When cell towers fail, what happens to customers? In a resilient network where customers may simply move from a failed tower to another one nearby, the answer is not always clear. Nor can it be captured by manual assessments that count the number of failed towers and estimate population density, thus ignoring how an outage may be dispersed over a wide area.
What’s needed is an in-depth analysis of network data, and it's why AT&T researchers created the AT&T Tower Outage and Network Analyzer, which looks at the complex interactions among customers and towers to intelligently assess the impact to all customers in the immediate area. Read more.
Machine speech translation is hard to do; it requires recognizing the language, transcribing it, doing the translation—all while the person is talking. Processing requirements are enormous.
In this Popular Mechanics interview, Mazin Gilbert describes how AT&T Research is incorporating machine learning and cloud technology to enhance its speech technologies—representing over 30 years of research—and make smooth, seamless translation a reality.
Imagine arriving home and signaling your digital availability by moving pebbles from one bowl to another: one bowl for close friends only (or for text messages only), another for wider availability. It's a refreshingly simple use of physical objects to maintain control in a plugged-in age. And it is the idea behind Lana Yarosh's Availabowls project, which works by attaching programmable RFID tags to pebbles. For more information about Availabowls, see this article.
Application battery performance "sexy"? Yes, according to this article, which describes how IBM is integrating the AT&T Application Resource Optimizer (ARO) with its own development software. The goal is to enable enterprises to create mobile apps that use less battery power and are more network-friendly.
Created at AT&T Research, ARO is a free diagnostic tool available from the AT&T Developer Program.
Joint-Family: Adaptive BitRate Video-on-Demand Streaming over Peer-to-Peer Networks with Realistic Abandonment Patterns.
Kyung Hwang, Vijay Gopalakrishnan, Rittwik Jana, Seungjoon Lee, Vishal Misra, K. K. Ramakrishnan, Dan Rubenstein
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