Theoretical analysis of the relationship between the Brillouin gain coefficient and the strain in the optical-fiber sensors

The relation between the power of the Brillouin signal and the strain is one of the bases of the distributed fiber sensors of temperature and strain. The coefficient of the Bfillouin gain can be changed by the temperature and the strain that will affect the power of the Brillouin scattering. The relation between the change of the Brillouin gain coefficient and the strain is thought to be linear by many researchers. However, it is not always linear based on the theoretical analysis and numerical simulation. Therefore, errors will be caused if the relation between the change of the Brillouin gain coefficient and the strain is regarded as to be linear approximately for measuring the temperature and the strain. For this reason, the influence of the parameters on the Brillouin gain coefficient is proposed through theoretical analysis and numerical simulation.

GRATDet:Smart Contract Vulnerability Detector Based on Graph Representation and Transformer

Smart contracts have led to more efficient development in finance and healthcare,but vulnerabilities in contracts pose high risks to their future applications.The current vulnerability detection methods for contracts are either based on fixed expert rules,which are inefficient,or rely on simplistic deep learning techniques that do not fully leverage contract semantic information.Therefore,there is ample room for improvement in terms of detection precision.To solve these problems,this paper proposes a vulnerability detector based on deep learning techniques,graph representation,and Transformer,called GRATDet.The method first performs swapping,insertion,and symbolization operations for contract functions,increasing the amount of small sample data.Each line of code is then treated as a basic semantic element,and information such as control and data relationships is extracted to construct a new representation in the form of a Line Graph(LG),which shows more structural features that differ from the serialized presentation of the contract.Finally,the node information and edge information of the graph are jointly learned using an improved Transformer-GP model to extract information globally and locally,and the fused features are used for vulnerability detection.The effectiveness of the method in reentrancy vulnerability detection is verified in experiments,where the F1 score reaches 95.16%,exceeding stateof-the-art methods.

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

Improved recognition of Echinococcus granulosus protoscoleces using visual saliency and scale-invariant features

Echinococcosis—a parasitic disease caused by Echinococcus granulosus or Echinococcus multilocularis larvae—occurs in many regions in the world. This disease can pose a serious threat to public health and thus requires a convenient and cost-effective method for early detection. So, we developed a novel method based on visual saliency and scale-invariant features that detects the tapeworm parasites. This method improves upon existing bottom-up computational saliency models by introducing a visual attention mechanism. The results indicated that the proposed method offers a higher level of both accuracy and computational efficiency when detecting Echinococcus granulosus protoscoleces, which in turn could improve early detection of echinococcosis.

Synthesis and photoluminescence properties of washboard belt-like ZnSe nanostructures

Washboard belt-like zinc selenide(ZnSe)nanostructures are successfully prepared by a simple chemical vapor deposition(CVD)technology without catalyst.The phase compositions,morphologies and optical properties of the nanostructures are investigated by X-ray diffraction(XRD),scanning electron microscopy(SEM),high-resolution transmission electron microscopy(HRTEM)and photoluminescence(PL)spectroscop,respectively.A vapor-liquid mechanism is proposed for the formation of ZnSe belt-like structures.Strong PL from the ZnSe nanostructure can be tuned from 462 nm to 440 nm with temperature varying from 1000°C to 1200°C,and it is demonstrated that the washboard belt-like ZnSe nanostructures have potential applications in optical and sensory nanotechnology.This method is expected to be applied to the synthesis of other II-VI groups or other group’s semiconducting materials.

Self-diffractionin Porous Silicon/PMMA-DR1 Composite Fil ms

Two-layer structure consisting of PS/PMMA-DR1 composite film planar waveguide layer on porous silicon cladding layer was fabricated in our experiment. The induced grating based on the third nonlinear optical properties was formed by interaction of two Nd∶YAG laser beams at 1064nm in the porous silicon/PMMA-DR1 waveguide. The diffraction efficiency of the first order diffracted light is measured to be about 0.2% of the total output.

DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement

Poor illumination greatly affects the quality of obtained images.In this paper,a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement.DEANet combines the frequency and content information of images and is divided into three subnetworks:decomposition,enhancement,and adjustment networks,which perform image decomposition;denoising,contrast enhancement,and detail preservation;and image adjustment and generation,respectively.The model is trained on the public LOL dataset,and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.

