A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

Content based image retrieval(CBIR)techniques have been widely deployed in many applications for seeking the abundant information existed in images.Due to large amounts of storage and computational requirements of CBIR,outsourcing image search work to the cloud provider becomes a very attractive option for many owners with small devices.However,owing to the private content contained in images,directly outsourcing retrieval work to the cloud provider apparently bring about privacy problem,so the images should be protected carefully before outsourcing.This paper presents a secure retrieval scheme for the encrypted images in the YUV color space.With this scheme,the discrete cosine transform(DCT)is performed on the Y component.The resulting DC coefficients are encrypted with stream cipher technology and the resulting AC coefficients as well as other two color components are encrypted with value permutation and position scrambling.Then the image owner transmits the encrypted images to the cloud server.When receiving a query trapdoor form on query user,the server extracts AC-coefficients histogram from the encrypted Y component and extracts two color histograms from the other two color components.The similarity between query trapdoor and database image is measured by calculating the Manhattan distance of their respective histograms.Finally,the encrypted images closest to the query image are returned to the query user.

Quantitative analysis of the content of nitrogen and sulfur in coal based on laserinduced breakdown spectroscopy: effects of variable selection

Coal is a crucial fossil energy in today’s society,and the detection of sulfir(S) and nitrogen(N)in coal is essential for the evaluation of coal quality.Therefore,an efficient method is needed to quantitatively analyze N and S content in coal,to achieve the purpose of clean utilization of coal.This study applied laser-induced breakdown spectroscopy(LIBS) to test coal quality,and combined two variable selection algorithms,competitive adaptive reweighted sampling(CARS) and the successive projections algorithm(SPA),to establish the corresponding partial least square(PLS) model.The results of the experiment were as follows.The PLS modeled with the full spectrum of 27,620 variables has poor accuracy,the coefficient of determination of the test set(R^2 P) and root mean square error of the test set(RMSEP) of nitrogen were 0.5172 and 0.2263,respectively,and those of sulfur were0.5784 and 0.5811,respectively.The CARS-PLS screened 37 and 25 variables respectively in the detection of N and S elements,but the prediction ability of the model did not improve significantly.SPA-PLS finally screened 14 and 11 variables respectively through successive projections,and obtained the best prediction effect among the three methods.The R^2 P and RMSEP of nitrogen were0.9873 and 0.0208,respectively,and those of sulfur were 0.9451 and 0.2082,respectively.In general,the predictive results of the two elements increased by about 90% for RMSEP and 60% for R2 P compared with PLS.The results show that LIBS combined with SPA-PLS has good potential for detecting N and S content in coal,and is a very promising technology for industrial application.

Molecular Spectra and Dissociation Dynamics of Oxalyl Chloride: Effect of External Electrical Fields

Oxalyl chloride is a highly toxic and caustic substance, which widely exists in human production and life as a kind of volatile organic compound. Based on the density functional theory B3 LYP at 6-311++G(d, p) level, the influences of external electric field on the bond length, bond energy, dipole moment and dissociation mechanism are optimized. The results indicate that the C_1–Cl_3 bond length increases while the C_4–Cl_6 bond decreases. At the same time, the carbon-carbon bond length gradually increases with the increase of electric field. The total energy decreases while the dipole moment gradually increases with the increase of electric field. In the infrared spectra, the vibration frequency of the carbon-chlorine(C_4–Cl_6) bond decreases while the vibration frequency of the carbon-oxygen bond increases. In the ultraviolet-visible spectra, the wavelength of the strongest absorption peak increases as the external electric field increases and shows an observable red shift phenomenon. Additionally, single point energies of oxalyl chloride along the carbon-carbon bond are scanned with the equation-of-motion coupled cluster method restricted to single and double excitations(EOM-CCSD) method and the potential energy curves under different external electric fields are obtained. The dissociation barrier in potential energy curve decreases because of the breakage of carbon-carbon bond with the increase of external electric field. These results provide reference for further researches on the properties of oxalyl chloride and offer a theoretical basis for the study of oxalyl chloride degradation.

Electronic Structure and Physical Characteristics of Dioxin Under External Electric Field

Dioxin is a highly toxic and caustic substance,which widely existed in the atmosphere,soil and water with tiny particles.Dioxin pollution has become a major problem that concerns the survival of mankind,which must be strictly controlled.The bond length,bond angle,energy,dipole moment,orbital energy level distribution of dioxin under the external field are investigated using DFT(density functional theory)on basis set level of B3LYP/6-31G(d,p).The results indicate that with the increase of the electric field,the length of one Carbon-Oxygen bond increases while another Carbon-Oxygen bond decreases.The energy gradually decreases with the electric field,while the change of the dipole moment has an opposite trend.In the infrared spectra,the vibration frequency decreases with the electric field increasing and shows an obvious red shift.Moreover,the ultraviolet-visible absorption spectra under different electric fields are analyzed with TD-DFT(time-dependent density functional theory)method.The wavelength of the strongest absorption peak increases and occurs red shift with the increase of the electric field.All the above results can provide reference for further research on the properties of dioxin under different external electric field.

