The objective of this study is to cross-compare three algorithms for retrieving surface soil moisture (SSM) from ESA's Sentinel-1 (S-1) data. The context is provided by the large scientific and application interes...
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The objective of this study is to cross-compare three algorithms for retrieving surface soil moisture (SSM) from ESA's Sentinel-1 (S-1) data. The context is provided by the large scientific and application interest in SSM products at high resolution and regional/continental scale that can be retrieved from S-1 data alone or in combination with other missions such as NASA/SMAP and ESA/SMOS. Of the three investigated algorithms, one inverts a scattering model exploiting a Bayesian approach, whereas the other two are changedetection approaches. The cross-comparison is carried out by using both simulated and experimental data. Strengths and weaknesses of the three algorithms are identified and discussed.
In this paper, we study the internal incremental Davies-Bouldin (iiDB) cluster validity index in the context of streaming data analysis. We extend the original index to a more general version parameterized by the expo...
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In this paper, we study the internal incremental Davies-Bouldin (iiDB) cluster validity index in the context of streaming data analysis. We extend the original index to a more general version parameterized by the exponent of membership weights. Then we illustrate how the iiDB can be used to analyze and understand the performance of the Extended Robust Online Streaming Clustering (EROLSC) algorithm. We give examples that illustrate the appearance of a new cluster, the effect of different cluster sizes, handling of outlier data samples, and the effect of the input order on the resultant cluster history.
This paper presents Digital Video Watermarking based on Cuckoo Search nature inspired algorithm involving scene changedetection. Two famous lévy flight algorithms associated with Cuckoo Search algorithm are pres...
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ISBN:
(纸本)9781538619605
This paper presents Digital Video Watermarking based on Cuckoo Search nature inspired algorithm involving scene changedetection. Two famous lévy flight algorithms associated with Cuckoo Search algorithm are presented known as Mantegna Algorithm and McCulloch algorithm. In this paper, we explained why cuckoo search algorithm is efficient in digital watermarking followed by watermarking of the video frames only during scene changedetection in the video. Finally, we compared the results of both the lévy flight algorithms - Mantegna algorithm and McCulloch algorithm on the basis of imperceptibility and robustness of the watermarked image.
This study addresses the problem of detecting change points in streaming data. Herein, we focus on detecting changes with low complexity and high accuracy to manage large-volume, high-velocity data. To this end, we pr...
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ISBN:
(纸本)9781538627150
This study addresses the problem of detecting change points in streaming data. Herein, we focus on detecting changes with low complexity and high accuracy to manage large-volume, high-velocity data. To this end, we propose a novel method of detecting changes via data compression, by introducing minimum description length (MDL) change statistics to an adaptive windowing regime. The time complexity of the resulting algorithm, Sequential Compression with Adaptive Windowing (SCAW), is optimal except for a logarithmic factor, and is theoretically justified through exponential upper bounds on error probabilities. Moreover, SCAW can be used to detect arbitrary types of changes by choosing an appropriate compressor. We also introduce the notion of asymptotic reliability as a criterion of change point detectionalgorithms, and determine SCAW's parameter using this criterion. Finally, we demonstrate the effectiveness of the proposed method in experiments using synthetic and real-world data from markets and industrial machinery.
Assessing terrain ahead of a robot when repeating previously driven safe paths can be accomplished by looking for geometric changes (e.g., due to the appearance of humans or other obstacles). Previous work has shown t...
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ISBN:
(纸本)9781538626825
Assessing terrain ahead of a robot when repeating previously driven safe paths can be accomplished by looking for geometric changes (e.g., due to the appearance of humans or other obstacles). Previous work has shown that the incorporation of data-driven learning and place-dependence are useful aspects of making terrain classification viable in challenging terrain. This paper presents a learning, place-dependent (LPD) terrain classifier that uses a probabilistic model of the terrain to improve detection of small obstacles in uncluttered terrain while avoiding false positives in more challenging environments. Specifically, a Gaussian mixture model is used to account for multi-height terrain cells that arise in heavily vegetated areas (where both a ground plane and overhanging vegetation can occupy the same cell). A variational Bayesian technique is used to automatically determine the number of components required for each cell using a Dirichlet prior on mixing proportions and a Normal-Inverse-Wishart prior on the means and covariances of the components. The probabilistic nature of the model allows for the detection of much smaller obstacles in regions that exhibit low variance in the terrain surface, whilst still avoiding false positives in regions where the terrain is highly cluttered (e.g., vegetation). The algorithm is tested on almost 10 km of autonomous traverse and is shown to be able to classify a wider range of obstacles than two baseline change-detectionalgorithms based on absolute geometric differences.
