scholarly journals Portable Rice Disease Spores Capture and Detection Method Using Diffraction Fingerprints on Microfluidic Chip

Micromachines ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 289 ◽  
Author(s):  
Yang ◽  
Chen ◽  
Li ◽  
Li ◽  
Zou ◽  
...  
Keyword(s):  
Microfluidic Chip ◽  
Detection Method ◽  
Fungal Spores ◽  
Large Field ◽  
Average Error ◽  
Crop Diseases ◽  
Rice Disease ◽  
Two Parameters ◽  
The Cost ◽  
Spore Detection

Crop diseases cause great harm to food security, 90% of these are caused by fungal spores. This paper proposes a crop diseases spore detection method, based on the lensfree diffraction fingerprint and microfluidic chip. The spore diffraction images are obtained by a designed large field of view lensless diffraction detection platform which contains the spore enrichment microfluidic chip and lensless imaging module. By using the microfluidic chip to enrich and isolate spores in advance, the required particles can be captured in the chip enrichment area, and other impurities can be filtered to reduce the interference of impurities on spore detection. The light source emits partially coherent light and irradiates the target to generate diffraction fingerprints, which can be used to distinguish spores and impurities. According to the theoretical analysis, two parameters, Peak to Center ratio (PCR) and Peak to Valley ratio (PVR), are found to quantify these spores. The correlation coefficient between the detection results of rice blast spores by the constructed device and the results of microscopic artificial identification was up to 0.99, and the average error rate of the proposed device was only 5.91%. The size of the device is only 4 cm × 4 cm × 5 cm, and the cost is less than $150, which is one thousandth of the existing equipment. Therefore, it may be widely used as an early detection method for crop disease caused by spores.

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

Detection of Microbubbles Using the Hough Transform

2013 ◽  
Vol 378 ◽  
pp. 478-482
Author(s):  
Yoshihiro Mitani ◽  
Toshitaka Oki
Keyword(s):  
Image Processing ◽  
Error Rate ◽  
Hough Transform ◽  
Detection Method ◽  
Experimental Results ◽  
Average Error ◽  
Image Processing Techniques ◽  
Average Error Rate ◽  
Processing Techniques

The microbubble has been widely used and shown to be effective in various fields. Therefore, there is an importance of measuring accurately its size by image processing techniques. In this paper, we propose a detection method of microbubbles by the approach based on the Hough transform. Experimental results show only 4.49% of the average error rate of the undetected microbubbles and incorrectly detected ones. This low percentage of the error rate shows the effectiveness of the proposed method.

scholarly journals A peduncle detection method of tomato for autonomous harvesting

2021 ◽  
Author(s):  
Jiacheng Rong ◽  
Guanglin Dai ◽  
Pengbo Wang
Keyword(s):  
Pose Estimation ◽  
Pitch Angle ◽  
Detection Method ◽  
Geometric Model ◽  
Average Error ◽  
Long Distance ◽  
End Effector ◽  
Key Points ◽  
Yaw Angle ◽  
Cutting Point

AbstractFor automating the harvesting of bunches of tomatoes in a greenhouse, the end-effector needs to reach the exact cutting point and adaptively adjust the pose of peduncles. In this paper, a method is proposed for peduncle cutting point localization and pose estimation. Images captured in real time at a fixed long-distance are detected using the YOLOv4-Tiny detector with a precision of 92.7% and a detection speed of 0.0091 s per frame, then the YOLACT +  + Network with mAP of 73.1 and a time speed of 0.109 s per frame is used to segment the close-up distance. The segmented peduncle mask is fitted to the curve using least squares and three key points on the curve are found. Finally, a geometric model is established to estimate the pose of the peduncle with an average error of 4.98° in yaw angle and 4.75° in pitch angle over the 30 sets of tests.

scholarly journals Comparative evaluation of cost effective extraction free molecular technique for detection of SARS-CoV-2 with reference to standard VTM based RT-qPCR method

2021 ◽  
Author(s):  
Megh Singh Dhakad ◽  
Sanjib Gogoi ◽  
Ansu Kumari ◽  
Aashish Kumar Singh ◽  
Manoj B. Jais ◽  
...  
Keyword(s):  
Detection Method ◽  
Cost Effective ◽  
Molecular Technique ◽  
Rt Pcr ◽  
Index Method ◽  
Resource Limited ◽  
Qpcr Method ◽  
Kappa Value ◽  
The Cost ◽  
Higher Sensitivity

