Abstract: A method and system for intelligent monitoring and intelligent alerting. One or more data inputs are received from one or more systems. A weight is attributed to each data input based on the data input, the source of the data input, and other factors. One or more video inputs are received from one or more video sources. Image analysis is performed on the video data to determine one or more video parameters. The video parameters are assigned one or more weights based on the video source and other factors. A series of rules are evaluated using the data inputs, the video inputs, and their respective weights. An associated action is performed for each rule that is activated. The present invention may be used for various security and safety purposes, including fighting crime, detecting and preventing terrorist activity, and ensuring safety procedures are followed.

Abstract: The present invention is a video surveillance, storage, and alerting system having surveillance cameras, video analytics devices, audio sensory devices, other sensory devices, and a plurality of data storage devices. A network management module monitors network status of all subsystems including cameras, servers, storage devices, etc. and shows actively monitored areas on a physical map. A vehicle information module retrieves information from a law enforcement database about vehicles detected in the video data based on the vehicle's license plate, including information about stolen vehicles, as well as warrant, wanted person, and mug shot information for registered drivers of the vehicles. Video tips are received and processed from anonymous and non-anonymous sources.

Abstract: Hashing functions have many practical applications in data storage and retrieval. Perfect hashing functions are extremely difficult to find, especially if the data set is large and without large-scale structure. There are great rewards for finding good hashing functions, considering the savings in computational time such functions provide, and much effort has been expended in this search. This in mind, we present a strong competitive evolutionary method to locate efficient hashing functions for specific data sets by sampling and evolving from the set of polynomials over the ring of integers mod n. We find favorable results that seem to indicate the power and usefulness of evolutionary methods in this search. Polynomials thus generated are found to have consistently better collision frequencies than other hashing methods. This results in a reduction in average number of array probes per data element hashed by a factor of two.