Exploring quantum technologies on superconducting hybrid devices
Professor: Bhaskaran Muralidharan
UID: ID03
Department/Domain: Electrical + Physics
CNN based automatic trait extraction from RGBB imagery
Professor: Adinarayana J
UID: ID04
Department/Domain: AI/ML + Agriculture
ID01
Description: The proposal is to design micro components
which are part of a high pressure gas handling system. This
system will operate at high power (100HP) and will require agile
controllers for fail-safe operation. Multi-sensor (temperature,
pressure, stress) data acquisition and rapid analysis will be
key to robust system operation. Materials (metal alloys) which
are stable under high pressure will be reviewed. Finally,
simulation and design of precision cooling architectures for
hot-spot mitigation is required.
Number of students: 2
Year of study: Students entering 2nd year, Students
entering 3rd year, Students entering 4th/5th year
CPI eligibility criteria:
Prerequisites NONE
Duration: 3 months
Learning outcome: Simulations, Technology Review
Weekly time commitment: 20 hours
General expectations: Interaction level should be high
Assignment: Internet review of high pressure compressor
technology
Instructions for assignment: General understanding is
key.Good written, oral communication desired
ID02
Description:"a. We wish to explore musical tone
production using quantum concepts via quantum algorithms. Basic
exposure to keyboard playing/music notation might be required.
b. We wish to adapt classic video games using quantum concepts
toward enthusing a quantum- pedagogue for making quantum
phenomena accessible."
Number of students: 2
Year of study Students entering 2nd year, Students
entering 3rd year
CPI eligibility criteria: 9
Prerequisites: QM basics, aptitude in music and or
gaming.
Duration: 2 years
Learning outcome: using quantum algorithms for very new
types of applications
Instructions for assignment: Will be discussed upon
selection.
ID03
Description: On our developed computational quantum
transport platform we will explore superconducting nanoscale
hybrid systems for one the following applications : a)
Superconducting - hybrid single photon emission/detecting b)
topological qubits c) topological entanglement entropy and d)
solid state entanglement production. Any one of the above themes
may be discussed and finalised after a few weeks of self
study/literature survey.
Number of students: 2
Year of study: Students entering 3rd year
CPI eligibility criteria: 9
Prerequisites: Background EP/EE. Devices courses/solid
state and basic QM
Instructions for assignment: Will be discussed upon
selection.
ID04
Description: " Precise estimation of agronomic variables
(SUCH AS Leaf Area and Biomass) using RGB image dataset
consisting data from different plant growth stages "
Number of students: 1
Year of study: Students entering 3rd year
CPI eligibility criteria:8
Prerequisites:Some knowledge of ML/DL
Duration: 2 months.
Learning outcome: concept of crop phenotyping, image
processing initial concepts, knowledge of UAV imagery
processing, CNN model building and hyperparameter tuning
Weekly time commitment: At least 4-5 hours per day
General expectations: I expect the student should be
sincere and my experience is not that good
Assignment: "Precision Agriculture, Plant Phenomics,
Frontiers in Plant Sciences; Kar, S., Purbey, V., Suradhaniwar,
S., Korbu, L.B., Kholová, Durbha, S.S., Adinarayana, J. and
Vadez, V. (2020). An Ensemble Machine Learning Approach for
Determination of the Optimum Sampling Time for
Evapotranspiration Assessment from High-Throughput Phenotyping
Data. Computers and Electronics in Agriculture,
https://doi.org/10.1016/j.compag.2021.105992 Kar, S., Garin, V.,
Kholová, J., Vadez, V., Durbha, S.S., Tanaka, R., Iwata, H.,
Urban, M.O. and Adinarayana, J. (2020). SpaTemHTP: A Data
Analysis Pipeline for Efficient Processing and Utilization of
Temporal High-Throughput Phenotyping Data. Frontiers in Plant
Science, 11, p.1746
(https://www.frontiersin.org/articles/10.3389/fpls.2020.552509/full)
Kar, S., Tanaka, R., Korbu, L.B., Kholová, J., Iwata, H.,
Durbha, S.S., Adinarayana,J. and Vadez, V. (2020). Automated
discretization of ‘transpiration restriction to increasing VPD’
features from outdoors high-throughput phenotyping data. Plant
methods, 16(1), pp.1-20
(https://plantmethods.biomedcentral.com/articles/10.1186/s13007-020-00680-8)"
Instructions for assignment: Will be discussed upon
selection.
ID05
Description:Driver warning technologies hold the
potential to reduce traffic crashes and save precious human
lives. Scientists and engineers hypothesize that the
measurements obtained from advanced driving simulators can
predict equivalent measurements in the real world that lead to a
better understanding of the driver behaviour in critical driving
situations. The aim of the project is to study the variations in
driver state using a combination of driver behavioural and
driver physiological signals using sensor technologies in
driving simulator. Driver warning can be developed through
statistical analysis and AI techniques.
Number of students: 2
Year of study:Students entering 4th/5th year
CPI eligibility criteria:8
Prerequisites: Data analysis tools
Duration: 6-8 months
Learning outcome: Statistical data analysis and modeling,
exposure to driver simulators and sensors