Rydberg atoms can be used for high-precision, self-calibrating measurements of microwave and terahertz fields due to their large polarizability and electric dipole moments and high sensitivity to external electric fields. In this paper, the research progress of using the Electromagnetically Induced Transparency(EIT) effect and the Autler-Townes(AT) splitting of Rydberg atoms to achieve high-precision detection of microwave and terahertz fields, is reviewed, as well as the research of Rydberg atoms in fast terahertz imaging.

Aiming at the requirements of millimeter and sub millimeter wave radiometer for quasi optical feed network, a 23~425 GHz ten channels quasi optical feed network is designed by GRASP software, using flat plane to achieve a double layers quasi optical feed network and reduce the size,using polarization grids and Frequency Selective Surfaces(FSS) to separate different channels. Through simulating the specifications of each channel, the test results are consistent with the simulation results.

In this paper, a different network architecture is proposed for intelligent segmentation of THz security inspection images based on Generative Adversarial Network(GAN) and multi-head attention mechanism. The algorithm is prone to address the problems of low-resolution THz images, blurred edges of dangerous goods, and inability to segment dangerous goods efficiently. More realistic images are obtained by studying the feature map of the deep discriminator. The multi-head attention mechanism is introduced to improve the recognition ability of the model to the characteristics of dangerous goods. A large number of experimental results of segmentation of terahertz security inspection images show that the proposed GAN has better generalization ability at the same depth than the traditional Convolution Neural Networks(CNN). The introduction of multi-head attention mechanism strengthens the model's learning of the characteristics of dangerous goods, which also has a good effect in the case of unknown dangerous goods category. The Intersection Over Union(IOU) index is 9.6% higher than that of RestNet-50, 21.3% higher than that of RestNet-18, and 12.3% higher than that of U-Net. The research is conducive to image segmentation algorithms for more accurate and efficient processing of THz security images, which broadens further applications of THz intelligent security systems.

Terahertz Time Domain Spectroscopy(THz-TDS) is employed to detect the Zinc Citrate (ZC) dihydrate and its dehydration kinetics. Results show that there are two prominent absorption peaks: one at 1.91 THz, the other at 1.77 THz for the ZC dihydrate, while there is no featuring absorption peak for ZC anhydrate at room temperature. When the temperature increases, the absorption peak of ZC dihydrate at 1.91 THz decreases and gradually disappears. According to the variation of the absorption peak area of ZC dihydrate at 1.91 THz under different temperatures and heating time, the enthalpy change of ZC dihydrate transforming to anhydrate is 381.13 J/g(232.6 kJ/mol) by using the Arrhenius equation, and the error is 4.2% compared with the traditional Differential Scanning Calorimetry(DSC) measurement. These results indicate that THz-TDS can be used as an efficient and fast technique for the detection of ZC dihydrate and its dehydration kinetics.

To design the optimal protection scheme for cultural relics of different materials and production techniques, the cultural heritage scientists are in constant search of the most effective nondestructive testing method for the overall survey of the cultural relics. Due to the unique properties of terahertz wave's low energy, strong penetrability for non-metallic and nonpolar substances, simultaneous measurement of amplitude and phase information, strong anti-interference capacity, etc., terahertz technology has been gaining an edge over other conventional nondestructive testing methods. In this review paper, the latest progress of terahertz technology applied in nondestructive testing of cultural relics are introduced, and the terahertz spectra characteristics of various cultural relics materials are illustrated. The principle and characteristics of terahertz imaging detection technology are expounded. The key issues in the application of terahertz nondestructive testing technology for various cultural relics are discussed, and the most successful and representative application examples of terahertz technology in cultural relics science are listed. Finally, we present the developing trend of terahertz nondestructive testing technology in the field of cultural relics.

