Dr.Madhu Patil
@bgscet.ac.in
Professor
BGS College of Engineering and Technology
EDUCATION
B.E, Mtech,P.hD
RESEARCH INTERESTS
Wireless network, Signal processing, Communication
Scopus Publications
Scopus Publications
H. V. Chaitra, Madhu Patil, G. Manjula, M. K. Bindiya, and E. Naresh
Springer Science and Business Media LLC
Ravi Kumar Barwal, Neeraj Raheja, B R Mohan, Yamuna U, Sai Sudha Gadde, and Madhu Patil
Auricle Technologies, Pvt., Ltd.
Nowadays, Tumour is one of the important reasons of human death worldwide, producing about 9.6 million people in 2018. BC (breast cancer) is the common reason for cancer deaths in females. BC is a type of cancer that can be treated when detected early. The main motive of this analysis is to detect cancer early in life using ML (machine learning) techniques. The features of the people included in the WDBC (Wisconsin diagnostic breast cancer) and Coimbra BC datasets were classified by SVOF-KNN, KNN, and Naïve Bayes techniques. The pre-processing data phase was applied to the datasets before classification. After the data pre-processing steps, three classification methods were applied to the data. Specificity and Sensitivity rates were used to calculate the success of the techniques. As an outcome of the BC diagnosis classification, the SVOF-KNN technique was found with a 91 percent specificity rate and 90 percent sensitivity rate. When the outcomes attained from feature extraction and selection are calculated. It is seen that feature extraction, selection, and data pre-processing techniques improve the specificity and sensitivity rate of the detection system.
Sheetal Singrihalli Hemaraj, Shylashree Nagaraja, Sunitha Yariyur Narasimhaiah, and Madhu Patil
Institute of Advanced Engineering and Science
<p>Bug finding is a critical component of the verification flow and is resource intensive.In a typical week, a debug engineer writes triages, which take up significant amount of time that could be spent debugging another unique issue, and the lack of standardization in scripting causes maintainability issues in functional verification bug triage. A framework that allows customizable triage script generation is developed based on inputs from the engineer deploying YAML isn’t another markup language (YAML) files and practical extraction and report language (PERL) scripting, and this methodology is made automated and is standardized across projects to ensure maximum benefit going forward. The use of auto-triage in the project of functional verification bug triage has contributed to a 18% increase in triaged signatures on average, from 40% before its use to 58% after. A similar earlier project vs. current project comparison shows a 20% uplift. The triaged inputs that are parsed are currently being fed to a machine learning algorithm, which will help further improve the debug efficiency. As part of future work, the information from input YAML files can be used to analyze simulation failure attributes, hence improving the overall efficiency of debugging.</p>
Manjula B M, Madhu Patil, Deekshith K S, and Prateek P Deshapande
IEEE
It is critical to obtain information about the fetus early in pregnancy to avert stillbirth. Medical personnel use Cardiotocography (CTG) to monitor the fetus's health in the hospital, however, it is not possible to record continuous long-duration signals using this method. As a result, constant and long-term monitoring of fetal electrocardiogram[1] signals is required to determine the health condition utilizing portable instruments. The invasive approach is superior to an invasive method for measuring ECG signals. To retrieve FECG encoded in the mother ECG, compact electronics and advanced signal processing techniques were required. Because the Fetal Heartbeat from the abdomen is frequently contaminated or interfered with by the Maternal Heartbeat, which is essentially noise. As a result, an attempt is made to separate the Fetal Heartbeat from the interfering Maternal Heartbeat in this case. The Adaptive [2] Noise Canceller (ANC) is used to remove the signal's noise content. Different adaptive filtering schemes, such as Single Input Single Output (SISO) on ANC, where adaptive algorithms such as least mean squares (LMS), Normalized least mean squares (NLMS), and leaky least mean [7] squares (L-LMS) are implemented in MATLAB and simulation results show the extracted FECG noise-free signal.
Madhu Patil and Chirag Sharma
Springer Singapore
Madhu Patil and Chirag Sharma
Institute of Advanced Engineering and Science
<p>Wireless sensor network (WSN) has attained enormous growth in recent times due to availability of tiny and low cost sensor devices. The sensor network is been adopted by various organization for various application services such environment monitoring, surveillance etc.. The WSN are powered by batteries and are deployed in non-rechargeable remote location. Preserving batteries of these devices is most desired. Many methodologies have been proposed in recent time to improve the lifespan of sensor network among them clustering technique is the most sorted out technique. The drawback of existing technique the cluster head energy degrades very fast due to long transmission which requires amplification as a result energy is lost to the node that is surrounding the cluster head. They did not consider the packet failure likelihood among inter and intra as a results there exist scheduling bottleneck and degrades the energy of sensor devices. To overcome this work present a packet failure estimation model and hop selection optimization model for inter cluster transmission. Experiments are conducted for lifetime efficiency for varied sensor devices for proposed and existing. The result shows that the proposed model performs better than existing in term of network lifetime and energy efficiency.</p>
Madhu Patil and Chirag Sharma
IEEE
Wireless sensor based communication system is an ever growing sector in the industry of communication. Wireless infrastructure is a network that enables correspondence between various devices associated through a infrastructure protocol. In WSN enhancing the life span of network depends on the energy dissipation of the sensor devices. Reducing the energy dissipation of sensor devices will improve the lifetime and device failure which help in better connectivity and coverage of sensor network. There has been various approach that has been developed to improve energy efficiency of sensor network among that clustering is a significant technique that help in improving the network lifetime by reducing the energy consumption but the issues with existing methodology is the energy inefficiency in selecting the cluster head which result in loss of connectivity which reducing the life time of network. To overcome this here the author proposes a design for cluster head selection based on connectivity of node. The experimental result shows that the proposed PS-LEAH perform better than existing LEACH in term of network lifetime, number of active device.