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The Copernicus high-resolution layer imperviousness density (HRL IMD) for 2018 is a 10 m resolution raster showing the degree of soil sealing across Europe. The imperviousness gradation (0–100%) per pixel is determined by semi-automated classification of remote sensing imagery and based on calibrated NDVI. The product was assessed using a within-pixel point sample of ground truth examined on very high-resolution orthophoto for the section of the product covering Norway. The results show a high overall accuracy, due to the large tracts of natural surfaces correctly portrayed as permeable (0% imperviousness). The total sealed area in Norway is underestimated by approximately 33% by HRL IMD. Point sampling within pixels was found to be suitable for verification of remote sensing products where the measurement is a binomial proportion (e.g., soil sealing or canopy coverage) when high-resolution aerial imagery is available as ground truth. The method is, however, vulnerable to inaccuracies due to geometrical inconsistency, sampling errors and mistaken interpretation of the ground truth. Systematic sampling inside each pixel is easy to work with and is known to produce more accurate estimates than a simple random sample when spatial autocorrelation is present, but this improvement goes unnoticed unless the status and location of each sample point inside the pixel is recorded and an appropriate method is applied to estimate the within-pixel sampling accuracy. [ABSTRACT FROM AUTHOR]

Automatic control of intelligent vehicles depends heavily on accurate lane-level location information. The traditional digital map cannot meet the automatic driving requirements, and it is very difficult for lane-level road maps (LRMs) to achieve a good balance between the representation accuracy and data storage volume. To tackle this problem, we obtain the recursive definition of a lane line by analyzing road lanes using the fractal geometry theory. Subsequently, we construct a recursive feedback system and model it. On this basis, an autonomous storage optimization LRM generation strategy based on cascaded NURBS is devised. According to the input data density, this framework generates an LRM with the minimum data storage volume within the error constraint. It is mainly composed of two modules: (1) a random fractal and (2) an adaptive iteration memory optimizer, based on cascaded NURBS error feedback. First, the whole lane is divided into several meta lane line modules. Second, data sampling/interpolation is carried out iteratively according to the NURBS fitting error and symmetry of road geometry. In this way, the storage space used to store lane lines and other information is automatically optimized. Last, a simulation experiment is carried out by using a dataset obtained from a park. The simulation results show that the proposed method can achieve high accuracy and high storage efficiency. Under the constraint of a 0.1 m curve fitting error, the average memory demand of the lane line elements is less than 2.4 bytes/m. [ABSTRACT FROM AUTHOR]

This paper builds on an earlier examination of the influence of sampling size and analyte surface density on the accuracy and precision of measurements using surface-enhanced Raman scattering (SERS) to read out heterogeneous immunoassays. Quantitation using SERS typically relies on interrogating a small area on the sample surface by using a micrometer-sized laser spot for signal generation. The information obtained using such a small portion of sample is then projected as being representative of the much larger sample, which can compromise the accuracy and precision of the measurement due to undersampling. For a heterogeneous immunoassay interrogated by SERS, quantitation is, therefore, sensitive to the size of the analyzed area and the surface density of the measured analyte. To identify conditions in which sampling error poses a threat to accuracy and precision, a simulation of a SERS immunoassay was developed and compared to experimental results. The simulation randomly distributes adsorbates across the capture surface and then measures the density of adsorbates inside areas of analysis of different sizes. This approach mimics the analysis of a heterogeneous immunoassay when using a Raman microscope with different laser spot sizes. The results of the simulations, which were confirmed experimentally by comparison to an immunoassay of human immunoglobulin G (IgG) show that the accuracy and precision of the measurement improved with larger analysis areas and higher analyte concentrations due to the increased apparent homogeneity of the analyte within the area of analysis. By imposing a threshold on precision (5%), we also begin to establish a framework for the parameters necessary to achieve reliable quantitative measurements (e.g., laser spot size, analyte concentration, and sample volume). [ABSTRACT FROM AUTHOR]

