AR Signal Research Articles

Tree-structured nonlinear signal modeling and prediction

We develop a regression tree approach to identification and prediction of signals that evolve according to an unknown nonlinear state space model. In this approach, a tree is recursively constructed that partitions the p-dimensional state space into a collection of piecewise homogeneous regions utilizing a 2/sup p/-ary splitting rule with an entropy-based node impurity criterion. On this partition, the joint density of the state is approximately piecewise constant, leading to a nonlinear predictor that nearly attains minimum mean square error. This process decomposition is closely related to a generalized version of the thresholded AR signal model (ART), which we call piecewise constant AR (PCAR). We illustrate the method for two cases where classical linear prediction is ineffective: a chaotic double-scroll signal measured at the output of a Chua-type electronic circuit and a second-order ART model. We show that the prediction errors are comparable with the nearest neighbor approach to nonlinear prediction but with greatly reduced complexity.

Determination of 40Ca+ in the Presence of 40Ar+: An Illustration of the Utility of Time-Gated Detection in Pulsed Glow Discharge Mass Spectrometry

The glow discharge ionization source operated in the pulsed, or modulated, power mode affords a number of distinct advantages over its steady-state counterpart. It is well-known that pulsed plasma operation permits the application of higher instantaneous powers by allowing time for the sample to cool. This minimizes sample overheating while effecting higher sputtering yields and lower limits of detection. The presence of discrete time regimes affords the added advantage of temporal selectivity. Such selectivity allows the observation of analyte ions during a time regime in which their signal is at a maximum while that of electron ionized background species is declining. Significantly, time regimes are found when no background argon ion signals are observable but analyte ion signals remain. This means that discrimination against isobaric interferences arising from the discharge gas is possible. A prime example of the utility of this advantage arises in the determination of calcium with an argon glow discharge. Both the major argon and calcium isotopes are found at a nominal m/z of 40. Time-gated mass spectrometeric detection during the afterpeak time regime enables the ready determination of (40)Ca(+) in samples at the ppm level. A linear calibration curve is obtained that also demonstrates the elimination of the (40)Ar(+) signal from mass spectra obtained with either a dc or rf glow discharge ion source.

Robust Time-Varying AR Parameter Estimation

A new method for estimating time-varying parameters of nonstationary AR signal models, based on robust recursive least squares with variable forgetting factors, is described. The robust estimator differs from the conventional one by the insertion of nonlinear residual transformation, which has to suppress the influence of the spiky noise or outliers, while the VFF's are adapted to the signal via the robustified generalized likelihood ratio detection scheme. The method has good adaptability in the nonstationary situations, and gives low bias and low variance at the stationary situations. The feasibility of the approach is demonstrated with both simulation and experimental data.

Estimation of noisy quantized Gaussian AR time-series with randomly varying observation coefficient

Presents an estimation algorithm for the parameters of Gaussian auto-regressive AR processes from one-bit quantized observation sequences. The input signal to the quantizer is the AR signal corrupted by multiplicative white Gaussian noise. The estimation algorithm is computationally inexpensive as it involves counting the number of occurrences of particular patterns of zeros and ones in the observation sequence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Multirate modeling of AR/MA stochastic signals and its application to the combined estimation-interpolation problem

The use of the Kalman filter is investigated in this work for interpolating and estimating values of an AR or MA stochastic signal when only a noisy, down-sampled version of the signal can be measured. A multirate modeling theory of the AR/MA stochastic signals is first derived from a block state-space viewpoint. The missing samples are embedded in the state vector so that missing signal reconstruction problem becomes a state estimation scheme. Next, Kalman state estimation theory is introduced to treat the combined estimation-interpolation problem. Some extensions are also discussed for variations of the original basic problem. The proposed Kalman reconstruction filter can be also applied toward recovering missing speech packets in a packet switching network with packet interleaving configuration. By analysis of state estimation theory, the proposed Kalman reconstruction filters produce minimum-variance estimates of the original signals. Simulation results indicate that the multirate Kalman reconstruction filters possess better estimation/interpolation performances than a Wiener reconstruction filter under adequate numerical complexity.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

New Results on the ARMA Modeling of AR Signal Plus White Noise Process

New Results on the ARMA Modeling of AR Signal Plus White Noise Process

Sevoflurane inhibited beta-adrenoceptor-G protein bindings in myocardial membrane in rats.

