Bharath Sudharsan

images

Status

Ph.D. @ Confirm SFI Research Centre | Ex ML Infrastructure Engg Intern @ ARM, Ireland | Ex Research Intern @ AI Institute, UofSC, USA | Ex Embedded System Engineer @ Four Corners Technologies.

Fields

IoT Devices, Optimization Methods, TinyML, ML Systems, Edge Computing, Applied ML.

Activities

Researcher, Implementation Engineer, Technical Program Committee.

Contact

bharath.sudharsan@insight-centre.org

+353-899836498

I am a Ph.D. student advised by Dr. Muhammad Intizar Ali and Prof. John G Breslin. My research interests include the following:

  • Design and implement algorithms to improve the Resilience, Interoperability, Scalability (RIS) of IoT devices.
  • Deep optimization, deployment, and efficient execution of a wide range of ML models on AIoT boards, small CPUs, and MCUs based devices.
  • Design resource-friendly ML model training algorithms to create self-learning devices that can locally re-train themselves on-the-fly (after deployment) using the unseen real-world data.

In 2020 - 2021, I have published 14 first-author full papers in venues such as IEEE Internet Computing, IOTJ, ECML PKDD, ACM IoT, IEEE SCC, IEEE UIC, etc., and provided demos, short papers at PerCom, ICCPS, IoTDI, WF-IoT, SenSys, Middleware, UbiComp. I obtained my Masters from NUI Galway in Electronics and Computer Engineering. My Master's project was supervised by Prof. Peter Corcoran. Prior to research, I was an Embedded System Engineer at Four Corners Technologies, where we developed IoT smart solutions for retails, workspace, kiosks, and outdoor billboards. I was the hardware guy in this firm, where my role in these projects was to design-build-program the wireless embedded system (hardware) of the devices, then connect its data stream to their cloud services.

