En-Bing Lin

Wavelet Analysis of ECG Signals

This study evaluated the effectiveness of different types of wavelets and thresholds to process electrocardiograms. An electrocardiogram, or ECG, shows the electrical activity in the heart and can be used to detect abnormalities. The first process used term-by-term thresholding to denoise ECGs. The second process denoised and compressed ECGs using global thresholding. The effectiveness was determined by using the signal-to-noise ratio (SNR) and the percentage root mean square difference (PRD).

Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.

Background Exhaled aerosol patterns, also called aerosol fingerprints, provide clues to the health of the lung and can be used to detect disease-modified airway structures. The key is how to decode the exhaled aerosol fingerprints and retrieve the lung structural information for a non-invasive identification of respiratory diseases. Objective and Methods In this study, a CFD-fractal analysis method was developed to quantify exhaled aerosol fingerprints and applied it to one benign and three malign conditions: a tracheal carina tumor, a bronchial tumor, and asthma. Respirations of tracer aerosols of 1 µm at a flow rate of 30 L/min were simulated, with exhaled distributions recorded at the mouth. Large eddy simulations and a Lagrangian tracking approach were used to simulate respiratory airflows and aerosol dynamics. Aerosol morphometric measures such as concentration disparity, spatial distributions, and fractal analysis were applied to distinguish various exhaled aerosol patterns. Findings Utilizing physiology-based modeling, we demonstrated substantial differences in exhaled aerosol distributions among normal and pathological airways, which were suggestive of the disease location and extent. With fractal analysis, we also demonstrated that exhaled aerosol patterns exhibited fractal behavior in both the entire image and selected regions of interest. Each exhaled aerosol fingerprint exhibited distinct pattern parameters such as spatial probability, fractal dimension, lacunarity, and multifractal spectrum. Furthermore, a correlation of the diseased location and exhaled aerosol spatial distribution was established for asthma. Conclusion Aerosol-fingerprint-based breath tests disclose clues about the site and severity of lung diseases and appear to be sensitive enough to be a practical tool for diagnosis and prognosis of respiratory diseases with structural abnormalities.

Comparisons between Rough Set Based and Computational Applications in Data Mining

Rough set theory is a set theory for the study of information systems which are characterized by insufficient and incomplete information. An information system can be regarded as a set-valued system. Some of its attribute values may be subsets of an attribute domain. One of our objectives of this study is to find rules, relationships and classifications of such a system and to develop applications to data mining. Information systems can be represented in various ways. One approach is to use attribute systems in which each system can be interpreted as an ordered pair (U, R); where U is a non-empty set of all finite objects under consideration and R is an equivalence relation on U. This approach is called the rough set approach. Rough Set theory originated from Pawlaks seminal work (1). It has been conceived as a tool to conceptualize, analyze and classify various types of data. It has been developed as a tool to classify objects which are only roughly described. The available information provides a partial discrimination among them although they are considered as different objects. In other words, objects considered distinct could happen to have the same or similar description, as far as a set of attributes is considered. The theory extends the classical crisp set to a rough (or approximate) set by defining lower and upper approximations for any subset of a non-empty universe. It is based on the concept that every object of the universe is associated with some information (data or knowledge). Objects characterized by the same information are considered indiscernible. Thus an elementary set can be any set of all indiscernible entities, and it forms the basic granule of knowledge (2)-(4). Information granulation is a collection of granules, with a granule being a clump of objects (points) which are drawn toward an object.

Definability of approximations in reflexive relations

Considering a reflexive relation R on a fixed nonempty set U, four different constructions of lower and upper approximations are described by using the so-called R-successor or/and R-predecessor sets of each object of the set U. The first two of the four constructions of lower and upper approximations are well known, and one pair is presented in this paper for the first time. The lower and upper approximations in each pair are mutually dual, and all the four upper approximations discussed in this paper are extensive and monotonic. If the reflexive relation R is further assumed to be symmetric, the four constructions of lower and upper approximations are induced to the commonly used lower and upper approximations. The primary goal of this paper is to study definability of approximations in reflexive relations via a special kind of neighborhood systems, called total pure reflexive neighborhood systems. It is shown that such neighborhood systems give a unified framework for definability of the four constructions.

Multiresolution Analysis of DNA Sequences

A wavelet based method for transforming DNA sequences is illustrated by using the small subunit of the ribosome. This paper discusses the application of multi resolution analysis on FASTA-formatted DNA sequences using biorthogonal wavelets. Once transformed, the data could be used for pairwise or multiple sequence alignments needed for studies of evolutionary relationships or for gene finding. Further studies of wavelet based methods are also mentioned.

Neighborhood Systems and Variable Precision Generalized Rough Sets

In this paper, we present the connection between the concepts of Variable Precision Generalized Rough Set model (VPGRS-model) and Neighborhood Systems through binary relations. We provide characterizations of lower and upper approximations for VPGRS-model by introducing minimal neighborhood systems. Furthermore, we explore generalizations by investigating variable parameters which are limited by variable precision. We also prove some properties of lower and upper approximations for VPGRS-model.

Definability of rough approximations for binary relations and cloud computing

We establish a characterization of upper approximation for binary relations. Based on this characterization, the R-definability of Slowinski and Vanderpooten is alternatively defined in terms of lower and upper approximations. In addition, some necessary conditions for R-definability are presented.

A conservative and variation preserving finite volume method for non-overlapping meshes of reaction and diffusion in angiogenesis

We propose a conservative and variation preserving finite volume method for reaction and diffusion in angiogenesis. The reaction domain keeps changing the morphology and length, and its mesh is non-uniform and does not overlap with the diffusion mesh. These facts make it very challenging to develop a numerical method that conserves the mass when transferring data between the reaction and diffusion domains. We prove the method developed in this work not only conserves the mass locally but also retains the variation in the reaction domain. In contrast, the direct interpolation may smear out the reaction data in the data transfer process. This method is applied to the growth factor reaction and diffusion problems in angiogenesis. Reaction and diffusion of growth factors in angiogenesis.Reaction and the diffusion meshes are non-overlapping.Conservative and reaction-variation preserving finite volume method.Handle non-uniform discretization and arbitrary shaped reaction domains.

On variable precision of generalized rough sets

We generalize Variable Precision Rough Set Theory (VPRST) to Variable Precision of Generalized Rough Sets (VPGRS) through reflexive relations and provide characterizations of lower and upper approximations for VPGRS.