The LAMBDA method has been widely used in GNSS for fixing integer ambiguities. It can also solve any integer least squares (ILS) problem arising from other applications. For real time applications with high dimensions, the computational speed is crucial. A modified LAMBDA method (MLAMBDA) is presented. Several strategies are proposed to reduce the computational complexity of the LAMBDA method. Numerical simulations show that MLAMBDA is (much) faster than LAMBDA. The relations between the LAMBDA method and some relevant methods in the information theory literature are pointed out when we introduce its main procedures.

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

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Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application. Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters, comprising leaf area index (LAI; m 2 green leaf area m −2 soil) and green leaf chlorophyll density (GLCD; mg chlorophyll m −2 soil), using stepwise multiple regression (SMR) models and support vector machines (SVMs). Four transformations of the rice canopy data were made, comprising reflectances (R), first-order derivative reflectances (D1), second-order derivative reflectances (D2), and logarithm transformation of reflectances (LOG). The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI, with a root mean square error (RMSE) of 1.0496 LAI units. The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD, with an RMSE of 523.0741 mg m −2 . The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters, but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data. The assessment of biophysical vegetation properties, such as leaf area index (LAI) and green leaf chlorophyll density (GLCD), is a major goal of remote sensing in agriculture. Remote-sensing-based assessments of these variables are made possible as a result of the strong contrast between spectral reflectances of vegetation and the soil background and the dramatic reflectance changes associated with changing vegetative cover. Based on this contrast, numerous vegetation indices (VIs) have been developed during the past few decades, which are highly correlated with the amount of vegetation. The most common of these indices use the red and near-infrared (NIR) canopy reflectances in the form of ratios, such as the ratio VI [1] and the normalized difference vegetation index [2], and as linear combinations of red and NIR reflectances [3,4]. These indices generally use averaged spectral information over broad bandwidths [5], resulting in the loss of critical information available in specific narrow bands [6], and potentially limiting the accurate estimates of agricultural crop and natural vegetation biophysical and biochemical variables [7,8]. In addition, many of these vegetation indices are strongly influenced by the soil background, resulting in soil-dependent VI-biophysical relationships [9,10]. Further improvements in quantifying vegetation are possible using spectral data from distinct narrow bands, as indicated by numerous hyperspectral studies using field spec-

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The global optimization of complicated nonlinear systems is mathematically intractable and such an optimization extensively exists in science and engineering. Once an objective function has many local extreme points, the traditional optimization methods may not obtain the global optimization efficiently. A genetic algorithm (GA) based on the genetic evolution of a species provides a robust procedure to explore broad and promising regions of solutions and to avoid being trapped at the local optimization. However, the computational amount is very large. To reduce computations and to improve the computational accuracy, a method based on the two-point crossover and two-point mutation of the hybrid accelerating genetic algorithm with Hooke-Jeeves searching operator is developed for systems optimization. With the shrinking of searching range, the method gradually directs to optimal result by the excellent individuals obtained by Gray code genetic algorithm embedding with Hooke-Jeeves searching operator and Hooke-Jeeves algorithm. The efficiency of the new algorithm is verified by application of several test functions. The comparison of our GA with six existing other algorithms is presented. This algorithm overcomes the Hamming-cliff phenomena in other existing genetic methods, and is proved to be very efficient for the given environmental systems optimization.

China’s water shortage problem is becoming increasingly severe. Improving water use efficiency is crucial to alleviating China’s water crisis. This paper evaluates the water use efficiency of 31 provinces and municipalities in China by using the data envelopment analysis (DEA) method. When the usual DEA model has too many indexes selected, it will cause the majority of the decision making units (DMUs) efficiency values be one, which leads to invalid evaluation results. Therefore, by using the entropy weight method, a new synthetic set of indexes is constructed based on the original indexes. The new synthetic set of indexes retains the full information of the original indexes, and the goal of simplifying the number of indexes is achieved. Simultaneously, by empowering the original indexes, the evaluation using synthetic indexes can also avoid the impact of industrial structure and labor division on water use efficiency. The results show that in China’s northeastern grain producing areas, water use efficiency is higher due to the high level of agricultural modernization. The provinces in the middle reaches of the Yangtze River have the lowest water use efficiency due to water pollution and water waste. In general, China’s overall water use efficiency is low, and there is still much room for improvement.

An efficient regularization approach is proposed for decoding underdetermined multiple input multiple output (MIMO) systems. The main idea is to transform an underdetermined integer least squares problem to an equivalent overdetermined integer least squares problem by using part of the transmit vector to do a regularization. Some strategies are proposed to enhance the efficiency of this approach. Specifically, we discuss how many entries of the transmit vector should be chosen and how to choose them when we do the regularization. An empirical formula for the regularization parameter is presented. Simulation results indicate that this modified approach can be much more efficient than current approaches for any square constellation higher than 4QAM.