  • [Apr-2021] ECML PKDD 2021, our 'Machine Learning Meets Internet of Things: From Theory to Practice' tutorial accepted [Link] [Tutorial Website]
  • [Mar-2021] Security, Privacy and Trust in the Internet of Things (SPT-IoT) workshop at PerCom '21. Presented our 'Edge2Guard: Botnet Attacks Detecting Offline Models for Resource-Constrained IoT Devices' paper [Link]
  • [Mar-2021] PerCom 2021, presented our paper 'Ultra-fast Machine Learning Classifier Execution on IoT Devices without SRAM Consumption' [Link]
  • [Feb-2021] Our research featured on the Confirm Smart Manufacturing website [Link]
  • [Jan-2021] Our COVID-away paper is also made available at the World Health Organization's global literature on coronavirus disease page [Link]
  • [Oct-2020] Presented papers "RCE-NN and Edge2Train" in IoT Conference [Pics] [Pics]
  • [Oct-2020] Our "Avoid Touching Your Face" paper won the Second place in IoT-HSA workshop [Link]
  • [Aug-2020] Presented "Adaptive Strategy to Improve the Quality of Communication for IoT Edge Devices" in the institute meeting at the Data Science Institute, NUI Galway.
  • [Aug-2020] Attended the IEEE Virtual World Forum on Internet of Things 2020 - Tutorial Program [Cert]
  • [Jan-2020] First flight of my first DIY drone. Features: Altitude hold, auto level, GPS lock, and return to home [Video]
  • [Dec-2019] Presented poster "Smart speaker design and implementation with biometric authentication and advanced voice interaction capability" at 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science [Poster] [Pics]
  • [Dec-2019] Presented paper "AI Vision: Smart speaker design and implementation with object detection custom skill and advanced voice interaction capability" at 11th IEEE - International Conference on Advanced Computing (ICoAC)
  • [Oct-2019] Organised Deep Learning with PyTorch workshop with Bharathi Raja & Shardul at Insight Centre for Data Analytics [Pics]
  • [Apr 2019] Provided a bench demo of MEngg project at NUIG
  • [Mar 2019] Presented poster for our "Assess, Respond, Monitor, Strengthen Glove (ARMS glove) for stroke" at Blackstone Launchpad, NUIG
  • [Feb 2019] Presented MEngg project poster titled "Design and realization of a wireless smart-speaker" at NUIG [Poster]
According to CORE Rankings Portal: [A*] - flagship conference, a leading venue in a discipline area. [A] - excellent conference, and highly respected in a discipline area. [B] - good to very good conference, and well regarded in a discipline area. [C] - other ranked conference venues that meet minimum standards.
Research
  • 2021: First author. [A] Enabling Machine Learning on the Edge using SRAM Conserving Efficient Neural Networks Execution Approach @ European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) [Paper].
  • 2021: First author. [9.5] Impact Factor. ML-MCU: A Framework to Train ML Classifiers on MCU-based IoT Edge Devices @ IEEE Internet of Things Journal (IoTJ) [Paper] [Code].
  • 2021: First author. [B] Train++: An Incremental ML Model Training Algorithm to Create Self-Learning IoT Devices. Accepted @ International Conference on Ubiquitous Intelligence and Computing (UIC) [Code].
  • 2021: First author. [B] Globe2Train: A Framework for Distributed ML Model Training using IoT Devices Across the Globe. Accepted @ International Conference on Ubiquitous Intelligence and Computing (UIC).
  • 2021: First author. [A] An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware. Accepted @ IEEE International Conference on Services Computing (SCC).
  • 2021: First author. [B] Towards Distributed, Global, Deep Learning using IoT Devices @ IEEE Internet Computing Journal (IEEE IC) [Paper].
Demo, Short Paper, and Tutorial
  • 2021: First author. [A] Machine Learning Meets Internet of Things: From Theory to Practice @ European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) [Paper] [Tutorial Website].
  • 2021: [A] GNOSIS- Query-Driven Multimodal Event Processing for Unstructured Data Streams @ The International Middleware Conference (Middleware).
  • 2021: First author. [A*] ElastiCL: Elastic Parameters Quantization for Communication Efficient Collaborative Learning in IoT @ The ACM Conference on Embedded Networked Sensor Systems (SenSys).
  • 2021: [A*] Air Quality Sensor Network Data Acquisition, Cleaning, Visualization, and Analytics: A Real-world IoT Use Case @ The ACM international joint conference on pervasive and ubiquitous computing (UbiComp) [Code].
  • 2021: First author. TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers @ IEEE World Forum on Internet of Things (WF-IoT) [Paper] [Code].
  • 2021: First author. Porting and Execution of Anomalies Detection Models on Embedded Systems in IoT @ ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI) [Paper].
  • 2021: First author. SRAM Optimized Porting and Execution of Machine Learning Classifiers on MCU-based IoT Devices @ International Conference on Cyber-Physical Systems (ICCPS) [Paper].
Applied Research
  • 2021: First author. Edge2Guard: Botnet Attacks Detecting Offline Models for Resource-Constrained IoT Devices @ PerCom Workshop (SPT-IoT) [Paper] [Code].
  • 2021: First author. OWSNet: Towards Real-time Offensive Words Spotting Network for Consumer IoT Devices @ IEEE World Forum on Internet of Things (WF-IoT) [Paper].
  • 2020: First author. Avoid Touching Your Face: A Hand-to-face 3D Motion Dataset (COVID-away) and Trained Models for Smartwatches @ Internet of Things based Health Services and Applications (IoT-HSA) [Paper] [Code].
