title: A Surgical implant conductor with increased radio frequency alternating current resistance

authors: Scott, Jonathan B.

abstract: In one aspect the invention provides a surgical implant conductor formed from a length of conductive material which exhibits increased radio frequency alternating current resistance. This conductive material defines an exterior surface where at least a portion of the exterior surface of the conductor defines a region with a roughened surface. Preferably the roughening of the exterior surface increases the area of the exterior surface when compared to a non-roughened surface, resulting in a reduction in the effective cross-section area of the conductor used to transport alternating currents.

text: A Surgical implant conductor with increased radio frequency alternating current resistance In one aspect the invention provides a surgical implant conductor formed from a length of conductive material which exhibits increased radio frequency alternating current resistance. This conductive material defines an exterior surface where at least a portion of the exterior surface of the conductor defines a region with a roughened surface. Preferably the roughening of the exterior surface increases the area of the exterior surface when compared to a non-roughened surface, resulting in a reduction in the effective cross-section area of the conductor used to transport alternating currents.

year: 2016

title: Optical full Hadamard matrix multiplexing and noise effects

authors: Streeter, Lee

subjects: Hadamard multiplexing

abstract: Hadamard multiplexing provides a considerable SNR boost over additive random noise but Poisson noise such as photon noise reduces the boost. We develop the theory for full H-matrix Hadamard transform imaging under additive and Poisson noise effects. We show that H-matrix encoding results in no effect on average on the noise level due to Poisson noise sources while preferentially reducing additive noise. We use this result to explain the wavelength-dependent varying SNR boost in a Hadamard hyperspectral imager and argue that such a preferential boost is useful when the main noise source is indeterminant or varying.

text: Optical full Hadamard matrix multiplexing and noise effects Hadamard multiplexing provides a considerable SNR boost over additive random noise but Poisson noise such as photon noise reduces the boost. We develop the theory for full H-matrix Hadamard transform imaging under additive and Poisson noise effects. We show that H-matrix encoding results in no effect on average on the noise level due to Poisson noise sources while preferentially reducing additive noise. We use this result to explain the wavelength-dependent varying SNR boost in a Hadamard hyperspectral imager and argue that such a preferential boost is useful when the main noise source is indeterminant or varying.

year: 2009

title: Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources?

authors: Death, Russell G.

abstract: Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.

text: Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources? Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.

year: 2017