RF Emitter Pattern Analysis for Monitored Volume Identification
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Solution Overview
Problem
There is a need to identify individuals within a monitored volume, particularly in environments where security is a concern, and to provide forensic data on the presence and historical locations of RF-emitting devices associated with those individuals, as well as to facilitate anonymous meetings between individuals using smart devices.
Innovation Solution
The Freya system utilizes RF-emitting devices to create unique identifiers and patterns of life by detecting and analyzing RF, visible, infrared, ultraviolet, and acoustic data, correlating this information with video data to provide detailed analytics, and integrating with databases for forensic and security applications, including the Marco app for anonymous meetings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional scanning methods (SPECT, PET, MRI, CT) are used to image the entire human body, then comprehensive anatomical coverage is achieved, but the scan time becomes excessively long (e.g., 30 minutes or more for a whole-body PET scan)
Solution Approach 1:
The patent divides the body into multiple smaller volumes of interest (VOIs) that are scanned sequentially rather than scanning the entire body at once. Each VOI is imaged independently, allowing the system to focus computational and scanning resources on specific regions, thereby reducing the total scan time while maintaining comprehensive anatomical coverage across all divided segments.
Solution Approach 2:
The system performs preliminary localization to identify and define volumes of interest before conducting the full imaging scan. This preliminary step allows the system to pre-segment the body into relevant anatomical regions, enabling subsequent rapid scanning of only those identified volumes rather than performing a comprehensive whole-body scan from scratch.
2Loss of time
If the monitored volume is reduced to a smaller region, then the scan time is reduced, but the ability to detect patterns spanning multiple body regions is lost
Solution Approach 1:
The pattern recognition system is designed to be universal and adaptable, capable of detecting various types of patterns (anatomical, physiological, pathological) across different body regions. The system can analyze multiple segmented volumes and integrate their data to identify patterns that span across region boundaries, maintaining versatile pattern detection capability while working with reduced individual volume sizes.
Solution Approach 2:
The system transitions from analyzing three-dimensional volumes to extracting and analyzing two-dimensional projection data or feature representations. By converting volumetric data into lower-dimensional feature spaces, the system can efficiently process multiple small volumes and reconstruct comprehensive patterns that span across original body regions, effectively adding a dimensional transformation layer to the analysis process.
3Measurement precision
If multiple complete anatomical scans are performed to ensure comprehensive coverage, then detection accuracy is improved, but the radiation exposure and scan time increase significantly
Solution Approach 1:
The patent segments the body into multiple volumes of interest and scans each volume with optimized parameters rather than performing multiple complete whole-body scans. This segmentation allows the system to achieve comprehensive detection accuracy by covering all relevant anatomical regions with appropriate scan settings for each specific volume, while minimizing total radiation exposure by avoiding redundant scanning of the entire body multiple times.
Solution Approach 2:
The system dynamically adjusts scanning parameters (such as radiation dose, scan duration, and imaging mode) based on the specific volume being scanned and the type of pattern being sought. By optimizing parameters for each segmented volume rather than using fixed high-dose whole-body settings, the system maintains high detection accuracy while significantly reducing overall radiation exposure and energy consumption.
Data Source
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AI summary
This document presents a system to identify patterns of life assocated with the users of radio frequency emitting devices radiating within a monitored volume. These recurring commonalities in human activity can be derived from collected data and subsequent analytics that provide intelligence about those emitters and associated humans present within the monitored volume. The Freya system provides unique identifiers for detected emitters; insights into the current network relationships between emitters, past and current; human relational networks within the monitored volume; and can identify previous emitter locations prior to detection by the Freya system. These patterns of life provide a foundation for predicting interactions between humans associated with emitters active on the Internet of Things (IoT). This capability provides enhanced security capabilities as required by authorities for sensitive venues and events.