  • 2020: First author. RCE-NN: A Five-stages Pipeline to Execute Neural Networks (CNNs) on Resource-constrained IoT Edge Devices @ International Conference on the Internet of Things (IoT) [Paper].
  • 2020: First author. Edge2Train: A Framework to Train Machine Learning Models (SVMs) on Resource Constrained IoT Edge Devices @ International Conference on the Internet of Things (IoT) [Paper] [Code].
  • 2020: First author. Adaptive strategy to improve quality of communication for IoT edge devices @ IEEE World Forum on Internet of Things (WF-IoT) [Paper].
  • Page Under Construction
01
Researcher
CONFIRM SFI Research Centre for Smart Manufacturing, Ireland. May 2019 - Present
Below is the list of applied research projects that I contributed to as a researcher at CONFIRM:
  • We built 'COVID-away models' to reduce the spread of the current global pandemic. When our model is deployed on smartwatches, it can trigger a timely notification (e.g. vibration) when the hand of the smartwatch user is moved (unintentionally) towards the face [Repo]. This work is featured on the Confirm website [Link] and made available on the WHO's global literature on coronavirus disease page [Link]. Also won Second place in IoT-HSA'20 workshop [Link].
  • We designed 'Edge2Guard', which are resource-friendly standalone botnet attacks detecting models that enable resource-constrained IoT devices to instantly detect IoT attacks without depending on networks or any external protection mechanisms [Repo].
  • We designed 'Adaptive Strategy' to improve the quality of communication for IoT edge devices. When devices within an IoT system are equipped with our strategy, they can adapt according to dynamic context whilst ensuring the highest level of communication quality, thus, improving the overall resilience of the entire IoT system [Article].
02
Machine Learning Infrastructure (DevOps) Intern
ARM, Galway, Ireland. June - Oct 2021
ARM teams are entering a new growth phase to develop innovative technologies and products for new markets. By joining the ML Infrastructure Galway team, I contributed to ARM by; (i) enabling the ML software teams to ensure they successfully adopt the latest and most comprehensive DevOps practices; (ii) performing Cloud setup and Board Farm Management to enable globally distributed ARM research, engineering teams to craft software that powers the next generation of ML + CV based mobile apps, portable devices, home automation, smart cities, self-driving cars. Following are the principles/systems/devices/technology that I learned and worked with during the internship.
  • Setting Gerrit mirrors for repositories. Gerrit Code Review (usage of git commands such as checkout, push, pull, checkout, config, status, add, commit). Gerrit connection settings, replication events.
  • Usage of HashiCorp Vault, Engpwdb (Engg Password Database). Create, store, use Jenkins API keys, tokens, secret text in Jobs.
  • Commissioning and setup of Single Board Computers (SBCs) such as Hikey960, Odroid, Raspberry Pi, Pumpkin i500. During hardware shipping - awareness of trade compliance, shipping regulations, and IP.
  • QEMU emulation of development boards inside both Windows and Ubuntu as hosts.
  • Create sites using Atlassian Confluence to document procedures and investigation findings of given tasks. Created the following sites: (i) Development boards emulation using QEMU. (ii) AWS EC2 Status by Project. (iii) Jenkins credentials creation, usage in groovy, handling .netrc files. (iv) Implementing Gerrit mirror. (v) Adding Hikey board as a CI agent.
  • Using ServiceNow for tasks such as resolve internal IT issues, obtain fixed IPs for dev boards, request details (hypervisor platform, FQDN, Rhev manager, etc.) of static hosts in use.
  • Automation using Ansible. After given hosts (fresh) setup in Nagios, 3lds, etc. adding it to AWX inventories. Then, running full setup jobs (from .yml code) for new hosts.
  • Flashing and rooting of Android smartphones to enable seamless Neural Network model performance benchmark on GPU in Pixel 4a. Usage of Magisk Manager, SDK Platform Tools, Team Win Recovery Project (TWRP), Odin, other tools.
  • Usage of Jenkins Pipeline (adding dev boards as agents), European Association for Language Testing and Assessment (EALATA), open source cloud computing infrastructure (OpenStack).
  • Scripting in Bash & Python, advanced debugging in Ubuntu environment. Exposed to using Groovy scripting.
  • Theoretical knowledge and basic usage of AWS CLI and services: Lambda, SNS, Load balancer, EC2, Optimization, Pricing, SQS, ECS, EKS, Fargate.
  • Setup and usage of Nagios (Industry-standard IT infrastructure monitoring) to monitor multiple parameters (load, temp, uptime, kernel, ping, LDAP security check, etc.) on various types of dev boards, agents, servers, and local host machines. Tasks involve add hosts .cfg to Nagios server, add authorised key on client, install plugins on client (OS specific), set groupings, removing particular/specified Nagios checks/monitoring on the device, Slack-Nagios integration for alerts.
  • Daily usage of Atlassian JIRA for scrum sprints, create-assign-resolve-validate tickets, manage backlog tasks, issue tracking, prioritizing work, roadmap planning.
  • Contributed to preparing ARM teams for migrating jobs running on in-house machines to AWS (bakery setup to start using agents from AWS): Communication with global ARM teams, preparing roadmap, tracking status by team/project, understanding Groovy script of various teams, then alter by using sshagent in the groovy scripts instead of relying on private SSH keys on disk.
