Kofi M. Odame

Overview

Professor Odame is an expert in analog integrated circuits for nonlinear signal processing. His research group is developing ultra low power circuits for implantable and wearable biomedical devices and next generation image sensors. For more information visit the Analog Lab.

Research Interests

Analog integrated circuits; low power sensor interfaces; nonlinear signal processing

Education

BSc, Electrical and Computer Engineering, Cornell University 2002
MSc, Electrical and Computer Engineering, Cornell University 2004
PhD, Electrical and Computer Engineering, Georgia Institute of Technology 2008

Awards

Jeff Crowe '78 Grand Prize, 2019 Dartmouth Entrepreneurs Forum
Neukom/IQBS CompX Faculty Grant (2011–2012)
Analog Devices Career Development Assistant Professor of Engineering (2008–2012)
Georgia Institute of Technology Tower Award (2008)
Cornell Institute for African Development Fellowship (2002–2003)
Cornell International Scholars and Students Award (1998–2002)

Professional Activities

Senior Member, IEEE
Venture Advisor, TandemLaunch
Standing Member, NIH/CSR BCHI Study Section (7/2019–9/2020)
Standing Member, NIH/CSR Clinical Informatics and Digital Health Study Section (9/2020–6/2025)

Research Projects

Electrical impedance tomography in pulmonary and cardiac applications

Electrical impedance tomography in pulmonary and cardiac applications

The most common application, so far, of electrical impedance tomography (EIT) has been pulmonary monitoring of patients. In particular, we have been pursuing EIT for cardiac output monitoring (a related application) and EIT as a surrogate for pulmonary function tests.
Custom ICs for Prostate imaging

Custom ICs for Prostate imaging

We are providing advanced integrated circuits (ICs) for sensing to Professor Ryan Halter, who is combining them with transrectal electrical impedance tomography and electrical impedance sensing biopsy devices to improve the accuracy of ultrasound-guided prostate biopsy procedures.
Asthma symptom monitoring

Asthma symptom monitoring

Our asthma symptom monitoring solution is a wearable device that automatically detects and tracks cough, wheezing severity, lung function and inhaler use. The device provides objective information about the patient’s level of asthma control and level of compliance with the asthma plan.
Cardiac output monitoring

Cardiac output monitoring

We are developing a device for continuous, non-invasive monitoring of a patient's cardiac hemodynamic status, allowing for physician intervention before the clinical symptoms of decompensation are observed, and potentially avoiding hospitalization.

Selected Publications

A Power Adaptive, 1.22 pW/Hz, 10 MHz Read-Out Front-End for Bio-Impedance Measurement, M. Takhti and K. Odame, in IEEE Transactions on Biomedical Circuits and Systems, vol. 13, no. 4, p. 725–734, Aug. 2019.
An Optimized Recurrent Unit For Ultra-Low-Power Keyword Spotting, J. Amoh, K. Odame, in Journal Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technology, vol. 3, no. 2, p. 36, 2019.
Deep Neural Networks For Identifying Cough Sounds, J. Amoh, K. Odame, in IEEE Transactions on Biomedical Circuits and Systems, vol. 10, no. 5, p. 1003–1011, 2016.
A Bandpass Filter With Inherent Gain Adaptation for Hearing Applications, K. Odame, D.V. Anderson, P. Hasler, in IEEE Transactions on Circuits and Systems I, vol. 55, no. 3, p. 786–795, 2008.
An Efficient Oscillator Design Based on OTA Nonlinearity, K. Odame and P. Hasler, in Proc. of IEEE International Symposium on Circuits and Systems, p. 921–924, 2007.
The Translinear Principle: A General Framework for Implementing Chaotic Oscillators, K. Odame and B. Minch, in International Journal of Bifurcation and Chaos, vol. 15, no. 8, p. 2559–2568, 2005.

Patents

Acoustic sensor with an acoustic object detector for reducing power consumption in front-end circuit | 9964433
Hearing-aid noise reduction circuitry with neural feedback to improve speech comprehension | 9906872
Electroencephalography monitoring device having a self-adaptive analog-to-digital converter | 8655438