Confinement of Bloch surface waves in a graphene-based one-dimensional photonic crystal and sensing applications

Bloch surface waves（BSWs） are excited in one-dimensional photonic crystals（Ph Cs） terminated by a graphene monolayer under the Kretschmann configuration. The field distribution and reflectance spectra are numerically calculated by the transverse magnetic method under transfer-matrix polarization, while the sensitivity is analyzed and compared with those of the surface plasmon resonance sensing method. It is found that the intensity of magnetic field is considerably enhanced in the region of the terminated layer of the multilayer stacks, and that BSW resonance appears only in the interface of the graphene and solution. Influences of the graphene layers and the thickness of a unit cell in Ph Cs on the reflectance are studied as well. In particular, by analyzing the performance of BSW sensors with the graphene monolayer,the wavelength sensitivity of the proposed sensor is 1040 nm/RIU whereas the angular sensitivity is 25.1°/RIU. In addition,the maximum of figure of merit can reach as high as 3000 RIU^-1. Thus, by integrating graphene in a simple Kretschmann structure, one can obtain an enhancement of the light–graphene interaction, which is prospective for creating label-free,low-cost and high-sensitivity optical biosensors.

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

Coronaviruses are a well-known family of viruses that can infect humans or animals.Recently,the new coronavirus(COVID-19)has spread worldwide.All countries in the world are working hard to control the coronavirus disease.However,many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system,which leads to the mass spread of diseases.As a powerful tool,artificial intelligence(AI)has been successfully applied to solve various complex problems ranging from big data analysis to computer vision.In the process of epidemic control,many algorithms are proposed to solve problems in various fields of medical treatment,which is able to reduce the workload of the medical system.Due to excellent learning ability,AI has played an important role in drug development,epidemic forecast,and clinical diagnosis.This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.

Probing Ultrafast Dissociation Dynamics of Chloroiodomethane in the B Band by Time-Resolved Mass Spectrometry

Ultrafast dissociation dynamics of chloroiodomethane （CH2ICl） in the B band is studied by femtosecond time- resolved time-of-flight （TOF） mass spectrometry. Time-resolved TOF mass signal of parent ion （CH2ICl＋） and main daughter ion （CH2Cl＋） are obtained. The curve for the transient signal of CH2ICl＋ is simple and can be well fitted by an exponential decay convoluted with a Gaussian function. The decay constant determined to be less than 35 fs reflects the lifetime of the B band. Significant substituent effects on photodissociation dynamics of CH2IC1 compared with CH3I are discussed. The dissociation time from the parent ion CH2IC1＋ to the daughter ion CH2Cl＋ is determined in the experiment. The optimized geometry of the ionic state of CH2ICl and the ionization energy are calculated for further analysis of the measurements. In addition, compared with the parent ion, a new decay component with time constant of -596 fs is observed for CH2Cl＋, and reasonable mechanisms are proposed for the explanation.

Omnidirectional and compact Tamm phonon-polaritons enhanced mid-infrared absorber

Narrow band mid-infrared(MIR)absorption is highly desired in thermal emitter and sensing applications.We theoretically demonstrate that the perfect absorption at infrared frequencies can be achieved and controlled around the surface phonon resonance frequency of silicon carbide(SiC).The photonic heterostructure is composed of a distributed Bragg reflector(DBR)/germanium(Ge)cavity/SiC on top of a Ge substrate.Full-wave simulation results illustrate that the Tamm phonon-polaritons electric field can locally concentrate between the Ge cavity and the SiC film,contributed to the improved light-phonon interactions with an enhancement of light absorption.The structure has planar geometry and does not require nano-patterning to achieve perfect absorption of both polarizations of the incident light in a wide range of incident angles.Their absorption lines are tunable via engineering of the photon band-structure of the dielectric photonic nanostructures to achieve reversal of the geometrical phase across the interface with the plasmonic absorber.

Abnormal driving behavior identification based on direction and position offsets

Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.

