In this paper, we explore the use of redundant range differences in signal estimation and detection. Redundant range differences are known to lie in a certain subspace. This information forms our basis of estimation a...
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In this paper, we explore the use of redundant range differences in signal estimation and detection. Redundant range differences are known to lie in a certain subspace. This information forms our basis of estimation and detection algorithms. In addition to this information, we also use the configuration of the base stations to check the consistency of range difference estimates. In summary we propose the shrunken estimator as an improvement over the least squares estimator for range difference smoothing. Shrunken estimator is known to give less mean square error compared to the least squares estimator. For detection purposes we propose an method that can passively detect the presence of a signal form redundant range differences which is based on matched subspace detectors.
In this paper, a gene immune detection algorithm with complement operator in the artificial immune system is proposed. Such problems as gene detection, immune memory and niching strategy in the algorithms are discusse...
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In this paper, a gene immune detection algorithm with complement operator in the artificial immune system is proposed. Such problems as gene detection, immune memory and niching strategy in the algorithms are discussed. Application on the area of anomaly detection is considered. Shown by simulation experiments, the algorithm joined vaccine operator and complement operator improves the problem of hole and keeps variety of antibodies. And its detecting time is also decreased obviously.
One of the most promising applications of synthetic aperture radar (SAR) imagery is change detection. However, the success of change detection algorithms is highly dependent on the type of change being detected. With ...
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One of the most promising applications of synthetic aperture radar (SAR) imagery is change detection. However, the success of change detection algorithms is highly dependent on the type of change being detected. With the advent of multi-channel SAR systems (multi-frequency and/or polarimetric) new algorithms to improve change detection are being developed to take advantage of the additional information. However, these algorithms are still hindered by the speckle phenomena of radar imaging. Adaptive neighborhood filtering techniques have been shown to be effective to reduce speckle, but the impact on change detection has not been explored. The research presented explores the impact of an adaptive filtering algorithm on the probability of detection for synthesized changes.
The paper presents a method for content based still-image replica detection. This method uses a compact image signature which depends on image content and is invariant to many widely used image processing techniques, ...
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ISBN:
(纸本)9781424433643
The paper presents a method for content based still-image replica detection. This method uses a compact image signature which depends on image content and is invariant to many widely used image processing techniques, such as lossy compression, resizing, resampling, color enhancements and simple rotations. The signature is designed to be usable in big image database: it has small size (a few dozen bytes), the extraction is fast and the comparison of image signatures is very fast. More than million of image signatures per second can be compared on a modern PC. Usage of the method within a framework for digital rights infringements detection in the World Wide Web is also discussed.
Shot boundaries provide the basis for almost all high-level video content analysis approaches, validating it as one of the major prerequisites for efficient video indexing and retrieval in large video databases. The s...
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Shot boundaries provide the basis for almost all high-level video content analysis approaches, validating it as one of the major prerequisites for efficient video indexing and retrieval in large video databases. The successful detection of both gradual and abrupt transitions is necessary to this end. In this paper a new gradual transition detection algorithm is proposed, based on novel features exhibiting less sensitivity to local or global motion than previously proposed ones. These features, each of which could serve as a stand-alone transition detection approach, are then combined using a machine learning technique, to result in a meta-segmentation scheme. Besides significantly improved performance, advantage of the proposed scheme is that there is no need for threshold selection, as opposed to what would be the case if any of the proposed features were used by themselves and as is typically the case in the relevant literature. Comparison of the proposed approach with four popular algorithms of the literature reveals the significantly improved performance of it.
A new QRS complex detection algorithm based on the empirical mode decomposition (EMD) is proposed in this paper. The EMD can first decompose the ECG signal into a series of oscillatory components called intrinsic mode...
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A new QRS complex detection algorithm based on the empirical mode decomposition (EMD) is proposed in this paper. The EMD can first decompose the ECG signal into a series of oscillatory components called intrinsic mode functions (IMFs). Then with the soft- threshold denoising method on the first three IMFs, we construct the detection layer that is suitable for QRS detection. Using the corresponding relationship between the feature points of QRS complex and the modulus maxima of the detection layer, the QRS complex detection is realized. The proposed EMD-based method was validated through experiments on the MIT-BIH arrhythmia database and a QRS detection rate of 99.34% was achieved.
