Degree Of Data Reduction Research Articles

Developing nonuniform sampling strategies to improve sensitivity and resolution in 1,1-ADEQUATE experiments.

Nonuniform sampling (NUS) strategies are developed for acquiring highly resolved 1,1-ADEQUATE spectra, in both conventional and homodecoupled (HD) variants with improved sensitivity. Specifically, the quantile-directed and Poisson gap methods were critically compared for distributing the samples nonuniformly, and the quantile schedules were further optimized for weighting. Both maximum entropy and iterative soft thresholding spectral estimation algorithms were evaluated. All NUS approaches were robust when the degree of data reduction is moderate, on the order of a 50% reduction of sampling points. Further sampling reduction by NUS is facilitated by using weighted schedules designed by the quantile method, which also suppresses sampling noise well. Seed independence and the ability to specify the sample weighting in quantile scheduling are important in optimizing NUS for 1,1-ADEQUATE data acquisition. Using NUS yields an improvement in sensitivity, while also making longer evolution times accessible that would be difficult or impractical to attain by uniform sampling. Theoretical predictions for the sensitivity enhancements in these experiments are in the range of 5-20%; NUS is shown to disambiguate weak signals, reveal some n JCC correlations obscured by noise, and improve signal strength relative to uniform sampling in the same experimental time. This work presents sample schedule development for applying NUS to challenging experiments. The schedules developed here are made available for general use and should facilitate the broader utilization of ADEQUATE experiments (including 1,1-, 1,n-, and HD- variants) for challenging structure elucidation problems.

A new multi-resolution based method for estimating local surface roughness from point clouds

From some empirical and theoretical research on the digital elevation model (DEM) accuracy obtained for different source data densities, it can be observed that when the same degree of data reduction is applied to a whole area, the rate of change in the DEM error is statistically greater in local areas where the surface is rougher. Based on this observation, it is possible to characterize surface roughness or complexity from the differences between two digital elevation models (DEMs) built using point clouds that represent the same terrain surface but are of different spatial resolutions (or data spacings). Following this logic, a new approach for estimating surface roughness is proposed in this article. Numerical experiments are used to test the effectiveness of the approach. The study datasets considered in this article consist of four elevation point clouds obtained from terrestrial laser scanning (TLS) and airborne light detection and ranging (LiDAR). These types of topographical data are now used widely in Earth science and related disciplines. The method proposed was found to be an effective means of quantifying local terrain surface roughness.

Extracting conflict-free information from multi-labeled trees

BackgroundA multi-labeled tree, or MUL-tree, is a phylogenetic tree where two or more leaves share a label, e.g., a species name. A MUL-tree can imply multiple conflicting phylogenetic relationships for the same set of taxa, but can also contain conflict-free information that is of interest and yet is not obvious.ResultsWe define the information content of a MUL-tree T as the set of all conflict-free quartet topologies implied by T, and define the maximal reduced form of T as the smallest tree that can be obtained from T by pruning leaves and contracting edges while retaining the same information content. We show that any two MUL-trees with the same information content exhibit the same reduced form. This introduces an equivalence relation among MUL-trees with potential applications to comparing MUL-trees. We present an efficient algorithm to reduce a MUL-tree to its maximally reduced form and evaluate its performance on empirical datasets in terms of both quality of the reduced tree and the degree of data reduction achieved.ConclusionsOur measure of conflict-free information content based on quartets is simple and topologically appealing. In the experiments, the maximally reduced form is often much smaller than the original tree, yet retains most of the taxa. The reduction algorithm is quadratic in the number of leaves and its complexity is unaffected by the multiplicity of leaf labels or the degree of the nodes.

Aggressive Data Reduction for Damage Detection in Structural Health Monitoring

While wireless sensors are increasingly adopted in various applications, the need of developing data reduction methods to alleviate data transmission rate issue between the sensors and the data interpretation unit becomes more urgent. This article presents a new data reduction method for sensors used in structural health monitoring application. Our goal is to achieve an effective data reduction capability while maintaining adequate power for damage detection. We propose to establish an explicit measure of damage detection capability for the features in the response signals and use this measure to select the subset of the features that balance between the degree of data reduction and the damage detection capability. We also explore a computationally efficient procedure searching for the best subset of the features. This new method is tested on experimentally obtained Lamb wave signals for beam damage detection. Performance comparisons with respect to the existing methods demonstrate the strength of the proposed method.

What Is Wrong with Compression Ratio in Lossy Image Compression?

Editor: We read with interest the article by Dr Ringl and colleagues (1) in the September 2006 issue of Radiology, in which they evaluate the effect of using the lossy image-compression algorithm, Joint Photographic Experts Group (JPEG) 2000, on computed tomographic (CT) images. We found the most interesting part of the article to be in the Discussion section, in which the authors stated that application of the highest acceptable compression ratio (CR) might be restricted because the quality of lossy compressed images depends on the level of image noise and the structural content of the image. We would like to further highlight this important issue that has been often neglected in many studies evaluating lossy image compression in radiology. For diagnostic accuracy, it is of utmost importance that the amount of image detail preservation in lossy image compression is constant, predictable, and controlled. Image compression can be performed by means of two compression modes—namely, a constant quality factor (QF) and a constant CR. The effects of these modes on image detail degradation were studied recently (2). It was demonstrated that image detail degradation greatly depends on image content in a constant CR compression mode. In other words, more image details would be lost on the more complex images and vice versa. On the other hand, constant degree of image detail degradation, which is crucial for diagnostic accuracy, was achieved only with a QF compression mode. However, the scales for adjusting QF with the most often used JPEG and JPEG 2000 lossy compression methods are not standardized, although the methods themselves are ISO (International Organization for Standardization) standards, which makes comparative evaluation of different lossy compression methods infeasible (3). This fact could be the reason that CR has become a de facto standard for expressing the degree of image data reduction in radiology, and it is even used in the U.S. Food and Drug Administration guidance (4). Nevertheless, it has to be emphasized that CR depends not only on the degree of data reduction but also on the image content of original image (2) and noise (5), which is in accordance with the theory of data compression (6). In conclusion, CR is by definition a measure of file size reduction and as such is not suitable to expressing the degree of image detail degradation in lossy image compression.