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Radar high-resolution range profiles (HRRPs) are typical high-dimensional, non-Gaussian and interdimension dependently distributed data, the statistical modelling of which is a challenging task for HRRP based target recognition. Assuming the HRRP data follow interdimension dependent Gaussian distribution, factor analysis (FA) was recently applied to describe radar HRRPs and a two-phase procedure was used for model selection, showing promising recognition results. Besides the interdimensional dependence, this paper further models the non-Gaussianity of the radar HRRP data by local factor analysis (LFA). Moreover, since the two-phase procedure suffers from extensive computation and inaccurate evaluation on high-dimensional finite HRRPs, we adopt an automatic Bayesian Ying-Yang (BYY) harmony learning, which determines the component number and the hidden dimensionalities of LFA automatically during parameter learning. Experimental results show incremental improvements on recognition accuracy by three implementations, progressively from a two-phase FA, to a two-phase LFA, and then to an automatically learned LFA by BYY harmony learning.

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For some groups of organisms, DNA barcoding can provide a useful tool in taxonomy, evolutionary biology, and biodiversity assessment. However, the efficacy of DNA barcoding depends on the degree of sampling per species, because a large enough sample size is needed to provide a reliable estimate of genetic polymorphism and for delimiting species. We used a simulation approach to examine the effects of sample size on four estimators of genetic polymorphism related to DNA barcoding: mismatch distribution, nucleotide diversity, the number of haplotypes, and maximum pairwise distance. Our results showed that mismatch distributions derived from subsamples of ≥20 individuals usually bore a close resemblance to that of the full dataset. Estimates of nucleotide diversity from subsamples of ≥20 individuals tended to be bell‐shaped around that of the full dataset, whereas estimates from smaller subsamples were not. As expected, greater sampling generally led to an increase in the number of haplotypes. We also found that subsamples of ≥20 individuals allowed a good estimate of the maximum pairwise distance of the full dataset, while smaller ones were associated with a high probability of underestimation. Overall, our study confirms the expectation that larger samples are beneficial for the efficacy of DNA barcoding and suggests that a minimum sample size of 20 individuals is needed in practice for each population.

An adaptive direction-dependent DVF regularization method has been developed to model the sliding tissue motion of the thoracic and abdominal organs. The overall motion estimation accuracy has been improved especially near the chest wall and abdominal wall where large organ sliding motion occurs.

This paper presents a new web mining scheme for parallel data acquisition. Based on the Document Object Model (DOM), a web page is represented as a DOM tree. Then a DOM tree alignment model is proposed to identify the translationally equivalent texts and hyperlinks between two parallel DOM trees. By tracing the identified parallel hyperlinks, parallel web documents are recursively mined. Compared with previous mining schemes, the benchmarks show that this new mining scheme improves the mining coverage, reduces mining bandwidth, and enhances the quality of mined parallel sentences.

This paper addresses automated mapping of the remaining wall thickness of metallic pipelines in the field by means of an inspection robot equipped with nondestructive testing (NDT) sensing. Set in the context of condition assessment of critical infrastructure, the integrity of arbitrary sections in the conduit is derived with a bespoke robot kinematic configuration that allows dense pipe wall thickness discrimination in circumferential and longitudinal direction via NDT sensing with guaranteed sensing lift-off (offset of the sensor from pipe wall) to the pipe wall, an essential barrier to overcome in cement-lined water pipelines. A tailored covariance function for pipeline cylindrical structures within the context of a Gaussian Processes has also been developed to regress missing sensor data incurred by a sampling strategy folllowed in the field to speed up the inspection times, given the slow response of the pulsed eddy current electromagnetic sensor proposed. The data gathered represent not only a visual understanding of the condition of the pipe for asset managers, but also constitute a quantative input to a remaining-life calculation that defines the likelihood of the pipeline for future renewal or repair. Results are presented from deployment of the robotic device on a series of pipeline inspections which demonstrate the feasibility of the device and sensing configuration to provide meaningful 2.5D geometric maps. K E Y W O R D S gaussian process, inspection harsh environments, mapping, NDT, pipeline robot 1 | MOTIVATION-A TAXONOMY OF NDT INSPECTION TECHNIQUES Nondestructive testing (NDT) or evaluation (NDE) is extensively used by the energy and water industry to assess the integrity of their network assets, particularly their larger and most critical conduits (generally refered to as those larger than 350 mm in diameter), in their decision-making process leading their renewal/repair/rehabilitation programs. The key advantage of NDT/NDE is that the structure of the asset is not compromised in estimating its condition. The sensing modality to use is strongly influenced by the material of the asset. Grey Cast Iron (CI) pipelines remain the bulk of the buried critical water infrastructure in the developed world as that was the material of choice for mass production with the advent of the Industrial Revolution in the middle of the 18th century (alongside its less brittle relative of Ductile Iron since 1950s), until carbon steel, asbestos cement, or plastic pipelines (PVC) among other materials made them redundant over the years. The nonhomogeneity of the CI produce means that sensing techniques widely used in the (mild) carbon steel networks in the energy pipeline sector, such as ultrasonics or electromagnetic acoustic transducers, are inadequate for CI, and the underlying techniques of most commercial propositions for CI are instead based on either magnetics (e.g., magnetic flux leakage, pulsed eddy current [PEC], and remote field eddy currents), or the study of the propagation of pressure waves in the pipeline...