5,683 research outputs found

    Spin-Mediated Consciousness: Theory, Experimental Studies, Further Development & Related Topics

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    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

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    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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