The Beatles PSYOPS, Epilogue

EMI-Capitol Records invented:
The Beatles
The Beach Boys
Judy Garland
James Taylor

Warner Brothers Arts & Repertoire Rep Ted Templeman:
https://en.wikipedia.org/wiki/Ted_Templeman
https://en.wikipedia.org/wiki/Van_Morrison
https://en.wikipedia.org/wiki/The_Doobie_Brothers
https://en.wikipedia.org/wiki/Van_Halen

Columbia Records CBS created KISS:
https://en.wikipedia.org/wiki/Clive_Davis
https://en.wikipedia.org/wiki/Gene_Simmons
https://en.wikipedia.org/wiki/Paul_Stanley
https://en.wikipedia.org/wiki/Whitney_Houston (Sony)

Warner Brothers NYC Music Building creations:
https://en.wikipedia.org/wiki/Madonna
https://en.wikipedia.org/wiki/Billy_Idol

RCA NYC Brill Building creations:
https://en.wikipedia.org/wiki/Don_Kirshner
https://en.wikipedia.org/wiki/Neil_Sedaka
https://en.wikipedia.org/wiki/Neil_Diamond

https://en.wikipedia.org/wiki/Body_area_network

Abstract:
In the healthcare sector, the health status and biological, and physical activity of the patient are monitored among different sensors that collect the required information about these activities using Wireless body area network (WBAN) architecture. Sensor-based human activity recognition (HAR), which offers remarkable qualities of ease and privacy, has drawn increasing attention from researchers with the growth of the Internet of Things (IoT) and wearable technology. Deep learning has the ability to extract high-dimensional information automatically, making end-to-end learning. The most significant obstacles to computer vision, particularly convolutional neural networks (CNNs), are the effect of the environment background, camera shielding, and other variables. This paper aims to propose and develop a new HAR system in WBAN dependence on the Gramian angular field (GAF) and DenseNet. Once the necessary signals are obtained, the input signals undergo pre-processing through artifact removal and median filtering. In the initial stage, the time series data captured by the sensors undergoes a conversion process, transforming it into 2-dimensional images by using the GAF algorithm. Then, DenseNet automatically makes the processes and integrates the data collected from diverse sensors. The experiment results show that the proposed method achieves the best outcomes in which it achieves 97.83% accuracy, 97.83% F-measure, and 97.64 Matthews correlation coefficient (MCC).