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The ability for a smart speaker to localize a user based on his/her voice opens the door to many new applications. In this paper, we present a novel system, MAVL, to localize human voice. It consists of three major components: (i) We first develop a novel multi-resolution analysis to estimate the AoA of time-varying low-frequency coherent voice signals coming from multiple propagation paths; (ii) We then automatically estimate the room structure by emitting acoustic signals and developing an improved 3D MUSIC algorithm; (iii) We finally re-trace the paths using the estimated AoA and room structure to localize the voice. We implement a prototype system using a single speaker and a uniform circular microphone array. Our results show that it achieves median errors of 1.49 degree and 3.33 degree for the top two AoAs estimation and achieves median localization errors of 0.31m in line-of-sight (LoS) cases and 0.47m in non-line-of-sight (NLoS) cases. 

Acoustic ranging is a technique for estimating the distance between two objects using acoustic signals, which plays a critical role in many applications, such as motion tracking, gesture/activity recognition, and indoor localization. Although many ranging algorithms have been developed, their performance still degrades significantly under strong noise, interference and hardware limitations. To improve the robustness of the ranging system, in this paper we develop a Deep learning based Ranging system, called DeepRange. We first develop an effective mechanism to generate synthetic training data that captures noise, speaker/mic distortion, and interference in the signals and remove the need of collecting a large volume of training data. We then design a deep range neural network (DRNet) to estimate distance. Our design is inspired by signal processing that ultra-long convolution kernel sizes help to combat the noise and interference. We further apply an ensemble method to enhance the performance. Moreover, we analyze and visualize the network neurons and filters, and identify a few important findings that can be useful for improving the design of signal processing algorithms. Finally, we implement and evaluate DeepRangeusing 11 smartphones with different brands and models, 4 environments (i.e., a lab, a conference room, a corridor, and a cubic area), and 10 users. Our results show that DRNet significantly outperforms existing ranging algorithms. 

Passive radio-frequency identification (RFID) tags are attractive because they are low cost, battery-free, and easy to deploy. This technology is traditionally being used to identify tags attached to the objects. In this paper, we explore the feasibility of turning passive RFID tags into battery-free temperature sensors. The impedance of the RFID tag changes with the temperature and this change will be manifested in the reflected signal from the tag. This opens up an opportunity to realize battery-free temperature sensing using a passive RFID tag with already deployed Commercial Off-the-Shelf (COTS) RFID reader-antenna infrastructure in supply chain management or inventory tracking. However, it is challenging to achieve high accuracy and robustness against the changes in the environment. To address these challenges, we first develop a detailed analytical model to capture the impact of temperature change on the tag impedance and the resulting phase of the reflected signal. We then build a system that uses a pair of tags, which respond differently to the temperature change to cancel out other environmental impacts. Using extensive evaluation, we show our model is accurate and our system can estimate the temperature within a 2.9 degree centigrade median error and support a normal read range of 3.5 m in an environment-independent manner. 

The popularity of smartphones has grown at an unprecedented rate, which makes smartphone based imaging especially appealing. In this paper, we develop a novel acoustic imaging system using only an off-the-shelf smartphone. It is an attractive alternative to camera based imaging under darkness and obstruction. Our system is based on Synthetic Aperture Radar (SAR). To image an object, a user moves a phone along a predefined trajectory to mimic a virtual sensor array. SAR based imaging poses several new challenges in our context, including strong self and background interference, deviation from the desired trajectory due to hand jitters, and severe speaker/microphone distortion. We address these challenges by developing a 2-stage interference cancellation scheme, a new algorithm to compensate trajectory errors, and an effective method to minimize the impact of signal distortion. We implement a proof- of-concept system on Samsung S7. Our results demonstrate the feasibility and effectiveness of acoustic imaging on a mobile.