The traditional passive-resistance test of all electronic fuze adopts external on resistance test equipment or general on resistance test circuit design, which has nothing to do with the electrical performance test of full electronic fuze, and can not avoid the risk of the failure product burning after power on. Therefore, a systematic design idea is proposed to diagnose the electrical performance of passive-resistance and full electronic fuze. This paper designs a full-automatic test control architecture based on ARM chip and FPGA dual core architecture and a 24 bit high-precision four-wire passive-resistance test circuit. The passive-resistance of the external interface part of the all electronic fuze and the working characteristics of the fuze are comprehensively tested. The test results show that the design can be utilized to test the passive-resistance with high precision(the measuremetn accuracy is ±0.1%) as well as the comprehensive working performance after power on.

The multiple features are selected and classified based on Nonlinear Correlation Information Entropy(NCIE) for the target recognition problem of Synthetic Aperture Radar(SAR) image. The Gaussian mixture model is employed to model the probability distributions of different kinds of features and then the KL(Kullback-Leibler) divergence is utilized to evaluate the similarity among different kinds of features. The NCIE values of different combinations of features are calculated and the one with the maximum entropy is chosen as the optimal. The joint sparse representation model is employed to represent and classify the selected features. Experiments are conducted based on the MSTAR data under the standard operating condition and extended operating condition. The results show the effectiveness of the proposed method.

The emitter threat level evaluation is an important topic in electronic countermeasures. To deal with the disadvantages of the current methods for emitter threat level evaluation, the UTilites Additives DIScriminantes(UTADIS) method, which is a method of multicriteria classification, is introduced. It is indicated that UTADIS can produce reasonable and accurate evaluations of emitter threat level that reflect the preference of decision makers and can give more information of emitter threat degree. Thus, UTADIS has advantages in performance and applications and is a new way to effectively evaluate the emitter threat level.

This study investigates artificial noise aided Multiuser Multiple-Input Single-Output (MU-MISO) broadcast wiretap system designs in slow fading channel environment. We adopt a beamforming technique with artificial noise to achieve secure multiuser communication and optimize system performance. To overcome the complexity of this model, a novel optimization scheme using semi-closed-form expressions and Monte Carlo method is employed to derive the relationship between transmission parameters and secure transmission performance. In this article, we detail the procedure of our new method, and conduct some heuristic simulation works. The simulation results reveal how power allocation ratio and information rate influence the multiuser system secure transmission probability and effective secrecy throughput of the multiuser system. We compare the multiuser system security and throughput performance with each user's performance, which helps us to verify the security ability of our method. Our research results extend the traditional single-user artificial noise design method to multi-user scenarios, and provide ideas for solving the optimization problem of multi-user broadcast communication.

As a common structure of power electronic system, the unequal length multiconductor transmission lines model has a crosstalk problem which may affect the normal operation of the whole system. In this paper, the Finite Difference Time Domain(FDTD) format is employed to differentially discretize the transmission line equation, and the Kirchhoff’s law is utilized to process the connection positions of different multiconductor systems. Then, the iterative formulas of the voltage and current on the multiconductor transmission lines are derived, and the simultaneous solutions of all voltages and currents on the entire model are realized. Finally, the effectiveness of the method in analyzing the electromagnetic interference problem of electrically large transmission lines is verified by a numerical example.

It is one of the key technologies of underwater pulsed laser detection to suppress the backscattered noise caused by water transmission in signal processing. Based on the correlation between two adjacent detections of backscattering in the same water area, an improved Normal Least Mean Square (NLMS) adaptive filtering algorithm is proposed. The step factor is controlled according to the intensity of backscattering and the size of the filtered target signal to make it achieve good filtering effect for target signals at different distances and intensities. The algorithm is verified by simulation and swimming pool experiment. The results show that the filter algorithm can effectively filter the backscattered noise with a lower order for targets with different reflection strengths at different distances, and retain the target echo signal. Compared with the existing adaptive filtering method, it greatly reduces the computing resources required for filtering, and has certain application value in underwater pulsed laser detection systems.

