Supramolecular engineering of cellulose-polyaniline composites using CuTA for superior energy storage applications.

Cellulose, the most abundant biomass, is highly appreciated for its robustness, biodegradability, and renewability, garnering significant interest for innovative applications in sustainable functional materials. Composites of cellulose and polyaniline (PANI) are particularly promising for flexible supercapacitors because of their ease of processing, excellent electrical conductivity, and high theoretical specific capacitance. However, challenges persist due to the tendency of PANI to agglomerate and the weak interfacial interactions between PANI and cellulose fibers (CFs).

Using cluster skeleton as prototype for data labeling.

A new approach, designed for clustering data whose underlying distribution shapes are arbitrary, is presented. This study is concerned with the use of the skeleton of a cluster as its prototype, which can represent the cluster more closely than that of using a single data point. The given data set is then partitioned into those skeleton-represented clusters without any prior knowledge nor assumptions of hidden structures.

Dynamic fuzzy neural networks-a novel approach to function approximation.

In this paper, an architecture of dynamic fuzzy neural networks (D-FNN) implementing Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended radial basis function (RBF) neural networks is proposed. A novel learning algorithm based on D-FNN is also presented. The salient characteristics of the algorithm are: 1) hierarchical on-line self-organizing learning is used; 2) neurons can be recruited or deleted dynamically according to their significance to the system's performance; and 3) fast learning speed can be achieved.

Fast orthogonal forward selection algorithm for feature subset selection.

Feature selection is an important issue in pattern classification. In the presented study, we develop a fast orthogonal forward selection (FOFS) algorithm for feature subset selection. The FOFS algorithm employs an orthogonal transform to decompose correlations among candidate features, but it performs the orthogonal decomposition in an implicit way.

RBF neural network center selection based on Fisher ratio class separability measure.

For classification applications, the role of hidden layer neurons of a radial basis function (RBF) neural network can be interpreted as a function which maps input patterns from a nonlinear separable space to a linear separable space. In the new space, the responses of the hidden layer neurons form new feature vectors. The discriminative power is then determined by RBF centers.

Bounds of the incremental gain for discrete-time recurrent neural networks.

As a nonlinear system, a recurrent neural network generally has an incremental gain different from its induced norm. While most of the previous research efforts were focused on the latter, this paper presents a method to compute an effective upper bound of the former for a class of discrete-time recurrent neural networks, which is not only applied to systems with arbitrary inputs but also extended to systems with small-norm inputs. The upper bound is computed by simple optimizations subject to linear matrix inequalities (LMIs).

GenSoFNN: a generic self-organizing fuzzy neural network.

Existing neural fuzzy (neuro-fuzzy) networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical training data.

Shape indexing using self-organizing maps.

In this paper, we propose a novel approach to generate the topology-preserving mapping of structural shapes using self-organizing maps (SOMs). The structural information of the geometrical shapes is captured by relational attribute vectors. These vectors are quantised using an SOM.

Face recognition with radial basis function (RBF) neural networks.

A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition, is presented. In order to avoid overfitting and reduce the computational burden, face features are first extracted by the principal component analysis (PCA) method. Then, the resulting features are further processed by the Fisher's linear discriminant (FLD) technique to acquire lower-dimensional discriminant patterns.

Communication channel equalization using complex-valued minimal radial basis function neural networks.

A complex radial basis function neural network is proposed for equalization of quadrature amplitude modulation (QAM) signals in communication channels. The network utilizes a sequential learning algorithm referred to as complex minimal resource allocation network (CMRAN) and is an extension of the MRAN algorithm originally developed for online learning in real-valued radial basis function (RBF) networks. CMRAN has the ability to grow and prune the (complex) RBF network's hidden neurons to ensure a parsimonious network structure.

Design and fabrication of a high performance multilayer piezoelectric actuator with bending deformation.

A new bending mode multimorph actuator was designed and fabricated successfully by a multiple screen printing process. Unlike the conventional bimorph actuator in which the bend occurs in the thickness direction, the bend in the multimorph actuator occurs in the widthwise direction because of synchronistical deformation of each single monolithic layer in the multilayer structure. The theoretical analysis and experimental measurements were conducted to study the performance of this type of actuator, and a comparison was made with the conventional bimorph actuator.

POPFNN-CRI(S): pseudo outer product based fuzzy neural network using the compositional rule of inference and singleton fuzzifier.

A pseudo-outer product based fuzzy neural network using the compositional rule of inference and singleton fuzzifier [POPFNN-CRI(S)] is proposed in this paper. The correspondence of each layer in the proposed POPFNN-CRI(S) to the compositional rule of inference using standard T-norm and fuzzy relation gives it a strong theoretical foundation. The proposed POPFNN-CRI(S) training consists of two phases; namely: the fuzzy membership derivation phase using the novel fuzzy Kohonen partition (FKP) and pseudo Kohonen partition (PFKP) algorithms, and the rule identification phase using the novel one-pass POP learning algorithm.

Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance.

