Aniket Dhole

Hi, I am Aniket.


I am a graduate student at Northeastern University in Boston for my Masters of Science (MS) in Computer Vision and Machine Learning under Electrical and Computer Engineering (ECE). I have my undergrad at Pimpri Chinchwad College of Engineering in Computer Engineering, working on Computer Vision projects in Agriculture and Healthcare using Edge and Parallel Computing

Currently am a Research Assistant at SiliconSynapse Lab, Northeastern University, working under the guidance of Professor Alireza Ramezani on simulation and testing of Legged+Aerial Robot named "Husky Carbon: A multimodal robot with aerial and walking capabilities". Also have been working on the Aerobat: bird-like flapping robot for autonomous control.

I am the Co-Founder and Software Engineer at Vuna Technologies (VunaTec) where I was working on making Drones with Multispectral Cameras and a Farm Mapping Software to predict and avoid Pre-Harvest losses for farmers.

I have experience in IoT Devices and Robotics, where I explored areas such as Computer Networks, Hardware Interfacing, Navigation, Inverse Kinematics and Path Planning of Robots.

My Work Portfolio Resume

Research and Patent

Hovering Control of Flapping Wings in Tandem with Multi-Rotors

Aniket Dhole, Bibek Gupta, Adarsh Salagame, Alireza Ramezani et al.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)

This work focuses on stabilizing the flight dynamics of Northeatern's tailless bat-inspired micro aerial vehicle, Aerobat. Unlike traditional insect-style designs, Aerobat incorporates morphing wings and lacks a tail, making flight control more complex. To stabilize its position and orientation during hovering, we employ a guard design with small thrusters and combine it with a flapping system and a multi-rotor. We assume the guard cannot directly observe Aerobat's states and propose the use of an observer to estimate these states for closed-loop hovering control of the Guard-Aerobat platform.

paper (preprint published) - October 2023
Patent - IOT BASED PORTABLE HEALTH MONITORING SYSTEM USING EDGE COMPUTING

Aniket Dhole, Mohit Gandhi, Shrishail Kumbhar, Harsh Singhal, Dr. Sonal Gore

India - Intellectual Property (Application ID- 202221028427)

Due to social distancing norms, several restrictions have been established in public settings due to the COVID-19 pandemic. In offices and schools, there are no automated systems or procedures for managing large groups of people. Some systems use camera footage of workspaces to verify whether individuals are wearing masks, and temperature checks are done manually by designated authorities and processed on massive servers. The paper contains a proposed prototype of a portable device that can manage if individuals entering the workspace are wearing masks, and have an appropriate heart pulse rate using M5Stack Core2, ESP32 Camera Module, and distance sensors. For optimization and fast Mask Detection Model which will run entirely on the device, Tensorflow Lite and Edge Computing are used. The mask detection model achieves an accuracy of 87.8%. Here the focus was on edge computing with limited RAM usage and with an optimized MobileNetV1 model.

patent (published) - June 2022
video
Radiomics for Parkinson’s Disease classification using Advanced Texture-based Biomarkers

Sonal Gore, Aniket Dhole, Shrishail Kumbhar, Jayant Jagtap

Methods-X Journal by Elsevier, Also available at PubMed by National Library of Medicine

Parkinson's disease (PD) is one of the neurodegenerative diseases whose complete cure is not found to date. Therapies and medications are supportive methods to deal with symptoms. There is always a requirement of medical domain expertise to diagnose PD manually. Since manual diagnosis leads to a time-consuming process, an automatic technique has always been useful in such complex tasks. Magnetic resonance imaging (MRI) based computer-aided diagnosis helps medical experts to diagnose PD more precisely and fast. Texture-based radiomic analysis is carried out on 3D MRI scans of T1 weighted and resting-state modalities.Classification accuracies were obtained from 61.11% to 83.33% and area under the curve-receiver operating characteristics (AUC-ROC) values range from 0.43 to 0.86 using four variants of LBP.

paper (published) - September 2023
Review of Deep Learning Models for Mask Detection and Medical Sensors for IoT based Health Care System

Aniket Dhole, Mohit Gandhi, Shrishail Kumbhar, Harsh Singhal, Sonal Gore

IEEE International Conference on Computational Intelligence and Computing Applications-21

The growth of medical sensors like heart rate,blood sugar, and other health monitoring sensors is huge.Along with the use of sensors in devices and healthcare systems, the use of image classification models like mask detection on edge devices is of growing demand. The survey consists of various techniques used in modern healthcare devices and various other methods like sensor fusion and wireless sensors to collect and monitor health data. And it also includes a comparison of multiple mask detection models which were deployed on embedded devices like Raspberry Pi, Nvidia Jetson and cameras like OpenMV, ESP32Cam and deep learning models like MobileNetV1, InceptionV4, and YOLO Tiny which were optimized using TensorFlow Lite.

paper (published) - Oct 2021
Parallel and Edge Computing Techniques for Computer Vision Models on Embedded Devices

Aniket Dhole, Mohit Gandhi, Shrishail Kumbhar

Springer- International conference on Emerging trends and Innovations in ICT (ICEI)

