Reconstruction Method for Nonconcyclic Dual-Source Circular Cone-Beam CT with a Large Field of View

In industrial computed tomography (CT), it is often required to inspect large objects whose size is beyond a reconstructed field of view (FOV). Some multiscan modes have been developed to acquire the complete CT projection data for a larger object using small panel detectors. In this paper, we give a non-concyclic dual-source circular cone-beam scanning geometry based on the idea of multiscan modes and propose a backprojection-filtration-based (BPF) reconstruction algorithm without data rebinning. Since the FOV calculated according to this nonconcyclic dual-source circular CT scanning geometry is larger than cardiac dual-source CT scanning geometry, our method can reconstruct larger horizontal slices (i.e., the slices perpendicular to rotation axis) than cardiac dual-source CT. The quality of CT images is expected to be superior to those obtained using larger panel detectors. The simulation results have indicated that CT images obtained by the proposed method are satisfying

A Novel System Anomaly Prediction System Based on Belief Markov Model and Ensemble Classification

Computer systems are becoming extremely complex, while system anomalies dramatically influence the availability and usability of systems. Online anomaly prediction is an important approach to manage imminent anomalies, and the high accuracy relies on precise system monitoring data. However, precise monitoring data is not easily achievable because of widespread noise. In this paper, we present a method which integrates an improved Evidential Markov model and ensemble classification to predict anomaly for systems with noise. Traditional Markov models use explicit state boundaries to build the Markov chain and then make prediction of different measurement metrics. A Problem arises when data comes with noise because even slight oscillation around the true value will lead to very different predictions. Evidential Markov chain method is able to deal with noisy data but is not suitable in complex data stream scenario. The Belief Markov chain that we propose has extended Evidential Markov chain and can cope with noisy data stream. This study further applies ensemble classification to identify system anomaly based on the predicted metrics. Extensive experiments on anomaly data collected from 66 metrics in PlanetLab have confirmed that our approach can achieve high prediction accuracy and time efficiency