Decoding algorithm and architecture for BCH codes under the Lee Metric

The Lee metric measures the circular distance between two elements in a cyclic group and is particularly appropriate as a measure of distance for data transmission under phase-shift-keying modulation over a white noise channel. In this paper, using newly derived properties on Newton?s identities, we initially investigate the Lee distance properties of a class of BCH codes and show that (for an appropriate range of parameters) their minimum Lee distance is at least twice their designed Hamming distance. We then make use of properties of these codes to devise an efficient algebraic decoding algorithm that successfully decodes within the above lower bound of the Lee error-correction capability. Finally, we propose an attractive design for the corresponding VLSI architecture that is only mildly more complex than popular decoder architectures under the Hamming metric; since the proposed architecture can also be used for decoding under the Hamming metric without extra hardware, one can use the proposed architecture to decode under both distance metrics (Lee and Hamming).     

A method for improving the classification speed of clustering algorithms which use a Euclidean distance metric

Many pattern recognition computer programs use one of the clustering algorithm techniques. Often these algorithms use a Euclidean distance metric as a similarity measure. A scheme is proposed where both the Euclidean metric and a more simple city-block metric are utilized together to reduce overall classification time. The relation between the Euclidean and city-block distances is introduced as a scalar function. The bounds of the function are given and used to decide whether classification of each pattern vector is to be achieved by the computationally slow Euclidean distance or the faster city-block distance. The criteria is that the classification should be identical to the original Euclidean only scheme.     

On beamforming with finite rate feedback in multiple-antenna systems

We study a multiple-antenna system where the transmitter is equipped with quantized information about instantaneous channel realizations. Assuming that the transmitter uses the quantized information for beamforming, we derive a universal lower bound on the outage probability for any finite set of beamformers. The universal lower bound provides a concise characterization of the gain with each additional bit of feedback information regarding the channel. Using the bound, it is shown that finite information systems approach the perfect information case as (t-1)2/sup -B/t-1/, where B is the number of feedback bits and t is the number of transmit antennas. The geometrical bounding technique, used in the proof of the lower bound, also leads to a design criterion for good beamformers, whose outage performance approaches the lower bound. The design criterion minimizes the maximum inner product between any two beamforming vectors in the beamformer codebook, and is equivalent to the problem of designing unitary space-time codes under certain conditions. Finally, we show that good beamformers are good packings of two-dimensional subspaces in a 2t-dimensional real Grassmannian manifold with chordal distance as the metric.     

一种基于距离调节的聚类算法

针对k-means算法不适合凹形样本空间的问题,提出了一种基于距离调节的聚类算法.算法中引入了一种调节最短路径距离作为算法的相似度函数,该函数可以使经过高密度数据区域的两点距离缩短,而经过低密度数据区域的两点距离加长,由此来缩小类间样本的相似度,同时加大类间的相似度,以及更好的聚类.实验结果证明,该算法对凹状的聚类样本空间具有很好的聚类效果.     

基于L1范数稀疏距离测度学习的单类分类算法

 已有单类分类算法通常采用欧氏测度描述样本间相似关系,然而欧氏测度有时难以较好地反映一些数据集样本的内在分布结构,为此提出一种用于改善单类分类器描述性能的高维空间单类数据距离测度学习算法,与已有距离测度学习算法相比,该算法只需提供目标类数据,通过引入样本先验分布正则化项和L1范数惩罚的距离测度稀疏性约束,能有效解决高维空间小样本情况下的单类数据距离测度学习问题,并通过采用分块协调下降算法高效的解决距离测度学习的优化问题.学习得到的距离测度能容易地嵌入到单类分类器中,仿真实验结果表明采用学习得到的距离测度能有效改善单类分类器的描述性能,特别能够改善覆盖分类的描述能力,从而使得单类分类器具有更强的推广能力.     

