Abstract: A method of encrypted communications comprising: by the sender; generating a first symmetric key, calculating the first symmetric key based on the intended spatiotemporal identifiers; encrypting the decipherable message with the coordinates of the first symmetric key to produce an encrypted message Dm; generating a second symmetric key; encrypting the encrypted message Dm with the coordinates of the second symmetric key to produce an encrypted message Cm; receiving the public key from the receiver; calculating a point P1 and a point P2; transmitting the encrypted message Cm, point P1, and point P2; by the receiver; transmitting the public key to the sender; determining the actual spatiotemporal identifiers; receiving the encrypted message Cm, point P1, and point P2; calculating the second symmetric key, decrypting the encrypted message Cm asymmetrically to reproduce the encrypted message Dm; decrypting the encrypted message Dm symmetrically with the actual spatiotemporal identifiers.

Abstract: A method of training, verification, and advanced notice for an autonomous system, comprising: by a machine learning classifier, wherein an adversary vehicle, and a primary vehicle are on the same path; creating a path position probability transition matrix, wherein the path position probability transition matrix comprises an acceleration parameters array of one or more acceleration parameters for a potential position, sorting a sample path, and using Ne elite path; creating a samples array, wherein the sample array is one or more paths; evaluating a sample path; sorting the sample path, based at least in part a custom score of a custom score function and selecting a subset of the sample paths as Ne elite paths; using the Ne elite paths to update the path probability transition matrix and the acceleration parameters array; and repeating until the Ne elite paths stabilizes for a predetermined number of iterations.

Abstract: In one embodiment, a method for authenticating a user with an electronic device is disclosed. The method incudes receiving digital sensor data from a motion sensor over a signal acquisition time period; deleting a beginning portion of the digital sensor data prior to the signal acquisition time period; suppressing signal components in the data associated with voluntary movement of the user; signal processing the suppressed digital sensor data to extract signal features representing neuro muscular tone of the user; tabulating the extracted signal features over periods of time into a feature vector table; executing a predictive model with the feature vector table; generating a numerical degree of matching level based on the feature vector table and the user parameter set; and making a determination to either authorize the user or not based on the numerical degree of matching level. The predictive model is trained by a user parameter set.

Abstract: Methods, and systems, for Multiagent Pathfinding for Non Dynamic Programming Problems, including a CE method which provides for sampling from a complex probability distribution that is not necessarily known in a closed form. The applications of this method include rare-event simulation, variance reduction for estimation problems, and stochastic optimization. The method iteratively searches for a probability distribution that is “close” to the intended distribution, where the closeness of distributions is measured using the Kullback-Liebler (KL) divergence between the distributions. At each step, the method generates samples according to a current candidate distribution from the family. Next, it uses those current candidate distribution samples to move the distribution toward a new candidate distribution that is closer in the sense of KL divergence to the target distribution.

Abstract: Methods, and systems, for Multiagent Pathfinding for Non Dynamic Programming Problems, including a CE method which provides for sampling from a complex probability distribution that is not necessarily known in a closed form. The applications of this method include rare-event simulation, variance reduction for estimation problems, and stochastic optimization. The method iteratively searches for a probability distribution that is “close” to the intended distribution, where the closeness of distributions is measured using the Kullback-Liebler (KL) divergence between the distributions. At each step, the method generates samples according to a current candidate distribution from the family. Next, it uses those current candidate distribution samples to move the distribution toward a new candidate distribution that is closer in the sense of KL divergence to the target distribution.

Abstract: In one embodiment, a method for authenticating a user with an electronic device is disclosed. The method incudes receiving digital sensor data from a motion sensor over a signal acquisition time period; deleting a beginning portion of the digital sensor data prior to the signal acquisition time period; suppressing signal components in the data associated with voluntary movement of the user; signal processing the suppressed digital sensor data to extract signal features representing neuro muscular tone of the user; tabulating the extracted signal features over periods of time into a feature vector table; executing a predictive model with the feature vector table; generating a numerical degree of matching level based on the feature vector table and the user parameter set; and making a determination to either authorize the user or not based on the numerical degree of matching level. The predictive model is trained by a user parameter set.

Abstract: The present invention comprises a method of implementing state machines between regions in which there are communication delays between the regions.

Abstract: An electronic controller connectible to an external system comprises: a plurality of logical networks each capable of assuming a plurality of different states and arranged according to a hierarchy of different networks according to a hierarchy of different states having a one-to-one mapping between states and networks such that any transition between two states affects the two associate networks and their respective lower-order networks. Each of the logical networks includes a storage element for storing its state, means for determining its next state, and means for receiving control signals from, or for outputting control signals to, the immediately higher level logical network and the immediately lower level logical network for determining their respective states. At least some of the logical networks include means for receiving control signals from, or for outputting control signals to, the external system.