scholarly journals Rating of Speech Quality in Mobile Phone Networks

2003 ◽  
Vol 15 (1) ◽  
pp. 29-45 ◽  
Author(s):  
Khalid A. Al-Mashouq
Keyword(s):  
Mobile Phone ◽  
Speech Quality ◽  
Mobile Phone Networks

Where the Imaginary Meets the Virtual

2020 ◽  
pp. 161-198
Author(s):  
Neville Bolt
Keyword(s):  
Social Networks ◽  
Social Movements ◽  
Mobile Phone ◽  
Information Age ◽  
Digital Technologies ◽  
Network Effect ◽  
The Internet ◽  
Digital Revolution ◽  
Slow Moving ◽  
Mobile Phone Networks

Chapter 6 examines how the insurgent landscape has been transformed by the digital revolution; how migrant disaporas and social networks have been brought closer together by digital technologies in the Information Age, and how social movements, once below the radar of states or emergent states, affect and outmaneuver slow-moving bureaucracies. This begs the question: is Propaganda of the Deed active or reactive, truly strategic or opportunistic? The answer lies closer to strategic opportunism, offering a strategy of fluidity able to capitalize on the switch from a one-to-many model of historic communications to a many-to-many model of contemporary communications. Indeed, it exploits to the full the network effect across the Internet and mobile phone networks.

RF Propagation Characteristics of In-Cabin CDMA Mobile Phone Networks

2006 ◽  
Author(s):  
G.A. Breit ◽  
H. Hachem ◽  
J. Forrester ◽  
P. Guckian ◽  
K.P. Kirchoff ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Propagation Characteristics ◽  
Rf Propagation ◽  
Mobile Phone Networks

The end of life treatment of second generation mobile phone networks: Strategies to reduce the environmental impact

2005 ◽  
Vol 25 (5) ◽  
pp. 540-566 ◽  
Author(s):  
Wolfram Scharnhorst ◽  
Hans-Jörg Althaus ◽  
Mischa Classen ◽  
Olivier Jolliet ◽  
Lorenz M. Hilty
Keyword(s):  
Environmental Impact ◽  
Mobile Phone ◽  
End Of Life ◽  
Second Generation ◽  
Mobile Phone Networks ◽  
End Of Life Treatment

Examining Visitors' Characteristics and Behaviors in Tourist Destinations Through Mobile Phone Users' Location Data

2020 ◽  
pp. 279-310
Author(s):  
Masahide Yamamoto
Keyword(s):  
Population Structure ◽  
Mobile Phone ◽  
Spatial Statistics ◽  
Population Data ◽  
Statistical Population ◽  
Identification Process ◽  
Tourist Destinations ◽  
Location Data ◽  
Mobile Phone Networks ◽  
Operational Data

This chapter uses Mobile Kukan Toukei™ (mobile spatial statistics) to collect the location data of mobile phone users in order to count the number of visitors at specific tourist destinations and examine their characteristics. Mobile Kukan Toukei is statistical population data created by an operational data of mobile phone networks. It is possible to estimate the population structure of a region by gender, age, and residence using this service of the company. The locations and characteristics of the individuals obtained herein are derived through a non-identification process, aggregation processing, and concealment processing. Therefore, it is impossible to identify specific individuals. This chapter attempts to identify the number of visitors in different periods and their characteristics based on the location data of mobile phone users collected by the mobile phone company. In addition, it also attempts to demonstrate an alternative method to more accurately infer the number of visitors in specific areas.

Developing a Method for Visualizing Population Movements

2015 ◽  
pp. 205-221
Author(s):  
Matthew Kwan ◽  
Colin Arrowsmith ◽  
William Cartwright
Keyword(s):  
Mobile Phone ◽  
Large Population ◽  
Large City ◽  
Population Movements ◽  
Movement Vector ◽  
Mobile Phone Networks ◽  
Do So

This chapter describes a technique for visualizing the movements of a population in a region at a point in time. It is suitable for cases where a large population is spread throughout the region and can move in all directions, for example the population of a large city. By repeatedly clustering movement vector arrows it can visually summarize the movements of millions of individuals, and do so with moderate computing resources. The technique is designed to work with data captured from mobile phone networks, but other sources of data can also be used.

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