The effects of sevoflurane on the beta-adrenoceptor (beta AR) system, consisting of the receptor, stimulatory guanosine triphosphate (GTP) binding protein (Gs), and adenylate cyclase (AC), were studied in rat myocardial membrane. Cyclic adenosine monophosphate (cAMP) production rate was measured as cAMP generation stimulated by l-isoproterenol, guanosine 5"-O-(3-thiotriphosphate) (GTP gamma S), and forskolin, respectively. The receptor studies were performed by using the dihydroalprenolol ([3H]DHA) binding method. Nonspecific binding was determined in the presence of l-propranolol. beta AR binding capacity determined by the maximum number of binding sites (Bmax) and the dissociation constant (Kd) for [3H]DHA were calculated. beta AR affinity for agonists was assessed by an inhibition constant (Ki), calculated from the concentration of l-isoproterenol to produce half-maximum inhibition of [3H]DHA binding (IC50). At 2.0 mM sevoflurane, cAMP production rate was significantly depressed by 87.1% +/- 4.8% with l-isoproterenol and by 79.4% +/- 7.0% with GTP gamma S, but not significantly depressed by forskolin. In the presence of 0-2.0 mM sevoflurane, Kd for [3H]DHA and Ki for l-isoproterenol increased in a concentration-dependent manner, whereas Bmax remained unchanged. The ratio of high affinity state against the high and low affinity state of the adrenoceptor decreased significantly from 0.60 +/- 0.15 to 0.35 +/- 0.12. We conclude that sevoflurane might depress the beta AR signal transduction system by reducing ligand-receptor bindings and disrupting the relationship between the receptor and Gs.

Ar atom emission as a probe of craze formation and craze growth in polystyrene

AbstractA report of measurements of Ar emission during the loading of polystyrene and high impact polystyrene in vacuum is presented. Argon was introduced into the material prior to the experiment by storing the samples in an Ar atmosphere. The development of crazes during loading was monitored by videotaped visual observations and scattered light measurements. Increased Ar emission is observed at the onset of crazing, provided that the crazes intersect the surface. The strength of the Ar signal depends upon the extent of crazing; especially intense signals are observed from samples which display significant crazing prior to fracture. High‐impact polystyrene shows intense emissions at yield which soon decay due to the depletion of Ar from the near surface material. The emission intensity rises again prior to fracture, when surface crazes become connected to crazes in the bulk. Thus the emission of volatile species during deformation reflects the growth of crazes intersecting the surface, as well as changes in the “connectivity” of the craze network. © 1993 John Wiley &amp; Sons, Inc.

A generalized parametric PR-QMF design technique based on Bernstein polynomial approximation

A generalized, parametric, perfect-reconstruction quadrature-mirror-filter (PR-QMF) design technique based on Bernstein polynomial approximation in the magnitude-square domain is presented. The parametric nature of this solution provides useful insights to the PR-QMF problem. Several well-known orthonormal wavelet filters, PR-QMFs, are shown to be the special cases of the proposed technique. Energy compaction performances of a few popular signal decomposition techniques are presented for AR(1) signal sources. It is observed that the hierarchical QMF filter banks considered outperform the block transforms as expected. >

Spectral peak enhancement methods for 1‐D and 2‐D signals and their applications

AbstractIn this paper, we propose a method to enhance the maximum spectral peaks of signal components corrupted by noise for one‐ and two‐dimensional (1‐D and 2‐D) signals under the condition that no a priori information is given. This method does not use a conventional filter where the passband is one and the stopband is zero but uses a filter where the spectral characteristics of the signal to be extracted from the input signal are the desired characteristics; that is, the filter is dependent on the input signal. For 1‐D signals, we demonstrate that the signal components can be satisfactorily enhanced by iterative processing using the fast Fourier transform (FFT) given an “autoregressive (AR signal + white noise signal” and a “harmonic signal + white noise signal.” For 2‐D signals, the amount of computation is decreased significantly and good spectral enhancement is possible compared to conventional methods. Furthermore, the possible application of this method to the design of narrowband plane wave detection filters, which are difficult to design using conventional methods, is presented.

Lapped transforms for efficient transform/subband coding

Two lapped transforms for subband/transform coding of signals are introduced: a version of the lapped orthogonal transform (LOT), which can be efficiently computed for any transform length; and the modulated lapped transform (MLT), which is based on a modulated quadrature mirror (QMF) bank. The MLT can also be efficiently computed by means of a type-IV discrete sine transform (DST-IV). The LOT and the MLT are both asymptotically optimal lapped transforms for coding an AR(1) signal with a high intersample correlation coefficient. The coding gains of the LOT and MLT of length M are higher than that of the discrete cosine transform (DCT) of the same length; they are actually close to the coding gains obtained with a DCT of length 2M. An MLT-based adaptive transform coder (ACT) for speech signals is simulated; the code is essentially free from frame rate noise and has a better spectral resolution that DCT-based ATC systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Fast adaptive least squares algorithms for power spectral estimation

Power spectrum estimation is of great importance in various applications of signal processing, such as geophysics and communications. In this paper two new fast algorithms are presented that adaptively compute a least squares estimate of the power spectrum of a time series. This is achieved by modeling the input as an AR signal of order m and simultaneous minimization of the sum of the forward and backward prediction error energies. The first algorithm is of the 0 (m2) type, and the second of 0(m) requiring 9m multiplications and additions.