Anusha K. S. received her B. Tech. degree in Electronics and Instrumentation from Cochin Institute of Science and Technology, Kerala, India in 2006. She received her M. Tech degree in Instrumentation and Control from National Institute of Technology Calicut in 2013. She also received her MBA in HRM and Finance from Mahatma Gandhi University, Kottayam in 2009. She has been working as a faculty in IES College of Engineering, Trichur. Currently she serves as Assistant Professor (Sr. Gr.) at the Department of Electronics and Communication Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore Campus. She is also pursuing her Ph. D. in Wireless Sensor Network domain. Her research interests are also in Instrumentation and Artificial Intelligence.
Education
- Pursuing: Ph. D. in Wireless Sensor Networks
Amrita Vishwa Vidyapeetham - 2011 - 2013: M.Tech in Instrumentation and Control
NIT Calicut - 2007 - 2009: MBA in HRM and Finance
M. G. University
Professional Experience
| Year | Affiliation |
| January 1, 2018 - Present | Assistant Professor (Sr. Gr.), Amrita Vishwa Vidyapeetham Domain : Teaching, research , projects, administrative duties in dept. and campus |
| November 9, 2013 - January 1, 2018 | Assistant Professor, Amrita Vishwa Vidyapeetham Domain : Teaching, research , projects, administrative duties in dept. and campus |
| July 15, 2013 - November 9, 2018 | Faculty Associate, Amrita Vishwa Vidyapeetham Domain : Teaching, administrative duties in dept |
| August 1, 2006 - July 22, 2011 | Lecturer, IES College of Engineering, Trichur Domain : Teaching, projects, administrative duties in dept. and campus |
Academic Responsibilities
| S. No | Position | Class / Batch | Responsibility |
| 1. | Class Adviser | 2015 - 19 | Academic and administrative duties |
| 2. | Publication coordinator | Dept. | Publication status update of department |
Undergraduate Courses Handled
- Fundamentals of Electrical Technology
- Industrial Instrumentation I
- Electrical & Electronics Measurements I
- Control systems
- Industrial Instrumentation II
- Sensors and Signal Conditioning
- Industrial Automation
- Analytical Instrumentation
- Power plant Instrumentation
Post-Graduate / PhD Courses Handled
- Biomedical Instrumentation (BME)
- Biomedical Instrumentation Lab (BME)
Innovations in Teaching - Learning
| Innovation Method | Description with Tools used |
| Note submission as a CA component | - |
Participation in Faculty Development / STTP / Workshops /Conferences
| SNo | Title | Organization | Period | Outcome |
| 1. | RTCSP | Amrita, Coimbatore. | February 26 - 27, 2014 | Research |
| 2. | National seminar on Curriculum design for sustainable and societal development: A road map | Amrita, Coimbatore. | August 12 - 13, 2016 | Curriculum design. |
Organizing Faculty Development / STTP / Workshops / Conferences
| SNo | Title | Organization | Period | Outcome |
| 1. | ISTE Work on Signals & Systems | IIT Kharagpur | January 2 - 12, 2014 | Research |
| 2. | National Workshop on IPBA | Amrita, Coimbatore. | June 12 - 13, 2015 | Research |
| 3. | National Workshop on BiSAC | Amrita, Coimbatore. | December 17 - 19, 2015 | Research |
Publications
| Year of Publication | Title |
|---|---|
|
2019 |
Anusha K. S., Ramanathan, R., and Jayakumar, M., “Link distance-support vector regression (LD-SVR) based device free localization technique in indoor environment”, Engineering Science and Technology, an International Journal, 2019.[Abstract] Indoor localization using device free localization (DFL) in wireless sensor networks is gaining momentum nowadays due to the potential benefits of DFL. The techniques used in DFL can be broadly classified as statistical methods, compressive sensing, machine learning, radio tomographic method etc. Whenever loss factor and noise involved in the setup is unpredictable, techniques based on machine learning for target detection improves the result to a greater extend. The adaptability nature of support vector machines eased our choice of machine learning algorithm and support vector machine regression (SVR) is the proposed machine learning approach to address prediction of target position. Proposed link distance-support vector machine (LD-SVR) model uses link distance based DFL of single and multiple targets in indoor environment. Performance of the proposed model using SVR is analysed using parameters mean error and probability distribution function of mean error for various number of nodes and targets by imparting measurement error. The simulation results are found to be very much promising in a 3D room environment. The maximum value of mean error due to measurement error effect on link distance is less than 1 m. More »» |
|
2019 |
Anusha K. S., Dr. Ramanathan R., and Dr. Jayakumar M., “Device Free Localisation Techniques in Indoor Environments”, Defence Science Journal (DSJ), vol. 69, no. 4, pp. 378-388, 2019.[Abstract] The location estimation of a target for a long period was performed only by device based localisation technique which is difficult in applications where target especially human is non-cooperative. A target was detected by equipping a device using global positioning systems, radio frequency systems, ultrasonic frequency systems, etc. Device free localisation (DFL) is an upcoming technology in automated localisation in which target need not equip any device for identifying its position by the user. For achieving this objective, the wireless sensor network is a better choice due to its growing popularity. This paper describes the possible categorisation of recently developed DFL techniques using wireless sensor network. The scope of each category of techniques is analysed by comparing their potential benefits and drawbacks. Finally, future scope and research directions in this field are also summarised. More »» |
|
2014 |
G. R, Sundaram, L., P, S., Unnithan, V. Ramachandr, and Anusha K. S., “PC Based Virtual Oscilloscope Based On Sound Card and Scilab”, International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 5, no. 3, pp. 216-220, 2014.[Abstract] Oscilloscopes are used for displaying and analyzing electrical signals. Basic electrical components or circuit modules can be tested for their working. In today’s world, PC based measurements have become more affordable and easy to use, thus opening the door for “virtual instrumentation”. This paper describes about developing a PC based virtual oscilloscope. Data acquisition part has been done using sound card as A/D converter and a software manager was developed for processing the signals with the help of SCILAB. The PC based virtual oscilloscope can be used by undergraduate students for analyzing signals without the use of traditional oscilloscope. More »» |
| Year of Publication | Title |
|---|---|
|
2012 |
Anusha K. S., Mathews, M. T., and Puthankattil, S. D., “Classification of Normal and Epileptic EEG Signal Using Time & Frequency Domain Features through Artificial Neural Network”, in 2012 International Conference on Advances in Computing and Communications, 2012.[Abstract] Epilepsy is one of the important brain disorders, characterized by sudden recurrent and transient disturbances of mental function and movements of body, which is caused from excessive neuronal activity due to highly frequent electrochemical impulses from the neurons. This excessive discharge is shown in EEG as epileptic spikes which are complementary source of information in diagnosis and localization of epilepsy. Currently there are many techniques for the diagnosis and monitoring of epilepsy. Artificial Neural Networks (ANN) have proved to be an effective approach for a broad spectrum of applications for EEG signals because of its self-adaptation and natural way to organize and implement the redundancy. This paper proposes a neural-network-based automated epileptic EEG detection system that uses Feed forward Artificial Neural Network incorporating sliding window technique for pattern recognition. This work utilizes 100 single channel EEG signals obtained from the database of Epilepsy Centre in Bonn, Germany. The algorithm was trained with 50 datasets and tested for 25 normal data and 25 epileptic data sets. The performance of classification using Feed forward Artificial Neural Network gave a high success rate of 93.37% for distinguishing normal signals and 95.5% for epileptic signals. More »» |
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