3016046
Asymptotic analysis of a fast algorithm for efficient multiple frequency estimation
20811204
Asymptotic analysis of a fast algorithm for efficient multiple frequency estimation
Based on an asymptotic analysis of the contraction mapping (CM) method of Li and Kedem (1993), a bandwidth shrinkage rule is proposed for fast and accurate estimation of the frequencies of multiple sinusoids from noisy measurements. The CM frequency estimates are defined as the fixed points of a contractive mapping formed by the lag-one autocorrelation coefficient calculated from the output. of a parametric filter applied to the observed time series. With bandwidth parameters judiciously chosen according to the asymptotic analysis, the algorithm is shown to be able to accommodate possibly poor initial values of precision O(n/sup -1/3/) and converge to a final estimate whose accuracy is arbitrarily close to O(n/sup -3/2/), the optimal error rate for frequency estimation under the Gaussian assumption. The total computational complexity of the algorithm is shown to be O(n log n), which is comparable to that of n-point fast Fourier transform (FFT). A novelty in the asymptotic analysis is that it accommodates closely spaced frequencies by allowing not only the filter bandwidth but also the frequency separation to be functions of the sample size n. This enables an assessment of the accuracy of the frequency estimates for given bandwidths and initial values in situations where some or all of the frequencies are close to each other.
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. >
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.
14215995
Spin detection hardware for improved management of multithreaded systems
Spinning is a synchronization mechanism commonly used in applications and operating systems. Excessive spinning, however, often indicates performance or correctness (e.g., livelock) problems. Detecting if applications and operating systems are spinning is essential for achieving high performance, especially in consolidated servers running virtual machines. Prior research has used source or binary instrumentation to detect spinning. However, these approaches place a significant burden on programmers and may even be infeasible in certain situations. In this paper, we propose efficient hardware to detect spinning in unmodified applications and operating systems. Based on this hardware, we develop 1) scheduling and power policies that adaptively manage resources for spinning threads, 2) system support that helps detect when a multithreaded program is livelocked, and 3) hardware performance counters that accurately reflect system performance. Using full-system simulation with SPEC OMP, SPLASH-2, and Wisconsin commercial workloads, we demonstrate that our mechanisms effectively improve the management of multithreaded systems
15267641
Estimating Joint Probabilities from Marginal Ones
Estimating joint probabilities plays an important role in many data mining and machine learning tasks. In this paper we introduce two methods, minAB and prodAB, to estimate joint probabilities. Both methods are based on a light-weight structure, partition support. The core idea is to maintain the partition support of itemsets over logically disjoint partitions and then use it to estimate joint probabilities of item-sets of higher cardinalitiess. We present extensive mathematical analyses on both methods and compare their performances on synthetic datasets. We also demonstrate a case study of using the estimation methods in Apriori algorithm for fast association mining. Moreover, we explore the usefulness of the estimation methods in other mining/learning tasks. Experimental results show the effectiveness of the estimation methods.
200438
Nanoparticle-enhanced proton computed tomography: a Monte Carlo simulation study
Proton computed tomography (pCT) has the potential to improve the accuracy of proton treatment planning, which is currently based on X-ray computed tomography (XCT). However, at this time no method exists for pCT to selectively increase the contrast of tumor tissues with respect to normal tissues. One possibility to achieve this goal is to bind gold-nanoparticles, i.e., microscopic structures in the size range of 1 nm to several hundred nanometers, to the surface of tumor cells with cell-seeking antibodies conjugated to the surface of the nanoparticles. In order to test the suitability of this approach, we used the Monte Carlo transport code GEANT4, to provide simulated histories of 200 MeV protons going through a cylindrical water phantom with embedded inhomogeneities enhanced with traces of gold. The energy loss of several million protons traversing the cylinder from 180 different angles formed the basis for reconstructing the single proton CT image representing the relative volume electron density map of the phantom using an approximate filtered back projection algorithm. Our results demonstrate that with hundred nanoparticles of 100 nm diameters per tumor cell, it may be possible to enhance the visibility of tumors in pCT.
13900916
Detection of cognitive binding during ambiguous figure tasks by wavelet coherence analysis of EEG signals
It is hypothesized that the perception of an alternative image in ambiguous figures would be manifested as an increase in synchronization of EEG signals over multiple scalp sites as "cognitive binding" occurs. A statistical time-frequency analysis (STFA) approach is taken to detect cognitive binding. The STFA approach consists of an analysis filter bank that extracts useful components from the input EEG signals and certain statistical parameters at the output that characterize the synchronization patterns over multiple scalp sites. With the help of wavelet-packet filters and wavelet coherence measures, cognitive binding is detected over many frequency bands by statistical testing.
15890486
Interval Data Clustering with Applications
Interval data is described by a group of variables, each of which contains a range of continuous values instead of the traditional single continuous or discrete value. Traditional data analysis simply replaces each interval by its representative (e.g., center or mean) and ignores the structure information of intervals. In this paper, we study the problem of clustering interval data using the modified or extended interval data dissimilarity measures. Our contributions are two-fold. First, we discuss various approaches for measuring the dissimilarities/distances between interval data, investigate the relations among them, and present a comprehensive experimental study on clustering interval data. We show that the extended interval data clustering achieves better performance than traditional ones and produces more meaningful and explanatory results. Second, we propose a two-stage approach for clustering interval data by exploiting the relations between the traditional distances and the modified distances. Experimental results show the effectiveness of our approach