An improved ellipse propagation model for location estimation in facilitating ubiquitous computing
Positioning is a crucial technology for ubiquitous computing. A directional propagation model - the ellipse propagation model (EPM) is proposed by our research group for locating a mobile station (MS) within a radio cellular network with an accuracy that can enable a number of location based services to realize ubiquitous computing. By using a geometric algorithm, the location of the mobile station can be estimated. However, since one parameter in our geometric algorithm is fixed, errors may be induced as the surrounding environment changes. In view of this, we would like to propose a new algorithm - the iterative algorithm to provide the positioning based on EPM. With the technical support of two local mobile phone operators, we have conducted a series of experiments using real data and experiment results showed that the proposed iterative algorithm outperforms the geometric algorithm by a good margin of 18% in terms of average error.

title: A selector method for providing mobile location estimation services within a radio cellular network
abstract: Mobile location estimation or mobile positioning is becoming an important service for a mobile phone network. It is well-known that GPS can provide accurate location estimation, but it is also a known fact that GPS does not perform well in urban areas like downtown New York and cities like Hong Kong. Then many mobile location estimation approaches based on radio cellular networks have been proposed to compensate the problem of the lost of GPS signals in providing location services to mobile users in metropolitan areas. In this paper, we present a selector method with the linear discriminant analysis (LDA) among different kinds of mobile location estimation technologies we had proposed in previous work in order to combine their merits, then provide a more accurate estimation for location services. We build up a three-level binary tree to classify these four algorithms. These three levels are named as Stat-Geo level, CG-nonCG level and CT-EPM level. And these success ratios of these three levels are 85.22%, 88.45% and 88.89% respectively. We have tested our selector method with real data taken in Hong Kong and it is proven that it outperforms other existing location estimation algorithms among different kinds of terrains.

title: Optimizing parallel execution of detailed wireless network simulation
abstract: With parallel and discrete event simulation (PDES) techniques, the runtime performance of detailed wireless network simulation can be improved significantly without compromising fidelity of the simulation results. However, modelling characteristics of wireless communications such as signal propagation and interference may severely hinder the potential speedup yielded by PDES. This paper proposes various optimization techniques to address three major concerns in achieving efficient parallel execution of wireless network simulation: i.e., (1) reducing communication and computation overhead of simulating signal propagation across multiple logical processes; (2) reducing synchronization overhead among logical processes; (3) minimizing event scheduling overhead within individual logical processes. These techniques have been implemented in a parallel version of GloMoSim and QualNet. The experimental results with mobile ad hoc networking scenarios demonstrate that the proposed optimization techniques can improve the performance of parallel wireless network simulation by up to an order of magnitude.

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: 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: Authenticating Query Results in Data Publishing
abstract: We propose a communication-efficient authentication scheme to authenticate query results disseminated by untrusted data publishing servers. In our scheme, signatures of multiple tuples in the result set are aggregated into one and thus the communication overhead incurred by the signature keeps constant. Next attr-MHTs (tuple based Merkle Hash Tree) are built to further reduce the communication overhead incurred by auxiliary authentication information (AAI). Besides the property of communication-efficiency, our scheme also supports dynamic SET operations (UNION, INTERSECTION) and dynamic JOIN with immunity to reordering attack.

title: QoS analysis for component-based embedded software: Model and methodology
abstract: Component-based development (CBD) techniques have been widely used to enhance the productivity and reduce the cost for software systems development. However, applying CBD techniques to embedded software development faces additional challenges. For embedded systems, it is crucial to consider the quality of service (QoS) attributes, such as timeliness, memory limitations, output precision, and battery constraints. Frequently, multiple components implementing the same functionality with different QoS properties (measurements in terms of QoS attributes) can be used to compose a system. Also, software components may have parameters that can be configured to satisfy different QoS requirements. Composition analysis, which is used to determine the most suitable component selections and parameter settings to best satisfy the system QoS requirement, is very important in embedded software development process. In this paper, we present a model and the methodologies to facilitate composition analysis. We define QoS requirements as constraints and objectives. Composition analysis is performed based on the QoS properties and requirements to find solutions (component selections and parameter settings) that can optimize the QoS objectives while satisfying the QoS constraints. We use a multi-objective concept to model the composition analysis problem and use an evolutionary algorithm to determine the Pareto-optimal solutions efficiently.