Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatia...Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.展开更多
A 16-bit 170 MS/s pipelined ADC implemented in 0.18 m CMOS process is presented in this paper. An improved digital calibration method and a linearized sampling front-end are employed to achieve a high SFDR. The enlarg...A 16-bit 170 MS/s pipelined ADC implemented in 0.18 m CMOS process is presented in this paper. An improved digital calibration method and a linearized sampling front-end are employed to achieve a high SFDR. The enlarged full scale range makes it possible to obtain a high SNR with smaller sampling capacitors, thus achieving higher speed and low power. This ADC attains an SNR of 77.2 d BFS, an SFDR of 97.6 d Bc for a 10 MHz input signal, while preserving an SFDR 〉 80 d Bc up to 300 MHz input frequency. The ADC consumes 430 mW from a1.8 V supply and occupies a 17 mm^2 active area.展开更多
基金funded by the Natural Science and Engineering Research Council (NSERC) of Canada (No. RGPIN-2014-04100)
文摘Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.
基金Project supported by the National Science and Technology Major Project(No.2009ZX01034-002-001-016)
文摘A 16-bit 170 MS/s pipelined ADC implemented in 0.18 m CMOS process is presented in this paper. An improved digital calibration method and a linearized sampling front-end are employed to achieve a high SFDR. The enlarged full scale range makes it possible to obtain a high SNR with smaller sampling capacitors, thus achieving higher speed and low power. This ADC attains an SNR of 77.2 d BFS, an SFDR of 97.6 d Bc for a 10 MHz input signal, while preserving an SFDR 〉 80 d Bc up to 300 MHz input frequency. The ADC consumes 430 mW from a1.8 V supply and occupies a 17 mm^2 active area.
