In this paper, a high transmission metasurface for subwavelength focusing of terahertz waves was proposed. A full 2π phase coverage with high transmission at target frequency was designed by varying the lengths of the cross resonators. The high transmission characteristics of the resonators were analyzed and the performance of the focusing was also discussed. The results show that the maximum transmission of the resonators reaches 0.92 and the terahertz wave can be focused with at the focusing point of a full width at half-maximum of 143 μm, which agrees well with the full width at half-maximum of 139 μm obtained by Huygens' principle. This device with the characteristics of flexible, thin and easy-integration exhibits the potential applications in THz imaging and communications, and also can be extended in the design of other planar THz components easily.
Optically tunable negative refractive metamaterials composed of electric response split-ring resonator (eSRR) and magnetic response split-ring resonator (mSRR) are theoretically investigated in terahertz (THz) region. A negative refractive band is achieved when both the eSRR and mSRR are fabricated on one substrate. Meanwhile, the optically tunable response is realized by filling the photo conductive semiconductor GaAs in the capacitive regions of eSRR. The electric response frequency varies with the pump laser fluence, therefore, the electric response frequency is controlled to overlap with the magnetic response frequency, and the negative refractive of the metamaterials can be controlled flexibly. The extracted constitutive parameters illuminate that a bandwidth 60GHz of negative refractive is realized when eSRR and mSRR combine together (with no GaAs), while a negative band width 30GHz is realized at the same structure parameters (with GaAs) when the pump laser fluence increases to 0.4mJ/cm2. Furthermore, the transmission spectra changes from dual band to single band with the variation of pump laser fluence.
Hyperspectral imaging technique and artificial neural network were used to investigate the feasibility of the nondestructive prediction for firmness and soluble solids content (SSC) of “Red” and “Green” plums. And the standard normal variation (SNV) was adopted to preprocess original spectral reflectance of region of interests. Then 5 and 28 characteristic wavelengths were selected from 256 full wavelengths by the methods of successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. An error back propagation (BP) network model was proposed based on selected characteristic variables to predict firmness and SSC of plums. The SSC prediction accuracy of CARS-BP model in calibration set (rc = 0.989, RMESC = 0.451 °Brix) was slightly higher than SPA-BP model (rc = 0.978, RMESC = 0.589 °Brix), while the SSC prediction accuracy of SPA-BP model in prediction set (rp = 0.964, RMESP = 0.778 °Brix) was slightly higher than CARS-BP model (rp = 0.955, RMESP = 0.851 °Brix).
KEYWORDS: Hyperspectral imaging, Data modeling, Principal component analysis, Convolution, Image processing, Nondestructive evaluation, Data acquisition, Process modeling, Spectroscopy, Injuries
Aiming at the problem that kiwifruit invisible damage is difficult to detect and identify by conventional detection methods, this paper proposes to use the visible near-infrared hyperspectral imaging technology to detect the identify and identify models based on deep learning VGG-16 neural network. Detection and recognition of hyperspectral images of kiwifruit invisible damage. The network is implemented by the caffe framework and python and is a 16-layer deep learning neural network. The reflection spectroscopy images of 50 kiwifruit samples were obtained at wavelengths of 400-1000 nm. According to whether they were subjected to invisible damage, they were classified into invisible damage and undamage, with 40 and 10 samples respectively. The training set and the test set are used to obtain the implicit damage discriminant model by using the principal component analysis image obtained from the spectral data as the input image of deep learning. The experimental results show that the highest accurate recognition rate reaches 100% and has a good recognition effect.
The difference between the initial wavelength of the output signal caused by the error during the tunable laser tuning process will lead to the decrease of the azimuth resolution of the synthetic aperture lidar. In order to reduce the initial wavelength error, a scheme of filtering using a cascaded micro-ring M-Z optical band-pass filter is proposed. Using the digital filter design method by cascade micro-loop assisted M-Z interferometer to achieve elliptic filter. The 24-order elliptic filter is used to realize the filter with the transition band of 0.001nm, the passband ripple of 1dB and the stopband ripple of 60dB. The use of digital filter design cascaded micro-ring M-Z interferometer optical filter, not only to achieve the desired results, but also can improve the design efficiency of optical filters. After the optimization of the parameters of the cascaded microring M-Z filter, a filter with a large bandwidth, a flat top and a very small transition band can be obtained. Used to control the initial wavelength error can achieve the desired effect.
It is difficult to monitor and predict the crops early diseases in that the crop disease monitoring is usually monitored by visible light images and the availabilities in early warning are poor at present. The features of common nondestructive testing technology applied to the crop diseases were analyzed in this paper. Based on the changeable characteristics of the virus from the incubation period to the onset period of crop activities, the multilevel composite information monitoring scheme were designed by applying infrared thermal imaging, visible near infrared hyperspectral imaging, micro-imaging technology to the monitoring of multilevel information of crop disease infection comprehensively. The early warning process and key monitoring parameters of compound monitoring scheme are given by taking the temperature, color, structure and texture of crops as the key monitoring characteristics of disease. With overcoming the deficiency that the conventional monitoring scheme is only suitable for the observation of diseases with naked eyes, the monitoring and early warning of the incubation and early onset of the infection crops can be realized by the composite monitoring program as mentioned in this paper.
This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.
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