  • Usage of Git web interface, Git GUI, Git CLI, Git LFS, IntelliJ, Slack Workspace, Slack alerts, Slack Apps and Integrations - App directory.
  • Basic level implementation of Continuous Integration (CI), Continuous Delivery (CD), containerization technologies (Docker, Kubernetes), Assisted in CI/CD of other DevOps teams in ARM.
  • Worked in an Agile Team: Contributed to meetings such as stand-ups, retrospectives, refinements.
03
Research Intern
Artificial Intelligence Institute, University of South Carolina, Columbia, USA.Oct 2020 - Nov 2021
My responsibilities as an intern are to drive impact by; (i) Collaborating with scientists and engineers on Machine Learning Systems research; (ii) Supporting quick concept development of new and emerging ideas; (iii) Open sourcing high-quality code and reproducible results for the TinyML community and TensorFlow Lite, Micro users; (iv) Publishing the performed state-of-the-art research work as a paper in IEEE Intelligent Systems Journal. Below is the list of applied research projects that I contributed to during my internship.
  • We designed and implemented 'OWSNet' for consumer IoT devices, which is a real-time offensive words spotting network. Our OWSNet is designed to ensure a healthy verbal environment and avoid harmful incidents by policing the usage of language. [Paper].
  • ECML PKDD 2021, our 'Machine Learning Meets Internet of Things: From Theory to Practice' tutorial accepted. [Proposal] [Tutorial Website].
05
Teaching Experience
NUI Galway, Ireland. May 2019 - June 2021
As a Teaching Support Staff (TSS), Teaching Assistant (TA) in the below modules, I have taught numerous technologies to students from various departments (CS, EEE, Industrial Engineering), year of study (first to final year), and programs (bachelors, masters, doctoral). My responsibilities include delivering lectures, organizing tutorial labs, creating assignments, providing feedback for student reports, and assisting lecturers.
  • 2021: TA and lab supervisor for Tools and Techniques for Large Scale Data Analytics (CT5105) @ School of CS. Supervisor Matthias Nickles.
  • 2021: Lab supervisor for Microprocessor Systems Engineering (EE224), Electrical Circuits and Systems (EE230) @ School of EEE. Supervisor Prof. Martin Glavin.
  • 2021: TSS for Research Skills in Artificial Intelligence (CT5144) @ School of CS. Supervisor Prof. David O'Sullivan.
  • 2021: TA for Data Visualisation (CT5100), Web and Network Science (CT5113) @ School of CS. Supervisor Dr. Conor Hayes.
  • 2020: TSS for Professional Skills - I (CT1112) and 2nd reader/examiner @ School of CS. Supervisor Prof. David O'Sullivan.
  • 2019: TA for Fundamentals of EEE - I (EE130), Fundamentals of Engineering (EI140) @ School of EEE. Supervisor Prof. John G Breslin.
06
R&D - Embedded System Engineer
Four Corners Technologies (4CT) Pvt. Ltd, India.Oct 2016 - Nov 2018
At 4CT, we developed IoT smart solutions for retails, workspace, kiosks, and outdoor billboards. I was the hardware guy in this firm, where my role was to design-build-program the wireless embedded system (hardware) of the devices, then connect its data stream to their cloud services. Below is the list of projects that I contributed to:
  • Workspace Occupancy Monitoring: We designed a wireless embedded system with Panasonic Grid Eye thermal sensor to monitor the workspace occupancy. This occupancy data was sent to our web app to generate client requirement-based meaningful insights such as; Rich visualization & reporting of building &workspace utilization, detailed occupancy patterns, extensive reporting of occupancy by department & by function, etc.
  • Remote Hoardings Monitoring: We designed an IP66 grade Linux-based IoT camera with 4G connectivity and integrated multiple outdoor LDR sensors. 250 of our IoT cameras were installed across the state to monitor the outdoor billboards to provide view clarity, material & installation quality, pillar quality, lighting quality, live stream, etc when requested by the billboard owners or clients via our billboard management system.
  • Retail Sense, ‘Progressive business decisions with live data at your Fingertips’. We designed Retail Sense, which is a low-cost camera-based wireless footfall people counter. The raw footfall count was sent to our web app, where it was converted into meaningful information that revealed patterns and profittable insight which was used to make key decisions on; ideal stafing levels and placement based on the hour, day, month & season, facility’s layout and operations, etc.
  • e-Health Kiosks: We designed an MCU based embedded system with Height (MaxBotix Ultrasonic), Weight (load cells mapped to a 24 bit ADC) & Heart rate (Max30100 Pulse Oximetry) sensors interfaced with it. This board computes the height (cms) weight (Kgs) and heart rate (BPM & SPO2) and sends it to the system of the Digital Signage kiosk via USB.
07
IoT R&D Intern
Flamenco Tech India Pvt. Ltd.Jun - Aug 2016
  • Sensor Integration into client's smart parking system: Installed and integrated hundreds of ceiling mount parked car detection ultrasonic sensors into client's smart parking system.
Embedded Hardware Design for Offline and Real-time IoT Edge Analytics
  • Multi-sensor, wireless, Low-power Embedded system design using various ARM MCUs. Embedded architecture-aware software development using PICCCS, Keil, or other Embedded development Tools, IDEs & Debuggers (JTAG).