Nowadays, running computer vision models on embedded devices like Raspberry Pi and Nvidia Jetson has become ubiquitous. But the main issue is the limited performance on these devices due to smaller CPUs and power factors. To solve this, we have proposed research on various parallel processing techniques to get complete optimal performance of computer vision models like GoogleNet, Squeezenet, and Mobilenet on a Raspberry Pi using OpenVino Toolkit. We tested and compared these models' interpretation on factors like CPU, RAM Utilization, and Inference Time using Two Neural Compute Sticks and analyzed it on different Intel Processors. The results using Two Neural Sticks were significant than typical processors and increased by a factor of 2 to 3 for all models. So using these results, we can directly use the technique for the suitable model.

paper (accepted) - November 2021
Topical Survey on Computing Solutions for Plant Disease Classification using Deep Learning Techniques

Aniket Dhole, Mohit Gandhi, Shrishail Kumbhar, Harsh Singhal, Sonal Gore

Advances in Image and Data Processing using VLSI Design

A major problem in agriculture is plant disease that is not recognized in the early stages, due to which, the people working in this industry face resulting losses, such as lost income, loss of time and effort, etc. We have surveyed different hardware implementations of plant disease detection on embedded devices, such as the Raspberry Pi, field-programmable gate arrays (FPGAs) with very large scale integration (VLSI), and ARM processors that use frameworks such as TinyML and TFLite. And studied and analyzed major deep learning algorithms and techniques, such AlexNet, long short-term memory (LSTM), LeNet-5, and ResNet, which have been used for plant disease detection.

paper (published) - May 2021

Projects

DIY Farm Drone(NDVI)

Drone made from scratch which will map the farm using MultiSpectral Camera to calculate the health of Plant using NDVI (Normalized Difference Vegetation Index)

Health(AI)ness

HealtAIness helps users to exercise and train for health and fitness using 3D Spatial Camera for pose estimation using DepthAI Framework.

Cheer Up

A Robot which dances and guides with your medice schedule to cheer you up all day.

EMOJO Mental Health Chatbot

A Visual Chatbot for your Mental Health ,made using Raspberry Pi,Display Screen and F.R.I.E.N.D.S TV Series Corpus

nRF5340 Oscilloscope Band

Measure and Analyse Voltages on the Go with nRF5340. A hand band to easy the process of measuring voltage and current of circuits.

Mango Plant Disease Detection

It helps in classifying the diseases of mango leaves for our Mango Farm in India using Tensorflow and OpenVino in Drones

Coding-Kid

An Online Coding and Logic Question Solving Platform for Kids in Grade 5th to 10th with Online Coding Compiler.

Work Experience

SiliconSynapse Lab, Northeastern University

Research Assistant

September 2022 - Present

● Devising development of a multimodal: aerial + legged robots focusing on testing of gait optimization and contact force analysis
● Crafting position stabilization and control algorithms for aerial thrusters and leg joints, utilizing ROS, C++, Python and Matlab
● Integrated and programmed drivers for various sensors like IMU, GPS, MoCap Cameras for EtherCat network to communicate with real time Speedgoat and STM32 controllers
● Currently researching on Position Estimation and Autonomous control of Tiny Flapping systems in enclosed environments using SLAM and Vision based methods

website
Northeastern University

Teaching Assistant (EECE2150 : Circuits and Signals: Biomedical Applications)

September 2023 - Present

● Teaching Assistant for the EECE Circuits and Signals (Biomed Apps) Course, responsible for guiding 80 students in the analysis of circuits using tools like LTSpice and Matlab.
● Collaborated with students on the implementation of intricate circuits for the purpose of reading and comprehending ECG signals, utilizing equipment such as oscilloscopes and multimeters.

website
Vuna Technologies

Co-Founder and Software Engineer

November 2021 - September 2022

● Executed software and hardware integration for drones, cameras, and farm mapping, enabling Computer Vision-driven analysis of mango farms for plant health detection and harvest prediction using Python & Embedded C++
● Using techniques like NDVI(Normalized Difference Vegetation Index) and Object Detection to detect Disease and calculate Ripeness of fruits in farm.
● Used TinyML to deploy a Machine Learning model on a Arduino Nano BLE to predict the date of harvest using plant health and historical data
● Led and mentored a dynamic team of five developers, securing $20,000 in funding and overseeing all technical facets of startup

website
IoTIoT.in

IoT Intern

June 2020 - December 2020

●Built a Parallel Computing Framework which automatically allocates the task to threads and cores using OpenMP.
● Parallelised Computer Vision programs like person detection, vehicle re-id using OpenCV and OpenMP to increase its speed by 220%.
● Worked on networking programs of ZMQ Server to efficiently send socket requests to server concurrently.

website
Team Automatons

Robotics Engineer and Technical Team Member

August 2018 - November 2019

● Programmed Robotics Path Planning,Inverse Kinematics, Localization & Computer Vision on robots using Python & C++.
● Developed and analysed Embedded and Printed Circuit Boards.
● Worked on prototyping and design of Robots in mechanical manufacturing and Computer Aided Design aspects.
● Tested and implemented robot programs on Raspberry Pi, Arduino, STM32 and ESP32 Boards.

website

Skills

● Languages : Python , C/C++ , Javascript (React.js, Express)
● Frameworks: OpenCV, OpenMP , Tensorflow
● Technologies: Computer Vision, Robotics and Path Planning , Parallel Computing , Machine Learning , Embedded Systems, Drones
● Electronics : Arduino,ESP32,nRF and Raspberry Pi

Awards

My Personal Work Lab