In this paper a robust and adaptive method of circular object detection is proposed. The proposed method aims to limit the need for parameter adjustment when applied to a wide range of different images, by implementin...
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In this paper a robust and adaptive method of circular object detection is proposed. The proposed method aims to limit the need for parameter adjustment when applied to a wide range of different images, by implementing a hybrid algorithm using the circular Hough transform for region segmentation, and a refined localised version of the Randomized Circle detection algorithm to perform circular object detection. As a result the overall speed and accuracy are improved when compared to other circle detection algorithms.
In this paper, we propose a novel detection algorithm for the elementary signal estimator of an IDMA system considering channel estimation error. To develop the algorithm, we derive new probability density function of...
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In this paper, we propose a novel detection algorithm for the elementary signal estimator of an IDMA system considering channel estimation error. To develop the algorithm, we derive new probability density function of decision variable reflecting channel estimation error and modify the conventional algorithm based on it. Through computer simulations, it is shown that the proposed algorithm achieves lower bit error rate than the conventional one. This performance enhancement is provided with negligible increase of its computational complexity.
Intrusion detection systems (IDSs) can easily create thousands of alerts per day, up to 99% of which are false positives (i.e. alerts that are triggered incorrectly by benign events). This makes it extremely difficult...
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Intrusion detection systems (IDSs) can easily create thousands of alerts per day, up to 99% of which are false positives (i.e. alerts that are triggered incorrectly by benign events). This makes it extremely difficult for managers to analyze and react to attacks. This paper presents a novel method for handling IDS alerts more efficiently. It introduces outlier detection technique into this field, and designs a special outlier detection algorithm for identifying true alerts and reducing false positives. This algorithm uses frequent attribute values mined from historical alerts as the features of false positives, and then filters false alerts by the score calculated based on these features. We also proposed a two-phrase framework, which not only can filter newcome alerts in real time, but also can learn from these alerts and automatically adjust the filtering mechanism to new situations. Moreover our method needs no domain knowledge and little human assistance, so it is more practical than current ways. We have built a prototype implementation of our method. And the experiments on DARPA 2000 and real-world data have proved that this model has high performance.
An onset detection system based on linear prediction with scalable complexity is proposed in this work. One unique feature of the proposed onset detection algorithm is that it can offer a trade-off between complexity ...
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An onset detection system based on linear prediction with scalable complexity is proposed in this work. One unique feature of the proposed onset detection algorithm is that it can offer a trade-off between complexity and detection accuracy by adjusting its parameters. Consequently, it can be used in consumer electronics such as karaoke performance evaluation and automatic visual effect generation in portable media players.
We propose a method of shot boundary detection based on the co-occurrence of global motion in video stream. In addition to the conventional features based on appearance and local motion, we apply ST (Space-Time) patch...
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ISBN:
(纸本)9781424421749
We propose a method of shot boundary detection based on the co-occurrence of global motion in video stream. In addition to the conventional features based on appearance and local motion, we apply ST (Space-Time) patch analysis for detecting global motion in video stream. And then we perform shot boundary detection by constructing AdaBoost classifiers which represent the co-occurrence of global motion and the conventional features. Experimental results show that our method had 3.8% higher F-measure value than that of the conventional method for gradual shot boundary detection.
A spectral clustering intrusion detection approach is presented in this paper. The basic idea of the approach is to compute the similarities between the training data points, then to construct the affinity matrix, and...
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A spectral clustering intrusion detection approach is presented in this paper. The basic idea of the approach is to compute the similarities between the training data points, then to construct the affinity matrix, and to get the clusters according the main eigenvector of this affinity matrix. With the classified data instances, anomaly data clusters can be easily identified by normal cluster ratio. The benefits of the approach lie in that it is accurate in clustering and it needn 't labeled training data sets. Using the data sets of KDD99, the experiment result shows that this approach can detect intrusions efficiently in the real network connections.
The comprehension of the clustering result is a problem that hasn't yet resolve, which having important meaning to the usage of the cluster result and the evaluation of the cluster effect. We put forward the metho...
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The comprehension of the clustering result is a problem that hasn't yet resolve, which having important meaning to the usage of the cluster result and the evaluation of the cluster effect. We put forward the method discovering attribute feature cluster for any clustering result based on outlier detection technique, and put forward an outlier detection algorithm based on even distribution pattern. Through carrying on outlier analysis to all data cluster attribute descriptions, we discovered the feature attribute of each data cluster, and then carried out the comprehension to the clustering result. The remarkable point lies in the method doesn't only aim at a particular clustering algorithm, but also the analysis of any the clustering algorithm result. Experiment to the UCI data set indicated, the method submitted in this paper obtained better result.
This paper investigates the effects of channel estimation errors on zero-forcing (ZF) vertical Bell laboratories layered space time (V-BLAST) detection. An analytical method is presented to derive the symbol error pro...
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This paper investigates the effects of channel estimation errors on zero-forcing (ZF) vertical Bell laboratories layered space time (V-BLAST) detection. An analytical method is presented to derive the symbol error probability (SEP) of the signals detected at each stage. The effects of imperfect channel estimation on the SEP performance of V-BLAST detection are then studied. It is shown that ZF-VBLAST detection is very sensitive to the channel estimation errors under high signal to noise ratio (SNR). It is also shown that when optimal ordering is adopted, the effects of channel estimation errors are more significant on the latter detection stages.
The rapid and accurate detection of anomalies in network traffic has always been a challenging task, and is absolutely critical to the efficient operation of the network. The availability of numerous different detecti...
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The rapid and accurate detection of anomalies in network traffic has always been a challenging task, and is absolutely critical to the efficient operation of the network. The availability of numerous different detection algorithms makes it difficult to choose a suitable configuration. An algorithm may have a high detection rate for high rate attacks, but might behave unfavorably when faced with attacks with gradually increasing rates. This paper proposes an online parallel anomaly detection system that implements multiple anomaly detection algorithms in parallel to detect anomalies in real-time. The main idea is to aggregate the detection data from multiple algorithms to come up with a single anomaly metric. We evaluate this system with realistic attacks on the DETER testbed. Our results show improved true positive and false negative rates for both high intensity and slow-rise ramped floods. Furthermore, the system is able to detect attacks separated by as little as 15 seconds with a high true positive rate.
In this paper, we examine the bit error rate (BER) performance of direct sequence spread spectrum (DS-SS) systems using the parity bit selected spreading technique. Suboptimum detection is done by determining which sp...
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In this paper, we examine the bit error rate (BER) performance of direct sequence spread spectrum (DS-SS) systems using the parity bit selected spreading technique. Suboptimum detection is done by determining which spreading waveform is most likely used by the transmitter before attempting to detect the data. The bit error rate performance of such a system is dominated by the probability that the receiver incorrectly identifies the correct spreading sequence employed by the transmitter. We investigate the probability that, when the receiver does not correctly identify the correct spreading waveform as that used by the transmitter, it identifies the correct spreading waveform as the second most likely, third most likely etc. Based on these statistics, we propose a new detection algorithm that improves the BER performance by determining the two most likely employed spreading sequences to identify the transmitted information bits. The performance of this new technique is compared to maximum likelihood detection (MLD).
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