Multi-manifold metric learning for face recognition based on image sets

In this paper, we propose a new multi-manifold metric learning (MMML) method for the task of face recognition based on image sets. Different from most existing metric learning algorithms that learn the distance metric for measuring single images, our method aims to learn distance metrics to measure the similarity between manifold pairs. In our method, each image set is modeled as a manifold and then multiple distance metrics among different manifolds are learned. With these distance metrics, the intra-class manifold variations are minimized and inter-class manifold variations are maximized simultaneously. For each person, we learn a distance metric by using such a criterion that all the learned distance metrics are person-specific and thus more discriminative. Our method is extensively evaluated on three widely studied face databases, i.e., Honda/UCSD database, CMU MoBo database and YouTube Celebrities database, and compared to the state-of-the-arts. Experimental results are presented to show the effectiveness of the proposed method.     

Analysis of D2D‐pairing scheme based on Voronoi diagram in cellular networks

This paper presents a device‐to‐device (D2D)–pairing scheme based on Voronoi diagram, which establishes D2D links by the relation of transmission powers and link distances. Using the principle of stochastic geometry and channel inversion, the coverage probability is analyzed, we observe that the co‐channel interference caused by cellular networks is stable in theory, and the influence of link distance parameter is highlighted as well. According to the second‐order moment of link distance, the new metric space of D2D terminals set is constructed, in which Voronoi diagram is degenerated into Delaunay triangles set to represent the potential D2D links. In addition, on the basis of the mini‐max length criterion of Delaunay triangle, the D2D communication range–limiting algorithm is designed. Finally, the more intuitive results compared with conventional D2D communications are displayed by simulations. Owing to restraining the D2D communication range, lower co‐channel interference can be obtained. Specially, the D2D‐pairing scheme is approximate local optimal under the D2D communication range–limiting algorithm, and it can provide more fairness than greedy algorithm.     

Bounds on packings of spheres in the Grassmann manifold

We derive the Gilbert-Varshamov and Hamming bounds for packings of spheres (codes) in the Grassmann manifolds over R and C. Asymptotic expressions are obtained for the geodesic metric and projection Frobenius (chordal) metric on the manifold.     

基于对称KL距离的用户行为时序聚类方法

网络用户随时间变化的行为分析是近年来用户行为分析的热点,通常为了发现用户行为的特征需要对用户做聚类处理。针对用户时序数据的聚类问题,现有研究方法存在计算性能差,距离度量不准确的缺点,无法处理大规模数据。为了解决上述问题,该文提出基于对称KL距离的用户行为时序聚类方法。首先将时序数据转化为概率模型,从划分聚类的角度出发,在距离度量中引入KL距离,用以衡量不同用户间的时间分布差异。针对实网数据中数据规模大的特点,该方法在聚类的各个环节针对KL距离的特点做了优化,并证明了一种高效率的聚类质心求解办法。实验结果证明,该算法相比采用欧式距离和DTW距离度量的聚类算法能提高4%的准确度,与采用medoids聚类质心的聚类算法相比计算时间少了一个量级。采用该算法对实网环境中获取的用户流量数据处理证明了该算法拥有可行的应用价值。     

Image distance metric learning based on neighborhood sets for automatic image annotation

Since there is semantic gap between low-level visual features and high-level image semantic, the performance of many existing content-based image annotation algorithms is not satisfactory. In order to bridge the gap and improve the image annotation performance, a novel automatic image annotation (AIA) approach using neighborhood set (NS) based on image distance metric learning (IDML) algorithm is proposed in this paper. According to IDML, we can easily obtain the neighborhood set of each image since obtained image distance can effectively measure the distance between images for AIA task. By introducing NS, the proposed AIA approach can predict all possible labels of the image without caption. The experimental results confirm that the introduction of NS based on IDML can improve the efficiency of AIA approaches and achieve better annotation performance than the existing AIA approaches.     

Target tracking by lightweight blind particle filter in wireless sensor networks

For realizing robust target tracking with wireless sensor networks in the circumstance where the propagation parameters of the characteristic signal emitted by the target are unknown, a novel tracking algorithm under the particle filter framework is proposed. We propose a scheme to realize particle weight calculation without the prior knowledge about the propagation parameters of the target's characteristic signal. With the use of the monotonic relationship of the distance and the received signal strength, we define the signal characteristic sequence and particle distance sequence and utilize the modified sequence distance between the signal characteristic sequence and the particle distance sequence as the criterion to calculate the particle weight blindly with simple lightweight operations. Simulation results demonstrate the effectiveness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd.     

