@pccoer.com
Assistant Professor, E&TC Engineering
Pimpri Chinchwad College of Engineering & Research, Ravet
Electrical and Electronic Engineering, Signal Processing, Artificial Intelligence, Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Dnyaneshwar P. Landge
IEEE
Mayur Patil, Tanmay Waware, Atharva Yawalkar, Vijayalaxmi Kumbhar, Maithili Andhare, and Arti Tekade
IEEE
We have discussed about counter-based SAR ADC in this research paper. A significant component of the high-speed application of ADCs is the SARs critical path. ADCs needed for long term and battery-operated applications typically consume relatively less power. Applications requiring low power, moderate resolution, and medium speed is best suited for SAR ADC. Dynamic latch is employed in our ADC to boost performance and achieve low power consumption. We have demonstrated a 45nm CMOS-simulated, 4-bit low power SAR ADC. Utilizing an ADC design with the maximum amount of simplification, which consists of a dynamic latch comparator, in this paper we are primarily focusing on increasing the sampling frequency of the SAR ADC in order to get high conversion rate. The continuous time analogue low pass filter, which is typically used in front of the ADC to avoid aliasing, was also explored in this paper. Active-RC filters and operational transconductance-C filters are investigated and developed. Results from simulations and measurements are offered to illustrate the performance and functionality.
Maithili Shailesh Andhare, Vijayalaxmi Sandeep Kumbhar, and Arti Avinash Tekade
IEEE
Cybercriminals and hackers are actively pursuing critical city infrastructures that rely on smart "Industrial Internet of Things (IIoT)" devices. Regardless of the fact that it has prompted a number of interests in recent decades, there isn’t an accurate approach for Industrial IoT attack detection. Prior to actually developing an appropriate approach for detecting Industrial IoT attacks, it’s indeed necessary to have knowledge of previous literature works. As a result, a concise and conceptual literature evaluation is conducted in this research work, including the most applicable methodologies dedicated to IIoT attack detection. All of the research papers gathered is from the years 2020 to 2022. Furthermore, each of the gathered publications is examined in terms of a variety of criteria, including the information source, attack detection methodologies, and performance metrics. Finally, current study gaps in the literature have been highlighted, and this will serve as a benchmark for future IIoT threat detection researchers.