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During the 1950s and 1960s the United States conducted and filmed over 200 atmospheric nuclear tests establishing the foundations of atmospheric nuclear detonation behavior. Each explosion was documented with about 20 videos from three or four points of view. Synthesizing the videos into a 3D video will improve yield estimates and reduce error factors. The videos were captured at a nominal 2500 frames...
In 1950 Abraham Wald proved that every admissible statistical decision rule is either a Bayesian procedure or the limit of a sequence of such procedures. He thus provided a decision-theoretic justification for the use of Bayesian inference, even for non-Bayesian problems. It is often assumed that his result also justified the use of Bayesian priors to solve such problems. However, the principles one...
The problem of coherent multi-polarization SAR change detection assuming the availability of image pairs, collected from N multiple polarimetric channels, is addressed in this paper. At the design stage, it is assumed that the reference and test images from the same polarimetric channel may exhibit a power mismatch. The change detection problem is formulated as a binary hypothesis testing problem,...
The proposed ECG compression method presents the new beat segmentation algorithm. Because this proposed compression method uses the residual difference between original ECG signal beat and the reference ECG beat, the ECG signal must be separated into each beat before doing the compression process. That is the duty of beat segmentation process. Therefore, this process is important step of the selective...
This paper proposes a novel machine-learning framework for facial-expression recognition, which is capable of processing images fast and accurately even without having to rely on a large-scale dataset. The framework is derived from Support Vector Machines (SVMs) but distinguishes itself in three key ways. First, the measure of the samples normalization is based on the Perturbed Subspace Method (PSM),...
Text information contained in scene images is very useful for image understanding. In this paper, we propose a high-level representation named stroke bank for scene character recognition. Inspired by the work of object bank, we train stroke detectors and use detectors' maximal output as features. Specifically, we collect training samples for stroke detectors based on labeled key points. We also propose...
Robust feature plays an important role in many vision based applications. This paper proposes a fast extreme illumination robust feature in affine space. It inherits the techniques of extreme point location and main orientation computation from SIFT (Scale Invariant Feature Transform) algorithm, and adopts the rotation and scale invariant circular binary pattern based histograms in the affine space...
When we apply AdaBoost in pedestrian detection, a large number of examples are needed to train a detector. Except for designing features, a reasonable utilization of training examples is also significant to the detection accuracy and training time. In this paper, we propose a new method, named Weight-Loss Control Sampling (WLCS), to deal with the negative training examples by improving the training...
The main objective of this work is to alleviate this problem by imposing the matching results from a classifier based on a set of constructed weighted templates to the boosting framework. The integration of global contour templates and local HOGs is through the adjustment of the hyperplane from the support vector machine. The concept behind is to bias the hyperplane and make it consistent with the...
This paper deals with coherent multi-polarization SAR change detection assuming the availability of reference and test images collected from N multiple polarimetric channels. At the design stage, the change detection problem is formulated as a binary hypothesis testing problem and the principle of invariance is used to come up with decision rules sharing the Constant False Alarm Rate (CFAR) property...
In a multi-hypothesis cognitive radar system based on Chernoff's sequential detection paradigm, the radar returns are used to learn from the environment which probing (or control) signal should be selected next, among a set of M prescribed waveforms. For this active sensing scenario, we introduce a model — referred to as strong or weak — in which each probing signal elicits a “strong” response from...
Cognitive users are expected to be capable of exploring spectrum holes over a wide range of frequencies. Motivated by the sparse characteristic of underutilized spectrum, we consider sparse spectrum sensing using compressive sensing techniques for cognitive orthogonal frequency division multiplexing (OFDM) systems. The spectrum sensing problem is formulated as a multi-subcarrier detection problem,...
Orthogonal experimental design (OED) is a well-known method in statistical experiment which aims to reduce the testing complexity. This paper tries applying OED to reduce the computational complexity in Multiple-input Multiple-output (MIMO) detection. Several times of sorted QR decomposition (SQRD) algorithm are applied firstly to get the diverse solutions. Then a subspace is constructed with the...
A relatively new and important branch of Mutation Analysis involves model mutations. In our attempts to realize model-clone detector testing, we found that there was little mutation research on Simulink, which is a fairly prevalent modeling language, especially in embedded domains. Because Simulink model mutations are the crux of our model-clone detector testing framework, we want to ensure that we...
In this paper, we study the problem of ‘test-driving’ a detector, i.e. allowing a human user to get a quick sense of how well the detector generalizes to their specific requirement. To this end, we present the first system that estimates detector performance interactively without extensive ground truthing using a human in the loop. We approach this as a problem of estimating proportions and show that...
Detector adaptation is a challenging problem and several methods have been proposed in recent years. We propose multi class boosted random ferns for detector adaptation. First we collect online samples in an unsupervised manner and collected positive online samples are divided into different categories for different poses of the object. Then we train a multi-class boosted random fern adaptive classifier...
The spectrum sensing performance is significantly degraded by the primary user's status changes as arriving or leaving randomly in cognitive radio networks. This paper presents an improved energy detector (ED) with weights to improve detection performance in this situation. The idea is derived from the concept of unequal scale sampling such that the sampling points in the sensing period are endowed...
Testing, calibration, and calibration frequency requirements for portable radiation detectors used to directly measure alpha, beta, photon, and neutron radiation are defined. Also defined are the calibration and testing requirements for alpha and beta-gamma measurements of surface contamination levels using portable radiation detectors. Portable radiation detectors may be battery or line powered.
In this paper, we review our recent work on detecting weak patterns that are sparse and localized on a graph. This problem is relevant to many applications including detecting anomalies in sensor and computer networks, brain activity, co-expressions in gene networks, disease outbreaks etc. We characterize such a class of weak and sparse graph-structured patterns by small subsets of weakly activated...
Distributed sensor systems composed of spatially distributed micro sensor nodes have been proposed for large scale monitoring applications. In these systems, nodes aggregate their sensor data to provide real time information about the underlying state. To extend the lifetime each node of the system has to limit the complexity of the sequential fusion algorithm. In this paper we derive optimal likelihood...
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