Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

Ningxia Hui Autonomous Region locates in north west of China. Recent 10 years, the economic and social level of Ningxia improved much. Therefore, land cover influenced by the natural and human activities changed greatly. Remote sensing united GIS is an effective method to observing and overseeing the long-term and insignificant change of the environment or the ecosystem. TM images are the essential information sources of land-cover. In semi-arid and arid, especially in the transition belt of the physical geography, the method would be more useful to discover the change mechanism of land-cover. During the 10 years, farmland had great change, and about 86% was compensated by grassland. Rapidly increased population is the most important driver in the land cover change. So construction area also increased quickly. Comparing land cover change with socioeconomic variables, we found that the land cover changes with the little human influence were impacted by the climate alternation. Also climate alternation is another important driver. Among main land cover types, the changes are consistent with the climate change.

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The land desertification is one of the major environmental problems in the Source Region of the Yangtze River, Qinghai Province. With the Three Gorges Dam construction, more and more attention is paid to restoring the degraded eco-environment of the region. In this study, the sandy land was classified into three types, and the desertified land was also inventoried correspondingly into three levels. Through interpreting TM images in 1986 and 2000, the databases of sandy land at two times had been established, respectively. And then the desertified land data was derived through overlaying the databases of sandy land. The result shows that there is 1,459,365 ha of sandy land in the region in 2000, accounting for 9.89% of all study area. There is 11,877 ha of newly sandy land resulted from land desertification, and sandy land has increased by 7.35% during about 14 years in the end of the 20th century.

A compact Self-Injection Locked Oscillator based Doppler Radar (SILO-Radar) at 5 GHz has been studied, analytically and numerically. The SILO-Radar consists of just a cross-coupled oscillator and a Schottky diode baseband detector, with the help of the Hilbert transform at the baseband to extract the Doppler phase information. Both analytical analysis and PSpice simulation have been performed to verify the validity of the SILO-Radar. The compact, low-power and low-cost SILO-Radar has many potential consumer applications such as auto-driving radars, Unmanned Aerial Vehicle (UAV) navigation radars, remote heartbeat/respiration healthcare biomedical radars and so on.