IET Signal Process.最新文献_第8页

Reconstruction algorithm using exact tree projection for tree-structured compressive sensing 基于精确树投影的树结构压缩感知重构算法

IET Signal Process. Pub Date : 2016-07-01 DOI: 10.1049/iet-spr.2015.0351

Maojiao Wang, Xiaohong Wu, Wenhui Jing, Xiaohai He

引用次数: 7

Novel N-scan GM-PHD-based approach for multi-target tracking 基于n扫描gm - phd的多目标跟踪新方法

IET Signal Process. Pub Date : 2016-07-01 DOI: 10.1049/iet-spr.2014.0480

Mahdi Yazdian Dehkordi, Z. Azimifar

{"title":"Novel N-scan GM-PHD-based approach for multi-target tracking","authors":"Mahdi Yazdian Dehkordi, Z. Azimifar","doi":"10.1049/iet-spr.2014.0480","DOIUrl":"https://doi.org/10.1049/iet-spr.2014.0480","url":null,"abstract":"The GM-PHD-based filter has been proposed as an alternative of the PHD filter to estimate the first-order moment of the multi-target posterior density. The GM-PHD filter utilises a weighted summation of Gaussian components to estimate the target states. This filter and its recent variants perform state extraction of the targets based on the target weights. However, due to different uncertainties such as noisy observation, miss-detection, clutter or occlusion, the weight of a target is decreased and the estimation of the target is lost in some steps. In this study, the authors develop a simple and effective N-scan approach which employs the weight history of targets to improve the performance of the GM-PHD-based methods. They propose to assign a label, a weight history and a binary confidence indicator to each Gaussian component and propagate them in time. Then, they explain a novel N-scan state extraction algorithm to estimate the target states based on their histories in the N last steps. To study the efficiency of the proposed N-scan approach, it is applied on the GM-PHD filter as well as its several recent variants. The experimental results provided for various uncertainties show the effectiveness of the method.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129781417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Bayesian compressive sensing for primary user detection 基于贝叶斯压缩感知的主用户检测

IET Signal Process. Pub Date : 2016-07-01 DOI: 10.1049/iet-spr.2015.0529

M. Başaran, Serhat Erküçük, H. A. Çırpan

{"title":"Bayesian compressive sensing for primary user detection","authors":"M. Başaran, Serhat Erküçük, H. A. Çırpan","doi":"10.1049/iet-spr.2015.0529","DOIUrl":"https://doi.org/10.1049/iet-spr.2015.0529","url":null,"abstract":"In compressive sensing (CS)-based spectrum sensing literature, most studies consider accurate reconstruction of the primary user signal rather than detection of the signal. Furthermore, possible absence of the signal is not taken into account while evaluating the spectrum sensing performance. In this study, Bayesian CS is studied in detail for primary user detection. In addition to assessing the signal reconstruction performance and comparing it with the conventional basis pursuit approach and the corresponding lower bounds, signal detection performance is also considered both analytically and through simulation studies. In the absence of a primary user signal, the trade-off between probabilities of detection and false alarm is studied as it is equally important to determine the performance of a CS approach when there is no active primary user. To reduce the computation time and yet achieve a similar detection performance, finally the effect of number of iterations is studied for various systems parameters including signal-to-noise-ratio, compression ratio, mean value of accumulated energy and threshold values. The presented framework in this study is important in the overall implementation of CS-based approaches for primary user detection in practical realisations such as LTE downlink OFDMA as it considers both signal reconstruction and detection.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"23 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132693008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Iterative sequential Monte Carlo algorithm for motif discovery 基序发现的迭代序贯蒙特卡罗算法

IET Signal Process. Pub Date : 2016-07-01 DOI: 10.1049/iet-spr.2014.0356

M. Bataineh, Z. Al-qudah, A. Al-Zaben

{"title":"Iterative sequential Monte Carlo algorithm for motif discovery","authors":"M. Bataineh, Z. Al-qudah, A. Al-Zaben","doi":"10.1049/iet-spr.2014.0356","DOIUrl":"https://doi.org/10.1049/iet-spr.2014.0356","url":null,"abstract":"The discovery of motifs in transcription factor binding sites is important in the transcription process, and is crucial for understanding the gene regulatory relationship and evolution history. Identifying weak motifs and reducing the effect of local optima, error propagation and computational complexity are still important, but challenging tasks for motif discovery. This study proposes an iterative sequential Monte Carlo (ISMC) motif discovery algorithm based on the position weight matrix and the Gibbs sampling model to locate conserved motifs in a given set of nucleotide sequences. Three sub-algorithms have been proposed. Algorithm 1 (see Fig. 1) deals with the case of one motif instance of fixed length in each nucleotide sequence. Furthermore, the proposed ISMC algorithm is extended to deal with more complex situations including unique motif of unknown length in Algorithm 2, unique motif with unknown abundance in Algorithm 3 (see Fig. 2) and multiple motifs. Experimental results over both synthetic and real datasets show that the proposed ISMC algorithm outperforms five other widely used motif discovery algorithms in terms of nucleotide and site-level sensitivity, nucleotide and site-level positive prediction value, nucleotide-level performance coefficient, nucleotide-level correlation coefficient and site-level average site performance.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124869598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Criterion for non-existence of limit cycles in 2D state-space digital filters described by the Fornasini-Marchesini second model with finite wordlength non-linearities 有限字长非线性Fornasini-Marchesini第二模型描述的二维状态空间数字滤波器极限环不存在的判据

