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Randomly Penalized Symbol Flipping Decoding of Non-Binary LDPC Codes

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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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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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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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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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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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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Identification of Key Nodes in Complex Networks Based on Network Representation Learning

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IEEE ACCESS 2023年 11卷 128175-128186页

作者： Zhang, Heping Zhang, Sicong Xie, Xiaoyao Zhang, Taihua Yu, Guojun Guizhou Normal Univ Key Lab Informat & Comp Sci Guizhou Prov Guiyang 550001 Peoples R China Guizhou Normal Univ Sch Mech & Elect Engn Guiyang 550025 Peoples R China

Recently, some research has utilized machine learning methods to identify critical nodes in complex networks. However, existing approaches often lack a comprehensive consideration of network structural features during node feature extraction. Benefiting from the powerful feature extraction capability of network representation learning methods, a simple and effective algorithm for identifying key nodes in complex networks, termed Network Representation Learning and Key Node Identification (NRL_KNI), is proposed. The NRL_KNI algorithm utilizes network embedding techniques for learning node feature representations, followed by clustering and the utilization of quota-based limited sampling to obtain sampled nodes. Subsequently, these sampled nodes are employed to train a regression model for predicting the diffusion capability of unsampled nodes. To rank node influences, a Local Structure Influence Score (LSIS) based on the local structure is introduced to evaluate nodes' final impact. Experimental results on eight real-world datasets demonstrate that the NRL_KNI algorithm generally outperforms traditional centrality methods and network representation learning-based methods in terms of the Jaccard similarity coefficient and Kendall's Tau correlation coefficient evaluation metrics.

关键词： Representation learning Complex networks Training Predictive models Machine learning algorithms Clustering algorithms partitioning algorithms Regression analysis key node identification network representation learning regression model

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Energy-Aware Partitioned Scheduling of Imprecise Mixed-Criticality Systems

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2023年第11期42卷 3733-3742页

作者： Zhang, Yi-Wen Chen, Rong-Kun Gu, Zonghua Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Umea Univ Dept Appl Phys & Elect S-90187 Umea Sweden

We consider partitioned scheduling of an imprecise mixed-criticality (IMC) taskset on a uniform multiprocessor platform, with the earliest deadline first-virtual deadline (EDF-VD) as the uniprocessor task scheduling algorithm, and address the optimization problem of finding a feasible task-to-processor assignment and low-criticality (LO) mode processor speed with the objective of minimizing the system's average energy consumption in LO mode. We propose a task-to-processor assignment algorithm criticality-unaware worst-fit decreasing (CU-WFD) algorithm, which allocates tasks with the worst-fit decreasing (WFD) heuristic method based on utilization values at their respective criticality levels. We determine the energy-efficient speed for each processor based on EDF-VD scheduling, and present our algorithm energy-efficient partitioned scheduling for imprecise mixed-criticality (EEPSIMC) with the CU-WFD heuristic algorithm to minimize system energy consumption. The experimental results show that our proposed algorithm has good performance in terms both schedulability ratio and normalized energy consumption compared to seven comparison baselines.

关键词： Task analysis Job shop scheduling Heuristic algorithms Energy consumption partitioning algorithms Switches Energy efficiency Earliest deadline first-virtual deadline (EDF-VD) energy-aware scheduling imprecise mixed-criticality (IMC) multiprocessor partitioned scheduling

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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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Efficient Phase-Only Dual- and Multi-Beam Pattern Synthesis With Accurate Beam Direction and Power Control Employing Partitioned Iterative FFT

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第4期71卷 3719-3724页

作者： Li, Ming Liu, Yanhui Bao, Zhiming Chen, Liyang Hu, Jun Guo, Y. Jay Univ Technol Sydney Global Big Data Technol Ctr Sydney NSW 2007 Australia Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China Xiamen Univ Dept Elect Sci Fujian 361005 Peoples R China Xiamen Univ Inst Electromagnet & Acoust Fujian 361005 Peoples R China

In this communication, a novel phase-only method based on a partitioned iterative Fourier transform (PIFT) is presented to efficiently synthesize dual- and multibeam patterns for uniformly spaced linear and planar arrays. To achieve accurate mainlobe peak directions, the PIFT integrates a partitioned beam calibration strategy into the IFT to successively calibrate the beams by partitioning the multibeam pattern into single-beam patterns and calibrating them individually. Besides, the peak powers of the multiple beams are precisely controlled through proper mainlobe corrections. Several numerical examples of synthesizing dual- and multibeam patterns with accurately controlled peak directions and beam powers as well as reduced sidelobe levels (SLLs) are presented to demonstrate the effectiveness and efficiency of the proposed method. Synthesized results are validated with full-wave simulation. Comparisons with other phase-only methods are also presented to show the superiority of the proposed method.

关键词： Calibration Fourier transforms Iterative methods Indexes Power control partitioning algorithms Genetic algorithms Beam calibration beam scanning multibeam pattern synthesis partitioned iterative Fourier transform (PIFT)

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