Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration

The approach of using primarily satellite observations to estimate ecosystem gross primary production (GPP) without resorting to interpolation of many surface observations has recently shown promising results. Previous work has shown that the remote sensing based greenness and radiation (GR) model can give accurate GPP estimates in crops. However, the feasibility of its application and the model calibration to other ecosystems remain unknown. With the enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) images and the surface based estimates of photosynthetically active radiation (PAR), we provide an analysis of the GR model for estimating monthly GPP using flux measurements at fifteen sites, representing a wide range of ecosystems with various canopy structures and climate characteristics. Results demonstrate that the GR model can provide better estimates of GPP than that of the temperature and greenness (TG) model for the overall data classified as non-forest (NF), deciduous forest (DF) and evergreen forest (EF) sites. Calibration of the GR model is also conducted and has shown reasonable results for all sites with a root mean square error of 47.18 g C/m²/month. Different coefficients acquired for the three plant functional types indicate that there are shifts of importance among various factors that determine the monthly vegetation GPP. The analysis firstly shows the potential use of the GR model in estimating GPP across biomes while it also points to the needs of further considerations in future operational applications.     

Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model

A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R² greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R² greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice.     

Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure

Increasing interests in a global environment and climate change have led to studies focused on the changes in the multinational Arctic region. To facilitate Arctic research, a spatial data infrastructure (SDI), where Arctic data, information, and services are shared and integrated in a seamless manner, particularly in light of today's climate change scenarios, is urgently needed. In this paper, we utilize the knowledge-based approach and the spatial web portal technology to prototype an Arctic SDI (ASDI) by proposing (1) a hybrid approach for efficient service discovery from distributed web catalogs and the dynamic Internet; (2) a domain knowledge base to model the latent semantic relationships among scientific data and services; and (3) an intelligent logic reasoning mechanism for (semi-)automatic service selection and chaining. A study of the influence of solid water dynamics to the bio-habitat of the Arctic region is used as an example to demonstrate the prototype.     

Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China

Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geo-environment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou.     

A new paradigm for urban surveillance with vehicular sensor networks

We consider a new application paradigm of vehicular sensor networks (VSN). Currently, vehicles are equipped with forward facing cameras to assist forensic investigations of events by proactive image-capturing from streets and roads. Due to content redundancy and storage imbalance in this in-network distributed storage system, how to maximize its storage capacity becomes a nontrivial challenge. In other words, how to maximize the average lifetime of sensory data (i.e., images generated by cameras) in the network is a fundamental problem to be solved. This paper presents, VStore, a cooperative storage solution in vehicular sensor networks for mobile surveillance, which has been designed to support redundancy elimination and storage balancing throughout the network. Compared with existing works, we propose a novel storage architecture for urban surveillance and deal with challenges in a mobile scenario. Field testing was carried out with a trace-driven simulator, which utilized about 500 taxis in Shanghai. The testing results showed that VStore can largely prolong the average lifetime of sensory data by cooperative storage.     

Sliding mode control of switched hybrid systems with stochastic perturbation

This paper is concerned with the sliding mode control (SMC) of a continuous-time switched stochastic system. A sufficient condition for the existence of reduced-order sliding mode dynamics is derived and an explicit parametrization of the desired sliding surface is also given. Then, a sliding mode controller is then synthesized for reaching motion. Moreover, the observer-based SMC problem is also investigated. Some sufficient conditions are established for the existence and the solvability of the desired observer and the observer-based sliding mode controller is synthesized. Finally, numerical examples are provided to illustrate the effectiveness of the proposed theory.     

Robust refinement methods for camera calibration and 3D reconstruction from multiple images

This paper proposes robust refinement methods to improve the popular patch multi-view 3D reconstruction algorithm by Furukawa and Ponce (2008). Specifically, a new method is proposed to improve the robustness by removing outliers based on a filtering approach. In addition, this work also proposes a method to divide the 3D points in to several buckets for applying the sparse bundle adjustment algorithm (SBA) individually, removing the outliers and finally merging them. The residuals are used to filter potential outliers to reduce the re-projection error used as the performance evaluation of refinement. In our experiments, the original mean re-projection error is about 47.6. After applying the proposed methods, the mean error is reduced to 2.13.     

ADS5287及其在MIMO接收机中的应用

介绍了TI公司模数转换器ADS5287的性能特点.用ADS5287作为模数转换器设计了MIMO中频接收机电路,完成了系统的PCB设计,并使用Hyperlynx软件中的Boradsim工具对设计中重要的高速信号线做了信号完整分析.     

新型SPLL在蓄电池充放电装置中的应用研究

叙述了双同步旋转坐标变换原理,提出了一种基于此变换的新型锁相环(SPLL)设计方法,阐述了新型SPLL在蓄电池充放电装置中的具体应用方案,该方案能够在电网电压发生畸变及不平衡条件下利用双同步旋转坐标变换快速、准确地锁定电网电压相位。根据该应用方案建立的装置控制策略将大大提高基于可逆PWM整流器的蓄电池充放电装置的充放电性能和效率。仿真结果验证了方案的可行性和有效性。