Data Clustering Method Using a Modified Gaussian Kernel Metric and Kernel PCA

Most hyper‐ellipsoidal clustering (HEC) approaches use the Mahalanobis distance as a distance metric. It has been proven that HEC, under this condition, cannot be realized since the cost function of partitional clustering is a constant. We demonstrate that HEC with a modified Gaussian kernel metric can be interpreted as a problem of finding condensed ellipsoidal clusters (with respect to the volumes and densities of the clusters) and propose a practical HEC algorithm that is able to efficiently handle clusters that are ellipsoidal in shape and that are of different size and density. We then try to refine the HEC algorithm by utilizing ellipsoids defined on the kernel feature space to deal with more complex‐shaped clusters. The proposed methods lead to a significant improvement in the clustering results over K‐means algorithm, fuzzy C‐means algorithm, GMM‐EM algorithm, and HEC algorithm based on minimum‐volume ellipsoids using Mahalanobis distance.     

Non-Euclidian geographic routing in wireless networks

Greedy geographic routing is attractive for large multi-hop wireless networks because of its simple and distributed operation. However, it may easily result in dead ends or hotspots when routing in a network with obstacles (regions without sufficient connectivity to forward messages). In this paper, we propose a distributed routing algorithm that combines greedy geographic routing with two non-Euclidian distance metrics, chosen so as to provide load balanced routing around obstacles and hotspots. The first metric, Local Shortest Path, is used to achieve high probability of progress, while the second metric, Weighted Distance Gain, is used to select a desirable node among those that provide progress. The proposed Load Balanced Local Shortest Path (LBLSP) routing algorithm provides loop freedom, guarantees delivery when a path exists, is able to efficiently route around obstacles, and provides good load balancing.     

Analysis of Enhanced Collision Avoidance Scheme Proposed for IEEE 802.11e-Enhanced Distributed Channel Access Protocol

In enhanced distributed channel access (EDCA) protocol, small contention window (CW) sizes are used for frequent channel access by high-priority traffic (such as voice). But these small CW sizes, which may be suboptimal for a given network scenario, can introduce more packet collisions, and thereby, reduce overall throughput. This paper proposes enhanced collision avoidance (ECA) scheme for AC_VO access category queues present in EDCA protocol. The proposed ECA scheme alleviates intensive collisions between AC_VO queues to improve voice throughput under the same suboptimal yet necessary (small size) CW restrictions. The proposed ECA scheme is studied in detail using Markov chain numerical analysis and simulations carried out in NS-2 network simulator. The performance of ECA scheme is compared with original (legacy) EDCA protocol in both voice and multimedia scenarios. Also mixed scenarios containing legacy EDCA and ECA stations are presented to study their coexistence. Comparisons reveal that ECA scheme improves voice throughput performance without seriously degrading the throughput of other traffic types.     

Performance analysis of Viterbi decoder using channel stateinformation in COFDM system

This paper relates to the application method of channel state information (CSI) to the Viterbi (maximum likelihood) decoder in the digital terrestrial television broadcasting system. The proposed Viterbi decoder uses the CSI derived from the pilots inserted in the transmitter of the COFDM (coded orthogonal frequency division multiplexing) system. The CSI is calculated by interpolation using the pilots in the receiver. The active real (I) and imaginary (Q) data after equalization are transferred to the branch metric calculation block that decides the euclidean distance for soft decision decoding and also the estimated CSI values are transferred to the same block. After calculating the euclidean distance for the soft decision decoding, the euclidean distance of the branch metric is multiplied by CSI. To do so, new branch metric values that consider each carrier state information are obtained. We simulated this method in Rayleigh fading defined in the ETSI standard. From the simulation, this method has better performance of about 0.15 dB to 0.7 dB and 2.2 dB to 2.9 dB in the Rayleigh channel than that of conventional soft decision Viterbi decoding with or without a bit interleaver where the constellation is QPSK, l6-QAM and 64-QAM     

