Some perspectives on the analysis and control of complementarity systems

This paper is devoted to presenting controllability and stabilizability issues associated to a class of nonsmooth dynamical systems, namely complementarity dynamical systems. The main existing results are summarized, and some possible research directions are provided. Convex analysis and complementarity problems are claimed to be the main analysis tools for control related studies. This paper mainly focuses on mechanical applications.     

The Metaflow architecture

The Metaflow architecture, a unified approach to maximizing the performance of superscalar microprocessors, is introduced. The Metaflow architecture exploits inherent instruction-level parallelism in conventional sequential programs by hardware means, without relying on optimizing compilers. It is based on a unified structure, the DRIS (deferred-scheduling, register-renaming instruction shelf), that manages out-of-order execution and most of the attendant problems. Coupling the DRIS with a speculative-execution mechanism that avoids conditional branch stalls results in performance limited only be inherent instruction-level parallelism and available execution resources. Although presented in the context of superscalar machines, the technique is equally applicable to a superpipelined implementation. Lightning, the first implementation of the Metaflow architecture, which executes the Sparc RISC instruction set is described     

Indoor Air Quality: Exploring Policy Options to Reduce Human Exposures

Deciding between the different policy approaches available for reducing human exposures to indoor pollutants is an exceptionally complex task. These options can range from waiting until more definitive information is available to enacting regulatory standards, with many variations in between. This paper presents some of the factors policy-makers must consider in establishing indoor air quality policies, and the role researchers should play in ensuring that indoor air policies are based on the best available scientific information.     

Neural networks and blood cell identification

The objective of this project is to propose a method of identifying cells found in human blood and to classify them based upon their morphological features using neural networks. The project focuses on three major blood cell types, namely, erythrocytes, leukocytes and platelets. The data are collected using peripheral blood smears from clinical patients. The image acquisition requires 100x magnification on all the blood smears, the preprocessing involves the use of median and edge enhance filters; the feature extraction is done by performing the wavelet transform on the images. Finally classification of the blood cell types is done using ALOPEX and Back Propagation trained neural networks. The efficacy of both networks is then compared by comparing their outputs and number of iterations required to reach the final result.     

Penetrating ocular injuries: an epidemiologic and retrospective study

Penetrating injuries of the eye are an important cause of unilateral visual loss. We studied a series of 82 cases of penetrating injuries treated here from 1987 through 1993. The injuries were caused by sharp objects in 66% and blunt trauma in 6%. The prognosis after a penetrating injury is greatly influenced by the nature of the injury and the extent of the initial drainage. Among factors associated with an unfavorable visual outcome were diminished preoperative visual acuity and scleral wounds with dense vitreous hemorrhage.