Baseline for Artificial Intelligence Learning of Indications and Warnings from a Cellular Kit - AI Sensor Network (BAILIWICK: AISN)

The BAILIWICK-AISN Phase II effort represents a significant leap forward in the field of explosive event detection, supply chain monitoring, and geospatial analytics. Building upon the successful Phase I research, this Phase II project aims to further develop and refine the BAILIWICK-AISN system to address critical technical objectives: 1. Characterize Explosive Events with Smartphone Sensors:†This objective focuses on utilizing machine learning models running on smartphones to detect explosive testing events and distinguish them from other percussive events. The Phase II approach involves the evaluation of various algorithms for detection and data collection efforts. 2. Correlate Smartphone Data with Seismograph and Standard Data Sources:†This objective seeks to establish a correlation between smartphone sensor data and seismograph outputs, to verify the capability of smartphones to serve as viable sensors to detect seismographic blast / testing events. The project will explore data fusion methods and develop models to achieve this correlation. 3. Detect Smartphone Sensor-Based Indirect Indicators of Testing Activities:†The goal here is to detect activities indirectly related to weapons generation and testing, such as heavy trucking, construction, and more, from smartphone sensor data. The Phase II approach continues to utilize machine learning techniques to expand the set of supported labels and enhance detection capabilities. 4. Trace Testing-related Supply Chains with Smartphone Location Data:†This objective involves tracking and monitoring discrete locations and facilities related to weapons testing, supply chains, and clandestine activities using smartphone location data. 5. Establish Performant Graphical User Interfaces:†The final objective focuses on creating user-friendly interfaces for data visualization and interaction. The project will develop APIs to enable integration with third-party systems. Technical Approach:†The technical approach for BAILIWICK-AISN Phase II combines advanced machine learning algorithms, sensor data analysis, and geospatial analytics. It leverages the extensive capabilities of Knowmadics' existing 360 Aware enterprise software while enhancing it with novel R&D advancements. The approach includes controlled target signature and distractor signatures data collection, model development, testing and evaluation, and the creation of APIs to support data integration and visualization. Commercialization Strategy:†BAILIWICK-AISN's commercialization strategy involves targeting existing 360 Aware customers initially, expanding market penetration, and capturing new customers in the broader security market. The technology will be offered as an enhancement to the 360 Aware suite, with licensing options tailored to meet customer needs. Strategic partnerships, will then facilitate licensing of BAILIWICK-AISN analytics for integration with third-party C2 and/or security enterprise software providers.†