Correlation filtering target tracking algorithm is based on the correlation between the target template and the image to be tested to realize the location and tracking of the target. Especially, when the kernelized correlation filters (KCF) is proposed which is faster and more accurate, the correlation filtering target tracking algorithm is pushed to a new height. However, with in-depth study of the KCF algorithm, it is found that the correlation filter has some serious shortcomings in the anti-occlusion performance. Especially in the case of a short-term disappearance of the target, it is extremely easy to lost the target. In order to solve this problem, a method combining KCF with the forward and backward error detecting algorithm is proposed. It detects the occlusion phenomenon by the forward-backward error algorithm, and retains the original target template in time after the occlusion occurs. Finally, it re-locates the position of the target by combing the prediction in a small range with the original template. Experimental results show that this method can effectively solve the occlusion condition when the target disappears completely and perform effective tracking after the target reappears.

The performance of multi-pulse position modulation inter-satellite optical communication system is analyzed. For the shot-limited system, the analytical expression of the average symbol error rate (ASER) is given. For the thermal-limited system, the analytical expression of tight upper bound on the ASER is derived. The validity of theoretical calculations is verified. The results of numerical simulations show that the system performance degrades with the increase of jitter variance, and for the different power regions, the performance of the system varies with the different beam waist radiuses. When the number of signal slots per symbol is constant, increasing the number of time slots per symbol will increase the system performance, but the bandwidth utilization reduces as well. Therefore, when selecting system parameters, the above factors need to be considered comprehensively.

Based on the combination of Raman scattering and optical time domain reflection techniques, the distributed optical fiber is used for the simulation experiment of leakage detection and location of tap water pipes. The temperature detection signal is obtained under no leakage or slight leakage of a tap water pipe by a distributed optical fiber sensor. In the experiment, the absolute distance method is firstly used for clustering the temperature detection signals to identify whether there is leakage in the tap water pipe or not. Then, as for the no-leakage signals, a selective average threshold method is used for the determination of a threshold signal. Finally, the difference signal between the detection signal and the threshold signal is used for the identification of leakage location of a tap water pipe. The results show that the whole system is stable and can be used to accurately identify the leakage of tap water pipes. Moreover, the use of the selective average threshold method can accurately locate the leakage point.

The indoor optical wireless channel can be considered as quasi-static multipath channel with inherently low-pass characteristic. Moreover, the visible light communication is usually a power limited system. In order to make full use of the channel spectrum resources and save the energy, the adaptive bit-power loading algorithms of Chow, Hughes-Hartogs and Fischer are applied to the asymmetrically-clipped optical orthogonal frequency division multiplexing (ACO-OFDM) system is proposed. The process of adaptive bit-power loading is introduced, and the performance of the adaptive ACO-OFDM is compared with that of the adaptive direct current optical OFDM (DCO-OFDM). The results show that, compared with the equal bit loading method, the adaptive load algorithm can save about 15% for the optical power and about 30% for the electric power when the performance of bit error rate is guaranteed. Fischer algorithm can save the most optical and electrical power, while Hughes-Hartogs algorithm needs more power. At the same transmit rate, the adaptive ACO-OFDM is more economical than the adaptive DCO-OFDM.

A wavelength demodulation system combined wavelength scanning with time division multiplexing for fiber Bragg grating(FBG)array is proposed. Wavelength demodulation for a large capacity FBG array with a tree topology structure is realized by the pulse modulation of the optical signal with a wavelength scanning. The FBG array wavelength demodulation system is is built and the ability of wavelength demodulation for FBG arrays with a high degree of reusability is verified. The permanence of the wavelength demodulation for FBG array is tested. The experimental results show that this system can achieve the identification and demodulation of the wavelength for the FBG in each subarray with the wavelength accuracy of 2.5 pm and the fluctuation range is within ±2 pm.

A structure of tunable dual-bandpass microwave photonic filter (MPF) based on chirped fiber Bragg grating (CFBG) is proposed and experimentally demonstrated. By using two cascaded CFBGs with different central reflection wavelengths, bandwidths and chirp parameters, and the different delays of the modulated light signal in this structure are introduced. Consequently, two different frequency passbands are realized. On this basis, by adjusting the variable optical delay line in Mach-Zehnder interferometer (MZI) to change the free spectral range of MPF, the central frequencies of the two passbands can be tuned. Two different passbands with the central frequencies of 6.75 GHz and 16.07 GHz are obtained in the experiment. By adjusting the optical path difference between the two arms of MZI, the tuning of the central frequency of MPF is achieved. The proposed dual-bandpass MPF has a simple structure and is of good tunability and stability.

