Most current capacitive RF-MEMS switch technology is based on conventional dielectric materials such as SiO2 and
Si3N4. However, they suffer not only from charging problems but also stiction problems leading to premature failure of
an RF-MEMS switch. Ultrananocrystalline diamond (UNCD(R) (2-5 nm grains) and nanocrystalline diamond (NCD) (10-
100 nm grains) films exhibit one of the highest Young's modulus (~ 980-1100 GPa) and demonstrated MEMS resonators
with the highest quality factor (Q ≥10,000 in air for NCD) today, they also exhibit the lowest force of adhesion among
MEMS/NEMS materials (~10 mJ/m2-close to van der Waals' attractive force for UNCD) demonstrated today. Finally,
UNCD exhibits dielectric properties (fast discharge) superior to those of Si and SiO2, as shown in this paper. Thus,
UNCD and NCD films provide promising platform materials beyond Si for a new generation of important classes of
high-performance MEMS/NEMS devices.
We proposed an approach based on reconstructive sparse representations to segment tissues in optical images of
the uterine cervix. Because of large variations in image appearance caused by the changing of the illumination
and specular reflection, the color and texture features in optical images often overlap with each other and are not
linearly separable. By leveraging sparse representations the data can be transformed to higher dimensions with
sparse constraints and become more separated. K-SVD algorithm is employed to find sparse representations
and corresponding dictionaries. The data can be reconstructed from its sparse representations and positive
and/or negative dictionaries. Classification can be achieved based on comparing the reconstructive errors. In
the experiments we applied our method to automatically segment the biomarker AcetoWhite (AW) regions in
an archive of 60,000 images of the uterine cervix. Compared with other general methods, our approach showed
lower space and time complexity and higher sensitivity.
In this paper, we propose a new method for automated detection and segmentation of different tissue types in digitized uterine cervix images using mean-shift clustering and support vector machines (SVM) classification on cluster features. We specifically target the segmentation of precancerous lesions in a NCI/NLM archive of 60,000 cervigrams. Due to large variations in image appearance in the archive, color and texture features of a tissue type in one image often overlap with that of a different tissue type in another image. This makes reliable tissue segmentation in a large number of images a very challenging problem. In this paper, we propose the use of powerful machine learning techniques such as Support Vector Machines (SVM) to learn, from a database with ground truth annotations, critical visual signs that correlate with important tissue types and to use the learned classifier for tissue segmentation in unseen images. In our experiments, SVM performs better than un-supervised methods such as Gaussian Mixture clustering, but it does not scale very well to large training sets and does not always guarantee improved performance given more training data. To address this problem, we combine SVM and clustering so that the features we extracted for classification are features of clusters returned by the mean-shift clustering algorithm. Compared to classification using individual pixel features, classification by cluster features greatly reduces the dimensionality of the problem, thus it is more efficient while producing results with comparable accuracy.