Students Involved:
Arko Datta (M.Tech)
Rajib Kumar Chatterjee (PhD)
Ananya Banerjee (PhD)
Anirban Ghosh (M.Tech)
References:
AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer https://arxiv.org/abs/2204.11484
Github Code: https://github.com/prasenjit52282/AQuaMoHo/tree/master
Efficient Air Quality Index Prediction on Resource-Constrained Devices using TinyML: Design, Implementation, and Evaluation: https://drive.google.com/file/d/1jmtHQFOSUN7YcLuU33-QTvocgkPTe4ok/view?usp=sharing
On-Device Training Under 256KB Memory (MIT) : https://arxiv.org/abs/2206.15472
Source Code: https://github.com/mit-han-lab/mcunet
Exploring Indoor Health: An In-depth Field Study on the Indoor Air Quality Dynamics: https://arxiv.org/abs/2310.12241
TinyML on-device neural network training : https://drive.google.com/file/d/1ryTFHdZpyu0gu9Sm2D3bIpZTP99mAEKv/view?usp=sharing
Low Power TinyML for Image Recognition: https://drive.google.com/drive/u/0/folders/1iEyTkaa5thZnmYGWYhZGV1BAIBq4-ncq
Calculating an actionable indoor air quality index: https://www.breeze-technologies.de/blog/calculating-an-actionable-indoor-air-quality-index/
Students Involved:
Shraban Maiti (M.Tech)
Moumita Moitra (PhD)
Hanzla Ansari (M.Tech)
Sangram Behera (M.Tech)
References:
MCUNet: Tiny Deep Learning on IoT Devices: https://arxiv.org/abs/2007.10319
MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
On-Device Training Under 256KB Memory (MIT) : https://arxiv.org/abs/2206.15472
Source Code: https://github.com/mit-han-lab/mcunet
Low Power TinyML for Image Recognition: https://drive.google.com/drive/u/0/folders/1iEyTkaa5thZnmYGWYhZGV1BAIBq4-ncq
Students Involved:
Almamun Shaikh (PhD)
Partha Pratim Dasgupta (PhD)
Bhabatosh Sarkar (M.Tech)
Gurpreet Singh (M.Tech)
References:
An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment: https://www.sciencedirect.com/science/article/pii/S2542660523001713
LimitAccess: on-device TinyML based robust speech recognition and age classification: https://link.springer.com/article/10.1007/s44163-023-00051-x
Implementation of Urban Environment Noise Classification Application on a Low Power Microcontroller: https://drive.google.com/file/d/1j20lsoF769lhqdMxguCS8UI9TIYDcFtD/view?usp=sharing
Just Listen: Prototyping with TinyML to Augment Everyday Sound: https://drive.google.com/drive/u/0/folders/1iEyTkaa5thZnmYGWYhZGV1BAIBq4-ncq
Low Power TinyML for Image Recognition: https://drive.google.com/drive/u/0/folders/1iEyTkaa5thZnmYGWYhZGV1BAIBq4-ncq
Students Involved:
Arnab Chakraborty (PhD)
N V S L Bhavani Raparthi (M.Tech)
Nikhil Venunath Matkawala (M.Tech)
Gurram Meghashyam (M.Tech)
References:
A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition: https://drive.google.com/file/d/1zdl7NV2Q91NcJh_jQA-NlHoF6ij8baUg/view?usp=drive_link
A novel sEMG-based dynamic hand gesture recognition approach via residual attention network: https://drive.google.com/file/d/1hCEi6knrGWVbS8YFTrWk2bx3oC6-lU4V/view?usp=drive_link
Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements : https://drive.google.com/file/d/1FKfBd5WBsVGEsLfjuZkxdKM56N6Ci4XK/view?usp=sharing
Arduino-Based Myoelectric Control: Towards Longitudinal Study of Prosthesis Use: https://drive.google.com/file/d/1sSO8xCGv9ivogNRPB6LvMyY16hwqLX3A/view?usp=sharing
Low Power TinyML for Image Recognition: https://drive.google.com/drive/u/0/folders/1iEyTkaa5thZnmYGWYhZGV1BAIBq4-ncq
Students Involved:
Malay Kr Majhi (PhD)
Vidyarnab Bagchi (M.Tech)
Debanga Saikia (M.Tech)
Pamir Ghosh (M.Tech)
References:
Low Power TinyML for Image Recognition: https://drive.google.com/drive/u/0/folders/1iEyTkaa5thZnmYGWYhZGV1BAIBq4-ncq
Source Code: https://github.com/mit-han-lab/mcunet
Low Power TinyML for Image Recognition: https://drive.google.com/drive/u/0/folders/1iEyTkaa5thZnmYGWYhZGV1BAIBq4-ncq