Publishing in International Journal of Automation Technology

2007 ◽  
Vol 1 (1) ◽  
pp. 4-4
Author(s):  
Yusuf Altintas
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Position Control ◽  
Mechanical Design ◽  
Image Processing Algorithm ◽  
Academic Knowledge ◽  
Software Modules ◽  
Integration Of Disciplines ◽  
The Individual ◽  
Automation Technology

Automation technology is created by integrating mechanical design, dynamics, control, sensors, actuators, electronics and real time software engineering knowledge into a single system. While there are a number of journals which focus on the individual subjects, a sole journal like IJAT which presents the integration of disciplines to create automation products has been missing. Although automation covers rather a large spectrum, we encourage the authors to submit their articles with the details of technology integration. While mathematical details of a position control of a single axis machine may be more suitable to be presented in a pure control journal, the integration of mechanical drives, motors, sensors, control law, trajectory generation and real time software modules constitutes an excellent example for an automation technology. Similarly, while an image processing algorithm would be narrow, integration of image processing, timing, coordination with moving machinery, hardware and software lay out describes an automation technology. The aim of the journal is to bring theory, design and integration together which leads to the creation of a novel automation technology. The journal is expected to be a key resource for automation engineers in industry and academia while disseminating archival academic knowledge to the society.

Image Processing Algorithm for Real-Time Crack Inspection in Hole Expansion Test

2019 ◽  
Vol 20 (7) ◽  
pp. 1139-1148 ◽  
Author(s):  
Seungho Choi ◽  
Kwangyoon Kim ◽  
Jaeho Lee ◽  
Sung Hyuk Park ◽  
Hye-Jin Lee ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Hole Expansion ◽  
Expansion Test ◽  
Hole Expansion Test

Art as a Living System: Interactive Computer Artworks

Leonardo ◽  
1999 ◽  
Vol 32 (3) ◽  
pp. 165-173 ◽  
Author(s):  
Christa Sommerer ◽  
Laurent Mignonneau
Keyword(s):  
Image Processing ◽  
Human Computer Interaction ◽  
Real Time ◽  
Artificial Life ◽  
Real Life ◽  
Living System ◽  
Living Systems ◽  
Time Interaction ◽  
The Individual ◽  
Interactive Installations

The authors design computer installations that integrate artificial life and real life by means of human-computer interaction. While exploring real-time interaction and evolutionary image processes, visitors to their interactive installations become essential parts of the systems by transferring the individual behaviors, emotions and personalities to the works' image processing. Images in these installations are not static, pre-fixed or predictable, but “living systems” themselves, representing minute changes in the viewers' interactions with the installations' evolutionary image processes.

scholarly journals Size and mass prediction of almond kernels using machine learning image processing

2019 ◽  
Author(s):  
Sriram K Vidyarthi ◽  
Rakhee Tiwari ◽  
Samrendra K Singh
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Average Length ◽  
Ensemble Model ◽  
Image Processing Algorithm ◽  
Yield And Quality ◽  
Digital Fingerprint ◽  
The Mean ◽  
The Individual ◽  
Vital Parameters

AbstractAfter harvesting almond crop, accurate measurement of almond kernel sizes is a significant specification to plan, develop and enhance almond processing operations. The size and mass of the individual almond kernels are vital parameters usually associated with almond quality, particularly head almond yield. In this study, we propose a novel methodology that combines image processing and machine-learning ensemble that accurately measures the size and mass of whole raw almond kernels (classification - Nonpareil) simultaneously. We have developed an image-processing algorithm using recursive method to identify the individual almond kernels from an image and estimate the size of the kernels based on the occupied pixels by a kernel. The number of pixels representing an almond kernel was used as its digital fingerprint to predict its size and mass. Various popular machine learning (ML) models were implemented to build a stacked ensemble model (SEM), predicting the mass of the individual almond kernels based on the features derived from the pixels of the individual kernels in the image. The prediction accuracy and robustness of image processing and SEM were analyzed using uncertainty quantification. The mean error in estimating the average length of 1000 almond kernel was 3.12%. Similarly, mean errors associated with predicting the 1000 kernel mass were 0.63%. The developed algorithm in almond imaging in this study can be used to facilitate a rapid almond yield and quality appraisals.

Autonomous Real-Time Monitoring of Crops in Controlled Environment Agriculture

2019 ◽  
Author(s):  
Saba Faryadi ◽  
Mohammadreza Davoodi ◽  
Javad Mohammadpour Velni
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Directed Graphs ◽  
Controlled Environment ◽  
Real Time Monitoring ◽  
Collection System ◽  
Image Processing Algorithm ◽  
Ground Vehicle ◽  
Controlled Environment Agriculture ◽  
And Performance

Abstract In this work, we develop a system that can be used for real-time monitoring of multiple important areas in controlled environment agriculture (and in particular greenhouses) using an autonomous ground vehicle (AGV). To model the greenhouse layout, as well as the tasks that should be accomplished by the AGV, we generate two weighted directed graphs. Based on those graphs, an algorithm is then proposed for finding the optimal (in the sense of traveled distance) trajectory of the vehicle with the goal of precisely monitoring important areas in the greenhouse. Furthermore, a data collection system and image processing algorithm is proposed and implemented so that the vehicle: (i) can capture images and detect changes that have occurred on the crops in real time, and (ii) construct (if needed) a map of the plant rows, when arriving at each one of the important areas. Based on this work, the images can either be stitched onboard the vehicle and then sent to a server or be sent directly to the server and then processed (stitched) there. Both simulation and experimental results are provided to demonstrate the effectiveness and performance of the proposed system.

scholarly journals An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data

2020 ◽  
Vol 12 (4) ◽  
pp. 674 ◽  
Author(s):  
Luca Pulvirenti ◽  
Giuseppe Squicciarino ◽  
Elisabetta Fiori ◽  
Paolo Fiorucci ◽  
Luca Ferraris ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Vegetation Index ◽  
Commission Error ◽  
Management Service ◽  
Image Processing Algorithm ◽  
Burned Areas ◽  
Processing Chain ◽  
Sentinel 2 ◽  
Burned Forest

A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%.

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