scholarly journals Intelligent Color Based Object Sorting using Robotics

2019 ◽  
Vol 9 (1) ◽  
pp. 43-48
Author(s):  
Rani A. Magadum et al., Rani A. Magadum et al., ◽  
Keyword(s):  
Object Sorting

scholarly journals Automatic Pneumatic Power Based Object Sorting System by Using MITSUBISHI PLC

2015 ◽  
Vol 9 (1) ◽  
pp. 91-97
Author(s):  
P. Choudesh Varma ◽  
◽  
G. Venkateswarlu ◽  
Keyword(s):  
Object Sorting ◽  
Pneumatic Power ◽  
Sorting System

scholarly journals Object Detection and Sorting using IoT

2014 ◽  
Vol 3 (04) ◽  
pp. 01-04
Author(s):  
Mr. Dharmesh Dhabliya, Ms. Ritika Dhabalia
Keyword(s):  
Internet Of Things ◽  
Object Detection ◽  
Conventional Method ◽  
Industrial Internet Of Things ◽  
Huge Number ◽  
Recycling Industry ◽  
Color Sensor ◽  
Industrial Internet ◽  
Wide Range ◽  
Object Sorting

Color based object sorting has a significant impact in food and processing Industries. Hand picking process in sorting the huge number of objects in industry is very common and laborious task, and time consuming as well, which needs many labors and this conventional method is prone to error. The proposed work aims to replace the hand-picking process by Industrial Internet of Things. The goal of the technique is to sort and count the objects in to different bins accord to their color. A Color sensor, TCS 230 will identify the object and with the help of motors they are made to drop into different bins. The identification of the object is made with the help of frequency concept. As it known that different colors have different wave lengths, so are the different frequencies (f=c/λ). For each frequency, the motor rotates to different angles and thus container is attached to motor is also made to rotate to a certain angle, and the object is made to drop into the bin by a jerk. This action details regarding number of objects manufactured are sent to the IoT server, where the vendor and customer will know the details remotely. This proposed work finds a wide range of usage in fruit industry (to pick the unripen fruit), in candy industry, in grain industry (to remove the black stones from the grains), in recycling industry. 

Should we Ignore Dimensional Risk Factors in Prevention of Schizophrenia? Signposts to Prevention

2000 ◽  
Vol 34 (1_suppl) ◽  
pp. A22-A25 ◽  
Author(s):  
Nathaniel McConaghy
Keyword(s):  
Intervention Studies ◽  
Low Risk ◽  
Cognitive Factors ◽  
Abstract Thinking ◽  
Effective Prevention ◽  
Genetic Transmission ◽  
Cognitive Markers ◽  
P300 Potentials ◽  
Object Sorting ◽  
Sorting Test

Objective The objective was to outline the development of the concept of allusive thinking as a genetic marker of predisposition to schizophrenia and relate this to other cognitive markers of this predisposition. Method Publications were reviewed which were considered relevant to the objective. Results Allusive thinking as detected clinically could be measured objectively from subjects' performance on an Object Sorting Test. Using this test it was shown that parents, both of patients with schizophrenia and of university students with allusive thinking, themselves showed allusive thinking, indicating it was familially transmitted. Subjects with allusive thinking showed reduced cortical evoked brain P300 potentials, suggesting the transmission was genetic. The hypothesis that allusive thinking was associated with weaker cortical inhibitory processes was supported by the finding that subjects with such thinking chose more remote word associations. It was suggested that reasons allusive thinking has not been used as a marker in intervention studies is that as a dimension of abstract thinking, marked allusive thinking is not associated with a high risk of developing schizophrenia, and that administration of the Object Sorting Test is time-consuming. Other dimensional cognitive factors, such as psychoticism and perceptual anhedonia and aberration, are independent of allusive thinking and are also associated with a low risk of developing schizophrenia. Genetic transmission of schizophrenia would appear to involve a number of predisposing factors distributed dimensionally in the population with the contribution of each factor being small. Conclusions As they are associated with only a low risk of predisposition to schizophrenia, cognitive markers may not be of immediate value in the prevention of schizophrenia when compared with the less specific markers used for this purpose. However, it would seem that their study will be necessary if the nature of the genetic transmission of the illness is to be understood. This understanding could be expected to ultimately lead to more effective prevention.

Novel Technique for Real Time Individual Object Sorting

2007 ◽  
Author(s):  
A. Golfarelli ◽  
M. Battigaglia ◽  
R. Codeluppi ◽  
M. Tartagni
Keyword(s):  
Real Time ◽  
Individual Object ◽  
Novel Technique ◽  
Object Sorting

Image Processing and Artificial Neural Network for Robot Application

2014 ◽  
Vol 658 ◽  
pp. 678-683 ◽  
Author(s):  
Cristian Pop ◽  
Sanda Margareta Grigorescu ◽  
Erwin Christian Lovasz
Keyword(s):  
Neural Network ◽  
Robot Vision ◽  
Working Environment ◽  
Shape Classification ◽  
Input And Output ◽  
Different Types ◽  
Object Sorting ◽  
Feature Based ◽  
Position And Orientation ◽  
Vision Application

This paper presents a robot vision application, implemented in MATLAB working environment, developed for feature-based object recognition, object sorting and manipulation, based on shape classification and its pose calculus for proper positioning. The application described in this article, designed to detect, identify, classify and manipulate objects is based on previous robot vision applications that are presented in more detail in [1]. The idea underlying the mentioned applications is to determine the type, position and orientation of the work pieces (in those cases different types of bearings). Taking it further, in the presented application, objects that show shape with a gradual level of complexity are used. For this reason pattern recognition are discriminated by training a two layers neural network. The network is presented and also the input and output vectors.

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