Cold nuclear fusion reactor And new modern physics

Modern physics classical particle quantization Orbital motion model general solutio

Motion Segmentation from a Moving Monocular Camera

Identifying and segmenting moving objects from a moving monocular camera is difficult when there is unknown camera motion, different types of object motions and complex scene structures. To tackle these challenges, we take advantage of two popular branches of monocular motion segmentation approaches: point trajectory based and optical flow based methods, by synergistically fusing these two highly complementary motion cues at object level. By doing this, we are able to model various complex object motions in different scene structures at once, which has not been achieved by existing methods. We first obtain object-specific point trajectories and optical flow mask for each common object in the video, by leveraging the recent foundational models in object recognition, segmentation and tracking. We then construct two robust affinity matrices representing the pairwise object motion affinities throughout the whole video using epipolar geometry and the motion information provided by optical flow. Finally, co-regularized multi-view spectral clustering is used to fuse the two affinity matrices and obtain the final clustering. Our method shows state-of-the-art performance on the KT3DMoSeg dataset, which contains complex motions and scene structures. Being able to identify moving objects allows us to remove them for map building when using visual SLAM or SFM.Comment: Accepted by IROS 2023 Workshop on Robotic Perception And Mapping: Frontier Vision and Learning Technique

Statistical delay distribution analysis on high-speed railway trains

Published by SpringerOpen, Heidelber

Open Area Concealed Weapon Detection (CWD) Sensor System Development

The detection of concealed weapons is a key requirement when considering the personal security of individuals in a public environment, such as a sporting event, airports, festivals, schools or universities etc. Hence, being able to efficiently discover any illicit items hidden within luggage or underneath the clothes of an individual, for example, is essential. The development of a concealed weapon detection (CWD) system, which efficiently addresses the issue of accurate identification and classification of dangerous objects, will aid in minimising the potential danger for a high volume of individuals in open area environments. Searching all visitors who pass through security points is normally an inefficient process, comprising of individual manual inspection, which often leads to congestion at the entrance of the event. Conversely, highly sophisticated systems with minimal manual intervention, utilising image scanning, are typically claimed to be a high risk to personal privacy and the possible leakage of confidential information, such as identification of belongings, where carried items underneath clothes are displayed on the screen, even if no weapon is detected. The traditional weapon detection process depends upon the manual recognition of a threat with currently available commercial systems generally being unable to achieve the accurate recognition of potential threat objects from other non-threat items, often resulting in what `the generation of false alarms'. Therefore, the development of a CWD system to accurately determine and categorise different illicit targets, such as knives and guns etc, in real time and efficiently monitor the public security in an open area environment, is increasingly becoming an essential requirement. Hence an innovative CWD solution that uses the pulse-induction (PI) technique to recognise and classify threat objects, through the novel characterisation of the induced electromagnetic signal utilising a sigma delta analogue to digital modulation device to yield an analysable signature is proposed. In comparison, to typical digital conversion processes, with excessive data samples required to provide distinguishable object signal characteristic information, the system features a single bit data flow from a sigma delta, to simplify the analogue sampling measurement. The sigma delta modulating approach facilitated a novel algorithm development to accurately identify potential weapons, enabling features (shape, size and material) of a target object to be identifiable within the signature. The weapon detection scheme delivers the signature evaluation based on marked points of the single bit stream facilitating the specific threat characteristics of the detected target to be identified in real-time. A practical, FPGA based implementation of the object identification procedure proved the concept of an algorithm to identify object characteristics of threat objects, principally that of a typical hand-held weapon (knife) through the identification of weapon characteristics, e.g. edge sharpness, thus efficiently differentiating between potential threats from other objects of similar shape, mass, etc. All the key aspects of an open area weapon detection system, operating in real-time, have been proven, thus future development and implementation of the proposed algorithm for an individual sensor could be expanded to form a multi-detection system to track a weapon trajectory, contributing to the development of an accurate and efficient identification of weapons in an open area environment