Organization profile

Organisation profile

Instrumentation and Control Engineering is a multi-disciplinary branch which includes electrical, electronics and instrumentation. It enriches the students to excel in the area of automation where the students are exposed to various subjects related to measurements and control.

The department was established in 2001 and has expertise in the field of Applied Electronics, Sensor Technology, Control Systems, Process Control, etc.

Fingerprint The fingerprint is based on mining the text of the scientific documents related to the associated persons. Based on that an index of weighted terms is created, which defines the key subjects of research unit

Controllers Engineering & Materials Science
Distillation columns Engineering & Materials Science
Neural networks Engineering & Materials Science
Sensors Engineering & Materials Science
Support vector machines Engineering & Materials Science
Calibration Engineering & Materials Science
Actuators Engineering & Materials Science
Classifiers Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 1997 2019

Application of multiresolution analysis for automated detection of brain abnormality using MR images: A comparative study

Gudigar, A., Raghavendra, U., San, T. R., Ciaccio, E. J. & Acharya, U. R., 01-01-2019, In : Future Generation Computer Systems. 90, p. 359-367 9 p.

Research output: Contribution to journalArticle

Multiresolution analysis
Magnetic resonance
Brain
Neurology
Magnetic resonance imaging
4 Citations

Age-related Macular Degeneration detection using deep convolutional neural network

Tan, J. H., Bhandary, S. V., Sivaprasad, S., Hagiwara, Y., Bagchi, A., Raghavendra, U., Krishna Rao, A., Raju, B., Shetty, N. S., Gertych, A., Chua, K. C. & Acharya, U. R., 01-10-2018, In : Future Generation Computer Systems. 87, p. 127-135 9 p.

Research output: Contribution to journalArticle

Neural networks
Screening
Aging of materials
Costs

An efficient data mining framework for the characterization of symptomatic and asymptomatic carotid plaque using bidimensional empirical mode decomposition technique

Molinari, F., Raghavendra, U., Gudigar, A., Meiburger, K. M. & Rajendra Acharya, U., 23-02-2018, (Accepted/In press) In : Medical and Biological Engineering and Computing. p. 1-15 15 p.

Research output: Contribution to journalArticle

Data mining
Ultrasonics
Decomposition
Elasticity
Screening

Equipment

Discrete time control systems
Programmable logic controllers
Automation
cards
India
data acquisition
controllers
cameras

Press / Media

Researchers at MAHE discover method to fabricate superhydrophobicity

Santhosh K V

17-09-18

1 item of media coverage

Press/Media: Press / Media

Bettering the solar battery

Vidya S Rao

19-04-17

1 item of media coverage

Press/Media: Press / Media