  • Experienced working in power-constrained typologies-based wireless environments. Solid knowledge of wireless & wired communication systems, protocols & peripherals such as BLE, Wi-Fi, LTE, GPS/GNSS, CoAP, MQTT, 6LoWPAN, Z-Wave, ZigBee, LoRaWAN, SigFox, AMQP, XMPP, HTTP/2.0. Digital, Analog, I2S, USB, UART, CAN, I2C, SPI, RS232 & RS485.
  • Hands on embedded system design experience using Panasonic’s PaPIRs, Grid-EYE infrared arrays, MaxBotix’s range finders, Maxim Integrated’s healthcare sensors, range of Thermoelectric Peltier Modules, Interlink’s FSRs & Flex sensors, Melexis’c contactless IR temp sensor, ST’s FlightSense ToF technology, ReSpeaker mic-arrays, and others.
  • Schematic Capture, PCB Layout, Fab package release (Gerber, Drill, BOM, etc.) to build mixed-signal hardware using Proteus or Eagle.
  • Experienced using Digi’s wireless SOCs & networks, IntelMovidius NCS, Nvidia Jetson Nano, Leap motion, other SBCs & MCUs like Raspberry Pis, Intel NUC series, Google Coral, LattePanda boards, STM32 blue pills, Espressif modules, Nordic SoC’s, Arduino boards, etc.
Write Deployment Ready IoT Applications in C, Embedded C, C++, Python for Edge Devices, Fog and Cloud Servers
  • Design, build, maintain efficient and reliable code.
  • Familiar with Unix environment, Shell scripting and Git-based source control systems.
  • Writing code utilizing concepts from multi-threading, RTOS, OOPS. Experienced using inline functions, volatile keywords, macros, interrupts, virtual, friend functions, etc.
  • Experienced in setting up hosting environments such as Azure, ThingsSpeak, Dweet, IBM Watson, Node-Red, Digital Ocean, AWS, etc. Then connect the data stream from IoT edge devices to thus configured remote cloud for IoT analytics, historical data storage, etc.
  • Firm experience in selection of hardware budget and computation requirement based chips i.e. from a range of 8, 16 & 32-bit MCUs, microprocessors, FPGAs, and programing them to solve a wide range of problems in the given IoT use-case.
Use case based ML Model Creation, Deep Optimization, and Efficient Hardware Deployment
  • A solid foundation on Neural Network components such as weights, activation function (such as Sigmoid, Tanh, Softmax, ReLU), different types of network layers (such as Conv1D, DepthwiseConv2D, MaxPool1D, Dropout, Average, Reshape, etc.), training, validation, testing process, gradients, partial derivatives, chain rule, backpropagation, optimizers (such as GD, SGD, mini-batch GD, Adam), loss functions, etc. Experienced in implementing various types of NNs in Keras, Tensorflow Lite framework, and using libraries such as Numpy, SciPy, Pandas, etc. for precomputing.
  • Exposure to Deep Learning with classical computer vision techniques for image recognition, object detection & tracking, and acoustic scene identification.
  • Experienced in optimizing model size, workload, operations, perform quantization-aware training and post-training quantization. Converting and stitching models with main IoT application/program, followed by building executable binaries and deployment on any given hardware.
  • Firm knowledge in designing resource-friendly end-to-end advanced edge analytics, signal processing, and computer vision pipelines.
  • Design of use-case based anomaly detection, forecasting & classification model, followed by its hardware-software co-optimization and deep compression to enable its accomodation on resource-constrained devices.
  • Solid understanding of popular models such as MobileNet, SqueezeNet, Inception V1, MnasNet, NASNet mobile, DenseNet, DeepLabv3, PoseNet, EAST, etc. Experienced in optimizing such models in multiple aspects, followed by their efficient deployment and execution on any target device.
ML Datasets Creation, Analysis, Processing and Visualization
  • Perform Exploratory Data Analysis (EDA) for a given dataset and generate profile reports that contains; quantile statistics, descriptive statistics, most frequent values, histogram, correlations, missing values, file and image analysis, text analysis, etc.
  • Experienced in building ML datasets: Recently created a multi-sensor dataset named COVID-away. It contains the recording of accelerometer, gyroscope, barometric pressure & rotation vector data for 2071 dynamic hand-to-face movements, performed with various postures (standing, leaning, slouching, etc.) and wrist orientations (variations in Roll, Pitch, and Yaw).
  • Frequent usage of data visualization Plotly, Seaborn and Matplotlib libraries in research environment to present ML model performance, visual comparison of various analytics results, etc.
Additional Skills
  • Handling multiple development aspects from edge to cloud, support quality & manufacturing groups, technical documentation, clear verbal & written communication, creating release notes, releasing & archiving projects, using change management systems (JIRA). Basics of Docker, Matlab & LabVIEW.
  • Ensuring design complies with Industrial/relevant standards, ensure all health, safety, environmental & regulatory requirements are met. Knowledge of PCB manufacturing processes, assembly & equipment.
  • Experienced using Blackboard learning management system, Qwiklabs, BlueJeans, and CoderPad.
  • Experienced in prototyping without using PCBs: After the design phase, I build the first prototype manually. Thus, I learned to handle and build prototypes using surface mount technology (SMT), improved hand soldering techniques, reflow soldering, learned PCB etching, drilling, components assembly, etc. at home.