Species tree estimation from multi-locus datasets is complicated by processes such as incomplete lineage sorting (ILS) that result in different loci having different trees. Summary methods, which estimate species trees by combining gene trees, are popular but their accuracy is impaired by gene tree estimation error. Other approaches have been developed that only use the site patterns to estimate the species tree, and so are not impacted by gene tree estimation issues. In particular, PAUP ∗ provides a method in which SVDquartets is used to compute a set Q of quartet trees (i.e., trees on four leaves), and then a heuristic search is used to combine the quartet trees into a species tree T , seeking to maximize the number of quartet trees in Q that agree with T . The PAUP ∗ method based on SVDquartets (henceforth referred to as SVDquartets + PAUP ∗ ) is increasingly used in phylogenomic studies due to its ability to reconstruct species trees without needing to estimate accurate gene trees. We present SVDquest ∗ , a new method for constructing species trees using site patterns that is guaranteed to produce species trees that satisfy at least as many quartet trees as SVDquartets + PAUP ∗ . We show that SVDquest ∗ is competitive with ASTRAL and ASTRID (two leading summary methods) in terms of topological accuracy, and tends to be more accurate than ASTRAL and ASTRID under conditions with relatively high gene tree estimation error. SVDquest ∗ is available in open source form at https://github.com/pranjalv123/SVDquest . [ABSTRACT FROM AUTHOR]

Present computational techniques for coupled finite-volume/Monte-Carlo codes for plasma edge modeling under ITER or DEMO conditions face serious challenges with respect to computational time and accuracy. In this paper, scaling laws for different error contributions are assessed and practical procedures for error estimation are proposed. First results on a 1D and a 2D test case are discussed. [ABSTRACT FROM AUTHOR]

This paper deals with area-based subpixel image registration under the rotation-isometric scaling-translation transformation hypothesis. Our approach is based on parametrical modeling of geometrically transformed textural image fragments and maximum-likelihood estimation of the transformation vector between them. Due to the parametrical approach based on the fractional Brownian motion modeling of the local fragments' texture, the proposed estimator \mboxML_\mathrmfBm (ML stands for “maximum likelihood” and fBm stands for “fractal Brownian motion”) has the ability to better adapt to real image texture content compared with other methods relying on universal similarity measures such as mutual information or normalized correlation. The main benefits are observed when assumptions underlying the fBm model are fully satisfied, e.g., for isotropic normally distributed textures with stationary increments. Experiments on both simulated and real images and for high and weak correlations between registered images show that the \mboxML_\mathrmfBm estimator offers significant improvement compared with other state-of-the-art methods. It reduces translation vector, rotation angle, and scaling factor estimation errors by a factor of about 1.75–2, and it decreases the probability of false match by up to five times. In addition, an accurate confidence interval for \mboxML_\mathrmfBm estimates can be obtained from the Cramér–Rao lower bound on rotation-scaling-translation parameter estimation error. This bound depends on texture roughness, noise level in reference and template images, correlation between these images, and geometrical transformation parameters. [ABSTRACT FROM AUTHOR]

Zonal occupant level is of great practical interest for building energy saving under normal operations and for fast evacuation under emergency. Though there are many existing sensing systems to estimate this information, the problem is still challenging due to the privacy concerns, the random human movement, and the accumulative error. In this paper, we consider this important problem and focus on infrared beam systems that monitor the zonal arrival and departure events. We make the following contributions. First, a rule (i.e., Rule 1) based on the stay time is developed to reduce the accumulated estimation error in each zone. Second, a rule (i.e., Rule 2) is designed to coordinate the estimation among neighboring zones. A decentralized estimation method is then developed using these two rules. Third, the advantage of this method is demonstrated through simulation results and field tests. We hope this work brings insight to zonal occupant level estimation in buildings in more general situations. [ABSTRACT FROM AUTHOR]

In large databases, the amount and the complexity of the data calls for data summarization techniques. Such summaries are used to assist fast approximate query answering or query optimization. Histograms are a prominent class of model-free data summaries and are widely used in database systems. So-called self-tuning histograms look at query-execution results to refine themselves. An assumption with such histograms, which has not been questioned so far, is that they can learn the dataset from scratch, that is—starting with an empty bucket configuration. We show that this is not the case. Self-tuning methods are very sensitive to the initial configuration. Three major problems stem from this. Traditional self-tuning is unable to learn projections of multi-dimensional data, is sensitive to the order of queries, and reaches only local optima with high estimation errors. We show how to improve a self-tuning method significantly by starting with a carefully chosen initial configuration. We propose initialization by dense subspace clusters in projections of the data, which improves both accuracy and robustness of self-tuning. Our experiments on different datasets show that the error rate is typically halved compared to the uninitialized version. [ABSTRACT FROM AUTHOR]

