MDDAS 2008 Workshop Organization
A combination capacitor and transistor structure is described wherein the capacitor is formed integrally with the emitter electrode of the transistor. The transistor is formed in a monolithic integrated circuit using generally known techniques and constitutes a vertically integrated PNP device. The emitter electrode of the transistor which comprises a P+ diffusion region is of a predetermined area which is large enough to form the bottom plate of the capacitor. The top plate of the capacitor is formed by growing a dielectric material over the diffused emitter region and then forming metallization thereover. The combination capacitor and transistor structure may be utilized in a bias network for biasing the output stage of an operational amplifier in a class AB mode. The capacitor formed in the combination structure may be utilized as the compensation capacitor in such operational amplifier which utilizes pole splitting techniques. The improvement provided by the invention reduces the surface area of the semiconductor die chip required to form the capacitor and transistor which facilitates greater device density on a particular die chip.

title: Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation
abstract: Alarm correlation for fault management in large telecommunication networks demands scalable and reliable algorithms. In this paper, we propose a clustering based alarm correlation approach using sequential proximity between alarms. We define two novel distance metrics appropriate for measuring similarity between alarm sequences obtained from interval-based division: 1) the two-digit binary metric that values the occurrences of two alarms in neighboring intervals to tolerate the false separation of alarms due to interval-based alarm sequence division, and 2) the sequential ordering-based distance metric that considers the time of arrival for different alarms within the same interval. We validate both metrics by applying them with hierarchical clustering using real-world cellular network alarm data. The efficacy of the proposed sequential proximity based alarm clustering is demonstrated through a comparative study with existing similarity metrics.

title: A model-based approach to adding autonomic capabilities to network fault management system
abstract: Adding autonomic capabilities to network management systems provides great promise in delivering high QoS while lowering operation and maintenance cost. In this paper, we present a model-based approach to adding autonomic capabilities to a fault management system for cellular networks. We propose the use of modeling techniques to specify software failures and their dispositions at the model level for the target system. This facilitates the deployment of a control loop for adding autonomic capabilities into the system architecture, which include self-monitoring, self-healing, and self-adjusting. Our case study on the intelligent network fault management system illustrates the proposed approach by adding and deploying these autonomic capabilities derived from self-model specifications, to mitigate the risk of specified failures and maintain the level of healthiness of the system, dynamically and effectively.

title: A Context-Aware Policy Model to Support Autonomic Networking
abstract: Autonomic networking requires that the services and resources offered by the network at any specific time adapt to evolving changes in user needs, business goals and environmental conditions. This paper describes a rich and extensible context-aware policy model as part of the FOCALE autonomic network architecture. This context-aware policy model is built explicitly to support context-awareness that in turn enables the control loops in FOCALE to be adaptive.

title: Enhancing Software Product Line Maintenance with Source Code Mining
abstract: Large-scale reuse and accelerated software development have been some of the key attractions behind software product lines. Various strategies and processes have been developed to facilitate product line development, maintenance, and evolution. However, experiences with software product lines also showed that it is a rather challenging task to maintain software product lines and families over a long period of time. The time and effort needed to manage and maintain product lines increase and quality degrades as product lines evolve. Without proper methods and tools to support the evolution, the cost can outweigh the benefits. 
 
This paper describes an approach to simplifying the maintenance of software product lines and improving software quality by integrating traditional software maintenance practices with pattern-based source code mining for defect detection and correction. Our case studies were performed in an industrial setting where the evolution of multiple mobile phone models of a product line was investigated.

title: Model-Driven Adaptive Self-healing for Autonomic Computing
abstract: Self-healing is a vital property that an autonomic system must possess in order to provide robust performance and survivability. The promise of self-healing depends on other properties that the system should provide, which include self-monitoring and self-configuring. Autonomic systems further require self-healing behavior to adapt to changes in user needs, business goals, and environmental conditions such that self-healing decisions are made dynamically and adaptively according to the system context. In this paper, we propose a model-driven approach that leverages modeling techniques, reliability engineering methodologies, and aspect-oriented development to realize an adaptive self- healing paradigm for autonomic computing.

title: Large area nanosize array stamp for UV-based nanoimprint lithography fabricated by size reduction process
abstract: A novel size reduction process using electron beam lithography (EBL) combining with wet etching technique is developed as a possible solution for producing large area and low cost nanopattern stamp for UV-based nanoimprint lithography (UV-NIL). In the first step, a microstructure stamp with 1.4@mm periodical pore array and aspect ratio of 1:1 was formed over a 1inch^2 area on a quartz substrate. This process was carried out using common electron beam lithography (EBL) equipment, which was easily available in the modern integrated circuits (IC) semiconductor factory. Afterwards, with a controlled wet etching technique, the pore array was changed into tip patterns with the line width below 100nm and the period keeping as before. The uniformities and nanopattern accuracies were investigated to identify its possibility as a UV-NIL stamp by AFM and SEM. Finally, as a demonstration, the as obtained stamp was used as a positive stamp to replicate the nanotips into UV-curable resist successfully by a UV-NIL process. The method developed for the mold of nanoimprint lithography would be a simple and low price approach to fabricate large area UV-NIL stamp and the nanotip array structures would be widely used in two dimensional (2D) photonic crystal application.

