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An algorithm of weak optical signal processing in lidar measurements under intensive background radiation is described. The algorithm is based on transformation of the Poisson photoelectron flow forming at the output of the photod...
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An algorithm of weak optical signal processing in lidar measurements under intensive background radiation is described. The algorithm is based on transformation of the Poisson photoelectron flow forming at the output of the photodetector photocathode into a flux with sub-Poisson statistics due to its stochastic-determined “rarefaction.” The quality coefficients of the proposed algorithm are estimated.
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Several countries suffer from the existence of millions of buried landmines in their territories. These landmines have indefinite life, and may still cause horrific personal injuries and economic dislocation for decades after a wa...
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Several countries suffer from the existence of millions of buried landmines in their territories. These landmines have indefinite life, and may still cause horrific personal injuries and economic dislocation for decades after a war has finished. Therefore, there is a growing demand by these countries for reliable landmine inspection systems. There are several landmine detection techniques that can be used for this purpose. Each technique is suitable for detection under some conditions depending on the type of the landmine case, the explosive material, and the soil. This paper presents an overview of some of the existing landmine detection techniques. These techniques are briefly described and their merits and drawbacks are highlighted and compared. The purpose of this comparison is to shows the ideal conditions and the challenges for each technique. Furthermore, a comparison between landmine detection techniques from the points of view of cost, complexity, speed, safety, false alarm rate and effect of environmental conditions is presented.
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With facial recognition software becoming more widely used, especially in mobile apps such as Snapchat, boundary detection will continue to be one of the primary areas of interest in enhancing software performance. Edge detection ...
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With facial recognition software becoming more widely used, especially in mobile apps such as Snapchat, boundary detection will continue to be one of the primary areas of interest in enhancing software performance. Edge detection is paramount in discriminating objects so they can be used and processed. This paper will present a fuzzy system for boundary detection. The proposed system will then compare it to traditional methods of edge detection using MATLAB's image processing toolbox. The proposed fuzzy system allows for more effective tool since the membership functions can be more defined and robust to accommodate different images, as well as tailored to result in a more effective processed image due to ambiguity. The results of the simulation of the fuzzy system shows that it was more capable of dealing with ambiguities in the input images, so in pictures where the edges were not so clear or required more detail, the fuzzy system was more capable since it can be better tailored for use.
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Due to the intensive development of the digital business, malicious software and other cyber threats are becoming more common. In order to increase the level of security there are needed appropriate special countermeasures, which ...
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Due to the intensive development of the digital business, malicious software and other cyber threats are becoming more common. In order to increase the level of security there are needed appropriate special countermeasures, which are able to remain effective when new types of threats occur and which allow to detect cyber attacks targeting on a set of information system resources in fuzzy conditions. Different attacking effects on the corresponding resources generate various sets of anomalies in a heterogeneous parametric environment. There is known a tuple model of the formation of a set of basic components that allow to identify cyber attacks. For its effective application a formal implementation of the approach to the formation of sets of basic detection rules is necessary. For this purpose, there has been developed a method that focuses on solving problems of cyber attacks detection in computer systems, which is implemented through three basic steps: formation of anomaly identifiers subsets; formation of decisive functions; formation of conditional detection expressions. Using this method, it is possible to form the necessary set of detection rules, which determine the level of anomalous state of values in a heterogeneous parametric environment, characteristic for the impact of a certain type of attack. The use of this method at the creation intrusion detection systems will expand their functionality regarding the cyber attacks detection in a weakly formalized fuzzy environment.
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Molecular spectroscopic detection plays a crucial role in gas chromatography (GC). Some detectors constitute element-selective spectroscopy, where an element-containing species generates the detected signal, e.g. flame photometric...
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Molecular spectroscopic detection plays a crucial role in gas chromatography (GC). Some detectors constitute element-selective spectroscopy, where an element-containing species generates the detected signal, e.g. flame photometric detection (S, P, Cu); chemiluminescence detection (S, N). These respond with selective response, usually with excellent analyte detectability and reduced matrix interferences. Classical molecular spectroscopic detectors Fourier transform infrared, nuclear magnetic resonance, ultraviolet respond by giving a spectrum characteristic of the (intact) molecule. Molecular structure response plays multi-faceted roles: it produces a unique spectrum of a molecule, provided it is resolved by the column and presented to the detector as a single compound; or the chromatogram can be generated by responding to the total signal, or selectively to a given component of the signal. This review summarises the response, sensitivities, applicability, and recent literature reports of molecular spectroscopic detection. Hyphenation with dual detection and brief comments on multidimensional GC is included. (C) 2017 Elsevier B.V. All rights reserved.
