Aim To develop a high speed and high resolution dynamic rangefinding device for the measurement of large distances.Methods The device was comprised of an ultrasonic transmitter and a receiver,and a receiver , and a co...Aim To develop a high speed and high resolution dynamic rangefinding device for the measurement of large distances.Methods The device was comprised of an ultrasonic transmitter and a receiver,and a receiver , and a continuous ultrasonic wave amplitude-modulated by a low- frequency acoustic signal was used. The rangefinding was achieved by detecting the phase difference between the transmitted and received ultrasonic signals. The design principle. hard- ware implementation , experimental results and performance analysis of the device are included. Results and Conclusion Experiments show that the accuracy of the device are included. within 1.5m while its dynamic data update rate can be up to 40kHz.展开更多
A three-dimensional positioning method for global positioning system(GPS)receivers based on three satellites was proposed.In the method,the measurement equation used for positioning calculation was expanded by means o...A three-dimensional positioning method for global positioning system(GPS)receivers based on three satellites was proposed.In the method,the measurement equation used for positioning calculation was expanded by means of two measures.In this case,the measurement equation could be solved,and the function of positioning calculation could be performed.The detailed steps of the method and how to evaluate the positioning precision of the method were given,respectively.The positioning performance of the method was demonstrated through some experiments.It is shown that the method can provide the three-dimensional positioning information under the condition that there are only three useful satellites.展开更多
Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the I...Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.展开更多
文摘Aim To develop a high speed and high resolution dynamic rangefinding device for the measurement of large distances.Methods The device was comprised of an ultrasonic transmitter and a receiver,and a receiver , and a continuous ultrasonic wave amplitude-modulated by a low- frequency acoustic signal was used. The rangefinding was achieved by detecting the phase difference between the transmitted and received ultrasonic signals. The design principle. hard- ware implementation , experimental results and performance analysis of the device are included. Results and Conclusion Experiments show that the accuracy of the device are included. within 1.5m while its dynamic data update rate can be up to 40kHz.
基金Project (ZYGX2010J119)supported by the Fundamental Research Funds for the Central Universities of China
文摘A three-dimensional positioning method for global positioning system(GPS)receivers based on three satellites was proposed.In the method,the measurement equation used for positioning calculation was expanded by means of two measures.In this case,the measurement equation could be solved,and the function of positioning calculation could be performed.The detailed steps of the method and how to evaluate the positioning precision of the method were given,respectively.The positioning performance of the method was demonstrated through some experiments.It is shown that the method can provide the three-dimensional positioning information under the condition that there are only three useful satellites.
基金the Project"The Basic Research on Internet of Things Architecture"supported by National Key Basic Research Program of China(No.2011CB302704)supported by National Natural Science Foundation of China(No.60802034)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education(No.20070013026)Beijing Nova Program(No.2008B50)"New generation broadband wireless mobile communication network"Key Projects for Science and Technology Development(No.2011ZX03002-002-01)
文摘Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.
