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Survivable Routing, Spectrum, Core and Band Assignment in Multi-Band Space Division Multiplexing Elastic Optical Networks

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2022年第11期40卷 3442-3455页

作者： Luo, Zhihuan Yin, Shan Zhao, Ligang Wang, Zhenhao Zhang, Wenchao Jiang, Liyou Huang, Shanguo Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

The rapid growth in Internet traffic has contributed to the need to expand network transmission capacity. Multi-band (MB) using existing standard single-mode fibers (SMFs) in the free band is an ideal way to increase the capacity of the fiber. However, the introduction of MB has transferred space division multiplexing - elastic optical networks (SDM-EONs) to multi band-SDM-EONs (MB-SDM-EONs), and changed routing, spectrum and core assignment (RSCA) problem to the routing, spectrum, core and band assignment (RSCBA) problem. At the same time, it introduces a new issue named stimulated Raman scattering (SRS), which affects transmission quality. Although an expanded fiber can carry more services, a large number of services will be interrupted if a link fails, which will bring huge losses. Therefore, it is necessary to propose a reasonable protection strategy to ensure reliable transmission quality. In this paper, the survivable RSCBA problem in MB-SDM-EONs is studied, which copes with link failures and considers SRS in signal-to-noise ratio (SNR) analysis. In order to solve the problem, we propose a band partition protection scheme: working and protection resources are allocated in different frequency bands for increasing SNR in a fault-free network. We formulate this problem and band partition protection scheme as an integer linear programming (ILP) model, which takes into account inter-core crosstalk and SNR analysis. Furthermore, a heuristic algorithm based on genetic algorithm is proposed for large-scale networks. We evaluate the performance of the proposed approaches through experimental simulation. The results demonstrate that both proposed approaches are effective in finding the optimal solutions.

关键词： Optical fiber networks Signal to noise ratio Resource management C-band Routing partitioning algorithms Optical scattering Genetic algorithm (GA) multi band (MB) routing- spectrum- core-band assignment (RSCBA) signal-to-noise ratio (SNR) space division multiplexing (SDM) survivability

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Cluster-Volume-Based Merging Approach for Incrementally Evolving Fuzzy Gaussian Clustering-eGAUSS

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第9期28卷 2222-2231页

作者： Skrjanc, Igor Univ Ljubljana Fac Elect Engn Ljubljana 1000 Slovenia

In this article, a new dynamic merging approach for incrementally evolving clustering is presented. This means that the cluster partitions are incrementally learned on-line from streams of data. The criterion of merging is based on the comparison between the sum of volumes of two clusters that fulfill the criteria of a minimal number of samples in the cluster and the expected volume of the newly generated merged cluster. The newly generated merged cluster is conducted by using the weighted averaging of cluster centers and the calculation of the joint covariance matrix from the covariance matrices of the clusters. It has been shown that the proposed new evolving algorithm eGAUSS+ together with the new merging concept is very easy to implement, can work on higher-dimensional data sets, can perform all necessary computation on-line, and can produce reliable clusters.

关键词： Merging Covariance matrices Clustering algorithms Data models partitioning algorithms Eigenvalues and eigenfunctions Computational modeling Data stream dynamic merging evolving clustering evolving cluster models incremental learning volume of hyperellipsoids

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Randomly Penalized Symbol Flipping Decoding of Non-Binary LDPC Codes

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第1期70卷 639-654页

作者： Zhao, Zhanzhan Jiao, Xiaopeng Mu, Jianjun He, Yu-Cheng Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Huaqiao Univ Xiamen Key Lab Mobile Multimedia Commun Xiamen 361021 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Symbol flipping decoding (SFD) of non-binary low-density parity-check (NB-LDPC) codes has lower implementation complexity at a cost of performance degradation, when compared with other belief propagation decoding algorithms. This paper proposes modified SFD algorithms based on prediction (SFDP) for NB-LDPC codes by introducing random penalty items into the flipping metrics. Two types of noise with uniform and Gaussian distributions are considered. A probabilistic analysis shows theoretically that the randomly penalized SFDP algorithms can be advantageous in the correct flipping probability over their original versions. Simulation results show that the proposed penalizing methods exhibit significant performance gains for both regular and irregular NB-LDPC codes with different rates and alphabet sizes in both the additive white Gaussian noise (AWGN) channel and the binary symmetric channel (BSC).