My scholarly service to the research community.

01
Tutorial Organizer
ECML PKDD.Sept 2021
Wrote [Proposal] for a half-day tutorial titled 'ML meets IoT: From Theory to Practice', which was accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Tutorial agenda, slides, and code can be found at the [Tutorial Website].

02
Programme Committee Member
Journals, International Conferences and Workshops.2019 - Present
My role as a Research Paper Reviewer/Programme Committee Member in the below venues is to evaluate submissions based on the quality, completeness, and accuracy of the presented research. Also, provide feedback on the submitted article, suggest improvements and make a recommendation to the editor about whether to accept, reject or request changes to the article.
  • Taylor and Francis Applied Artificial Intelligence Journal.
  • Future Generation Computer Systems Journal.
  • IEEE Access Journal.
  • International Conference on Artificial Neural Networks (ICANN).
  • Global IoT Summit (GIoTS).
  • IEEE World Forum on Internet of Things (WF-IoT).
  • International Conference on the Internet of Things (IoT).
  • International Conference on Machine Learning Technologies (ICMLT).
  • Cross Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE).
  • International Conference on Information System and Data Mining (ICISDM).
  • Embedded and Mobile Deep Learning (ACM MobiSys workshop).
  • Internet of Things based Health Services and Applications (IoT-HSA).
Embedded Hardware Design for Offline and Real-time IoT Edge Analytics
  • Multi-sensor, wireless, Low-power Embedded system design using various ARM MCUs. Embedded architecture-aware software development using PICCCS, Keil, or other Embedded development Tools, IDEs & Debuggers (JTAG).
  • Experienced working in power-constrained typologies-based wireless environments. Solid knowledge of wireless & wired communication systems, protocols & peripherals such as BLE, Wi-Fi, LTE, GPS/GNSS, CoAP, MQTT, 6LoWPAN, Z-Wave, ZigBee, LoRaWAN, SigFox, AMQP, XMPP, HTTP/2.0. Digital, Analog, I2S, USB, UART, CAN, I2C, SPI, RS232 & RS485.
  • Hands on embedded system design experience using Panasonic’s PaPIRs, Grid-EYE infrared arrays, MaxBotix’s range finders, Maxim Integrated’s healthcare sensors, range of Thermoelectric Peltier Modules, Interlink’s FSRs & Flex sensors, Melexis’c contactless IR temp sensor, ST’s FlightSense ToF technology, ReSpeaker mic-arrays, and others.
  • Schematic Capture, PCB Layout, Fab package release (Gerber, Drill, BOM, etc.) to build mixed-signal hardware using Proteus or Eagle.
  • Experienced using Digi’s wireless SOCs & networks, IntelMovidius NCS, Nvidia Jetson Nano, Leap motion, other SBCs & MCUs like Raspberry Pis, Intel NUC series, Google Coral, LattePanda boards, STM32 blue pills, Espressif modules, Nordic SoC’s, Arduino boards, etc.
Write Deployment Ready IoT Applications in C, Embedded C, C++, Python for Edge Devices, Fog and Cloud Servers
  • Design, build, maintain efficient and reliable code.
  • Familiar with Unix environment, Shell scripting and Git-based source control systems.
  • Writing code utilizing concepts from multi-threading, RTOS, OOPS. Experienced using inline functions, volatile keywords, macros, interrupts, virtual, friend functions, etc.
  • Experienced in setting up hosting environments such as Azure, ThingsSpeak, Dweet, IBM Watson, Node-Red, Digital Ocean, AWS, etc. Then connect the data stream from IoT edge devices to thus configured remote cloud for IoT analytics, historical data storage, etc.
  • Firm experience in selection of hardware budget and computation requirement based chips i.e. from a range of 8, 16 & 32-bit MCUs, microprocessors, FPGAs, and programing them to solve a wide range of problems in the given IoT use-case.
Use case based ML Model Creation, Deep Optimization, and Efficient Hardware Deployment
  • A solid foundation on Neural Network components such as weights, activation function (such as Sigmoid, Tanh, Softmax, ReLU), different types of network layers (such as Conv1D, DepthwiseConv2D, MaxPool1D, Dropout, Average, Reshape, etc.), training, validation, testing process, gradients, partial derivatives, chain rule, backpropagation, optimizers (such as GD, SGD, mini-batch GD, Adam), loss functions, etc. Experienced in implementing various types of NNs in Keras, Tensorflow Lite framework, and using libraries such as Numpy, SciPy, Pandas, etc. for precomputing.
  • Exposure to Deep Learning with classical computer vision techniques for image recognition, object detection & tracking, and acoustic scene identification.
  • Experienced in optimizing model size, workload, operations, perform quantization-aware training and post-training quantization. Converting and stitching models with main IoT application/program, followed by building executable binaries and deployment on any given hardware.
  • Firm knowledge in designing resource-friendly end-to-end advanced edge analytics, signal processing, and computer vision pipelines.
  • Design of use-case based anomaly detection, forecasting & classification model, followed by its hardware-software co-optimization and deep compression to enable its accomodation on resource-constrained devices.
  • Solid understanding of popular models such as MobileNet, SqueezeNet, Inception V1, MnasNet, NASNet mobile, DenseNet, DeepLabv3, PoseNet, EAST, etc. Experienced in optimizing such models in multiple aspects, followed by their efficient deployment and execution on any target device.
ML Datasets Creation, Analysis, Processing and Visualization
  • Perform Exploratory Data Analysis (EDA) for a given dataset and generate profile reports that contains; quantile statistics, descriptive statistics, most frequent values, histogram, correlations, missing values, file and image analysis, text analysis, etc.
  • Experienced in building ML datasets: Recently created a multi-sensor dataset named COVID-away. It contains the recording of accelerometer, gyroscope, barometric pressure & rotation vector data for 2071 dynamic hand-to-face movements, performed with various postures (standing, leaning, slouching, etc.) and wrist orientations (variations in Roll, Pitch, and Yaw).
  • Frequent usage of data visualization Plotly, Seaborn and Matplotlib libraries in research environment to present ML model performance, visual comparison of various analytics results, etc.
Additional Skills
  • Handling multiple development aspects from edge to cloud, support quality & manufacturing groups, technical documentation, clear verbal & written communication, creating release notes, releasing & archiving projects, using change management systems (JIRA). Basics of Docker, Matlab & LabVIEW.
  • Ensuring design complies with Industrial/relevant standards, ensure all health, safety, environmental & regulatory requirements are met. Knowledge of PCB manufacturing processes, assembly & equipment.
  • Experienced using Blackboard learning management system, Qwiklabs, BlueJeans, and CoderPad.
  • Experienced in prototyping without using PCBs: After the design phase, I build the first prototype manually. Thus, I learned to handle and build prototypes using surface mount technology (SMT), improved hand soldering techniques, reflow soldering, learned PCB etching, drilling, components assembly, etc. at home.
Nov 2019 - Mar 2020
Liveliness Detection Sub-system for Digital Voice Assistants

