Applications of Unsupervised Clustering Algorithms to Aircraft Identification Using High Range Resolution Radar

Identification of aircraft from high range resolution (HRR) radar range profiles requires a database of information capturing the variability of the individual range profiles as a function of viewing aspect. This database can be a collection of individual signatures or a collection of average signatures distributed over the region of viewing aspect of interest. An efficient database is one which captures the intrinsic variability of the HRR signatures without either excessive redundancy typical of single-signature databases, or without the loss of information common when averaging arbitrary groups of signatures. The identification of \u27natural\u27 clustering of similar HRR signatures provides a means for creating efficient databases of either individual signatures, or of signature templates. Using a k-means and the Kohonen self organizing feature net, we identify the natural clustering of the HRR radar range profiles into groups of similar signatures based on the match quality metric used within a Vector Quantizer classification algorithm. This greatly reduces the redundancy in such databases while retaining classification performance. Such clusters can be useful in template-based algorithms where groups of signatures are averaged to produce a template. Instead of basing the group of signatures to be averaged on arbitrary regions of viewing aspect, the averages are taken over the signatures containing intake natural clusters which have been identified

TBI Contusion Segmentation from MRI using Convolutional Neural Networks

Traumatic brain injury (TBI) is caused by a sudden trauma to the head that may result in hematomas and contusions and can lead to stroke or chronic disability. An accurate quantification of the lesion volumes and their locations is essential to understand the pathophysiology of TBI and its progression. In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of patients with TBI. The CNN architecture proposed here was based on a state of the art CNN architecture from Google, called Inception. Using a 3-layer Inception network, lesions are segmented from multi-contrast MR images. When compared with two recent TBI lesion segmentation methods, one based on CNN (called DeepMedic) and another based on random forests, the proposed algorithm showed improved segmentation accuracy on images of 18 patients with mild to severe TBI. Using a leave-one-out cross validation, the proposed model achieved a median Dice of 0.75, which was significantly better (p<0.01) than the two competing methods.Comment: https://ieeexplore.ieee.org/abstract/document/8363545/, IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018

RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Active Data Manipulation

Federated learning (FL) has recently emerged as a privacy-preserving approach for machine learning in domains that rely on user interactions, particularly recommender systems (RS) and online learning to rank (OLTR). While there has been substantial research on the privacy of traditional FL, little attention has been paid to studying the privacy properties of these interaction-based FL (IFL) systems. In this work, we show that IFL can introduce unique challenges concerning user privacy, particularly when the central server has knowledge and control over the items that users interact with. Specifically, we demonstrate the threat of reconstructing user interactions by presenting RAIFLE, a general optimization-based reconstruction attack framework customized for IFL. RAIFLE employs Active Data Manipulation (ADM), a novel attack technique unique to IFL, where the server actively manipulates the training features of the items to induce adversarial behaviors in the local FL updates. We show that RAIFLE is more impactful than existing FL privacy attacks in the IFL context, and describe how it can undermine privacy defenses like secure aggregation and private information retrieval. Based on our findings, we propose and discuss countermeasure guidelines to mitigate our attack in the context of federated RS/OLTR specifically and IFL more broadly

DEC-QED: A flux-based 3D electrodynamic modeling approach to superconducting circuits and materials

Modeling the behavior of superconducting electronic circuits containing Josephson junctions is crucial for the design of superconducting information processors and devices. In this paper, we introduce DEC-QED, a computational approach for modeling the electrodynamics of superconducting electronic circuits containing Josephson junctions in arbitrary three-dimensional electromagnetic environments. DEC-QED captures the non-linear response and induced currents of BCS superconductors and accurately captures phenomena such as the Meissner effect, flux quantization and Josephson effects. Using a finite-element construction based on Discrete Exterior Calculus (DEC), DEC-QED can accurately simulate transient and long-time dynamics in superconductors. The expression of the entire electrodynamic problem in terms of the gauge-invariant flux field and charges makes the resulting classical field theory suitable for second quantization

Speedup of Interval Type 2 Fuzzy Logic Systems Based on GPU for Robot Navigation

As the number of rules and sample rate for type 2 fuzzy logic systems (T2FLSs) increases, the speed of calculations becomes a problem. The T2FLS has a large membership value of inherent algorithmic parallelism that modern CPU architectures do not exploit. In the T2FLS, many rules and algorithms can be speedup on a graphics processing unit (GPU) as long as the majority of computation a various stages and components are not dependent on each other. This paper demonstrates how to install interval type 2 fuzzy logic systems (IT2-FLSs) on the GPU and experiments for obstacle avoidance behavior of robot navigation. GPU-based calculations are high-performance solution and free up the CPU. The experimental results show that the performance of the GPU is many times faster than CPU