Hukam Mere Aka
Hukam Mere Aka is the Gtalk program which chats to everybody. It replys if it knows
what to reply, if not than you can teach it what to reply. Apart from this it also
replys to stock quotes. You can get price of any stock by following method as shown
here.
To get stock price for company on NSE enter
share n CompanyCode
i.e. share n rel
This will reply all stock price on NSE starting with rel. If you know exact company
code than you can get particular company's stock price. Here after company code
it is showing the price of stock listed on NSE. In bracket it is showing displacement
of price. After that Date and Time.
To use Hukam Mere Aka add hukammereaka@gmail.com as your baddy. It will automaticaly
add your id in it. Enjoy it.
Asterisk Servers
What is Asterisk
Asterisk is the world’s leading open source telephony engine and tool kit. Offering
flexibility unheard of in the world of proprietary communications, Asterisk empowers
developers and integrators to create advanced communication solutions...for free.
Asterisk® is released as open source under the GNU General Public License (GPL),
and it is available for download free of charge. Asterisk® is the most popular open
source software available, with the Asterisk Community being the top influencer
in VoIP.
Asterisk as a switch (PBX)
Asterisk can be configured as the core of an IP or hybrid PBX, switching calls,
managing routes, enabling features, and connecting callers with the outside world
over IP, analog (POTS), and digital (T1/E1) connections. Asterisk runs on a wide
variety of operating systems including Linux, Mac OS X, OpenBSD, FreeBSD and Sun
Solaris and provides all of the features you would expect from a PBX including many
advanced features that are often associated with high end (and high cost) proprietary
PBXs. Asterisk's architecture is designed for maximum flexibility and supports Voice
over IP in many protocols, and can interoperate with almost all standards-based
telephony equipment using relatively inexpensive hardware.
Asterisk as a gateway
It can also be built out as the heart of a media gateway, bridging the legacy PSTN
to the expanding world of IP telephony. Asterisk’s modular architecture allows it
to convert between a wide range of communications protocols and media codecs.
Asterisk as a feature/media server
Need an IVR? Asterisk’s got you covered. How about a conference bridge? Yep. It’s
in there. What about an automated attendant? Asterisk does that too. How about a
replacement for your aging legacy voicemail system? Can do. Unified messaging? No
problem. Need a telephony interface for your web site? Ok.
Asterisk in the call center
Asterisk has been adopted by call centers around the world based on its flexibility.
Call center and contact center developers have built complete ACD systems based
on Asterisk. Asterisk has also added new life to existing call center solutions
by adding remote IP agent capabilities, advanced skills-based routing, predictive
and bulk dialing, and more.
Asterisk in the network
Internet Telephony Service Providers (ITSPs), competitive local exchange carriers
(CLECS) and even first-tier incumbents have discovered the power of open source
communications with Asterisk. Feature servers, hosted services clusters, voicemail
systems, pre-paid calling solutions, all based on Asterisk have helped reduce costs
and enabled flexibility.
Asterisk everywhere
Asterisk has become the basis for thousands of communications solutions. If you
need to communicate, Asterisk is your answer.
Supported platforms
Asterisk® is primarily developed on GNU/Linux for x/86 and runs on GNU/Linux for
PPC along with OpenBSD, FreeBSD, and Mac OS X. Other platforms and standards-based
UNIX-like operating systems should be reasonably easy to port for anyone with the
time and requisite skill to do so. Asterisk® is available in Debian Stable and is
maintained by the Debian VoIP Team.
Supported hardware
Asterisk® needs no additional hardware for Voice over IP. For interconnection with
digital and analog telephony equipment, Asterisk® supports a number of hardware
devices, most notably all of the hardware manufactured by Digium®, the creator of
Asterisk®.
