Volatility forecasting with range models: An evaluation of new alternatives to the CARR model

The aim of this paper is to analyze the forecasting ability of the CARR model proposed by Chou (2005) using the S&P 500. We extend the data sample, allowing for the analysis of different stock market circumstances and propose the use of various range estimators in order to analyze their forecasting performance. Our results show that there are two range-based models that outperform the forecasting ability of the GARCH model. The Parkinson model is better for upward trends and volatilities which are higher and lower than the mean while the CARR model is better for downward trends and mean volatilities.

An Evaluation of Local Feature Combiners for Robot Visual Localization

In the last decade, local image features have been widely used in robot visual localization. To assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image to those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, we compare several candidate combiners with respect to their performance in the visual localization task. A deeper insight into the potential of the sum and product combiners is provided by testing two extensions of these algebraic rules: threshold and weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance. The voting method, whilst competitive to the algebraic rules in their standard form, is shown to be outperformed by both their modified versions.

An evaluation of SAFIRE’s potential to reduce the dose received by paediatric patients undergoing CT: a narrative review

Introduction: The purpose of this review is to gather and analyse current research publications to evaluate Sinogram-Affirmed Iterative Reconstruction (SAFIRE). The aim of this review is to investigate whether this algorithm is capable of reducing the dose delivered during CT imaging while maintaining image quality. Recent research shows that children have a greater risk per unit dose due to increased radiosensitivity and longer life expectancies, which means it is particularly important to reduce the radiation dose received by children. Discussion: Recent publications suggest that SAFIRE is capable of reducing image noise in CT images, thereby enabling the potential to reduce dose. Some publications suggest a decrease in dose, by up to 64% compared to filtered back projection, can be accomplished without a change in image quality. However, literature suggests that using a higher SAFIRE strength may alter the image texture, creating an overly ‘smoothed’ image that lacks contrast. Some literature reports SAFIRE gives decreased low contrast detectability as well as spatial resolution. Publications tend to agree that SAFIRE strength three is optimal for an acceptable level of visual image quality, but more research is required. The importance of creating a balance between dose reduction and image quality is stressed. In this literature review most of the publications were completed using adults or phantoms, and a distinct lack of literature for paediatric patients is noted. Conclusion: It is necessary to find an optimal way to balance dose reduction and image quality. More research relating to SAFIRE and paediatric patients is required to fully investigate dose reduction potential in this population, for a range of different SAFIRE strengths.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.