For high dimensional data, if no preprocessing is carried out before inputting patterns to classifiers, the computation required may be too heavy. For example, the number of hidden units of a radial basis function (RBF) neural network can be too large. This is not suitable for some practical applications due to speed and memory constraints.

Structural pattern recognition using genetic algorithms with specialized operators.

This paper presents a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. The objective of our work is to improve the GA-based ARG matching procedures leading to a faster convergence rate and better quality mapping between a scene ARG and a set of given model ARGs. In this study, potential solutions are represented by integer strings indicating the mapping between scene and model vertices.

Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks.

A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks, such as multilayer perceptrons (MLPs) trained with the backpropagation (BP) algorithm, is that they require a large amount of computation for learning. We propose a single-layer functional-link ANN (FLANN) in which the need for a hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials.

Study on eye gaze estimation.

There are two components to the human visual line-of-sight: pose of human head and the orientation of the eye within their sockets. We have investigated these two aspects but will concentrate on eye gaze estimation. We present a novel approach called the "one-circle" algorithm for measuring the eye gaze using a monocular image that zooms in on only one eye of a person.

Learning capability and storage capacity of two-hidden-layer feedforward networks.

The problem of the necessary complexity of neural networks is of interest in applications. In this paper, learning capability and storage capacity of feedforward neural networks are considered. We markedly improve the recent results by introducing neural-network modularity logically.

Color filter array demosaicking: new method and performance measures.

Single-sensor digital cameras capture imagery by covering the sensor surface with a color filter array (CFA) such that each sensor pixel only samples one of three primary color values. To render a full-color image, an interpolation process, commonly referred to as CFA demosaicking, is required to estimate the other two missing color values at each pixel. In this paper, we present two contributions to the CFA demosaicking: a new and improved CFA demosaicking method for producing high quality color images and new image measures for quantifying the performance of demosaicking methods.

Issues in implementing a knowledge-based ECG analyzer for personal mobile health monitoring.

Advances in sensor technology, personal mobile devices, and wireless broadband communications are enabling the development of an integrated personal mobile health monitoring system that can provide patients with a useful tool to assess their own health and manage their personal health information anytime and anywhere. Personal mobile devices, such as PDAs and mobile phones, are becoming more powerful integrated information management tools and play a major role in many people's lives. We focus on designing a health-monitoring system for people who suffer from cardiac arrhythmias.

Modeling and Identification of practical ultrasound transducers in ultrasound imaging systems.

This paper provides linear analysis for a practical ultrasound imaging system with single-element transducers. A proper eighth-order linear ARMA model with inputs and outputs of voltage traces is given to present the transfer characteristics of such a practical system, so that echo signals containing tissue information can be collected and analyzed more properly.

Distributed personal health information management system for dermatology at the homes for senior citizens.

A distributed personal health information management system (D-PHIMS) has been tested at a nursing home for the senior citizens (NHSC) in Singapore. The personal health information management system (PHIMS) from the University of Washington was customized to Singapore's context for teledermatology. A clinical trial commenced in October 2005 is ongoing and the survey results obtained indicate that the participants are satisfied with the D-PHIMS system.

Master-slave robotic system for therapeutic gastrointestinal endoscopic procedures.

Flexible endoscopy is used to inspect and treat disorders of the gastrointestinal (GI) tract without the need for creating an artificial opening on the patient's body. Simple surgical procedures (like polypectomy and biopsy) can be performed by introducing a flexible tool via a working channel to reach the site of interest at the distal end. More technically demanding surgical procedures like hemostasis for arterial bleeding, or suturing to mend a perforation cannot be effectively achieved with flexible endoscopy.

A new scheme and reconstruction algorithm for dual source circular CT.

Circular cone beam scanning has been a most popular scheme for computed tomography (CT) imaging, which is simple and can achieve symmetric projection data of the interested object. Many algorithms have been developed for circular cone beam CT. Many of these are FDK type algorithms, which can achieve good reconstruction quality when cone angle is small but may cause image deformation and density reduction at off plane when the cone angle is large.

An iterative constrained least squares filter for ultrasound image deconvolution.

This paper describes how the recently developed constrained least squares (CLS) filtering algorithm can be made iterative to improve the resolution gain (RG) of medical ultrasound images. We propose the use of the iterative CLS (ICLS) filter, by incorporating the recently proposed ultrasound tissue model, to account for the random fluctuations of the tissue signal within the received ultrasound radio frequency (RF) echo signal. The resulting improvement in RG is demonstrated by eight different abdomen ultrasound images where progressive improvements in both the axial and lateral directions can be observed.

UWB microwave breast cancer detection: generalized models and performance prediction.

This paper presents a generic framework for the modeling of ultra-wideband (UWB) signal propagation in human breast, which facilitates system-level simulations and provides performance prediction. The clutter associated with the breast tissue heterogeneity is modeled through several key parameters depending on the tissue compositions. Subsequently, important channel properties such as the backscatter energy and the probability density function of time-of-arrival are derived.