Ernest C.M. Lam’s research while affiliated with Hong Kong Baptist University and other places

We propose a new web hosting service with statistical capacity guarantee. By using this service, each customer subscribes to a certain capacity; that is, the speed that the web hosting system processes and transfers the web content, for his/her web site. The system provides the requested capacity to each web site with a probability of p where p is a given quality-of-service (QoS) requirement. To fulfill this QoS requirement, the system measures the traffic statistics of each web site and manages statistical resource sharing among the web sites. As a result, the system can better utilize its resources for hosting more web sites. The proposed web hosting service involves two new decision problems: (1) an admission control problem in which the system decides whether it can accommodate a new web site and (2) an assignment problem in which the system assigns web sites to server clusters to fulfill the QoS requirement. We have formulated these decision problems and designed efficient algorithms as solutions. The simulation results demonstrate that web hosting systems that use the proposed service can host significantly more web sites via statistical resource sharing among web sites.

In this paper, a novel approach to feature extraction with wavelet and fractal theories is presented as a powerful technique in pattern recognition. The motivation behind using fractal transformation is to develop a high-speed feature extraction technique. A multiresolution family of the wavelets is also used to compute information conserving micro-features. In this study, a new fractal feature is reported. We employed a central projection method to reduce the dimensionality of the original input pattern, and a wavelet transform technique to convert the derived pattern into a set of subpatterns, from which the fractal dimensions can readily be computed. The new feature is a measurement of the fractal dimension, which is an important characteristic that contains information about the geometrical structure. This new scheme includes utilizing the central projection transformation to describe the shape, the wavelet transformation to aid the boundary identification, and the fractal features to enhance image discrimination. The proposed method reduces the dimensionality of a 2-D pattern by way of a central projection approach, and thereafter, performs Daubechies' wavelet transform on the derived 1-D pattern to generate a set of wavelet transform subpatterns, namely, curves that are non-self-intersecting. Further from the resulting non-self-intersecting curves, the divider dimensions are computed with a modified box-counting approach. These divider dimensions constitute a new feature vector for the original 2-D pattern, defined over the curve's fractal dimensions. We have conducted several experiments in which a set of printed Chinese characters, English letters of varying fonts and other images were classified. Based on the formulation of our new feature vector, the experiments have satisfying results.

In this paper, we propose a novel wavelet domain HMM using block to strike a delicate balance between improving spatial adaptability of contextual HMM (CHMM) and modeling a more reliable HMM. Each wavelet coefficient is modeled as a Gaussian mixture model, and the dependencies among wavelet coefficients in each subband are described by a context structure, then the structure is modified by blocks which are connected areas in a scale conditioned on the same context. Before denoising the signal, efficient expectation maximization (EM) algorithms are developed for fitting the HMMs to observational signal data. Parameters of trained HMM are used to modify wavelet coefficients according to the rule of minimizing the mean squared error (MSE) of the signal. Then, the reverse wavelet transformation is utilized to modify wavelet coefficients. Finally, experimental results are given. The results show that the block hidden Markov model (BHMM) is a powerful yet simple tool in signal denoising.

Hidden Markov models (HHMs), while well applied in fields such as speech recognition and optical character recognition, have not been used in post-classification for search engines. We explore the use of HMMs for optimization of search engines tasks, specifically focusing on how to construct a new model structure to improve the classification of web pages. We show that a manually constructed new structure model that contains only two states and two classes of observations per field can produce good classification results, and discuss strategies for learning the model structure automatically from data. We also demonstrate that the use of new structure model to classify the search results using some search engines and some different search keywords provide a significant improvement in search accuracy. Our models are applied to the task of post-classifying the web pages selected by the search engine Google, and achieve a classification accuracy of 93.4.

In this paper, a novel approach to feature extraction based on fractal theory is presented as a powerful technique in pattern recognition. This paper presents a new fractal feature that can be applied to extract the feature of two-dimensional objects. It is constructed by a hybrid feature extraction combining wavelet analysis, central projection transformation and fractal theory. New fractal feature and fractal signatures are reported. A multiresolution family of the wavelets is also used to compute information conserving micro-features. We employed a central projection method to reduce the dimensionality of the original input pattern. A wavelet transformation technique to transform the derived pattern into a set of sub-patterns. Its fractal dimension can readily be computed, and to use the fractal dimension as the feature vectors. Moreover, a modified fractal signature is also used to distinguish the distinct handwritten signatures. We expect that the proposed fractal method can also be used for improving the extraction and classification of features in pattern recognition.

In this paper, we are investigating the utility of several emerging techniques to extract features. A novel method of feature extraction is proposed, which includes utilizing the central projection transformation (CPT) to describe the shape, the wavelet transformation to aid in the boundary identification, and the fractal features to enhance image discrimination. It reduces the dimensionality of a two-dimensional pattern by way of a central projection approach, and thereafter, performs Daubechies' wavelet transform on the derived one-dimensional pattern to generate a set of wavelet transform sub-patterns, namely, curves that are non-self-intersecting. The divider dimensions are computed from these curves with a modified box-counting approach. These divider dimensions constitute a new feature vector for the original two-dimensional pattern, defined over the curve's fractal dimensions. We have conducted several experiments in which a set of printed Chinese characters, English letters of varying fonts and other images were classified. Based on the Euclidean distance between the different feature vectors, the experiments have satisfying results.

This chapter describes a conceptual framework for designing and developing software agents that will enable customized electronic commerce (CEC) and highlights several important constructs as well as their interrelationships within the framework. In particular, it examines the enabling technologies under two categories, namely, on-line cataloging and recommendation. In order to demonstrate some of the key characteristics of customized electronic commerce, this chapter also presents three prototyped software agents, namely, ETA (Electronic Tour Agent), EPA (Electronic Property Agent), and EAA (Electronic Auction Agent).

This chapter describes a conceptual framework for designing and developing software agents that will enable customized electronic commerce (CEC) and highlights several important constructs as well as their interrelationships within the framework. In particular, it examines the enabling technologies under two categories, namely, on-line cataloging and recommendation. In order to demonstrate some of the key characteristics of customized electronic commerce, this chapter also presents three prototyped software agents, namely, ETA (Electronic Tour Agent), EPA (Electronic Property Agent), and EAA (Electronic Auction Agent).

As the interest in fractal geometry rises, the applications are getting more and more numerous in many domains. The aim of the authors is that these concepts can also be applied to feature extraction of patterns and that they can help, to a certain extent, to ease the solution of many problems. In this paper, the proposed method reduces the dimensionality of a two-dimensional pattern by way of a central projection approach, and thereafter, performs Daubechies' wavelet transformation on the derived one-dimensional pattern to generate a set of wavelet transformation sub-patterns, namely, curves that are non-self-intersecting. Further from the resulting nonself-intersecting curves, the divider dimensions are computed with modified box-counting approach. These divider dimensions constitute a new feature vector for the original two-dimensional pattern, defined over the curve's fractal dimensions