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This paper presents a 2-2 multi-stage-noise-shaping (MASH) ΔΣ analog-to-digital converter (ADC), in which an integrator with dual double sampling (DDS) technique and stability compensation is proposed to save the area of capacitors without increasing the thermal noise. In addition, a fast-charge correlated-level-shifting (CLS) input buffer is also proposed to improve the linearity with acceptable power consumption. The buffer and the ADC are fabricated in a 0.18-μm CMOS process with an active area of 2.41 mm2, with 3∼5V supply voltage for the input buffer and 1.8-V power supply for the ADC, the overall ADC chip achieves a signal-to-noise-and-distortion ratio (SNDR) of 103.3 dB, a spurious-free dynamic range (SFDR) of 113.2 dB and an integral nonlinearity (INL) of ±2.5 ppm/full-scale range (FSR). The overall power consumption is 14.88 mW. [ABSTRACT FROM AUTHOR]

Zero-inflated models have been used successfully to describe microbial data with excess of zero-counts. Some sampling plans such as single sampling plan (SSP), resubmitted single sampling plan (RSSP) and quick switching system (QSS) have been developed for the zero-inflated Poisson (ZIP) distribution. Two new plans such as repetitive group sampling (RGS) and multiple dependent state (MDS) sampling are proposed to compare their performance with the existing sampling plans. Additionally, we develop double sampling (DS) and sequential sampling plans for the ZIP data. The plan parameters of the sampling plans are determined by using the unity value approach. The MDS sampling plan is more economical than the other sampling plans, including SSP, RSSP, RGS and QSS, in terms of the required sample size and average sample number in most cases. The probability of a zero in the ZIP data affects the sample size required for all sampling plans. Practical guidance for selecting sampling plans under ZIP is also provided. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed an estimator for finite population mean using auxiliary information in form of attributes under double sampling. We have also derived the expression of mean squared error of the proposed estimator. The proposed estimator is compared with the simple mean per unit, usual double sampling ratio and product estimators. An empirical study is carried out to judge the merit of the proposed estimator. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose a class of chain ratio-type exponential estimators for population mean in double sampling using two auxiliary variates. The properties of the proposed class of estimators are studied under large sample approximation. It has been shown that the proposed class of estimators is more efficient than the ratio estimator in double sampling , Chand’s (1975) chain-type ratio estimator and Singh and Vishwakarma (2007) exponential ratio estimator. An empirical study is carried out in support of the present study. [Copyright &y& Elsevier]

Abstract: Spatially patchy populations present challenges for precise estimation of abundance from surveys, which typically result in high estimation errors compared to surveys of more evenly distributed species. In this study, we used simulations to evaluate the performance of the Trawl and Acoustic Presence/Absence Survey design (TAPAS) in reducing the variability in estimated biomass. This approach is a double sampling design in which high-density patches observed in a first phase using hydroacoustics are subsequently more intensively sampled (relative to non-patch areas) in a second phase using trawls and area-swept methods. Information on Gulf of Alaska Pacific ocean perch (Sebastes alutus), a patchily distributed rockfish species, was used to parameterize the simulations. The performance of the TAPAS design depends upon the degree to which high acoustic backscatter represents “patch” areas of high density and variability, as the relationship between backscatter and abundance of a given species can be affected by areas unavailable for sampling (i.e., the “dead zone”) and the contribution of multiple species to the backscatter intensity. With a strong relationship between backscatter intensity and density, the TAPAS design resulted in improved precision compared to simple random sampling (SRS). Additionally, more intensive sampling of the patches occurred when areas of high backscatter intensity were randomly distributed over space rather than located in spatially correlated clusters. However, with a weak relationship between backscatter intensity and density, the precision of the TAPAS design was not substantially larger than SRS. The potential improvement in precision when a strong relationship exists between backscatter intensity and fish density offers motivation to continue to refine relationships between underlying fish density, acoustic backscatter, and trawl catches. [Copyright &y& Elsevier]

