National Institute of Technology, Karnataka

About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Computer science & Corrosion. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.

A High-Gain Circularly Polarized Antenna Using Zero-Index Metamaterial

Abstract: In this letter, a novel high-gain circularly polarized (CP) antenna based on the zero-index metamaterial (ZIM) is presented. A square ring with two asymmetrical splits is used as a unit cell to achieve high gain and circular polarization. The 9 × 9 periodic array unit cells act as an aperture efficient focusing metasurface lens and polarization converter for a primary source antenna. The focusing effect of the ZIM enhances the gain of the microstrip patch antenna placed above it by an amount of 5–6 dB. Also, the ZIM converts the linearly polarized wave emitted by the patch antenna into circularly polarized waves. The circular polarization is achieved by optimizing the two split gaps on the ring of the unit cell, which gives two orthogonal polarizations with the required phase. The proposed design is fabricated and verified experimentally. The prototype has measured impedance bandwidth from 7.04 to 7.68 GHz. The 3 dB axial ratio is achieved from 7.11 to 7.56 GHz, with a peak gain of 12.31 dBic at 7.45 GHz. The gain of around 11.5 dBic is achieved over the entire CP bandwidth with a good cross-polarization level.

Antibiofouling hollow-fiber membranes for dye rejection by embedding chitosan and silver-loaded chitosan nanoparticles

Abstract: The removal of toxic dyes from the wastewater and industrial effluents is a major environmental challenge. Various techniques have been employed for the removal of dyes, including the application of nano-sized adsorbents, nanocomposite membranes and photodegradation. Membrane filtration is an alterntive but suffers from drawbacks such as fouling. Here we present a simple approach for the development of antibiofouling membranes based on chitosan. The application of chitosan-based nanoparticles as additives for wastewater treatment is poorly explored. The chitosan and silver-loaded chitosan nanoparticles were synthesized by ionic gelation method and incorporated to fabricate hollow-fiber membranes by dry–wet spinning technique. The prepared membranes were characterized by morphological study, permeability test, antibiofouling study and dye rejection study. The nanocomposite hollow-fiber membranes displayed superior performance than their pristine form. The incorporation of 0.30 weight percent of the chitosan and silver-loaded chitosan nanoparticles into the hollow-fiber membranes enhanced the antifouling property with flux recovery ratio of 81.21 and 86.13%, respectively. The dye rejection results showed maximum rejection of 89.27 and 86.04% for Reactive Black 5 and Reactive Orange 16, respectively. Hence, it can be concluded that hollow-fiber membranes with silver-loaded chitosan nanoparticles are pertinent in developing antibiofouling membranes for the treatment of industrial dye effluents.

A two-tier network based intrusion detection system architecture using machine learning approach

Abstract: Intrusion detection systems are systems that can detect any kind of malicious attacks, corrupted data or any kind of intrusion that can pose threat to our systems. In our paper, we would like to present a novel approach to build a network based intrusion detection system using machine learning approach. We have proposed a two-tier architecture to detect intrusions on network level. Network behaviour can be classified as misuse detection and anomaly detection. As our analysis depends on the network behaviour, we have considered data packets of TCP/IP as our input data. After, pre-processing the data by parameter filtering, we build a autonomous model on training set using hierarchical agglomerative clustering. Further, data gets classified as regular traffic pattern or intrusions using KNN classification. This reduces cost-overheads. Misuse detection is conducted using MLP algorithm. Anomaly detection is conducted using Reinforcement algorithm where network agents learn from the environment and take decisions accordingly. The TP rate of our architecture is 0.99 and false positive rate is 0.01. Thus, our architecture provides a high level of security by providing high TP and low false positive rate. And, it also analyzes the usual network patterns and learns incrementally (to build autonomous system) to separate normal data and threats.