Abstract: A system and method providing Proof of Location or Proof of Location and Velocity consensus in a blockchain network using radio frequency (RF) signals. Nodes validate the location of other nodes in the network using PING-PONG round trip signal propagation time to determine maximum distances to other nodes. These maximum distances are then shared between nodes, whereupon the nodes use computational techniques to resolve validated, geospatial location of the other nodes. The sharing of measured maximum distances to local nodes is a form of Proof of Location consensus. The validated geospatial locations (and velocities) of local nodes are then written to the blockchain, creating a time history for each node. This information may be used by the blockchain operating rules to implement any number of security and other operational functions. The ability of the invention to operate without need for time synchronization between nodes is an advantage of the system.

Abstract: A system and method providing Proof of Location or Proof of Location and Velocity consensus in a blockchain network using radio frequency (RF) signals. Nodes validate the location of other nodes in the network using PING-PONG round trip signal propagation time to determine maximum distances to other nodes. These maximum distances are then shared between nodes, whereupon the nodes use computational techniques to resolve validated, geospatial location of the other nodes. The sharing of measured maximum distances to local nodes is a form of Proof of Location consensus. The validated geospatial locations (and velocities) of local nodes are then written to the blockchain, creating a time history for each node. This information may be used by the blockchain operating rules to implement any number of security and other operational functions. The ability of the invention to operate without need for time synchronization between nodes is an advantage of the system.

Abstract: A system and method providing Proof of Location or Proof of Location and Velocity consensus in a blockchain network using radio frequency (RF) signals. Nodes validate the location of other nodes in the network using PING-PONG round trip signal propagation time to determine maximum distances to other nodes. These maximum distances are then shared between nodes, whereupon the nodes use computational techniques to resolve validated, geospatial location of the other nodes. The sharing of measured maximum distances to local nodes is a form of Proof of Location consensus. The validated geospatial locations (and velocities) of local nodes are then written to the blockchain, creating a time history for each node. This information may be used by the blockchain operating rules to implement any number of security and other operational functions. The ability of the invention to operate without need for time synchronization between nodes is an advantage of the system.

Abstract: One variation of a method includes: accessing a handwriting sample comprising a set of user glyphs handwritten by a user; for each character in a set of characters, identifying a subset of user glyphs corresponding to the character in the handwriting sample, characterizing variability of a set of spatial features across the subset of user glyphs, and storing variability of the set of spatial features across the subset of user glyphs in a character container corresponding to the character; and compiling the set of character containers into a handwriting model for the user. The method further includes: accessing a text string comprising a combination of characters in the set of characters; for each instance of each character in the text string, inserting a set of variability parameters into the handwriting model to generate a synthetic glyph representing the character; and assembling the set of synthetic glyphs into a print file.

Abstract: One variation of a method includes: accessing a handwriting sample comprising a set of user glyphs handwritten by a user; for each character in a set of characters, identifying a subset of user glyphs corresponding to the character in the handwriting sample, characterizing variability of a set of spatial features across the subset of user glyphs, and storing variability of the set of spatial features across the subset of user glyphs in a character container corresponding to the character; and compiling the set of character containers into a handwriting model for the user. The method further includes: accessing a text string comprising a combination of characters in the set of characters; for each instance of each character in the text string, inserting a set of variability parameters into the handwriting model to generate a synthetic glyph representing the character; and assembling the set of synthetic glyphs into a print file.

Abstract: One variation of a method includes: accessing a handwriting sample comprising a set of user glyphs handwritten by a user; for each character in a set of characters, identifying a subset of user glyphs corresponding to the character in the handwriting sample, characterizing variability of a set of spatial features across the subset of user glyphs, and storing variability of the set of spatial features across the subset of user glyphs in a character container corresponding to the character; and compiling the set of character containers into a handwriting model for the user. The method further includes: accessing a text string comprising a combination of characters in the set of characters; for each instance of each character in the text string, inserting a set of variability parameters into the handwriting model to generate a synthetic glyph representing the character; and assembling the set of synthetic glyphs into a print file.

Abstract: One variation of a method includes: accessing a handwriting sample comprising a set of user glyphs handwritten by a user; for each character in a set of characters, identifying a subset of user glyphs corresponding to the character in the handwriting sample, characterizing variability of a set of spatial features across the subset of user glyphs, and storing variability of the set of spatial features across the subset of user glyphs in a character container corresponding to the character; and compiling the set of character containers into a handwriting model for the user. The method further includes: accessing a text string comprising a combination of characters in the set of characters; for each instance of each character in the text string, inserting a set of variability parameters into the handwriting model to generate a synthetic glyph representing the character; and assembling the set of synthetic glyphs into a print file.

Abstract: Systems and related methods for a training protocol are described. The system and methods may include a graphical user interface where after selecting an evaluated trainee and an evaluated task, a score input region populates certain features associated with the evaluated trainee and the evaluated task, including a score region. Furthermore, the systems and methods may further include generating a training report which is configured to receive a plurality of training subjects. In some aspects, generating the training report further includes preventing certain portions of the training report from further edits after a pre-determined event occurs.

Abstract: One variation of a method includes: accessing a handwriting sample comprising a set of user glyphs handwritten by a user; for each character in a set of characters, identifying a subset of user glyphs corresponding to the character in the handwriting sample, characterizing variability of a set of spatial features across the subset of user glyphs, and storing variability of the set of spatial features across the subset of user glyphs in a character container corresponding to the character; and compiling the set of character containers into a handwriting model for the user. The method further includes: accessing a text string comprising a combination of characters in the set of characters; for each instance of each character in the text string, inserting a set of variability parameters into the handwriting model to generate a synthetic glyph representing the character; and assembling the set of synthetic glyphs into a print file.