An efficient algorithm for partitioning parameterized polygons into rectangles

In this paper, we propose an algorithm for partitioning parameterized orthogonal polygons into rectangles. The algorithm is based on the plane-sweep technique and can be used for partitioning polygons which contain holes. The input to the algorithm consists of the contour of a parameterized polygon to be partitioned and the constraints for those parameters which reside in the contour. The algorithm uses horizontal cuts only and generates a minimum number of rectangles whose union is the original orthogonal polygon. The proposed algorithm can be used as the basis to build corner stitching data structure for parameterized VLSI layouts and has been implemented in Java programming language. Copyright © 2010 ACM, Inc.

The rural medicine rotation: Increasing rural recruitment through quality undergraduate rural experiences

TITLE: The Rural Medicine Rotation: Increasing Rural Recruitment through Quality Undergraduate Rural Experiences Eley Diann, University of Queensland, School of Medicine, Rural Clinical Division, Toowoomba 4350, Queensland Australia Baker Peter, University of Queensland, School of Medicine Rural, Clinical Division, Toowoomba 4350, Queensland Australia Chater Bruce, University of Queensland, Chair, Clinical School Management Committee, School of Medicine Rural Clinical Division, Queensland Australia CONTEXT: While rural background and rural exposure during medical training increases the likelihood of rural recruitment (Wilkinson, 2003), the quality and content of that exposure is the key to altering undergraduatesâ?? perceptions of rural practice. The Rural Clinical Division at University of Queensland (UQ) runs the Rural Medicine Rotation (RMR) within the School of Medicine. The RMR is one of five eight week clinical rotations in Year three and is compulsory for all students. The RMR provides the opportunity to learn from a wide range of health professionals and clinical exposure is not restricted to general practice but also includes remote area nursing, Indigenous health care, allied health professionals and medical specialists. Week 1 involves preparation for their rural placement with workshops and seminars and Week 8 consolidates their placement and includes case and project presentations and a summative assessment. Weeks 2-7 are spent living and working as part of the health team in different rural communities. SETTING: Rural communities in and around Queensland including locations such as Arnham Land, Thursday Island, Mt. Isa and Alice Springs METHOD: All aspects of the RMR are evaluated with surveys using both qualitative and quantitative free response questions, completed by all students at the end of the Week 8. RESULTS: Overall the RMR is evaluated highly and narratives offered by students show that the RMR provides a positive rural experience. The overall impact of the RMR for students in 2004 ranked 3.45 on a scale of 1 to 4 (1 = lowest and 4 = highest), and is exemplified by the following quote; â??I enjoyed my placement so much I am now considering rural medicine something I definitely had not considered beforeâ??. OUTCOME: The positive impact of the RMR on studentâ??s perceptions of rural medicine is encouraging and can help achieve the overall aim of increasing recruitment of the rural workforce in Australia.

Verifying abstract information flow properties in fault tolerant security devices

The verification of information flow properties of security devices is difficult because it involves the analysis of schematic diagrams, artwork, embedded software, etc. In addition, a typical security device has many modes, partial information flow, and needs to be fault tolerant. We propose a new approach to the verification of such devices based upon checking abstract information flow properties expressed as graphs. This approach has been implemented in software, and successfully used to find possible paths of information flow through security devices.

Face recognition robust to head pose from one sample image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.