Active Virtual Network Management Prediction: Complexity as a Framework for Prediction, Optimization, and Assurance

Research into active networking has provided the incentive to re-visit what has traditionally been classified as distinct properties and characteristics of information transfer such as protocol versus service; at a more fundamental level this paper considers the blending of computation and communication by means of complexity. The specific service examined in this paper is network self-prediction enabled by Active Virtual Network Management Prediction. Computation/communication is analyzed via Kolmogorov Complexity. The result is a mechanism to understand and improve the performance of active networking and Active Virtual Network Management Prediction in particular. The Active Virtual Network Management Prediction mechanism allows information, in various states of algorithmic and static form, to be transported in the service of prediction for network management. The results are generally applicable to algorithmic transmission of information. Kolmogorov Complexity is used and experimentally validated as a theory describing the relationship among algorithmic compression, complexity, and prediction accuracy within an active network. Finally, the paper concludes with a complexity-based framework for Information Assurance that attempts to take a holistic view of vulnerability analysis

Radar return from a continuous vegetation canopy

The radar backscatter coefficient, sigma deg, of alfalfa was investigated as a function of both radar parameters and the physical characteristics of the alfalfa canopy. Measurements were acquired with an 8-18 GHz FM-CW mobile radar over an angular range of 0 - 70 deg as measured from nadir. The experimental data indicates that the excursions of sigma deg at nadir cover a range of nearly 18 dB during one complete growing cycle. An empirical model for sigma deg was developed which accounts for its variability in terms of soil moisture, plant moisture and plant height

Fading characteristics of panchromatic radar backscatter from selected agricultural targets

An experiment was performed to empirically determine the fading characteristics of backscattered radar signals from four agricultural targets at 9 GHz. After a short review of the statistics of Rayleigh fading backscatter, the data processing method and results of the data are analyzed. Comparison with theory shows adequate agreement with the experimental results, provided of course, the targets are modeled in a correct manner

Remotely sensing wheat maturation with radar

The scattering properties of wheat were studied in the 8-18 GHz band as a function of frequency, polarization, incidence angle, and crop maturity. Supporting ground truth was collected at the time of measurement. The data indicate that the radar backscattering coefficient is sensitive to both radar system parameters and crop characteristics particularly at incidence angles near nadir. Linear regression analyses of the radar backscattering coefficient on both time and plant moisture content result in rather good correlation. Furthermore, by calculating the average time rate of change of the radar backscattering coefficient it is found that it undergoes rapid variations shortly before and after the wheat is harvested. Both of these analyses suggest methods for estimating wheat maturity and for monitoring the progress of harvest

Radar backscatter properties of milo and soybeans

The radar backscatter from fields of milo and soybeans was measured with a ground based radar as a function of frequency (8-18 GHz), polarization (HH and VV) and angle of incidence (0 deg-70 deg) during the summer of 1974. Supporting ground truth was gathered contemporaneously with the backscatter data. At nadir sigma deg of milo correlated highly, r = 0.96, with soil moisture in the milo field at 8.6 GHz but decreased to a value of r = 0.78 at a frequency of 17.0 GHz. Correlation studies of the variations of sigma deg with soil moisture in the soybean fields were not possible due to a lack of a meaningful soil moisture dynamic range. At the larger angles of incidence, however, sigma deg of soybeans did appear to be dependent on precipitation. It is suggested this phenomenon was caused by the rain altering plant geometry. In general sigma deg of both milo and soybeans had a relatively small dynamic range at the higher angles of incidence and showed no significant dependence on the measured crop parameters