Abstract: Disclosed embodiments relate to ensemble wireless signal classification and systems and devices the incorporate the same. Some embodiments of ensemble wireless signal classification may include energy-based classification processes and machine learning-based classification processes. In some embodiments, incremental machine learning techniques may be incorporated to add new machine learning-based classifiers to a system or update existing machine learning-based classifiers.

Abstract: Processing flows and related systems and methods are disclosed. A computing system includes one or more data interfaces, one or more other components, and a controller. The one or more data interfaces are configured to provide an interface to a data source. The one or more other components include one or more controller plugins, one or more processing nodes, or both the one or more controller plugins and the one or more processing nodes. The controller is configured to manage interactions between the one or more data interfaces and the one or more other components and enable a user to chain together the one or more data interfaces and the one or more other components according to one or more flows. The one or more controller plugins are configured to provide results of the one or more flows to one of a user interface and a system interface.

Abstract: Processing flows and related systems and methods are disclosed. A computing system includes one or more data interfaces, one or more other components, and a controller. The one or more data interfaces are configured to provide an interface to a data source. The one or more other components include one or more controller plugins, one or more processing nodes, or both the one or more controller plugins and the one or more processing nodes. The controller is configured to manage interactions between the one or more data interfaces and the one or more other components and enable a user to chain together the one or more data interfaces and the one or more other components according to one or more flows. The one or more controller plugins are configured to provide results of the one or more flows to one of a user interface and a system interface.

Abstract: Processing flows and related systems and methods are disclosed. A computing system includes one or more data interfaces, one or more other components, and a controller. The one or more data interfaces are configured to provide an interface to a data source. The one or more other components include one or more controller plugins, one or more processing nodes, or both the one or more controller plugins and the one or more processing nodes. The controller is configured to manage interactions between the one or more data interfaces and the one or more other components and enable a user to chain together the one or more data interfaces and the one or more other components according to one or more flows. The one or more controller plugins are configured to provide results of the one or more flows to one of a user interface and a system interface.

Abstract: Wireless signal classifiers and systems that incorporate the same may include an energy-based detector configured to analyze an entire set of measurements and generate a first signal classification result, a cyclostationary-based detector configured to analyze less than the entire set of measurements and generate a second signal classification result; and a classification merger configured to merge the first signal classification result and the second signal classification result. Ensemble wireless signal classification and systems and devices the incorporate the same are disclosed. Some ensemble wireless signal classification may include energy-based classification processes and machine learning-based classification processes. In some embodiments, incremental machine learning techniques may be incorporated to add new machine learning-based classifiers to a system or update existing machine learning-based classifiers.

Abstract: Disclosed embodiments relate to ensemble wireless signal classification and systems and devices the incorporate the same. Some embodiments of ensemble wireless signal classification may include energy-based classification processes and machine learning-based classification processes. In some embodiments, incremental machine learning techniques may be incorporated to add new machine learning-based classifiers to a system or update existing machine learning-based classifiers.

Abstract: Wireless signal classifiers and systems that incorporate the same may include an energy-based detector configured to analyze an entire set of measurements and generate a first single classification result, a cyclostationary-based detector configured to analyze less than the entire set of measurements and generate a second signal classification result; and a classification merger configured to merge the first signal classification result and the second signal classification result. Ensemble wireless signal classification and systems and devices the incorporate the same are disclosed. Some ensemble wireless signal classification may include energy-based classification processes and machine learning-based classification processes. In some embodiments, incremental machine learning techniques may be incorporated to add new machine learning-based classifiers to a system or update existing machine learning-based classifiers.

Abstract: A method and system directed to the communication of the trust characteristics of electronic messages using visual and audio postmarks. The postmarks may represent a particular trust level on a continuum that can range from suspicious to trusted. A message system may determine the baseline trust level of a message and assign a postmark to the message. The postmark can communicate the trust level of the message to a user without requiring the message to be opened or read. In one embodiment of the invention the user can create rules and filters to classify messages by trust level.

Abstract: An information analysis method is described and which includes steps of providing a data analysis framework for selectively holding a plurality of interrelated computer viewable views of selected data; interacting the plurality of computer viewable views; and evaluating the plurality of computer viewable views substantially simultaneously.