C-40 City is an upgraded SMART CITY (REAL ID Enslavement Camp)

7G MESH Network for Precision Medicine, Control, Harvesting. SWISS Eugeneics corporate military contractors for DAPRA:

Dust Networks (now part of Analogue Devices) & Kristifer Pister & Joy Weiss
Coast Aluminum
Dynavax Technologies (novel vaccines)
America Poly-Foam (Founded 1973, American Poly-Foam Co, Inc is a foam fabricator located in Hayward, CA)
CRISPR Therapeutics Gene Editing & Jennifer Doudna Nobel clown
DARPA, DOE, DOD, NATO, Sheriff Department, C-40 City, FEMA Camps.

Blockchain, hyperledger system & MOSA & SOSA software platforms operate:
REAL ID Security Access or Denial
TOKEN CREDIT MONEY (Crypto or CBDC Smart Contracts)
Universal Income benefits contingent upon social compliance
Human Behaviors & Compliance (IoB)
IoT, IoE & IoB, (GiG) Global Information Grid
SMART DUST is an inescapable control grid (breathable air water, soil, farm, plants)
Sources & Uses of TOKEN Credits
Trace, track & target all living creatures and QR encoded vehicles with tires.
Programmable Matter. Programmable Money. Programmable Contracts.
Synthetic Humans operating with Synthetic TOKEN Money.
Interaction of cellular network. Computer to Brain via BSN or WBAN graphene
Concrete, painted lines, SMART METERS, LED streetlights, Ring Camera, Drones.
SMART CITIES are 24-7 surveillance, ZERO TRUST, NO PRIVACY Kill Boxes.

Human Activity Recognition Radar (HARR) Abstract:
Radar systems are increasingly being employed in healthcare applications for human activity recognition due to their advantages in terms of privacy, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are however often complex, focusing on a single domain of radar, and requiring significant computational resources that prevent their deployment in embedded platforms which often have limited memory and computational resources. To address this issue, we present an adaptive magnitude thresholding approach for highlighting the region of interest in the multi-domain micro-Doppler signatures. The region of interest is beneficial to extract salient features, meanwhile it ensures the simplicity of calculations with less computational cost. The results for the proposed approach show an accuracy of up to 93.1% for six activities, outperforming state-of-the-art deep learning methods on the same dataset with an over tenfold reduction in both training time and memory footprint, and a twofold reduction in inference time compared to a series of deep learning implementations. These results can help bridge the gap toward embedded platform deployment.

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