A method of fiber Bragg grating sensing demodulation based on self-injection locking is proposed. This method adopts the structure of fiber laser, and the output wavelength of this fiber laser is locked at the reflection wavelength of the sensing grating by self-injection locking. Using the dispersion characteristic of the chirped fiber Bragg grating, the change of the wavelength of the laser is transformed into the change of the length of the resonant cavity. The wavelength change of the laser is measured by the laser beat frequency. The structure and measuring principle of this proposed method are described in detail, and the experimental research is carried out with the example of temperature sensing. This proposed method has the advantages of simple demodulation system, good stability, unlimited sensing distance and good compatibility.

A linear Fresnel concentrating power-generation system is favored by the market because of its small windward area and high land-occupation rate. However, the further reduction of the construction cost of the concentrating system is an ongoing problem. Therefore, we include several thin heat pipes instead of a crude heat pipe to solve this problem so that the total opening and primary reflector widths are increased to 1118.79 and 831.81 mm, respectively, for the compound parabolic condenser (CPC). Additionally, we study the light-gathering system for this design idea and find a CPC opening half-angle of 45°. For a working time of 6 h, the light that is incident on the primary mirror field is reflected into the CPC, and the rate is above 75%. Additionally, the instantaneous rate of light entry into the CPC is more than 75%. The experimental results showed that the optical efficiency reached 61% at noon in summer/sunny weather.

The remote sensing images have the multi-temporal, multi-semantics and multi-spectral characteristics. In view of the sensitive remote sensing images involving state secret information with low searching efficiency, it is a general trend to conduct a secure outsourcing search for remote sensing images based on a cloud platform. Thus, a scheme of secure outsourcing search for encrypted remote sensing images is proposed. The remote sensing image stored on the cloud platform is first encrypted by the scan mode, and then a double ciphertext is obtained through the exclusive or operation and the Johnson-Lindenstrauss (JL) transformation. The JL-transformed remote sensing images are used to search on the cloud platform, while the exclusive or encrypted remote sensing images are used to decrypt image. The experimental results show that this scheme can effectively guarantee the security of remote sensing images on the cloud platform. Meanwhile, the search accuracy of encrypted images is high and the search efficiency is 98.37% higher than that by homomorphic encryption. Moreover, the computational complexity and communication cost are low, and it is suitable for the deployment on the cloud computing platforms.

Aiming at the spoofing attacks for the current face authentication systems, the traditional spoofing attacks include displaying printed photos and replaying recorded videos. With the rapid development of three-dimensional (3D) printing technology, the 3D mask spoofing attack is becoming a new threat. On the basis of the shearlet transform and combining with the 3D geometric attributes and the local regional texture changes, a method by utilizing the multilayer autoencoder network to conduct the feature fusion-based classification to identify the attack mask is proposed for the 3D mask spoofing attack. The low-frequency sub-band and several high-frequency sub-bands are extracted from the 3D image of the target face by the non sub-sampled shearlet transform method. The scale space function is used to detect, locate and distribute the feature points and then to generate feature operators in the low-frequency sub-band . Then, the generated feature operators and the texture features extracted from the high-frequency sub-band are combined in series and fed into the stacked autoencoder network and the softmax classifier to conduct the bottleneck feature fusion-based classification. The experimental results in the BFFD database based on the flexible TPU material 3D print mask shows that, the multi-feature fusion method added the 3D geometric feature has an obvious improvement for the accuracy of the anti-spoofing performance against 3D mask attacks to compare with the previous method of using the texture feature alone.

In order to accurately achieve the effective ground information in the process of remote sensing image processing and shorten the classification time, the convolutional neural networks (CNN) model is introduced into the classification of remote sensing image features. First, the picture fuzzy weighted average (PFWG) improved CNN classification method is proposed. The fuzzy geometric clustering algorithm is used as a pre-processing unit to characterize the experimental samples, and for multi-source feature decision-making for remote sensing ground information. The classification process is simplified and the convergence of the CNN model is speeded up. The experimental results show that using PFWG improved CNN classification method, the overall classification accuracy reaches 93.73%, and the Kappa coefficient is 0.94. This method effectively compensates for the shortcoming of CNN itself which is not good enough for classification and has poor expression performance of remote sensing images. It has successfully completed an efficient classification task and has a certain anti-jamming capability.

