Observation of the thickness of Antarctic ice shelves is vital for predicting the rise in sea levels caused by melting of the world's largest fresh water reservoir. This paper proposes a ground-based radar system using a MIMO phased array antenna layout to accurately monitor thickness changes and provide 2 dimensional cross-range information of the ice shelf. The radar system used to collect data is a Step Frequency (SFCW) Radar using multiple transmitting and receiving antennas which operate orthogonally in time. The MIMO antenna layout is shown together with the beam pattern from the associated virtual array. The aperture synthesis of the MIMO array using digital beamforming technique is discussed. Results of the aperture synthesis, showing the cross-range and depth profile of the ice shelf to be observed will be shown. The study conducted using the MIMO antennas is part of a future plan to replace the SFCW Radar system with an FMCW system that will allow long term deployments to observe variability in the rate of change of the ice shelf thickness. One of the conclusions of the paper is that a linear version of the MIMO array will make more efficient use of the antennas than a 2D version of the array.

We present a method for detecting and labeling the edge structures in digital gray-scale images in two distinct stages: First, a variant of the cubic facet model is applied to detect the location, orientation and curvature of the putative edge points. Next, a relaxation labeling network is used to reinforce meaningful edge structures and suppress noisy edges. Each node label of this network is a 3D vector parameterizing the orientation and curvature information of the corresponding edge point. A hysteresis step in the relaxation process maximizes connected contours. For certain types of images, prefiltering by adaptive smoothing improves robustness against noise and spatial blurring.

We present a method for detecting and labeling the edge structures in digital grey-scale images in two distinct stages: 1) a variant of the cubic facet model detects location, orientation and curvature of the putative edge points; and 2) a relaxation labeling network reinforces meaningful edge structures and suppresses noisy edges. Each node label of this network is a 3D vector parametrizing the orientation and curvature of the corresponding edge point. A hysteresis step in the relaxation process maximizes connected contours. For certain images, prefiltering by adaptive smoothing improves robustness against noise and spatial blurring.

Military communications networks have to retain their connectivity when subject to quite severe damage. The "survivability" of such networks is conveniently defined by the probability of retaining a connection between representative pairs of terminals. Many methods of calculating this parameter analytically, or of estimating it statistically, have been published. However, because of the "combinatorial explosion," none have so far proved viable except for relatively simple networks (and often also unjustifiable implicit assumptions). A new approach is therefore developed in this paper, which reduces the computational load by several orders of magnitude, and makes significant practical networks amenable to analysis.