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The focus of this paper is the multimodal detection and localization of a ground moving target based on radio frequency (RF) and infrared (IR) data. The target radiates a low probability of intercept (LPI) RF signal received by multiple passive RF sensors at scene and is imaged by using a stationary IR camera concurrently. To obtain the multimodal detector proposed in this paper, first, the generalized...
One of the capabilities of diamond detectors is the measurement of neutron radiation, due to their carbon composition. In these work is described a campaign of measurements related with neutron irradiation using different sources: starting with a 252 Cf standard neutron source, continuing with a well-known nuclear reaction at a low-energy tandem accelerator and finishing with neutron radiation produced...
In PET image reconstruction, a point-spread-function (PSF) in the form of normal distribution is commonly used to model the detector response function. The PSF becomes asymmetrical off the center of the field-of-view. This effect has been modeled with dual-half normal distribution functions with different standard deviations on the left and right side. This method is subject to unequal noise in the...
Mining massive data streams in real-time is one of the contemporary challenges for machine learning systems. Such a domain encompass many of difficulties hidden beneath the term of Big Data. We deal with massive, incoming information that must be processed on-the-fly, with lowest possible response delay. We are forced to take into account time, memory and quality constraints. Our models must be able...
Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. In this paper, a travel time analysis and prediction model was established for urban road traffic sensors data based on the change point analysis algorithm and ARIMA model. Firstly, time series of travel time parameters were clustered by using change point mining algorithm after traffic...
Predicting the outcome of an action can help a robot detect failures in advance, and schedule action replanning before an error occurs. We propose using an interactive physics based simulator with the aim of collecting realistic data to be used for learning. We then show how we save and query for specific information from the data more effectively. The data from the simulation is used to learn a failure...
Real-world transceiver designs for multiple-input multiple-output (MIMO) wireless communication systems are affected by a number of hardware impairments that already appear at the transmit side, such as amplifier non-linearities, quantization artifacts, and phase noise. While such transmit-side impairments are routinely ignored in the data-detection literature, they often limit reliable communication...
As Internet attacks grow rapidly, firewalls or network intrusion systems are indispensable. Existing approaches usually use attack signatures, machine learning or data mining algorithms to detect and stop anomaly or malicious flow. Machine learning algorithms need a set of labeled data to train the detection model, while the labeled data set is not always available. In this paper, we proposed an anomaly...
The prediction of both, vehicular traffic and communication connectivity are important research topics. In this paper, we propose the usage of innovative machine learning approaches for these objectives. For this purpose, Poisson Dependency Networks (PDNs) are introduced to enhance the prediction quality of vehicular traffic flows. The machine learning model is fitted based on empirical vehicular...
Probability density function (pdf) estimation of sea clutter in synthetic aperture radar (SAR) imagery has a fundamental role in constructing a constant false alarm rate (CFAR) based ship detector. This paper proposes a semi-parametric sea clutter modeling method for SAR amplitude imagery. The pdf of sea clutter is estimated point by point for each amplitude value, by selecting an optimal component...
We highlight challenges associated with using conventional pattern recognition methods to test imagery from a persistent longwave infrared remote sensing experiment, where material surface emissivity and temperature among other factors dominate the apparent radiance observed at the sensor. We also propose a superior data model for the task.
Distributed estimation of a deterministic scalar parameter by using quantized data in the presence of spoofing attacks, which modify the statistical model of the physical phenomenon, is considered. The paper develops an efficient heuristic approach to jointly detect attacks and estimate under spoofing attacks that are undetectable by a traditional approach that relies on noticing the data is not consistent...
Our paper proposes a novel method for fire detection in riot videos acquired with handheld cameras and smart-phones. This is a typical example of computer vision in the wild, where we have no control over the data acquisition process, and the quality of the video data varies considerably. We propose a novel spatial model for fire, based on Gaussian mixtures and on color adjacency in the visible spectrum...
Over the past years improvements in the field of vehicle-2-infrastructure communication offer a lot of possibilities for interactive vehicle applications. The challenge of existing driver assistance algorithms is the fact that a lot of decisions are necessary to select the best driving strategy. The incoming information are mostly based on local sensor values. The disadvantage of this approach is...
Hyperspectral (HS) imaging is a complex way of taking the image of the scenery, where the rich spectral information is collected for any pixel of HS image. The rich spectral information can consequently be used for finding objects or identifying specific material in military utilization of HS imaging. Finding the objects as a spectral anomaly is one of specific tasks of HS image processing.
Higher demands for more reliable and maintainable JavaScript-based web applications have led to the recent development of MVC (Model-View-Controller) frameworks. One of the main advantages of using these frameworks is that they abstract out DOM API method calls, which are one of the leading causes of web application faults, due to their often complicated interaction patterns. However, MVC frameworks...
A detection methodology for marine debris presence after a natural disaster is described. The methodology is based both on a predictive model and a Bayesian hierarchical spatial method. The chosen fusion approach relies on auto-logistic regression to weight the outputs of multiple target detection algorithms, as well as to capture the intrinsic processes related to the presence of marine debris. The...
The Multiple Target Tracking (MTT) problem is one of the fundamental challenges in computer vision. In this paper, we propose a feasible detection and association based MTT system which uses a modified Deformable Part-Based Model (DPM) to generate detection results and then links detections into track lets to further form long trajectories. We first describe our modified DPM algorithm which could...
A desired speech signal in hands-free communication systems is often degraded by background noise and interferers. Data-dependent spatial filters for desired speech extraction depend on the power spectral density (PSD) matrices of the desired and the undesired signals, which are commonly estimated recursively using a signal model-based speech presence probability (SPP). The SPP and the PSD matrix...
Source separation problems are ubiquitous in the physical sciences; any situation where signals are superimposed calls for source separation to estimate the original signals. In this tutorial I will discuss the Bayesian approach to the source separation problem. This approach has a specific advantage in that it requires the designer to explicitly describe the signal model in addition to any other...
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