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X-ray angiography (angio) images are widely used to identify irregularities in the vascular system. Because of their high spatial resolution and the increasing amount of X-ray angio images generated, compression of these images is becoming paramount. In this paper, we propose a diagnostically lossless compression method based on automatic segmentation using ray-casting and α-shapes. The diagnostically relevant Region of Interest is separated from the background by exploiting the inherent symmetrical features of the image. The background-suppressed images are then losslessly encoded using DICOM-compliant lossless compression methods. Experimental results suggest that the proposed method correctly identifies the Region of Interest in X-ray angio images with an average segmentation accuracy of 98.4% and achieves more than 2 bits per pixel improvement on compression performance as compared to lossless compression with no background suppression.

Summary form only given. X-ray angiographic (angio) images are widely used for identifying irregularities in the vascular system. Because of their high spatial resolution and the increasingly amount of X-ray angio images generated, compression of these images is becoming increasingly appealing. In this paper, we introduce a diagnostically lossless compression scheme for X-ray angio images. The coding scheme relies on a novel method based on ray casting and a-shapes for distinguishing the clinically relevant Region of Interest from the background. The background is then suppressed to increase data redundancy, allowing to achieve a higher coding performance. Experimental results suggest that the proposed scheme correctly identifies the Region of Interest in X-ray angio images and achieves more than 2 bits per pixel reduction in average as compared to the case of compression with no background suppression. Results are reported here for 20 out of 25 images compressed using various lossless compression methods.

Cognitive radio technology is a promising technology to solve the inefficiency problem of the usage of spectrum, and with the help of the cooperative communication technology, the system can obtain more performance. This paper proposes a cooperative communication strategy based on physical-layer network coding (PNC) to work out the difficulty that the primary cannot communicate, which the secondary user can also access the spectrum. Meanwhile, we also discuss the power allocation scheme and relay selection strategy to maximum the system capacity. With amplify forward (AF), the secondary forwards the signal after orthogonal zing the single of the primary user and the secondary user. Simulation results showed that, with the cooperation of the secondary user, the primary user can communicates efficiently, the secondary user transmits availably as well. The system achieves the best performance by the power allocation scheme and relay selection strategy.

In this paper, we propose a method for modeling and analysis of fault detection in real-time systems. This approach is based on the model of time petri net (TPN), which derives from the timing analysis of the TPN models with guarded transitions. With the reachability analysis of each mark of TPN model, some finite length of test sequences are generated through computing the shortest path from the initial mark to each leaf node. Then we carry out input in the proper time intervals and check out whether the system satisfies the requirements for real time and the functions of software that according to the output results. This approach is well illustrated by means of modeling and analysis of a safety-critical system: Interlock Logic System.

Applications running on these platforms of grid computation execution environment may exhibit diverse and unpredictable performance because of platform heterogeneity and varying resource availability. The ability to monitor and manage distributed computing components is critical for enabling high-performance distributed computing. Monitoring data is needed to determine the source of performance problems and to tune the system and application for better performance. This paper researches and uses technology of agent to design an agent-based architecture. These intelligent agents are used to collect , analysis the performance parameters and metric, and to decide on the required modification on real-time.

In most problems of first-order rule learning, rule space is usually structured by thetas-subsumption operator. But in first-order rule space, thetas-subsumption is a quasi-ordering. If the number of coved training examples is used as the criterion for ranking candidates of hypothesis, there is a equivalent-class problem when searching along the quasi-ordering. Rules in an equivalent-class can't be distinguished according to their fitness function values. In another aspect, it makes the search to prefer longer rules, and would reduce the system efficiency and readability of learned rules. To solve these problems, in this paper, a new fitness function based on binding is presented. The contrast experiment has been done to show the effect of the new fitness function in guiding the search through first-order rule space

This paper proposes a new approach of human face recognition using partial least square (PLS) and covering algorithm. PLS constructs the component so that the correlation between the class variable and themselves maximized. Covering Algorithm is a kind of neural network method. The approach first processes the image with wavelet transform, and then eliminates the redundant features with PLS. It reduces the dimension of image by extracting the principal features. Finally, classifies the image by covering algorithm.

Software safety testing focuses on whether safety-critical software under test fulfills corresponding safety specifications. But unfortunately, it is very difficult to test software safety by using traditional operational reliability demonstration testing methods. The main reason is that different failures may have different impacts on safety. Damage size of a failure is a classified measurement of harm severity of the failure under the most adverse circumstances. Harm severity of a failure could be obtained through harm analysis. A classified software safety weight is the probability that failures in a subset of the corresponding damage size do not emerge. Software safety vector is a vector of classified software safety weights.