IET Signal Process. Pub Date : 2016-07-01 DOI: 10.1049/iet-spr.2014.0099

Neha Agarwal, H. Kar

引用次数: 6

Distributed binary majority voting via exponential distribution 通过指数分布的分布式二元多数投票

IET Signal Process. Pub Date : 2016-07-01 DOI: 10.1049/iet-spr.2015.0021

Saber Salehkaleybar, S. J. Golestani

引用次数: 2

Improved semi-blind spectrum sensing for cognitive radio with locally optimum detection 基于局部最优检测的认知无线电改进半盲频谱感知

IET Signal Process. Pub Date : 2016-07-01 DOI: 10.1049/iet-spr.2015.0006

M. Cardenas-Juarez, M. Ghogho, Ulises Pineda Rico, E. Stevens-Navarro

{"title":"Improved semi-blind spectrum sensing for cognitive radio with locally optimum detection","authors":"M. Cardenas-Juarez, M. Ghogho, Ulises Pineda Rico, E. Stevens-Navarro","doi":"10.1049/iet-spr.2015.0006","DOIUrl":"https://doi.org/10.1049/iet-spr.2015.0006","url":null,"abstract":"In cognitive radio, there might be some information about primary users’ signals available at secondary users’ receivers since communications systems usually employ training signals for channel estimation and synchronization purposes. This training information can be exploited along with data symbols to perform semi-blind detection of primary users’ signals. In the literature, it is considered that the locally optimal semi-blind detection metric is the linear combination of the energy detector (ED) and the matched filter, i.e. the hybrid detector. Locally optimum detection (LOD), known to be optimum in the low signal-to-noise ratio, is proposed here in the design of a weighted semi-blind locally optimum detector (WSBLOD) by focusing on linear modulation in presence of an unknown phase shift and additive white Gaussian noise. By using LOD, it is shown that for binary phase shift keying-modulated signals, the semi-blind detector test statistic consists not only in combining linearly the matched filter and the ED but also the pseudo-energy of the received signal. Then, the designed semi-blind detector is improved by optimising the weights of the matched filter, energy and pseudo-energy in the test statistic, which maximises the probability of detection. Simulation results show that the proposed WSBLOD outperforms the hybrid detector.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116826536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Discrete blind reconstruction method for multi-coset sampling 多共集采样的离散盲重建方法

IET Signal Process. Pub Date : 2016-07-01 DOI: 10.1049/iet-spr.2015.0391

N. Dong, Jianxin Wang, Hai Yu

引用次数: 2

Evolutionary clustering framework based on distance matrix for arbitrary-shaped data sets 基于距离矩阵的任意形状数据集进化聚类框架

IET Signal Process. Pub Date : 2016-07-01 DOI: 10.1049/iet-spr.2015.0335

Cong Liu, Chunxue Wu, Linhua Jiang

{"title":"Evolutionary clustering framework based on distance matrix for arbitrary-shaped data sets","authors":"Cong Liu, Chunxue Wu, Linhua Jiang","doi":"10.1049/iet-spr.2015.0335","DOIUrl":"https://doi.org/10.1049/iet-spr.2015.0335","url":null,"abstract":"Data clustering plays a key role in both scientific and real-world applications. However, current clustering methods still face some challenges such as clustering arbitrary-shaped data sets and detecting the cluster number automatically. This study addresses the two challenges. A novel clustering analysis method, named automatic evolutionary clustering method based on distance (AED) matrix, is proposed to determine the proper cluster number automatically, and to find the optimal clustering result as well. In AED, a distance matrix is first obtained by using a specific distance metric such as Euclidean distance metric or path distance metric, and then this distance matrix is partitioned by an evolutionary clustering framework. In this framework, a fixed-length representation scheme is implemented to represent the clustering result, a novel cross-over scheme is introduced to increase the convergence speed, and a validity index is proposed to evaluate the intermediate clustering results and the final clustering results. AED is systematically compared with some state-of-the-art clustering methods on both hyper-spherical and irregular-shaped data sets, and the experimental results suggest that the authors approach not only successfully detects the correct cluster numbers but also achieves better accuracy for most of test problems.","PeriodicalId":272888,"journal":{"name":"IET Signal Process.","volume":"3 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128781900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Modified Cramer-Rao bound of parameter estimation for single-channel mixtures of adjacent-frequency signals 邻接频率单通道混合信号参数估计的改进Cramer-Rao界

IET Signal Process. Pub Date : 2016-06-28 DOI: 10.1049/iet-spr.2016.0180

Yong Yang, Hua Peng, Dongling Zhang

引用次数: 2