wavelength of 532 nm from the same sites of the tissues.Using SFSA method,the difference in the randomness of spatial frequency structures in the micrograph images was analyzed using Gaussian functionfitting.The standard deviations,calculated from the spatial frequencies of the micrograph images were then analyzed using support vector machine(SVM)classifier.The key VRR biomolecularfingerprints of carotenoids,tryptophan,amide II,lipids and proteins(methylene/methyl groups)were also analyzed using SVM classifier.All three types of brain tissues were identified with high accuracy in the two approaches with high correlation.The results show that SFSA–VRR can potentially be a dual-modal method to provide new criteria for identifying the three types of human brain tissues,which are on-site,real-time and label-free and may improve the accuracy of brain biopsy.

Compressive imaging based on multi-scale modulation and reconstruction in spatial frequency domain

Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.

Specifications and control of spatial frequency errors of components in two-beam laser static holographic exposure for pulse compression grating fabrication

The large-aperture pulse compression grating(PCG) is a critical component in generating an ultra-high-intensity, ultra-short-pulse laser;however, the size of the PCG manufactured by transmission holographic exposure is limited to large-scale high-quality materials. The reflective method is a potential way for solving the size limitation, but there is still no successful precedent due to the lack of scientific specifications and advanced processing technology of exposure mirrors. In this paper, an analytical model is developed to clarify the specifications of components, and advanced processing technology is adopted to control the spatial frequency errors. Hereafter, we have successfully fabricated a multilayer dielectric grating of 200 mm × 150 mm by using an off-axis reflective exposure system with Φ300 mm. This demonstration proves that PCGs can be manufactured by using the reflection holographic exposure method and shows the potential for manufacturing the meter-level gratings used in 100 petawatt class high-power lasers.

A spatial frequency/spectral indicator-driven model for estimating cultivated land quality using the gradient boosting decision tree and genetic algorithm-back propagation neural network

Cultivated land quality(CLQ)is related to national food security.Rapid and high-precision monitoring of CLQ is crucial for the sustainable development of agriculture.However,current satellite image-based evaluation methods that only consider the crop's spatial spectrum characteristics in the key growth stages cannot accurately estimate CLQ.This study proposes a new method based on time-series spectral data of crop growth to improve the accuracy of CLQ estimation.This study was conducted in the Conghua District of Guangzhou,Guangdong Province,China.The results showed that seven spectral indicators were determined as the optimal indicators based on the gradient boosting decision tree(GBDT)and variance inflation factor(VIF).And the genetic algorithm-back propagation neural network(GA-BPNN)model provided more accurate CLQ estimates than the partial least squares regression(PLSR)model,indicating a nonlinear relationship between CLQ and the indicators.In addition,the GA-BPNN model with a normalized root mean square error(NRMSE)of 9.91%demonstrates the excellent potential for mapping CLQ over large areas.The model based on the seven optimal indicators of crop phenology provided higher accuracy than the GA-BPNN model based on the normalized difference vegetation index(NDVI)indicators in the spatial domain,significantly decreasing the NRMSE of the CLQ estimates by 3.17%.This further implied that the spectral indicators in the spatial frequency domain can improve the accuracy of estimating CLQ.

Resolution beyond classical limits with spatial frequency heterodyning

A technique for coherent imaging based on spatial frequency heterodyning is described. Three images corresponding to three physical measurements are recorded. For the first measurement, a scene is simply illuminated with a coherent beam and for measurements 2 and 3, the scene is projected with cosine and sine fringes, respectively. Due to spatial frequency heterodyning, upper and lower side hand information falls in the pass band of the imager. These bands are separated and correct phases and positions are assigned to these bands in the spatial frequency domain. An extension of bandwidth is achieved in the frequency domain and the inverse frequency domain data then give a high resolution coherent image.

Chip-compatible wide-field 3D nanoscopy through tunable spatial frequency shift effect

Spatial frequency shift(SFS) microscopy with evanescent wave illumination shows intriguing advantages, including large field of view(FOV), high speed, and good modularity. However, a missing band in the spatial frequency domain hampers the SFS superresolution microscopy from achieving resolution better than 3 folds of the Abbe diffraction limit. Here, we propose a novel tunable large-SFS microscopy, making the resolution improvement of a linear system no longer restricted by the detection numerical aperture(NA). The complete wide-range detection in the spatial frequency domain is realized by tuning the illumination spatial frequency actively and broadly through an angle modulation between the azimuthal propagating directions of two evanescent waves. The vertical spatial frequency is tuned via a sectional saturation effect, and the reconstructed depth information can be added to the lateral superresolution mask for 3D imaging. A lateral resolution of λ/9, and a vertical localization precision of ~λ/200(detection objective NA = 0.9) are realized with a gallium phosphide(GaP) waveguide. Its unlimited resolution enhancing capability is demonstrated by introducing a designed metamaterial chip with an unusual large refractive index. Besides the great resolution enhancement, this method shows better anti-noise capability than classical structured illumination microscopy without SFS tunability. This method is chip-compatible and can potentially provide a massproducible illumination chip module achieving the fast, large-FOV, and deep-subwavelength 3D nanoscopy.

