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In this paper we present a micro-Doppler (mD) system and a computationally efficient classifier for the purpose of distinguishing different means of transport for human beings (pedestrians, inline skaters and cyclists) based on their mD time-frequency signatures. Accuracies as high as 97% are obtained while keeping the overall computational cost low.
—This paper presents a novel hybrid CMOS/MEMS tilt sensor with a 5 o resolution over a 330 o range. The device uses a MEMS-based semicircular mass suspended from a rigid body, projecting a shadow onto the CMOS-based optical sensor surface. A one-dimensional photodiode array arranged as a uniformly segmented ring is then used to determine the tilt angle by… (More)
This paper presents an alternative approach for angular-rate sensing based on the way that the natural vestibular semicircular canals operate, whereby the inertial mass of a fluid is used to deform a sensing structure upon rotation. The presented gyro has been fabricated in a commercially available MEMS process, which allows for microfluidic channels to be… (More)
We report on the design and the collection of a multi-modal data corpus for cognitive acoustic scene analysis. Sounds are generated by stationary and moving sources (people), that is by omni-directional speakers mounted on people's heads. One or two subjects walk along predetermined systematic and random paths, in synchrony and out of sync. Sound is… (More)