Title : Suknya Rakshak: Wireless Sensor Networks based Security System for girls’ hostels
Capacity: Principal Investigator
Funding agency: Uttar Pradesh Council of Science and Technology, Lucknow (CST-UP)
Amount: 7,80,000/- Rs.
Duration: 24 Months
Objective:
i) Time-based tracking of the movement of students or inmates with the help of WSN based virtual fence and GPRS based communication.
ii) Designing of an energy efficient and fault-finding routing algorithm to improve the operational lifetime of WSN and fool-proof security.
iii) System design with scalability (can be made to work with all the hostels) and central monitoring mechanism to minimize the deployment and manpower cost
iv) Design a system which cannot be compromised/manipulated, is scalable (can be made to work with all the hostels) and provides a central monitoring mechanism to minimize the deployment and manpower cost.
v) Design of an automated system which identifies any kind of unauthorized movement in the hostel.
vi) Deployment of the system as a pilot project for a girls’ hostel and test the working.
Title: IoT Network Traffic Classification and attack detection based on Network Traffic Characteristics using Artificial Intelligence
Capacity: Co-PI
Funded by: Data Security Council of India, New Delhi (DSCI)
Sanctioned amount: 5,36,000/- Rs.
Duration: 6 months
Objective
The objective of proposed work are as follows:
i) Develop a coherent structure for IoT traffic classification. We will extract various features from the IoT network traffic and describe the importance of each feature for the classification.
ii) Use a variety of machine learning algorithms and the obtained classification results of each classifier will compare on the basis of accuracy, training time of algorithm, error rate etc.
iii) Develop an artificial model for identify the attack pattern by tracing IoT devices traffic.
Title: Paka Taranhar: Leaf disease detection bot with automatic sprayer
Capacity: Principle Investigator
Funded by: Bennett University, Gr. Noida, India
Sanctioned amount: Apprx. 1.1 Lakhs
Duration: 2 years
Objective
The objective of proposed work are as follows:
i) Design and implementation of deep learning-based algorithm for leaf disease detection
ii) Design and implementation of lane detection algorithm using computer vision and deep learning
iii) Integration of the various component to develop automated bot
iv) Test the performance of bot in real-life conditions
v) Development and simulation of automatic spray gun
vi) Prototype of bot with auto spray gun
Title: UAV compatible object detection model using Deep Learning
Company name: AutoMicroUAS Aerotech Pvt. Limited (http://automicrouas.com/)
Capacity: Project Lead
Team size: 4
Duration: Jan 2020 – May 2020
Objective
The objectives of proposed work are as follows:
i) Development of object detection model
ii) Design of UI for importing the data and display of the output
iii) Compression of object detection model to make it compatible with UAVs
Project name: Identification of dominant emotion of a person on video data
Company name: Empass Hire (empass.mobi)
Capacity: Project Lead
Team size: 4
Duration: Jan 2019 – May 2019
Objective
The objectives of proposed work are as follows:
i) Development of deep learning model for video analysis
ii) Extraction of dominant emotion of a person using video analysis
iii) Error analysis to improve the performance of model
Project name: Identification of dominant emotion of a person on audio data
Company name: Empass Hire (empass.mobi)
Capacity: Project Lead
Team size: 4
Duration: Jan 2019 – May 2019
Objective
The objectives of proposed work are as follows:
i) Development of deep learning model for audio analysis
ii) Extraction of dominant emotion of a person using audio analysis
iii) Error analysis to improve the performance of model
Project name: Identification of dominant emotion of a person on audio data
Company name: Empass Hire (empass.mobi)
Capacity: Project Lead
Team size: 4
Duration: Jan 2019 – May 2019
Objective
The objectives of proposed work are as follows:
i) Development of deep learning model for audio analysis
ii) Extraction of dominant emotion of a person using audio analysis
iii) Error analysis to improve the performance of model