Based on a multiple layer perceptron neural networks, this paper presents a real-time channel prediction model, which could predict channel parameters such as path loss (PL) and packet drop (PD), for dedicated short-range communications (DSRC). The dataset used for training, validating, and testing was extracted from experiments under several different road scenarios including highways, local areas, residential areas, state parks, and rural areas. The study shows that the proposed PL prediction model outperforms conventional empirical models. Meanwhile, the proposed PD prediction model achieves higher prediction accuracy than the statistical one. Moreover, the prediction model can operate in real-time, through updating its training set, to predict channel parameters. Such a model can be easily extended to the applications of autonomous driving, the Internet of Things (IoT), 5th generation cellular network technology (5G) and many others.

The existing aeroengine instability precursor detection methods can be summarized as applying advanced signal processing technologies to various signals from the compressor test rig rather than the whole engine. Besides, these methods seriously depend on the artificial designed feature and threshold and also ignore the limit on the sensors onboard. Thus, with the help of the powerful feature extraction ability of the deep neural network, a real-time surge prediction method based on the multi-branch feature fusion neural network (MBFFNN) is proposed. First, the dataset can be obtained by using overlapping slices to divide surge test data into a sample sequence and using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to label each sample precisely. Second, for each sample, the time-domain statistical parameters are calculated and the recurrence plot is obtained by using phase space reconstruction. Finally, the MBFFNN with mixed data type input is designed, and its performance is evaluated by the generated dataset. The experimental results show that compared with multilayer perceptron (MLP), long short-term memory (LSTM), and deep residual network (DRN), MBFFNN has the best performance on two datasets for different surge tests, which demonstrates that the proposed method for surge prediction can accurately judge the state of the aeroengine, identify the instability precursor before the surge, and give an early warning in advance.

By loading two printed patches to the dielectric resonator antenna (DRA), a compact wide-band hybrid dielectric resonator antenna with enhanced gain and low cross-polarization is presented. The proposed antenna utilizes a combination of a rectangular dielectric resonator and two printed patches. Due to the hybrid design, multiple resonances were obtained. By adding two air layers between the dielectric resonator and the printed patches, the bandwidth has been significantly improved. Compared to the traditional hybrid dielectric resonator antenna, the proposed antenna can achieve wide bandwidth, high gain, low cross-polarization, and even small size simultaneously. The prototype of the proposed antenna has been fabricated and tested. The measured −10 dB return loss bandwidth is 25.6% (1.7–2.2 GHz). The measured antenna gains are about 6.3 and 8.2 dBi in the operating frequency band. Low cross-polarization levels of less than −28.5 dB and −43 dB in the E-plane and H-plane are achieved. Moreover, the overall dimensions of the antenna are only 67 × 67 × 34 (mm³). The proposed antenna is especially attractive for small base antenna applications.

Simulated altitude test is an essential exploring, debugging, verification and validation means during the development of aero-engine. Free-jet engine test can simulate actual working conditions of aero-engine more realistically than direct-connect engine test but with relatively lower cost compared to propulsion wind tunnel test, thus becoming an important developing area of simulated altitude test technology. The Flight Conditions Simulating Control System (FCSCS) is of great importance to the Altitude Test Facility (ATF) but the development of that is a huge challenge. Aiming at improving the design efficiency and reducing risks during the development of FCSCS for ATFs, a Hardware- in-the-Loop (HIL) simulation system was designed and the mathematical models of key components such as the pressure stabilizing chamber, free-jet nozzle, control valve and aero-engine were built in this paper. Moreover, some HIL simulation experiments were carried out. The results show that the HIL simulation system designed and established in this paper is reasonable and effective, which can be used to adjust control parameters conveniently and assess the software and hardware in the control system immediately.

This paper proposes a unified system framework based on linear frequency modulation (LFM) and orthogonal frequency-division multiplexing (OFDM) as a solution for resource sharing, especially sharing space and hardware. The proposed OFDM-LFM framework can not only transmit data flow by OFDM but also make it possible to extract features from the signal by LFM. Further, the signal features are used to construct a pseudospectrum related to the target speed and distance, thereby estimating the speed and distance of the target. Unified means using signal features to estimate target speed and distance while ensuring data transmission including communication and remote control data information. Besides the traditional data transmission mode, the ability of estimating target speed and distance is an additional benefit. The proposed unified framework makes control of transmission easier and saves more hardware resources. The simulation results show that the proposed LFM-OFDM framework can not only transmit data information including communication and remote control data information, but also estimate the speed and distance of the target by its signal features.