Abstract: Embodiments provide for a learning-rooted IoT platform. In example embodiments, a plug-and-play base pad apparatus includes one or more ports, each configured for hosting a pluggable component. The plug-and-play base pad apparatus further includes one or more of an administration chip or a microcontroller configured to control the apparatus and the one or more ports. The plug-and-play base pad apparatus further includes a battery configured to power the apparatus. The plug-and-play base pad apparatus further includes a power management unit configured to monitor the battery and interface with charging mechanisms.

Abstract: A reconfigurable JTAG includes, in part, a core logic, a boundary scan chain cell, one or more reconfigurable blocks (RBs), and a reconfigurable block (RB) programming module. The RBs may include, in part, one or more reconfigurable boundary scan chain blocks (RBB) adapted to couple the boundary scan chain cell to the core logic and to input/output (I/O) ports of the reconfigurable JTAG. The RBs may also include, in part, one or more additional reconfigurable logic (ARL) blocks to provide enhanced logic for locking operations. The RB programmable module may communicate with a memory storing data for configuring the RBBs and ARLs. The RB programming module may configure the RBBs and ARLs based at least in part on the data stored in the memory to disable access to the I/O ports of the JTAG. The RB programming module may configure the RBBs to encrypt the I/O ports in accordance with a cipher algorithm.

Abstract: A mask apparatus that is configured to prevent airborne pathogens from reaching an individual by actively mitigating airborne droplets that have a pathogen. In one example, the mask apparatus comprises a sensor device, a mitigator device, and a controller. The sensor device comprises an aerosol detector that is configured to perform a light measurement of an airborne aerosol droplet in an area proximate to the wearable apparatus. The mitigator device is configured to initiate a mitigation action directed at the airborne aerosol droplet. The controller is data communication with the sensor device and the mitigator device. The controller is configured to determine that the airborne aerosol droplet has a pathogen based at least in part on the light measurement captured by the sensor device and cause the mitigator device to initiate the mitigation action based on the airborne aerosol droplet having the pathogen.

Abstract: Apparatus, systems, and methods described herein relate generally to on-the-go entity-to-entity charging for multi-level battery-powered entities in transportation systems. A method can include determining charge levels, current positions, battery configuration, and transport speeds for an electric vehicle (EV), identifying one or more EVs in need of charging, and mobilizing a nearby EV for on-the-go peer-to-peer charging. A processor, with a memory including computer program code, can be configured to receive current charge level data for mobile battery-powered entities, identify one or more EVs to be charged and one or more other EVs that have excess charge to transfer, and send charging instructions to the EVs. A routing and charge transaction scheduling algorithm can be used to optimize the route of one or more battery-powered entities and to schedule charge transactions between EVs and/or a charging entity. A heuristic battery architecture compiler can be used to optimize battery architecture.