The BOKZ-M star sensor (a unit for measuring star coordinates) is intended to determine the parameters of the orientation of the axes of its own coordinate system relative to the axes of the inertial system using the observations of the areas of the stellar sky. A large number of factors influence the accuracy of this determination, and the estimation of the contribution of every factor to the total error is not always possible. In general, it must be limited by an estimation of the total error according to the results of studying a longterm series of determinations of the orientation parameters obtained in the ground tests and in space flight. This paper presents accuracy estimations for the BOKZ-M device obtained during its flight operation on the Meteor-M satellite. [ABSTRACT FROM AUTHOR]

This paper presents a global localization system for an indoor autonomous vehicle equipped with odometry sensors and a radio-frequency identification (RFID) reader to interrogate tags located on the ceiling of the environment. The RFID reader can measure the phase of the signals coming from responding tags. This phase has non-univocal dependence on the distance robot tag, but in the considered frequency, it is really sensitive to a change in the position of the robot. For this reason, a multihypothesis Kalman filtering approach provides a really satisfactory performance even in the case that a very small density of tags is used: In the experimental tests, an average position estimation error of about 4 cm is achieved using only two tags for an area of about 5 \m^2. [ABSTRACT FROM PUBLISHER]

This paper evaluates the performance of six possible sampling designs to estimate the population abundance index for American lobster using computer simulations. These designs include simple random sampling (SRS), systematic sampling (SYS) and stratified random sampling with four stratification schemes (i.e., based on region, depth, sediment and region x depth). For the stratified random design with region and depth being used for stratification, we evaluated the performances of different strategies for allocating sampling efforts. Simulations were implemented on the "true" populations which were estimated annually from 2002 to 2008 for both spring and fall based on a general additive model model developed in a separate study. Relative Estimation Error (REE), Relative Bias (RB) and design effect were used to measure the precision, accuracy and efficiency of mean estimation for different designs. On average, SYS tended to yield the most precise and efficient estimate of mean with specified sample size. However, its estimates tended to be biased and its performance varied with sample sizes and realizations of "true" population, thus changed with lobster distribution. Appropriate stratification, such as using depth to determine strata, significantly improved the precision and efficiency over SRS. Sediment, which is related to lobster distribution, was found to have little contribution to the improvement of the performance over SRS when it is used to determine strata. Also, allocating samples to each stratum based on variance or mean of previous year improved precision and efficiency. This study suggests that current design (i.e., region-depth stratified design) used in the survey had stable performance across years and seasons. [ABSTRACT FROM AUTHOR]

A number of algorithms to analyze crystalline porous materials and their porosities employ the Voronoi tessellation, whereby the space in the material is divided into irregular polyhedral cells that can be analyzed to determine the pore topology and structure. However, the Voronoi tessellation is only appropriate when atoms all have equal radii, and the natural generalization to structures with unequal radii leads to cells with curved boundaries, which are computationally expensive to compute. Therefore, high-throughput structure analysis codes utilize the radical Voronoi tessellation, which approximates the curved cells by polyhedra. In this study, we investigate the errors arising from this approximation reflected in the predicted values of two important porosity descriptors, the largest included and free sphere diameters. The maximum observed errors amount to ca. 0.6 Å for a test set comprised of zeolite and metal–organic framework materials. Following the work of Phillips et al. (Soft Matter, 2010, 6, 1693–1703), we describe a strategy to systematically decrease observed errors, and demonstrate that high accuracy (errors below 0.1 Å) can be obtained within the same algorithmic framework and at the expense of only an order of magnitude increase in computational cost. [ABSTRACT FROM AUTHOR]