Background and Objectives: The entire globe is undergoing an unprecedented challenge of COVID-19. Considering the need of rapid and accurate diagnostic tests for SARS-CoV-2, this study was planned to evaluate the cost effective extraction free RT-PCR technique in comparison to the standard VTM based RT-qPCR method. Materials and Methods: Paired swabs from nasopharynx and oropharynx were collected for SARS-CoV-2 testing, from 211 adult patients (≥18 years) in VTM and plain sterile tubes (dry swabs). These samples were processed and RT-qPCR was carried out as per standard protocols. Results: 54.5% of the patients were females and 45.5% were males with sex ratio 1:1.19 (M: F). 38.86% were symptomatic, of which fever (86.59%), cough (79.23%) and breathlessness (46.34%) were the most common symptoms. The positivity by VTM based method and index method was 31.27% and 13.27% respectively. Of the 27 inconclusive results from index method, 37.04% were positive, 48.15% were negative by VTM based method. However, in 40 inconclusive results by VTM based method, 90% were negative and rest remained inconclusive by index method. The sensitivity and specificity of the index method were 39.39% and 85.71% respectively. The overall agreement between VTM based method and index method was 49.59% with estimated Kappa value of 0.19. Conclusion: VTM based method showed higher sensitivity compared to the index method. The higher positivity by VTM based method, suggests that VTM based method could plausibly be a better detection method of SARS-CoV-2. Still, the index method might add value in a resource limited setups for detection of SARS-CoV-2.  

Application of Parametric Cost Estimation Model to Telecommunication Networks

2011 ◽  
pp. 150-167
Author(s):  
Swadesh Kumar Samanta ◽  
John Woods ◽  
Mohammed Ghanbari
Keyword(s):  
Cost Estimation ◽  
Research Effort ◽  
Real Data ◽  
Telecommunication Networks ◽  
Estimation Model ◽  
Parametric Cost Estimation ◽  
Cost Estimation Model ◽  
Two Parameters ◽  
The Cost ◽  
Telecom Network

The parametric cost estimation approach has proved to be an efficient method for analyzing complex systems such as spacecraft, missiles, ships, buildings, etc where cost varies according to a number of parameters. The cost to provision a telecom network also depends on a number of parameters; but little research effort has been applied to estimate cost using this approach. In estimating the cost of a telecom network, most published research has considered two parameters; distance and bandwidth of a link and ignored the effects of other parameters. We have modelled the cost based on distance, bandwidth, geographical terrain and technology simultaneously using a parametric cost estimation methodology applied to real data obtained from the Indian Telecom Company, BSNL. Using the model, we show how a cost optimized network can be designed given the real world constraints. The applicability of our model to determine revenue sharing mechanism for an international call is also demonstrated. [Article copies are available for purchase from InfoSci-on-Demand.com]

scholarly journals Achieving Fairness in the Stochastic Multi-Armed Bandit Problem

2020 ◽  
Vol 34 (04) ◽  
pp. 5379-5386
Author(s):  
Vishakha Patil ◽  
Ganesh Ghalme ◽  
Vineet Nair ◽  
Y. Narahari
Keyword(s):  
Learning Algorithm ◽  
Complete Characterization ◽  
Bandit Problem ◽  
Fairness Constraints ◽  
Two Parameters ◽  
The Cost ◽  
Primary Contribution ◽  
Theoretical Results ◽  
Natural Way

We study an interesting variant of the stochastic multi-armed bandit problem, which we call the Fair-MAB problem, where, in addition to the objective of maximizing the sum of expected rewards, the algorithm also needs to ensure that at any time, each arm is pulled at least a pre-specified fraction of times. We investigate the interplay between learning and fairness in terms of a pre-specified vector denoting the fractions of guaranteed pulls. We define a fairness-aware regret, which we call r-Regret, that takes into account the above fairness constraints and extends the conventional notion of regret in a natural way. Our primary contribution is to obtain a complete characterization of a class of Fair-MAB algorithms via two parameters: the unfairness tolerance and the learning algorithm used as a black-box. For this class of algorithms, we provide a fairness guarantee that holds uniformly over time, irrespective of the choice of the learning algorithm. Further, when the learning algorithm is UCB1, we show that our algorithm achieves constant r-Regret for a large enough time horizon. Finally, we analyze the cost of fairness in terms of the conventional notion of regret. We conclude by experimentally validating our theoretical results.

