A Fast Vaccine Distribution Mechanism in Worm Defense System for Mobile Network

2006 ◽  
Author(s):  
Ye Guo ◽  
Miaoliang Zhu
Keyword(s):  
Mobile Network ◽  
Defense System ◽  
Worm Defense ◽  
Vaccine Distribution

An Efficient Worm Defense System Based Signature Extraction

2009 ◽  
Vol 5 (1) ◽  
pp. 23-23
Author(s):  
Hao Tu ◽  
Zhitang Li ◽  
Bin Liu ◽  
Yejiang Zhang
Keyword(s):  
Defense System ◽  
Signature Extraction ◽  
Worm Defense

In Shortly about HIV and AIDS

2020 ◽  
Vol 2020 ◽  
Author(s):  
Sinisa Franjic
Keyword(s):  
Opportunistic Infections ◽  
Hiv And Aids ◽  
The Body ◽  
Hiv Disease ◽  
Infected Person ◽  
Defense System ◽  
Infected Mother ◽  
Unprotected Sexual Intercourse ◽  
Hiv Virus ◽  
Immunodeficiency Virus

HIV is a human immunodeficiency virus, a retrovirus that causes AIDS in the event of untreated infection. The virus causes the weakness and inability of the organism's defense system. HIV disease is a chronic progressive process that begins with the onset of HIV virus into the body, and over time (in adults over 10 years), a gradual destruction of the immune system occurs. The host during the infection becomes vulnerable and is suffering from complications of opportunistic infections and malignancies. A person infected with HIV feels good until developing AIDS, does not notice any changes in health, and has no specific external signs of infection. The only way to detect infection at this stage is HIV testing. HIV is transmitted: unprotected sexual intercourse with the infected person, exchange of needles, syringes or accessories with infected persons when taking drugs, with infected mother on her baby before, during or after delivery (breastfeeding).

Adaptive Video Streaming Using Bandwidth Estimation for 3.5G Mobile Network

2009 ◽  
Vol E92-B (12) ◽  
pp. 3893-3902
Author(s):  
Hyeong-Min NAM ◽  
Chun-Su PARK ◽  
Seung-Won JUNG ◽  
Sung-Jea KO
Keyword(s):  
Video Streaming ◽  
Mobile Network ◽  
Bandwidth Estimation ◽  
Adaptive Video Streaming ◽  
Adaptive Video

Study of Ad hoc Networks with Reference to MANET, VANET, FANET

2017 ◽  
Vol 7 (7) ◽  
pp. 390 ◽  
Author(s):  
Bodhy Krishna .S
Keyword(s):  
Ad Hoc Networks ◽  
Ad Hoc Network ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Mobile Network ◽  
Wireless Ad Hoc Network ◽  
Mobile Ad Hoc ◽  
Autonomous Nodes ◽  
Hoc Networks ◽  
Network Administration

A wireless ad hoc network is a decentralized type of wireless network. It is a type of temporary computer-to-computer connection. It is a spontaneous network which includes mobile ad-hoc networks (MANET), vehicular ad-hoc networks (VANET) and Flying ad-hoc networks (FANET). A MANET is a network that has many free or autonomous nodes often composed of mobile devices that can operate without strict top-down network administration [1]. A VANET is a sub form of MANET. It is a technology that uses vehicles as nodes in a network to create a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. This paper discusses the characteristics of these three ad-hoc networks.

SIMULASI NEURAL NETWORK UNTUK PREDIKSI LOKASI USER PADA MOBILE NETWORK

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Alexander Driyarkoro ◽  
Nurain Silalahi ◽  
Joko Haryatno
Keyword(s):  
Neural Network ◽  
Mobile Station ◽  
Mobile Network ◽  
Learning Rate ◽  
Noise Factor ◽  
Optimal Learning ◽  
Air Interface ◽  
Location Area ◽  
Optimal Learning Rate

Prediksi lokasi user pada mobile network merupakan hal sangat penting, karena routing panggilan pada mobile station (MS) bergantung pada posisi MS saat itu. Mobilitas MS yang cukup tinggi, terutama di daerah perkotaan, menyebabkan pencarian (tracking) MS akan berpengaruh pada kinerja sistem mobile network, khususnya dalam hal efisiensi kanal kontrol pada air interface. Salah satu bentuk pencarian adalah dengan mengetahui perilaku gerakan yang menentukan posisi MS. Dari MSC/VLR dapat diketahui posisi MS pada waktu tertentu. Karena location area suatu MS selalu unik dari waktu ke waktu, dan hal itu merupakan perilaku (behaviour) MS, maka dapat dibuat profil pergerakannya. Dengan menggunakan Neural Network (NN) akan diperoleh location area MS pada masa yang akan datang. Model NN yang digunakan pada penelitian ini adalah Propagasi Balik. Beberapa parameter NN yang diteliti dalam mempengaruhi kinerja prediksi lokasi user meliputi noise factor, momentum dan learning rate. Pada penelitian ini diperoleh nilai optimal learning rate = 0,5 dan noise factor = 1.

The distributed intelligent defense system

1989 ◽  
Author(s):  
MARK ROBINSON ◽  
TIMOTHY GIAMBRA ◽  
PETER KOGGE
Keyword(s):  
Defense System
Sign in / Sign up

Export Citation Format

Share Document