Automated measurement of vessel properties in birch and poplar wood

Holzforschung ◽  
2010 ◽  
Vol 64 (3) ◽  
Author(s):  
Fang Fiona Chen ◽  
Robert Evans
Keyword(s):  
Red Light ◽  
Wood Property ◽  
Digital Microscopy ◽  
Core Component ◽  
Automated Method ◽  
Manual Counting ◽  
Wide Range ◽  
Property Measurement ◽  
Processing Techniques ◽  
Microscopy Images

Abstract A method is proposed for the automated quantitative analysis of vessel characteristics in birch and poplar species. The method combines image-processing techniques with robust statistical approaches for automatically identifying vessels from digital microscopy images obtained by transmitted red light. The proposed method has been tested over a wide range of birch and poplar samples from different growth environments. Performance of the automatic vessel identification routine was assessed using results obtained by manual counting. The automated method produced fast and reliable vessel measurements and was robust to variations within and between samples. The approach has been merged into the wood property measurement system SilviScan as a core component of the hardwood analysis set for research and commercial use.

Ochrona dziedzictwa kulturowego a działania na dużych szybkozmiennych zbiorach danych – perspektywa kryminologiczna

2019 ◽  
pp. 5-22
Author(s):  
Szymon Buczyński
Keyword(s):  
New Technology ◽  
Big Data Analytics ◽  
Technological Innovations ◽  
Cultural Organizations ◽  
Econometric Methods ◽  
The Arts ◽  
Huge Data ◽  
Wide Range ◽  
Share Data ◽  
Processing Techniques

Recent technological revolutions in data and communication systemsenable us to generate and share data much faster than ever before. Sophisticated data tools aim to improve knowledge and boost confdence. That technological tools will only get better and user-friendlier over the years, big datacan be considered an important tool for the arts and culture sector. Statistical analysis, econometric methods or data mining techniques could pave theway towards better understanding of the mechanisms occurring on the artmarket. Moreover crime reduction and prevention challenges in today’sworld are becoming increasingly complex and are in need of a new techniquethat can handle the vast amount of information that is being generated. Thisarticle provides an examination of a wide range of new technological innovations (IT) that have applications in the areas of culture preservation andheritage protection. The author provides a description of recent technological innovations, summarize the available research on the extent of adoptionon selected examples, and then review the available research on the eachform of new technology. Furthermore the aim of this paper is to explore anddiscuss how big data analytics affect innovation and value creation in cultural organizations and shape consumer behavior in cultural heritage, arts andcultural industries. This paper discusses also the likely impact of big dataanalytics on criminological research and theory. Digital criminology supports huge data base in opposition to conventional data processing techniques which are not only in suffcient but also out dated. This paper aims atclosing a gap in the academic literature showing the contribution of a bigdata approach in cultural economics, policy and management both froma theoretical and practice-based perspective. This work is also a startingpoint for further research.

Growth Response and Carbohydrate Status of Gibberellin-treated Spectral Filter-grown Chrysanthemum Plants

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 522c-522
Author(s):  
Anuradha Tatineni ◽  
Sonja L. Maki ◽  
Nihal C. Rajapakse
Keyword(s):  
Plant Height ◽  
Growth Regulation ◽  
Red Light ◽  
Sucrose Phosphate Synthase ◽  
Spectral Filter ◽  
Spectral Filters ◽  
Wide Range ◽  
Ornamental Crops ◽  
Carbohydrate Levels ◽  
Carbohydrate Status

Interest in the use of non- (or less) chemical methods to reduce the height of ornamental crops has increased tremendously. Manipulation of greenhouse light quality is one alternative for plant growth regulation. We have shown that eliminating far-red light from the greenhouse environment with liquid CuSO4 spectral filters is effective in reducing the height of a wide range of plants though plant carbohydrate status is also altered under CuSO4 filter. In previous studies, application of GA3 reversed both the reduction of plant height and carbohydrate status of CuSO4 spectral filter grown plants. It has been proposed that GAs enhance the activity of the enzyme sucrose phosphate synthase to regulate carbohydrate levels. In the present study the role of exogenously applied GA19, GA1, and GA3 in overcoming the reduction of plant height and carbohydrate levels was investigated. Chrysanthemum plants were treated weekly for 4 weeks with saturating doses of GA19, GA1 and GA3 (25 μg) or the growth retardants paclobutrazol and prohexadione. GA1 was also applied with paclobutrazol and prohexadione to assess whether response to GAs is altered under CuSO4 filter. GA1 and GA3 promoted growth similarly under control or CuSO4 filter. GA19 was least effective in promoting growth under CuSO4 filter. In summary, these results suggest that gibberellin physiology is altered under spectral filters with the conversion of GA19 a possible point of regulation. The correlation between the carbohydrate status and the growth of the plants will be discussed.

