Stochastic Event Detection and Quantization for Catastrophe Prevention

Schools, stadiums, malls, and other locations that are frequented by large volumes of people can be described as Soft Targets and Crowded Places (ST-CPs). The relative lack of comprehensive security measures at these locations unfortunately results in ST-CPs being seen as prime targets for mass attacks that require little to no planning, including shootings, stabbings, and bombings. These attacks are significant security risks, as they are often difficult to detect and are carried out rapidly. To effectively prevent such attacks and minimize the damage caused, it is necessary to implement intelligent surveillance systems that are able to conduct risk assessments in real time and from a distance. When covering such large and dynamic areas as ST-CPs, it is important to use network control systems that consist of numerous individual sensors placed at various key locations. These sensors must communicate rapidly and reliably in order to ensure a swift response, meaning that data must be shared quickly and without degradation. Much of our research has been focused on Quantized Stabilization Stochastic Network Control Systems. Quantization errors are among the most common compression issues found in network communications. Our work is concerned with the most viable strategies for minimizing such errors, including methods such as feedback stabilization of Markov jump linear systems with time varying delay. Soft Target Engineering to Neutralize the Threat Reality (SENTRY) is committed to protecting against the threats to ST-CPs detailed above, particularly through the use of semiautonomous Virtual Sentries placed at critical points that process large amounts of information to facilitate real-time responses.