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مرکز اطلاعات علمی SID1
اسکوپوس
دانشگاه غیر انتفاعی مهر اروند
ریسرچگیت
strs
Issue Info: 
  • Year: 

    2004
  • Volume: 

    1
  • Issue: 

    4
  • Pages: 

    279-293
Measures: 
  • Citations: 

    455
  • Views: 

    43270
  • Downloads: 

    28126
Keywords: 
Abstract: 

Yearly Impact:

View 43270

Download 28126 Citation 455 Refrence 0
Issue Info: 
  • Year: 

    2013
  • Volume: 

    2
  • Issue: 

    4
  • Pages: 

    223-230
Measures: 
  • Citations: 

    0
  • Views: 

    67934
  • Downloads: 

    26915
Abstract: 

A Wireless Sensor Network (WSN) is a wireless decentralized structure network consists of many nodes. Nodes can be fixed or mobile. WSN applications typically observe some physical phenomenon through sampling of the environment so determine the location of events is an important issue in WSN. Wireless LOCALIZATION used to determine the position of nodes. The precise LOCALIZATION in WSNs is a complex issue that requires consideration of many prominent aspects such as energy consumption at the nodes as well as the algorithm execution time. In this article, we optimize a system called Spotlight. The spotlight is a LOCALIZATION system that delivers high-location estimation accuracy at low cost. We propose several methods to reduce execution time compared with previous methods in Spotlight. We proposed ILS, LAS and PAS methods that improve execution time about 25%, 50% and 75%. Execution time of the proposed scheme is restricted by the size of deployment area. Furthermore, in these methods, there is no need to equip the nodes with any special hardware.

Yearly Impact:

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Author(s): 

MOHAMMADI M.R. | RAIE A.

Issue Info: 
  • Year: 

    2011
  • Volume: 

    7
  • Issue: 

    -
  • Pages: 

    1-5
Measures: 
  • Citations: 

    462
  • Views: 

    21057
  • Downloads: 

    29437
Keywords: 
Abstract: 

Yearly Impact:

View 21057

Download 29437 Citation 462 Refrence 0
گارگاه ها آموزشی
Author(s): 

Journal: 

Applied Sciences

Issue Info: 
  • Year: 

    2019
  • Volume: 

    9
  • Issue: 

    12
  • Pages: 

    0-0
Measures: 
  • Citations: 

    462
  • Views: 

    14822
  • Downloads: 

    29437
Keywords: 
Abstract: 

Yearly Impact:

View 14822

Download 29437 Citation 462 Refrence 0
Author(s): 

CHEN Y. | OLIVER D.S.

Issue Info: 
  • Year: 

    2009
  • Volume: 

    14
  • Issue: 

    4
  • Pages: 

    0-0
Measures: 
  • Citations: 

    463
  • Views: 

    31648
  • Downloads: 

    29533
Keywords: 
Abstract: 

Yearly Impact:

View 31648

Download 29533 Citation 463 Refrence 0
Issue Info: 
  • Year: 

    2019
  • Volume: 

    8
  • Issue: 

    1
  • Pages: 

    39-43
Measures: 
  • Citations: 

    0
  • Views: 

    23284
  • Downloads: 

    27176
Abstract: 

Pterygium is a triangle-shaped fibrovascular hyperplasia of the bulbar conjunctiva on the cornea. The purpose of this study was to analyze Proteoglycans (PGs) by Immunohistochemistry (IHC) in pterygium tissues and to compare the results with normal conjunctiva. Twenty-four patients (14 males) undergoing primary pterygium excision and 17 healthy individuals (10 males), undergoing extracapsular cataract surgery, were included. Pterygium tissues and normal conjunctiva tissues were surgically removed. The tissue sections were fixed in 2% paraformaldehyde and incubated with monoclonal antibodies against PGs anti-mouse IgG. Immunohistochemical study showed stronger expression of keratan sulfate in the stroma of the pterygium compared to normal conjunctiva. An increased expression of heparan sulfate was observed in the epithelial layer and around the pterygium vessels. On the other hand, dermatan sulfate showed an increased expression and LOCALIZATION not only in the sub-epithelial area of the pterygium and normal conjunctiva, yet throughout the stroma of the pterygium. The differences in the expression and LOCALIZATION of the studied extracellular matrix proteoglycans in the pterygium tissue compared to normal conjunctiva may explain the tissue hyperplasia, structure, and the functional properties in pterygium. . .

