The ECE Department's mission is to carry out advanced research and development in various areas of Electronics & Communication Engineering with different application domains,and to train and educate, at both undergraduate and postgraduate levels, engineers of outstanding ability who can become innovators and new product creators.
The vision of the department is to be a world class department which:
- Is globally respected for its research capability, with some research groups being considered as among the leaders globally and within the country.
- Has thriving UG and PG programs
- Is socially relevant, globally linked and industry facing
Upcoming Events at IIIT-D
Date : 17th May 2022
Time: 4 PM
This talk gives an introduction to physical layer of wireless local area networks (WLAN PHY). It first covers OFDM, MIMO, channel models, evolution of 802.11 standards and PHY packet format. The main part of this talk mid-dives into OFDM packet detection, transmit beamforming and automatic gain control. We also briefly mention the applications of these techniques in 802.11be (7th generation WiFi).
Date : 6th to 10th June 2022
A five-day virtual FDP Program/IEEE SPS Summer Seasonal School on "AI in Healthcare" is being jointly organized by IEEE SPS Society, SBILab ECE Department, Centre of Artificial Intelligence, Centre of Excellence Health Care. This FDP Program / IEEE SPS Summer Seasonal School is targeted towards students, researchers, and professionals interested in learning about AI in Healthcare.
A. Agrawal, A. Chauhan, M. K. Shetty, Girish M. P., M. D.Gupta, A. Gupta, “ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects,” at Computers in Biology and Medicine,2022.
Studies showed that many COVID-19 survivors develop sub-clinical to clinical heart damage, even if subjects did not have underlying heart disease before COVID. Since Electrocardiogram (ECG) is a reliable technique for cardiovascular disease diagnosis, this study analyzes the 12-lead ECG recordings of healthy and post-COVID (COVID-recovered) subjects to ascertain ECG changes after suffering from COVID-19.Read More
B. Fatimah, P. Aggarwal, P. Singh, A. Gupta, “A comparative study for predictive monitoring of COVID-19 pandemic” at Applied Soft Computing, 2022.
COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. This study presents an optimized ARIMA model to forecast COVID-19 cases.Read More
A. Goel, A. Majumdar, “K-means Embedded Deep Transform Learning for Hyperspectral Band Selection” at IEEE Geoscience and Remote Sensing Letters ( Early Access ), 2022.
In clustering based hyperspectral band selection techniques 2D images of each band are usually taken as input samples. Some form of feature extraction on these images is performed before they are input to the clustering algorithm. The clustering algorithm returns the cluster centroids; the bands closest to the centroids are selected as representative bands for each cluster.
A. Verma, A. V. Subramanyam, Z. Wang, S. Satoh,R. R. Shah, "Unsupervised Domain Adaptation for Person Re-identification via Individual-preserving and Environmental-switching Cyclic Generation," in IEEE Transactions on Multimedia, 2022.
Unsupervised domain adaptation for person re-identification (Re-ID suffers severe domain discrepancies between source and target domains. To reduce the domain shift caused by the changes of context, camera style, or viewpoint, existing methods in this field fine-tune and adapt the Re-ID model with augmented samples, either through translating source samples to the target style or by assigning pseudo labels to the target.Read More
News and Events
The “CloudLab: Physical Lab Experiments in Online Mode” project has received funding under the DST Prayas scheme. Team members are Anmol Goyal (BTech ECE), Mihir Chaturvedi (BTech ECE), Khagendra Joshi (Lab Engineer), and Dr. Sumit Darak (Faculty ECE).
Hardware-based courses are challenging due to limited manpower, ever-evolving hardware platforms, and high maintenance cost. During the pandemic, over 95% of the universities dropped hardware-based courses and lab exercises due to a lack of a remote hardware access system. This has led to significant compromise on course objectives and impacted the availability of trained manpower in academia as well as industry. Though many universities do not have sufficient funds to maintain state-of-the-art hardware labs, resource-rich universities like IIIT Delhi would like to improve the utilization of hardware resources.
S. Santosh, S. Darak, “Multi-armed Bandit Algorithms on Zynq System-on-Chip: Go Frequentist or Bayesian?” is accepted in IEEE Transactions on Neural Networks and Learning Systems (Impact factor: 14.255).
Multi-armed Bandit (MAB) algorithms identify the best arm among multiple arms via exploration-exploitation trade-off without prior knowledge of arm statistics. Their usefulness in wireless radio, IoT, and robotics demand deployment on edge devices, and hence, a mapping on system-on-chip (SoC) is desired. Theoretically, the Bayesian approach-based Thompson Sampling (TS) algorithm offers better performance than the frequentist approach-based Upper Confidence Bound (UCB) algorithm.
We are elated to announce that our PhD students Aakanksha Tewari and Akanksha Sneh (supervised by Dr. Shobha Ram and Dr. Sumit J Darak) have won the Qualcomm Innovation Fellowship (QIF) India, 2022.
Their innovation titled: Software/Hardware Prototype of IEEE 802.11ad/ay Based Joint Radar-Communication Transceiver was under the Advances in Communication Techniques and Theory domain.
A. Lord, S. J. Savory, M. Tornatore, A. Mitra, “Flexible Technologies to Increase Optical Network Capacity, ” has been accepted in Proceedings of IEEE Journal.
Increased global traffic puts tough requirements not just on fiber communications links, but on the entire network. This manifests itself in multiple ways including how to optimize wavelength routing around the network; how to maximize the benefits arising from fine-control DSP with increasingly accurate real-time monitoring; and how to best deploy Multiband or multiple fiber connectivity.