scholarly journals Clay-Based Brick Porosity Estimation Using Image Processing Techniques

2019 ◽  
Vol 29 (1) ◽  
pp. 1226-1234
Author(s):  
Safa Jida ◽  
Hassan Ouallal ◽  
Brahim Aksasse ◽  
Mohammed Ouanan ◽  
Mohamed El Amraoui ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Computer Vision ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Porosity Estimation ◽  
Microscopy Images ◽  
Scanning Electron

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.

Cassava Processing in Sub-Saharan Africa: the Implications for Expanding Cassava Production

1994 ◽  
Vol 23 (3) ◽  
pp. 197-205 ◽  
Author(s):  
Felix Izu Nweke
Keyword(s):  
Food Security ◽  
Water Supply ◽  
Sub Saharan Africa ◽  
Wide Range ◽  
Intensive Use ◽  
Cassava Production ◽  
Sub Saharan ◽  
Processing Techniques ◽  
Technical Solutions ◽  
Poor Soil

Cassava makes an important contribution to improving food security and rural incomes in sub-Saharan Africa, as it is tolerant of drought and poor soil and its cultivation does not require much labour. However, the fresh roots are bulky and perishable and need to be processed before they can be marketed; processing also removes the cyanogens which make many varieties poisonous in their raw form. Cassava roots are turned into granules, flours, pastes and chips, with a wide range of flavours and appearances for different areas and markets. Many different processing techniques are used, some of which make intensive use of fuelwood while others require a plentiful water supply. These requirements, as well as the need for a good transport and marketing infrastructure, limit the expansion of cassava production in sub-Saharan Africa, but technical solutions are being found.

scholarly journals Asbestos Detection with Fluorescence Microscopy Images and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4582
Author(s):  
Changjie Cai ◽  
Tomoki Nishimura ◽  
Jooyeon Hwang ◽  
Xiao-Ming Hu ◽  
Akio Kuroda
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Occupational Safety ◽  
Graphics Processing Unit ◽  
Reference Method ◽  
Processing Unit ◽  
Fiber Concentration ◽  
Art Object ◽  
Wide Range ◽  
Microscopy Images

Fluorescent probes can be used to detect various types of asbestos (serpentine and amphibole groups); however, the fiber counting using our previously developed software was not accurate for samples with low fiber concentration. Machine learning-based techniques (e.g., deep learning) for image analysis, particularly Convolutional Neural Networks (CNN), have been widely applied to many areas. The objectives of this study were to (1) create a database of a wide-range asbestos concentration (0–50 fibers/liter) fluorescence microscopy (FM) images in the laboratory; and (2) determine the applicability of the state-of-the-art object detection CNN model, YOLOv4, to accurately detect asbestos. We captured the fluorescence microscopy images containing asbestos and labeled the individual asbestos in the images. We trained the YOLOv4 model with the labeled images using one GTX 1660 Ti Graphics Processing Unit (GPU). Our results demonstrated the exceptional capacity of the YOLOv4 model to learn the fluorescent asbestos morphologies. The mean average precision at a threshold of 0.5 ([email protected]) was 96.1% ± 0.4%, using the National Institute for Occupational Safety and Health (NIOSH) fiber counting Method 7400 as a reference method. Compared to our previous counting software (Intec/HU), the YOLOv4 achieved higher accuracy (0.997 vs. 0.979), particularly much higher precision (0.898 vs. 0.418), recall (0.898 vs. 0.780) and F-1 score (0.898 vs. 0.544). In addition, the YOLOv4 performed much better for low fiber concentration samples (<15 fibers/liter) compared to Intec/HU. Therefore, the FM method coupled with YOLOv4 is remarkable in detecting asbestos fibers and differentiating them from other non-asbestos particles.