Blackstone Launchpad - NUIG

Using the grant, we designed a light-weight Infineon radar-based sub-system and integrated it with Alexa digital voice assistant. Our sub-system enables Alexa to intelligently differentiate live human voices from voices coming out of speakers, thus making Alexa not react to wake word calls and voice commands from non-lively objects.

Nov 2018 - Feb 2019
Assess, Respond, Monitor, Strengthen Glove (ARMS glove) for stroke

Blackstone Launchpad - NUIG

Project, Poster & Bench Demo: Post hand paralysis or injury, patients often require lengthy, repeated and therapists supervised clinical training to regain muscular control and function. Using the grant, we built a wearable named Assess, Respond, Monitor, Strengthen (ARMS) glove that facilitates patients to perform various supervised interventions at their convenient place and time without the presence of therapists.

  • Paper: Avoid Touching Your Face - IoT-HSA Workshop

    Presented our paper titled "Avoid Touching Your Face: A Hand-to-face 3D Motion Dataset (COVID-away) and Trained Models for Smartwatches" in the IoT-HSA workshop and won the Second place [Link].

  • Secure Baggage using PSc - OpenGovDataHack

    PSc is a sensor-based embedded system with BLE, which we built to act as a virtual locker for securing the belongings of passengers using public transports. We were the runner up of NIC-IAMAI #OpenGovDataHack conducted across 7 cities nationwide and qualified for final presentation before Shri Ravi Shankar Prasad, Minister of Electronics & Information Technology, Govt of India. The project also got nominated for Tata Consultancy Services (TCS) best project award. Also participated in Smart India Hackathon by The Ministry of Civil Aviation, India [Link].

  • Smart Portable IoT Vaccine Monitor - Mouser IoT Design Contest

    Second place, National level, India: When the environment is not optimal, the efficacy of vaccines is lost, especially when health workers carry vaccines in a portable box during door-to-door polio campaigns. Our device continuously monitors the vaccines using multiple sensors and runs local analytics to ensure vaccine efficacy is preserved. The timely alerts from our device prevent administering less potent vaccines during campaigns. [Link].

  • Gesture Control Glove - Atmel India University Program Embedded Design Contest

    Finalist, National level: During seminars/presentations, to provide a seamless user-machine interaction, we built a wireless sensor-based wearable that helps the presenter achieve improved synchronization while performing presentation control tasks such as window switching, scrolling, slide navigation, audio-video controls, etc.

  • Compliance Training - ARM

    Completed the following training courses that are intended to provide key guidance on how to conduct oneself when working in a organization [Cert].

    • Introduction to Patents, Processes for Third Party IP, Open Source and Standards.
    • Active Shooter or Armed Threat.
    • Introduction to Open Source Software and Licenses.
    • Introduction to Intellectual Property Law.
    • Security Awareness, AI Ethics Awareness Training for Engineering.
    • Code of Conduct Trade Compliance, Training and Annual Policy Acceptances.

  • Research Integrity - Engineering and Technology - Epigeum, Oxford University Press

    Planning your research, Research with human participants, Managing and protecting interests, Financial interests and intellectual property, Research record, Research communication, Data interpretation and presentation, Case studies and advice: Pressure to publish, Advocacy [Cert].

  • Health Research and Data Protection Training - NUI Galway

    Ethics and DP, Health research Legal framework, mandatory DPIA, PBD&D, Health research projects before GDPR, GDPR, Personal data & SCPD, Data transfers, HRR overview, What is health research, Suitable & specific measures, Legal Basis, Explicit consent, HRCDC, Anonymisation & Pseudonymisation & Techniques, Combining datasets, software & MDR, Explanations, Covid-19 & Health Research.