AUTO-TUNING PARALLEL SKELETONS

2012 ◽  
Vol 22 (02) ◽  
pp. 1240005 ◽  
Author(s):  
ALEXANDER COLLINS ◽  
CHRISTIAN FENSCH ◽  
HUGH LEATHER
Keyword(s):  
Execution Time ◽  
Human Expert ◽  
Programming Abstraction ◽  
Parallel Skeletons ◽  
Starting Point ◽  
Monte Carlo Search ◽  
Exploratory Data ◽  
Automatic Search ◽  
Two Parameters ◽  
The Cost

Parallel skeletons are a structured parallel programming abstraction that provide programmers with a predefined set of algorithmic templates that can be combined, nested and parameterized with sequential code to produce complex programs. The implementation of these skeletons is currently a manual process, requiring human expertise to choose suitable implementation parameters that provide good performance. This paper presents an empirical exploration of the optimization space of the FastFlow parallel skeleton framework. We performed this using a Monte Carlo search of a random subset of the space, for a representative set of platforms and programs. The results show that the space is program and platform dependent, non-linear, and that automatic search achieves a significant average speedup in program execution time of 1.6× over a human expert. An exploratory data analysis of the results shows a linear dependence between two of the parameters, and that another two parameters have little effect on performance. These properties are then used to reduce the size of the space by a factor of 6, reducing the cost of the search. This provides a starting point for automatically optimizing parallel skeleton programs without the need for human expertise, and with a large improvement in execution time compared to that achievable using human expert tuning.

scholarly journals How to Convert Biological Carbon Into Graphite for AMS

Radiocarbon ◽  
2006 ◽  
Vol 48 (3) ◽  
pp. 325-336 ◽  
Author(s):  
Girma Getachew ◽  
Seung-Hyun Kim ◽  
Betty J Burri ◽  
Peter B Kelly ◽  
Kurt W Haack ◽  
...  
Keyword(s):  
Sample Preparation ◽  
Detection Method ◽  
Kinetic Studies ◽  
Environmental Research ◽  
Educational Materials ◽  
Tracer Studies ◽  
Isotope Tracer ◽  
Biological And Environmental Samples ◽  
Multistep Procedure ◽  
The Cost

Isotope tracer studies, particularly radiocarbon measurements, play a key role in biological, nutritional, and environmental research. Accelerator mass spectrometry (AMS) is now the most sensitive detection method for 14C, but AMS is not widely used in kinetic studies of humans. Part of the reason is the expense, but costs would decrease if AMS were used more widely. One component in the cost is sample preparation for AMS. Biological and environmental samples are commonly reduced to graphite before they are analyzed by AMS. Improvements and mechanization of this multistep procedure is slowed by a lack of organized educational materials for AMS sample preparation that would allow new investigators to work with the technique without a substantial outlay of time and effort. We present a detailed sample preparation protocol for graphitizing biological samples for AMS and include examples of nutrition studies that have used this procedure.

Studies on CCD On-Line Detection in Curve Grinding

2006 ◽  
Vol 505-507 ◽  
pp. 607-612
Author(s):  
Yong Hong Zhang ◽  
Hui Qiang Tang ◽  
Quan Lin Bu ◽  
De Jin Hu
Keyword(s):  
Detection Method ◽  
Field Of View ◽  
Large Field ◽  
Work Piece ◽  
Working Process ◽  
Tolerance Zone ◽  
Fitting Method ◽  
On Line ◽  
Image Edge ◽  
Curve Fitting Method

An on-line image measurement system for curve grinding was schemed out according to the working process. Because of interaction between detection precision and field of view, it is difficult to realize high detection precision at a large field of view. In order to settle this problem, a detection method based on circular tolerance zone was presented according to grinding process and wheel shape. Real-time images of work piece can be gathered while using synchronal control and outer trigger technology. Using curve fitting method, the work piece image edge can be located to sub-pixel values. Experiments show that the proposed method in this paper is effective, and its detection precision and results are reasonable.

scholarly journals Steel Reinforcement and Concrete for Multipurpose Hall Building with Seismic Design

2019 ◽  
Vol 8 (3S3) ◽  
pp. 543-547
Keyword(s):  
Reinforced Concrete ◽  
Seismic Design ◽  
Soil Type ◽  
Steel Reinforcement ◽  
Lower Amount ◽  
Public Building ◽  
Two Parameters ◽  
The Cost ◽  
Cost Of Construction ◽  
New Buildings

Multipurpose hall is a public building of people assembly for various function and activities. It can be converted to be a temporary shelter during disaster like flood and earthquake. After experiencing tremors from both local and distant earthquakes, the time has come to implement the seismic design to new buildings in Malaysia to ensure public safety. The implementation of seismic design also affecting the cost of construction, especially materials. Therefore, this paper presents the taking off results for reinforced concrete multipurpose hall building with seismic design. In this study two parameters namely as soil type and concrete grade had been considered as design variable. Result from design and taking off demonstrated that the amount of steel reinforcement is strongly influenced by both parameters. The usage of steel for reinforced concrete buildings with seismic design is estimated to increase around 3% to 59% depend on soil type and concrete grade. Results also demonstrated that higher concrete grade require lower amount of steel as reinforcement.

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