EXPRESS: On the Limit of Detection in Infrared Spectroscopic Imaging

2021 ◽  
pp. 000370282110509
Author(s):  
Laurin Lux ◽  
Yamuna Dilip Phal ◽  
Pei-Hsuan Hsieh ◽  
Rohit Bhargava
Keyword(s):  
Limit Of Detection ◽  
Protein Microarray ◽  
Spectroscopic Imaging ◽  
Single Frequency ◽  
Systematic Analysis ◽  
Protein Mass ◽  
Ir Imaging ◽  
Wide Range ◽  
Ft Ir ◽  
Processing Techniques

Infrared (IR) spectroscopic imaging instruments’ performance can be characterized and optimized by an analysis of their limit of detection (LoD). Here we report a systematic analysis of the LoD for Fourier transform IR (FT-IR) and discrete frequency IR (DFIR) imaging spectrometers. In addition to traditional measurements of sample and blank data, we propose a decision theory perspective to pose the determination of LoD as a binary classification problem under different assumptions of noise uniformity and correlation. We also examine three spectral analysis approaches, namely absorbance at a single frequency, sum of absorbance over selected frequencies and total spectral distance – to suit instruments that acquire discrete or contiguous spectral bandwidths. The analysis is validated by refining the fabrication of a bovine serum albumin protein microarray to provide eight uniform spots from 2.8 nL of solution for each concentration over a wide range (0.05 -10 mg/mL). Using scanning parameters that are typical for each instrument, we estimate a LoD of 0.16 mg/mL and 0.12 mg/mL for widefield and line scanning FT-IR imaging systems, respectively, usingthespectraldistanceapproach,and0.22mg/mLand0.15mg/mL using an optimal set of discrete frequencies. As expected, averaging and the use of post-processing techniques such as minimum noise fraction (MNF) transformation results in LoDs as low as 0.075 mg/mL that correspond to a spotted protein mass of 112 fg/pixel. We emphasize that these measurements were conducted at typical imaging parameters for each instrument and can be improved using the usual trading rules of IR spectroscopy. This systematic analysis and methodology for determining the LoD can allow for quantitative measures of confidence in imaging an analyte’s concentration and a basis for further improving IR imaging technology.

scholarly journals Enabling Smart Buildings by Indoor Visible Light Communications and Machine Learning

2019 ◽  
Author(s):  
Shuping Dang ◽  
Guoqing Ma ◽  
Basem Shihada ◽  
Mohamed-Slim Alouini
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Data Transmission ◽  
Visible Light Communications ◽  
Monitoring And Control ◽  
Smart Building ◽  
Wide Range ◽  
Tremendous Amount ◽  
Processing Techniques ◽  
And Control

<pre>The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers. The SB is capable of sensing, acquiring and processing a tremendous amount of data as well as performing proper action and adaptation accordingly. With rapid increases in the number of connected nodes and thereby the data transmission demand in SBs, conventional transmission and processing techniques are insufficient to provide satisfactory services. To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing and reasoning abilities. In this regard, we envision an SB framework enabled by indoor VLC and ML in this article.</pre>

scholarly journals Deep Learning Based Ethiopian Car’s License Plate Detection and Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 5730-5737
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Learning Model ◽  
Motor Vehicles ◽  
License Plate ◽  
License Plate Recognition ◽  
Computer Algorithms ◽  
Wide Range ◽  
Processing Techniques ◽  
Deep Learning Model

Digital Image Processing is application of computer algorithms to process, manipulate and interpret images. As a field it is playing an increasingly important role in many aspects of people’s daily life. Even though Image Processing has accomplished a great deal on its own, nowadays researches are being conducted in using it with Deep Learning (which is part of a broader family, Machine Learning) to achieve better performance in detecting and classifying objects in an image. Car’s License Plate Recognition is one of the hottest research topics in the domain of Image Processing (Computer Vision). It is having wide range of applications since license number is the primary and mandatory identifier of motor vehicles. When it comes to license plates in Ethiopia, they have unique features like Amharic characters, differing dimensions and plate formats. Although there is a research conducted on ELPR, it was attempted using the conventional image processing techniques but never with deep learning. In this proposed research an attempt is going to be made in tackling the problem of ELPR with deep learning and image processing. Tensorflow is going to be used in building the deep learning model and all the image processing is going to be done with OpenCV-Python. So, at the end of this research a deep learning model that recognizes Ethiopian license plates with better accuracy is going to be built.

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