Naval Surface Warfare Center

About: Naval Surface Warfare Center is a facility organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Radar & Sonar. The organization has 2855 authors who have published 3697 publications receiving 83518 citations. The organization is also known as: NSWC.

Magnetostrictive Properties of Galfenol Alloys Under Compressive Stress

Abstract: Fe–Ga alloys, in which the α-Fe structure is maintained, are rich sources of high strength, low cost magnetostrictive alloys for transducer and vibration reduction applications. Although the magnetostriction of Fe itself is very low, when a relatively small fraction of the Fe atoms are replaced by Ga, the magnetostriction, (3/2)λ100, increases greatly. Until recently, the highest magnetostriction was found with the replacement of Fe by Al (Alfenol). In this paper, we present our measurements of magnetostriction on Fe1−x Gax , 0.13 ≤ x ≤ 0.24, (Galfenol). With the substitution of 19% Ga for Fe in Fe1−x Gax , a 12-fold increase in magnetostriction to ∼ 400 ppm occurs, even though Ga is non-magnetic. In these alloys, the saturation magnetizations remain high, Ms ∼= 1.7 T, and the Curie temperatures are far above room temperature, TC ∼= 700◦C. In most alloys studied, the magnetostrictions and magnetizations are fully saturated in fields less than 24 kA/m, even under compressive stresses >100 MPa. For x = 0.24 (near Fe3Ga), an anomalous increase in magnetostriction with temperature occurs with a peak magnetostriction above room temperature. Small additions of Ni and Mo to the binary Fe–Ga alloys decrease the room temperature value of λ100.

Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography

Abstract: Computer-assisted detection of microcalcifications in mammographic images will likely require a multistage algorithm that includes segmentation of possible microcalcifications, pattern recognition techniques to classify the segmented objects, a method to determine if a cluster of calcifications exists, and possibly a method to determine the probability of malignancy. This paper focuses on the first three of these stages, and especially on the classification of segmented local bright spots as either calcification or noncalcification. Seven classifiers (linear and quadratic classifiers, binary decision trees, a standard backpropagation network, 2 dynamic neural networks, and a K-nearest neighbor) are compared. In addition, a postprocessing step is performed on objects identified as calcifications by the classifiers to determine if any clusters of microcalcifications exist. A database of digitized film mammograms is used for training and testing. Detection accuracy of individual and clustered microcalcificat...