Abstract: The available calibration experiences about rain intensity gauges relying on the weighing measuring principle are based on laboratory tests performed under constant reference flow rate conditions. Although the Weighing Gauges (WG) do provide better performance than more traditional Tipping Bucket Rain Gauges (TBR) under constant reference flow rates, dynamic effects do impact on the accuracy of WG measurements under real-world/time-varying rainfall conditions. The most relevant biases are due to the response time of the measurement system and the derived systematic delay in assessing the exact weight of the volume of cumulated precipitation collected in the container. This delay assumes a relevant role in case high resolution rainfall intensity (RI) time series are sought from the instrument, as is the case of many hydrologic and meteo-climatic applications (the one-minute time resolution recommended by the WMO for rainfall intensity measurements is here assumed). A significant sampling error is also attributable to some kind of weighing gauge, which affects the low intensity range as well. A laboratory investigation of the accuracy and precision of a modern weighing gauge manufactured by OTT (Pluvio2) under unsteady-state reference RI conditions is here addressed. Three different laboratory test conditions are applied: single and double step variations of the reference flow rate and a simulated real-world event. The preliminary development and validation of a suitable rainfall simulator for the generation of time-variable reference intensities is presented. The generator is demonstrated to have a sufficiently short time response with respect to the expected instrument behavior in order to ensure effective comparison of the measured vs. reference intensities. The measurements obtained from the WG are compared with those derived from a traditional TBR (manufactured by Casella) under the same laboratory conditions. The TBR measurements have been corrected to account for systematic mechanical errors and comparison is also proposed after applying further algorithms to reduce the sampling errors. Results indicate that the performance of the investigated WG under unsteady (real world) conditions in the laboratory is comparable or even lower than what can be obtained from more traditional TBRs, even in case corrections for sampling errors are not applied. [Copyright &y& Elsevier]

Abstract: Accuracy and precision of number, mean weight and biomass estimation at time of harvest of farmed Atlantic salmon were investigated using harvest-time data from 240 pens belonging to three large salmon producers in mid-Norway. The same database was used for statistical analysis of potential factors associated with estimation errors of the three outcome variables in a generalized linear model using a sandwich estimator for handling clustering and dependencies in the dataset. The results show high accuracy and poor precision of estimates where, at the pen level, mean errors are close to zero, but approximately 50% of estimates fall outside 3% errors and approximately 10% remain outside ±10% of the true values. The mean absolute biomass estimation error on pen level was 5.1%. Errors were reduced when figures were aggregated at the site and company level due to opposite directed errors cancelling each other. The symmetric distribution around zero suggests that the poor precision is an effect of random errors. Mortality above median was found to be statistically associated with underestimation of the fish numbers for harvest. Error in number estimation was found to be inversely associated with errors in mean weight estimation. Norwegian salmon production is regulated by maximum allowed biomass per license where random errors in stock estimation may contribute to overstocking and subsequent regulatory actions. The poor precision in stock estimation is currently an obstacle to further development and profitability in the industry. Correcting the factors identified in this study may give minor improvements in stock estimation while major improvements will require new methods and/or equipment. [Copyright &y& Elsevier]

The purpose of a theory of sampling is to answer two questions: How should one select a sample?—How much material should be selected? Parts I (qualitative approach), II and III (quantitative approach) of this series propose answers to these two fundamental questions. These answers are not entirely new (answers have been formulated since 1950), but a scientific theory is a living structure that has to be kept up to date. At a course given in Brasilia in 1998, pointed questions were raised which suggested that the introduction to the qualitative approach had to be clarified. Part I represents the most updated introduction to theory of sampling (TOS). More than 200 scientific papers, books, lectures and courses on sampling theory—and practice—have been published or offered to the public by the author over a period of 50 years. A brief, chronological account of the development history of TOS is presented for the first time in part IV—with a comprehensive literature survey as part V. [Copyright &y& Elsevier]

Location estimation in wireless sensor networks (WSNs) has received tremendous attention in recent times. Improved technology and efficient algorithms systematically empower WSNs with precise location identification. However, while algorithms are efficient in improving the location estimation error, the factor of the network lifetime has not been researched thoroughly. In addition, algorithms are not optimized in balancing the load among nodes, which reduces the overall network lifetime. In this paper, we have proposed an algorithm that balances the load of computation for location estimation among the anchor nodes. We have used vector-based swarm optimization on the connected dominating set (CDS), consisting of anchor nodes for that purpose. In this algorithm, major tasks are performed by the base station with a minimum number of messages exchanged by anchor nodes and unknown nodes. The simulation results showed that the proposed algorithm significantly improves the network lifetime and reduces the location estimation error. Furthermore, the proposed optimized CDS is capable of providing a global optimum solution with a minimum number of iterations. [ABSTRACT FROM AUTHOR]