title: Comparison between Voxel-based Morphometry and Volumetric Analysis in Schizophrenia
abstract: This study compares the voxel-based morphometry (VBM) and region-of-interest (ROI)-based volumetric analysis in schizophrenia. Magnetic resonance imaging (MRI) data of 50 unmedicated schizophrenia patients and 82 healthy controls were examined. ROI- based volumetric analysis was performed by tracing caudates on anterior commissure and posterior commissure (ACPC)-positioned MRI. The comparison of 20 subjects in the lower and upper 25% for traced caudate size and the comparison between patients and controls revealed significantly reduced gray matter concentration in the caudate through VBM. Caudate volume (absolute and relative) revealed that patients with schizophrenia had smaller caudate nuclei than controls. The ROC results demonstrated that the performance of VBM (area under the curve (AUC): 61%) was similar to that of ROI-based volumetric analysis (AUC: 61% for relative ROI volume) and their correlation was 0.51 (p<0.05). These results suggest that to be an alternative to ROI-based volumetry in schizophrenia, cautious interpretation of VBM may be needed.

title: A component-oriented software toolkit for patient-specific finite element model generation
abstract: A component-oriented software system, i.BioMech (image-based biomechanical modeling) is proposed for generation of patient-specific finite element model. It applies a systematic software engineering approach to patient/subject-specific meshing and assignment of material properties. The prototype program is based on the component object model (COM), which enables ease of combination of existing mesh generation algorithms and material property assignment schemes, and incorporation of new ones. It also facilitates utilization by other programming languages or platforms. Data input comprises a series of medical images captured from the patient. The output is a patient-specific finite element model for computational analysis using commercially available finite element software. The prototype software system provides a framework to compare the different finite element mesh generation methods as well as schemes for material property assignment. Our focus is on patient/subject-specific modeling of the human vertebrae.

title: The nature of knowledge and its influence on knowledge sharing practice: experiences from building the MACROS system
abstract: This study investigates how the interactive influences of the nature of knowledge and multiple organizational and technological factors - trust, leadership, incentives and issues, group size and variety, implementation strategy, and technology - facilitated and/or impeded the knowledge sharing processes. Using a case study approach, the research focuses on the modifying effects of four characteristics of knowledge- codifiability, context-embeddedness, practice-embeddedness, and dynamics- on the processes and outcomes of knowledge sharing in a case of building the multi-purpose access for customer relations & operational support (MACROS) system involving multiple organizations, divisions, and geographically separated offices. The case results suggest that modifying effects occurred along at least three dimensions- codifiability, context-embeddedness, and practice-embeddedness. The levels of codifiability appeared to dictate the implementation strategy; reduced context-embeddedness allowed for more effective group coordination; reduced context-embeddedness also enhanced trust; codified knowledge is more effective than uncodified knowledge in demonstrating concrete incentives; and technology interacts with context-embeddedness and practice-embeddedness. The results of this study have theoretical and practical implications for a larger set of problems encountered in sizable organizations. More specifically, even though the relevance of the nature of knowledge has been widely acknowledged by I. Nonaka, et. al. (2001), it is not always clear how it comes into play. This study conceptualized the nature of knowledge as a modifying variable, and the results provide a new and more comprehensive framework for investigating the relevance of the nature of knowledge in knowledge management research.

title: Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy
abstract: Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large data sets. Most existing outlier detection methods only deal with static data with relatively low dimensionality. Recently, outlier detection for high-dimensional stream data became a new emerging research problem. A key observation that motivates this research is that outliers in high-dimensional data are projected outliers, i.e., they are embedded in lower-dimensional subspaces. Detecting projected outliers from high-dimensional stream data is a very challenging task for several reasons. First, detecting projected outliers is difficult even for high-dimensional static data. The exhaustive search for the outlying subspaces where projected outliers are embedded is a NP problem. Second, the algorithms for handling data streams are constrained to take only one pass to process the streaming data with the conditions of space limitation and time criticality. The currently existing methods for outlier detection are found to be ineffective for detecting projected outliers in high-dimensional data streams. 
In this thesis, we present a new technique, called the Stream Project Outlier deTector (SPOT), which attempts to detect projected outliers in high-dimensional data streams. SPOT employs an innovative window-based time model in capturing dynamic statistics from stream data, and a novel data structure containing a set of top sparse subspaces to detect projected outliers effectively. SPOT also employs a multi-objective genetic algorithm as an effective search method for finding the outlying subspaces where most projected outliers are embedded. The experimental results demonstrate that SPOT is efficient and effective in detecting projected outliers for high-dimensional data streams. The main contribution of this thesis is that it provides a backbone in tackling the challenging problem of outlier detection for high-dimensional data streams. SPOT can facilitate the discovery of useful abnormal patterns and can be potentially applied to a variety of high demand applications, such as for sensor network data monitoring, online transaction protection, etc.