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
14541123
An evolutionary algorithm with population immunity and its application on autonomous robot control
The natural immune system is an important resource full of inspirations for the theory researchers and the engineering developers to design some powerful information processing methods aiming at difficult problems. Based on this consideration, a novel optimal-searching algorithm, the immune mechanism based evolutionary algorithm - IMEA, is proposed for the purpose of finding an optimal/quasi-optimal solution in a multi-dimensional space. Different from the ordinary evolutionary algorithms, on one hand, due to the long-term memory, IMEA has a better capability of learning from its experience, and on the other hand, with the clonal selection, it is able to keep from the premature convergence of population. With the simulation on autonomous robot control, it is proved that IMEA is good at the task of adaptive adjustment (offline), and it can improve the robot's capability of reinforcement learning, so as to make itself able to sense its surrounding dynamic environment.
7144886
Khatri-Rao Unitary Space-Time Modulation
Based on the Khatri-Rao matrix product, we propose a novel unitary space-time modulation design called KR-USTM in this paper. Different from existing USTM schemes, such as the systematic approach and space-time frequency-shift keying (ST-FSK), KR-USTM does not require any computer search and can be applied to any number of transmit antennas. Moreover, the special structure of KR-USTM also makes it a high-rate scheme and achieve full antenna diversity as well as lower decoding complexity. Simulation results show that the proposed KR-USTM constellation achieves error performance comparable to existing USTM designs at low rates, while it outperforms them at high rates.
17142699
Feature Selection with Kernel Class Separability
Classification can often benefit from efficient feature selection. However, the presence of linearly nonseparable data, quick response requirement, small sample problem and noisy features makes the feature selection quite challenging. In this work, a class separability criterion is developed in a high-dimensional kernel space, and feature selection is performed by the maximization of this criterion. To make this feature selection approach work, the issues of automatic kernel parameter tuning, the numerical stability, and the regularization for multi-parameter optimization are addressed. Theoretical analysis uncovers the relationship of this criterion to the radius-margin bound of the SVMs, the KFDA, and the kernel alignment criterion, providing more insight on using this criterion for feature selection. This criterion is applied to a variety of selection modes with different search strategies. Extensive experimental study demonstrates its efficiency in delivering fast and robust feature selection.
752937
In cloud, do MTC or HTC service providers benefit from the economies of scale?
Cloud computing, which is advocated as an economic platform for daily computing, has become a hot topic for both industrial and academic communities in the last couple of years. The basic idea behind cloud computing is that resource providers, which own the cloud platform, offer elastic resources to end users. In this paper, we intend to answer one key question to the success of cloud computing: in cloud, do many task computing (MTC) or high throughput computing (HTC) service providers, which offer the corresponding computing service to end users, benefit from the economies of scale? To the best of our knowledge, no previous work designs and implements the enabling system to consolidate MTC and HTC workloads on the cloud platform and no one answers the above question. Our research contributions are threefold: first, we propose an innovative usage model, called dynamic service provision (DSP) model, for MTC or HTC service providers. In the DSP model, the resource provider provides the service of creating and managing runtime environments for MTC or HTC service providers, and consolidates heterogeneous MTC or HTC workloads on the cloud platform; second, based on the DSP model, we design and implement Dawningcloud, which provides automatic management for heterogeneous workloads; third, a comprehensive evaluation of Dawningcloud has been performed in an emulatation experiment. We found that for typical workloads, in comparison with the previous two cloud solutions, Dawningcloud saves the resource consumption maximally by 46.4% (HTC) and 74.9% (MTC) for the service providers, and saves the total resource consumption maximally by 29.7% for the resource provider. At the same time, comparing with the traditional solution that provides MTC or HTC services with dedicated systems, Dawningcloud is more cost-effective. To this end, we conclude that for typical MTC and HTC workloads, on the cloud platform, MTC and HTC service providers and the resource service provider can benefit from the economies of scale.
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.
30275519
Traffic Intelligent Optimization and Local Traffic-Flow Control Inside Shanghai-EXPO-Area