Abstract: To meet Kyoto Protocol obligations, New Zealand is required to estimate forest carbon stock change over the first commitment period (2008–2012). New Zealand has three subcategories of forest, namely: Natural forest; Pre-1990 forest; and Post-1989 forest. The Post-1989 forest carbon inventory undertaken in 2008 used discrete return airborne LiDAR and ground-based measurements of 0.06ha circular plots located on a 4-km×4-km grid. The national carbon stock estimate was based on a double sampling scheme consisting of 246 plots from which both ground and LiDAR data were obtained, supplemented with 46 additional plots assessed using only LiDAR. This paper describes the relationships established between carbon stocks estimated using ground-based measurements and LiDAR metrics. A regression model explaining 74% of the variation in total carbon was developed using LiDAR 30th percentile height (P30ht) and canopy cover (%Cover). The regression estimator improved the precision of the national carbon stock estimate in 2008 by 6% compared to the ground-based estimate. This inventory indicates that it is possible to reduce the cost of obtaining carbon stock estimates to a specified level of precision using a combination of ground-based and LiDAR measurements in a double sampling approach. The theoretical maximum improvement in precision expected in 2012, when additional LiDAR data are expected to be available, is 50–55%. [Copyright &y& Elsevier]

• Double-sampling Bayesian scheme with state-dependent inspection intervals. • Markovian deterioration with self-announced failures. • Double samplings are allowed. • Analytic derivation of cost function without the need for discretization. • Improved economic outcome compared to previous Bayesian policies. The aim of this study is to propose a Condition-Based Maintenance and Monitoring (CBM) policy which employs a Bayesian inspection scheme. A single unit system, which is subject to both operational deterioration and catastrophic failures, is considered. The equipment may operate in two different non-observable states (healthy and unhealthy). The unhealthy state is characterized by higher operational cost and higher proneness to failure. Failures are self-announced (directly observable) and thus, corrective maintenance is implemented immediately. A new double-sampling Bayesian control chart with state-dependent variable inspection frequency is proposed. The process operation is analytically modeled through a six-state Markov process, while, unlike all previous Bayesian models, there is no need for discretization of the unhealthy-state probabilities. At each inspection instance all available information regarding the equipment condition is utilized in order to schedule future inspections and preventive maintenance actions and detect possible operation in the unhealthy state. The critical parameters, namely the duration of the inspection intervals, the sample sizes and the preventive maintenance times, which minimize the expected total cost per time unit, are determined. Numerical comparisons with three other Bayesian CBM models are conducted to demonstrate the effectiveness of the proposed policy. [ABSTRACT FROM AUTHOR]

Abstract: We formulate closed-form Bayesian estimators for two complementary Poisson rate parameters using double sampling with data subject to misclassification and error free data. We also derive closed-form Bayesian estimators for two misclassification parameters in the modified Poisson model we assume. We use our results to determine credible sets for the rate and misclassification parameters. Additionally, we use MCMC methods to determine Bayesian estimators for three or more rate parameters and the misclassification parameters. We also perform a limited Monte Carlo simulation to examine the characteristics of these estimators. We demonstrate the efficacy of the new Bayesian estimators and highest posterior density regions with examples using two real data sets. [Copyright &y& Elsevier]

In forest inventories, various sampling techniques, also known as sampling designs, are used for the collection of the necessary information, which will provide precise sample estimates of the tree population characteristics at a low cost. The traditional single objective optimization in sampling is usually achieved either by minimizing the variance of the sample estimates assuming constraints on the cost of all the measurements in the sample or by minimizing the cost assuming constraints on the variance. Another quite realistic approach is to consider the optimization as a multiple objective optimization problem where the cost and the variance are simultaneously minimized functions. This paper proposes variations of a random search algorithm known as simulated annealing algorithm (SAA) for finding optimal sampling designs. The algorithm is first described for the single objective optimization problem and then is extended for the case of multi-objective optimization. The methods are referred to the double sampling procedure but can be extended to every multivariate sampling procedure. Examples of the applications of the algorithms to real inventory problems are presented. [Copyright &y& Elsevier]

