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Sampling Strategy

D. Scholarios, Beatrice I.j.m. Van Der Heijden · University of Twente Research Information · 2005

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Chapter 10 explores strategies for sampling the multidimensional optical data cube, which includes spatial, spectral, temporal, polarization, and coherence information. It emphasizes the challenge of mapping high-dimensional object data onto 2D focal plane arrays and introduces the concept of separating object, field, and measurement data cubes. The chapter outlines three primary sampling strategies: interleaved (e.g., Bayer filters), temporal scanning (e.g., tunable filters), and parallel sampling (e.g., camera arrays). Feature-specific measurement is discussed using principal component analysis (PCA) to optimize sampling based on object statistics. Spectral and temporal imaging are analyzed through examples like AVIRIS and bead datasets, showing how multiscale sampling can improve efficiency. The chapter also covers dynamic range management via multiple exposures and tone mapping, autofocus strategies using reinforcement learning, and lens design in the context of microcamera arrays. It concludes with advanced topics like interferometric focal planes and compressive video encoding, highlighting the need for efficient analog-to-digital conversion and low-power image signal processing to enable high-throughput computational imaging.

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Sampling strategy.

Joy John Mammen (3129411), Edwin Sam Asirvatham (12360156), Jeyaseelan Lakshmanan (2337460) et al. · Figshare · 2022

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Sampling Strategy.

Oathokwa Nkomazana (753799), Robert Mash (336013), Sheila Shaibu (784585) et al. · Figshare · 2015

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Sampling Strategy

Taylor Kohut, William Fisher, Lorne Campbell et al. · OSF Preprints (OSF Preprints)

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Sampling Strategy

Taylor Kohut, William Fisher, Lorne Campbell et al. · OSF Preprints (OSF Preprints) · 2016

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Sampling strategy.

Roy Cherian (8307810), Gem Le (8307813), James Whall (8307816) et al. · Figshare · 2020

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Sampling strategy.

Rasmita Shrestha (18611354), Aditya Shakya (18611357), Pratik Khanal (9235659) et al. · Figshare · 2024

<div>BackgroundThe Government of Nepal initiated a family-based National Health Insurance Program (NHIP) in April 2016, aiming to ensure universal health coverage (UHC) by enhancing access to and utilization of quality health services. However, NHIP, in its initial years of implementation, encountered challenges such as low population coverage, a high dropout rate, and concerns among the insured regarding the quality of healthcare services. There is a dearth of information regarding user satisfaction with the NHIP in Nepal. This study aimed to assess user satisfaction with NHIP at the household level in Nepal.MethodsWe conducted a cross-sectional study among 347 households in the Ilam district using a multi-stage random sampling method. Face-to-face interviews were conducted with household heads enrolled in NHIP. A semi-structured questionnaire was used to collect the data. The multivariable logistic regression analysis was done to identify the predictors of satisfaction level.ResultsOverall, 53.6% of the insured were satisfied with the NHIP, while 31.1% had comprehensive knowledge about the NHIP. Factors such as gender (AOR: 1.80, 95% CI: 1.08–3.00), distance to the first point of contact (AOR: 2.15, 95% CI: 1.24–3.74), waiting time (AOR: 2.02, 95% CI: 1.20–3.42), availability of diagnostic services (AOR: 1.90, 95% CI: 1.05–3.45), availability of prescribed medicine (AOR: 3.90, 95% CI: 1.97–7.69), perceived service quality (AOR: 2.20, 95% CI: 1.15–4.20), and the behavior of service providers (AOR: 3.48, 95% CI: 1.04–11.63) were significantly associated with user satisfaction.ConclusionThe satisfaction level among NHIP users was deemed moderate. This study highlighted several factors, such as gender, distance to the first point of contact, waiting time, availability of diagnostic services and prescribed medicine, perceived service quality, and the behavior of service providers, as key determinants impacting user satisfaction. Recognizing the pivotal role of user satisfaction, health insurance stakeholders must prioritize it to ensure higher retention rates and coverage within NHIP.</div>

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Sampling Strategy

2021

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Sampling Strategy

Taylor Kohut, Lorne Campbell, William Fisher et al. · OSF Preprints (OSF Preprints) · 2022

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Sampling strategy.

