Stiction issues and actuation of RF LIGA-MEMS variable capacitors

High aspect ratio variable capacitors have been fabricated using deep X-ray lithography and electroplating. Stiction phenomena applicable to high aspect ratio devices are presented, including the conditions for stiction to occur and the critical dimensions of structures. Actuation tests at 3 GHz are also presented and show a maximum capacitance of 0.86 pF with no actuation voltage and a minimum capacitance of 0.70 pF with an actuation voltage of 20 V just before pull-in, which gives a tuning range of 1.23:1. Corresponding Q-factor values are 49.3 and 70.8 respectively. After pull-in, the measured capacitance is 0.61 pF, corresponding to a tuning range of 1.41:1, with a maximum Q-factor of 102.9.     

Feature-subspace aggregating: ensembles for stable and unstable learners

This paper introduces a new ensemble approach, Feature-Subspace Aggregating (Feating), which builds local models instead of global models. Feating is a generic ensemble approach that can enhance the predictive performance of both stable and unstable learners. In contrast, most existing ensemble approaches can improve the predictive performance of unstable learners only. Our analysis shows that the new approach reduces the execution time to generate a model in an ensemble through an increased level of localisation in Feating. Our empirical evaluation shows that Feating performs significantly better than Boosting, Random Subspace and Bagging in terms of predictive accuracy, when a stable learner SVM is used as the base learner. The speed up achieved by Feating makes feasible SVM ensembles that would otherwise be infeasible for large data sets. When SVM is the preferred base learner, we show that Feating SVM performs better than Boosting decision trees and Random Forests. We further demonstrate that Feating also substantially reduces the error of another stable learner, k-nearest neighbour, and an unstable learner, decision tree.     

Implementing Compositional Analysis Using Intersection Types With Expansion Variables

A program analysis is compositional when the analysis result for a particular program fragment is obtained solely from the results for its immediate subfragments via some composition operator. This means the subfragments can be analyzed independently in any order. Many commonly used program analysis techniques (in particular, most abstract interpretations and most uses of the Hindley/Milner type system) are not compositional and require the entire text of a program for sound and complete analysis.System is a recent type system for the pure λ-calculus with intersection types and the new technology of expansion variables. System supports compositional analysis because it has the principal typings property and an algorithm based on the new technology of β-unification has been developed that finds these principal typings. In addition, for each natural number k, typability in the rank-k restriction of System is decidable, so a complete and terminating analysis algorithm exists for the rank-k restriction.This paper presents new understanding that has been gained from working with multiple implementations of System and β-unification-based analysis algorithms. The previous literature on System presented the type system in a way that helped in proving its more important theoretical properties, but was not as easy to follow as it could be. This paper provides a presentation of many aspects of System that should be clearer as well as a discussion of important implementation issues.     

Isolation kernel: the X factor in efficient and effective large scale online kernel learning

Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. Current state-of-the-art large scale online kernel learning focuses on improving efficiency. Two key approaches to gain efficiency through approximation are (1) limiting the number of support vectors, and (2) using an approximate feature map. They often employ a kernel with a feature map with intractable dimensionality. While these approaches can deal with large scale datasets efficiently, this outcome is achieved by compromising predictive accuracy because of the approximation. We offer an alternative approach that puts the kernel used at the heart of the approach. It focuses on creating a sparse and finite-dimensional feature map of a kernel called Isolation Kernel. Using this new approach, to achieve the above aim of large scale online kernel learning becomes extremely simple—simply use Isolation Kernel instead of a kernel having a feature map with intractable dimensionality. We show that, using Isolation Kernel, large scale online kernel learning can be achieved efficiently without sacrificing accuracy.

     

Relationship of head restraint positioning to driver neck injury in rear-end crashes

Insurance claims were examined for evidence of neck injuries to drivers of passenger cars struck in the rear. Neck injury rates were significantly lower for male drivers, elderly drivers, and drivers in less severe crashes. Even after accounting for differences in driver demographics and crash severity, neck injury rates were significantly lower for drivers of cars with head restraints that were more likely to be behind the heads of motorists.     

Dialogical Argument as an Interface to Complex Debates

The Magtalo prototype environment harnesses dialogue protocols to support flexible, intuitive interaction with data in complex, contentious domains and provides mechanisms for eliciting structured knowledge. Magtalo represents the first example of an implemented online system that uses a closely specified argument-based dialogue protocol combined with a rich language for representing monologic argument to provide a tool for intuitive user exploration of a disagreement space. This approach also enables the expansion of argument resources through knowledge elicitation, which is structured by the argument dialogue protocol. The continuing aim of our research is to use advances in argumentation theory to push the practice of argumentation technology in providing tools and interfaces that have wide appeal.     

Musculoskeletal symptoms among mobile hand-held device users and their relationship to device use: A preliminary study in a Canadian university population

The study aims were, in a population of university students, staff, and faculty (n = 140), to: 1) determine the distribution of seven measures of mobile device use; 2) determine the distribution of musculoskeletal symptoms of the upper extremity, upper back and neck; and 3) assess the relationship between device use and symptoms. 137 of 140 participants (98%) reported using a mobile device. Most participants (84%) reported pain in at least one body part. Right hand pain was most common at the base of the thumb. Significant associations found included time spent internet browsing and pain in the base of the right thumb (odds ratio 2.21, 95% confidence interval 1.02–4.78), and total time spent using a mobile device and pain in the right shoulder (2.55, 1.25–5.21) and neck (2.72, 1.24–5.96). Although this research is preliminary, the observed associations, together with the rising use of these devices, raise concern for heavy users.     

Efficient synthesis of gaussian filters by cascaded uniform filters

Gaussian filtering is an important tool in image processing and computer vision. In this paper we discuss the background of Gaussian filtering and look at some methods for implementing it. Consideration of the central limit theorem suggests using a cascade of ``simple' filters as a means of computing Gaussian filters. Among ``simple' filters, uniform-coefficient finite-impulse-response digital filters are especially economical to implement. The idea of cascaded uniform filters has been around for a while [13], [16]. We show that this method is economical to implement, has good filtering characteristics, and is appropriate for hardware implementation. We point out an equivalence to one of Burt's methods [1], [3] under certain circumstances. As an extension, we describe an approach to implementing a Gaussian Pyramid which requires approximately two addition operations per pixel, per level, per dimension. We examine tradeoffs in choosing an algorithm for Gaussian filtering, and finally discuss an implementation.