3381503
Finite-alphabet information and multivariate blind deconvolution and identification of linear systems
This article addresses the problem of estimating a multivariate linear system from its output when the input is an unobservable sequence of random vectors with finite-alphabet distribution. By explicitly utilizing the finite-alphabet property, an estimation method is proposed under the traditional inverse filtering paradigm as a generalization of a univariate method that has been studied previously. Identifiability of multivariate systems by the proposed method is proved mathematically under very mild conditions that can be satisfied even if the input is nonstationary and has both cross-channel and serial statistical dependencies. Statistical super-efficiency in estimating both parametric and nonparametric systems is also established for an alphabet-based cost function.
8716577
Blind identification and deconvolution of linear systems driven by binary random sequences
The problem of blind identification and deconvolution of linear systems with independent binary inputs is addressed. To solve the problem, a linear system is applied to the observed data and adjusted so as to produce binary outputs. It is proved that the system coincides with the inverse of the unknown system (with scale and shift ambiguities), whether it is minimum or nonminimum phase. These results are derived for nonstationary independent binary inputs of infinite or finite length. Based on these results, an identification method is proposed for parametric linear systems. It is shown that under some mild conditions, a consistent estimator of the parameter can be obtained by minimizing a binariness criterion for the output data. Unlike many other blind identification and deconvolution methods, this criterion handles nonstationary signals and does not utilize any moment information of the inputs. Three numerical examples are presented to demonstrate the effectiveness of the proposed method. >
23492812
Singularity analysis of redundant parallel manipulators
The general methods of singularity analysis of a general parallel manipulator are not suitable for that of a redundant parallel manipulator, thereby This work presents a method for the identification of the singularities of redundant parallel manipulators. Firstly, the theory of singular value decomposition is employed to analyze the velocity equation of a mechanism, and the relationship between the generalized input velocities and the generalized output velocities is researched. Then the physical interpretation of the singularity of a redundant parallel manipulator is discussed, and the relationship between the singularity and the singularity values of the Jacobian matrix of a mechanism is studied. Finally, based on above analysis, the singularities of a redundant manipulator are divided into three basic types and a method for computing the singularity set is presented and formulated. As an example, a 3 degree-of-freedom redundant manipulator is researched through using this method, and all the singular configurations of this manipulator are found out and pictured.
16509721
A location system using asynchronous distributed sensors
This paper describes a location system using an asynchronous distributed sensor network. The sensors' clocks operate independently and are not explicitly synchronized. The motion of the source, or the object, between two emitted pulses will cause the inter-arrival time of these two pulses at each sensor to vary. This inter-arrival time is a function of the source location and motion and is independent of the clock offsets among sensors. Thus, the independently calculated time-difference-of-arrival (TDOA) of successive pulses at each sensor provides a reliable source of information to estimate the source position as well as its motion. The Cramer-Rao lower bound (CRLB) is derived to assess the performance of the proposed method and to show that its performance can approach that of a synchronous network given a sufficient number of sensors and moderate source movement. Some sub-optimal but effective source trajectory tracking methods are proposed which further relax the assumption of source motion between consecutive pulses. The simulation results show that the proposed estimation and tracking methods are quite good.
9813108
CoFD : An Algorithm for Non-distance Based Clustering in High Dimensional Spaces
The clustering problem, which aims at identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity clusters, has been widely studied. Traditional clustering algorithms use distance functions to measure similarity and are not suitable for high dimensional spaces. In this paper, we propose CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the maximum likelihood principle, CoFD is to optimize parameters to maximize the likelihood between data points and the model generated by the parameters. Experimental results on both synthetic data sets and a real data set show the efficiency and effectiveness of CoFD.
7621113
Detecting emotion in music
Music is not only for entertainment and for pleasure, but has been used for a wide range of purposes due to its social and physiological effects. Traditionally musical information has been retrieved and/or classified based on standard reference information, such as the name of the composer and the title of the work etc. These basic pieces information will remain essential, but information retrieval based on these are far from satisfactory. Huron points out that since the preeminent functions of music are social and psychological, the most useful characterization would be based on four types of information: the style, emotion, genre, and similarity [Huron,2000].
8121416
Biclustering Protein Complex Interactions with a Biclique Finding Algorithm
Biclustering has many applications in text mining, Web clickstream mining, and bioinformatics. When data entries are binary, the tightest biclusters become bicliques. We propose a flexible and highly efficient algorithm to compute bicliques. We first generalize the Motzkin-Straus formalism for computing the maximal clique from L1 constraint to Lp constraint, which enables us to provide a generalized Motzkin-Straus formalism for computing maximal-edge bicliques. By adjusting parameters, the algorithm can favor biclusters with more rows less columns, or vice verse, thus increasing the flexibility of the targeted biclusters. We then propose an algorithm to solve the generalized Motzkin-Straus optimization problem. The algorithm is provably convergent and has a computational complexity of O(/E/) where /E/ is the number of edges. Using this algorithm, we bicluster the yeast protein complex interaction network. We find that biclustering protein complexes at the protein level does not clearly reflect the functional linkage among protein complexes in many cases, while biclustering at protein domain level can reveal many underlying linkages. We show several new biologically significant results.