Information Purification Network for Remote Sensing Image Super-Resolution

Recently,several well-performing deep convolutional neural networks were proposed for remote sensing image super-resolution(SR).However,these methods rarely consider that remote sensing images are corruptible by additional noise,blurring,and other factors.Therefore,to eliminate the interference of these factors,especially the noise,we propose a novel information purification network(IPN)for remote sensing image SR.The proposed information purification block(IPB)can process channel-wise features differently by channel separation and rescale spatial-wise features adaptively through the proposed multi-scale spatial attention mechanism.We further design an information group to explore a more powerful expressive combination of IPBs.Moreover,long and short skip connections can transmit abundant low-frequency information,making IPBs pay more attention to high-frequency information.We mix the images under various degradation models as training data in the training phase.In this way,the network can directly reconstruct various degraded images.Experiments on AID and UC Merced Land-Use datasets under multiple degradation models demonstrate that the proposed IPN performs better than state-of-the-art methods.

Optical and electrical properties of blue-light polyfluorence/porous silicon composites

Optical and electrical properties of composites formed by mixing porous silicon （PS） and poly （9, 9- diocty-2, 7-fluorene- co-4, 4＇-butoxydiphenyl） （PFP） have been studied by Fourier transform-infrared spectroscopy （FT-IR）, scanning electron microscopy （SEM） and photoluminescence （PL） spectroscopy. The optical spectra show that porous silicon is incorporated into the polymer without significant change in the polymer properties. The FT-IR spectroscopy has detected the existence of specific interactions, which may be attributed to non-conjugated alkoxy segment. By fitting the current-voltage （/-V） curve of PFP/PS structure with the modified standard ecluation, the n factor and I0 are cletermined

Preparation and photoluminescence of TiO_2/PS composite system

A TiO2/porous silicon(PS) composite system is prepared by chemical vapor deposition.The crystal form with anatase phase of the samples is evaluated by X-ray diffraction and ultraviolet-visible(UV-vis) absorbance spectra,and the morphology with microsphere of TiO2particles is characterized by scanning electron microscopy.The composite system formed by this technique gives a broad blue luminescence and the mechanism of photoluminescence with TiO2/PS is also discussed.

Acoustic analysis of the vowel system in Hotan dialect and its contribution to dialect recognition of Uyghur dialects

Based on the actual needs of speech application research such as speech recognition and voiceprint recognition,the acoustic characteristics and recognition of Hotan dialect were studied for the first time.Firstly,the Hetian dialect voice was selected for artificial multi-level annotation,and the formant,duration and intensity of the vowel were analyzed to describe statistically the main pattern of Hetian dialect and the pronunciation characteristics of male and female.Then using the analysis of variance and nonparametric analysis to test the formant samples of the three dialects of Uygur language,the results show that there are significant differences in the formant distribution patterns of male vowels,female vowels and whole vowels in the three dialects.Finally,the GUM-UBM(Gaussian Mixture Model-Universal Background Model),DNN-UBM(Deep Neural Networks-Universal Background Model)and LSTM-UBM(Long Short Term Memory Network-Universal Background Model)Uyghur dialect recognition models are constructed respectively.Based on the Mel-frequency cepstrum coefficient and its combination with the formant frequency for the input feature extraction,the contrastive experiment of dialect i-vector distinctiveness is carried out.The experimental results show that the combined features of the formant coefficients can increase the recognition of the dialect,and the LSTM-UBM model can extract more discriminative dialects than the GMM-UBM and DNN-UBM.

Reflection z-scan for measuring the nonlinear refractive index of porous silicon

An experimental investigation on the nonlinear refractive index of nanoporous silicon at wavelengths of 532 nm and 1064 nm is reported by the reflection z-scan(RZ-scan) method with picosecond pulses.The porous silicon(PS) does not need to be peeled from silicon substrate.The method uses a p-polarized beam with oblique incidence.The modification of the reflected beam intensity gives the information of the surface nonlinear refractive index.The index of porous silicon at 1064 nm is at the same order of magnitude as that obtained by the conventional transmission z-scan technique,and the measured absolute value of nonlinear refractive index n2 at 532 nm is two orders of magnitude higher than that at 1064 nm.

Immunosensor application based on Raman spectroscopy of porous silicon

Porous silicon （PS） suitable for optical detection of immunoreaction is fabricated. The structure of immunosensor is prepared by the following steps： oxidization, silanization, glutaraldehyde cross-linker, and covalent binding of antibody. When antigen is added into the immunosensor, the Raman intensity is estimated to be linearly reduced according to the concentration of the surface protective antigen protein A （spaA） of below 4.0 μg ml-1. The ultimate detection limit is 1.412 × 102 pg ml-1. Controlled experiments are also presented with non-immune antigen of the spaA, and results show that the immunosensor has high specificity. Compared with the conventional enzyme-linked immuno sorbent assay （ELISA）, this method is quick, inexpensive, and label-free.

