The problem of recursive identification of autoregressive processes which are subject to parameter jumps of unknown magnitude occurring at unknown times is addressed. A sequential procedure for tracking the parameters...
详细信息
The problem of recursive identification of autoregressive processes which are subject to parameter jumps of unknown magnitude occurring at unknown times is addressed. A sequential procedure for tracking the parameters, detecting the parameter jumps and estimating the points of change is presented which is based on generalized likelihood ratio (GLR) techniques and application of two adaptive ladder filters: the unnormalized growing memory and sliding memory least squares covariance ladder algorithms. From the prediction error energies which are available from these algorithms, the relevant GLR statistics for detection and location of the parameter jumps is computed and after each jump detection the growing memory ladder algorithm is reinitialized by means of the sliding memory filter estimates.
An automated tracking algorithm for Doppler radar storms is presented. Potential storms in Doppler radar images are hypothesized as regions of high water density (high intensity in the radar images) using a merge-and-...
详细信息
An automated tracking algorithm for Doppler radar storms is presented. Potential storms in Doppler radar images are hypothesized as regions of high water density (high intensity in the radar images) using a merge-and-split region growing algorithm. Potential storms are verified by a relaxation labelling scheme that attempts to finds the best tracks based on spatio-temporal storm consistency. Temporal consistency is ensured by requiring temporal coherence of storm properties, which include size, average intensity, radial velocity variance (computed from the Doppler radial velocity images), storm shape and orientation and neighbourhooding storm disparity. Spatial consistency requires neighbouring storm tracks with common storms to compete with a winner-take-all strategy. The property coherence framework is adaptive, allowing additional properties to be added or deleted as appropriate. The tracking algorithm allows storm merging and splitting via a construction called pseudo-storms. Several tracks for Doppler storm radar data supplied by AES are given as examples of the algorithms performance.< >
Anomaly-based Intrusion detection is a key research topic in network security due to its ability to face unknown attacks and new security threats. Moreover, new solutions should cope with scalability issues derived fr...
详细信息
Anomaly-based Intrusion detection is a key research topic in network security due to its ability to face unknown attacks and new security threats. Moreover, new solutions should cope with scalability issues derived from the growth of the Internet traffic. To this aim random aggregation through the use of sketches represents a powerful prefiltering stage that can be applied to backbone data traffic with a performance improvement wrt traditional static aggregations at subnet level. In the paper we apply the CUSUM algorithm at the bucket level to reveal the presence of anomalies in the current data and, in order to improve the detection rate, we correlate the data corresponding to traffic flows aggregation based on different fields of the network and transport level headers. As a side effect, the correlation procedure gives some hints on the typology of the intrusions since different attacks determine the variability of the statistics associated to specific header fields. The performance analysis, presented in this paper, demonstrates the effectiveness of the proposed approach, confirming the goodness of CUSUM as a change-point detection algorithm.
The all-pairs shortest paths problem in weighted graphs is investigated. An algorithm called the hidden paths algorithm, which finds these paths in time O(m*+n n/sup 2/ log n), where m* is the number of edges particip...
详细信息
The all-pairs shortest paths problem in weighted graphs is investigated. An algorithm called the hidden paths algorithm, which finds these paths in time O(m*+n n/sup 2/ log n), where m* is the number of edges participating in shortest paths, is presented. It is argued that m* is likely to be small in practice, since m*=O(n log n) with high probability for many probability distributions on edge weights. An Omega (mn) lower bound on the running time of any path-comparison-based algorithm for the all-pairs shortest paths problem is proved.< >
The negative selection algorithm proposed by Forrest et al. (1994) is a very significant changedetection algorithm based on the generation process of T-Cells process in biological system. But when negative selection ...
详细信息
ISBN:
(纸本)9810475241
The negative selection algorithm proposed by Forrest et al. (1994) is a very significant changedetection algorithm based on the generation process of T-Cells process in biological system. But when negative selection algorithm is used in distributed intrusion detection, the first problem that we meet is how to distribute the detectors in all detection workstations. To resolve this problem, this paper proposed a novel distributed negative selection algorithm based on the original negative selection algorithm. The core of this distributed negative selection algorithm is the distributing strategy. Two kinds of distributing strategies, random distributing strategy and greedy distributing strategy are given. Then we compared the performance of random distributing strategy and greedy distributing strategy. The experimental results show that: (1) distributed negative selection algorithm can avoid the problem of single point failure, when a part of detection workstations fails, the detection rate does not descend quickly; and (2) when some detection workstations fail, greedy distributing strategy can maintain better detection rate than random distributing strategy.