Kris A. Christensen (3797896), Eric B. Rondeau (8726934), David R. Minkley (8726931) et al. · Figshare · 2020

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Sampling Strategy

D. Scholarios, Beatrice I.j.m. Van Der Heijden · University of Twente Research Information · 2005

Cited by 0

Sampling Strategy

OSF Preprints (OSF Preprints) · 2016

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Sampling strategy.

Samuel Chatio (288467), Paul Welaga (391464), Philip Teg-Nefaah Tabong (2150050) et al. · Figshare · 2019

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Sampling strategy.

Sven Schwabe (12477380), Franziska A. Herbst (4675615), Stephanie Stiel (4675624) et al. · Figshare · 2024

<div>BackgroundSuicide assistance is as a complex process comprising a series of stages, ranging from initial consultations with patients about end-of-life options to counselling bereaved family members. The role of healthcare professionals and other practitioners in this process varies according to national regulations and procedural requirements. After a decision of the German Federal Constitutional Court in 2020, suicide assistance is unregulated in Germany but official data about the implementation of suicide assistance is lacking.AimsThe project “ASEP–Assisted suicide in Germany: Exploring the practice” aims to generate and disseminate scientific knowledge on the practice of assisted suicide in Germany, based on the experiences of practitioners and family members.MethodsASEP is a prospective, observational, qualitative study comprising three phases. In Phase 1, expert interviews will be conducted to identify involved professional groups, issues and stages of the implementation of suicide assistance in Germany. In phase 2, experiences of practitioners and family members, who were involved in the practice of suicide assistance in Germany, will be collected via semi-structured interviews and analysed. In phase 3, findings of phase 2 will be discussed in focus groups with experts to identify linkages between the practice of assisted suicide and the healthcare system and to develop recommendations on how these linkages should be organised.DiscussionThe results of this study will gain insights into the practice of suicide assistance for the first time in Germany. The findings are expected to inform scientific societies, professional association, and policy makers about the current practice and provide recommendations for better linkages of assisted suicide with the healthcare system. In this way, the project provides an evidence-based impetus for a more transparent and well-founded handling of assisted suicide in the German healthcare system.Trial registrationThe study was prospectively registered in the German Clinical Trials Register (Deutsches Register Klinischer Studien) (Registration N° <a href="https://clinicaltrials.gov/ct2/show/DRKS00034574" target="_blank">DRKS00034574</a>; date of registration: 04 July 2024). The study is searchable under the International Clinical Trials Registry Platform Search Portal of the World Health Organization, under the German Clinical Trials Register number.</div>

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Sampling strategy.

Terje Kristensen (5665387), Erik Næsset (802278), Mikael Ohlson (802279) et al. · Figshare · 2015

Sampling strategy for the eight boreal forest plots used in this study, illustrated by plot K2. Filled squares represent 73 sample points for field layer vegetation, mosses and the organic top soil layer. Filled circles indicate location of standing trees. Stand density measures were calculated at a plot scale (dotted circle) and in subplots with a radius of 5 m around for each of the 73 sampling points (full circles). For illustration purposes only five of the density circles are displayed.

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Sampling Strategy

Scholarios, D., van der Heijden, Beatrice · Data Archiving and Networked Services (DANS) · 2005

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SAMPLING STRATEGY 3

Michael J. Kunk, Robert P. Wintsch · 1996

This report is preliminary and has not been edited or reviewed for conformity with U.S. Geological Survey editorial standards and stratigraphic nomenclature. Any use of trade names is for descriptive purposes only and does not imply endorsement by the U.S.G.S.

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Natural sampling strategy

Cecil R. Hallum, J. P. Basu · 1979

A natural stratum-based sampling scheme and the aggregation procedures for estimating wheat area, yield, and production and their associated prediction error estimates are described. The methodology utilizes LANDSAT imagery and agrophysical data to permit an improved stratification in foreign areas by ignoring political boundaries and restratifying along boundaries that are more homogeneous with respect to the distribution of agricultural density, soil characteristics, and average climatic conditions. A summary of test results is given including a discussion of the various problems encountered.

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Probability sampling strategy.

Raquel De Boni (326318), Pedro Luis do Nascimento Silva (326320), Francisco Inácio Bastos (326322) et al. · Figshare · 2015

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