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A Physical Layer algorithm for Estimation of Number of Tags in UHF RFID Anti-Collision Design

Computers, Materials & Continua 2019年第7期61卷 399-408页

作者： Zhong Huang Jian Su Guangjun Wen Wenxian Zheng Chu Chu Yijun Zhang Yibo Zhang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China Nanjing University of Information Science&Technology Nanjing210044China Shenzhen Intellifusion Technologies Co. Shenzhen518000China School of Foreign Languages University of Electronic Science and Technology of ChinaChengdu611731China Department of Mechanical Engineering University of British ColumbiaVancouverCanada

A priori knowledge of the number of tags is crucial for anti-collision protocols in slotted UHF RFID *** number of tags is used to decide optimal frame length in dynamic frame slotted ALOHA(DFSA)and to adjust access probability in random access *** researches estimate the number of tags in MAC layer based on statistics of empty slots,collided slots and successful ***,a collision detection algorithm is employed to determine types of time *** three types are distinguished because of lack of ability to detect the number of tags in single time *** this paper,a physical layer algorithm is proposed to detect the number of tags in a collided *** shift algorithm is utilized,and some properties of backscatter signals are *** results verify the effectiveness of the proposed solution in terms of low estimation error with a high SNR range,outperforming the existing MAC layer approaches.

关键词： UHF RFID anti-collision cluster algorithm

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Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data

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BMC BIOINFORMATICS 2007年第1期8卷 1-11页

作者： Wang, Zailong Yan, Pearlly Potter, Dustin Eng, Charis Huang, Tim H-M Lin, Shili Ohio State Univ Math Biosci Inst Columbus OH 43210 USA Dept Mol Virol Immunol & Med Genet Columbus OH 43210 USA Ohio State Univ Ctr Comprehens Canc Human Canc Genet Program Columbus OH 43210 USA Ohio State Univ Dept Stat Columbus OH 43210 USA Cleveland Clin Fdn Cleveland Clin Genomic Med Inst Cleveland OH 44195 USA

Background: In order to recapitulate tumor progression pathways using epigenetic data, we developed novel clustering and pathway reconstruction algorithms, collectively referred to as heritable clustering. This approach generates a progression model of altered DNA methylation from tumor tissues diagnosed at different developmental stages. The samples act as surrogates for natural progression in breast cancer and allow the algorithm to uncover distinct epigenotypes that describe the molecular events underlying this process. Furthermore, our likelihood-based clustering algorithm has great flexibility, allowing for incomplete epigenotype or clinical phenotype data and also permitting dependencies among variables. Results: Using this heritable clustering approach, we analyzed methylation data obtained from 86 primary breast cancers to recapitulate pathways of breast tumor progression. Detailed annotation and interpretation are provided to the optimal pathway recapitulated. The result confirms the previous observation that aggressive tumors tend to exhibit higher levels of promoter hypermethylation. Conclusion: Our results indicate that the proposed heritable clustering algorithms are a useful tool for stratifying both methylation and clinical variables of breast cancer. The application to the breast tumor data illustrates that this approach can select meaningful progression models which may aid the interpretation of pathways having biological and clinical significance. Furthermore, the framework allows for other types of biological data, such as microarray gene expression or array CGH data, to be integrated.

关键词： cluster algorithm Methylation Profile Methylation Data Progression Model Phenotype Center

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Visualization methods for statistical analysis of microarray clusters

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BMC BIOINFORMATICS 2005年第1期6卷 1-10页

作者： Hibbs, MA Dirksen, NC Li, K Troyanskaya, OG Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Lewis Sigler Inst Integrat Genom Princeton NJ 08544 USA

Background: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue. Results: We present several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive and are dynamically linked together for comprehensive analysis. Further, our approach applies to both protein and gene expression microarrays, and our architecture is scalable for use on both desktop/laptop screens and large-scale display devices. This methodology is implemented in GeneVAnD (Genomic Visual ANalysis of Datasets) and is available at http://***/GeneVAnD. Conclusion: Incorporating relevant statistical information into data visualizations is key for analysis of large biological datasets, particularly because of high levels of noise and the lack of a gold-standard for comparisons. We developed several new visualization techniques and demonstrated their effectiveness for evaluating cluster quality and relationships between clusters.

