In this research, we introduce NeuroCamTags, a battery-free platform designed to detect a range of rich human interactions and activities in entire rooms and floors without the need for batteries. The NeuroCamTag system comprises low-cost tags that harvest ambient light energy and utilize high-frequency modulation of light-emitting diodes (LEDs) for wireless communication. These visual signals are captured by an available neuromorphic camera, which boasts temporal resolution and frame rates an order of magnitude higher than those of conventional cameras. We present an event processing pipeline that allows simultaneous localization and identification of multiple unique tags. NeuroCamTags offer a wide range of functionalities, providing battery-free wireless sensing for various physical stimuli, including changes in temperature, contact, button presses, key presses, and even sound cues. Our empirical evaluations demonstrate impressive accuracy at long ranges up to 200 feet. In addition to these findings, we consider a range of applications such as battery-free input devices, tracking of human movement, and long-range detection of human activities in various environments such as kitchens, workshops, etc. By reducing reliance on batteries, NeuroCamTags promotes eco-friendliness and opens doors to exciting possibilities in smart environment technology.
@article{IMWUT24_Scott,author={Scott, Danny and Bringle, Matthew and Fahad, Imran and Morales, Gaddiel and Zahid, Azizul and Swaminathan, Sai},title={NeuroCamTags: Long-Range, Battery-free, Wireless Sensing with Neuromorphic Cameras},year={2024},issue_date={September 2024},publisher={Association for Computing Machinery},address={New York, NY, USA},volume={8},number={3},url={https://doi.org/10.1145/3678529},doi={10.1145/3678529},journal={Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},month=sep,articleno={122},numpages={25},keywords={Activity sensing, Battery-free, Context Aware Computing, Wireless sensing}}
ArXiv
BanglaNum–A Public Dataset for Bengali Digit Recognition from Speech
Mir Sayeed Mohammad, Azizul Zahid, and Md Asif Iqbal
@article{mohammad2024banglanum,title={BanglaNum--A Public Dataset for Bengali Digit Recognition from Speech},author={Mohammad, Mir Sayeed and Zahid, Azizul and Iqbal, Md Asif},journal={arXiv preprint arXiv:2403.13465},year={2024},month=mar,url={https://doi.org/10.48550/arXiv.2403.13465},doi={10.48550/arXiv.2403.13465},}