|
|
Creator | Title | Description | Subject | Date |
| 1 |
 | Mathews, V. John | An efficient algorithm for joint estimation of differential time delays and frequency offsets | ABSTRACT This paper introduces an efficient algorithm that jointly estimates differential time delays and frequency offsets between two signals. The approach is a two-step procedure. First, the differential frequency offsets are estimated from measurement of the autocorrelation functions of the rec... | | 1992 |
| 2 |
 | Mathews, V. John | Sufficient stability bounds for slowly varying direct-form recursive linear filters and their applications in adaptive IIR filters | Abstract-This correspondence derives a sufficient time-varying bound on the maximum variation of the coefficients of an exponentially stable time-varying direct-form homogeneous linear recursive filter. The stability bound is less conservative than all previously derived bounds for time-varying IIR ... | | 1999 |
| 3 |
 | Mathews, V. John | Techniques for bilinear time series analysis | This paper reviews the general problem of nonlinear time series analysis. The special case of bilinear time series analysis is discussed in detail. The stability of the estimated nonlinear system models is of particular importance. We discuss a simple sufficient condition for the stability of s... | | 1993 |
| 4 |
 | Mathews, V. John; Dubow, Joel | A stable adaptive Hammerstein filter employing partial orthogonalization of the input signals | Abstract This paper presents an algorithm that adapts the parameters of a Hammerstein system model. Hammerstein systems are nonlinear systems that contain a static nonlinearity cascaded with a linear system. In this work, the static nonlinearity is modeled using a polynomial system and the linear f... | | 2002 |
| 5 |
 | Mathews, V. John | Blind identification of QAM signals using a likelihood-based algorithm | This paper presents a method for automatically identifying different QAM modulations. This method identifies the modulation type as the hypothesis for which the likelihood function of the amplitudes of the received signal is the maximum. The derivation of the likelihood functions assumes additive wh... | | 2013-01-01 |
| 6 |
 | Mathews, V. John | Blind identification of bilinear systems | Abstract-This paper is concerned with the blind identification of a class of bilinear systems excited by non-Gaussian higher order white noise. The matrix of coefficients of mixed input-output terms of the bilinear system model is assumed to be triangular in this work. Under the additional assumptio... | | 2003 |
| 7 |
 | Mathews, V. John | Stochastic mean-square performance analysis of an adaptive Hammerstein filter | ABSTRACT This paper presents an almost sure (a.s.) mean-square performance analysis of an adaptive Hammerstein filter for the case when the measurement noise in the desired response signal is a martingale difference sequence. The system model consists of a series connection of a memoryless nonlinea... | | 2004 |
| 8 |
 | Mathews, V. John | Adaptive polynomial filters | While linear filter are useful in a large number of applications and relatively simple from conceptual and implementational view points. there are many practical situations that require nonlinear processing of the signals involved. This article explains adaptive nonlinear filters equipped with polyn... | | 1991 |
| 9 |
 | Mathews, V. John | Adaptive parallel-cascade truncated volterra filters | Abstract-This paper studies adaptive truncated Volterra filters employing parallel-cascade structures. Parallel-cascade realizations implement higher order Volterra systems a s a parallel connection of multiplicative combinations of lower order truncated Volterra systems. A normalized LMS adaptive f... | | 1998 |
| 10 |
 | Mathews, V. John | Parameter estimation for a bilinear time series model | ABSTRACT This paper presents a direct approach to the estimation of the parameters associated with a bilinear time series model. The approach depends critically on the expressions for certain higher-order statistics of the signals that satisfy the bilinear model. These expressions are linear in mo... | | 1991 |
| 11 |
 | Harrison, Reid R. | Mobile robot navigation in enclosed large-scale space | In a large-scale s ace, navigation may occur among very dispersed landmarks, further apart than the range of sensing of an autonomous vehicle. In this work we investigate the feasibility of construction of a landmark-based cognitive map, whose elements are the obstacles perceived by a robotic vehicl... | | 1994-01-01 |
| 12 |
 | Furse, Cynthia M. | Novel inverse methods for wire fault detection and diagnosis | Abstract?In recent years, methods used to locate and diagnose wiring faults have increased in complexity and variety. However, there is much research yet to take place in order to develop high resolution models for accurately analysis of the effects of small faults, gradual impedance discontinuities... | | 2011 |
| 13 |
