On Faster I DDQ Measurements

The purpose of this paper is to introduce a new I _DDQ measurement technique based on active successive approximations, called ASA-I _DDQ. This technique has unique features facilitating a speed-up in I _DDQ measurement. Experimental results suggest that a significant speed-up factor (up to 4) can be obtained over the QuiC-Mon technique. Such a speed-up is a key element in the replacement of single-threshold I _DDQ testing since it amplifies the effectiveness of post-processing techniques.     

A decision support system for interactive decision making - Part II: analysis and output interpretation

For pt.I see ibid., p.42-55 (2003). The development of a comprehensive decision support system, GMCR II, for the systematic study of real-world interactive decision problems is presented. The companion paper (Part I), discusses how GMCR II elicits, stores, and manages conflict models; here (Part II), the focus is on GMCR IIs analysis and output interpretation subsystems. Specifically, this paper describes the powerful and efficient analysis engine contained in GMCR II, its informative output presentation and interpretation facilities, and a number of follow-up analyses. Furthermore, an illustrative case study is used to demonstrate how GMCR II can be conveniently applied in practice.     

On-demand learning for a wireless campus

The current Internet and wireless networks are harsh environments for transporting high-bandwidth multimedia data. We examine the technical issues involved, and describe an end-to-end solution to support a Web-based learn-on-demand system that operates in a wireless campus environment.     

Switched-Current Filters Revisited: Square-Root Domain Sampled-Data Filters

In this brief, the well-known switched-current (SI) filtering technique is revisited using the concept of the square-root domain (SRD) filtering. It is proved that SI filters are a subclass of the SRD filters, where sampled-data signal processing is performed. This is achieved by considering typical lossless and lossy SRD sampled-data integrator configurations, using a set of complementary SRD operators which are based on the quadratic I-V relationship of MOS transistor operated in the saturation. Circuit examples are given, where linear-domain integrator and third-order filter configurations were derived using appropriate SRD sampled-data building blocks     

Parametric effects on the transient Voltage performance of a DC superconducting cable

The objective of this work was to determine the significant parameters of a 3-GW 200-kV dc superconducting cable system which influence the transient voltage distribution in the various parts of the cable. The cable system consists of four coaxial metallic cylinders. It was found that the dielectric constant and the electrical resistivity of the soil significantly affect the severity of the transient voltages; lower dielectric constant and higher resistivity of the soil will increase the magnitudes of the transient voltages by increasing the earth-return impedance. It was also found that the effect of the conductor internal impedances of the cable is insignificant. Shorting the coaxial cylinders of the cryogen flow and the cryostat will lessen the severity of the transient voltages. Grounding the second, third, and fourth cylinders at regular intervals with low-impedance grounding impedance will also improve the transient performance of the cable. More research is needed to evaluate these procedures.     

Intelligent process analytics for CRM

Many organisations use decision models in their processes such as tables or trees to provide decision support to their operational divisions. For example, in fault management, customer contact centre operators usually use a decision model in the form of prescribed interviews. Based on the answers given by customers, the operator navigates through the decision model to reach an assessment of the problem. In order to achieve customer satisfaction and operational excellence, it is very important to constantly monitor the performance of a decision model not only on an overall level, but also on the level of individual decisions. In this paper we present a configurable business process analytics tool, known as the intelligent Universal Service Management System, that constantly monitors decision data and is capable of optimising the decisions based on high-level business objectives. We explain the various features of the software and show how it can be used to optimise decision processes. We also show how we can easily provide a customised version to monitor the performance of provision processes.     

Profile-Driven Component Placement for Cluster-Based Online Services

The growth of the Internet and of various intranets has spawned a wealth of online services, most of which are implemented on local-area clusters using remote invocation (for example, remote procedure call/remote method invocation) among manually placed application components. Component placement can be a significant challenge for large-scale services, particularly when application resource needs are workload dependent. Automatic component placement has the potential to maximize overall system throughput. The key idea is to construct (offline) a mapping between input workload and individual-component resource consumption. Such mappings, called component profiles, then support high-performance placement. Preliminary results on an online auction benchmark based on J2EE (Java 2 Platform, Enterprise Edition) suggest that profile-driven tools can identify placements that achieve near-optimal overall throughput.     

Minimax estimation of deterministic parameters in linear models with a random model matrix

We consider the problem of estimating an unknown deterministic parameter vector in a linear model with a random model matrix, with known second-order statistics. We first seek the linear estimator that minimizes the worst-case mean-squared error (MSE) across all parameter vectors whose (possibly weighted) norm is bounded above. We show that the minimax MSE estimator can be found by solving a semidefinite programming problem and develop necessary and sufficient optimality conditions on the minimax MSE estimator. Using these conditions, we derive closed-form expressions for the minimax MSE estimator in some special cases. We then demonstrate, through examples, that the minimax MSE estimator can improve the performance over both a Baysian approach and a least-squares method. We then consider the case in which the norm of the parameter vector is also bounded below. Since the minimax MSE approach cannot account for a nonzero lower bound, we consider, in this case, a minimax regret method in which we seek the estimator that minimizes the worst-case difference between the MSE attainable using a linear estimator that does not know the parameter vector, and the optimal MSE attained using a linear estimator that knows the parameter vector. For analytical tractability, we restrict our attention to the scalar case and develop a closed-form expression for the minimax regret estimator.