Mobile Object Tracking in Panoramic Video and LiDAR for Radiological Source-Object Attribution and Improved Source Detection

The addition of contextual sensors to mobile radiation sensors provides valuable information about radiological source encounters that can assist in adjudication of alarms. This study explores how computer-vision based object detection and tracking analyses can be used to augment radiological data from a mobile detector system. We study how contextual information (streaming video and LiDAR) can be used to associate dynamic pedestrians or vehicles with radiological alarms to enhance both situational awareness and detection sensitivity. Possible source encounters were staged in a mock urban environment where participants included pedestrians and vehicles moving in the vicinity of an intersection. Data was collected with a vehicle equipped with 6 NaI(Tl) 2 inch times 4 inch times 16 inch detectors in a hexagonal arrangement and multiple cameras, LiDARs, and an IMU. Physics-based models that describe the expected count rates from tracked objects are used to correlate vehicle and/or pedestrian trajectories to measured count-rate data through the use of Poisson maximum likelihood estimation and to discern between source-carrying and non-source-carrying objects. In this work, we demonstrate the capabilities of our source-object attribution approach as applied to a mobile detection system in the presence of moving sources to improve both detection sensitivity and situational awareness in a mock urban environment

Background and Anomaly Learning Methods for Static Gamma-ray Detectors

Static gamma-ray detector systems that are deployed outdoors for radiological monitoring purposes experience time- and spatially-varying natural backgrounds and encounters with man-made nuisance sources. In order to be sensitive to illicit sources, such systems must be able to distinguish those sources from benign variations due to, e.g., weather and human activity. In addition to fluctuations due to non-threats, each detector has its own response and energy resolution, so providing a large network of detectors with predetermined background and source templates can be an onerous task. Instead, we propose that static detectors use simple physics-informed algorithms to automatically learn the background and nuisance source signatures, which can them be used to bootstrap and feed into more complex algorithms. Specifically, we show that non-negative matrix factorization (NMF) can be used to distinguish static background from the effects of increased concentrations of radon progeny due to rainfall. We also show that a simple process of using multiple gross count rate filters can be used in real time to classify or ``triage'' spectra according to whether they belong to static, rain, or anomalous categories for processing with other algorithms. If a rain sensor is available, we propose a method to incorporate that signal as well. Two clustering methods for anomalous spectra are proposed, one using Kullback-Leibler divergence and the other using regularized NMF, with the goal of finding clusters of similar spectral anomalies that can be used to build anomaly templates. Finally we describe the issues involved in the implementation of some of these algorithms on deployed sensor nodes, including the need to monitor the background models for long-term drifting due to physical changes in the environment or changes in detector performance.Comment: 12 pages, 6 figures, accepted for publication in IEEE Transactions on Nuclear Scienc

The EU-ToxRisk method documentation, data processing and chemical testing pipeline for the regulatory use of new approach methods

Hazard assessment, based on new approach methods (NAM), requires the use of batteries of assays, where individual tests may be contributed by different laboratories. A unified strategy for such collaborative testing is presented. It details all procedures required to allow test information to be usable for integrated hazard assessment, strategic project decisions and/or for regulatory purposes. The EU-ToxRisk project developed a strategy to provide regulatorily valid data, and exemplified this using a panel of > 20 assays (with > 50 individual endpoints), each exposed to 19 well-known test compounds (e.g. rotenone, colchicine, mercury, paracetamol, rifampicine, paraquat, taxol). Examples of strategy implementation are provided for all aspects required to ensure data validity: (i) documentation of test methods in a publicly accessible database; (ii) deposition of standard operating procedures (SOP) at the European Union DB-ALM repository; (iii) test readiness scoring accoding to defined criteria; (iv) disclosure of the pipeline for data processing; (v) link of uncertainty measures and metadata to the data; (vi) definition of test chemicals, their handling and their behavior in test media; (vii) specification of the test purpose and overall evaluation plans. Moreover, data generation was exemplified by providing results from 25 reporter assays. A complete evaluation of the entire test battery will be described elsewhere. A major learning from the retrospective analysis of this large testing project was the need for thorough definitions of the above strategy aspects, ideally in form of a study pre-registration, to allow adequate interpretation of the data and to ensure overall scientific/toxicological validity.Toxicolog

Mapping the Minimum Detectable Activities of Gamma-Ray Sources in a 3-D Scene

The ability to formulate maps of minimum detectable activities (MDAs) that describe the sensitivity of an ad hoc measurement that used one or more freely moving radiation detector systems would be significantly beneficial for the conduct and understanding of many radiological search activities. In a real-time scenario with a free-moving detector system, an MDA map can provide useful feedback to the operator about which areas have not been searched as thoroughly as others, thereby allowing the operator to prioritize future actions. Similarly, such a calculation could be used to inform subsequent navigation decisions of autonomous platforms. Here we describe a near real-time MDA mapping approach that can be applied when searching for point sources using detected events in a spectral region of interest (ROI) while assuming a constant, unknown background rate. We show the application of this MDA mapping method to a real scenario, a survey of the interior of a small building using a handheld detector system. Repeated measurements with no sources and with 137Cs sources of different strengths yield results consistent with the estimated thresholds and MDA values; namely, that for background-only measurements no sources are seen above threshold anywhere in the scene, while when sources are present they are detected above the thresholds calculated for their locations

60 GHz WLAN applications and implementation aspects

Various wireless applications are currently under development for the unlicensed 60GHz band. This paper describes three examples with different system requirements. The first two are point-to-multipoint wireless networks (in an airplane and in a car) and the third one is a short range point-to-point connection. Special requirements of the applications are a high number of users for the point-to-multipoint connection and a high data rate of 10Gbit/s for the point-to-point connection system. Implementation aspects are pointed out, which are important to demonstrate the functionality of the system in a relevant environment and are key aspects to develop the related products. For example, integration aspects of the antenna into an airplane passenger seat and the receiver concept of the radio frequency-(RF) front-end to reducing the power consumption at ultrahigh data rates are described. Additionally, to determine the geometrical system architecture, ray-tracing simulations inside an aircraft and inside a car were performed

3-D Object Tracking in Panoramic Video and LiDAR for Radiological Source-Object Attribution and Improved Source Detection

Networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spectroscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments. This study investigates the use of Light Detection and Ranging (LiDAR) and streaming video to enable real-time object detection and tracking, and the fusion of this tracking information with radiological data for the purposes of enhanced situational awareness and increased detection sensitivity. This work presents an object detection, tracking, and novel source-object attribution analysis that is capable of operating in real time. By implementing this analysis pipeline on a custom-developed system that comprises a static 2 in. \times 4 in. \times16 in. NaI(Tl) detector colocated with a 64-beam LiDAR and four monocular cameras, we demonstrate the ability to accurately correlate trajectories from tracked objects to spectroscopic gamma-ray data in real time and use physics-based models to reliably discriminate between source-carrying and nonsource-carrying objects. In this work, we describe our approach in detail and present a quantitative performance assessment that characterizes the source-object attribution capabilities of both video and LiDAR. Additionally, we demonstrate the ability to simultaneously track pedestrians and vehicles in a mock urban environment and use this tracking information to improve both detection sensitivity and situational awareness using our contextual-radiological data fusion methodology