See Hardware List
Features
Asterisk-based telephony solutions offer a rich and flexible feature set. Asterisk®
offers both classical PBX functionality and advanced features which interoperates
with traditional standards-based telephony systems and Voice over IP systems. See Feature Set
Supported protocols
Asterisk® supports a wide range of protocols for the handling and transmission of
voice over traditional telephony interfaces including H.323, Session Initiation
Protocol (SIP), Media Gateway Control Protocol (MGCP), and Skinny Client Control
Protocol (SCCP). Using the Inter-Asterisk eXchange (IAX™) Voice over IP protocol
Asterisk® merges voice and data traffic seamlessly across disparate networks. The
use of Packet Voice allows Asterisk® to send data such as URL information and images
in-line with voice traffic, allowing advanced integration of information. Asterisk®
provides a central switching core, with four APIs for modular loading of telephony
applications, hardware interfaces, file format handling, and codecs. It allows for
transparent switching between all supported interfaces, allowing it to tie together
a diverse mixture of telephony systems into a single switching network. See the
Asterisk glossary for a list of terms.
Nural Network
An artificial neural network (ANN), often just called a "neural network" (NN), is
a mathematical model or computational model based on biological neural networks.
It consists of an interconnected group of artificial neurons and processes information
using a connectionist approach to computation. In most cases an ANN is an adaptive
system that changes its structure based on external or internal information that
flows through the network during the learning phase.
In more practical terms neural networks are non-linear statistical data modeling
tools. They can be used to model complex relationships between inputs and outputs
or to find patterns in data.
A neural network is an interconnected group of nodes, akin to the vast network of
neurons in the human brain.
There is no precise agreed-upon definition among researchers as to what a neural
network is, but most would agree that it involves a network of simple processing
elements (neurons), which can exhibit complex global behavior, determined by the
connections between the processing elements and element parameters. The original
inspiration for the technique was from examination of the central nervous system
and the neurons (and their axons, dendrites and synapses) which constitute one of
its most significant information processing elements (see Neuroscience). In a neural
network model, simple nodes (called variously "neurons", "neurodes", "PEs" ("processing
elements") or "units") are connected together to form a network of nodes — hence
the term "neural network." While a neural network does not have to be adaptive per
se, its practical use comes with algorithms designed to alter the strength (weights)
of the connections in the network to produce a desired signal flow. These networks
are also similar to the biological neural networks in the sense that functions are
performed collectively and in parallel by the units, rather than there being a clear
delineation of subtasks to which various units are assigned (see also connectionism).
Currently, the term Artificial Neural Network (ANN) tends to refer mostly to neural
network models employed in statistics, cognitive psychology and artificial intelligence.
Neural network models designed with emulation of the central nervous system (CNS)
in mind are a subject of theoretical neuroscience (computational neuroscience).
In modern software implementations of artificial neural networks the approach inspired
by biology has more or less been abandoned for a more practical approach based on
statistics and signal processing. In some of these systems neural networks, or parts
of neural networks (such as artificial neurons) are used as components in larger
systems that combine both adaptive and non-adaptive elements. While the more general
approach of such adaptive systems is more suitable for real-world problem solving,
it has far less to do with the traditional artificial intelligence connectionist
models. What they do, however, have in common is the principle of non-linear, distributed,
parallel and local processing and adaptation.
Grid Computing
Grid computing is a form of distributed computing whereby a "super and virtual computer"
is composed of a cluster of networked, loosely-coupled computers, acting in concert
to perform very large tasks. This technology has been applied to computationally-intensive
scientific, mathematical, and academic problems through volunteer computing, and
it is used in commercial enterprises for such diverse applications as drug discovery,
economic forecasting, seismic analysis, and back-office data processing in support
of e-commerce and web services.
What distinguishes grid computing from typical cluster computing systems is that
grids tend to be more loosely coupled, heterogeneous, and geographically dispersed.
Also, while a computing grid may be dedicated to a specialized application, it is
often constructed with the aid of general purpose grid software libraries and middleware.
Grids versus conventional supercomputers
"Distributed" or "grid" computing in general is a special type of parallel computing[citation
needed] which relies on complete computers (with onboard CPU, storage, power supply,
network interface, etc.) connected to a network (private, public or the Internet)
by a conventional network interface, such as Ethernet. This is in contrast to the
traditional notion of a supercomputer, which has many processors connected by a
local high-speed computer bus. The primary advantage of distributed computing is
that each node can be purchased as commodity hardware, which when combined can produce
similar computing resources to a multiprocessor supercomputer, but at lower cost.