Yearly Impact:

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strs
Author(s): 

SIADAT M.R. | SOLTANIANZADEH H.

Issue Info: 
  • Year: 

    2000
  • Volume: 

    34
  • Issue: 

    1 (67)
  • Pages: 

    9-24
Measures: 
  • Citations: 

    0
  • Views: 

    805
  • Downloads: 

    306
Keywords: 
Abstract: 

Hippocampus is an important structure of the human brain limbic system. The variations in the volume and architecture of this structure have been related to certain neurological diseases. Accurate and reproducible volumetry of hippocampus from MRI is required for diagnosis and treatment of patients. This can be done using the following three steps: 1) determining hippocampus approximate location 2) determining its accurate boundaries and 3) calculating its volume. This paper presents an automated method for the first step. The proposed approach employs a search-based vision for information extraction from MRI and an expert system for information analysis. In the development of the expert system a shell of expert systems named VPEXPERT is used. Information analysis is done using rules. that are based on anatomy and symmetry of the human brain. Results show that the proposed approach is promising for object LOCALIZATION from complex medical images. The approach has been applied to 128 images from 6 patients. In these studies, all of the images without hippocampus were correctly identified. In addition, in 58.3% of the images containing hippocampus, the structure was properly localized. has been applied to 128 images from 6 patients. In these studies, all of the images without hippocampus were correctly identified. In addition, in 58.3% of the images containing hippocampus, the structure was properly localized.

Yearly Impact:

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Issue Info: 
  • Year: 

    2016
  • Volume: 

    28
  • Issue: 

    4 (109)
  • Pages: 

    83-89
Measures: 
  • Citations: 

    0
  • Views: 

    1036
  • Downloads: 

    221
Abstract: 

The aim of the present study was to immunohistochemical LOCALIZATION of Cytochrome P450 Aromatase in male goat reproduction system. Aromatase is a terminal enzyme which transforms androgens in to estrogens. This study investigated the immunohistochemical LOCALIZATION of aromatase in native male goat testis, efferent ductules of epididymis, vesicle seminal and prostate glands using polyclonal anti aromatase antibody as primary antibody and anti-mouseIgG (HRP) Polyclonal antibody as secondary antibody. Samples of testis and accessory glands collected from five native male goat (2-4 years old), and for IHC test were kept in Bowen solution. Then paraffin block and histological section for IHC test were prepared. Immunoreactions was observed in stages of spermatogenesis, Leydig cells, efferent ductules of epididymis, cytoplasm of vesicle seminal and prostate glands cells. The results show that aromatase in testicular cells and sexual glands converts androgens to estrogens and may be locally estrogens have a paracrine and autocrine activates in reproductive tract.

Yearly Impact:

View 1036

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Author(s): 

GOSHVARPOUR A.

Issue Info: 
  • Year: 

    2016
  • Volume: 

    20
  • Issue: 

    3
  • Pages: 

    353-368
Measures: 
  • Citations: 

    465
  • Views: 

    19797
  • Downloads: 

    30016
Keywords: 
Abstract: 

Yearly Impact:

View 19797

Download 30016 Citation 465 Refrence 0
Issue Info: 
  • Year: 

    2011
  • Volume: 

    3
  • Issue: 

    2
  • Pages: 

    47-56
Measures: 
  • Citations: 

    0
  • Views: 

    76108
  • Downloads: 

    20721
Abstract: 

Localizing sensors in a sensor network is one of the severe bottlenecks that must be dealt with, before exploiting these kinds of networks efficiently. While there has been many techniques and methods proposed for the issue, most of them suffer from low accuracy, or impose extra costs to the network.A Support Vector Machine (SVM) based method has already been proposed which uses machine learning techniques to achieve a fairly accurate estimate of the location of the nodes. In this paper, we propose to use probabilistic SVM, which is more powerful than the existing method.Moreover, an innovative post processing step called ARPoFiL will be proposed that provides even more improvement to the accuracy of the location of the sensor nodes. We will show analytically and experimentally that probabilistic SVM integrated with ARPoFiL completely outperforms the existing method, particularly in sparse networks and rough environments with lots of coverage holes.

Yearly Impact:

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