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Herndon, VA, United States

Jin H.,Xian University of Science and Technology | Jin H.,Network Security Technology | Wang Y.,Xian University of Science and Technology
Infrared Physics and Technology | Year: 2014

This paper proposes a novel image fusion scheme based on contrast pyramid (CP) with teaching learning based optimization (TLBO) for visible and infrared images under different spectrum of complicated scene. Firstly, CP decomposition is employed into every level of each original image. Then, we introduce TLBO to optimizing fusion coefficients, which will be changed under teaching phase and learner phase of TLBO, so that the weighted coefficients can be automatically adjusted according to fitness function, namely the evaluation standards of image quality. At last, obtain fusion results by the inverse transformation of CP. Compared with existing methods, experimental results show that our method is effective and the fused images are more suitable for further human visual or machine perception. © 2014 Elsevier B.V. All rights reserved.


Yu D.,Hangzhou Dianzi University | Zhang Y.,Hangzhou Dianzi University | Ge J.,Hangzhou Dianzi University | Wu W.,Network Security Technology
Proceedings - International Computer Software and Applications Conference | Year: 2013

Structural design patterns address concerns related to high-level structures for applications being developed. Accurately recovered instances of structural design patterns support development related tasks like program comprehension and reengineering. However, the detection of structural design pattern instances is not always a straightforward task. The lack of documentation, the ad-hoc nature of programming and the possible variants of pattern instances often lead to the low accuracy of detection. In this paper, we present an approach to the detection of instances of structural design patterns using source codes. We first transform the source codes and predefined patterns into graphs, with the classes as nodes and the relations as edges. We then identify the instances of sub-patterns that would be the possible constituents of pattern instances by means of subgraph discovery. The sub-pattern instances are further merged by joint classes to see if the collective matches one of the predefined patterns. Compared with existing approaches, our approach focuses on simple sub-patterns, not complicated patterns. In this way, it can not only simplify the detection process, but also detect multiple pattern instances at a time. The results of the experiments on detecting pattern instances of Adapter, Bridge, Composite, Decorator and Proxy from 4 open source software systems demonstrate that our approach obtains better precision than the existing approaches. © 2013 IEEE.


Yu D.,Hangzhou Dianzi University | Geng P.,Hangzhou Dianzi University | Wu W.,Network Security Technology
Proceedings - Asia-Pacific Software Engineering Conference, APSEC | Year: 2012

Software Product Line Engineering organizes the commonality and the variability of domain feature model in order to achieve large-scale software reuse. Although there are a variety of approaches to the construction of domain feature models, they are however difficult to locate inconsistency caused by frequent changes occurring in the process of evolution. This paper presents a novel approach to the construction of domain feature model and its trace ability with corresponding requirements. It first constructs a set of feature models for individual applications within same domain and their trace ability to corresponding application requirements. Then, it merges all application feature models to form the domain feature model and constructs the trace ability between features in different models. It finally extracts the domain requirements and the trace ability between domain requirements and domain features. The case of software product line for labor market monitoring applications verifies this new approach, and shows that it can not only construct domain feature model automatically and effectively, but also help locate affected requirements while features change or vice versa. © 2012 IEEE.


Sui L.,Xian University of Science and Technology | Sui L.,Network Security Technology | Duan K.,Xian University of Science and Technology | Liang J.,Xian University of Technology | Hei X.,Xian University of Science and Technology
Optics Express | Year: 2014

A double-image encryption is proposed based on the discrete fractional random transform and logistic maps. First, an enlarged image is composited from two original images and scrambled in the confusion process which consists of a number of rounds. In each round, the pixel positions of the enlarged image are relocated by using cat maps which are generated based on two logistic maps. Then the scrambled enlarged image is decomposed into two components. Second, one of two components is directly separated into two phase masks and the other component is used to derive the ciphertext image with stationary white noise distribution by using the cascaded discrete fractional random transforms generated based on the logistic map. The cryptosystem is asymmetric and has high resistance against to the potential attacks such as chosen plaintext attack, in which the initial values of logistic maps and the fractional orders are considered as the encryption keys while two decryption keys are produced in the encryption process and directly related to the original images. Simulation results and security analysis verify the feasibility and effectiveness of the proposed encryption scheme. © 2014 Optical Society of America.


Li H.,Network Security Technology | Hu C.,Network Security Technology
Proceedings - IEEE INFOCOM | Year: 2013

Fine-grained traffic identification (FGTI) reveals the context/purpose of each packet that flows through the network nodes/links. Instead of only indicating the application/protocol that a packet is related to, FGTI further maps the packet to a meaningful user behavior or application context. In this paper, we propose a Rule Organized Optimal Matching (ROOM) for fast and memory efficient fine-grained traffic identification. ROOM splits the identification rules into several fields and elaborately organizes the matching order of the fields. We formulate and model the optimal rule organization problem of ROOM mathematically, which is demonstrated to be NP-hard, and then we propose an approximate algorithm to solve the problem with the time complexity of O(N 2) (N is the number of fields in a rule). In order to perform evaluations, we implement ROOM and related work as real prototype systems. Also, real traces collected in wired Internet and mobile Internet are used as the experiment input. The evaluations show very promising results: 1.6X to 104.7X throughput improvement is achieved by ROOM in the real system with acceptable small memory cost. © 2013 IEEE.

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