An operational modal analysis method in frequency and spatial domain

A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.

Image Fusion Using Wavelet Transformation and XGboost Algorithm

Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same item.Image fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual perception.Image fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on application.The research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet transform.Image fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient manner.More precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain characteristics.To tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is proposed.The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice.The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images.This study enhances the performance of the eXtreme Gradient Boosting(XGBoost)algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection.The performance of images is improved by segmenting them employing the K-Means algorithm.The segmentation method aids in identifying specific regions of interest,using Particle Swarm Optimization(PCA)for trait selection and XGBoost for data classification.Extensive trials confirm the model’s exceptional visual performance,achieving an accuracy of up to 97.067%and providing good objective indicators.

Spatial Distribution Characteristics of Surface Tidal Currents in the Southwest of Taiwan Strait

This study was conducted on the spatial distribution characteristics of surface tidal currents in the southwestern Taiwan Strait based on the quasi-harmonic analysis of current data obtained by two high frequency surface wave radar(HFSWR) systems. The analysis shows that the tidal current pattern in the southwestern Taiwan Strait is primarily semi-diurnal and influenced significantly by shallow water constituents. The spatial distribution of tidal current ellipses of M2 is probably affected by the interaction between two different systems of tide wave, one from the northern mouth of Taiwan Strait and the other from the Bashi Channel. The directions of the major axes of M2 tidal current ellipses coincide roughly with the axis of the Taiwan Strait. The spatial distribution of the magnitudes of the probable maximum current velocity(PMCS) shows gradual increase of the velocity from northeast to southwest, which is in accordance with the spatial distribution of the measured maximum current velocity(MMCS). The directions of the residual currents are in accordance with the direction of the prevailing monsoon wind at the Taiwan Strait and the direction of the Taiwan warm current during summer. The bathymetry also shows a significant effect on the spatial distribution characteristics of tidal currents.

Research on Frequency of the Aftershock Sequence of the Wenchuan Earthquake Based on Coseismic Coulomb Stress Change

By the aftershock frequency estimation method based on the calculation of coseismic static Coulomb stress changes and rate-and state-dependent fault constitutive law,we calculate the frequency of "direct "aftershocks of the Wenchuan earthquake related to coseismic static Coulomb stress changes in its aftershock zone and the areas nearby.It shows that the frequency is significantly lower than the truth in the main rupture zone,especially in the southern rupture zone,due to the decrease of stress level on the rupture plane of the main shock resulting from coseismic Coulomb stress change.The study also shows that the effect of the Coulomb stress change on the duration of aftershock activity is associated with the lower limit magnitude.The duration is about 15-16 months for aftershocks above ML4.0,and close to 60 months for aftershocks above ML3.5.In this period,the ratio of the"direct"aftershocks caused by coseismic Coulomb stress change ranges between 44.7% to48.6%,which suggests that,even in the "effective"period of coseismic Coulomb stress changes,about half of the aftershocks on the main shock rupture plane are independent of coseismic Coulomb stress changes.It is pointed out that those aftershocks may be related to the afterslip or the viscoelastic relaxation,which are time dependent cases.

Ghost imaging based on the control of light source bandwidth

A scheme to improve the quality in ghost imaging(GI)by controlling the bandwidth of light source(BCGI)is proposed.The theoretical and numerical results show that the reconstruction result with high quality can be obtained by adjusting the bandwidth range of the light source appropriately,and the selection criterion of the bandwidth is analyzed by the power distribution of the imaging target.A proof-of-principle experiment is implemented to verify the theoretical and numerical results.In addition,the BCGI also presents better anti-noise performance when compared with some popular GI methods.

Visual response characteristics of neurons in the second visual area of marmosets

The physiological characteristics of the marmoset second visual area(V2) are poorly understood compared with those of the primary visual area(V1). In this study, we observed the physiological response characteristics of V2 neurons in four healthy adult marmosets using intracortical tungsten microelectrodes. We recorded 110 neurons in area V2, with receptive fields located between 8° and 15° eccentricity. Most(88.2%) of these neurons were orientation selective, with half-bandwidths typically ranging between 10° and 30°. A significant proportion of neurons(28.2%) with direction selectivity had a direction index greater than 0.5. The vast majority of V2 neurons had separable spatial frequency and temporal frequency curves and, according to this criterion, they were not speed selective. The basic functional response characteristics of neurons in area V2 resemble those found in area V1. Our findings show that area V2 together with V1 are important in primate visual processing, especially in locating objects in space and in detecting an object's direction of motion. The methods used in this study were approved by the Monash University Animal Ethics Committee, Australia(MARP 2009-2011) in 2009.

