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Rolant Gini J. currently serves as Assistant Professor at the Department of Electronics and Communication Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore Campus. Her areas of research include Signal Processing in various fields and especially for Bio Medical Applications, Embedded Systems and published researches based on the same. She works rigorously in providing real life solutions at affordable cost. She is very keen in developing good engineers so she is very passionate about teaching. She teaches different subjects in the areas such as signal processing, Microcontrollers, VLSI, circuits etc., and discusses various real life systems to make them visualize engineering aspects in everything. She motivates her students also towards developing a better engineer in them.


  • Pursuing: Ph. D. in Bio Medical Signal Processing
    Amrita Vishwa Vidyapeetham
  • 2008 - 2010: M. E. in VLSI Design
    Anna University, Coimbatore

Professional Experience

Year Affiliation
July 1, 2016 – Till date Assistant Professor (Sr. Gr.), Amrita Vishwa Vidyapeetham
Domain : Teaching, Research
December 1, 2012 – June 31, 2016 Assistant Professor (Or. Gr.), Amrita Vishwa Vidyapeetham
Domain : Teaching, Research
July 1, 2008 - November 30, 2010 Lecturer, Amrita Vishwa Vidyapeetham
Domain : Teaching, Research

Academic Responsibilities

SNo Position Class / Batch
1. Class Adviser 2013– 17 2017 - 21

Undergraduate Courses Handled

  1. Digital Signal Processing
  2. Wavelet Signal Processing & its Applications
  3. Signals and Systems
  4. VLSI System Design
  5. Introduction to Embedded Systems
  6. Microprocessor & Microcontroller
  7. Introduction to Microcontroller and its Applications
  8. Computer System Architecture
  9. Digital Circuits and systems
  10. Electronics Engineering
  11. Electrical Engineering

Post-Graduate / PhD Courses Handled

  1. VLSI Signal Processing (VLSI Design)

Innovations in Teaching - Learning

SNo Innovation Method Description with Tools used
1. Real Time signals for analysis, decompose and reconstruction Real signals for better understanding, design. Work done using Matlab

Participation in Faculty Development / STTP / Workshops /Conferences

SNo Title Organization Period Outcome
1. Workshop on Advanced Electronic Circuit Analysis Amrita VishwaVidyapeetham, Coimbatore July 7 - 9, 2016 Lab evaluation development for UG
2. ITAA Training ITAA, International Board of Certification July 9 - 10, 2015 Students psychological problems and handling
3. Two-week ISTE Workshop on Signals & Systems MHRD & IIT Kharagpur January 2 - 12, 2014 Basic plan
4. Two-week ISTE Workshop on Analog Electronics MHRD & IIT Kharagpur June 4 - 14, 2013 Teaching and Learning
5. Two-week ISTE Workshop on Basic Electronics MHRD & IIT Bombay June 28 – July 8, 2011 Teaching and Learning

Organizing Faculty Development / STTP / Workshops /Conferences

SNo Title Organization Period Outcome
1. National Workshop on Image Processing for Biomedical applications (IPBA 2016) Amrita VishwaVidyapeetham, Coimbatore December 16 - 17, 2016 Research
2. National Workshop on Biomedical Signal Processing and Conditioning (BiSAC 2015) Amrita VishwaVidyapeetham, Coimbatore December 17 - 19, 2015 Teaching, Learning & Research
3. National Workshop on Image Processing for Biomedical Applications (IPBA 2015) Amrita VishwaVidyapeetham, Coimbatore June 12 - 13, 2015 Teaching, Learning &Research

Products Developed

SNo Product Name / Domain Description Resource Industry if any
1. EMI Shield   UG/PG Projects  


Publication Type: Conference Proceedings

Year of Publication Title


J. Joseph, J. Gini, R., and Dr. K. I. Ramachandran, “Removal of BW and Respiration Noise in abdECG for fECG Extraction”, Advances in Signal Processing and Intelligent Recognition Systems, vol. 678. Springer International Publishing, Cham, pp. 3-14, 2018.[Abstract]