关键词： cluster algorithm Microarray Data Singular Value Decomposition Visualization Method cluster Quality

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A clustering-based portfolio strategy incorporating momentum effect and market trend prediction

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CHAOS SOLITONS & FRACTALS 2018年 117卷 1-15页

作者： Lu, Ya-Nan Li, Sai-Ping Zhong, Li-Xin Jiang, Xiong-Fei Ren, Fei East China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China East China Univ Sci & Technol Res Ctr Econophys Shanghai 200237 Peoples R China Acad Sinica Inst Phys Taipei 115 Taiwan Zhejiang Univ Finance & Econ Sch Finance Hangzhou 310018 Zhejiang Peoples R China Ningbo Dahongying Univ Coll Informat Engn Ningbo 315175 Zhejiang Peoples R China

The hierarchical clustering algorithm has been proved useful in portfolio investment, which is one of the hottest issues in finance. In our new portfolio strategy, central, peripheral and dispersed portfolios constructed from clusters detected using unweighted and weighted modularity are compared according to their past performances, and the optimal portfolio is used in the investment period only if the market index return predicted by the LR, WMA or BP models is positive to avoid losses when the market drops. Our strategy is tested using the daily data of Chinese A-share market from January 4, 2008 and December 31, 2016, and the average investment return during different moving investment periods and 200 repeated runs is calculated. We find that although incorporating dispersed portfolio into our strategy has no significant effect in raising the investment return, it shows a similar performance as the peripheral portfolio, and the strategy constructed using unweighted modularity generally outperforms its counterpart by using weighted modularity. In addition, the market trend prediction can refine the investment return of our strategy. In brief, the strategy constructed using the BP model and unweighted modularity has the best investment return, which also outperforms the Markowitz portfolio. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Financial network cluster algorithm Portfolio strategy Momentum effect Market trend prediction

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Constructing financial network based on PMFG and threshold method

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2018年 495卷 104-113页

作者： Nie, Chun-Xiao Song, Fu-Tie East China Univ Sci & Technol Sch Business Dept Finance Shanghai 200237 Peoples R China

Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP algorithm to represent PTN in low dimensional Euclidean space. The results show that the community corresponds well to the cluster in the Euclidean space. Further, we studied the dynamics of the community structure and constructed the normalized mutual information (NMI) matrix. Based on the real data in the market, we found that the volatility of the market can lead to dramatic changes in the community structure, and the structure is more stable during the financial crisis. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Planar maximally filtered graph Threshold network Community structure cluster algorithm

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Incremental genetic K-means algorithm and its application in gene expression data analysis

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BMC BIOINFORMATICS 2004年第1期5卷 172-172页

作者： Lu, Y Lu, SY Fotouhi, F Deng, YP Brown, SJ Univ So Mississippi Dept Biol Sci Hattiesburg MS 39406 USA Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Kansas State Univ Div Biol Manhattan KS 66506 USA

Background: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. Results: In this paper, we propose a new clustering algorithm, Incremental Genetic K-means algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at http://***/proj/FGKA/***. Conclusions: Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.

关键词： cluster algorithm Functional Category Mutation Operator Mutation Probability Gene Expression Data Analysis

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The simple-cubic lattice gas with nearest-neighbour exclusion: Ising universality

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PHYSICA A 1996年第1-2期232卷 369-374页

作者： Heringa, JR Blote, HWJ TECH UNIV DELFT LAB TECH NATUURKUNDENL-2600 GA DELFTNETHERLANDS

The lattice gas with nearest neighbour-exclusion on the simple cubic lattice is studied by means of statistically accurate Monte Carlo simulations with an efficient cluster algorithm. Our results for critical exponents are y(h) = 2.47(1) and y(t) = 1.60(2). These results agree well with the three-dimensional Ising universality class. We explain the discrepancy with an earlier study. The critical activity is z(c) = 1.0559 (1). The size distribution of the clusters indicates that the percolation threshold of the cluster formation process of the Monte Carlo algorithm coincides with the critical point.

