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Unimodular waveforms with good correlation properties are desired for multiple‐input‐multiple‐output radar to achieve an increased transmitting/receiving virtual aperture. In this study, a new optimisation framework is introduced to design waveforms with good correlations. It is shown that many existing waveform design problems can be incorporated into this framework. Since the considered problem is in general highly non‐linear and non‐convex, an iterative optimisation method based on majorisation‐minimisation is developed. By carefully devising the surrogate function, the only requirement is to deal with a series of simple problems, which have closed‐form solutions. The proposed algorithm can be implemented via fast Fourier transform and hence is computationally efficient. In addition, the proposed algorithm is extended to deal with spectral constraints. Numerical results demonstrate the efficiency of the proposed algorithm compared with the state‐of‐the‐art techniques.

Action recognition is usually a central problem for many practical applications, such as video annotations, video surveillance and human computer interaction. Most action recognition approaches are based on localized spatio-temporal features that can vary significantly when the viewpoint changes. However, their performance rapidly drops when the viewpoints of the training and testing data are different. In this paper, we propose a transfer learning framework for view-invariant action recognition by the way of sharing image stitching feature among different views. Experimental results on multi-view action recognition IXMAS dataset demonstrate that our method produces remarkably good results and outperforms baseline methods.

Since the main plain and basin areas in the Yellow River basin are regions of great importance to the social and economic development of China, it is important work to understand the groundwater balance and circulation in these regions. We have selected the Yellow River source region and the Yinchuan, Hubao, Taiyuan and Guanzhong basins, as well as the Lower Yellow River zone for study on groundwater balance and circulation. This paper shows characteristics of stable isotope ratios of hydrogen and oxygen, and water quality in the main plain and basin areas in the Yellow River basin. Moreover, the groundwater fl ow system and water balance in these regions are estimated by the results

The paper reviews regional geology in the Yellow River basin from viewpoints of stratigraphy and structure. Based on the geologic features, water-bearing formations are classified into following 5 types: Loose sediment type pore water-bearing formation, Clastic rock type fissurepore water bearing formation, Carbonate rock type formation, Magmatic and metamorphic rock type formation, and Frozen layer type formation. Groundwater in the Yellow river basin is classifi ed into 12 systems on the basis of hydrogeological conditions and characteristics of recharge, runoff and discharge of groundwater. Each system is described in detail, and distribution map for classifi ed systems are shown in the map. Three cross sections are illustrated as representative examples for upper, middle and lower reaches respectively.

By study and simulation on the theoretic model and principle of electronic nose system in artificial olfactory system, this paper proposes an electronic nose system which combines gas sensor array and artificial neural network recognition algorithm. In order to testify systematic rationality and experimental realizability which is implemented in multiple gas measurement, we discuss related methods and steps which apply the system to quality and quantity of multiple gas. Through the analysis on experiments of gas detection which combines gas sensor array and pattern recognition technology, we apply BP feed forward neural network to analyze three gases in quality and quantity. Our research involves experimental configuration method of low concentration gas, input, output and hidden layer element determination method of BP feed forward network in quality and quantity analysis of multiple gases, signal pretreatment algorithm and gas sensor array response vector normalization. The experimental data show that the system designed in this paper makes recognition of 3 gases with 100% qualitative recognition and lower error of quantitative recognition.