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TinyML-CAM
Code for MobiCom paper 'TinyML-CAM: 80 FPS Image Recognition in 1 Kb RAM'ML-MCU
Code for IoT Journal paper 'ML-MCU: A Framework to Train ML Classifiers on MCU-based IoT Edge Devices'CNN_on_MCU
Code for paper 'Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware'Train_plus_plus
Code for paper 'Train++: An Incremental ML Model Training Algorithm to Create Self-Learning IoT Devices'OTA-TinyML
Code for IEEE Internet Computing Journal paper 'OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices'COVID-away
Code for paper 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'Edge2Guard
Code for PerCom paper 'Edge2Guard: Botnet Attacks Detecting Offline Models for Resource-Constrained IoT Devices'ML-Classifiers-on-MCUs
Supplementary material for IEEE Services Computing paper 'An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware'profile
Link: https://bharathsudharsan.github.io/profile/Alexa-Smart-Speaker
A modern Smart-speaker with an advanced microphone array, camera module interfaced with Pi capable of performing AI-based tasksAVRGait
Optimized-One-vs-One-Algorithm
Code for AAAI poster 'Training up to 50 Class ML Models on 3 $ IoT Hardware via Optimizing One-vs-One Algorithm'Edge2Train
Code for IoT paper 'Edge2Train: a framework to train machine learning models (SVMs) on resource-constrained IoT edge devices'ML-Model-Combining
Code for BigData paper 'Ensemble Methods for Collective Intelligence: Combining Ubiquitous ML Models in IoT'Tiny-Impute
On-device Hybrid Anomaly Detection and Data ImputationAir-Quality-IoT-Analytics
Repo and code of the UbiComp-ISWC 2021 paper: 'Air Quality Sensor Network Data Acquisition, Cleaning, Visualization, and Analytics: A Real-world IoT Use Case'ECML-Tutorial-ML-Meets-IoT
Repository of the ECML PKDD 2021 tutorial title 'Machine Learning Meets Internet of Things: From Theory to Practice'Reducing-Overprocessing-of-DNNs
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