Rapid identification of volatile organic compounds and their isomers in the atmosphere

Isomers are widely present in volatile organic compounds(VOCs),and it is a tremendous challenge to rapidly distinguish the isomers of VOCs in the atmosphere.In this work,laserinduced breakdown spectroscopy(LIBS)technology was developed to online distinguish VOCs and their isomers in the air.First,LIBS was used to directly detect halogenated hydrocarbons(a typical class of VOCs)and the characteristic peaks of the related halogens were observed in the LIBS spectra.Then,comparing the LIBS spectra of various samples,it was found that for VOCs with different molecular formulas,although the spectra are completely the same in elemental composition,there are still significant differences in the relative intensity of the spectral lines and other information.Finally,in light of the shortcomings of traditional LIBS technology in identifying isomers,machine learning algorithms were introduced to develop the LIBS technique to identify the isomers of atmospheric VOCs,and the recognition results were very good.It is proved that LIBS combined with machine learning algorithms is promising for online traceability of VOCs in the atmospheric environment.

Fabrication and magnetic properties of 4SC(NH_2)_2–Ni_(0.97)Cu_(0.03)Cl_2 single crystals

Single crystals of 4SC（NH2）2–Ni1-xCux Cl2（x = 0.03）（Cu-DTN） containing spin S = 1/2 Cu^2＋and S = 1 Ni^2＋cations are synthesized by slow evaporation methods. Structural characterization demonstrates that the Cu-DTN is of a tetrahedral structure with lattice parameter c being 9.0995 A, which is 1.32% expansion compared with that of parent material DTN due to the larger radius of the Cu ion. Direct current（DC） susceptibility measurements show that both the antiferromagnetic exchange interaction at low temperature and the large anisotropy of susceptibilities are suppressed after doping the Cu ion, which could be related to the structural distortion and the increase of the super-exchange paths in Cu-DTN.

Attribute-Based Access Control Scheme with Efficient Revocation in Cloud Computing

Attribute-based encryption(ABE) supports the fine-grained sharing of encrypted data.In some common designs,attributes are managed by an attribute authority that is supposed to be fully trustworthy.This concept implies that the attribute authority can access all encrypted data,which is known as the key escrow problem.In addition,because all access privileges are defined over a single attribute universe and attributes are shared among multiple data users,the revocation of users is inefficient for the existing ABE scheme.In this paper,we propose a novel scheme that solves the key escrow problem and supports efficient user revocation.First,an access controller is introduced into the existing scheme,and then,secret keys are generated corporately by the attribute authority and access controller.Second,an efficient user revocation mechanism is achieved using a version key that supports forward and backward security.The analysis proves that our scheme is secure and efficient in user authorization and revocation.

A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features

Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic environment.These difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature availability.To address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor features.The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier(SKNNC).Further the more specific vehicle type such as bus,truck,sedan or van is recognized by the second stage classification,which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier(KSRC).A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR.To improve VTR efficiency,the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image.Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.

A Dual-Chaining Watermark Scheme for Data Integrity Protection in Internet of Things

Chaining watermark is an effective way to verify the integrity of streaming data in wireless network environment,especially in resource-constrained sensor networks,such as the perception layer of Internet of Things applications.However,in all existing single chaining watermark schemes,how to ensure the synchronization between the data sender and the receiver is still an unsolved problem.Once the synchronization points are attacked by the adversary,existing data integrity authentication schemes are difficult to work properly,and the false negative rate might be up to 50 percent.And the additional fixed group delimiters not only increase the data size,but are also easily detected by adversaries.In this paper,we propose an effective dual-chaining watermark scheme,called DCW,for data integrity protection in smart campus IoT applications.The proposed DCW scheme has the following three characteristics:(1)In order to authenticate the integrity of the data,fragile watermarks are generated and embedded into the data in a chaining way using dynamic grouping;(2)Instead of additional fixed group delimiters,chained watermark delimiters are proposed to synchronize the both transmission sides in case of the synchronization points are tampered;(3)To achieve lossless integrity authentication,a reversible watermarking technique is applied.The experimental results and security analysis can prove that the proposed DCW scheme is able to effectively authenticate the integrity of the data with free distortion at low cost in our smart meteorological Internet of Things system.

Steganalysis of LSB Matching Using Characteristic Function Moment of Pixel Differences

Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) matching,which is the prototype of many advanced information hiding methods.Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels.From another aspect,this paper calculates the differences among pixel pairs and proves that the histogram of difference values will be smoothed by stego noises.We calculate the difference histogram characteristic function(DHCF) and deduce that the moment of DHCFs(DHCFM) will be diminished after stego bits are hidden in the image.Accordingly,we compute the DHCFMs as the discriminative features.We calibrate the features by decreasing the influence of image content on them and train support vector machine classifiers based on the calibrated features.Experimental results demonstrate that the DHCFMs calculated with nonadjacent pixels are helpful to detect stego messages hidden by LSB matching.

