4 resultados para Evaluation performance
em WestminsterResearch - UK
Resumo:
This paper reports on a Field Programmable Gate Array (FPGA) implementation as well as prototyping for real-time testing of a low complexity high efficiency decimation filter processor which is deployed in conjunction with a custom built low-power jitter insensitive Continuous Time (CT) Sigma-Delta (Σ-Δ) Modulator to measure and assess its performance. The CT Σ-Δ modulator/decimation filter cascade can be used in integrated all-digital microphone interfaces for a variety of applications including mobile phone handsets, wireless handsets as well as other applications requiring all-digital microphones. The work reported here concentrates on the design and implementation as well as prototyping on a Xilinx Spartan 3 FPGA development system and real-time testing of the decimation processing part deploying All-Pass based structures to process the bit stream coming from CT Σ-Δ modulator hence measuring in real-time and fully assessing the modulator's performance.
Resumo:
In this study we propose the use of the performance measure distribution rather than its punctual value to rank hedge funds. Generalized Sharpe Ratio and other similar measures that take into account the higher-order moments of portfolio return distributions are commonly used to evaluate hedge funds performance. The literature in this field has reported non-significant difference in ranking between performance measures that take, and those that do not take, into account higher moments of distribution. Our approach provides a much more powerful manner to differentiate between hedge funds performance. We use a non-semiparametric density based on Gram-Charlier expansions to forecast the conditional distribution of hedge fund returns and its corresponding performance measure distribution. Through a forecasting exercise we show the advantages of our technique in relation to using the more traditional punctual performance measures.
Resumo:
Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.
Resumo:
Reconfigurable computing is becoming an important new alternative for implementing computations. Field programmable gate arrays (FPGAs) are the ideal integrated circuit technology to experiment with the potential benefits of using different strategies of circuit specialization by reconfiguration. The final form of the reconfiguration strategy is often non-trivial to determine. Consequently, in this paper, we examine strategies for reconfiguration and, based on our experience, propose general guidelines for the tradeoffs using an area-time metric called functional density. Three experiments are set up to explore different reconfiguration strategies for FPGAs applied to a systolic implementation of a scalar quantizer used as a case study. Quantitative results for each experiment are given. The regular nature of the example means that the results can be generalized to a wide class of industry-relevant problems based on arrays.