33935023
Artificial Immune Strategies Improve the Security of Data Storage
A novel artificial immune strategies based data storage model, called AIS-DS, is proposed for dealing with the problem of resources sharing in a storage area network (SAN). Especially for the multi-user's tasks, this technology has some essential features for ensuring the security and privacy of information and/or data, because a SAN here can be regarded exclusive for each user with its own vaccines (a kind of special codes assigned for this user), and on the other hand, damage or interference to the disk or type to some extent in a local area of SAN, will not destroy the integrity of the saved data. Furthermore, with AIS-DS, the privacy of user's coded/decoded data is guaranteed even if the disk is physically handed by some other unwanted users.
15857389
A novel genetic algorithm based on immunity
A novel algorithm, the immune genetic algorithm (IGA), is proposed based on the theory of immunity in biology which mainly constructs an immune operator accomplished by two steps: 1) a vaccination and 2) an immune selection. IGA proves theoretically convergent with probability 1. Strategies and methods of selecting vaccines and constructing an immune operator are also given. IGA is illustrated to be able to restrain the degenerate phenomenon effectively during the evolutionary process with examples of TSP, and can improve the searching ability and adaptability, greatly increase the convergence rate.
10512188
An immune neural network used for classification
Based on an analysis of immune phenomena in nature and utilizing performances of ANN, a novel network mode, i.e., an immune neural network (INN), is proposed which integrates the immune mechanism and the function of neural information processing. The learning algorithm of an INN selects an excitation function and adaptive algorithm of the network. This model makes it easy for a user to utilize directly the characteristic information of a problem and to simplify the original structure by adjusting the excitation function with prior knowledge, improving the working efficiency and searching accuracy. A theoretical analysis and a simulation test for the twin-spiral problem show that, compared with an artificial neural network, INN is not only effective but also feasible. INN can simplify the structure of the existent model and show good working performance.
3194016
A Novel Model of Artificial Immune Network and Simulations on Its Dynamics
A novel model of artificial immune network is presented at first, and then a simulative research work is made on its dynamic behaviors. In this model, a B cell makes a key role that takes antigens in, so as to generate antibodies as its outputs. Under five different kinds of adjustment by suppressor T cells, number of antibodies will keep to a certain degree through influencing the B cell’s activation. On the other hand, with help T cells, different B cells could cooperate from each other, which makes the system’s dynamic behavior appear more complex, such as phenomena of limit cycle, chaos, etc. Simulative results show that 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.
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).
31999867
A Novel Classifier with the Immune-Training Based Wavelet Neural Network
After analyzing the classification and training ability of a wavelet neural network (WNN), a novel WNN learning scheme integrating immunity based evolutionary algorithm (IDEA) is proposed, in which, IDEA is an evolutionary algorithm with an embedded immune mechanism. When WNN is used as a classifier, the process of seeking the least mean-square error (LMS) of an optimal problem is equivalent to that of finding the wavelet feature with maximal separability, namely, maximizing its separable division. On the other hand, with the capability of robust learning of its evolutionary process, IDEA is able to eliminate local degenerative phenomenon due to blindfold behaviors of original operators in the existing evolutionary algorithms. In the case of the twin-spiral problem, experimental simulation shows the feasibility of WNN training with the IDEA based learning algorithm.
46259333
PN-based Security Design for Data Storage
For improving the security and privacy of data storage, a novel model based on code division multiple access, called PNCD-DS, is proposed for dealing with the problem of resources sharing in a storage area network (SAN). This technology differs from existing such as spac e or time division multiple access, it has some essential features for ensuring the security and privacy of user's information and/or data. Through decreasing the information intensity by multi times during user's data storage and then recovering it normal when scan, the method makes the interference from other users or damages to the data weaken enormously. Therefore, damage to some extent in some local area of SAN will not destroy the integrity of saved data. On the other hand, because a SAN here can be regarded exclusive for each user with its own special code, the data management could be simplified. Due to coded when data storage and/or backup and decoded when scan, the probability of user's data leakage is also very low even if the disk is physical ly handed by some other users. Key Words Security, confidentiality, database, storage area network, PN sequence
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.
15237813
Research on an Immune Mechanism Based Intelligent Spam Filter
With regard to the problem that the traditional antispam filter is usually incapable of recognizing unknown and mutational characters, a novel anti-spam filter based on immune mechanisms is proposed which employs multiple principles and mechanisms of the artificial immune system, such as the self tolerance, immune recognition, immune learning and immune memory. Through combining the idea of intelligent decision support system with the extension of classical rough set theory, a novel algorithm is provided for finding minimal attributes extraction and updating the attributes base. Simulation shows that this technique is able to reduce and improve the availability of the normal anti-spam systems.
14475706
Efficiency, Fairness and Competitiveness in Nash Bargaining Games
Recently, [8] defined the class of Linear Nash Bargaining Games (LNB) and obtained combinatorial, polynomial time algorithms for several games in this class. [8] also defines two natural subclasses within LNB, UNB and SNB, which contain a number of natural Nash bargaining games. In this paper we define three basic game theoretic properties of Nash bargaining games: price of bargaining, fairness and full competitiveness. We show that for each of these properties, a game in UNB has this property iff it is in SNB.
16050541
Energy-efficiency bounds for deep submicron VLSI systems in the presence of noise
In this paper, we present an algorithm for computing the bounds on energy-efficiency of digital very large scale integration (VLSI) systems in the presence of deep submicron noise. The proposed algorithm is based on a soft-decision channel model of noisy VLSI systems and employs information-theoretic arguments. Bounds on energy-efficiency are computed for multimodule systems, static gates, dynamic circuits and noise-tolerant dynamic circuits in 0.25-/spl mu/m CMOS technology. As the complexity of the proposed algorithm grows linearly with the size of the system, it is suitable for computing the bounds on energy-efficiency for complex VLSI systems. A key result presented is that noise-tolerant dynamic circuits offer the best trade off between energy-efficiency and noise-immunity when compared to static and domino circuits. Furthermore, employing a 16-bit noise-tolerant Manchester adder in a CDMA receiver, we demonstrate a 31.2%-51.4% energy reduction over conventional systems when operating in the presence of noise. In addition, we compute the lower bounds on energy dissipation for this CDMA receiver and show that these lower bounds are 2.8/spl times/ below the actual energy consumed, and that noise-tolerance reduces the gap between the lower bounds and actual energy dissipation by a factor of 1.9/spl times/.