关键词： lattice gas universality cluster algorithm

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Model order selection for bio-molecular data clustering

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BMC BIOINFORMATICS 2007年第s2期8卷 1-13页

作者： Bertoni, Alberto Valentini, Giorgio Univ Milan DSI Milan Italy

Background: cluster analysis has been widely applied for investigating structure in bio-molecular data. A drawback of most clustering algorithms is that they cannot automatically detect the "natural" number of clusters underlying the data, and in many cases we have no enough "a priori" biological knowledge to evaluate both the number of clusters as well as their validity. Recently several methods based on the concept of stability have been proposed to estimate the "optimal" number of clusters, but despite their successful application to the analysis of complex bio-molecular data, the assessment of the statistical significance of the discovered clustering solutions and the detection of multiple structures simultaneously present in high-dimensional bio-molecular data are still major problems. Results: We propose a stability method based on randomized maps that exploits the high-dimensionality and relatively low cardinality that characterize bio-molecular data, by selecting subsets of randomized linear combinations of the input variables, and by using stability indices based on the overall distribution of similarity measures between multiple pairs of clusterings performed on the randomly projected data. A chi(2)-based statistical test is proposed to assess the significance of the clustering solutions and to detect significant and if possible multi-level structures simultaneously present in the data (e.g. hierarchical structures). Conclusion: The experimental results show that our model order selection methods are competitive with other state-of-the-art stability based algorithms and are able to detect multiple levels of structure underlying both synthetic and gene expression data.

关键词： cluster algorithm Chronic Lymphocytic Leukemia Follicular Lymphoma Stability Index Random Projection

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Reusable components for partitioning clustering algorithms

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ARTIFICIAL INTELLIGENCE REVIEW 2009年第1-4期32卷 59-75页

作者： Delibasic, Boris Kirchner, Kathrin Ruhland, Johannes Jovanovic, Milos Vukicevic, Milan Univ Belgrade Fac Org Sci Belgrade Serbia Univ Jena Fac Econ & Business Adm Jena Germany

clustering algorithms are well-established and widely used for solving data-mining tasks. Every clustering algorithm is composed of several solutions for specific sub-problems in the clustering process. These solutions are linked together in a clustering algorithm, and they define the process and the structure of the algorithm. Frequently, many of these solutions occur in more than one clustering algorithm. Mostly, new clustering algorithms include frequently occurring solutions to typical sub-problems from clustering, as well as from other machine-learning algorithms. The problem is that these solutions are usually integrated in their algorithms, and that original algorithms are not designed to share solutions to sub-problems outside the original algorithm easily. We propose a way of designing cluster algorithms and to improve existing ones, based on reusable components. Reusable components are well-documented, frequently occurring solutions to specific sub-problems in a specific area. Thus we identify reusable components, first, as solutions to characteristic sub-problems in partitioning cluster algorithms, and, further, identify a generic structure for the design of partitioning cluster algorithms. We analyze some partitioning algorithms (K-means, X-means, MPCK-means, and Kohonen SOM), and identify reusable components in them. We give examples of how new cluster algorithms can be designed based on them.

关键词： cluster algorithm Partitioning clustering Reusable component Generic Kohonen SOM K-means X-means MPCK-means

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Perturbation theory predictions and Monte Carlo simulations for the 2D O(n) non-linear sigma-models

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NUCLEAR PHYSICS B 1997年第1-3期500卷 513-543页

作者： Alles, B Buonanno, A Cella, G IST NAZL FIS NUCL I-56126 PISA ITALY

By using the results of a high-statistics (O(10(7)) measurements) Monte Carlo simulation we test several predictions of perturbation theory on the O(n) non-linear sigma-model in 2 dimensions. We study the O(3) and O(8) models on large enough lattices to have a good control on finite-size effects. The magnetic susceptibility and three different definitions of the correlation length are measured. We check our results with large-n expansions as well as with standard formulae for asymptotic freedom up to 4 loops in the standard and effective schemes. For this purpose the weak coupling expansions of the energy up to 4 loops for the standard action and up to 3 loops for the Symanzik action are calculated. For the O(3) model we have used two different effective schemes and checked that they lead to compatible results. A great improvement in the results is obtained by using the effective scheme based on the energy at 3 and 4 loops. We find that the O(8) model follows very nicely (within few per mille) the perturbative predictions. For the O(3) model an acceptable agreement (within few per cent) is found. (C) 1997 Elsevier Science B.V.

关键词： Monte Carlo Symanzik action cluster algorithm higher order in perturbation theory perturbation and 1/n expansion

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