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This paper presents an ultra-low-power intersignal time-delay-to-digital converter. It introduces polarity-coincidence adaptive time-delay estimation, a mixed-signal processing technique that consumes only 78.2nW for a 3-channel delay estimation. A 0.18um CMOS implementation of the converter has been tested and characterized with controlled and real-life stimuli. This analog-to-feature converter has further been used to estimate the time difference of arrival in an audio-based vehicle-bearing IoT system. 

With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this paper, we build a wearable system that uses multi-channel audio sensors embedded in a headset to help detect and locate cars from their honks, engine and tire noises, and warn pedestrians of imminent dangers of approaching cars. We demonstrate that using a segmented architecture consisting of headset-mounted audio sensors, a front-end hardware platform that performs signal processing and feature extraction, and machine learning based classification on a smartphone, we are able to provide early danger detection in real-time, from up to 60m away, and alert the user with low latency and high accuracy. To further reduce power consumption of the battery-powered wearable headset, we implement a custom-designed integrated circuit that is able to compute delays between multiple channels of audio with nW power consumption. A regression-based method for sound source localization, AvPR, is proposed and used in combination with the IC to improve the granularity and robustness of localization. 

With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this article, we present PAWS, a smartphone platform that utilizes an embedded wearable headset system mounted with an array of MEMS microphones to help detect, localize, and warn pedestrians of the imminent dangers of approaching cars. 

The aim of this demo is to explore the implementation of an ultra-low-power analog-to-feature ASIC to an IoT embedded system. The custom integrated circuit, designed to optimize the power consumption of a traditional sound-source localization systems, is capable of extracting the time-difference of arrival (TDoA) between 4 microphones consuming only 78.2nW. An end-to-end embedded system is presented; a microphone array is connected to the ASIC that converts the TDoA to digital information and sends it to a host computer. A machine-learning algorithm, running in the host, is then used to detect the bearing of the sound-source. During the demonstration, the audience is able to verify the benefits and drawbacks of the custom integrated circuit solution, both in the perspective of the signal-processing performance of the ASIC, and the impact it introduces to the complexity of the system’s integration. 

With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this paper, we build a wearable system that uses multi-channel audio sensors embedded in a headset to help detect and locate cars from their honks, engine and tire noises, and warn pedestrians of imminent dangers of approaching cars. We demonstrate that using a segmented architecture and implementation consisting of headset-mounted audio sensors, a front-end hardware that performs signal processing and feature extraction, and machine learning based classification on a smartphone, we are able to provide early danger detection in real-time, from up to 60m distance, near 100% precision on the vehicle detection and alert the user with low latency.