Spatial sampling methods for improved communication for wireless relay robots

Invasive pathogens are known to cause major damage to the environments they invade. Effective control of such invasive pathogens depends on early detection. In this paper we focus on sampling with the aim of detecting an invasive pathogen. To that end, we introduce the concept of optimized spatial sampling, using spatial simulated annealing, to plant pathology. It has been mathematically proven (15) that this optimization method converges to the optimum allocation of sampling points that give the largest detection probability. We show the benefits of the method to plant pathology by (i) first illustrating that optimized spatial sampling can easily be applied for disease detection, and then we show that (ii) combining it with a spatially explicit epidemic model, we can develop optimum sample schemes, i.e., optimum locations to sample that maximize the probability of detecting an invasive pathogen. This method is then used as baseline against which other sampling methods can be tested for their accuracy. For the specific example case of this paper, we test (i) random sampling, (ii) stratified sampling as well as (iii) sampling based on the output of the simulation model (using the most frequently infected hosts as sample points), and (iv) sampling the hosts closest to the outbreak point.

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Comparison of different spatial sampling methods for validation of GEOV1 FVC product over heterogeneous and homogeneous surfaces

10.1117/12.2241618 ◽

2016 ◽

Author(s):

Yanling Ding ◽

Yong Ge ◽

Maogui Hu ◽

Hongyan Zhang

Keyword(s):

Sampling Methods ◽

Spatial Sampling

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A Multi-Level Non-Uniform Spatial Sampling Method for Accuracy Assessment of Remote Sensing Image Classification Results

Applied Sciences ◽

10.3390/app10165568 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5568

Author(s):

Zhenhua Wang ◽

Lizhi Xu ◽

Qing Ji ◽

Wei Song ◽

Lingqun Wang

Keyword(s):

Remote Sensing ◽

Spatial Correlation ◽

Sampling Method ◽

Sampling Methods ◽

Accuracy Assessment ◽

Remote Sensing Image ◽

Spatial Sampling ◽

Remote Sensing Images ◽

Multi Level ◽

Sample Points

Accuracy assessment of classification results has important significance for the application of remote sensing images, which can be achieved by sampling methods. However, the existing sampling methods either ignore spatial correlation or do not consider spatial heterogeneity. Here, we proposed a multi-level non-uniform spatial sampling method (MNSS) for the accuracy assessment of classification results. Taking the remote sensing image of Kobo Askov, Texas, USA, as an example, the classification result of this image was obtained by Support Vector Machine (SVM) classifier. In the proposed MNSS, the studied spatial region was zoned from high to low resolution based on the features of spatial correlation. Then, the sampling rate of each zone was deduced from the low to high resolution based on the spatial heterogeneity. Finally, the positions of sample points were allocated in each zone, and the classification results of the sample points were obtained. We also used other sampling methods, including a random sampling method (SRS), stratified sampling method (SS), and spatial sampling of the gray level co-occurrence matrix method (GLCM), to obtain the classification results of the sample points (2-m resolution). Five categories of ground objects in the same region were used as the ground truth data. We than calculated the overall accuracy, Kappa coefficient, producer accuracy, and user accuracy to estimate the accuracy of the classification results. The results showed that MNSS was the strictest inspection method as shown by the minimum value of accuracy. Moreover, MNSS overcame the shortcoming of SRS, which did not consider the spatial correlation of sample points, and overcame the shortcomings of SS and GLCM, which had redundant information between sample points. This paper proposes a novel sampling method for the accuracy assessment of classification results of remote sensing images.

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Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100163873 ◽

1996 ◽

Vol 54 ◽

pp. 282-283

Author(s):

Badrinath Roysam ◽

Hakan Ancin ◽

Douglas E. Becker ◽

Robert W. Mackin ◽

Matthew M. Chestnut ◽

...

Keyword(s):

Image Analysis ◽

Structural Changes ◽

Sampling Methods ◽

Spatial Clustering ◽

Three Dimensional ◽

Field Of View ◽

Cell Counting ◽

Depth Of Field ◽

Tissue Cell ◽

Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

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Pengaruh Tekanan Waktu, Locus Of Control , Tindakan Supervisi, Dan Materialitas Terhadap Penghentian Prematur Atas Prosedur Audit Studi Kasus Pada Kantor Akuntan Publik Daerah Istimewa Yogyakarta Tahun 2015

Kajian Bisnis STIE Widya Wiwaha ◽

10.32477/jkb.v24i1.216 ◽

2017 ◽

Vol 24 (1) ◽

pp. 35-53

Author(s):

Sulastiningsih Sulastiningsih ◽

Intan Ayu Candra

Keyword(s):

Locus Of Control ◽

Premature Termination ◽

Sampling Methods ◽

Time Pressure ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Test Reliability ◽

Friedman Test ◽

Independent Variables ◽

Public Accountant

The purpose of this study is to prove: (1) Time pressure, locus of control, the action of supervision and materiality partially affect the premature termination of the audit procedures (2) Time pressure, locus of control, supervision and materiality simultaneously affect the premature termination on the audit procedures. This research was conducted in Public Accountant firm in Yogyakarta region of which total 12 samples of KAP, by distributing 105 questionnaires, and 57 questionnaires were returned (54%). 34 of the returned questionnaires can be processed (34%). The samples in this study were determined by using non-probability sampling, one of purposive sampling methods. Data analysis consisted of: (1) validity test, reliability test and classical assumption. The result showed that the instruments used are quite reliable and valid (2) multiple linear regression analysis. The results are (a) Some of independent variables partially affect premature termination of the audit procedure, while the action of supervision does not influence premature termination of audit procedures (b) All independent variables influence simultaneously to the premature termination of the audit procedures (c) All independent variables showed that as much as 55% it affects on premature termination of the audit procedures, the rest of it are influenced by other variables. (3) Friedman Test. The result shows that there are order of priority of audit procedures being terminated.

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