High-Temperature Annealing Induced He Bubble Evolution in Low Energy He Ion Implanted 6H-SiC

Bubble evolution in low energy and high dose He-implanted 6H-SiC upon thermal annealing is studied. The （0001）-oriented 6H-SiC wafers are implanted with 15keV helium ions at a dose of 1×10^17 cm^-2 at room temperature. The samples with post-implantation are annealed at temperatures of 1073, 1173, 1273, and 1473K for 30rain. He bubbles in the wafers are examined via cross-sectional transmission electron microscopy （XTEM） analysis. The results present that nanoscale bubbles are almost homogeneously distributed in the damaged layer of the as-implanted sample, and no significant change is observed in the He-implanted sample after 1073 K annealing. Upon 1193 K annealing, almost full recrystallization of He-implantation-induced amorphization in 6H-SiC is observed. In addition, the diameters of He bubbles increase obviously. With continually increasing temperatures to 1273K and 1473 K, the diameters of He bubbles increase and the number density of lattice defects decreases. The growth of He bubbles after high temperature annealingabides by the Ostwald ripening mechanism. The mean diameter of He bubbles located at depths of 120-135 nm as a function of annealing temperature is fitted in terms of a thermal activated process which yields an activation energy of 1.914＋0.236eV.

Dynamically Tunable Perfect Absorbers Utilizing Hexagonal Aluminum Nano-Disk Array Cooperated with Vanadium Dioxide

A tunable perfect absorber composed of hexagonal-arranged aluminum nano-disk array embedded in the vanadium dioxide(VO_2) film is proposed. The aim is to achieve the tunability of resonance absorption peak in the visible and near-infrared regimes. Numerical results reveal that the absorption peak achieves a large tunability of 76.6% while VO_2 undergoes a structural transition from insulator phase to metallic phase. By optimizing the structural parameters, an average absorption of 95% is achieved from 1242 to 1815 nm at the metallic phase state. In addition, the near unity absorption can be fulfilled in a wide range of incident angle(0°–60°) and under all polarization conditions. The method and results presented here would be beneficial for the design of active optoelectronic devices.

Compressive Sensing Sparse Sampling Method for Composite Material Based on Principal Component Analysis

Signals can be sampled by compressive sensing theory with a much less rate than those by traditional Nyquist sampling theorem,and reconstructed with high probability,only when signals are sparse in the time domain or a transform domain.Most signals are not sparse in real world,but can be expressed in sparse form by some kind of sparse transformation.Commonly used sparse transformations will lose some information,because their transform bases are generally fixed.In this paper,we use principal component analysis for data reduction,and select new variable with low dimension and linearly correlated to the original variable,instead of the original variable with high dimension,thus the useful data of the original signals can be included in the sparse signals after dimensionality reduction with maximize portability.Therefore,the loss of data can be reduced as much as possible,and the efficiency of signal reconstruction can be improved.Finally,the composite material plate is used for the experimental verification.The experimental result shows that the sparse representation of signals based on principal component analysis can reduce signal distortion and improve signal reconstruction efficiency.

Recrystallization Phase in He-Implanted 6H-SiC

The evolution of the recrystallization phase in amorphous 6H-SiC formed by He implantation followed by thermal annealing is investigated. Microstructures of reerystallized layers in 15 keV He＋ ion implanted 6H-SiC （0001） wafers are characterized by means of cross-sectional transmission electron microscopy （XTEM） and high-resolution TEM. Epitaxial recrystallization of buried amorphous layers is observed at an annealing temperature of 900℃. The recrystallization region contains a 3C-SiC structure and a 6H-SiC structure with different crystalline orientations. A high density of lattice defects is observed at the interface of different phases and in the periphery of He bubbles. With increasing annealing to 1000℃, 3C-SiC and columnar epitaxial growth 6H-SiC become unstable, instead of [0001] orientated 6H-SiC. In addition, the density of lattice defects increases slightly with increasing annealing. The possible mechanisms for explanation are also discussed.

Using wavelet multi-resolution nature to accelerate the identification of fractional order system

Because of the fractional order derivatives, the identification of the fractional order system（FOS） is more complex than that of an integral order system（IOS）. In order to avoid high time consumption in the system identification, the leastsquares method is used to find other parameters by fixing the fractional derivative order. Hereafter, the optimal parameters of a system will be found by varying the derivative order in an interval. In addition, the operational matrix of the fractional order integration combined with the multi-resolution nature of a wavelet is used to accelerate the FOS identification, which is achieved by discarding wavelet coefficients of high-frequency components of input and output signals. In the end, the identifications of some known fractional order systems and an elastic torsion system are used to verify the proposed method.