US Government As Represented by the Secretary of the Army

Ford Global Technolgoies, LLC

Thomas J Meitzler

Samuel Edward Ebenstein

Gregory Hugh Smith

Thomas P Reynolds

Harpreet Singh

Elena N Bankowski

James Stewart Rankin, Ii

Yelena Mordechai Rodin

Jason A Schrader

Michael Nranian

Ilya N Krivorotov

Andrei N Slavin

Ivan Wong

David Bednarz

Euijung Sohn

Vasyl S Tyberkevych

One example is an antenna structure with a metamaterial having a flexible metamaterial layer, a two-dimensional antenna layer and a spacer layer. The flexible metamaterial layer has a metamaterial thickness allowing the metamaterial layer to be attached to a curved conducting surface of a vehicle. The metamaterial layer is formed with a two-dimensional array of elements having a passive magnetic property with the array of elements formed with elongated individual elements each having a top end and a bottom end. The elongated individual elements have curved outer surfaces between the top end and the bottom end. The two-dimensional antenna layer receives electromagnetic signals. The spacer layer is located between the metamaterial layer and the antenna layer separating the metamaterial layer and the antenna layer.

Antenna structure with metamaterial

A spintronic electronic apparatus having a multilayer structure. The apparatus includes a substrate, having disposed in succession upon the substrate; a bottom interface layer; a pinned layer; a tunneling barrier; a free layer; and a top interface layer, wherein the apparatus operates as a non-resonant magnetic tunnel junction in a large amplitude, out-of-plane magnetization precession regime having weakly current dependent, large diode volt-watt sensitivity when external microwave signals that exceed a predetermined threshold current and have a frequency that is lower than a predetermined level excite the magnetization precession.

Spintronic electronic device and circuits

A system incorporating sensor enhanced composite armor structure. The structure has one layer including ceramic tiles and ceramic-material transducers, wherein the ceramic tiles and the transducers fit closely together so that the one layer is ballistically windowless. The structure has other layers composed of armor material stacked with the one layer, the one layer and the other layers forming a plate which as a unit has a set or group of fundamental frequencies. The system includes logic means to analyze only signals resulting from vibrations transmitted from the one transducer to the other transducer through the one layer. The logic means thereby derives a signal fingerprint which characterizes a state of damage to the plate.

Self diagnostic composite armor

A unit of modular armor. The module has a box-like, optically opaque outer shell of ballistic-protection material enclosed to exclude light from the module's interior. The shell contains transparent armor plates and the shell contains a self-diagnostic system for ascertaining whether the plates have been damaged. The self-diagnostic system includes a first PC board disposed along first edges of the plates, the board being divided into strips on which are mounted rows of lights. The self-diagnostic system further includes a second PC board at second, opposed edges of the plates divided into strips on which are mounted rows of light receptors. The PC boards incorporate circuitry for illuminating the rows of the lights in a row-by-row sequence, and for allowing activation only of the receptors directly opposed to illuminated lights. This circuitry has an analysis means for determining the health of the plates in response to signals from the receptors. The armor module has a transparent, elastomeric, adhesive matrix within the shell in which each of the plates and boards are suspended and encapsulated, wherein the matrix occupies all space not occupied by other elements within the shell.

Self diagnostic armor structure

A method for testing cameras includes positioning a camera such that an image of a target is displayed on a monitor within at least one default image parameter. The method further includes engaging a dimming condition of a light source from the camera, thereby generating a lighting condition signal. Subsequently the camera receives the lighting condition signal and displays it on the monitor. A group of observers then analyze the lighting condition signal. The method continues through engaging a second dimming condition of the light source from the first camera, thereby generating a second lighting condition signal, which is also displayed on the monitor. The group of observers then analyzes the second lighting condition signal.

Method for camera calibration and quality testing

A method of determining the visibility of a target in a background uses search time as the output. A set of images of the target in the background is made, and selected input variables in each image are measured. The input variables relate to target size, target juxtaposition relative to the viewer's location, luminance of the target and luminance of the background scene. One version of our method additionally uses wavelet edge points as an input variable. Each input variable, as well as the output variable, has several applicable membership functions by which membership values are assigned to the variables. Choosing membership functions for variables is done by fuzzy rules. Applying the fuzzy rules and membership functions produces multiple values for the output variable. These values are defuzzified to obtain a crisp end result. This result can disqualify proposed target designs or to help select among several good designs of the target.

Fuzzy logic technique to determine search time and probability of detection for targets of interest in background scenes

A method to determine the probability of detection, P(t), of targets within infrared-imaged, pixelated scenes includes dividing the scenes into tar blocks and background blocks. A plurality of .DELTA.T metrics are applied to the blocked scenes to derive various .DELTA.T values for each scene. Factor analysis is then used on the various .DELTA.T values to derive a relative .DELTA.T for each scene. The scenes are divided again, into cells of pixels, in accordance with a plurality of clutter metrics. These clutter metrics are used to derive various clutter values for each scene. Factor analysis is used on the various clutter values to derive relative clutter values for each scene. The relative .DELTA.T values and the relative clutter values are used in determining the probabilities of detection of the targets in the scenes. Based on the probabilities of detection, the optimum scene or set of scenes is selected.

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Troy, MI, United States of America

Thomas J Meitzler