Performance Mining for Batch Processing Using the Performance Spectrum

2019 ◽  
pp. 172-185 ◽  
Author(s):  
Eva L. Klijn ◽  
Dirk Fahland
Keyword(s):  
Batch Processing

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

The Coming of Multiple-Access Computers

1967 ◽  
Vol 71 (676) ◽  
pp. 235-236 ◽  
Author(s):  
M. V. Wilkes
Keyword(s):  
Engineering Design ◽  
Multiple Access ◽  
Batch Processing ◽  
Central Processor ◽  
Storage Space ◽  
Typical Problem ◽  
Design Engineers ◽  
Processing Techniques ◽  
Digital Computers

When digital computers first became available many of us expected that they would have an early and significant impact on engineering design. This did not happen, and the reasons why it did not are worth examining. For one thing, the early computers were not nearly large enough; engineers do not build things out of spheres and parallelograms as mathematicians do, and quite a lot of storage space is needed to describe a typical problem in engineering design. Unfortunately, the big computers when they came were so expensive that it was not considered economic to allow users to handle them personally, and batch-processing techniques were introduced. The result was to create a barrier between the computer and the design engineer, and to make it impossible for him to get results of any kind without a delay amounting to a few hours at the very best, and often to much more. Emphasis in fact, was put on the efficiency with which the central processor of the computer was used and no regard at all was paid to the efficiency with which the users—in this case programmers and design engineers—worked. We are on the threshold of a development which, there is every reason to hope, will change the situation radically.

scholarly journals GenoProfiler: batch processing of high-throughput capillary fingerprinting data

2006 ◽  
Vol 23 (2) ◽  
pp. 240-242 ◽  
Author(s):  
F. M. You ◽  
M.-C. Luo ◽  
Y. Q. Gu ◽  
G. R. Lazo ◽  
K. Deal ◽  
...  
Keyword(s):  
High Throughput ◽  
Batch Processing

scholarly journals A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2033
Author(s):  
Raegeun Oh ◽  
Yifang Shi ◽  
Jee Woong Choi
Keyword(s):  
Particle Swarm Optimization ◽  
Motion Analysis ◽  
Hybrid Method ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Optimization Method ◽  
Batch Processing ◽  
Swarm Optimization ◽  
Newton Raphson ◽  
Target Motion Analysis

Bearing-only target motion analysis (BO-TMA) by batch processing remains a challenge due to the lack of information on underwater target maneuvering and the nonlinearity of sensor measurements. Traditional batch estimation for BO-TMA is mainly performed based on deterministic algorithms, and studies performed with heuristic algorithms have recently been reported. However, since the two algorithms have their own advantages and disadvantages, interest in a hybrid method that complements the disadvantages and combines the advantages of the two algorithms is increasing. In this study, we proposed Newton–Raphson particle swarm optimization (NRPSO): a hybrid method that combines the Newton–Raphson method and the particle swarm optimization method, which are representative methods that utilize deterministic and heuristic algorithms, respectively. The BO-TMA performance obtained using the proposed NRPSO was tested by varying the measurement noise and number of measurements for three targets with different maneuvers. The results showed that the advantages of both methods were well combined, which improved the performance.

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