关键词： Decoding Measurement Prediction algorithms partitioning algorithms Reliability Iterative decoding Probabilistic logic Gaussian noise non -binary LDPC codes random penalty symbol flipping decoding uniform noise

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Toward Occlusion Handling in Visual Tracking via Probabilistic Finite State Machines

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第4期50卷 1726-1738页

作者： Liu, Chenghuan Huynh, Du Q. Reynolds, Mark Univ Western Australia Sch Comp Sci & Software Engn Perth WA 6009 Australia

Visual tracking has been an active research area in computer vision for decades. However, the performance of existing techniques is still challenged by various factors, such as occlusion and change in appearance of the target. In this paper, we propose a novel framework based on correlation filtering and probabilistic finite state machines (FSMs) to handle occlusion. In our tracking framework, the target is partitioned into several parts whose occlusion states are automatically detected. A set of states for the target is defined in terms of the combination of the parts' occlusion states. The probabilistic FSMs are then used to model the target's state transitions so as to reduce the effect of noise in the output response maps of correlation filters. Our target model's update strategy is adaptable online depending on the estimated state of the target. Extensive experiments have been performed on several public benchmarks and the proposed algorithm achieves competitive results against state-of-the-art techniques.

关键词： Target tracking Correlation Visualization partitioning algorithms Probabilistic logic Adaptation models Support vector machines Correlation filters occlusion handling probabilistic finite state machines visual tracking

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SPAID - AN ARCHITECTURAL SYNTHESIS TOOL FOR DSP CUSTOM APPLICATIONS

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 1989年第2期24卷 426-435页

作者： HAROUN, BS ELMASRY, MI VLSI Group Electrical Engineering Department University of Waterloo Waterloo ONT Canada

SPAID, a design tool that maps digital signal processing (DSP) algorithms into a multibus VLSI architecture is discussed. Algorithm structure, design style of functional units (FU), and parallelism of the architecture are all explored in the design space. SPAID supports pipelined and multicycle functional units. The synthesized processor is a self-timed element externally, while it is internally synchronous. SPAID explores a wider space, and produces better solutions compared with existing synthesis systems on an elliptic filter benchmark design example. A multibus multifunctional unit processor matched to the implemented algorithm is synthesized using SPAID.< >

关键词： Digital signal processing Signal processing algorithms Signal synthesis Throughput Algorithm design and analysis Integrated circuit synthesis Very large scale integration Parallel processing partitioning algorithms Signal design

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GRAPH MONOMORPHISM algorithms

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1980年第4期10卷 189-196页

作者： GHAHRAMAN, DE WONG, AKC AU, T UNIV WATERLOO WATERLOO N2L 3G1 ONTARIO CANADA CARNEGIE MELLON UNIV PITTSBURGH PA 15213 USA

A characterization of graph morphism is presented and utilized toward solving the graph monomorphism problem. The proposed model is used to formulate necessary conditions for the existence of monomorphisms and to develop decision-tree search algorithms for that problem. Examples are provided to elucidate the principal features of the proposed algorithms.

关键词： Gold Information retrieval Decision trees Chemicals Dictionaries Image processing Polynomials Sufficient conditions partitioning algorithms Councils

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Performance-Driven Clustering of Asynchronous Circuits

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2014年第2期33卷 197-209页

作者： Dimou, Georgios D. Beerel, Peter A. Lines, Andrew M. Intel Corp Commun & Storage Infrastruct Grp Calabasas CA 91302 USA Univ So Calif Ming Hsieh Dept Elect Engn Los Angeles CA 90089 USA

This paper proposes the method of generating asynchronous circuits from hardware description language specifications by clustering the synthesized gates into asynchronous pipeline stages while preserving liveness, meeting throughput and latency constraints, and minimizing area. This method provides a form of automatic pipelining in which the throughput of the overall design is not limited to the clock frequency or the level of pipelining in the original Register-Transfer Level (RTL) specification. The method is design-style agnostic and is thus applicable to many asynchronous design styles.