Electrocardiogram (ECG) signals are one of the most important diagnostic tools for any doctor, especially a cardiologist. It is important that the fetus present inside the abdomen undergoes a fetal ECG recording to assess the health of the fetus. Complications like disturbance because of movement of abdominal muscles are usually present during the recording and leads to the wrong diagnosis of the fetus ECG. In this paper, the signal in dispute had been altered in the proposed method so as to eliminate the wandering of the baseline, respiration noise and also expel the noise from other sources. The acquired abdominal ECG signal in a noninvasive manner had been considered for extracting the fetal ECG after eliminating the noise. The windowed zero mean method is used where the first step is segmentation. In segmentation, the abdominal ECG signal is divided into set of samples based on window size. Zero mean is applied across each of the windowed abdominal ECG signals to address the issue of baseline wandering and respiration noise. This is followed by the application of a bandpass filter to cancel the high-frequency noise component. This process results in an ECG signal that almost has no complications as present before. The fetal ECG signal that is procured using such a method is now easier to diagnose as compared to the acquired signal which contains noise. Thus, for a fetus, this can help in proper diagnosis. It is further noted that this method is very reliant on using and is lucid. It can be used to augment and alter signals where such complications arise in the field of medicine and clinical diagnosis

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R. J. Gini, Chakravarthy, P. Deepan, Dr. K. I. Ramachandran, and Anand, P., “Modeling of a System for fECG Extraction from abdECG”, Advances in Intelligent and Soft Computing, ISDA, vol. 736. Springer International Publishing, pp. 568-579, 2018.[Abstract]

The objective of this paper is to move a step ahead in investigation and create a feasible, cost effective fetal ECG analysis tool for clinical practice which will be easy for usage by any non-skilled personal and provide actionable medical information such as the QRS complex of fetal ECG, fetal HR etc. In this method, a composite abdominal ECG is subjected to a pre-processing stage which involves filtering and normalization, then fed into the `thresholding and peak finding' stage to detect the maternal ECG peaks. The next stage involves construction of the MLE of maternal ECG embedded in the abdominal ECG. After this, the constructed MLE which represent the maternal ECG is subtracted from the abdominal ECG to obtain fetal ECG along with a smidgen of noise. This noise which adulterates the fetal ECG is removed by filtering, done at the post processing stage. Thresholding and peak finding is done at the post processed signal to calculate the fetal HR. This paper puts forth a promising possibility of implementing the proposed algorithm in any suitable hardware model, since an average Accuracy of 76.8% and average Sensitivity of 90.7% is attained.

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T. Abhay, Kayalvizhi, N. M. N., and J. Gini, R., “Estimating Correlation between Arterial Blood Pressure and Photoplethysmograph”, The 16th International Conference on Biomedical Engineering. IFMBE Proceedings, vol. 61. Springer Verlag, pp. 47-52, 2017.[Abstract]

Photoplethysmograph (PPG) and Arterial Blood Pressure (ABP) are good indicators of cardiovascular performance. Although ABP is more widely employed, the invasive procedure of signal acquisition may cause skin rashes and inconvenience to the patients. Also, it does not allow continuous monitoring of cardiac activity. PPG, on the other hand, uses infrared light to measure the blood volume changes, which is a simple, noninvasive and can be used for continuous measurement. This paper focuses on analyzing the similarity between ABP and PPG using various features like average slope, peak position, time period, elasticity, amplitude of the signal. A segmentation algorithm was used to segment out cycles of ABP and PPG from physionet database taken from 19 patients with respiratory failure and the values of each feature were extracted for each person. Considering the population, using Pearson’s correlation coefficient, the coefficient for the average slope of the PPG and peak to peak amplitude of ABP was found to be 0.55 indicating that other factors such as vessel diameter, thickness must be considered. The upstroke time period of both ABP and PPG was found to have a small difference in the range of 0.02s to 0.1s, whereas the time period of the heart cycles remained the same irrespective of the disease or healthy condition. The peak value of both ABP and PPG was found to occur with constant time difference. The elasticity with peak to peak amplitude of ABP was found to have a correlation of 0.822, and with systolic blood pressure, a correlation of 0.7622. When considered for individuals, parameters like the diastolic average slope of PPG and systolic blood pressure were found to have a good correlation coefficient ranging from 0.6 to 0.96 among other parameters which include systolic average slope, maximum and minimum slope of PPG, and the diastolic blood pressure.

