Geometric Cross-Modal Comparison of Heterogeneous Sensor Data

In this work, we address the problem of cross-modal comparison of aerial data streams. A variety of simulated automobile trajectories are sensed using two different modalities: full-motion video, and radio-frequency (RF) signals received by detectors at various locations. The information represented by the two modalities is compared using self-similarity matrices (SSMs) corresponding to time-ordered point clouds in feature spaces of each of these data sources; we note that these feature spaces can be of entirely different scale and dimensionality. Several metrics for comparing SSMs are explored, including a cutting-edge time-warping technique that can simultaneously handle local time warping and partial matches, while also controlling for the change in geometry between feature spaces of the two modalities. We note that this technique is quite general, and does not depend on the choice of modalities. In this particular setting, we demonstrate that the cross-modal distance between SSMs corresponding to the same trajectory type is smaller than the cross-modal distance between SSMs corresponding to distinct trajectory types, and we formalize this observation via precision-recall metrics in experiments. Finally, we comment on promising implications of these ideas for future integration into multiple-hypothesis tracking systems.Comment: 10 pages, 13 figures, Proceedings of IEEE Aeroconf 201

Pediatric meningiomas in The Netherlands 1974–2010: a descriptive epidemiological case study

The purpose of this study was to review the epidemiology and the clinical, radiological, pathological, and follow-up data of all surgically treated pediatric meningiomas during the last 35 years in The Netherlands. Patients were identified in the Pathological and Anatomical Nationwide Computerized Archive database, the nationwide network and registry of histopathology and cytopathology in The Netherlands. Pediatric patients of 18 years or younger at first operation in 1974-2009 with the diagnosis meningioma were included. Clinical records, follow-up data, radiological findings, operative reports, and pathological examinations were reviewed. In total, 72 patients (39 boys) were identified. The incidence of operated meningiomas in the Dutch pediatric population is 1:1,767,715 children per year. Median age at diagnosis was 13 years (range 0-18 years). Raised intracranial pressure and seizures were the most frequent signs at presentation. Thirteen (18 %) patients had neurofibromatosis type 2 (NF2). Fifty-three (74 %) patients had a meningioma World Health Organization grade I. Total resection was achieved in 35 of 64 patients. Fifteen patients received radiotherapy postoperatively. Mean follow-up was 4.8 years (range 0-27.8 years). Three patients died as a direct result of their meningioma within 3 years. Four patients with NF2 died as a result of multiple tumors. Nineteen patients had disease progression, requiring additional treatment. Meningiomas are extremely rare in the pediatric population; 25 % of all described meningiomas show biological aggressive behavior in terms of disease progression, requiring additional treatment. The 5-year survival is 83.9 %, suggesting that the biological behavior of pediatric menigiomas is more aggressive than that of its adult counterpart

Optimizing Heterogeneous Platform Allocation Using Reinforcement Learning

In this paper, we consider the problem of distinguishing a target within an environment using multiple mobile heterogeneous sensing platforms. When used efficiently, the diversity of sensing platforms offers gains over a set of platforms equipped with identical sensors. To optimize over the placement of the platforms, one needs a mechanism for combining the distributed multi-domain sensor data as well as a control to translate that data into coordinated platform movement. Here, we address the latter. By assuming complete sharing of information between platforms, we can establish a baseline for platform movement behavior and add noise incrementally to ensure robustness under noisy communication. The heterogeneity of the platforms removes one axis of symmetry from the problem of mapping platforms over an environment. This means that it is not sufficient to select a set of positions and send platforms based on proximity. We need to select a mapping over all of the platforms so that the platform placed in each location is the one which has the sensor configuration best equipped for information gain there. As each platform accumulates information on the targets within its field of view, we use a modified version of the Upper Confidence Bound algorithm to determine the value of placing each platform in that sector. We also use this algorithm to encourage exploration of sectors which have been unobserved for long periods of time. By assuming random uniform target movement, we can efficiently estimate the environment transitions forward in time. This allows us to generate best trajectories for each platform based on expected target behavior and jointly select their movements. We demonstrate that by framing the problem of distinguishing a target as a partially-observable Markov decision process we can allocate platforms in a way that minimizes search time and displays gains over the same scenario with homogeneous sensing platforms

Kernel Based Method for Distributed Derived Feature Tracking in High Dimensions

Modern sensing systems are increasingly heterogeneous and decentralized. These systems require new methods for efficiently combining data across distributed networks when centralized data fusion centers are impractical due to communications limitations. Consensus and innovation algorithms are a class of algorithms for fusing sensor data over distributed networks without the need for full connectivity to a centralized system. We present a novel method for combining a consensus and innovation framework with kernel density estimation to track complex non-observable features of targets over a high dimensional space. The goal of our method is to track multiple targets over time while categorizing their long term behavior. Instantaneous features of the targets are used both as tracking tools and combined over time to establish higher-order features of the targets\u27 long term behavior. We assume that the communication bandwidth in the network is low, and that real-time identification of specific long term behaviors, such as a pattern of suspicious activity, is a priority. We compare the capabilities and limitations of our method with common modern tracking methods including particle filtering and multi-hypothesis testing. Results are given for an example scenario of a heterogeneous set of sensors identifying a suspicious target vehicle from traffic data. The instantaneous measured features include the location, color, speed, and fuel consumption