In order to improve the coherent among different pixels, a spatial image adaptive steganography algorithm is proposed based on the framework of minimizing the embedding distortion function. The Daubechies wavelet construction filter is used to predict the residual weight of cover and the cost value is obtained. A mean filter is used to smooth the cost value. The messages combined with syndrome trellis coding are embedded. Two different image features are used for anti-steganography analysis. Experimental results demonstrate that the anti-detection ability of the proposed algorithm is similar to the Hill-pass, Low-pass, Low-pass (HILL) steganography algorithm when the payload is small and is significantly better than the mainstream steganography algorithms such as wavelet obtained weights and spatial-universal wavelet relative distortion (S-UNIWARD ) when the payload is large.

In the existing adaptive multi-scale block-slice compression sensing algorithms, the role of high-frequency information in the reconstruction process is neglected, resulting in the not-complete-reconstruction of the edge contours of images. Moreover, the fixed block size is used in the process of compressing blocks, and thus the sparsity of the image itself is not fully used. In view of the above deficiencies, a multi-scale block adaptive sampling rate compression sensing algorithm is proposed. This algorithm makes full use of the high-frequency and low-frequency signals after wavelet transform, and simultaneously improves the fixed size block of images. First, the spatial filtering algorithm based on adaptive neighborhood features is used to eliminate the blockness in the low frequency part. Second, as for the high frequency part, the size of the image block is adaptively selected according to the texture features, and thus the sample block size is automatically partitioned and the sampling rate is adaptive. Finally, the images with different amounts of texture information are compressed and reconstructed. The results show that the reconstruction effect by the proposed method is obviously superior to those by the existing adaptive sampling rate algorithms.

In the process of diagnosis using medical image, it is necessary to do image enhancement efficiently in order to mine more information for decision-making as much as possible. However, the traditional medical image enhancement algorithm has some shortcomes, such as creating noise and fuzziness. Therefore, an image enhancement algorithm based on shearlet domain and improved Pal-King algorithm is proposed. First, the shearlet transform is used to decompose the image two parts, high frequency part and low frequency part. Then the adaptive threshold denoising method is used to denoise the image efficiently. After that, the inverse shear wave transform is used to reconstruct the image. Finally, Pal-King algorithm is used to enhance contrast to highlight the details of the image. In order to verify the validity of this algorithm, the processing results of the proposed algorithm are compared with shear wave, fractional differential and the improved Pal-King enhancement method respectively by using the self-built image database. Results show that both the enhancement effect and contrast of image by the proposed algorithm has significant improvements.

In order to effectively fuse the audio and visual features in the task of speaker recognition, a multimodal long short-term memory network (LSTM) with depth-gate is proposed. First, a multi-layer LSTM model is established for each type of individual features. Then the depth-gate is used to connect the memory cells in the upper and lower layers, and the connection between the upper and lower layers is enhanced, which improves the classification performance of the feature itself. At the same time, the connection among layer models can be learned by sharing the output of hidden layers and the weight of each gate unit among different models. The experimental results show that this method can be used to effectively fuse the audio and video features and improve the accuracy of speaker recognition. Moreover, this method is robust to external disturbance.

Due to the absorption and scattering of the light, underwater images often have the problems of low contrast, detail blurring and color distortion. An underwater image restoration method based on scene depth estimation and white balance is proposed. First, the underwater image is divided to the scene depth related patches by using Sobel edge detection and morphological closed operation. Regression analysis of the relationship between the RGB channel and the scene depth is carried out to obtain scene depth images and to estimate the background light underwater. Second, for the heavily attenuated color channel, its inverse channel is used for correcting the transmission estimation. Third, the backscattering is eliminated by inversely calculating the underwater optical imaging model. Finally, the improved white balance algorithm is utilized to achieve better color correction of underwater images and the restored underwater images are obtained. Compared with four kinds of typical underwater image restoration methods, the experimental results demonstrate that the proposed method can effectively improve the detail clarity and color fidelity of underwater images with low quality and low illumination, while restoring the natural visual effects.

