Aatmesh Shrivastava
Northeastern University, MA·Engineering·Early-career
Small electronic devices are spreading everywhere, from wireless sensors to wearable and implantable health monitors. Two problems limit them: how to power them without batteries or wires, and how to run smart processing such as machine learning on very little energy.
- Fields
Engineering
Biomedical Engineering
Computer Science
- Topics
RF energy harvesting
wireless power transfer
distributed beamforming
- Grants
- 2 known grants, through Aug 2027
What they work on
- Develop a distributed beamforming technique that combines RF energy from multiple Wi-Fi or Bluetooth-type transmitters and directs it to a remote IoT receiver, using closed-loop optimization between transmitters and receiver.
- Build an ultra-low-power backscatter communication link, where the receiver signals by reflecting radio waves, plus an energy-detection platform that senses phase and frequency offsets among transmitters.
- Design a new RF-to-DC rectifier topology with maximum power tracking that converts radio waves to usable power efficiently over a wide range of input levels, and demonstrate it on test chips.
- Create a modeling and simulation tool that links analog circuit power, noise and linearity to machine-learning classification accuracy, so that ML hardware can be tuned for area, power and performance.
- Build an ultra-low-power analog system-on-chip using constant-transconductance sub-threshold circuits to detect arrhythmia and obstructive sleep apnea from ECG and pulse oximetry, aiming for up to 50-fold lower power.
More about the research
This research designs ultra-low-power integrated circuits (chips) to address both problems. One project harvests radio-wave energy for remote internet-of-things (IoT) devices. The other uses analog computing to run machine-learning inference on biomedical devices.
Why it matters
Battery-free wireless power could let IoT, biomedical and remote sensors run in places where wiring or battery replacement is impractical. Ultra-low-power ML chips could allow small wearable or implantable monitors to detect heart arrhythmias and obstructive sleep apnea on the device itself. That could support continuous monitoring and timely medical intervention for patients with these conditions.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Negar Reiskarimian
Massachusetts Institute of Technology, MA·Engineering
Demonstrate a low-power software-defined radio chip that resists harmonic interference, using a fully passive harmonic-reject mixer and a compact clocking scheme with sub-milliwatt static power.
Vanessa Chen
Carnegie Mellon University, PA·Engineering·Early-career
Design bio-inspired, time-domain analog classifiers that exploit transistor mismatch and nonlinearity to perform energy-efficient hierarchical classification at low supply voltages within a sensor.
Toshikazu Nishida
University of Florida, FL·Engineering
Develop zero-power magnetic field sensors and shear-stress sensors for self-powered, edge-of-the-node sensing in IoT systems.
The school
Northeastern University: 27 labs on PI Pages, $347M a year on research, 251 research doctorates a year.
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