This paper proposes an algorithm for handling multiple consecutive measurement biases that appear and disappear frequently, typically encountered in GNSS in small low flying UAVs due to range errors and signal blockin...
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ISBN:
(纸本)9781538609392
This paper proposes an algorithm for handling multiple consecutive measurement biases that appear and disappear frequently, typically encountered in GNSS in small low flying UAVs due to range errors and signal blocking. The method is derived as an extension to the well known GLR algorithm and is based on a corrected innovation sequence for detection and an identification stage based on least square estimation. A recursive (RLS) and a non-recursive (LS) solution is proposed in the identification stage. Results in a GNSS position error example show that the proposed algorithms are significantly better than the original algorithm in terms of estimation precision when biases appear and disappear frequently.
This paper investigates the influence of the signal to noise ratio (SNR) and the type of a noise on the performance of two adaptive novelty detection methods. The evaluated methods are Learning Entropy (LE) and Error ...
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ISBN:
(纸本)9781538607718
This paper investigates the influence of the signal to noise ratio (SNR) and the type of a noise on the performance of two adaptive novelty detection methods. The evaluated methods are Learning Entropy (LE) and Error and Learning Based Novelty detection (ELBND). The methods are compared in empirical way in classification framework. A classification based only on the error of the adaptive model was used as a reference. The research in this field is important, because a noise is present in every measured data and can drastically influence the result of tasks like the novelty detection. Moreover, various types of noise can influence the novelty detection in different ways, therefore the optimal method of adaptive novelty detection can be hard to choose. This assumption is supported by experimental results in this study.
作者：
Molloy, Timothy L.QUT
Fac Sci & Engn Sch Elect Engn & Comp Sci Brisbane Qld 4000 Australia
In this paper, we consider the problem of quickly detecting and isolating an abrupt change in an observed stochastic processes under a polynomial penalty for delays. We propose an auxiliary matrix cumulative sum (AMCU...
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ISBN:
(纸本)9781922107909
In this paper, we consider the problem of quickly detecting and isolating an abrupt change in an observed stochastic processes under a polynomial penalty for delays. We propose an auxiliary matrix cumulative sum (AMCUSUM) detection and isolation algorithm, and show that it is optimal under our quickest changedetection and isolation criterion with polynomial delay penalties in the asymptotic regime of few false alarms and false isolations. We also illustrate the AMCUSUM algorithm in simulations.
New tools are required to support and increase the reliability and safety of unmanned aerial vehicle (UAV) formation operations. This paper poses a new formation coordination changedetection problem as a quickest det...
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ISBN:
(纸本)9781922107909
New tools are required to support and increase the reliability and safety of unmanned aerial vehicle (UAV) formation operations. This paper poses a new formation coordination changedetection problem as a quickest detection of change in signal coordination on the basis of a worst case average detection delay cost, inspired by Lorden's criteria. This paper also poses a pragmatic nested changedetection algorithm for detecting formation coordination. The proposed algorithms are evaluated on both simulated and real measurement data.
The goal of this technical note is to design a switching signal estimator for a class of elementary continuous-time switching or switched systems. First, the elementary system is recast into a polynomial form and, sec...
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The goal of this technical note is to design a switching signal estimator for a class of elementary continuous-time switching or switched systems. First, the elementary system is recast into a polynomial form and, secondly, some tools borrowed from Algebraic Geometry are used to express the switching signal as a function of the time derivatives of the output and of the input.
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