Currently a number of image matching algorithms focus on measuring the distance between key points while ignoring the structure information of the images, hence are prone to mismatches. This paper presents an image matching algorithm using the geometric invariance of variance constraint coupling. With the help of Forstner operator, the interest value of pixels is calculated to detect the characteristics of the image. The gradient information of the image is calculated to obtain the direction value of the image. The circular neighborhood of image features is cut to obtain the fan-shaped sub domains. Based on the direction value of the image, the feature vector of the image feature is obtained by calculating the gray invariant moment in the fan-shaped sub domains. The region variance function is introduced to obtain the structure information of the image, which is added to the image feature matching process to constrain the results of Euclidean distance measurement and realize the image feature matching. Based on the geometric invariance between matching points, the matching image features are processed to get accurate image matching results. Experimental results show that compared with the existing matching techniques, this algorithm has higher matching accuracy, which up to 96.56%, 95.38% and 93.52% for non-transformed images, zoomed images and rotated images, respectively.

Aiming at the problem of low network coverage caused by uneven deployment and distribution of Wireless Sensor Network nodes, with the goal of maximizing wireless sensor network coverage, a network coverage optimization strategy based on Improved Firefly Algorithm(IFA) is proposed. This method uses the good point set method to initialize the population, improve the diversity of the population and lay the foundation for the global search. Simultaneously, it uses the sigmoid function with non-linear exponential decline as the inertia weight to balance the global and local search capabilities of the algorithm. Then, Gaussian disturbance strategy is employed to perturb individual position update and avoid the premature of the algorithm. The simulation results indicate that compared with Artificial Fish Swarm Algorithm(AFSA), seed Hybrid Particle Swarm Optimization(HSPSO) and Chaotic Glowworm Swarm Optimization(CGSO), this algorithm effectively enhance the network coverage rate and make the WSN more evenly distributed.

In order to improve the accuracy of image segmentation without changing the structure of original semantic segmentation models, an approach is proposed to train Semantic Segmentation models by using Generative Adversarial Network(SS-GAN). There are three steps related to this work: constructing the generative model of Fully Convolutional Network(FCN) structure to segment image preliminarily; constructing the adversarial model which can learn the high-order relationship between pixels and training it to improve the learning ability of generative model; adding the anti-loss to assist generative model training, encouraging generative network to learn the relationship between pixels independently. Experiments on Pattern Analysis, Statistical Modeling and Computational Learning (PASCAL VOC) and Cityscapes datasets show that the proposed method achieves better performance than the existing advanced methods, and improves Intersection over Union(IoU) by 1.56%/1.17% and 1.93%/ 1.55%, respectively.

Accurate and timely acquisition of the phenological information of winter wheat which is one of the main crops in China, is a prerequisite for its growth monitoring and yield estimation. Spaceborne Synthetic Aperture Radar(SAR) is an all-time and all-weather microwave remote sensing device, which can realize large-scale monitoring of periodic crop phenology. According to the time series Sentinel-1A SAR data, a phenological period identification method of winter wheat is proposed based on spaceborne dual-polarization SAR. This method uses eigenvalue decomposition and polarization scattering analysis to extract the backscattering coefficient, polarization entropy, scattering angle, etc., in different phenological stages to realize winter wheat phenology identification. The experimental results show that the overall accuracy of phenological phase identification reaches 79%. This method has practical value in winter wheat growth monitoring.

By using the 1 Pulse Per Second(1PPS) received from Global Positioning System(GPS), the disciplining method is studied for common secondary frequency source Temperature Compensated Crystal Oscillator(TCXO) and Coherent Population Trapping(CPT) atomic clock. A disciplining scheme of phase-locked loop is designed. The time difference between 1PPS from local frequency division and that received by GPS receiver is measured by Time-Digital Converter(TDC) to realize the locking of local signal to GPS time signal. TCXO has achieved frequency stability of 8.5 ′ 10-12 at 10 ks, and the average frequency accuracy at 35 ks after disciplining has been improved by more than 4 times. In addition, the noise model of CPT atomic clock is deeply studied and simulated through Matlab. The corresponding relationship between white frequency noise and random walk frequency noise on Allan variance curve is established. The results after disciplining by average filtering and Kalman filtering on CPT atomic clock are compared, and the frequency stability increases by an order of magnitude at 50 ks.