One goal of integrated vehicle health management (IVHM) for commercial airplane customers is to monitor sensor data to anticipate problems before flight deck effects (FDEs) ground the airplane for unplanned maintenanc...
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One goal of integrated vehicle health management (IVHM) for commercial airplane customers is to monitor sensor data to anticipate problems before flight deck effects (FDEs) ground the airplane for unplanned maintenance. Airplane subsystems - such as flight and environmental control systems, electrical and hydraulic power - can have a high number of associated parameters. Monitoring sensor data streams individually can be inefficient, and fail to detect problems. A research effort at The Boeing Company is investigating anomaly detection algorithms for multivariate time series of parametric data. The multivariate process monitoring techniques account for correlation between parameters, and therefore alert when relationships between parameters change, as well as when mean levels of individual parameters change. Since many traditional multivariate process monitoring techniques are not suited for the high number of parameters in airplane subsystems, this paper discusses using dimension reduction techniques. One example is principal component analysis (PCA). If the assumptions behind PCA are not met, then monitoring charts based on conventional PCA alone can show false alarms and bad detectability. Independent component analysis (ICA) is a recently developed method in which the goal is to decompose observed data into linear combinations of statistically independent components. ICA can be considered an extension of PCA since it uses PCA as an initial pre-whitening stage. Like projection pursuit density estimation, ICA searches for projections of the data that are most non-Gaussian.
Deadlock detection is a well-studied problem that may be considered solved from a theoretical point of view. However, specific cases may demand for specific solutions. One such specific case is deadlock detection in K...
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Deadlock detection is a well-studied problem that may be considered solved from a theoretical point of view. However, specific cases may demand for specific solutions. One such specific case is deadlock detection in Kahn Process Networks. The Kahn process network (KPN) is an expressive model of computation that is widely used to model and specify deterministic streaming applications. The processes in the network communicate point-to-point over FIFO channels whose sizes are undecidable in general. As a consequence, deadlock may occur and, therefore, a run-time deadlock detection mechanism is required. This can be organized in a centralized way, a distributed way, and a hierarchical way. Centralized and distributed procedures have been reported in the literature. In this paper, we propose a novel hierarchical approach for KPN deadlock detection at run time. We also give results for the implementation on the IBM Cell processor.
Presently, outlier mining is used for many areas such as telecommunication, finance and intrusion detection. However, finding outliers needs amounts of computation with most traditional algorithms. Thus, we propose a ...
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Presently, outlier mining is used for many areas such as telecommunication, finance and intrusion detection. However, finding outliers needs amounts of computation with most traditional algorithms. Thus, we propose a modified density based outlier mining algorithm in this paper. For every object in dataset, our algorithm need not judge whether there are core objects within the epsiv-neighborhood of it. In addition, the module information of data object is introduced in our algorithm and it can avoid large numbers of unnecessary computation to finding all outliers. The algorithm is applied on the intrusion dataset and experimental results show it obtains efficient performance for outlier mining while maintaining stable detection rates.
detection of anomalies in multivariate time series is an important data mining task with potential applications in medical diagnosis, ecosystem modeling, and network traffic monitoring. In this paper, we present a rob...
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detection of anomalies in multivariate time series is an important data mining task with potential applications in medical diagnosis, ecosystem modeling, and network traffic monitoring. In this paper, we present a robust graph-based algorithm for detecting anomalies in noisy multivariate time series data. A key feature of the algorithm is the alignment of kernel matrices constructed from the time series. The aligned kernel enables the algorithm to capture the dependence relationship between different time series and to support the discovery of different types of anomalies (including subsequence-based and local anomalies). We have performed extensive experiments to demonstrate the effectiveness of the proposed algorithm. We also present a case study that shows the utility of applying our algorithm to detect ecosystem disturbances in Earth science data.
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