Mauris Laoreet Aliquet

Nunc eget sapien arcu, nec blandit est. Curabitur et risus neque, quis tincidunt mauris. Nullam risus nulla, imperdiet quis fermentum et, venenatis et lacus. Quisque id lacus sagittis purus tincidunt vehicula et eget magna. Proin eu risus at libero imperdiet laoreet vitae non elit. In aliquet massa euismod turpis feugiat sollicitudin. Cras in metus non dui porttitor lacinia vel et ipsum. Donec et hendrerit erat. Maecenas volutpat rutrum enim, sagittis blandit dolor dictum eu. Aenean viverra mollis condimentum. Mauris risus diam, cursus a porta non, cursus sed ante. Duis ut risus quis mauris ultricies lobortis vitae non velit. Maecenas ultricies velit ut tellus elementum elementum.

Morbi vitae sem nibh. Mauris laoreet aliquet mi a laoreet. Integer malesuada adipiscing condimentum. Etiam gravida euismod tincidunt. Nullam feugiat ornare purus, vel pharetra turpis aliquam id. Aliquam at leo urna, ac pretium ligula. Quisque congue lacus vel dui porttitor id mattis metus vulputate. Cras iaculis commodo nunc, sit amet eleifend augue euismod vel. Integer id libero nec purus condimentum dapibus. Quisque mauris ipsum, tristique eget faucibus blandit, iaculis non augue. Curabitur lobortis, lorem sed pellentesque viverra, odio est vulputate tellus, vitae tristique mi elit eu nibh. Nullam vitae hendrerit arcu. Nullam eget semper elit. Aliquam felis quam, bibendum sed pellentesque nec, sollicitudin ac dolor.

Suspendisse nisi leo, tincidunt mattis sodales a, rhoncus nec nulla. Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Maecenas enim tellus, ultricies id bibendum non, ornare ac dui. Nulla in orci felis. Duis nec porta erat. Sed egestas volutpat nisl et ultrices. Aliquam varius hendrerit massa nec fringilla. Nulla vitae elit vel justo elementum scelerisque. Curabitur gravida nisi vitae est malesuada molestie.

RAVINDAR MOGILI

Associate Professor

M.Tech (CSE), (Phd)

14 Years of Teaching experience

Area of interest: Data Mining, Machine learning, Image processing

ravindermogili@gmail.com

Mobile: 9493142141

 

Most people associate a personal computer (PC) with the phrase computer. A PC is a small and relatively inexpensive computer designed for an individual use. PCs are based on the microprocessor technology that enables manufacturers to put an entire CPU on one chip. Personal computers at home can be used for a number of different applications including games, word processing, accounting and other tasks. Computers are generally classified by size and power as follows, although there is considerable overlap. The differences between computer classifications generally get smaller as technology advances, creating smaller and more powerful and cost-friendly components. Personal computer: a small, single-user computer based on a microprocessor. In addition to the microprocessor, a personal computer has a keyboard for entering data, a monitor for displaying information, and a storage device for saving data. Workstation: a powerful, single-user computer. A workstation is like a personal computer, but it has a more powerful microprocessor and a higher-quality monitor. Minicomputer: a multi-user computer capable of supporting from 10 to hundreds of users simultaneously. Mainframe: a powerful multi-user computer capable of supporting many hundreds or thousands of users simultaneously. Supercomputer: an extremely fast computer that can perform hundreds of millions of instructions per second.