Objective: In 1999, a survey of the health status of six rural central New York counties was performed using double sampling. The resulting impact of the methodology on health outcome prevalence estimation was assessed.Study design and setting: Six counties were double sampled using methodology described by Neyman. Prevalence of health outcomes was estimated for the initial survey responders, nonresponders who subsequently responded after being offered a financial incentive, and both groups combined.Results: Although both groups were similar on demographic variables, they differed significantly on several disease and health behavior prevalences. The failure to use double sampling would have resulted in overestimation of the prevalence of chronic disease by as much as 6.2% for females and 2.0% for males.Conclusion: Double sampling increased the generalizability of the results from approximately 35% of the population to over 70%, and decreased the bias in the estimation of many of the health end points. [Copyright &y& Elsevier]

Abstract: In estimating recreation visitation at the National Forest level in the US, annual counts of a number of types of visitation proxy measures were used. The intent was to improve the overall precision of the visitation estimate by employing the proxy counts. The precision of visitation estimates at sites that had proxy information versus those that did not is examined. Results show that using visitation proxy information reduces sampling needs by about 25%. Characteristics of the types of visitation proxy information that performed best and worst are discussed. [Copyright &y& Elsevier]

We consider the following type of two stage studies. First, a full scale study is conducted by observing an outcome Y, imperfect surrogate measure T of the unobserved covariate of interest X, and covariates U for all members of a cohort, where the cohort is a random sample from some infinite, underlying population. Second, a validation study is conducted, in which we ascertain the previously unmeasured covariate X for a subset of the subjects selected at random within each category/stratum of the cohort. Stratum membership can depend on Y, T and U from the full scale study. Several methods of analysis are now available that one can use to estimate the relationship between Y and X in two stage studies, but each method has potential limitations, especially when both Y and X are continuous. We present a method of analysis for two stage study data with continuous and/or discrete variables. The proposed method generalizes the mean score method of Reilly and Pepe (Biometrika 82 (1995) 299) in two ways. First, it uses an estimating equations approach to estimate mean scores. Second, it applies even when the outcome and independent variables are continuous, provided sampling of subjects for the validation study is random within categories. Despite its greater applicability, this new approach retains many of the consistency properties of the mean score method. One can use the method of analysis for effect estimation with many study designs when error in measurement or misclassification of independent variables has occurred. Like the mean score method, this method of analysis uses an estimated score function. We assume some “regularity conditions”, but make no distributional assumptions about the misclassified variable or the measurement error. A limited simulation study was performed to evaluate the proposed method and two examples are presented to illustrate the proposed method. [Copyright &y& Elsevier]

CMOS Image Sensors have many applications currently. They are present everywhere and almost everyone owns many cameras based on these sensors ranging from mobile phones (with several cameras), to tablets, computers, security devices and cars. Although they work pretty well as we increase their resolution and quality we face a few problems. One of these problems is Flicker noise. The other source of noise, thermal, evolved a lot recently and flicker noise is now the main limitation regarding improving the performance. This work presents a new method to reduce the flicker noise contribution in the overall sensor noise by reducing the time for Double Sampling, allowing to filters more correctly the flicker noise spectrum introduced by the pixel source follower amplifier. We also introduce a way to remove the column induced lag that comes from the column readout circuitry by using strong and stable references. We propose a new amplifier that is used as a buffer for the reference voltage. At the same time Double Sampling is performed. This solution contributes both to reduce the current consumption and time between samples which also contributes to a lower power heat dissipation. The developed circuit can be seen as a generic solution for applications that need stable references and high resolution, resulting in a massively parallel circuit, as it is common in CMOS image sensors. The proposed solution is tested with a circuit containing 3000 readout columns. [ABSTRACT FROM AUTHOR]