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Recent advances to hardware integration and realization of highly-efficient Compressive Sensing (CS) approaches have inspired novel circuit and architectural-level approaches. These embrace the challenge to design more optimal nonuniform CS solutions that consider device-level constraints for IoT applications wherein lifetime energy, device area, and manufacturing costs are highly-constrained, but meanwhile the sensing environment is rapidly changing. In this manuscript, we develop a novel adaptive hardware-based approach for non-uniform compressive sampling of sparse and time-varying signals. The proposed Adaptive Sampling of Sparse IoT signals via STochastic-oscillators (ASSIST) approach intelligently generates the CS measurement matrix by distributing the sensing energy among coefficients by considering the signal characteristics such as sparsity rate and noise level obtained in the previous time step. In our proposed approach, Magnetic Random Access Memory (MRAM)-based stochastic oscillators are utilized to generate the random bitstreams used in the CS measurement matrix. SPICE and MATLAB circuit-algorithm simulation results indicate that ASSIST efficiently achieves the desired non-uniform recovery of the original signals with varying sparsity rates and noise levels. 

This poster paper describes the authors’ single-year National Science Foundation (NSF) project DRL-1825007 titled, “DCL: Synthesis and Design Workshop on Digitally-Mediated Team Learning” which has been conducted as one of nine awards within NSF-18-017: Principles for the Design of Digital STEM Learning Environments. Beginning in September 2018, the project conducted the activities herein to deliver a three-day workshop on Digitally-Mediated Team Learning (DMTL) to convene, invigorate, and task interdisciplinary science and engineering researchers, developers, and educators to coalesce the leading strategies for digital team learning. The deliverable of the workshop is a White Paper composed to identify one-year, three-year, and five-year research and practice roadmaps for highly-adaptable environments for computer-supported collaborative learning within STEM curricula. As subject to the chronology of events, highlights of the White Paper’s outcomes will be showcased within the poster itself.