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R. J. Gini, Dr. K. I. Ramachandran, Nair, R. H., and Anand, P., “Portable Fetal ECG Extractor from abdECG”, 2016 International Conference on Communication and Signal Processing (ICCSP). pp. 0698 – 0701, 2016.[Abstract]

This paper aims at creating an affordable fECG extractor by simplifying the process of fECG extraction from abdECG. Even though invasive fECG extraction is more accurate, noninvasive method of extraction has been preferred during prenatal considering the fetus's health. This makes the noninvasive fECG extraction an emerging and required field of research. This paper gives a fundamental idea to create a prototype for extracting the fetal ECG from abdominal ECG. The abdECG has been preprocessed by normalization and filtering. Based on thresholding and first order differentiation, the maternal peak has been identified from the preprocessed abdECG signal. Using the identified maternal peaks, QRS complex of mECG has been identified and the same has been cancelled out from abdECG to cull out the fECG. The resultant signal has been a combination of fECG and noise. The fetal peaks have been identified from the culled out signal. The identified fetal peaks provide information like the QRS complex of the fetus, fetus heart rate, diagnosis of any congenital disorder and other anomalies. This simplified algorithm has been implemented with high level language C and executed using Raspberry Pi. The execution results with a second delay and Raspberry Pi can create a standalone platform at any place and is handy. The system resulted in 100% accuracy when the selected channel happened to be near the fetus's heart. Even in other cases, it has proven to be good and effective. This shows that the system is affordable and practically useable.

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V. N. Varghees, Manikandan, M. S., and J. Gini, R., “Adaptive MRI image denoising using total-variation and local noise estimation”, IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012. Nagapattinam, Tamil Nadu, pp. 506-511, 2012.[Abstract]

In this paper, we present an automated, adaptive image denoising method for removal of Rician noise from MRI images. The proposed method is based on the discretized total variation (TV) minimization model and the local noise estimation technique. The regularization parameter of the TV-based denoising method is adapted based on the standard deviation of noise in MRI image. The performance of the proposed method is evaluated using the brain MRI images corrupted by Rician noise with standard deviation ranging from 2 to 30. The quality of the denoised image is validated using both subjective visualization tests and objective quality metrics. The experimental results show that the proposed method achieves a significant improvement in the preservation of edges while simultaneously removing the Rician noise from a MR image. The adaptive TV filtering method is reasonably better than existing methods such as non-local filter, bilateral filter and multiscale linear minimum mean square-error estimation (LMMSE) approach. © 2012 Pillay Engineering College.

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N. V. Varghees, Manikandan, M. S., J. Gini, R., and Dr. Soman K. P., “A New Framework to Automatically Select Noise Model for Rician Noise Estimation in MR Images”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. Cochin, pp. 82-85, 2012.[Abstract]

In this paper, we study a set of histogram and higher-order statistical (HOS) features for automatically identifying the presence of large background in the magnitude MR images. The robustness and discriminative power of each individual feature and combining feature sets are investigated using different MR images including brain, cardiac, breast, spine, stomach and noisy images corrupted by Rician noise with different standard deviations, σ={5,10,15,20,25,30,35}. The performances of the identification approaches are evaluated in terms of sensitivity, specificity, and accuracy. Experimental results obtained on 2544 MR images show that an approach based on the kurtosis and histogram peak ratio (HPR) features outperforms significantly as compared to that of other approaches reported in this work. The proposed approach can be used for selection of distribution model (Rayleigh or Gaussian) for accurate estimation of Rician noise level in MR images having large or little background regions. © 2012 IEEE.

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A. S. Menon, J. Gini, R., Aishwarya, B., Balaji, C. C. G., Jaswanth, R., and Krishnadas, A., “Optimization of GALS CMP Architecture with DCT as Case Study”, ICECT 2011 - 2011 3rd International Conference on Electronics Computer Technology, vol. 3. Kanyakumari, pp. 330-333, 2011.[Abstract]

