Researchers in the Murmann Mixed Signal Group have developed a pipelined chip architecture with inverted residual and linear bottlenecks-based networks for energy efficient Machine Learning inference on edge devices.
Researchers at Stanford have modified the spatial construction of two-wave interferometers to enable high-precision acoustic sensors and accelerometers produced at scale.
The Murmann lab has developed a method for an extraction information from acoustic signals that utilizes low power consumption. N-path filters are used to decompose the original acoustic signals' waveform before downconverting to lower their Nyquist-rate bandwidth.
Stanford researchers have developed a method called KleinPAT, for creating sound models in seconds, making it cost effective to simulate sounds for many different objects in a virtual environment.
Engineers in Prof. Khuri-Yakub's laboratory have developed ultrasonic methods for non-invasive flow meters to accurately measure flow rate, pressure, velocity and other parameters of gas or liquid traveling through a pipe.
Engineers in Prof. Khuri-Yakub's laboratory have developed ultrasonic methods for non-invasive flow meters to accurately measure flow rate, pressure, velocity profile and other parameters of gas or liquid traveling through a pipe.
Stanford researchers have demonstrated the use of a coherent frequency-domain technique in microwave thermoacoustic imaging, which significantly improves signal-to-noise ratio (SNR) and reduces peak-power requirements without sacrificing resolution or other performance metrics