Abstract: Technologies are disclosed herein for providing an aerial transport network infrastructure for unmanned aerial vehicles, such as drones. The transport network infrastructure comprises models built from publicly available information, such as Federal Communications Commission and Department of Transportation (FCC-DOT) map data. An Electric Regulated Drone Operating System (ERDOS) executing within a drone includes logic for routing, queuing, monitoring, and rescuing using Level 5 automation techniques within the aerial transport network infrastructure.

Abstract: Technologies are disclosed herein for providing an aerial transport network infrastructure for unmanned aerial vehicles, such as drones. The transport network infrastructure comprises models built from publicly available information, such as Federal Communications Commission and Department of Transportation (FCC-DOT) map data. An Electric Regulated Drone Operating System (ERDOS) executing within a drone includes logic for routing, queuing, monitoring, and rescuing using Level 5 automation techniques within the aerial transport network infrastructure.

Abstract: The embodiments herein provide a method for scheduling an entity in a multi-core processor system including a big-core processor, and a little-core processor. The method includes detecting, by a scheduler, that a load contribution of the entity exceeds a load threshold. Further, the method includes determining, by the scheduler, whether the entity is one of a background entity, an IO intensive entity, a non-background entity, and a non-IO intensive entity based on at least one parameter. Further, the method includes instructing, by the scheduler, one of to schedule the entity on a little-core processor when the entity is at least one of the background entity and the IO intensive entity; and to schedule the entity on the big-core processor when the entity is at least one of the non-background entity and the non-IO intensive entity.

Abstract: The embodiments herein provide a method for scheduling an entity in a multi-core processor system including a big-core processor, and a little-core processor. The method includes detecting, by a scheduler, that a load contribution of the entity exceeds a load threshold. Further, the method includes determining, by the scheduler, whether the entity is one of a background entity, an IO intensive entity, a non-background entity, and a non-IO intensive entity based on at least one parameter. Further, the method includes instructing, by the scheduler, one of to schedule the entity on a little-core processor when the entity is at least one of the background entity and the IO intensive entity; and to schedule the entity on the big-core processor when the entity is at least one of the non-background entity and the non-IO intensive entity.

Abstract: In one aspect, a method of generating a prediction list of real-estate assets that have a specified probability of being placed for sale within a specified period of time includes the step of providing a list of real-estate assets. Each real-estate asset is associated with one or more real-estate assets attributes. The method includes the step of providing a training data set wherein the training data set comprises a past population of data associated with a plurality of real-estate assets and a set of training-data set attributes for each real-estate asset in the plurality of real-estate assets. The method includes providing a testing data set wherein the testing data set comprises another past population of data associated with the plurality of real-estate assets and a set testing-data set attributes for each real-estate asset in the plurality of real-estate assets, wherein the set of testing data set attributes comprises an updated version of the training data set attributes from a specified later time.