The quaternion spectral residual method for detecting the visual saliency regions of two images is proposed, by expressing the reference image and the distorted image as a pure quaternion matrix. Then both the method and the quaternion gradient features are employed to design color image quality evaluation, as well as visual saliency as the weight of the evaluation index. Numerical experiments are conducted on the TID2013 and CSIQ databases to calculate four kinds of objective evaluation indexes such as the Spearman rank correlation coefficient (SROCC), the Kendall rank correlation coefficient, the Pearson linear correlation coefficient, and the root mean squared error. The results show that the experimental SROCC value on TID2013 reaches 0.8169, which matches the subjective evaluation of humans.

In order to identify foliage and the adjacent obstacles in the vegetation scenes, an object detection algorithm of three-dimensional laser radar is proposed. The neighborhood characteristics of neighboring points are constructed in point cloud, and new characteristic parameters are extracted as determining criterion. Then the Gaussian mixture model is obtained by using the maximum expectation algorithm to characterize the distribution of the parameters. Finally, the priori model is established by using Markov random field. The optimal objective function is obtained by the graph-cut method under the maximum posteriori probability framework. This algorithm has been successfully applied to the unmanned platform. The experimental results show that the algorithm can effectively identify foliage and their adjacent obstacles, and the boundaries of obstacles can be detected clearly. Compared with traditional algorithms, the proposed algorithm is more robust and accurate, and its response time meets the demand of practical applications.

The theoretical structure for generating floating images from laser-ionized air is described and tested. A sensor mechanism by which users can interact with these images via touch is also proposed. The image-projection and touch feedback system uses a low-power femtosecond laser to ionize the air to create images and senses tactile interactions by detecting the laser echo. This method can be used for detecting a touch signal that changes the image position and for determining the location of a touch via triangulation. The proposed system can be applied in holographic projection imaging, fog-screen imaging, and laser imaging combined with ionized air. The holographic projection image or fog imaging screen is used to form a basic image, and the laser-ionized air is used to form images for touch interactions and buttons.

In order to evaluate and design the stitching algorithm better, a color-corrected stitched image quality assessment method is proposed. A stitched image database with five kinds of color differences is established by using the current color correction algorithm and stitching algorithm. To evaluate color-difference stitched images quality comprehensively, four features are extracted from pre-splicing image sequence and stitched image respectively. Then four features are combined to establish a relation model between features and quality through support vector regression algorithm, so as to predict color-difference stitched image quality. Experimental result shows that the proposed method can effectively evaluate color-difference stitched images quality.

The phase measuring deflection is used as a basic technical principle, a measurement system composed of an LED plate display, a measured mirror object and a CCD camera is constructed. The geometric and physical model for general geometric settings and the corresponding calibration method is proposed, which has low equipment, high flexibility and accuracy. The preliminary experimental results of concave mirror demonstrate the feasibility of this method.

The autonomous positioning technology of a multi-camera system can restore the spatial position and pose of the system by using the multiple cameras to observe the feature points in space, which can overcome the influence of the complicated environment and improve the precision of measurement by using the large field of view of multiple cameras. Aiming at the problem of the complex structure of a multi-camera system, posture recovery is rather difficult and time-consuming, a self-positioning method based on the graphic optimization model is proposed. On the basis of solving the efficient perspective-n-point-positioning problem to obtain the approximate position and pose of a multi-camera system, a graph optimization framework is used to model the observation problem of the multi-camera system and the spatial control points. Thus, the problem of the position and pose recovery is equivalent to the problem of the minimization reprojection error nonlinear optimization. Using a control field build by a video-simultaneous triangulation and resection system (VSTARS) and a multi-camera system build by a linear guideway, the results of the measurement and simulation experiment show that this method possesses a high measurement accuracy and a fast running speed.

A 5 kW disk laser is used for the butt filler wire welding of 1.1 mm thick advanced high strength steels. The laser process parameters are designed by the orthogonal test, and the tensile strength, microhardness, surface and cross section of these welded joints are analyzed by the universal mechanical test machine, microhardness tester, optical microscope and scanning electron microscope. In addition, the effects of laser process parameters on the mechanical properties and microstructures of the welded joints are studied. The results show that laser power and welding speed are the most important parameters influencing the performances of joints in the range of test parameters. Under the reasonable process parameters, that is, when the line energy is greater than the threshold value, the weld appearance with a uniform, continuous and smooth surface can be obtained, and the tensile strength is more than 90% of that of the base material. There exist for all the joints the phenomena of fusion zone hardening and lateral heat-affected zone softening. The microstructure of the fusion zone is mainly composed of lath martensite and proeutectoid ferrite. The microstructure of the heat affected zone is softened due to the emergence of large quantities of tempering martensite, which affects the tensile properties of joints.

