Channavva Kolanur
Verified @kletech.ac.in
Assistant Professor and Automation & Robotics
KLE Technological University
RESEARCH, TEACHING, or OTHER INTERESTS
Engineering, Multidisciplinary, Engineering, Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
C. B. Kolanur, Jyoti Bali, Shilpa Tanvashi, and Arunkumar Giriyapur
AIP Publishing
Madhu R. Koravanavar and C. B. Kolanur
IEEE
Respiratory infections are disorders that damage the lungs and airways and make it difficult for patients to breathe. Any part of the respiratory system has the potential to become infected or sick, which could have a variety of negative effects. Early detection of lung disorders results in a person's successful treatment. Using electronically recorded lung sounds, pulmonary diseases can be diagnosed. MelFrequency Cepstral Coefficient (MFCC), one of the Librosa machine learning library features, is used to check that such a brief speech segment is sufficiently steady to permit effective modeling. In this research, Librosa is one of the better preprocessing techniques used when working with audio data. The CNN model is applied to the collected dataset and obtained a train accuracy of 95% and a test accuracy of 85% when trained on extracted MFCC features. And also used a number of performance evaluation indicators, including as F1score, accuracy, precision, and recall. The outcomes of this research enable the use of deep learning models in clinical settings to improve doctors' judgment when identifying lung disorders.
Vineeth C Johnson, Jyoti Bali, Shilpa Tanvashi, and C B Kolanur
IEEE
A study on the FPGA development board PYNQ-Z2 for hardware acceleration is presented in this research paper. The experiment accelerates the tasks of optical character recognition (OCR) and image recognition using the FPGA on PYNQ-Z2. The output results on hardware acceleration (Processing system (PS) and Programmable Logic (PL)) are compared with the output results obtained while executing the same tasks on the Arm processor (Processing System (PS)) alone. In this experiment, a Long short-term memory (LSTM) neural network is used to implement OCR, and a Binarized neural network (BNN) is used to implement image recognition. LSTM and BNN here are quantized to reduce memory usage while implementing them on PYNQ-Z2.
C B Kolanur, R M Banakar, and G Rajneesh
IOP Publishing
Abstract The advent of industrial automation has brought about a substantial change in the human lifestyle and the way human interaction with machines. One of the major technological interventions in day-to-day life is the role of automation, which has uplifted the living standards in developed countries and fostered humans’ dependency for accomplishing recurring tasks. The latest and most adopted technology for remotely monitoring and controlling any machine or device is the Internet of Things-which has become one of the industry standards for automation. Multiple edge computation capable embedded devices and machines are connected to the internet as independent nodes, which can be monitored and controlled remotely with the use of IoT. Adapting this cutting-edge technology for household appliances to monitor and control them is often seen nowadays. This is fast and reliable but arrives with its own snags such as availability of internet at place where user and also the device/appliance is located, use of single control strategy which hampers and restricts the user always to carry the remote or to stay in the vicinity of the device to be controlled in case of wifi or Bluetooth controlled automation. The proposed research focuses on the adoption of multiple strategies to control each device/appliance to empower the end-user without being restricted to depend on a single strategy remote controlling system. The core part of the proposed research revolves around the Message Queuing Telemetry Transport (MQTT) protocol but can be activated with multiple routes such as touch-based sensing panels, cellphone-based controlling, web-enabled controlling or even gesture-based controlling of the appliances resulting in an ameliorative user experience and interactions with machines.