关键词： Asynchronous circuits circuit synthesis clustering algorithms partitioning algorithms

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Rate Distortion via Deep Learning

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第1期68卷 456-465页

作者： Li, Qing Chen, Yang Western Digital Res Milpitas CA 95035 USA Univ Michigan Dept Stat Ann Arbor MI 48109 USA Univ Michigan Michigan Inst Data Sci Ann Arbor MI 48109 USA

We explore the connections between rate distortion/lossy source coding and deep learning models, the Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs). We show that rate distortion is a function of the RBM log partition function and that RBM/DBN can be used to learn the rate distortion approaching posterior as in the Blahut-Arimoto algorithm. We propose an algorithm for lossy compressing of binary sources. The algorithm consists of two stages, a training stage that learns the posterior with training data of the same class as the source, and a compression/reproduction stage that is comprised of a lossless compression and a lossless reproduction. Theoretical results show that the proposed algorithm achieves the optimum rate distortion function for stationary ergodic sources asymptotically. Numerical experiments show that the proposed algorithm outperforms the reported best results.

关键词： Rate-distortion Deep learning Distortion Source coding partitioning algorithms Probability distribution Monte Carlo methods Rate distortion lossy source coding partition function restricted Boltzmann machine (RBM) deep belief network (DBN) Blahut-Arimoto (BA) algorithm block Gibbs sampling annealed importance sampling

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TDEC: Evidential Clustering Based on Transfer Learning and Deep Autoencoder

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第10期32卷 5585-5597页

作者： Jiao, Lianmeng Wang, Feng Liu, Zhun-Ga Pan, Quan Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Evidential clustering is a promising clustering framework using Dempster-Shafer belief function theory to model uncertain data. However, evidential clustering needs to estimate more parameters compared with other clustering algorithms, and thus the clustering performance of evidential clustering will be greatly affected if data is insufficient or contaminated. In addition, the existing evidential clustering algorithms can not well deal with high-dimensional data such as texts and images. To solve the above problems, an evidential clustering algorithm based on transfer learning and deep autoencoder (TDEC) is proposed. The TDEC utilizes deep autoencoder to obtain evidential clustering-friendly representations of the original data, and applies the maximum mean discrepancy (MMD) constraint between the source network and the target network, so that the network can learn domain-invariant features. The algorithm jointly trains the deep evidential clustering networks in the source domain and the target domain, and realizes the deep feature representations of high-dimensional data in the target domain for evidential clustering by minimizing reconstruction loss, entropy-based evidential clustering loss, MMD loss and the regular penalty term of the network parameters. In addition, an iterative optimization method to solve the TDEC objective function is proposed. Extensive experiments were conducted to evaluate the clustering performance of the proposed TDEC algorithm compared with the existing shallow transfer clustering algorithms and deep clustering algorithms. For both image and text clustering tasks, the proposed TDEC achieved approximately 5% performance improvement over the comparison algorithms on average. In addition, the practical application value of the proposed TDEC algorithm was demonstrated in unsupervised remote sensing image scene classification.

关键词： Clustering algorithms partitioning algorithms Transfer learning Evidence theory Feature extraction Fuzzy systems Approximation algorithms Deep autoencoder evidential clustering unsupervised transfer learning

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Enhancing Density Peak Clustering via Density Normalization

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第4期16卷 2477-2485页

作者： Hou, Jian Zhang, Aihua Bohai Univ Coll Engn Jinzhou 121013 Peoples R China

Clustering is able to find out implicit data distribution and is especially useful in data driven machine learning. Density based clustering has an attractive property of detecting clusters of arbitrary structures. The density peak algorithm makes use of two assumptions to detect cluster centers and then groups the other data. This approach is simple to implement and shown to be promising in many experiments. However, we find its clustering results are dependent on density kernel types and kernel parameters, and density difference across clusters also influences the results significantly. In this paper, we make a detailed study of the density peak algorithm and attribute the problems to the local density criterion in detecting cluster centers. We then use density normalization to relieve the influence of the problems, and present a density kernel to further improve clustering results. We conduct experiments with different types of datasets to demonstrate the performance of our approach.

关键词： Clustering algorithms Kernel Informatics partitioning algorithms Iris Machine learning Fault diagnosis Clustering data driven density normalization fault diagnosis

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