NS 2

NS (version 2) is an object-oriented, discrete event driven network simulator developed at UC Berkely written in C++ and OTcl. NS is primarily useful for simulating local and wide area networks. Although NS is fairly easy to use once you get to know the simulator, it is quite difficult for a first time user, because there are few user-friendly manuals. Even though there is a lot of documentation written by the developers which has in depth explanation of the simulator, it is written with the depth of a skilled NS user. The purpose of this project is to give a new user some basic idea of how the simultor works, how to setup simulation networks, where to look for further information about network components in simulator codes, how to create new network components, etc., mainly by giving simple examples and brief explanations based on our experiences. Although all the usage of the simulator or possible network simulation setups may not be covered in this project, the project should help a new user to get started quickly.

Latest Trends in Computer Science

1. Artificial intelligence and robotics

With the global robotics industry forecast to be worth US$38 billion by 2018, a large portion of this growth is down to the strength of interest and investment in artificial intelligence (AI) – one of the most controversial and intriguing areas of computer science research. The technology is still in its early stages, but tech giants like Facebook, Google and IBM are investing huge amounts of money and resources into AI research. There’s certainly no shortage of opportunities to develop real-world applications of the technology, and there’s immense scope for break-through moments in this field.

2. Big data analytics

Back in 2012, the Harvard Business Review branded data science the ‘sexiest job’ of the 21 century. Yes, you read that correctly. There has been a surge in demand for experts in this field and doubled efforts on the part of brands and agencies to boost salaries and attract data science talents. From banking to healthcare, big data analytics is everywhere, as companies increasingly attempt to make better use of the enormous datasets they have, in order to personalize and improve their services.

3. Computer-assisted education

The use of computers and software to assist education and/or training, computer-assisted education brings many benefits and has many uses. For students with learning disabilities, for instance, it can provide personalized instruction and enable students to learn at their own pace, freeing the teacher to devote more time to each individual. The field is still growing but promising, with many educators praising its ability to allow students to engage in active, independent and play-based learning.  

4. Bioinformatics

A fascinating application of big data, bioinformatics, or the use of programming and software development to build enormous datasets of biological information for research purposes, carries enormous potential. Linking big pharma companies with software companies, bioinformatics is growing in demand and offers good job prospects for computer science researchers and graduates interested in biology, medical technology, pharmaceuticals and computer information science.  

5. Cyber security

According to 2014 data from Burning Glass, cyber security jobs in the US grew by 74% between 2007 and 2013 – more than twice the rate of IT jobs overall, and raising concerns about the shortfall in qualified graduates. In February 2015, President Barack Obama spoke of the need to “collaborate and explore partnerships that will help develop the best ways to bolster our cyber security.” It’s not hard to understand why he might think so. We live in a hyper-connected world, in which absolutely everything – from banking to dating to governmental infrastructure – is done online. In today’s world, data protection is no longer optional, for either individuals or nations, making this another growing strand of computer science research.

Virtual Reality

A portable virtual reality benefit (VRS) will make the nearness and introduction of the sounds and sights of a genuine physical condition practically accessible wherever continuously using versatile media transmission gadgets and systems. Besides, the VRS is the change of a physical framework into its advanced portrayal in a three-measurement (3D) sight and sound configuration.

This  addresses one part of the idea of conveying a real interactive media condition to its virtual nearness wherever continuously .

A universal media transmission union (ITC) proposal archive, containing ITU's dreams on for the most part forward-looking and inventive administrations and system capacities, addresses the ability required in a telecom framework to permit portable access to constant sights and hints of a genuine physical condition in the challenge and types of a VRS scene .

By and by, the accessibility of a VRS is constrained to settled get to wonders in non-ongoing , for instance , excitement machines and different reproductions gear. There are additionally some constrained settled get to and ongoing administrations that require low information transmission rates, for example, net gatherings. In the last case, a client can encounter a restricted genuine condition instead of the previous instance of a non-genuine PC created condition.

These current virtual reality administrations don't permit client control in survey 3D situations, and they are for the most part constrained to review pictures on a screen in two measurements.

The VRS-competent frameworks, in any case, will permit rather 3D portrayals of remote genuine conditions. For example, a traveler in a prepare or in an auto could turn into a member in a phone call in a 3D situation or turn out to be practically present among the gathering of people in a show lobby or games stadium seeing a live show or occasion

History of Neural Networks

Neural system reenactments seem, by all accounts, to be a current improvement. Be that as it may, this field was built up before the appearance of PCs, and has made due no less than one noteworthy mishap and a few periods.

Numerous important progresses have been supported by the utilization of modest PC imitations. Taking after an underlying time of eagerness, the field survived a time of dissatisfaction and notoriety. Amid this period when financing and expert support was negligible, critical advances were made by moderately few reserchers. These pioneers could create persuading innovation which outperformed the restrictions distinguished by Minsky and Papert. Minsky and Papert, distributed a book (in 1969) in which they summed up a general sentiment disappointment (against neural systems) among specialists, and was along these lines acknowledged by most without further examination. At present, the neural system field appreciates a resurgence of intrigue and a comparing increment in financing.

The principal manufactured neuron was delivered in 1943 by the neurophysiologist Warren McCulloch and the scholar Walter Pits. In any case, the innovation accessible around then did not permit them to do excessively.

What is Neural Networks?

What is a Neural Network?

An Artificial Neural Network (ANN) is a data handling worldview that is motivated by the way organic sensory systems, for example, the cerebrum, prepare data. The key component of this worldview is the novel structure of the data handling framework. It is made out of an extensive number of very interconnected preparing components (neurones) working as one to take care of particular issues. ANNs, similar to individuals, learn by case. An ANN is designed for a particular application, for example, design acknowledgment or information order, through a learning procedure. Learning in organic frameworks includes changes in accordance with the synaptic associations that exist between the neurones. This is valid for ANNs too.