Image fusion based on median filters and SOFM neural networks: a three-step scheme

This paper presents a new image data fusion scheme by combining median filtering with self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2), which suppresses the residual noise components and thus further improves the image quality. It proves that such a three-step combination offers an impressive effectiveness and performance improvement, which is confirmed by simulations involving three image sensors (each of which has a different noise structure).

Modeling requirement driven architecture of adaptive healthcare system based on semiotics

In order to improve the quality of healthcare services, the integrated large-scale medical information system is needed to adapt to the changing medical environment. In this paper, we propose a requirement driven architecture of healthcare information system with hierarchical architecture. The system operates through the mapping mechanism between these layers and thus can organize functions dynamically adapting to user’s requirement. Furthermore, we introduce the organizational semiotics methods to capture and analyze user’s requirement through ontology chart and norms. Based on these results, the structure of user’s requirement pattern (URP) is established as the driven factor of our system. Our research makes a contribution to design architecture of healthcare system which can adapt to the changing medical environment.

Integrated clinical pathway management for medical quality improvement-based on a semiotically inspired systems architecture

Clinical pathway is an approach to standardise care processes to support the implementations of clinical guidelines and protocols. It is designed to support the management of treatment processes including clinical and non-clinical activities, resources and also financial aspects. It provides detailed guidance for each stage in the management of a patient with the aim of improving the continuity and coordination of care across different disciplines and sectors. However, in the practical treatment process, the lack of knowledge sharing and information accuracy of paper-based clinical pathways burden health-care staff with a large amount of paper work. This will often result in medical errors, inefficient treatment process and thus poor quality medical services. This paper first presents a theoretical underpinning and a co-design research methodology for integrated pathway management by drawing input from organisational semiotics. An approach to integrated clinical pathway management is then proposed, which aims to embed pathway knowledge into treatment processes and existing hospital information systems. The capability of this approach has been demonstrated through the case study in one of the largest hospitals in China. The outcome reveals that medical quality can be improved significantly by the classified clinical pathway knowledge and seamless integration with hospital information systems.

Filter for Car Tracking Based on Acceleration and Steering Angle

The motion of a car is described using a stochastic model in which the driving processes are the steering angle and the tangential acceleration. The model incorporates exactly the kinematic constraint that the wheels do not slip sideways. Two filters based on this model have been implemented, namely the standard EKF, and a new filter (the CUF) in which the expectation and the covariance of the system state are propagated accurately. Experiments show that i) the CUF is better than the EKF at predicting future positions of the car; and ii) the filter outputs can be used to control the measurement process, leading to improved ability to recover from errors in predictive tracking.

A Filter for Visual Tracking Based on a Stochastic Model for Driver Behaviour

A driver controls a car by turning the steering wheel or by pressing on the accelerator or the brake. These actions are modelled by Gaussian processes, leading to a stochastic model for the motion of the car. The stochastic model is the basis of a new filter for tracking and predicting the motion of the car, using measurements obtained by fitting a rigid 3D model to a monocular sequence of video images. Experiments show that the filter easily outperforms traditional filters.

Water-soluble, unimolecular containers based on amphiphilic multiarm star block copolymers

A new class of water-soluble, amphiphilic star block copolymers with a large number of arms was prepared by sequential atom transfer radical polymerization (ATRP) of n-butyl methacrylate (BMA) and poly( ethylene glycol) methyl ether methacrylate (PEGMA). As the macroinitiator for the ATRP, a 2-bromoisobutyric acid functionalized fourth-generation hyperbranched polyester (Boltorn H40) was used, which allowed the preparation of star polymers that contained on average 20 diblock copolymer arms. The synthetic concept was validated by AFM experiments, which allowed direct visualization of single molecules of the multiarm star block copolymers. DSC and SAXS experiments on bulk samples suggested a microphase-separated structure, in agreement with the core-shell architecture of the polymers. SAXS experiments on aqueous solutions indicated that the star block copolymers can be regarded as unimolecular micelles composed of a PBMA core and a diffuse PPEGMA corona. The ability of the polymers to encapsulate and release hydrophobic guests was evaluated using H-1 NMR spectroscopy. In dilute aqueous solution, these polymers act as unimolecular containers that can be loaded with up to 27 wt % hydrophobic guest molecules.

Development of novel brush-type chiral stationary phases based on terpenoid selectors: HPLC evaluation and theoretical investigation of enantioselective binding interactions

The terpenoid chiral selectors dehydroabietic acid, 12,14-dinitrodehydroabietic acid and friedelin have been covalently linked to silica gel yielding three chiral stationary phases CSP 1, CSP 2 and CSP 3, respectively. The enantiodiscriminating capability of each one of these phases was evaluated by HPLC with four families of chiral aromatic compounds composed of alcohols, amines, phenylalanine and tryptophan amino acid derivatives and beta-lactams. The CSP 3 phase, containing a selector with a large friedelane backbone is particularly suitable for resolving free alcohols and their derivatives bearing fluorine substituents, while CSP 2 with a dehydroabietic architecture is the only phase that efficiently discriminates 1, 1'-binaphthol atropisomers. CSP 3 also gives efficient resolution of the free amines. All three phases resolve well the racemates of N-trifluoracetyl and N-3,5-dinitrobenzoyl phenylalanine amino acid ester derivatives. Good enantioseparation of beta-lactams and N-benzoyl tryptophan amino acid derivatives was achieved on CSP 1. In order to understand the structural factors that govern the chiral molecular recognition ability of these phases, molecular dynamics simulations were carried out in the gas phase with binary diastereomeric complexes formed by the selectors of CSP 1 and CSP 2 and several amino acid derivatives. Decomposition of molecular mechanics energies shows that van der Waals interactions dominate the formation of the diastereomeric transient complexes while the electrostatic binding interactions are primarily responsible for the enantioselective binding of the (R)- and (S)-analytes. Analysis of the hydrogen bonds shows that electrostatic interactions are mainly associated with the formation of N-(HO)-O-...=C enantio selective hydrogen bonds between the amide binding sites from the selectors and the carbonyl groups of the analytes. The role of mobile phase polarity, a mixture of n-hexane and propan-2-ol in different ratios, was also evaluated through molecular dynamics simulations in explicit solvent. (c) 2006 Elsevier Ltd. All rights reserved.