Environmental radioactivity levels in Malaysian marine species

Radioactivity measurements were carried out on some Malaysian marine species using gross γ‐ray spectrometry. ¹³⁷Cs levels of the order of 7 to 10 Bq/dry kg were detected in the larger‐sized species whereas considerably lower levels were found in the smaller‐sized ones. No other artificial radioisotopes were present in significant concentrations. ⁴⁰K levels ranged from about 70 to 567 Bq/dry kg. The concentration factors for both ^l17Cs and ⁴⁰K in these samples were also reported.     

Photoelectric properties of MISM phthalocyanine structure

Langmuir-Blodgett films have been prepared from copper tetra-4-t-butylphthalocyanine. A voltage dependence of capacitance can be attributed to the presence of a Schottky depletion layer. The barrier potential was determined as l·4eV, and the carrier concentration about 10²⁴m^?3     

Pattern Recognition Theory in Nonlinear Signal Processing

A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.     

Multiresolution Design of Aperture Operators

The design of an aperture operator is based on adequately constraining the spatial domain and the graylevel range in order to diminish the space of operators and, consequently, the estimation error. The design of a resolution constrained operator is based on adequately combining information from two or more different resolutions and has the same motivation, that is, diminish the space of operators to facilitate design. This paper joins these approaches and studies multiresolution design of aperture operators for grayscale images. Spatial resolution constraint, range resolution constraint and the combination of both constraints are characterized, and the error increase by using the constrained filter in place of the optimal unconstrained one is analyzed. Pyramidal multiresolution design involves applying the resolution constraint approach hierarchically, from the higher to the lower resolution space. These approaches are also characterized and their error increase analyzed. The system that has been implemented to design pyramidal multiresolution operators is described and has its complexity (memory and runtime) analyzed. Several simulations and two applications for deblurring are shown and compared to optimal linear filters. The results confirm the usefulness of the approach.     

Markovian Analysis of Adaptive Reconstructive Multiparameter τ-Openings

A classical single-parameter -opening is a union of openings in which each structuring element is scaled by the same parameter. Multiparameter binary -openings generalize the model in two ways: first, parameters for each opening are individually defined; second, a structuring element can be parameterized relative to its overall shape, not merely sized. The reconstructive filter corresponding to an opening is defined by fully passing any grain (connected component) that is not fully eliminated by the opening and deleting all other grains. Adaptive design results from treating the parameter vector of a reconstructive multiparameter -opening as the state space of a Markov chain. Signal and noise are modeled as unions of randomly parameterized and randomly translated primary grains, and the parameter vector is transitioned depending on whether an observed grain is correctly or incorrectly passed. Various adaptive models are considered, transition probabilities are discussed, the state-probability increment equations are deduced from the appropriate Chapman-Kolmogorov equations, and convergence of the adaptation is characterized by the steady-state distribution relating to the Markov chain.