AT&T upgrades 3G network and takes over Starbucks Wi-Fi

AT&T reported last week that it will be upgrading its wireless network to “3G services” to an additional 80 US cities for a total of 350 by the end of 2008. The upgrade process will encompass more than 1,500 new sites, and cover the “top 100 US cities”.

In that announcement, AT&T stated “Other plans for the new year include completion of the nation’s first High Speed Uplink Packet Access (HSUPA)-enabled network by the middle of the year.” AT&T’s wording is odd; perhaps intentionally vague. But parsing the announcements a bit, it sounds to me like the announcement breaks down into three announcements:
1. AT&T is deploying “3G” wireless HSxPA (it’s variously stated as High Speed Packet Access, HS Downlink PA, HS Uplink PA) to an additional 80 cities, bringing their total “3G” deployment to 350 cities, including all of the “top” 100 US cities, by the end of 2008.
2. These new deployments will be the latest generation of HSxPA.
3. Coinciding with the timetable of the additional deployments, AT&T will com 942 plete the upgrades of all of its “3G” systems (350 total, including the “top” 100 US cities) by the end of 2008.

So, summarizing, by EOY 2008, AT&T will have HSxPA capability in 350 US cities, including the “top” 100 US cities.

In a kind-of-related announcement, AT&T announced that it is the new Wi-Fi service provider for Starbucks at their 7,000 company-owned stores, taking over from T-Mobile. There were a number of “sweeteners” to this deal, resulting in easier and cheaper Wi-Fi Internet Access for many. Those who use Starbucks “stored value” cards will be able to get two hours of Wi-Fi Internet Access free. Starbucks “partners” (employees) will be able to use Starbucks / AT&T Wi-Fi service for free. AT&T customers of fixed-line services will be able to use Starbucks Wi-Fi for free.

For corporate / enterprise users, “AT&T’s remote access services business customers will be able to access Wi-Fi service at Starbucks locations”. Also, T-Mobile customers and those who have existing T-Mobile Wi-Fi HotSpot accounts will be able to continue to use Starbucks “… at no additional cost”. Glenn Fleishman of Wi-Fi Networking News reports that Wayport also has a hand in the Starbucks / AT&T Wi-Fi deal - I’ll leave it to Glenn to explain the particulars.

The deal with AT&T will consolidate the two distinct data communications deals that every company-owned Starbucks store currently has- corporate Intranet via AT&T, and public Internet access via Wi-Fi from T-Mobile; now AT&T will do both.

And, as a final nod to the inanity of AT&T wireless customers not being able to (without paying a usage fee) use their iPhones, in Wi-Fi mode, at AT&T Wi-Fi HotSpots, comes this terse teaser: “AT&T will soon extend the benefits of Wi-Fi at Starbucks to its wireless customers.”

If this plays out the least bit logically, the real winners of these two announcements will be users of the “Version 2″ iPhone, expected to debut sometime this year, that will 8e7 be able to use the AT&T “3G” / HSxPA network and then “roam into” Starbucks for an even “higher bandwidth fix” for downloading big media files onto their iPhone along with their caffeine jolt, all for no additional charge and a seamless experience.

Radial Basis Neural Networks

During the last two decades, there is a tremendous growth in neural computing field with the invention of large number of new theories, new network topologies and learning algorithms. The overall objective of these discoveries was to develop/design a neural network model which has the ability to learn, to adapt and to generalize for a given input-output mapping with further enhancements for existing models. Architecture of neural networks have been developed from its simplest form, perceptron and to the other types such as multilayer feed forward networks, recurrent networks, Hopfield networks etc. There are various weaknesses exploit in these types of neural networks such as having complex architecture, having slow training procedure etc. In order to overcome these problems, a neural network type called Radial Basis Function Neural Network (RBFNN/RBNN) was developed recently. Radial basis neural networks have advantage of being simpler than other types of neural networks while providing faster training time. Also it has the property of universal approximation of functions.

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Mesothelioma International is dedicated to the fight against mesothelioma – a worldwide health crisis with roots in humanity's use of asbestos. Although the use of asbestos has now been banned in the European Union, it is estimated that 250,000 workers in Western Europe alone will have died from mesothelioma by 2029. It is our hope that this website will be helpful to those throughout Europe who have been affected by this epidemic.

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How Do We Evaluate Artificial Immune Systems?

What is an artificial immune system (AIS)? One answer is that an AIS is a model of the immune system that can be used by immunologists for explanation, experimentation and prediction activities that would be difficult or impossible in ‘wet-lab’ experiments. This is also known as ‘computational immunology.’ Another answer is that an AIS is an abstraction of one or more immunological processes. Since these processes protect us on a daily basis, from the ever-changing onslaught of biological and biochemical entities that seek to prosper at our expense, it is reasoned that they may be computationally useful. It is this form of AIS—methods based on immune abstractions—that will be studied here.

Although AIS is a relatively young field, advancing on many fronts, some central themes have become apparent—the question is, are these AIS delivering anything useful, or are they just another addition to the increasingly long line of approaches that are biologically inspired? These approaches include established paradigms such as genetic and evolutionary computation (GEC), artificial neural networks (ANN) and various forms of artificial life; as well as less established topics such as ant colony dynamics (Dorigo, 1992; Dorigo, 1999) and cell membrane computing (Paun, 2002). The intention here is to provide an assessment of prior developments in AIS, its current strengths, weaknesses and its overall usefulness.

Perhaps the biggest difficulty faced by AIS is that it has few application types for which it is undisputedly the most effective method. Despite the many points in its favor, this single point is enough to allow it to be ignored by many. Although two important areas have been identified in which AIS is unique in its ability to provide solutions, further impressive demonstrations of effectiveness will be required if AIS is to be pushed to the fore.