PSYCHOLOGICAL EVALUATION OF COLOR DESIGN

In this paper, a method of predicting psychological values of color design was performed by using random color patterns. The results were analyzed in terms of the Fourier transform of the color patterns, and it was found that the psychological values of the random color patterns depended not only on the zero frequency component but also on the dynamic components of the Fourier transform of the patterns. The application of the estimation method was discussed.

Three-dimensional quantication of protoporphyrin IX in photodynamic therapy using SFDI/DFT:A pilot experimental validation

Photodynamic therapy(PDT)dosimetry,induding light dose,photosensitizer dose and tissue oxygen,has been a research focus in PDT.In this work,we present a three-dimensional(3D)quantification of protoporphyrin X(PpIX)using combined spatial frequency domain imaging(SFDI)and diffuse fuorescence tomography(DFT).The SFDI maps both the distributions of tissue absorption and scattering properties at three wavelengths and accordingly provides the optical background for DFT and extracts the tissue oxygenation for assessing the therapeutic outcomes,while DFT dynamically monitors the 3D distribution of PpEX dose from measured fluorescence signals for the procedure optimization.A pilot in vrivo application in tumor nude models showed that the proposed SFDI/DFT is able to dynamically trace changes in the PpX concentration and tissue oaxygen during the treatment,rendering it a potentially powerful tool for PDT to improve clinical eficacy.

Sample indexed spatial orthogonal frequency division multiplexing

Optical spatial modulation （OSM） is a multiple-transmitter technique that can provide higher data rates with low system complexity as compared with single-input single-output systems. Orthogonal frequency division multiplexing （OFDM） is widely implemented to achieve better spectral efficiency in wireless channels. Asymmetrically clipped optical OFDM （O-OFDM） and DC-biased O-OFDM are two well-known O-OFDM techniques suitable for intensity-modulation direct-detection optical systems. In this work, sample indexed spatial OFDM （SIS-OFDM） is proposed to combine OSM and O-OFDM in a novel way and achieve significant per- formance gain. By assigning time-domain samples of the O-OFDM transmit symbol to different transmitters, SIS-OFDM achieves much better spectral efficiency and reduces computational complexity at the transmitter as compared with previous work that combines OSM with O-OFDM in the frequency domain. We also consider the impact of optical source biasing on overall performance, and the relative performance of imaging receiver （ImR） versus non-imaging receiver （NImR） design for our proposed SIS-OFDM technique. Results indicate that for an Ntx x Nrx multiple-input multiple-output configuration where Nix = N = 4, SIS-OFDM using ImR can achieve up to 135 dB of signal-to-noise ratio gain over comparable system using a NImR. Also, using Nc number of O-OFDM subcarriers provides up to Nsc × log2（Ntx） additional bits per symbol of spectral efficiency over technioues that combine OSM and O-OFDM in the freollencv domain.

Evolution of low-frequency noise passing through a spatial filter in a high power laser system

The theoretical model of spatial noise passing through a spatial filter is established in high power laser system under the small signal approximation. The transmission characteristic for a noise signal passing through spatial filters with different magnifications is analyzed by numerical simulation, according to the actual structure of the high power laser system. The results show that the spatial modulation period of low-frequency noise getting through the pinhole will be proportional to the magnification of the spatial filter. When the magnification is less than 1, the safe low-frequency noise will be extruded into the high-frequency region, which is the fast increasing part, and finally develops into the most dangerous part which can damage the optical devices. The conclusion of this research improves the relay imaging theory of a spatial filter and provides an important theoretical basis for a general design of high power laser systems.

High Spatial Resolution and High Temporal Frequency(30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets:Method Development and Validation

High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

Soft Computing Based Discriminator Model for Glaucoma Diagnosis

In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Interest (ROI)for glaucoma diagnosis must be selected at first to reduce the complexity of anysystem. The DMGD system uses a series of pre-processing;initial cropping by thegreen channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), bloodvessel detection and removal by Gaussian Derivative Filters (GDF) and inpaintingalgorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequencydomain features from LAWS are generated from the corresponding ROI forfurther classification using kernel based Support Vector Machine (SVM). TheDMGD system performances are validated using four fundus image databases;ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels;LinearKernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel,Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernelbased classifies from the properly segmented ROI.