This is due to the economies of scale of producing commodity hardware, compared
to the lower efficiency of designing and constructing a small number of custom supercomputers.
The primary performance disadvantage is that the various processors and local storage
areas do not have high-speed connections. This arrangement is thus well-suited to
applications in which multiple parallel computations can take place independently,
without the need to communicate intermediate results between processors. The high-end
scalability of geographically dispersed grids is generally favorable, due to the
low need for connectivity between nodes relative to the capacity of the public Internet.
There are also some differences in programming and deployment. It can be costly
and difficult to write programs so that they can be run in the environment of a
supercomputer, which may have a custom operating system, or require the program
to address concurrency issues. If a problem can be adequately parallelized, a "thin"
layer of "grid" infrastructure can allow conventional, standalone programs to run
on multiple machines (but each given a different part of the same problem). This
makes it possible to write and debug on a single conventional machine, and eliminates
complications due to multiple instances of the same program running in the same
shared memory and storage space at the same time.
Design considerations and variations
One feature of distributed grids is that they can be formed from computing resources
belonging to multiple individuals or organizations (known as multiple administrative
domains). This can facilitate commercial transactions, as in utility computing,
or make it easier to assemble volunteer computing networks. One disadvantage of
this feature is that the computers which are actually performing the calculations
might not be entirely trustworthy. The designers of the system must thus introduce
measures to prevent malfunctions or malicious participants from producing false,
misleading, or erroneous results, and from using the system as an attack vector.
This often involves assigning work randomly to different nodes (presumably with
different owners) and checking that at least two different nodes report the same
answer for a given work unit. Discrepancies would identify malfunctioning and malicious
nodes. Due to the lack of central control over the hardware, there is no way to
guarantee that nodes will not drop out of the network at random times. Some nodes
(like laptops or dialup Internet customers) may also be available for computation
but not network communications for unpredictable periods. These variations can be
accommodated by assigning large work units (thus reducing the need for continuous
network connectivity) and reassigning work units when a given node fails to report
its results as expected. The impacts of trust and availability on performance and
development difficulty can influence the choice of whether to deploy onto a dedicated
computer cluster, to idle machines internal to the developing organization, or to
an open external network of volunteers or contractors. In many cases, the participating
nodes must trust the central system not to abuse the access that is being granted,
by interfering with the operation of other programs, mangling stored information,
transmitting private data, or creating new security holes. Other systems employ
measures to reduce the amount of trust "client" nodes must place in the central
system such as placing applications in virtual machines. Public systems or those
crossing administrative domains (including different departments in the same organization)
often result in the need to run on heterogeneous systems, using different operating
systems and hardware architectures. With many languages, there is a tradeoff between
investment in software development and the number of platforms that can be supported
(and thus the size of the resulting network). Cross-platform languages can reduce
the need to make this tradeoff, though potentially at the expense of high performance
on any given node (due to run-time interpretation or lack of optimization for the
particular platform). Various middleware projects have created generic infrastructure,
to allow diverse scientific and commercial projects to harness a particular associated
grid, or for the purpose of setting up new grids. BOINC is a common one for academic
projects seeking public volunteers; more are listed at the end of the article. In
fact, the middleware can be seen as a layer between the hardware and the software.
On top of the middleware, a number of technical areas have to be considered, and
these may or may not be middleware independent. Example areas include SLA management,
Trust and Security, VO management, License Management, Portals and Data Management.
These technical areas may be taken care of in a commercial solution, though the
cutting edge of each area is often found within specific research projects examining
the field.
CPU scavenging
CPU-scavenging, cycle-scavenging, cycle stealing, or shared computing creates a
"grid" from the unused resources in a network of participants (whether worldwide
or internal to an organization). Typically this technique uses desktop computer
instruction cycles that would otherwise be wasted at night, during lunch, or even
in the scattered seconds throughout the day when the computer is waiting for user
input or slow devices. Volunteer computing projects use the CPU scavenging model
almost exclusively. In practice, participating computers also donate some supporting
amount of disk storage space, RAM, and network bandwidth, in addition to raw CPU
power. Since nodes are likely to go "offline" from time to time, as their owners
use their resources for their primary purpose, this model must be designed to handle
such contingencies.