The influence of R. E. Kalman—state space theory,realization, and sampled-data systems

Professor Rudolf Emil Kalman passed away on July 2, 2016. He left an indelible mark on system and control theory, and indeed established a firm basis for modern control theory. This note intends to give a brief historical account on his influence on modern system theory, particularly, state space theory, realization, and sampled-data systems, as well as elaborating upon his methodology.     

G.729E语音编码算法的C程序实现

介绍了11．8kbit/s CS-ACELP语音压缩算法G．729E的算法原理,对其关键性的技术原理及算法作了较详细的分析和说明,并通过Microsoft Visual C＋＋6．0构造编码和解码两个模块,实现了带背景噪声语音信号及音乐信号的压缩。     

A Review of: “Exploration of the Solar System by Infrared Remote Sensing”. By R. A. HANEL,B. J. CONRATH,D. E. JENNINGS and R. E. SAMUELSON. (Cambridge: Cambridge University Press, 1992.) [Pp. 458.] Price £75.00.

Crop classification is a key issue for agricultural monitoring using remote-sensing techniques. Synthetic aperture radar (SAR) data are attractive for crop classification because of their all-weather, all-day imaging capability. The objective of this study is to investigate the capability of SAR data for crop classification in the North China Plain. Multi-temporal Envisat advanced synthetic aperture radar (ASAR) and TerraSAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using different combinations of these SAR data and texture features. The results indicated that multi-configuration SAR data achieved satisfactory classification accuracy (best overall accuracy of 91.83%) in the North China Plain. ASAR performed slightly better than TerraSAR data acquired in the same time span for crop classification, while the combination of two frequencies of SAR data (C- and X-band) was better than the multi-temporal C-band data. Two temporal ASAR data acquired in late jointing and flowering periods achieved sufficient classification accuracy, and adding data to the early jointing period had little effect on improving classification accuracy. In addition, texture features of SAR data were also useful for improving classification accuracy. SAR data have considerable potential for agricultural monitoring and can become a suitable complementary data source to optical data.