3D model retrieval using principal plane analysis and dynamic programming

Three dimensional models play an important role in many applications; the problem is how to select the appropriate models from a 3D database rapidly and accurately. In recent years, a variety of shape representations, statistical methods, and geometric algorithms have been proposed for matching 3D shapes or models. In this paper, we propose a 3D shape representation scheme based on a combination of principal plane analysis and dynamic programming. The proposed 3D shape representation scheme consists of three steps. First, a 3D model is transformed into a 2D image by projecting the vertices of the model onto its principal plane. Second, the convex hall of the 2D shape of the model is further segmented into multiple disjoint triangles using dynamic programming. Finally, for each triangle, a projection score histogram and moments are extracted as the feature vectors for similarity searching. Experimental results showed the robustness of the proposed scheme, which resists translation, rotation, scaling, noise, and destructive attacks. The proposed 3D model retrieval method performs fairly well in retrieving models having similar characteristics from a database of 3D models.     

Fracture characteristics of a burst tested maraging steel rocket motor case

A 0.3 m diameter, 2 m long and 0.0015 m thick, 18 nickel 1800 MNm^–2 grade maraging steel motor case was designed, fabricated and burst tested to gain experience for using the steel as booster case material in satellite launch vehicles. The bursting occurred at 15.2 MPa for which the effective hoop stress worked out to be 1754 MNm^–2 almost equal to the ultimate tensile strength (1764 MNm^–2) of the material in the solution treated and aged condition. The failure analysis revealed that the material failed due to normal tensile overload fracture. The burst test data was used to arrive at fracture mechanics parameters like crack size, gross section area stress and the stress for leak before bursting.     

Robust pole clustering in a good ride quality region of aircraft for matrices with structured uncertainties

This paper presents a general analysis of robust pole clustering in a good ride quality region (GRQR) of aircraft for matrices with structured uncertainties. This region is an intersection of a ring and a horizontal strip, located in the left half-plane, which is a specific non-Ω-transformable region providing good ride quality of aircraft. The paper applies the Rayleigh principle along the norm theory to analyze robust pole clustering within this region since the generalized Lyapunov theory is not valid for non-Ω-transformable regions. Concerned uncertainties are structured/parametric uncertainties, including interval matrices. The results are useful for robust control analysis and design, especially, of robust good ride quality of aircraft, shuttles, vehicles and space station, as well as some industrial systems. An example of the F-16 dynamics for which GRQR is suitable is included to illustrate the results.     

Robot control and parameter estimation with only joint position measurements

Most adaptive control schemes for rigid robots assume velocities measurements to be available. Although it is possible to measure velocities by using tachometers, this increases costs and the signals delivered may be contaminated with noise. Since the use of encoders allows to read joint position pretty accurately, it is desirable to estimate joint velocities through an observer. This paper presents an adaptive scheme designed in conjunction with a linear observer. Boundedness of the estimated parameters and uniform ultimate boundedness for the tracking and observation errors are guaranteed. Experimental results are included to support the developed theory.     

Characterization of a negative cis-acting DNA element regulating the transcription of CYP2B1/B2 gene in rat liver

Polymeric films were deposited from hydroxyethylmethacrylate (HEMA) plasma on non-woven poly(butyleneterephtalate) (PBT) filter materials. To test the effect of deposition conditions on surface properties, film were deposited using a constant monomer flow rate and a discharge power ranging from 40-100 W. Surface composition and surface energetics were evaluated by Electron Spectroscopy for Chemical Analysis (ESCA) and contact angle measurement, respectively. Albumin (Alb) and fibrinogen (Fg) adsorption from single protein solutions to the plasma-coated filters was measured. Results illustrate the marked effects of the deposition condition on the surface composition, the surface field of forces, and the protein adsorption behavior. The latter is modeled by the application of the Good-van Oss-Chaudhury theory of Lewis acid-base contribution to interfacial energetics. Materials endowed with widely different properties are obtained from the same monomer and different deposition conditions, a result that must be taken into account both in the production step, to assure constant quality, and in the development of specifically tailored materials.     

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.     

Preface

Microsystem Technologies -     

Favorable influence of ssDNA-functionalized SWCNT on the navigation pattern of C. elegans

Microsystem Technologies - Single-Walled Carbon Nanotubes (SWCNTs) are widely used as potential carriers in drug delivery systems. The objective of this work was to observe the effects of pristine,...