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...
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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.
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...
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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...
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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.
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 ...
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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 ...
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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.
Optimistic replication allows mobile workspaces to remain accessible during interrupted network access, but require consecutive handling of any conflicts. When conflict handling requires manual intervention by the use...
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Optimistic replication allows mobile workspaces to remain accessible during interrupted network access, but require consecutive handling of any conflicts. When conflict handling requires manual intervention by the user, the system must not require that conflicts are resolved immediatley after their detection. We present a log-based algorithm for reconciling changes to replicated data that supports deferred manual conflict resolution, while sharing the favorable scalability properties of log-based approaches of small storage and communication overhead. Simulation results validate our design and show that it compares favorably in the relevant metrics with version vector-based designs.
The problem of detecting and isolating abrupt changes in signals and systems with random disturbances is addressed. A simple recursive changedetection/isolation algorithm is proposed. It is shown that its statistical...
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The problem of detecting and isolating abrupt changes in signals and systems with random disturbances is addressed. A simple recursive changedetection/isolation algorithm is proposed. It is shown that its statistical properties are close to an asymptotic optimal solution.
A calculation algorithm for hepatorenal contrast from real ultrasonic images is proposed for analysis research of time series change in patient condition by aging. It provides automatic calculation of kidney pelvis po...
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A calculation algorithm for hepatorenal contrast from real ultrasonic images is proposed for analysis research of time series change in patient condition by aging. It provides automatic calculation of kidney pelvis position based on fuzzy inference, which detects kidney and liver region for hepatorenal contrast to calculate. Experimental calculation results for 150 ultrasonic images taken in real treatment from Kochi Medical School hospital show that accuracy of kidney pelvis detection is 93% and that correlation coefficient of hepatorenal contrast with normal gamma-GT is 0.82. The proposed algorithm is being considered for use in analysis of condition change in Center of Medical Information Science, Kochi Medical School.
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