Gravity sensitive moss protonema — a model object of the space biology

1997 ◽  
Vol 3 (3-4) ◽  
pp. 34-39 ◽  
Author(s):  
O.T. Demkiv ◽  
◽  
Ya.D. Khorkavtsiv ◽  
O.R. Kardash ◽  
Kh.I. Chaban ◽  
...  
Keyword(s):  
Space Biology ◽  
Model Object ◽  
Moss Protonema

A solution to the tag-assignment problem for neural networks

1989 ◽  
Vol 12 (3) ◽  
pp. 381-397 ◽  
Author(s):  
Gary W. Strong ◽  
Bruce A. Whitehead
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Assignment Problem ◽  
Model Object ◽  
Parallel Processes ◽  
Illusory Conjunctions ◽  
Feature Processing ◽  
Human Object ◽  
Processing Activity ◽  
Normal Perception

AbstractPurely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions (the incorrect combination of features into perceived objects in a stimulus array) have been demonstrated empirically (Treisman 1986; Treisman & Gelade 1980). Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial processes that may solve the tag-assignment problem in normal perception. One component of the model extracts pooled features and another provides attentional tags that correct illusory conjunctions. Our approach addresses two questions: (i) How can objects be identified from simultaneously attended features in a parallel, distributed representation? (ii) How can the spatial selectional requirements of such an attentional process be met by a separation of pathways for spatial and nonspatial processing? Our analysis of these questions yields a neurally plausible simulation of tag assignment based on synchronizing feature processing activity in a spatial focus of attention.

Opportunities for Biotesting of the Water Environment for Heavy Metal Pollution Using a Plant Spirodela polyrhiza (L.) Schleid

2021 ◽  
Vol 25 (5) ◽  
pp. 52-57
Author(s):  
S.I. Alekseeva ◽  
Zh.M. Okhlopkova
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Growth And Development ◽  
Water Environment ◽  
Biological Testing ◽  
Spirodela Polyrhiza ◽  
Model Object ◽  
Chlorophyll A And B

The methods of biotesting of the aquatic environment based on the representative of the duckweed family (lat. Lemnaceae) greater duckweed (Spirodela polyrhiza (L.) Schleid) were considered. A review is presented on the use of greater duckweed as a model object in biological testing, in partic-ular, when exposed to heavy metals salts. When cultivated Spirodela polyrhiza with the addition of heavy metals salts, a change in the growth and development of plants in the experienced line of plants was revealed, as well as a decrease in the content of chlorophyll a and b.

3D Model Reconstruction from Multi-views of 2D Images using Radon Transform

2015 ◽  
Vol 74 (6) ◽  
Author(s):  
Siti Syazalina Mohd. Sobani ◽  
Nasrul Humaimi Mahmood ◽  
Nor Aini Zakaria ◽  
Ismail Ariffin
Keyword(s):  
Radon Transform ◽  
3D Model ◽  
Digital Camera ◽  
Computation Method ◽  
Model Reconstruction ◽  
Fixed Sequence ◽  
Model Object ◽  
3D Model Reconstruction ◽  
Images Processing ◽  
2D Images

This paper presents a simple computation method to reconstruct 3-dimensional (3D) model from a sequence of 2-dimensional (2D) images using a multiple-view camera setup. The 3D model is acquired by applying several images processing on few 2D images captured by digital camera with different angle of views. The setup for this study consisted of a digital camera mounted on a tripod stand focusing at a block of model object on a turntable with black floor and background. 36 different angles are used to capture the images where every view angle differs by ten degree (10°) with another view in a fixed sequence. The image processing applied on all 2D images to be reconstructed as 3D surface are image segmentation, Radon transform (RT), image filtering, morphological operation, edge detection, and boundary extraction. The results for 3D model reconstruction shows it is well reconstructed, with a smooth texture obtained using 3D mesh and Delaunay triangulation, while the shape is nearly identical to the original model while the remaining are distinguishable.  

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