  • GDPR Introductory Training - NUI Galway

    GDPR Principles Privacy by Design & Default; Material Scope; Territorial Scope; Data flows; Personal Data and Special Category Personal Data; Data Subject Rights; Lawful processing; Fairness & Transparency; Automated decision making & Profiling; Derogations and purpose limitations for research; Anonymisation & pseudonymisation; Combining Datasets; Security; Disclosure, Data Breach & Penalties; Roles & Relationships; When a DPIA is required.

  • Academic Publishing and Peer Review - Publons Academy

    Communication with editors, author and reviewer biases, evaluating data in tables and figures in the results section, structure and effectively communicate your constructive review.

  • English for Academic Purposes (EAP) workshop - English Language Centre, NUI Galway

    Applying academic conventions in the sections of a research paper. Applying appropriate language and communication strategies to academic tasks (describing trends, analyzing data, evaluating the arguments of others, supporting arguments with testimony and evidence, linking ideas logically and coherently). Academic writing (topic sentences, unity, internal cohesiveness, nominalization, sentence structure, strategies for summarising and synthesis).

  • Google IT Automation with Python (Professional Certificate) - Google via Coursera

    Configuration Management and the Cloud (Automation at Scale, Basic Monitoring & Alerting, Cloud Computing, Using Puppet) [Cert]. Crash Course on Python [Cert]. Using Python to Interact with the OS, (Regular Expression (REGEX), Automating System Administration Tasks with Python, Bash Scripting) [Cert]. Introduction to Git and GitHub (Using Git, Version Control Systems, Interacting with GitHub, Reverting Changes, Creating Pull Requests) [Cert]. Troubleshooting and Debugging Techniques (Improving Software Performance, Managing Scarce Resources, Advanced Troubleshooting, Understanding Errors, Finding the Root Cause of a Problem) [Cert]. Automating Real-World Tasks with Python (Serialization, Building a Solution, Creating and Translating Media Files, Interacting with Web Services)[Cert]. Final Professional Certificate [Cert].

  • Industrial IoT Markets and Security - University of Colorado Boulder via Coursera

    Automation deployment, IIoT software and services market, Real-time operating system for an IIoT node, Networking, wireless communication providers and protocols, Network Functions Virtualization and Software Defined Networks, Security solutions for end-node type devices [Cert].

  • Open Source and the 5G Transition - LinuxFoundationX via edX

    Open 5G network, Standards & software, Integrating 5G into business strategy, Considerations & going forward [Cert].

  • Applied AI with DeepLearning - IBM via Coursera

    Deep Learning Frameworks(Keras, TensorFlow, SystemML & DeepLearning4J), DeepLearning Applications(Anomaly Detector, Time Series Forecasting, Image classification & Sequence Classification), Scalingand Deployment(IBM Watson Visual Recognition, Tasks in ApacheSpark using DL4J & SystemML) [Cert].

  • Cybersecurity & the Internet of Things - University System of Georgia via Coursera

    Organizational Risks in industrial Sector, Application in Smart Grid, Security & Privacy Issues, Interoperability & Securityissues, Connected Home & Community, Consumer Wearables(Wearable Computing, Objective Metrics, Quantified Self) [Cert].

  • Architecting Smart IoT Devices - EIT Digital via Coursera

    Hardware & Software for EmS(MCU, SOC, FPGA, Cache, pipeline & coupling, Sensor Networks, Protocolstacks, Licenses, SensorTag Experiment), RTOS (Real-time Scheduling, Synchronisation and Communication, Device Drivers), System Finalisation (Code Tuning, Security, Realtime & Logical remote debugging, Simulation on host) [Cert].

  • MediaTek Smartphone Design Training Program - India & Taiwan

    Mobiledesignprocess, Performance testing & tuning, Basic BSP knowledge, Taiwan mobile phone eco-system tours, Real practice in MediaTek, Digital /Analogue /Cellular RF /Wireless Connectivity /Multimedia relative knowledge, Camera/Audio tuning, Power consumption & thermal design, Certification & regulation, Case study (measurement and debugging), Mobile market segmentation & positioning [Cert].

  • Fundamentals of Digital Image and Video Processing -North western University via Coursera

    Signals & System, Fourier Transforms & Sampling, Motion Estimation, Image Enhancement, Image Recovery, Lossless Compression, Video Compression, Image & video segmentation, Sparsity [Cert].

  • Introduction to Linux - LinuxFoundationX via edX

    Linux Philosophy & Community, Partitions, Filesystems, Boot process, Environment Variables, Permissions, Security, Commandline, Encryption, Bash Shell Scripting & Debugging [Cert].