We investigate the characteristics of dissimilar metals, such as TC4 Ti alloy/5052 Al alloy, joined via laser spot welding using a continuous fiber laser device of 1070 nm wavelength and 6 kW maximum power. Results indicate that with the same defocusing distance, two molten pools are formed during welding with a low laser power. Shrinkages, pores, and cracks occurred on the Al base metal. With increasing laser power, a part of the molten Ti flowed into the fusion zone, forming a nail shape, due to which mutual diffusion and reaction occurred between the Ti and Al atoms at the edge. TiAl2 and TiAl3 phases are produced close to the Ti and Al sides, respectively. A layer of TiAl3 intermetallic compound formed at the Ti-Al interface but not in the fusion zone. The TiAl3 layer became thicker as the laser power is increased. Joint strength first increases and then decreases with the increasing laser power at defocusing distances is less than +50 mm. The maximum fracture load is less than 3000 N. At the defocusing distance of +50 mm, two peak values of joint strength are observed with a variation of laser power. The fracture locations are distributed at the interface and the Al alloy base metal. Cleavage and ductile fractures are the corresponding forms of fractures. The maximum joint strength reaches 3571 N.

A certain amount of noise would be introduced by a dictionary update step using the K-means sequential generalized (SGK) denoising algorithm. To reduce the effect of noise interference on dictionary atoms, a seismic signal denoising algorithm is proposed based on controlled interference SGK (C-SGK) dictionary learning under a compressive sensing framework. The algorithm compares the signal-to-noise ratio and the threshold set in the dictionary update step, which determines whether to update the atom: the atoms should be sequentially updated only if the signal-to-noise ratio is greater than the threshold. The experimental results of synthesized and real seismic signal denoising in this study indicate that the proposed algorithm can effectively control noise interference. Compared with traditional SGK denoising, the proposed algorithm demonstrates a better denoising effect on seismic signals.

In order to solve the visual quality problem caused by the different structure or reflectivity of the object surface, a new adaptive illumination method based on camera-projector system is proposed. By adopting the dual-frequency grating fringes, both high and low frequency phase shift characteristics can be combined and used for unwrapping phase, which is more accurate than the traditional single frequency fringe unwrapping method. Thus, the necessary depth accuracy is improved in the measured process. Furthermore, different channels deal with different frequencies of fringe gratings by using the characteristic of the multiple channels of colored images. This method is twice as fast as the black-and-white image measurement method. Therefore, the dual-frequency phase shift adaptive method can meet the requirement of high speed and accuracy in machine vision measurement, and thus improves the on-line detection ability and can be widely used for industrial detection and image recognition.

Photonic crystal (PC), conventional camouflage fabric and infrared stealth coating are used as testing sample, which is attached tightly to a heater with a temperature of 60 ℃ respectively, to perform infrared stealth experiment in outdoor environments. Experimental results show that sunlight affects 8-14 μm waveband stealth effect of PC slightly, while 3-5 μm waveband is also the same in most angle range. Radiation from walls and atmosphere has a little effect on 3-5 μm and 8-14 μm stealth effects of PC. The PC has better infrared stealth performance in both middle and far infrared wavebands, compared with the other two conventional infrared stealth materials.

With polyethylene oxide as a polymer additive, the composite films are prepared out of the solutions with different ratios of CH3NH3Br and PbBr2 (Ⅱ). The influences of different feeding ratios on the morphology, crystallinity, photophysics and electroluminescence properties of the films are studied. The effects of the species and contents of polymer additives are further investigated. The results show that the multilayer structure devices have the maximum luminance efficiency of 6.4 cd·A-1, much higher than that of the single-layer devices.