A new sequential decoding algorithm based on branch metric

A new sequential decoding algorithm with an adjustable threshold and a new method of moving through the decoding tree is proposed. Instead of the path metric of the conventional sequential decoding algorithms, the proposed algorithm uses a branch metric based on maximum-likelihood criterion. Two new parameters, the jumping-back distance and going-back distance, are also introduced. The performance of the algorithm for long constraint length convolutional codes is compared to those of the other sequential decoding algorithms and the Viterbi algorithm. The results show that the proposed algorithm is a good candidate for decoding of convolutional codes due to its fast decoding capability and good bit error rate (BER) performance. This work was supported in part by the Research Foundation at Karadeniz Technical University under Grant 2004.112.004.01 and 2005.112.009.2.     

Mobile Robot Navigation Using Wireless Sensor Networks Without Localization Procedure

In this paper, algorithms for navigating a mobile robot through wireless sensor networks are presented. The mobile robot can navigate without the need for a map, compass, or GPS module while interacting with neighboring sensor nodes. Two navigation algorithms are proposed in this paper: the first uses the distance between the mobile robot and each sensor node and the second uses the metric calculated from one-hop neighbors’ hop-counts. Periodically measuring the distance or metric, the mobile robot can move toward a point where these values become smaller and finally come to reach the destination. These algorithms do not attempt to localize the mobile robot for navigation, therefore our approach permits cost-effective robot navigation while overcoming the limitations of traditional navigation algorithms. Through a number of experiments and simulations, the performance of the two proposed algorithms is evaluated.     

Information distance

While Kolmogorov (1965) complexity is the accepted absolute measure of information content in an individual finite object, a similarly absolute notion is needed for the information distance between two individual objects, for example, two pictures. We give several natural definitions of a universal information metric, based on length of shortest programs for either ordinary computations or reversible (dissipationless) computations. It turns out that these definitions are equivalent up to an additive logarithmic term. We show that the information distance is a universal cognitive similarity distance. We investigate the maximal correlation of the shortest programs involved, the maximal uncorrelation of programs (a generalization of the Slepian-Wolf theorem of classical information theory), and the density properties of the discrete metric spaces induced by the information distances. A related distance measures the amount of nonreversibility of a computation. Using the physical theory of reversible computation, we give an appropriate (universal, antisymmetric, and transitive) measure of the thermodynamic work required to transform one object in another object by the most efficient process. Information distance between individual objects is needed in pattern recognition where one wants to express effective notions of “pattern similarity” or “cognitive similarity” between individual objects and in thermodynamics of computation where one wants to analyze the energy dissipation of a computation from a particular input to a particular output     

Design and analysis of a content-based pathology image retrieval system

A prototype, content-based image retrieval system has been built employing a client/server architecture to access supercomputing power from the physician's desktop. The system retrieves images and their associated annotations from a networked microscopic pathology image database based on content similarity to user supplied query images. Similarity is evaluated based on four image feature types: color histogram, image texture, Fourier coefficients, and wavelet coefficients, using the vector dot product as a distance metric. Current retrieval accuracy varies across pathological categories depending on the number of available training samples and the effectiveness of the feature set. The distance measure of the search algorithm was validated by agglomerative cluster analysis in light of the medical domain knowledge. Results show a correlation between pathological significance and the image document distance value generated by the computer algorithm. This correlation agrees with observed visual similarity. This validation method has an advantage over traditional statistical evaluation methods when sample size is small and where domain knowledge is important. A multi-dimensional scaling analysis shows a low dimensionality nature of the embedded space for the current test set.     

Toward a generic evaluation of image segmentation.

Image segmentation plays a major role in a broad range of applications. Evaluating the adequacy of a segmentation algorithm for a given application is a requisite both to allow the appropriate selection of segmentation algorithms as well as to tune their parameters for optimal performance. However, objective segmentation quality evaluation is far from being a solved problem. In this paper, a generic framework for segmentation evaluation is introduced after a brief review of previous work. A metric based on the distance between segmentation partitions is proposed to overcome some of the limitations of existing approaches. Symmetric and asymmetric distance metric alternatives are presented to meet the specificities of a wide class of applications. Experimental results confirm the potential of the proposed measures.