16539998
Lossy to lossless image compression based on reversible integer DCT
A progressive image compression scheme is investigated using reversible integer discrete cosine transform (RDCT) which is derived from the matrix factorization theory. Previous techniques based on DCT suffer from bad performance in lossy image compression compared with wavelet image codec. And lossless compression methods such as IntDCT, I2I-DCT and so on could not compare with JPEG-LS or integer discrete wavelet transform (DWT) based codec. In this paper, lossy to lossless image compression can be implemented by our proposed scheme which consists of RDCT, coefficients reorganization, bit plane encoding, and reversible integer pre- and post-filters. Simulation results show that our method is competitive against JPEG-LS and JPEG2000 in lossless compression. Moreover, our method outperforms JPEG2000 (reversible 5/3 filter) for lossy compression, and the performance is even comparable with JPEG2000 which adopted irreversible 9/7 floating-point filter (9/7F filter).
31834649
A Novel Artificial Immune Network Model and Analysis on Its Dynamic Behavior and Stabilities
A novel model of artificial immune network is presented at first, and then a simulative research work is made on its dynamic behaviors. Simulation results show that the limit cycle and chaos may exist simultaneously when four units are in connection, and the network's characteristic has a close relationship with the intensity of suppressor T-cell's function, B-cell's characteristics and transconductance. Besides this, with Liapunov's method, the sufficient conditions for network's stability is studied, especially for the case of system's characteristics under the condition that the helper T-cells appear as a nonlinear function.
62767012
Incorporating prior knowledge into SVM for image retrieval
SVM based image retrieval suffers from the scarcity of labelled samples. In this paper, this problem is solved by incorporating prior knowledge into SVM. Firstly, some prior knowledge of image retrieval is discussed and constructed. After that, the knowledge is incorporated into SVM optimization as a constraint, and a new knowledge-based target function is formulated. Based on this, a framework of image retrieval with knowledge based SVM is proposed. Experimental results demonstrate that the proposed method can effectively improve the learning and retrieval performance of SVM, especially when the number of labelled samples is small.
14688887
Data Compression Algorithm Based on Hierarchical Cluster Model for Sensor Networks
A new distributed algorithm of data compression based on hierarchical cluster model for sensor networks is proposed, in which, the whole sensor network is mapped into a kind of hierarchical clusters model firstly, and then different wavelet transform models are used to commit data compression in inner and super clusters respectively. Theoretical analyses and simulation results show that, the above new methods have good performance of approximation, and can compress data and reduce the amount of data efficiently.
9891360
Image retrieval with SVM active learning embedding Euclidean search
Image retrieval with relevance feedback suffers from the small sample problem. Recently, SVM active learning has been proposed to tackle this problem, showing promising results. However, a small but sufficient number of initially labelled samples are still required to ensure subsequent efficient active learning and good retrieval performance. In the existing method, the user is asked to label more images before active learning starts. In this paper, a method of embedding Euclidean search into SVM active learning is proposed. With the help of Euclidean search, the adverse effect on retrieval performance due to lack of initially labelled samples can be reduced. Experimental results demonstrate the improvement by the proposed method, especially when the number of initially labelled samples is small.
12523556
Design Patterns of Scalable Cluster System Software
The design pattern of cluster system software has an important influence on scalability of massive cluster system. The paper presents design patterns of scalable cluster system software, including scalable software topologies and optimized communication modes. These design patterns have been widely applied in Dawning series of supercomputers and some results of performance evaluation show their good scalability
8484163
Near-Collision Attacks on MD4: Applied to MD4-Based Protocols
The most widely used hash functions from MD4 family have been broken, which lead to a public competition on designing new hash functions held by NIST. This paper focuses on one concept called near-collision resistance: computationally difficult to find a pair of messages with hash values differing in only few bits, which new hash functions should satisfy. In this paper, we will give a model of near-collisions on MD4, and apply it to attack protocols including HMAC/NMAC-MD4 and MD4(Password||Challenge). Our new outer-key recovery attacks on HMAC/NMAC-MD4 has a complexity of 272 online queries and 277 MD4 computations, while previous result was 288 online queries and 295 MD4 computations. Our attack on MD4(Password||Challenge) can recover 16 password characters with a complexity of 237 online queries and 221 MD4 computations, which is the first approach to attack such protocols.