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IoT and Image Processing Internship


  1. Designed and developed three IoT prototypes for Sports Authority of India. (Starting Block, Force Plate, Timing Gates)
  2. Interfaced multiple sensors to Arduino Uno(PCB designed with ATMEGA328P).
  3. Designed an Android App using Android Studio to provide live plotting of sensor data after backend processing.
  4. Designed a local server(Java Apache server with plotting sensor data in a GUI with backend data analysis) and main server for the database(Firebase).
  5. Developed a Facial Recognition system as a LockOut-Tagout (LOTO) system for Industrial IoT.
  6. Designed Haar Cascade classifier based face detection and recognition.
  7. Created a thorough dataset of 3 employees using Python.

Detecting Acceptable Air Contamination in Classrooms using Low Cost Sensors


In present scenario of the world, environmental pollution is one of the leading challenges. Most often the educational institutes and organizations in developing countries suffer from polluted environment due to overcrowded rooms, improper planning and poor infrastructure. Students/faculties in a classroom could suffer from health issues due to prolonged exposure to such environment. On an average a student/faculty is exposed to such environment for eight hours per day. A student/faculty could undergo physical as well as cognitive hazards. This paper tends to detect the duration for which a classroom environment can be considered healthy for a given number of students. We built an Air Quality Monitoring Unit using low cost gas sensors which could compare the air contamination level of the environment with specified standards to detect when the environment tends to get uncomfortable for students/faculties. This in turn could result in reduced absentees and improved performance of students/faculties. Some useful results came to our observation such as, in a class of 30 students the concentration level of CO 2 increases about 28.14% as compared to empty classroom whereas in a class of 40 students in the same classroom it increases about 55.33% in a duration of 2 hours.

AeT-Drone: Aerial Environment Sensing and Traffic Surveillance using sensor enabled Drone/UAV


The rise in the environmental pollution and degradation of air quality has dragged the attention of researchers and innovators. Due to the high cost Air Quality Monitoring Station(AQMS) can not be placed densely. However, spatial and temporal resolution of data from ground, manned aircraft and satellite measurements is relatively low and often inadequate for local and regional applications. Furthermore, taking measurements close to pollutant sources may not always be possible and it could be too dangerous or risky for manned aircraft to fly close to the ground. Together, these reasons promote the use of small, lightweight UAVs for a range of applications, including atmospheric measurements. To get fine grained data, we have developed an Environment monitoring device using some pollutant sensors and build a model to predict the pollutants’ concentration. UAVs cover large areas and can monitor remote, dangerous or difficult to access locations, increasing operational flexibility and resolution over land-based methods. Parallel to Environmental pollution, traffic congestion is a serious problem in a fast-paced world, especially in metropolitans. People lose hours of their precious time by waiting in traffic. We aim to find an optimized resource-allocation based solution by analyzing traffic data of distributed nodes in form of UAVs across the city. The end goal is to explore new possibilities in the field of traffic analysis by its fully automatic calculation of a wide range of traffic parameters such as speed, densities, time, and pollution levels.

3D Tracking and Geo-Localization of a target using Unmanned Aerial Vehicles


Optical tracking and triangulation of target objectsare quite challenging when the object is moving in the three dimensions. The task can be accomplished through Electro-Optical Tracking Systems. But ground based tracking systems can only track objects that are flying at high altitudes. For this reason, our project proposes a system that successfully localizes, tracks and triangulates both air-borne and ground-borne target objects, using the concept of aerial Electro-optical tracking systems, via the use of UAVs, namely quad-copters. Our project uses two quadcopters, individually equipped with an on-board camera and a companion computer that tracks a desired target object using image processing and sends the data to the ground station. The received data from the two drones are used to triangulate the objects location in real time at the ground station,along with virtual visualization of the entire triangulation in a 3D application and a User Interface application.

Intelligent Traffic Routing Based on Real-Time Congestion Analysis


The problem of traffic congestion has led to several issues like an increase in carbon dioxide emission including the inability to route emergency vehicles on a priority. Hence we aim to find an optimized approach, allocating resources based on the traffic condition at that particular instant. This could be realized by analysing the traffic data relayed by device nodes present all over the city in a real-time environment. The node is a cloud-linked camera-integrated MPU equipped with GSM communication. The captured video frames are processed which is then analysed collectively to suggest the most appropriate route to the vehicles.

