Generic shape feature extraction is a challenging task for image and video content analysis. We present a non-parametric statistics based method for extracting generic shape tokens based on a Perceptual Curve Partition and Grouping (PCPG) model. In this PCPG model, each curve is made up of Generic Edge Tokens (GET) connected at Curve Partitioning Points (CPP). The types of GET and CPP provide a set of basic shape descriptors for semantic vocabulary. The new implementation of the PCPG is based on: 1) An arctangent space is employed to signify the evidence of CPPs at pixel-level. 2) The pixels' sequential order is taken as heuristic to establish a bin order preserving arctangent histogram for locating CPPs by examining the continuity of generic feature criteria statistically. 3) A new CPP detection scheme is capable of detecting CPPs and classifying GETs on the fly. Experiments are presented for performance demonstration.
Learning the underlying model from distributed data is often useful for many distributed systems. In this paper, we study the problem of learning a non-parametric model from distributed observations. We propose a gossip-based distributed kernel density estimation algorithm and analyze the convergence and consistency of the estimation process. Furthermore, we extend our algorithm to distributed systems under communication and storage constraints by introducing a fast and efficient data reduction algorithm. Experiments show that our algorithm can estimate underlying density distribution accurately and robustly with only small communication and storage overhead.
This paper presents a method using only the rank of the measurements to separate a part's elevated response to parametric tests from its non-elevated response. The effectiveness of the proposed method is verified on the 130nm ASIC. Good die responses are correlated for same parametric tests at different conditions such as temperature, voltage and or other stress. Nonparametric correlation methods are used to calculate the intra-die correlation. When intra-die correlation is found to be low the elevated vectors that lower correlation are extracted and input to IDDQ-based diagnostic tools. Monte-Carlo simulations are described to obtain confidence bounds of the correlation for good die test response.
In this paper, we propose a statistical method to determine the setting of compiler options. Conventionally, programmers use standard - Ox settings which are provided by compiler developers. However, in order to obtain maximal performance, it is necessary to tune the compiler setting for the application as well as the underlying architecture. In this paper, we propose a methodology to configure compiler options automatically using profile information. We apply non-parametric statistical analysis, in particular the Mann-Whitney test, to decide whether to turn on or to turn off compiler flags. This approach produces compiler settings of gcc 33.1 for the SPEC2000 benchmark suite that outperform the standard - Ox switches on a Pentium 4 processor.
Online transient stability assessment (TSA) is of great necessity for fast awareness of transient instability caused by fault contingencies. In this paper, a non-parametric statistics based scheme is proposed for response-based online TSA. A critical clearing time-based stability margin index is defined as the predictive output and 14 kinds of severity indicators are proposed as input features for the TSA predictor. With no prior knowledge of the correlation structure, the non-parametric additive model is used as the basis of the predictor. To screen out the weakly correlated indicators and reduce the dimensionality of the input space, two-stage feature selection is fulfilled by non-parametric independence screening and group Lasso penalised regression successively. The predictor is then learnt by least-squares regression in the reduced multi-feature space. With phasor measurement unit measurements at generator buses, severity indicators can be computed in the real-time and fast evaluation of post-fault stability margin can be made by the offline-trained predictor. The effectiveness of the proposed non-parametric statistics based scheme is demonstrated in a modified New England 39-bus system and a practical 756-bus transmission system in China.
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. ► Incorporate robust regression into BPM to replace OLS regression. ► Implement BnPM by applying non-parametric permutation tests and cluster computer. ► Compare these approaches in simulation and empirical use. ► Demonstrate rBPM tolerates outliers, which makes the tool reliable.