 | Mathews, V. John | Output-error adaptive bilinear filters | This paper presents an overview of several gradient type recursive algorithms for adaptive nonlinear filters equipped with bilinear system models. Bilinear models are attractive because they can approximate a large class of nonlinear systems with great parsimony in the use of coefficients. Two al... | | 1991 |
| 14 |
 | Mathews, V. John | Output-error LMS bilinear filters with stability monitoring | ABSTRACT This paper introduces output-error LMS bilinear filters with stability monitoring. Bilinear filters are recursive nonlinear systems that belong to the class of polynomial systems. Because of the feedback structure, such models are able to represent many nonlinear systems efficiently. ... | | 1995 |
| 15 |
 | Myers, Chris J. | Learning genetic regulatory network connectivity from time series data | Abstract-Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This information can be used to understand the genetic regulatory network that generates the data. Typically, Bayesian analysis approaches are applied which neglect ... | | 2011 |
| 16 |
 | Mathews, V. John | Stochastic mean-square performance analysis of an adaptive Hammerstein filter | Abstract-This paper presents an almost sure mean-square performance analysis of an adaptive Hammerstein filter for the case when the measurement noise in the desired response signal is a martingale difference sequence. The system model consists of a series connection of a memoryless nonlinearity fol... | | 2006 |
| 17 |
 | Mathews, V. John | Adaptive algorithms for identifying recursive nonlinear systems | ABSTRACT This paper presents two fast least-squares lattice algorithms for adaptive non-linear filters equipped with system models involving nonlinear feedback. Such models can approximate a large class of non-linear systems adequately, and usually with considerable parsimony in the number of coeff... | | 1991 |
| 18 |
 | Mathews, V. John | A stable adaptive Hammerstein filter employing partial orthogonalization of the input signals | Abstract-This paper presents an algorithm that adapts the parameters of a Hammerstein system model. Hammerstein systems are nonlinear systems that contain a static nonlinearity cascaded with a linear system. In this paper, the static nonlinearity is modeled using a polynomial system, and the linear ... | | 2006 |
| 19 |
 | Mathews, V. John | Adaptive volterra filters using orthogonal structures | Abstract-This paper presents an adaptive Volterra filter that empolys a recently developed orthogonalization procedure of Gaussian signals for Volterra system identification. The algorithm is capable of handling arbitrary orders of nonlinearity P as well as arbitrary lengths of memory N for the syst... | | 1996 |
| 20 |
 | Mathews, V. John | Adaptive volterra filters using orthogonal structures | Abstract- This paper presents an adaptive Volterra filter that employs a recently developed orthogonalization procedure of Gaussian signals for Volterra system identification. The algorithm is capable of handling arbitrary orders of nonlinearity P as well as arbitrary lengths of memory N for the sys... | | 1995 |
| 21 |
 | Stevens, Kenneth; Myers, Chris J. | Average-case optimized technology mapping of one-hot domino circuits | This paper presents a technology mapping technique for optimizing the average-case delay of asynchronous combinational circuits implemented using domino logic and one-hot encoded outputs. The technique minimizes the critical path for common input patterns at the possible expense of making less commo... | | 1998 |
| 22 |
| Myers, Chris J.; Stevens, Kenneth | Average-case optimized technology mapping of one-hot domino circuits* | This paper presents a technology mapping technique for optimizing the average-case delay of asynchronous combinational circuits implemented using domino logic and one-hot encoded outputs. The technique minimizes the critical path for common input patterns at the possible expense of making less commo... | | 1998 |
| 23 |
 | Mathews, V. John | Identification of nonlinear, memoryless systems using chebyshev nodes | ABSTRACT This paper describes an approach for identification of static nonlinearities from input-output measurements. The approach is based on minimax approximation of memoryless nonlinear systems using Chebyshev polynomials. For memoryless nonlinear systems that are finite and continuous with fini... | | 2005 |
| 24 |
 | Tasdizen, Tolga | Three-dimensional alignment and merging of confocal microscopy stacks | We describe an efficient, robust, automated method for image alignment and merging of translated, rotated and flipped confocal microscopy stacks. The samples are captured in both directions (top and bottom) to increase the SNR of the individual slices. We identify the overlapping region of the two s... | | 2013-01-01 |
| 25 |
 | Mathews, V. John | Adaptive lattice bilinear filters | Abstract-This paper presents two fast least squares lattice algorithms for adaptive nonlinear filters equipped with bilinear system models. Bilinear models are attractive for adaptive filtering applications because they can approximate a large class of nonlinear systems adequately, and usually with... | | 1993 |