Exploring Collision Avoidance during Communication Over Sound for Healthy Environment


The rapid rise in Internet of Things (IoT) devices increases communication overhead. The communication is the most crucial part in IoT. However, most of the the traditional methods of communication emit harmful electromagnetic radiations which have severe impact on human health. To reduce the health hazards on the society due to such radiations, we have proposed a harmless mode of communication using the near-ultrasonic audible acoustic signal. We can use the sound signal as communication medium for sensitive systems like healthcare system, smart classroom system, and so on, where the occupants are vulnerable to harmful radiations. In this work, we have used Chirp Software Development Kit (SDK) as the tool for connecting the IoT devices through acoustic signal, which has the capability of multi path transmission where a device is allocated randomly a channel from a pool channels. However, with the rise in the number of devices in the system, the chances that a channel being allocated to many devices increase. It increases the chances of collision during communication, unless necessary precaution is taken in the channel allocation. By resolving the above issues when multiple node used same channel, we have achieved 80\% more network throughput than the existing technique. In addition, we have proposed an approach to increase the communication range with acoustic communication, which is another critical issue in the present context.

An ns3-based Energy Module of 5G NR User Equipments for Millimeter Wave Networks


This poster presents the design, development and test results of an energy consumption analysis module developed over ns3 Millimeter Wave (mmWave) communication for analyzing power consumption for 5G New Radio (NR) User Equipment (UE) during both continuous and discontinuous packet receptions. This module is important to analyze and explore the energy consumption behavior of the 5G communication protocols under the NR technology. The developed module includes the complete Radio Resource Control (RRC) state machine for 5G NR recommended by 3GPP Specification 38.840. To the best of our knowledge, the designed module is the first of its kind that provides a comprehensive energy analysis for the 5G NR UEs over mmWave communication.

An ns3-based Energy Module for 5G mmWave Base Stations


This poster presents the design, development, and test results of an energy consumption analysis module developed over ns3 Millimeter Wave (mmWave) communication, which can analyze the power consumption characteristics of 5G eNodeB/gNodeB Base Stations. This module is essential for research and exploration of the energy consumption behavior of the 5G communication protocols under the New Radio (NR) technology. To the best of our knowledge, the designed module is the first of its kind that provides a comprehensive energy analysis for the 5G mmWave base stations.

Contactless RF Sensing using mmWave


In this talk, we present the recent works on Radio Frequency based human sensing with a significant focus on mmwave. We thoroughly discuss the working principle of a mmWave FMCW Radar. Finally, we present a recent paper on motion robust human vital sign monitoring using mmWave.

Implementation of mmWave-energy Module and Power Saving Schemes in ns-3


Next-generation 5G New Radio (NR) cellular networks operating at mmWave frequencies are targeted to support diverse use cases, such as enhanced Mobile Broadband (eMBB), massive machine-type communications (mMTC), ultra-reliable and low latency communications (URLLC), etc. Energy-Efficiency is one of the key performance indicators for NR technology. User Equipment (UE) battery life significantly impacts the Quality of Experience (QoE) of the UE. Thus 5G NR standard is designed to have great flexibility on network operation modes to adapt to different requirements and trade-offs. 3GPP, in its 5G technical specification release, has proposed various power-saving schemes such as connected mode Discontinuous Reception (cDRX), RRC INACTIVE state, etc. In this work, we discuss the implementation and analysis of UE RRC state-based energy consumption module, including different power saving schemes in ns-3. We have thoroughly evaluated the module with the simulation study and validated the implementation with the 3GPP standards. The implementation source code is publicly available as open-source.

Demo Abstract: MARS -An mmWave-based Multi-user Activity Tracking Solution


Developing robust wireless sensing mechanisms for continuously monitoring human activities and presence is crucial for creating pervasive interactive intelligent spaces. The existing literature lacks solutions that continuously monitor multiple users’ activities without prior knowledge of the environment. This requires simultaneous localization and tracking of multiple subjects and identifying their activities at various scales, including macro-scale activities like walking and squats and micro-scale activities like typing or sitting. In this demo, we present MARS, a holistic system using a single off-the-shelf mmWave radar. MARS employs an intelligent model to sense both macro and micro activities and uses a dynamic spatial time-sharing approach to sense different subjects simultaneously. Our thorough evaluation demonstrates that MARS can continuously infer activities with over 93% accuracy and an average response time of approximately 2 seconds, even with five subjects performing 19 different activities.