Improved Detection Rates for Close Binaries Via Astrometric Observations of Gravitational Microlensing Events

In addition to constructing a Galactic matter mass function free from the bias induced by the hydrogen-burning limit, gravitational microlensing allows one to construct a mass function which is less affected by the problem of unresolved binaries (Gaudi & Gould). However, even with the method of microlensing, the photometric detection of binaries is limited to binary systems with relatively large separations of $b\gtrsim 0.4$ of their combined Einstein ring radius, and thus the mass function is still not totally free from the problem of unresolved binaries. In this paper, we show that by detecting distortions of the astrometric ellipse of a microlensing event with high precision instruments such as the {\it Space Interferometry Mission}, one can detect close binaries at a much higher rate than by the photometric method. We find that by astrometrically observing microlensing events, $\sim 50$ of binaries with separations of $0.1r_{\rm E}$ can be detected with the detection threshold of 3%. The proposed astrometric method is especially efficient at detecting very close binaries. With a detection threshold of 3% and a rate of 10%, one can astrometrically detect binaries with separations down to $\sim 0.01r_{\rm E}$.Comment: total 14 pages, including 5 Figures and no Table (For figure 1, please send a request mail to [email protected]), accepted to ApJ (Vol 525, 000), updated versio

Application of the Fisher-Rao metric to ellipse detection

The parameter space for the ellipses in a two dimensional image is a five dimensional manifold, where each point of the manifold corresponds to an ellipse in the image. The parameter space becomes a Riemannian manifold under a Fisher-Rao metric, which is derived from a Gaussian model for the blurring of ellipses in the image. Two points in the parameter space are close together under the Fisher-Rao metric if the corresponding ellipses are close together in the image. The Fisher-Rao metric is accurately approximated by a simpler metric under the assumption that the blurring is small compared with the sizes of the ellipses under consideration. It is shown that the parameter space for the ellipses in the image has a finite volume under the approximation to the Fisher-Rao metric. As a consequence the parameter space can be replaced, for the purpose of ellipse detection, by a finite set of points sampled from it. An efficient algorithm for sampling the parameter space is described. The algorithm uses the fact that the approximating metric is flat, and therefore locally Euclidean, on each three dimensional family of ellipses with a fixed orientation and a fixed eccentricity. Once the sample points have been obtained, ellipses are detected in a given image by checking each sample point in turn to see if the corresponding ellipse is supported by the nearby image pixel values. The resulting algorithm for ellipse detection is implemented. A multiresolution version of the algorithm is also implemented. The experimental results suggest that ellipses can be reliably detected in a given low resolution image and that the number of false detections can be reduced using the multiresolution algorithm

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Quantitative infrared thermography resolved leakage current problem in cathodic protection system

Leakage current problem can happen in Cathodic Protection (CP) system installation. It could affect the performance of underground facilities such as piping, building structure, and earthing system. Worse can happen is rapid corrosion where disturbance to plant operation plus expensive maintenance cost. Occasionally, if it seems, tracing its root cause could be tedious. The traditional method called line current measurement is still valid effective. It involves isolating one by one of the affected underground structures. The recent methods are Close Interval Potential Survey and Pipeline Current Mapper were better and faster. On top of the mentioned method, there is a need to enhance further by synthesizing with the latest visual methods. Therefore, this paper describes research works on Infrared Thermography Quantitative (IRTQ) method as resolution of leakage current problem in CP system. The scope of study merely focuses on tracing the root cause of leakage current occurring at the CP system lube base oil plant. The results of experiment adherence to the hypothesis drawn. Consequently, res

A Dynamic Localized Adjustable Force Field Method for Real-time Assistive Non-holonomic Mobile Robotics

Providing an assistive navigation system that augments rather than usurps user control of a powered wheelchair represents a significant technical challenge. This paper evaluates an assistive collision avoidance method for a powered wheelchair that allows the user to navigate safely whilst maintaining their overall governance of the platform motion. The paper shows that by shaping, switching and adjusting localized potential fields we are able to negotiate different obstacles by generating a more intuitively natural trajectory, one that does not deviate significantly from the operator in the loop desired-trajectory. It can also be seen that this method does not suffer from the local minima problem, or narrow corridor and proximity oscillation, which are common problems that occur when using potential fields. Furthermore this localized method enables the robotic platform to pass very close to obstacles, such as when negotiating a narrow passage or doorway