Neha Thomas

2papers

2 Papers

CVDec 5, 2022
FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views

Renuga Kanagavelu, Kinshuk Dua, Pratik Garai et al.

Federated Deep Learning frameworks can be used strategically to monitor Land Use locally and infer environmental impacts globally. Distributed data from across the world would be needed to build a global model for Land Use classification. The need for a Federated approach in this application domain would be to avoid transfer of data from distributed locations and save network bandwidth to reduce communication cost. We use a Federated UNet model for Semantic Segmentation of satellite and street view images. The novelty of the proposed architecture is the integration of Knowledge Distillation to reduce communication cost and response time. The accuracy obtained was above 95% and we also brought in a significant model compression to over 17 times and 62 times for street View and satellite images respectively. Our proposed framework has the potential to be a game-changer in real-time tracking of climate change across the planet.

ROJul 14, 2021
Sensorimotor-inspired Tactile Feedback and Control Improve Consistency of Prosthesis Manipulation in the Absence of Direct Vision

Neha Thomas, Farimah Fazlollahi, Jeremy D. Brown et al.

The lack of haptically aware upper-limb prostheses forces amputees to rely largely on visual cues to complete activities of daily living. In contrast, able-bodied individuals inherently rely on conscious haptic perception and automatic tactile reflexes to govern volitional actions in situations that do not allow for constant visual attention. We therefore propose a myoelectric prosthesis system that reflects these concepts to aid manipulation performance without direct vision. To implement this design, we built two fabric-based tactile sensors that measure contact location along the palmar and dorsal sides of the prosthetic fingers and grasp pressure at the tip of the prosthetic thumb. Inspired by the natural sensorimotor system, we use the measurements from these sensors to provide vibrotactile feedback of contact location and implement a tactile grasp controller that uses automatic reflexes to prevent over-grasping and object slip. We compare this system to a standard myoelectric prosthesis in a challenging reach-to-pick-and-place task conducted without direct vision; 17 able-bodied adults took part in this single-session between-subjects study. Participants in the tactile group achieved more consistent high performance compared to participants in the standard group. These results indicate that the addition of contact-location feedback and reflex control increases the consistency with which objects can be grasped and moved without direct vision in upper-limb prosthetics.