Aimed at the disadvantages of the lower azimuth resolution of the sky-wave radar and larger position error of traditional analytic algorithm, a new locating model using chaotic mutation grey wolf optimization algorithm to optimize the kernel extreme learning machine (KELM) is put forward. First, the piecewise linear chaotic map, adaptive Cauchy mutation strategy and non-linearity of the convergence factor are introduced into the grey wolf optimization algorithm to form an improved grey wolf algorithm. Then, the improved grey wolf optimization algorithm is used to optimize penalty coefficient and kernel parameter of the KELM. Finally, the optimized the KELM is applied to sky-wave radar location, making the established KELM model have the high steady-state prediction accuracy and generalization performance. The experimental results show that the predicted results of the proposed model are basically consistent with the measured values, and the prediction accuracy is higher than that of the KELM location model, which is optimized by the standard grey wolf algorithm. A new target location method is provided for sky-wave radar.

Chalcogenide glasses (ChGs) have a very wide range of infrared transmittance, extremely high linear and nonlinear refractive index. In this article, research progress on ChG photonic devices based on stimulated Brillouin scattering is reviewed, as well as applications of ChG fibers and waveguides in Brillouin fibers lasers, slow light generation, and microwave photonic filters. Moreover, current problems are summarized, and their potential developments are discussed.

The high-dimension, high-redundancy, high-noise and nonlinear characteristics of near-infrared spectroscopy data seriously affect the accuracy of spectral similarity measurement. Aiming at this problem, a similarity measurement method of the near infrared spectrum based on the grid division local linear embedding (GGLLE) algorithm is proposed. First, the high-dimensional spectral data is divided into multiple grid subspaces according to the expression of key chemical components in the spectrum. Second, two aspects for the local linear embedding (LLE) algorithm are improved, and the improved LLE algorithm is used to sequentially map the feature of each subspace from high- to low-dimensional space and calculate the similarity matrix of the generated subspace. Finally, the subspace similarity matrix is normalized, and the similarity matrix of the accumulated and generated spectral sample set is to be solved to realize a similarity measurement of the spectrum. Two sets of tobacco leaf spectral data provided by a tobacco company are selected to construct a model of the spectral similarity measurement. The accuracy of the similarity measurement is a criterion of the pros and cons of the algorithm. The experimental results show that the accuracy of the similarity measurement model constructed by the GGLLE algorithm is 93.3%, which is obviously better than the accuracies achieved by principal component analysis, stacked auto encoders, and LLE algorithms, which are 64.2%, 67.5%, and 82.5%, respectively. Thus, the effectiveness of the GGLLE algorithm is proved.

This study proposes a new technique that combines three-dimensional fluorescence spectra with algorithm combination methodology (ACM) to address issues associated with complex components of oil pollutants and the difficulty in identifying their overlapping spectra. By combining alternating trilinear decomposition (ATLD), self-weighted alternating trilinear decomposition (SWATLD), and parallel factor analysis (PARAFAC), ACM realizes the complementary advantages of using three algorithms. First, using carbon tetrachloride as the target contaminant, a three-component mixed solution of diesel, gasoline, and kerosene with different concentrations are prepared. Then, the three-dimensional fluorescence spectra of the mixed solution are measured using a F-7000 fluorescence spectrometer. Blank deduction and missing data recovery-principal component analysis are then employed as pretreatment methods to eliminate the scattering interference. Finally, ACM is used to decompose the three-dimensional spectral data matrix. Results are compared with the three separate algorithms for component analysis, revealing that ACM is insensitive to component concentration. The average recoveries for diesel, gasoline, and kerosene are 96.68%, 97.83% and 97.11%, respectively, which further indicated that this method is more universal and can be used for the qualitative and quantitative analyses of contaminants in oil mixtures.

A 3.7 fs pulsed laser is used to selectively pump the two lowest electronic transitions of semiconducting single-walled carbon nanotubes, which induces a low-frequency oscillation radial breathing mode (RBM) and a high-frequency oscillation axis stretching mode (GM) along the carbon tubes. By changing the pump bandwidth, the pump effects of the lowest single-excited-state and dual-excited-state on the amplitudes and initial phases of the two vibrational modes are detected. The research result indicates that the amplitudes of RBM and GM, as well as the initial phase of GM, are obviously modulated. With the influence of coherent electro-phonon coupling on phonon field, the parameters of the vibrational modes of the carbon nanotubes lattice structure are modulated. This provides a possibility to regulate the coherent lattice vibration properties by inducing the electronic coherent motion.