Improving the performance of Kriging based interpolation application with parallel processors

Guvendik, Celal; Genc, Ahmet Esat; Tamer, Özgür; Nil, Metin

doi:10.1109/siu.2012.6204645

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…al. (Güvendik et al, 2012) General definition of the Kriging algorithm will be introduced in the second section of this paper, which is going to be followed by the presentation of the proposed parallel architecture. At the fourth section, results of this application will be presented.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Architecture for Improving the Performance of the Kriging Algorithm

Tamer

Genc²

2023

UMAG

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Estimating missing data values by using interpolation algorithms is a well-known technique. Kriging is an optimized interpolation method based on regression against evaluated values from the surrounding observation points, weighted according to spatially varying values according to the covariance between these observation points. It has been widely used for estimating the missing geological data of the areas based on the measurements in close proximity. In this work we use the Kriging to recover the missing pixels of digital images. Even though Kriging is considered as successful on estimating the missing pixels, the algorithm has a high operation load, causing delays especially for live streaming videos. In this paper we propose a parallel architecture to improve the performance and reduce the operation time of the Kriging Algorithm for estimating the missing pixels. The proposed method can be applied on Field Programmable Gate Arrays (FPGA) and considerable performance improvement have been achieved depending on the number of logic blocks available inside the FPGA.

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Section: Introductionmentioning

confidence: 99%

A Parallel Architecture for Improving the Performance of the Kriging Algorithm

Tamer

Genc²

2023

UMAG

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Analyzing Kriging method and creating an application using Kriging

Gaikwad¹,

Sawant²

2016

2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

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Optimized Data Mining Techniques for Outlier Detection, Removal, and Management Zone Delineation for Yield Prediction

Roopa¹,

Kumar²,

Kumar³

et al. 2020

Modern Techniques for Agricultural Disease Management and Crop Yield Prediction

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Enormous agricultural data collected using sensors for crop management decisions on spatial data with soil parameters like N, P, K, pH, and EC enhances crop growth for soil type. Spatial data play vital role in DSS, but inconsistent values leads to improper inferences. From EDA, few observations involve outliers that deviates crop management assessments. In spatial data context, outliers are the observations whose non-spatial attributes are distinct from other observations. Thus, treating an entire field as uniform area is trivial which influence the farmers to use expensive fertilizers. Iterative-R algorithm is applied for outlier detection to reduce the masking/swamping effects. Outlier-free data defines interpretable field patterns to satisfy statistical assumptions. For heterogeneous farms, the aim is to identify sub-fields and percentage of fertilizers. MZD achieved by interpolation technique predicts the unobserved values by comparing with its known neighbor-points. MZD suggests the farmers with better knowledge of soil fertility, field variability, and fertilizer applying rates.

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Improving the performance of Kriging based interpolation application with parallel processors

Cited by 3 publications

References 2 publications

A Parallel Architecture for Improving the Performance of the Kriging Algorithm

A Parallel Architecture for Improving the Performance of the Kriging Algorithm

Analyzing Kriging method and creating an application using Kriging

Optimized Data Mining Techniques for Outlier Detection, Removal, and Management Zone Delineation for Yield Prediction

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