5,683 research outputs found
Spin-Mediated Consciousness: Theory, Experimental Studies, Further Development & Related Topics
We postulate that consciousness is intrinsically connected to quantum spin
since the latter is the origin of quantum effects in both Bohm and Hestenes
quantum formulisms and a fundamental quantum process associated with the
structure of space-time. Applying these ideas to the particular structures and
dynamics of the brain, we have developed a detailed model of quantum
consciousness. We have also carried out experiments from the perspective of our
theory to test the possibility of quantum-entangling the quantum entities
inside the brain with those of an external chemical substance. We found that
applying magnetic pulses to the brain when an anaesthetic was placed in between
caused the brain to feel the effect of said anaesthetic as if the test subject
had actually inhaled the same. We further found that drinking water exposed to
magnetic pulses, laser light or microwave when an anaesthetic was placed in
between also causes brain effects in various degrees. Additional experiments
indicate that the said brain effect is indeed the consequence of quantum
entanglement. Recently we have studied non-local effects in simple physics
systems. We have found that the pH value, temperature and gravity of a liquid
in the detecting reservoirs can be non-locally affected through manipulating
another liquid in a remote reservoir quantum-entangled with the former. In
particular, the pH value changes in the same direction as that being
manipulated; the temperature can change against that of local environment; and
the gravity can change against local gravity. We suggest that they are mediated
by quantum entanglement between nuclear and/or electron spins in treated liquid
and discuss the profound implications of these results. This paper now also
includes materials on further development of the theory and related topics.Comment: 92 pages; expanded content; minor corrections; for additional
information, please visit http://quantumbrain.or
SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos
In recent years, distracted driving has garnered considerable attention as it
continues to pose a significant threat to public safety on the roads. This has
increased the need for innovative solutions that can identify and eliminate
distracted driving behavior before it results in fatal accidents. In this
paper, we propose a Signal-Based anomaly detection algorithm that segments
videos into anomalies and non-anomalies using a deep CNN-LSTM classifier to
precisely estimate the start and end times of an anomalous driving event. In
the phase of anomaly detection and analysis, driver pose background estimation,
mask extraction, and signal activity spikes are utilized. A Deep CNN-LSTM
classifier was applied to candidate anomalies to detect and classify final
anomalies. The proposed method achieved an overlap score of 0.5424 and ranked
9th on the public leader board in the AI City Challenge 2023, according to
experimental validation results
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