Toward automatic annotation of in situ mRNA expression patterns of drosophila embryos
The in situ mRNA hybridization gene expression pattern images of Drosophila melanogaster in the course of embryogenesis provide spatial-temporal information of the expression patterns of a target gene. This information is critical for understanding the roles of genes during the development of embryos. Currently, the annotation of these images is often done by manually assigning a subset of image ontology terms to the images. This approach is time consuming and depends heavily on the consistency of the experts. Alternatively, if the annotation process can be automated or semi-automated, efficiency and consistency are likely to be greatly enhanced. We formulate this task as a pattern classification problem, and present preliminary results. We consider both the template-based global matching and the multi-objective classification based on neural networks (e.g. multi-layer perceptron). We develop a method to combine them to optimize efficiency and accuracy. This method has been applied to the gene expression pattern image database generated by the Berkeley Drosophila Genome Project.

title: Regularized B-spline network and its application to heart arrhythmia classification
abstract: This paper presents an effective learning scheme that combines B-spline modeling and regularized neural networks. Essential issues of structural design and learning process are discussed. Regularization theory is leveraged to design the topological structure of the network. A training algorithm is derived for the learning of both synaptic weights and B-spline coefficients. The approach is then applied to the medical problem of heart arrhythmia detection, particularly the detection of premature ventricular contraction. Promising results demonstrate the potential benefits of the proposed method.

title: Automatic detection of premature ventricular contraction using quantum neural networks
abstract: Premature ventricular contractions (PVCs) are ectopic heart beats originating from ventricular area. It is a common form of heart arrhythmia. Electrocardiogram (ECG) recordings have been widely used to assist cardiologists to diagnose the problem. In this paper, we study the automatic detection of PVC using a fuzzy artificial neural network named Quantum Neural Network (QNN). With the quantum neurons in the network, trained QNN can model the levels of uncertainty arising from complex classification problems. This fuzzy feature is expected to enhance the reliability of the algorithm, which is critical for the applications in the biomedical domain. Experiments were conducted on ECG records in the MIT-BIH Arrhythmia Database. Results showed consistently higher or same reliability of QNN on all the available records compared to the backpropagation network. QNN, however, has a relatively higher resource requirement for training.

title: Verification - a method of enhancing the recognizers of isolated and touching handwritten numerals
abstract: This paper investigates verification schemes and their applications to the recognition of both isolated and touching handwritten numerals. Definitions and functionality analyses of the verifiers are given. The measurement of precision rate is used to assess the system reliability in a class-specific manner. Verification-enhanced systems are proposed with extensive experiments conducted on both isolated and touching numerals. Two databases for touching numerals are built/organized to serve as standard data sets. Experimental results indicate a substantial improvement in system precision rates by the verification scheme, which proves the effectiveness of the proposed systems and justifies the important role of verifiers in OCR systems.

title: Unconstrained numeral pair recognition using enhanced error correcting output coding: a holistic approach
abstract: This paper describes a new approach to recognize touching numeral strings. Currently most methods for numeral string recognition require segmenting the string image into separate numerals. As a result, the recognition system heavily depends on the reliability of the segmentation module. This study explores the holistic strategy directly on the string images without segmentation. It builds the novel classifier by combining binary classifiers based on data-driven error correcting output coding (DECOC). The dimensions of input images are reduced using principal components analysis. Support vector machines are used as base learners. Experiments on NIST SD19 touching numeral pairs confirm that DECOC can achieve favorable performance compared with other multi-class holistic classifiers. The method provides the flexibility of controlling the computational complexity versus accuracy. We also discuss an implementation suitable for distributing computing by decomposing the ensemble into subtasks.