Trivedi et al. presented an effective LMP (locally most powerful) method (2005) to estimate the secret key for sequential steganography in mid- and high-frequency of DCT domain. In this paper, we proposed an improved ...
详细信息
Trivedi et al. presented an effective LMP (locally most powerful) method (2005) to estimate the secret key for sequential steganography in mid- and high-frequency of DCT domain. In this paper, we proposed an improved LMP algorithm, which not only can reliably estimate the secret key for mid- and high-frequency sequential embedding, but also for the case of low-frequency. Moreover, the application of improved algorithm for DWT sequential steganography is discussed. Results of experiment show the performance of improved algorithm is desirable for low-, mid- and high-frequency steganography in DCT domain and in the subbands of DWT domain.
Beam tracking methods are instrumental for efficient use of the multi-gigahertz bandwidth available at mmWave frequencies. In this paper, we propose a Multi-armed Bandit (MAB) based Reinforcement Learning (RL) algorit...
详细信息
ISBN:
(数字)9798350303582
ISBN:
(纸本)9798350303599
Beam tracking methods are instrumental for efficient use of the multi-gigahertz bandwidth available at mmWave frequencies. In this paper, we propose a Multi-armed Bandit (MAB) based Reinforcement Learning (RL) algorithm to periodically select transmitter-receiver beam pairs so as to maximize the average spectral efficiency. Contrary to a traditional Bayesian MAB-based approach, the MAB algorithm proposed by us can track a user as it moves across multiple correlation distances. The algorithm keeps track of the received signal strength to detect a change in the channel correlation and adjusts its strategy to adapt to the new channel conditions. We derive an upper bound on the regret of the proposed algorithm. The proposed algorithm is evaluated on channel data generated using the open-source simulator NYUSIM and is observed to outperform existing algorithms, thus removing the requirement of repeated initial access procedures.
This paper presents a new algorithm for detecting objects in images, one of the fundamental tasks of computer vision. The algorithm extends the representational efficiency of eigenimage methods to binary features, whi...
详细信息
This paper presents a new algorithm for detecting objects in images, one of the fundamental tasks of computer vision. The algorithm extends the representational efficiency of eigenimage methods to binary features, which are less sensitive to illumination changes than gray-level values normally used with eigenimages. Binary features (square subtemplates) are automatically chosen on each training image. Using features rather than whole templates makes the algorithm more robust to background clutter and partial occlusions. Instead of representing the features with real-valued eigenvector principle components, we use binary vector quantization to avoid floating point computations. The object is detected in the image using a simple geometric hash table and Hough transform. On a test of 1000 images, the algorithm works on 99.3%. We present a theoretical analysis of the algorithm in terms of the receiver operating characteristic, which consists of the probabilities of detection and false alarm. We verify this analysis with the results of our 1000-image test and we use the analysis as a principled way to select some of the algorithm's important operating parameters.
This paper describes a simulation study of model-based respiratory parameter tracking and event detection. An improved model of respiratory system mechanics was formulated to incorporate separate inspiratory mid expir...
详细信息
This paper describes a simulation study of model-based respiratory parameter tracking and event detection. An improved model of respiratory system mechanics was formulated to incorporate separate inspiratory mid expiratory resistances. Parameters were estimated and tracked using an extended Kalman filter. Events were detected using a multiple-model adaptive estimation (MMAE) algorithm.
A fuzzy reasoning based approach for ARMA order selection is discussed in this paper. The proposed method attempts to select the optimal ARMA order of a time-varying ARMA model. This method improves model validity-cri...
详细信息
A fuzzy reasoning based approach for ARMA order selection is discussed in this paper. The proposed method attempts to select the optimal ARMA order of a time-varying ARMA model. This method improves model validity-criterion based order selection, such as the AIC (Akaike's Information Criterion) and the MDL (Minimum Description Length), etc with applying both of a fuzzy reasoning method and a fuzzy c-means clustering method. These fuzzy methods are incorporated in the proposed method as follows: (1) Suppose the ARMA order of the reference time-varying model changes. The suitable ARMA order is selected by utilizing a recursive fuzzy reasoning method. (2) By using a fuzzy c-means clustering method, we detect the time at which the ARMA order of the reference model changes, and the clustering values are used for adaptive setting the forgetting factor in the recursive fuzzy reasoning method. The experimental results show that the proposed method effectively selects the ARMA orders of a time-varying ARMA model.
暂无评论