A STUDY OF THE IMPACTS OF FREEZE-THAW ON CLIFF RECESSION AT THE CALVERT CLIFFS IN CALVERT COUNTY, MARYLAND

The Calvert Cliffs, which form much of the western coastline of the Chesapeake Bay in Calvert County, Maryland, are actively eroding and destabilizing, resulting in a critical situation for many homes in close proximity to the slope's crest. Past studies have identified that where waves directly interact with the toe of the slope, wave action controls cliff recession; however, where waves do not regularly interact with the slope toe, the past work identified that freeze-thaw controls recession. This study investigated the validity of this second claim by analyzing the recession rate and freeze-thaw behavior of six study sites along the Calvert Cliffs that are not directly affected by waves. While waves do remove failed material from the toe, in these regions freeze-thaw is believed to be the dominant factor driving recession at the Calvert Cliffs. Past recession rates were calculated using historical aerial photographs and were analyzed together with a number of other variables selected to represent the freeze-thaw behavior of the Calvert Cliffs. The investigation studied sixteen independent variables and found that over 65% of recession at these study sites can be represented by the following five variables: (1) cliff face direction, (2 and 3) the percent of total cliff height composed of soil with freeze-thaw susceptibility F4 and F2, (4) the number of freeze-thaw cycles, and (5) the weighted shear strength. Future mitigation techniques at these sites should focus on addressing these variables and might include vegetation or addressing the presence of water along the face of the slope. Unmitigated, the Calvert Cliffs will continue to recede until a stable slope angle is reached and maintained.

Multi-antenna non-line-of-sight identification techniques for target localization in mobile ad-hoc networks

Target localization has a wide range of military and civilian applications in wireless mobile networks. Examples include battle-field surveillance, emergency 911 (E911), traffc alert, habitat monitoring, resource allocation, routing, and disaster mitigation. Basic localization techniques include time-of-arrival (TOA), direction-of-arrival (DOA) and received-signal strength (RSS) estimation. Techniques that are proposed based on TOA and DOA are very sensitive to the availability of Line-of-sight (LOS) which is the direct path between the transmitter and the receiver. If LOS is not available, TOA and DOA estimation errors create a large localization error. In order to reduce NLOS localization error, NLOS identifcation, mitigation, and localization techniques have been proposed. This research investigates NLOS identifcation for multiple antennas radio systems. The techniques proposed in the literature mainly use one antenna element to enable NLOS identifcation. When a single antenna is utilized, limited features of the wireless channel can be exploited to identify NLOS situations. However, in DOA-based wireless localization systems, multiple antenna elements are available. In addition, multiple antenna technology has been adopted in many widely used wireless systems such as wireless LAN 802.11n and WiMAX 802.16e which are good candidates for localization based services. In this work, the potential of spatial channel information for high performance NLOS identifcation is investigated. Considering narrowband multiple antenna wireless systems, two xvNLOS identifcation techniques are proposed. Here, the implementation of spatial correlation of channel coeffcients across antenna elements as a metric for NLOS identifcation is proposed. In order to obtain the spatial correlation, a new multi-input multi-output (MIMO) channel model based on rough surface theory is proposed. This model can be used to compute the spatial correlation between the antenna pair separated by any distance. In addition, a new NLOS identifcation technique that exploits the statistics of phase difference across two antenna elements is proposed. This technique assumes the phases received across two antenna elements are uncorrelated. This assumption is validated based on the well-known circular and elliptic scattering models. Next, it is proved that the channel Rician K-factor is a function of the phase difference variance. Exploiting Rician K-factor, techniques to identify NLOS scenarios are proposed. Considering wideband multiple antenna wireless systems which use MIMO-orthogonal frequency division multiplexing (OFDM) signaling, space-time-frequency channel correlation is exploited to attain NLOS identifcation in time-varying, frequency-selective and spaceselective radio channels. Novel NLOS identi?cation measures based on space, time and frequency channel correlation are proposed and their performances are evaluated. These measures represent a better NLOS identifcation performance compared to those that only use space, time or frequency.