6623361
A Hybrid Control Architecture for Connection Management in Translucent WDM Networks
Translucent WDM networks use a set of sparsely but strategically placed 2R or/and 3R regenerators to overcome physical impairments and wavelength collision introduced by fully transparent networks. In this paper, we concentrate on the study of control architectures and management approaches for connection establishment in translucent networks. A hybrid OCP (optical control plane) has been proposed, which needs the extensions of both routing and signaling protocol. In hybrid OCP, we combine the best features of routing based information updating and signaling based data collection and path evaluation, in order to achieve better performance. Simulations are conducted to compare hybrid OCP with two existing control architectures: signaling based OCP and routing based OCP. Numerical results show that hybrid OCP keeps a lower blocking probability than the other approaches, and also minimize the stability and scalability problems under various traffic conditions.
30833865
Noise-Aware Wavelength Assignment for Wavelength Switched Optical Networks
In transparent WSON (wavelength switched optical networks), signals are switched optically and propagate thousands of kilometers without electrical regeneration. Over such distances, physical impairments, such as crosstalk, ASE noise and so on, can accumulate along the path and lead to signal quality degradation. If the admission of a lightpath will either cause its BER to be too high, or sufficiently degrade the performance of the already established lightpaths, it must be blocked. Most of recent research only consider the first case, but ignore the second case, which will lead to service interruption. In this paper, a new noise-aware wavelength assignment scheme called NAWA has been proposed, which use IIES (Impairments impact Evaluation Scheme) to solve both problems mentioned above in a distributed way. Simulations have been conducted and numerical results show that: Compared with normal impairment-aware solutions, NAWA can eliminate the occurrence of service interruption, and achieve better performance in total blocking.
11915654
Hybrid Neural Network Architecture for On-Line Learning
Approaches to machine intelligence based on brain models use neural networks for generalization but they do so as signal processing black boxes. In reality, the brain consists of many modules that operate in parallel at different levels. In this paper we propose a more realistic biologically inspired hybrid neural network architecture that uses two kinds of neural networks simultaneously to consider short-term and long-term characteristics of the signal. The first of these networks quickly adapts to new modes of operation whereas the second one provides more accurate learning within a specific mode. We call these networks the surfacing and deep learning agents and show that this hybrid architecture performs complementary functions that improve the overall learning. The performance of the hybrid architecture has been compared with that of back-propagation perceptrons and the CC and FC networks for chaotic time-series prediction, the CATS benchmark test, and smooth function approximation. It is shown that the proposed architecture provides a superior performance based on the RMS error criterion.
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.
29608911
Password Recovery on Challenge and Response: Impossible Differential Attack on Hash Function
We propose practical password recovery attacks against two challenge-response authentication protocols using MD4. When a response is computed as MD4(Password||Challenge), passwords up to 12 characters are practically recovered. To recover up to 8 characters, we need 16 times the amount of eavesdropping and 16 times the number of queries, and the off-line complexity is less than 235 MD4 computations. To recover up to 12 characters, we need 210 times the amount of eavesdropping and 210 times the number of queries, and the off-line complexity is less than 240 MD4 computations.When a response is computed as MD4(Password||Challenge||Password), passwords up to 8 characters are practically recovered by 28 times the amount of eavesdropping and 28 times the number of queries, and the off-line complexity is less than 239 MD4 computations. Our approach is similar to the "Impossible differential attack", which was originally proposed for recovering the block cipher key. Good impossible differentials for hash functions are achieved by using local collision. This indicates that the presence of one practical local collision can damage the security of protocols.
15759515
An Effective Evidence Theory Based K-Nearest Neighbor (KNN) Classification
In this paper, we study various K nearest neighbor (KNN) algorithms and present a new KNN algorithm based on evidence theory. We introduce global frequency estimation of prior probability (GE) and local frequency estimation of prior probability (LE). A GE for a class is the prior probability of the class across the whole training data space based on frequency estimation; on the other hand, a LE for a class in a particular neighborhood is the prior probability of the class in this neighborhood space based on frequency estimation. By considering the difference between the GE and the LE of each class, we present a solution to the imbalanced data problem in some degree without doing re-sampling. We compare our algorithm with other KNN algorithms using two benchmark datasets. Results show that our KNN algorithm outperforms other KNN algorithms, including basic evidence based KNN.
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).