Providing QoS to Real and Interactive Data Applications in WiMAX Mesh Networks

WiMAX ◽  
2018 ◽  
pp. 209-234
Keyword(s):  
Mesh Networks ◽  
Interactive Data

Architecture of Meteorology Observing System Based on Wireless Sensor Networks

2012 ◽  
Vol 562-564 ◽  
pp. 1244-1247
Author(s):  
Jing Lin Du ◽  
Rong Liu ◽  
Jian Jun Xue ◽  
Jie Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Mesh Networks ◽  
Data Retrieval ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Web Based ◽  
Heterogeneous Wireless Sensor Networks ◽  
Interactive Data ◽  
Central Data

This paper presents an application platform for meteorology observing heterogeneous wireless sensor networks (WSNs) and a data fusion weather platform. We propose system architecture for wireless sensor networks and mesh networks for handling and storing sensor data streams in real-time. It collects data from WSN and continuously transmits physical or metrology conditions at different locations to a remote central data server. A web based platform designed to enable remote online access to WSN data with interactive data retrieval and visualization functionalities.

Proxy-Based IPv6 Neighbor Discovery Scheme for Wireless LAN Based Mesh Networks

2010 ◽  
Vol E93-B (5) ◽  
pp. 1151-1154
Author(s):  
Jihoon LEE ◽  
Seungwoo JEON ◽  
Jaehoon KIM
Keyword(s):  
Wireless Lan ◽  
Mesh Networks ◽  
Neighbor Discovery

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

COORDINATED DISTRIBUTED SCHEDULING IN WIRELESS MESH NETWORK

2012 ◽  
Vol 3 (3) ◽  
pp. 368-374
Author(s):  
Usha Kumari ◽  
Udai Shankar
Keyword(s):  
Network Performance ◽  
Mesh Networks ◽  
Mac Protocol ◽  
Scheduling Algorithm ◽  
Ieee 802.16 ◽  
Distributed Scheduling ◽  
Mesh Network ◽  
Fourth Generation ◽  
Network Nodes ◽  
Wireless Mesh

IEEE 802.16 based wireless mesh networks (WMNs) are a promising broadband access solution to support flexibility, cost effectiveness and fast deployment of the fourth generation infrastructure based wireless networks. Reducing the time for channel establishment is critical for low latency/interactive Applications. According to IEEE 802.16 MAC protocol, there are three scheduling algorithms for assigning TDMA slots to each network node: centralized and distributed the distributed is further divided into two operational modes coordinated distributed and uncoordinated distributed. In coordinated distributed scheduling algorithm, network nodes have to transmit scheduling message in order to inform other nodes about their transfer schedule. In this paper a new approach is proposed to improve coordinated distributed scheduling efficiency in IEEE 802.16 mesh mode, with respect to three parameter Throughput, Average end to end delay and Normalized Overhead. For evaluating the proposed networks efficiency, several extensive simulations are performed in various network configurations and the most important system parameters which affect the network performance are analyzed

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