Antioxidant and antihemolytic activities of methanol extract of Hyssopus angustifolius

This study was designed to evaluate antioxidant and antihemolytic activities of Hyssopus angustifolius flower, stem and leaf methanol extracts by employing various in vitro assays. The leaf extract showed the best activity in DPPH (63.2 ± 2.3 μg mL-1) and H2O2  (55.6 ± 2.6 μg mL-1) models compared to the other extracts. However, flower extract exhibited the highest Fe2+ chelating activity (131.4 ± 4.4 μg mL-1). The extracts exhibited good antioxidant activity in linoleic acid peroxidation and reducing power assays, but were not comparable to vitamin C. The stem (23.58 ± 0.7 μg mL-1) and leaf (26.21 ± 1 μg mL-1) extracts showed highest level of antihemolytic activity than the flower extract

Estimation and identification for 2-D block Kalman filtering

Includes bibliographical references.This correspondence is concerned with the development of a recursive identification and estimation procedure for 2-D block Kalman filtering. The recursive identification scheme can be used on-line to update the image model parameters at each iteration based upon the local statistics within a block of the observed noisy image. The covariance matrix of the driving noise can also be estimated at each iteration of this algorithm. A recursive procedure is given for computing the parameters of the higher order models. Simulation results are also provided

Model reduction method for a class of 2-D systems, A

Includes bibliographical references.A decomposition-aggregation scheme for reduction of dimensionality for a class of 2-D systems is introduced. This method, which is based upon the extension of the singular perturbation method in two dimensions, is used to decompose the original 2-D system into two reduced-order 2-D subsystems. These reduced order subsystems are shown to effectively capture the dynamical behavior of the original full-order system. Two numerical examples are provided that indicate the effectiveness of this method when used in image modeling applications.This work was supported in part by the Natural Sciences and Engineering Research Council of Canada, and by Fonds Pour la Formation de Chercheurs et L'aide la Recherche, Programme E'tablissment de Nouveaux Chercheurs

Full-plane block Kalman filter for image restoration, A

Includes bibliographical references.A new two-dimensional (2-D) block Kalman filtering method is presented which uses a full-plane image model to generate a more accurate filtered estimate of an image that has been corrupted by additive noise and full-plane blur. Causality is maintained within the filtering process by employing multiple concurrent block estimators. In addition, true state dynamics are preserved, resulting in an accurate Kalman gain matrix. Simulation results on a test image corrupted by additive white Gaussian noise are presented for various image models and compared to those of the previous block Kalman filtering methods

Two-dimensional adaptive block Kalman filtering of SAR imagery

Includes bibliographical references.Speckle effects are commonly observed in synthetic aperture radar (SAR) imagery. In airborne SAR systems the effect of this degradation reduces the accuracy of detection substantially. Thus, the elimination of this noise is an important task in SAR imaging systems. In this paper a new method for speckle noise removal is introduced using 2-D adaptive block Kalman filtering (ABKF). The image process is represented by an autoregressive (AR) model with nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which take into account not only the effect of speckles as a multiplicative noise but also those of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as that of the additive noise

Parameter estimation for two-dimensional vector models using neural networks

Includes bibliographical references.This correspondence addresses the problem of two-dimensional (2-D) vector image model parameter estimation using a new recursive least squares (RLS)-based learning method. Vector autoregressive (AR) models with various 1-D and 2-D, causal and noncausal regions of support (ROS) are considered. Numerical results are presented which demonstrate the usefulness of the proposed scheme for on-line implementation

Neural network directed Bayes decision rule for moving target classification

Includes bibliographical references.In this paper, a new neural network directed Bayes decision rule is developed for target classification exploiting the dynamic behavior of the target. The system consists of a feature extractor, a neural network directed conditional probability generator and a novel sequential Bayes classifier. The velocity and curvature sequences extracted from each track are used as the primary features. Similar to hidden Markov model (HMM) scheme, several hidden states are used to train the neural network, the output of which is the conditional probability of occurring the hidden states given the observations. These conditional probabilities are then used as the inputs to the sequential Bayes classifier to make the classification. The classification results are updated recursively whenever a new scan of data is received. Simulation results on multiscan images containing heavy clutter are presented to demonstrate the effectiveness of the proposed methods.This work was funded by the Optoelectronic Computing Systems (OCS) Center at Colorado State University, under NSF/REC Grant 9485502