Comparison of Video Laryngoscopy Technologies

Indirect laryngoscopy allows practitioners to “see around the corner” of a patient’s airway during intubation. Inadequate airway management is a major contributor to patient injury, morbidity and mortality. The purpose of the present study was to evaluate the video quality of commercially available video laryngoscopy systems. A team of four investigators at the University of Nebraska at Omaha and the Peter Kiewit Institute performed intubation simulations using a number of video laryngoscopy systems. Testing was done with a Laerdal Difficult Airway Manikin (Laerdal Medical Corp., Wappingers Falls, NY) in a setting that simulated difficult airways, adverse lighting conditions and various system configurations (e.g., maximizing screen contrast, minimizing screen brightness, maximizing screen color hue, etc.).

Applications of Linear Programming to Coding Theory

Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.