Wanlin Gao

Research on Resource Directory Service for Sharing Remote Sensing Data under Grid Environment

Because of the features of remote sensing data such as large quantity, complex format and wide distribution in heterogeneous storage devices of different geographic locations, it is quite difficult for integrating, exchanging and sharing remote sensing data effectively. On one hand, it is very hard to get continuous remote sensing data for some research institutions and individuals who urgently need a convenient way to share and acquire the data. On the other hand, the data producers seldom make full use of the precious data resources due to the lack of effective method supporting for the massive data sharing. Focusing on the contradictions between continuous accumulation and lowly utilization of remote sensing data resources, in this paper, an approach of sharing remote sensing data under grid environment is discussed and based on LDAP for registration, issue, management and search using a directory tree form. It improves sharing remote sensing data rapidly and conveniently and also maximizes the efficiency of data resource.

A Remote Sensing Image Process Method of Supervised Classification under Grid Environment

On the base of studying and analyzing the Globus Toolkit2.4 platform and remote sensing technology, this article uses the grid platform Globus Toolkit2.4 and the Bayesian classification to build a remote sensing image process method of supervised classification under the grid environment. This method can provide a preferred approach for the classification of remote sensing image.

M-A model of agricultural remote sensing monitoring metadata based on grid environment

The increase of the amount of agricultural remote sensing monitoring data makes it difficult for data storage and management thereby limiting the utilization of data resources. Considering the security and response time, the original data cannot be directly exposed on the Internet for user queries. Therefore, there is an urgent need to organize and describe agricultural remote sensing monitoring data effectively for users to understand and query. In this paper, based on a detailed analysis, for rational planning and organizing of agricultural remote sensing monitoring data resources, a M-A (Metadata of Agricultural Remote Sensing Monitoring Data) model is constructed with the study of data characteristics and the Grid environment. The M-A model structure and its contents are designed using the XML language which gives a relatively comprehensive description of agricultural remote sensing monitoring data and the Grid environment. In summary, the study of this paper provides a practical and effective support for data standardization, sharing, exchanging and integration under the Grid environment.

Research into a resource directory service based on Grid RLS

At present, massive amounts of data are distributed across different institutions, departments and scientific research institutes, and even at one site can be stored in heterogeneous storage systems. Therefore, it is hard to get the desired information from the massive data resource, which leads to the requirement of data sharing. In an effort to solve this problem, this paper studies Grid technology and RLS, for sharing remote sensing data. Furthermore, a resource directory service has been designed and implemented by using RLS in the Grid environment.

A Research on Grid-Based Geometric Correction for Remote Sensing Image

On the base of studying and analyzing the Globus Toolkit2.4 platform and remote sensing technology, this article uses the grid platform Globus Toolkit2.4 and the mode of parallel processing, develops a method of geometric correction for processing remote sensing images based on grid. It provides an approach for solving problems caused by RS image’s characteristics of complexity and time consuming.

Study and Implementation of Agricultural SMS Management System

In this paper, an agricultural SMS (Short Message Service) management system is constructed based on a platform of GSM modem to send and receive short messages. The platform will create tables in database that store short messages and provide interface for agricultural short message management system. Users can send request of query to the platform in short message through mobile phone. The system automatically analyzes and processes the query and inserts the results into the sending table which will be sent back to the user through the platform. The system has been applied actually and runs efficiently.

Research on Continuous Vital Signs Monitoring Based on WBAN

Vital signs are the indicators which evaluate the existence of health status and life quality. Hospitals may provide medical services for acute and chronic diseases and injuries. However, continuous monitoring and long-term treatment becomes difficult in such type diseases. In this paper, continuous vital signs monitoring system CVSMS based on wireless body area network WBAN is designed. And the gathered data is transmitted to a mobile phone via Bluetooth and then transferred to a remote server and stored in the database. In this way, a variety of vital signs such as body temperature, pulse rate, blood pressure, and ECG information would be acquired. Through the analysis and assessment of CVSMS, the results showed that the measurements are accurate and it provides an effective method for continuous health monitoring.

Compressed sensing based apple image measurement matrix selection

The purpose of this paper is to design a measurement matrix of apple image based on compressed sensing to realize low cost sampling apple image. Compressed sensing based apple image sampling method makes a breakthrough to the limitation of the Nyquist sampling theorem. By investigating the matrix measurement signal, the method can project a higher dimensional signal to a low-dimensional space for data compression and reconstruct the original image using less observed values. But this method requires that the measurement matrix and sparse transformation base satisfy the conditions of RIP or incoherence. Real time acquiring and transmitting apple image has great importance for monitoring the growth of fruit trees and efficiently picking apple. This paper firstly chooses sym5 wavelet base as apple image sparse transformation base, and then it uses Gaussian random matrices, Bernoulli random matrices, Partial Orthogonal random matrices, Partial Hadamard matrices, and Toeplitz matrices to measure apple images, respectively. Using the same measure quantity, we select the matrix that has best reconstruction effect as the apple image measurement matrix. The reconstruction PSNR values and runtime were used to compare and contrast the simulation results. According to the experiment results, this paper selects Partial Orthogonal random matrices as apple image measurement matrix.

Research on Voice Signal Acquisition and Recovery Algorithm Based on Compressive Sensing

This paper introduces a method based on compressive sensing to acquire voice signals from distributed wireless sensors. The method uses compressive sensing technology to sample, transmit and recover data to reduce the sampling rate and further compress the data. As the voice signals in the discrete cosine transform domains are approximately sparse, this paper builds a JSM-1 based model and uses the random Gaussian measurement matrix to measure the signal and recovers the signal using SOMP algorithm. The method can be applied to Internet of things in agriculture to monitor wireless sensor signals, which can significantly lower hardware costs and reduce power consumption. Whats more, the method can prevent the network structure damage caused by unbalanced power consumption and can be widely used.

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.