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For a connected graph G of order n ≥ 3 and an ordered factorization F = {G_1, G_2, …, G_k} of G into k spanning subgraphs G_i (1 ≤ i ≤ k), the color code of a vertex υ of G with respect to F is the ordered k-tuple c(υ) = (a...
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For a connected graph G of order n ≥ 3 and an ordered factorization F = {G_1, G_2, …, G_k} of G into k spanning subgraphs G_i (1 ≤ i ≤ k), the color code of a vertex υ of G with respect to F is the ordered k-tuple c(υ) = (a_1,a_2,…,a_k) where a_i = deg_(Gi) υ. If distinct vertices have distinct color codes, then the factorization F is called a detectable factorization of G; while the detection number det(G) of G is the minimum positive integer k for which G has a detectable factorization into k factors. We study detectable factorizations of cubic graphs. It is shown that there is a unique graph F for which the Pe-tersen graph has a detectable F-factorization into three factors. Furthermore, if G is a connected cubic graph of order (_3~(k+2)) with det(G) = k, then k ≡ 2 (mod 4) or k ≡ 3 (mod 4). We investigate the largest order of a connected cubic graph with prescribed detection number.
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The range of fire detection designs found in distilleries across Scotland can vary greatly. On one hand some of the smaller privately owned distilleries have relatively sparse detection capabilities in place, whereas others, with ...
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The range of fire detection designs found in distilleries across Scotland can vary greatly. On one hand some of the smaller privately owned distilleries have relatively sparse detection capabilities in place, whereas others, with the increasing corporatisation of the Scottish whisky distillation industry, demand their investments are protected, and so try to ensure their detection systems are in line with good practice with respect to loss prevention.
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We propose a viewpoint-independent object-detection algorithm that detects objects in videos based on their 2-D and 3-D information. Object-specific quasi-3-D templates are proposed and applied to match objects' 2-D contours and t...
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We propose a viewpoint-independent object-detection algorithm that detects objects in videos based on their 2-D and 3-D information. Object-specific quasi-3-D templates are proposed and applied to match objects' 2-D contours and to calculate their 3-D sizes. A quasi-3-D template is the contour and the 3-D bounding cube of an object viewed from a certain panning and tilting angle. Pedestrian templates amounting to 2660 and 1995 vehicle templates encompassing 19 tilting and 35 panning angles are used in this study. To detect objects, we first match the 2-D contours of object candidates with known objects' contours, and some object templates with large 2-D contour-matching scores are identified. In this step, we exploit some prior knowledge on the viewpoint on which the object is viewed to speed up the template matching, and the viewpoint likelihood for each contour-matched template is also assigned. Then, we calculate the 3-D widths, heights, and lengths of the contour-matched candidates, as well as the corresponding 3-D-size-matching scores. The overall matching score is obtained by combining the aforementioned likelihood and scores. The major contributions of this paper are to explore the joint use of 2-D and 3-D features in object detection. It shows that, by considering 2-D contours and 3-D sizes, one can achieve promising object detection rates. The proposed algorithms were evaluated on both pedestrian and vehicle sequences. It yielded significantly better detection results than the best results reported in PETS 2009, showing that our algorithm outperformed the state-of-the-art pedestrian-detection algorithms.
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The frequent usage of figurative language on online social networks, especially on Twitter, has the potential to mislead traditional sentiment analysis and recommender systems. Due to the extensive use of slangs, bashes, flames, a...
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The frequent usage of figurative language on online social networks, especially on Twitter, has the potential to mislead traditional sentiment analysis and recommender systems. Due to the extensive use of slangs, bashes, flames, and non-literal texts, tweets are a great source of figurative language, such as sarcasm, irony, metaphor, simile, hyperbole, humor, and satire. Starting with a brief introduction of figurative language and its various categories, this article presents an in-depth survey of the state-of-the-art techniques for computational detection of seven different figurative language categories, mainly on Twitter. For each figurative language category, we present details about the characterizing features, datasets, and state-of-the-art computational detection approaches. Finally, we discuss open challenges and future directions of research for each figurative language category.
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It becomes increasingly important to detect intrusions with unknown patterns in order to protect our business from cyber terrorism threats. This paper introduces data mining technologies designed for this purpose; SmartSifter (out...
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It becomes increasingly important to detect intrusions with unknown patterns in order to protect our business from cyber terrorism threats. This paper introduces data mining technologies designed for this purpose; SmartSifter (outlier detection engine), ChangeFinder (change-point detection engine), AccessTracer (anomalous behavior detection engine). All of them are able to learn statistical patterns of logs adaptively and to detect intrusions as statistical anomalies relative to the learned patterns. We briefly overview the principles of these engines and illustrate their applications to network intrusion detection, worm detection, and masquerader detection.
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