Abstract

Abstract

Abstract

Abstract
Orientation estimation plays a crucial role in robotics. Precise and reliable estimation of orientation, and the yaw angle in particular, is still a challenge and subject of great concern among researchers. This paper presents the development of a platform for yaw angle estimation by fusing inertial and magnetic sensing (a low-cost multi-sensorial system composed by both a digital compass and a gyroscope). A Kalman filter is used to estimate the error produced by the gyroscope. Experimental results indicate that the proposed solution is able to eliminate the drift effect produced by gyroscope data and, at the same time, has the capacity to react to fast orientation changes.

Abstract
The communication between two neurons is established by endogenous chemical particles aggregated in vesicles that move along the axons. It is known that an abnormal transport of these vesicles is correlated with neurodegenerative diseases. The quantification of the dynamics of vesicles movement can therefore be a window to study early detection of such diseases. Nevertheless, most of the studies in the literature rely on manual tracking techniques. In this paper we present a novel methodology for quantifying neurotransmitter vesicle dynamics by using a combination of image tracking and classification algorithms. We use confocal microscopy videos of living neurons to detect and classify vesicles using support vector machine (SVM), while motion is extracted via global nearest neighbor (GNN) tracking approach. Results of the classification algorithm are presented and compared to a ground truth dataset defined by experts. Sensitivity of 90% and specificity of 97% were obtained at a much lower computational cost than an established method from the literature (0.24s/frame vs. 125s/frame). These preliminary results suggest the great potential of the method and tool we have been developing for single particle movement dynamics measure in living cells.

Abstract