Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB.

A pair of parallel Stark plates are added to a CO laser magnetic resonance spectrometer to apply electric field in the absorption cell. This apparatus is used to measure the molecular electric dipole moment via Zeeman and Stark effects simultaneously. The saturated absorption spectra of NO (X²Π_3/2, ν = 1 ← 0) was observed and the electric dipole moments of NO were directly measured in the presence of an electric field. The dipole moments are determined as μ₀(ν = 0) = 0.1595(15) D, μ₁ (ν = 1) = 0.1425(16) D. The electric dipole moment of the vibrationally excited state (ν = 1) is determined for the first time. The dependence of the electric dipole moments on its nuclear distances is interpreted.

We have theoretically and experimentally investigated the laser-detected magnetic resonance induced by radio-frequency two-photon processes in $F_g=4$ of $D_1$ line of cesium atoms. The effective Hamiltonian for the interaction between the two radio-frequency magnetic fields and sublevels at the ground state of $F_g=4$ is derived. By appropriately selecting the frequencies of the two radio-frequency fields, the Raman two-photon process and the cascade two-photon process can occur, which induce the magnetic resonance detected through laser transmission spectra. The theoretical calculation results fit well with the experimental data. Our results maybe apply for measuring the high bandwidth radio-frequency magnetic fields in space and studying the magnetic-induction tomography.

The silicon pixel sensor is the core component of the vertex detector in the Circular Electron Positron Collider (CEPC). The JadePix3 is one of the chips designed to study the performance and design of pixel sensors. The chip is a design of the full-function large-size chip based on CMOS technology. To test all the functions and the performance of this chip, we designed a test system based on the IPbus framework. The test system controls the parameters and monitors the status of the pixel chip and off-chip components. The IPbus block read/write speed is extended by jumbo frame feature integration to the IPbus suite for meeting the readout specifications of the JadePix3. The robustness, scalability, and portability of this system have been verified by pulse test, cosmic test and laser test in the laboratory. This paper describes the JadePix3 DAQ and control system, the integration tests, results and performance are included.

With the demand for fast response of magnetic field measurement and the development of laser diode technology, self-oscillating laser-pumped atomic magnetometers have become a new development trend. In this work, we designed a portable self-oscillating VCSEL-pumped Cs atom magnetometer, including the probe (optical path) and circuits. The signal amplification and feedback loop of the magnetometer, VCSEL laser control unit, and atomic cell temperature control unit were realized. We tested the performance of the magnetometer in the metering station. Finally, The performance of the VCSEL-pumped magnetometer designed in this work was compared with that of a CS-3 lamp-pumped self-oscillating atomic magnetometer; their performance was found to be mostly in the same order of magnitude, while the power consumption of our magnetometer was 3 W less than that of the CS-3. This work represents an exploratory attempt to integrate and miniaturize a portable self-oscillating VCSEL-pumped Cs atomic magnetometer.

Emitter signal waveform recognition and classification are necessary survival techniques in electronic warfare systems. The emitters use various techniques for power management and complex intra-pulse modulations, which can create what looks like a noisy signal to an intercept receiver, so emitter signal waveform recognition at a low signal-to-noise ratio (SNR) has gained increased attention. In this study, we propose an autocorrelation feature image construction technique (ACFICT) combined with a convolutional neural network (CNN) to maintain the unique feature of each signal, and a structure optimization for CNN input layer called hybrid model is designed to achieve image enhancement of the signal autocorrelation, which is different from using a single image combined with CNN to complete classification. We demonstrate the performance of ACFICT by comparing feature images generated by different signal pre-processing algorithms, and the evaluation indicators are signal recognition rate, image stability degree, and image restoration degree. This paper simulates six types of the signals by combining ACFICT with three types of hybrid model, the simulation results compared with the literature show that the proposed methods not only has a high universality, but also better adapts to waveform recognition at low SNR environment. When the SNR is –6 dB, the overall recognition rate of the method reaches 88%.