GaInNAs/GaAs single-quantum-well (SQW) lasers have been grown bysolid-source molecular beam epitaxy. N is introduced by a home-madedc-active plasma source. Incorporation of N into InGaAs decreases thebandgap significantly. The highest N concentration of 2.6%in a GaInNAs/GaAsQW is obtained, corresponding to the photoluminescence (PL) peakwavelength of 1.57 µm at 10 K. The PL peak intensity decreases rapidlyand the PL full width at half maximum increases with the increasing Nconcentrations. Rapid thermal annealing at 850°C couldsignificantly improve the crystal quality of the QWs. An optimumannealing time of 5s at 850°C was obtained. TheGaInNAs/GaAs SQW laser emitting at 1.2 µm exhibits a highcharacteristic temperature of 115 K in the temperature range of20°C-75°C.

Superresolution mapping (SRM) is a method of predicting the spatial locations of land cover classes within mixed pixels in remotely sensed images. This paper proposes a novel SRM framework that is operated from the perspective of spatial regularization. Within the proposed framework, SRM aims to generate final superresolution land cover maps that conform to inputted fraction images, with spatial regularization intended for exploiting a priori knowledge about the land cover maps. Two SRM models are constructed by using maximal spatial dependence as the spatial regularization term and the L1 or L2 norm as the data fidelity term. The proposed models are evaluated by using synthetic Landsat, real IKONOS, and real Airborne Visible/Infrared Imaging Spectrometer images and compared with hard classification technologies, as well as pixel-swapping, Hopfield neural network, and Markov random field SRM models. We perform linear spectral mixture analysis (LSMA) and multiple endmember spectral mixture analysis (MESMA) to estimate fraction images. Results show that the accuracy of inputted fraction images plays an important role in the final superresolution land cover maps and that using MESMA fraction images results in higher accuracy than using LSMA fraction images. Moreover, the L-curve criterion is suitable for choosing the optimal regularization parameter in both SRM models. Compared with hard classification technologies and other SRM models, the proposed model derives the highest Kappa coefficients and lowest class area proportion errors when MESMA fraction images are used as input.

In recent years, sedimentation conditions in Dongting Lake have varied greatly because of signifi cant changes in runoff and sediment load in the Changjiang (Yangtze) River following the construction of Three Gorges Dam. The topography of the lake bottom has changed rapidly because of the intense exchange of water and sediment between the lake and the Changjiang River. However, time series information on lake-bottom topographic change is lacking. In this study, we introduced a method that combines remote sensing data and in situ water level data to extract a record of Dongting Lake bottom topography from 2003 to 2011. Multi-temporal lake land/water boundaries were extracted from MODIS images using the linear spectral mixture model method. The elevation of water/land boundary points were calculated using water level data and spatial interpolation techniques. Digital elevation models of Dongting Lake bottom topography in different periods were then constructed with the multiple heighted waterlines. The mean root-mean-square error of the linear spectral mixture model was 0.036, and the mean predicted error for elevation interpolation was -0.19 m. Compared with fi eld measurement data and sediment load data, the method has proven to be most applicable. The results show that the topography of the bottom of Dongting Lake has exhibited uneven erosion and deposition in terms of time and space over the last nine years. Moreover, lake-bottom topography has undergone a slight erosion trend within this period, with 58.2% and 41.8% of the lake-bottom area being eroded and deposited, respectively.

An adaptive identification method of simple dynamic recurrent neural network (SRNN) for nonlinear dynamic systems is presented in this paper. This method based on the theory that by using the inner-states feed-back of dynamic network to describe the nonlinear kinetic characteristics of system can reflect the dynamic characteristics more directly, deduces the recursive prediction error (RPE) learning algorithm of SRNN, and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control, due to its less weight values, simpler learning algorithm, higher identification speed, and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicate topological structure in usual dynamic recurrent neural network.

In this study, a replicon vaccine vector system for Japanese encephalitis virus (JEV) was established. The system included a trans-complementing cell line, a series of JEV DNA-based subgenomic replicons, and several encapsidated JEV propagation-deficient pseudoinfectious particles (PIPs). The DNA-based JEV replicon vectors, which deleted the structural coding region, could be able to self-replicate and express the reporter gene. A stable BHK packaging cell line named BHK-CME, which constitutively expressed the capsid protein C, the precursor membrane and envelope proteins (C-prM-E) of JEV, was generated. BHK-CME cells were used to trans-complement the JEV replicons and proved to package the JEV replicons into single-round infectious PIPs efficiently. The PIPs were produced in titers of up to 1.6*10 5 IU/ml. To investigate the efficacy of JEV replicon-based vaccines, four groups of female BALB/c mice were inoculated three times at 3-week intervals with the JEV PIPs and others. The JEV-specific antibody titers reached to 1:6400 and the neutralizing antibody titers reached 1:256 after three rounds of immunization with JEV PIPs. And the antisera collected from immunized mice were shown to be protective partially against lethal infection when passively transferred to susceptible weanling mice. These results demonstrated the value of the JEV replicon vector system for the development of new vaccine candidates.

In view of the data limitations for mountain wetland, we validated the application of high spatial resolution satellite imagery and DEM for mountain wetland conservation and restoration in this paper. Dajiuhu wetland, a rare sub-alpine wetland which is threatened by degradation and currently poorly understood, was select as the study area. Although there are scarce of basis data in Dajiuhu wetland, spatial patterns of the wetland vegetation could be obtained from the remote sensing interpretation, and the classification could achieve the plant formation level via integrating the high spatial resolution images with field survey. Total classification accuracy is about 83%. Despite of the widespread manmade drainage systems and the great changes on topography, hydrology, vegetations and land-cover, the potential suitable sites for wetland restoration could be modeled and located by the spatial analyzing to the result of satellite imagery classification and a high resolution (10m) DEM. Total area of the simulated potential lakes is 107.03hm(2), which is comparable with the original perennial lakes before 1950s. The research could provide basic data for the restoration of the Dajiuhu mountain wetland, and would be a useful tool to locate the potential suitable sites for wetland restoration.

A novel crosslinkable polyurethane is used as the core layer of the electro-optic (E-O) modulator. The refractive index and dispersion of this material have been detected by analyzing the F-P oscillation in transmission spectra. Calculated results from the effective index method are given to design the Mach-Zehnder and straight 5-layer ridge waveguide device (including the metal electrodes). With light at 1.31 mum being fiber coupled into waveguide, the mode properties of these devices have been demonstrated in a micron control system. The guided mode is accordant with the theoretical analysis

Square and circular resonator microlasers connected with two output waveguides are investigated for correlation of output powers from the two output ports.The square resonator microlasers with two output waveguides connected at the midpoint of one side and one vertex are fabricated and tested by measuring the output power versus injection current and the laser spectra of the two ports.The laser spectra indicated that the output power correlation between the two output ports is very weak because of different lasing modes in different ports of the square microlaser.Circular resonator microlaser with two output waveguides can realize single mode operation and has good output correlation from the two ports.So the output power from one port of the circular microlasers can be monitored by that of another port.