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From the Desk of the Vice Chancellor

Greetings!

As the Vice Chancellor of this premier university, I am extremely pleased to share this space to converse with all of you. The University of Madras is a post- sesquicentennial institution which has forged a glorious path for itself and has been the site for significant scientific discoveries as well as the beacon for national and regional societal transformation. It has successfully managed to hold aloft its tradition even while keeping up with the emerging trends. One reason for this is the way in which the university has kept alive its interactions with all the stakeholders.

The COVID 19 pandemic has introduced a ‘new normal’ and its impact is felt in the workings of the university as well. The students have been attending the lectures online. While online learning has the advantage of being anytime and anywhere, it has taken away the peer group interaction and peer learning which are integral aspects of the university experience. We look forward to welcoming you to the campus very soon. Meanwhile, our faculty and administration will continue to bring the best to the virtual classrooms. As students, you need to view the restrictions posed by the pandemic as a temporary deterrent. Your focus should be to gain the competencies to conduct application oriented research in order to evolve as useful citizens of our society.

Our university boasts of faculty with a high degree of knowledge and commitment to offer the best in teaching and research. Given the context of the pandemic, we need to redefine our teaching-learning processes to offer the best pedagogic experience to our students. Similarly, the post-pandemic era demands that we hone the employment potential and entrepreneurial capacity of our learners. This necessitates that we focus on sponsored research in cutting edge areas.

Our administrative staff have been the backbone of the university. While we move towards a transparent and complete e-governance model, we need more support from you. When the teaching-learning process at our university – from admission to certification -- is moving into the digital era, your skills and competencies need to keep pace.

At our university, we are gearing up for the final round of NAAC re-accreditation. This has offered us an opportunity to assess our Strengths, Weaknesses, Opportunities and Challenges. Further, it has made us more determined to reiterate our quality benchmarks in teaching, research and extension activities. We are aware of this responsibility and fully prepared for it because, our university has always encouraged individual thinking within the established frameworks. This is echoed in the words of Tim Burners Lee, who initiated the World Wide Web: “We are forming cells within a global brain and we are excited that we might start to think collectively. What becomes of us still hangs crucially on how we think individually.” Let us unite to make the educational experience at the University of Madras a synergy of the best minds and best thoughts.

Prof.Dr.S.Gowri

Vice-Chancellor
University of Madras

Dr. Mrs. PL. ChithraM.C.A., M.Phil., Ph.D

Professor and Head
Department of Network Systems and Information Technology
6

Awards

65

Publications

135

Seminars / Conference

5

Projects

3

Ph.D Awarded

9

Ph.D Present

Patents
S.No Title Description Patent Year Authority
1 VOXEON: VOXEL BASED BACK PROPAGATION NEURAL NETWORK FOR 3D LIDAR AIR BORNE POINT CLOUD CODEC   A neural network is a collection of layered neurons. Each neuron is a mathematical function that performs on an input value. Usually, a neuron handles the scalar value for processing the data. It is a very challenging task when 3D data is applied to a neural network. 3D data processing increases the space and time complexity 3 times than the vector data. Voxeon has been introduced to overcome this complexity problem. Voxeon is a combination of voxel and neuron in which a neuron handles the 3D point for processing. A common weight was generated for each 3D point to give the same importance for x,y, and z points in the point cloud data.    The working structure of a voxeon is given below ""  
  Single voxeon handles a 3D point coordinate x,y, and z. These coordinate values are multiplied by a generated weight w and then added with bias value b. Then, activation function f is applied on a resultant vector g to produce the output voxeon.
2021 INTELLECTUAL PROPERTY RIGHTS
2 IMAGE POLARITY CAPTCHA USING GAUSSIAN BASED EDGE DETECTED IMAGE WITH DEEP NEURAL NETWORK Image polarity CAPTCHA using Gaussian based edge detected image with Deep Neural   Network   1. The proposed Gaussian based edge detected image polarity captcha is designed for increasing user’s dexterity. 2. Gaussian based edge detection, extracts image features without affecting or altering the original features. 3. This edge detection has low error rate, and the edge point is accurately localized. 4. This image polarity captcha is more comfortable for human not for the machine. 5. Maintains moderate amount of images in the database and it consumes very less time with increased usability. 2021 INTELLECTUAL PROPERTY RIGHTS
3 PARALLEL ENCODER AND DECODER NETWORK FOR BRAIN TUMOR SEGMENTATION Parallel Encoder and Decoder Network for Brain Tumor Segmentation Abstract This research work proposes an automated parallel encoder and decoder network to segment brain tumor regions in Magnetic Resonance Imaging (MRI). This network contains two parallel and one downsampling block in encoding layer followed by one upsampling and one parallel block in decoding layer. All image slices from BRATS 2018 dataset are processed in the first parallel block of encoder layer. In this block, input slices are parallelly processed in four paths: hierarchy of three convolutional layers with 1 x 1 filters; hierarchy of three convolutional layers with 3 x 3 filters; hierarchy of three convolutional layers with 5 x 5 filters and hierarchy of three convolutional layers with 7 x 7 filters. The outcomes of four paths are cascaded to be processed in a convolution layer with 3 x 3 filters for extracting the aggregated feature map. These feature maps are then downsampled using max-pooling to perform dimensionality reduction. The reduced dimensions are again processed in parallel block to extract the downsampled features. These features have been processed in the hierarchy of unpooling and parallel block in decoding layer. Finally, softmax classification has been performed over the decoding layer outcome to segment complete, core and enhanced tumoral regions. The experimental results show higher segmentation results than the existing segmentation algorithms.  2021 INTELLECTUAL PROPERTY RIGHTS
// FLASH NEWS //
  • University of Madras attains category - 1 status from UGC |  NIRF Ranking - Ranked 39 in University Category 2024 |  University of Madras has been graded A++ in the NAAC Assessment

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