title: Recognition of handwritten numerals by Quantum Neural Network with fuzzy features
abstract: This paper describes a new kind of neural network – Quantum Neural Network (QNN) – and its application to the recognition of handwritten numerals. QNN combines the advantages of neural modelling and fuzzy theoretic principles. Novel experiments have been designed for in-depth studies of applying the QNN to both real data and confusing images synthesized by morphing. Tests on synthesized data examine QNN's fuzzy decision boundary with the intention to illustrate its mechanism and characteristics, while studies on real data prove its great potential as a handwritten numeral classifier and the special role it plays in multi-expert systems. An effective decision-fusion system is proposed and a high reliability of 99.10% has been achieved.

title: Image Rectification for Stereoscopic Visualization Without 3D Glasses
abstract: There exist various methods for stereoscopic viewing of images, most requiring that a viewer wears some special glasses. Recent technology developments have resulted in displays that enable 3D viewing without glasses. In this paper, we present results from our proposed approach to automatic rectification of two images of the same scene captured by cameras at general positions, so that the results can be viewed on a 3D display. Both simulated and real data experiments are presented.

title: Intermediary non-repudiation protocols
abstract: In commercial transactions, an intermediary might be involved to help transacting parties to conduct their business. Nevertheless, the intermediary may not be fully trusted. We introduce the concept of intermediary (or agent) in a non-repudiation protocol, define the aims of intermediary non-repudiation protocols, and analyze their security requirements. We present a simple scenario with only one recipient, followed by a more complicated framework where multiple recipients are involved and collusion between them is possible.

title: Defending against redirect attacks in mobile IP
abstract: The route optimization operation in Mobile IP Version 6 (MIPv6) allows direct routing from any correspondent node to any mobile node and thus eliminates the problem of "triangle routing" present in the base Mobile IP Version 4 (MIPv4) protocol. Route optimization, however, requires that a mobile node constantly inform its correspondent nodes about its new care-of addresses by sending them binding update messages. Unauthenticated or malicious binding updates open the door for intruders to perform redirect attacks, i.e., malicious acts which redirect traffic from correspondent nodes to locations chosen by intruders. How to protect binding update messages to defend against redirect attacks is a challenging problem given the open environment in which MIPv6 operates. In this paper, we first look at two solutions proposed by the IETF Mobile IP Working Group and point out their weaknesses. We then present a new protocol for securing binding update messages. We also show that our protocol achieves strong security and at the same time is highly scalable to wide spread deployment.

title: Rule-base technique for component adaptation to support QoS-based reconfiguration
abstract: Many critical application systems require reconfigurability in order to adapt under changing environment. To achieve system reconfigurability, it is possible to use adaptation mechanisms that are external to components or to use parameterized components. This paper focuses on making components reconfigurable in terms of QoS tradeoffs. We propose a new component parameterization technique which can transform individual components to reconfigurable ones such that they can support various QoS tradeoffs to achieve overall system QoS requirements. The parameterized components can be used to develop both statically and dynamically reconfigurable systems. The proposed technique is rule based, which provides flexibility and extensibility to enhance and improve the technique. A tool set has been developed based on the proposed technique to achieve semi-automated component parameterization.

title: A Data Fusion Approach to Mobile Location Estimation based on Ellipse Propagation Model within a Cellular Radio Network
abstract: Mobile location estimation is drawing considerable attention in the field of wireless communications. In this paper, we present a new estimator which considers all the information to reduce the effect of signal fluctuation and fading-the statistical estimation. The Statistical Estimation is derived from the information of the received signal strengths (RSSs) and the locations of their corresponding base stations (BSs) and then estimates the location of the mobile station (MS). The statistical estimation uses all the information to provide the estimation of the location of the MS, which can provide an accurate estimation and reduce the effect of signal fluctuation and fading. It is a data fusion method to handle the signal fluctuation and fading problem. We test our approach with real data collected from Hong Kong. Experimental results show that our approach outperforms other existing location estimation algorithms among different kinds of terrains. The improvements based on the geometric algorithm with EPM and the iterative algorithm with EPM are 18.87% and 4.46%, respectively.