Application of metal nanoparticles/porous silicon diffraction grating in rhodamine 6 G fluorescence signal enhancement

We present a technique for fabricating a fluorescence enhancement device composed of metal nanoparticles（NPs） and porous silicon（PSi） diffraction grating.The fluorescence emission enhancement properties of the PSi and the fluorescence enhancement of the probe molecules are studied on PSi gratings.The fluorescence enhancement of the probe molecules on a fluorescence enhancement device is further improved through the deposition of metal NPs onto the PSi grating.In comparison to metal NP/PSi devices,metal NP periodic distributions can produce a stronger fluorescence enhancement that couples with the PSi grating fluorescence enhancement to achieve an overall three-fold enhancement of the fluorescence intensity.

Template-directed synthesis of Ag nanowire arrays by a simple paired cell method for SERS

The silver （Ag） nanowire arrays with regular and uniform size were successfully fabricated inside the nanochannels of anodic aluminum oxide （AAO） template by a simple paired cell method. X-ray diffi＇action （XRD） and scanning elec- tron microscopy （SEM） results indicate that the as-synthesized samples are composed of face-centered cubic structure, and the average diameter is about 60-70 nm. Transmission electron microscopy （TEM） and the corresponding fast Fourier transformation （FFT） results show that Ag nanowires have a preferred single-crystal structure. Ultravio- let-visible （UV-vis） spectrum of Ag nanowire arrays exhibits UV emission band at 383 nm which can be attributed to the transverse dipole resonance ofAg nanowire arrays. A good surface-enhanced Raman scattering （SERS） spectrum is observed by excitation with a 514.5 nm laser, and the intensity of the SERS peak is about 23 times higher than that of the normal Raman peak measured from an empty AAO template. The high enhancement factor suggests that this method can be used to fabricate SERS sensor with high efficiency.

Influence of electrolyte temperature on spectral properties of porous silicon microcavity

A porous silicon microcavity （PSM） is highly sensitive for sensing applications due to its high surface area and a narrow resonance peak. In this letter, we fabricated the PSM by alternate current density from a low value to a high value during double-tank electrochemical anodization at different electrolyte temperatures. Results show that with the increase of the electrolyte temperature, the rate of the PS etching becomes faster and the refractive index of the PS layer becomes smaller. The thickness of the PS increases faster than the decrease of the refractive index of the PS.

Porous silicon biosensor for detection of variable domain of heavy-chain of HCAb antibody

In this paper, we produce porous silicon （PSi） by electrochemical etching, and it is the first time to evaluate the performance of label-free porous silicon biosensor for detection of variable domain of heavy chain of heavy-chain antibody （VHH）. The binding of hen egg white lysozyme （HEWL） and VHH causes a red shift in the reflection spectrum of the biosensor. The red shift is proportional to the VHH concenlration in the range from 14 gg.ml-I to 30 pg.ml~ with a detection limit of 0.648 ng.ml~. The research is useful for the development of label-free biosensor applied in the rapid and sensitive determination of small molecules.

RFCNet:Remote Sensing Image Super-Resolution Using Residual Feature Calibration Network

In the field of single remote sensing image Super-Resolution(SR),deep Convolutional Neural Networks(CNNs)have achieved top performance.To further enhance convolutional module performance in processing remote sensing images,we construct an efficient residual feature calibration block to generate expressive features.After harvesting residual features,we first divide them into two parts along the channel dimension.One part flows to the Self-Calibrated Convolution(SCC)to be further refined,and the other part is rescaled by the proposed Two-Path Channel Attention(TPCA)mechanism.SCC corrects local features according to their expressions under the deep receptive field,so that the features can be refined without increasing the number of calculations.The proposed TPCA uses the means and variances of feature maps to obtain accurate channel attention vectors.Moreover,a region-level nonlocal operation is introduced to capture long-distance spatial contextual information by exploring pixel dependencies at the region level.Extensive experiments demonstrate that the proposed residual feature calibration network is superior to other SR methods in terms of quantitative metrics and visual quality.

A label-free single photonic quantum well biosensor based on porous silicon for DNA detection

The single photonic quantum well （PQW） structures are successfully fabricated on ptype silicon wafer by electro chemical etching process, and are used for DNA detection firstly. The red shift of resonance peak is caused by the changing refractive index of PSi layer, which results from coupling of organic molecules into pores. When the porous silicon （PSi） based single PQW biosensors are immersed in complementary deoxyribonucleic acid （DNA） with differ ent concentrations ranging from 0.625 pM to 10,000 pM, a good linear relationship is observed between the red shift of resonance peak and the complementary DNA concentration. Experimental results show that the detection sensitivity of PSibased single PQW biosensors is 3.04 nm/M with a detection limit of 32 nM for 16base pair DNA oligonu cleotides.