University of Florida Research Foundation, Incorporated

Prabuddha Chakraborty

Swarup Bhunia

Christopher Vega

Patanjali Sristi Lakshmiprasanna Sriramakumara

Jonathan William Cruz

Reiner Dizon

Sandip Ray

Tamzidul Hoque

Robert Parker

Shuo Wang

Abdulrahman Alaql

Rohan Reddy Kalavakonda

Pravin Dasharth Gaikwad

Abhishek Anil Nair

Toan Trung Nguyen

Naren Masna

Parker Difuntorum

Saranyu Chattopadhyay

Joel B Harley

Lili Du

A joint compression and encryption system is configured to retrieve, from a local memory or image capture device, an image file including a first plurality of segments. The system is further configured to identify, for one or more segment of the first plurality of segments, a matching segment in a local segmentation repository. The system is further configured to compress remaining segments of the first plurality of segments for which no matching segment was identified into a compressed remaining segment set. The system is further configured to transmit, via an unsecure communication channel and to a second computing entity, the identifications of the matching segments, and the compressed remaining segment set. The system is further configured to, prior to retrieving the image file, perform a handshake or calibration process. The system is further configured to encrypt the matching segments using a key into an encrypted segment set and transmit the key to the second computing entity. In embodiments, the second computing entity, using the key, decrypts the encrypted segment set.

Predictive joint compression and encryption for images and videos

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.

Multi-level battery systems for battery-operated entities, methods for rapid charge transfer therebetween, and methods for optimizing entity routing and network charge distribution

Disclosed are various embodiments related to coordinated monitoring and responding to an emergency situation at a building structure as a supplement to a traditional emergency response. In some embodiments, a system comprises a computing device that is configured to receive sensor data from a sensor network. The sensor network includes monitoring units that monitor various locations of an infrastructure. The computing device determines an occurrence of an emergency event at a location in the infrastructure using an anomaly detector model based at least in part on the sensor data. A hybrid mobile unit is instructed by the computing device to navigate to the location of the emergency event. The hybrid mobile unit is configured to provide mobile sensor data associated with the location to confirm the emergency event.

Smart infrastructures and first-responder network for security and safety hazards

The present disclosure describes various embodiments of systems, apparatuses, and methods for drone-based administration of remotely located devices. One such method comprises deploying an unmanned aerial vehicle from a base station, wherein the base station assigns a maintenance order to the unmanned aerial vehicle for servicing of a remote device, traveling, by the unmanned aerial vehicle, to the location of the remote device, authenticating, by the unmanned aerial vehicle, a valid identification of the remote device; upon the remote device being authenticated by the unmanned aerial vehicle, servicing the remote device by at least charging a power supply of the remote device and transferring contents of a device log to the unmanned aerial vehicle; and after completing the servicing of the remote device; returning to the base station and transferring contents of the device log to the base station.

Drone-based administration of remotely located instruments and gadgets

Apparatus, systems, and methods described herein relate generally to autonomous mobile units carrying a modular configurable battery system that may attach and power mobile units in transportation systems. A method can include determining charge levels, current positions, and transport speeds for an electric vehicle (EV), identifying one or more EVs in need of charging, and mobilizing a Mobile Charging Station (MoCS) to deliver one or more external batteries. 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 the proximity of both MoCS and physical battery stations, 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 the EV and MoCS and/or the battery station.

System and method for a battery on wheels (BoW) for charging mobile battery-operated units

Apparatus, systems, and methods described herein relate generally to on-the-go entity-to-entity charging in transportation systems. A method can include determining charge levels, current positions, 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.

System and method for charging a network of mobile battery-operated units on-the-go

Embodiments of the present disclosure provide methods, apparatus, systems, computer program products, computing devices, and/or computing entities for obfuscating a hardware intellectual property (IP) design by locking the design based at least in part on a plurality of key-bits. In one embodiment, a method is provided comprising: generating a key vulnerability matrix for a locked version of the design and a plurality of attacks that comprises for each attack, a vector comprising a value for each key-bit identifying whether the attack successfully extracted a correct key value for the key-bit; and for each key-bit: determining whether the key-bit is vulnerable to an attack based on the values in the matrix; and responsive to being vulnerable: identifying a set of solutions to mitigate the attack; selecting a solution from the set; and inserting a key-gate type for the key-bit at a location identified by the selected solution into the design.

Learning guided obfuscation framework for hardware IP protection

Embodiments of the present disclosure provide methods, apparatus, and computer program products for generating an insertion netlist for a target circuit configured for inserting a malicious design alteration into the circuit based on a design identifying reference trigger nets. Features are extracted for each net identified in a netlist for the circuit. A set of reference trigger features is generated for each of the reference trigger nets. A net is selected from the netlist for each set of reference trigger features based on a similarity between the features of the net and the set of reference trigger features. The insertion netlist is generated that includes the circuit with the malicious design alteration inserted at each of the selected nets.

Systems and methods for machine intelligence based malicious design alteration insertion

Various examples are provided related to multi-purpose context-aware bumps (CABs) that can support dynamic adaptation of form factors and functionality. In one example, a CAB system can include sensors distributed in a traffic network and communicatively coupled to a remotely located computing environment; context-aware bumps (CABs) placed in the traffic network and communicatively coupled to the remotely located computing environment; and a CAB application configured to adjust a form factor of a CAB in response to information obtained from the sensors and/or CABs. In another example, a method can include receiving, by a remotely located computing environment, traffic information from sensors distributed in a traffic network or CABs placed in the traffic network; communicating, by the remotely located computing environment, a form factor control to a CAB in response to the traffic information; and adjusting a form factor of the CAB in response to the form factor control.

Growing community of inventors

Gainesville, FL, United States of America

Prabuddha Chakraborty