Coherence as an indicator to discern electromagnetically induced transparency and Autler-Townes splitting

Electromagnetically induced transparency (EIT) and Autler-Townes splitting (ATS) are generally characterized and distinguished by the width of the transparency created in the absorption profile of a weak probe in presence of a strong control field. This often leads to ambiguities, as both phenomena yield similar spectroscopic signature. However, an objective method based on the AIC test offers a quantitative way to discern the two regimes when applied on the probe absorption profile. The obtained transition value of control field strength was found to be higher than the value given by pole analysis of the corresponding off-diagonal density matrix element $\rho_{13}$. By contrast, we apply the test on ground state coherence $\rho_{12}$ and the measured coherence quantifier, which yielded a distinct transition point around the predicted value also in presence of noise. Our test accurately captures the transition between the two regimes, indicating that a proper measure of coherence is essential for making such distinctions.Comment: 5 pages, 4 figure

Motion capture sensing techniques used in human upper limb motion: a review

Purpose Motion capture system (MoCap) has been used in measuring the human body segments in several applications including film special effects, health care, outer-space and under-water navigation systems, sea-water exploration pursuits, human machine interaction and learning software to help teachers of sign language. The purpose of this paper is to help the researchers to select specific MoCap system for various applications and the development of new algorithms related to upper limb motion. Design/methodology/approach This paper provides an overview of different sensors used in MoCap and techniques used for estimating human upper limb motion. Findings The existing MoCaps suffer from several issues depending on the type of MoCap used. These issues include drifting and placement of Inertial sensors, occlusion and jitters in Kinect, noise in electromyography signals and the requirement of a well-structured, calibrated environment and time-consuming task of placing markers in multiple camera systems. Originality/value This paper outlines the issues and challenges in MoCaps for measuring human upper limb motion and provides an overview on the techniques to overcome these issues and challenges

Gun detection system using Yolov3

Based on current situation around the world, there is major need of automated visual surveillance for security to detect handgun. The objective of this paper is to visually detect the handgun in real time videos. The proposed method is using YOLO-V3 algorithm and comparing the number of false positive and false negative with Faster RCNN algorithm. To improve the result, we have created our own dataset of handguns with all possible angles and merged it with ImageNet dataset. The merged data was trained using YOLO-V3 algorithm. We have used four different videos to validate the results of YOLO-V3 compared to Faster RCNN. The detector performed very well to detect handgun in different scenes with different rotations, scales and shapes. The results showed that YOLO-V3 can be used as an alternative of Faster RCNN. It provides much faster speed, nearly identical accuracy and can be used in a real time environment