Globally Asynchronous Locally Synchronous (GALS) Chip multiprocessors with separate clocks for separate modules inside the chip are highly suited for processing number crunching and processing jobs that have to be done with limited energy. The GALS uses clock and voltage scaling jointly in system sub-modules to achieve low energy consumption rates. An advantage here is feasibility to use different clock rates for different modules in the chip. GALS allows up to 25 % energy savings in addition to clock as well as voltage scalability. However, a chronic drawback of GALS is additional communication latency between the various clock domains. In addition the component processors consume power even when they are idle. Latency to a great extent is reduced by implementing large inter-processor FIFOs buffers [3]. This work proposes to enhance the latency minimization by alternately enhance one processor domain to optimally manage latency and power wastage. Real Time System algorithms are used for managing the inter clock domain communication. This technique can be used to either enhance the FIFO buffer technique if area is not a consideration where only the efficiency is considered or as a standalone manager for handling the inter clock domain communications efficiently with reduced area and increase resource handling capability. Tri state buffers are put to use to switch clock and supply voltage to individual clock domains. For performance evaluation, Component processors to find the DCT were implemented with FIFO in between the modules for communication between processors and in turn reduce latency. Comparison of performance of the latency management processor enhanced GALS chip with FIFO buffer chip revealed increase of 15% throughput and 40% energy savings approximately as compared to 10% and 25% for the latter.

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Publication Type: Conference Paper

Year of Publication Title


R. J. Gini, Dr. K. I. Ramachandran, and Ceerthibala, U. K., “Approach to extract twin fECG for different cardiac conditions during prenatal”, in IFMBE Proceedings, 2017, vol. 61, pp. 104-108.[Abstract]

During multiple fetus pregnancy, degree of risk for distinguishing the information of mother and fetus health condition is high. A proper distinguishable ECG of each fetus and mother gives information about the health conditions of individuals. In case of multiple fetal conditions, the heartbeat of the fetuses will be almost at the same rate. This algorithm has been aimed to separate mECG and the fECGs of the individual fetus. First, the signal for different medical conditions like Fibrillation, Apnea, Ventricular Ectopy, Singleton and Normal has been considered. The synthetic abdECG signal for the above mentioned cases has been formulated by preprocessing and considered as the input signal. RPeak of mECG in the abdECG signal has been located using First Order Gaussian Differentiator and Zero Crossing Detector. QRS complex has been considered around the identified R-Peak of abdECG. Identified QRS has been removed from the abdECG signal to obtain fECG with residual noise. The QRS complexes of fECG are detected the same way as mECG QRS were detected, and is represented as binary signals. The separation of the fetal ECG is done based on the individual presence of the fetus in the signals using Inter-beat averaging and Inter-beat standard deviation of the binary signal. The algorithm has been tested for above mentioned cardiac conditions during prenatal. The algorithm has been able to achieve 99% accuracy for particular cardiac condition with overall system accuracy of 80.4%. The standard cardiac signals of different cases have been sourced from Physionet database to construct the abdECG. © Springer Nature Singapore Pte Ltd. 2017.

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R. H. Nair, J. Gini, R., and Dr. K. I. Ramachandran, “A simplified approach to identify the fetal ECG from abdECG and to measure the fHR”, in IFMBE Proceedings, 2015, vol. 52, pp. 23-26.[Abstract]

Fetal ECG (fECG) recording aids physicians to diagnose congenital disorders and other anomalies like asphyxia at the early stages of pregnancy. The fECG extraction has been an area of intensive research. Despite the existence of sophisticated and detailed algorithms – based on adaptive filters, independent component analysis (ICA), &c – filtering out the fECG, buried in the noise and mixed up with the maternal ECG (mECG) remains a challenging task. Some residues of mECG are always present in the fECG extracted with all such techniques. A simple algorithm has been developed here to identify the local maxima in the pre-processed abdominal ECG (abdECG) through thresholding; it locates the mECG peaks explicitly. At the outset, the abdECG has been refined by removing the baseline wander and power line interference at a pre-processing stage. With these as pivots the mECG component is eliminated and the fECG of good quality culled out. The fetal heart rate (fHR) and information required to know the condition of fetal heart can be extracted from this fECG effectively. Extraction of these information helps reducing the rate of fetal mortality, and improving the health condition of fetus as well as mother. Performance of the method is better than the conventional adaptive filtering method and the same is proven quantitatively. A processor based realization of the scheme adds to its credibility substantially to ensure its usability in practice. © Springer International Publishing Switzerland 2015.

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