Sri Ramakrishna Engineering College

About: Sri Ramakrishna Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Control theory. The organization has 1030 authors who have published 843 publications receiving 3822 citations.

Revisiting computer networking protocols by wireless sniffing on brain signal/image portals

Abstract: Brain signal/image processing is of significant attention not only the physiologist carrying out analysis and probe and the clinician investigating patients but further to the biomedical engineer who is vital for acquisition, processing, and interpreting the electroencephalogram signals by designing systems and algorithms for their control. The precious, abundant materials or information in the field of brain signal/image processing is distributed in the diverse scientific, technological and physiological periodicals, magazines, journals, international conference proceedings, and also in various portals/databases. Security threats or attacks may happen for a portal using data interruption, information interception, content modification and fabrication with new data. This study interprets the protocol layering information for the captured packets, image reconstruction after sniffing the packets, and the DNS/rDNS response times for a given portal/IP address using a Wireshark open source tool. Also, the security assessment results such as OS fingerprinting and port sweeping on the remote machines are performed using Nmap open source tool. Results are analyzed on specific brain signal/image processing portals around the globe located in USA, UK, and other countries.

A Comprehensive Survey on Effective Spectrum Sensing in 5G Wireless Networks through Cognitive Radio Networks

Abstract: Spectrum sensing is a challenging issue in cognitive radio network. In particular, wideband spectrum sensing gains more attention due to emerging 5G wireless networks characterized by high data rates in the order of hundreds of Gbps. Conventional sensing techniques uses samples for its observations based on Nyquist rate. Due to hardware cost and sampling rate limitations those techniques can sense only one band at a time. In spite of this issue secondary users have to sense multiple frequency bands using frontend technologies which leads into increased cost, time and complexity. Considering the facts and issues, compressive sensing was introduced to minimize the computation time by improving the sensing process even for high dimensional resources. Holding the essential information and reduces the sample size which is related to high dimensional data acquisition is performed in compressive sensing. In the last decade various researchers paid more attention to improve the performance of compressive sensing in cognitive radio networks based on sensing matrix, sparse representation and recovery process. This survey paper provides an in-depth analysis of conventional models and its sensing strategies in cognitive radio networks along with its merits and demerits to obtain a detailed insight about compressive sensing.