SELECTIVE SENSING: A DATA-DRIVEN NONUNIFORM SUBSAMPLING APPROACH FOR COMPUTATION-FREE ON-SENSOR DATA DIMENSIONALITY REDUCTION Anonymous

Abstract

Designing an on-sensor data dimensionality reduction scheme for efficient signal sensing has always been a challenging task. Compressive sensing is a state-of-the-art sensing technique used for on-sensor data dimensionality reduction. However, the undesired computational complexity involved in the sensing stage of compressive sensing limits its practical application in resourceconstrained sensor devices or high-data-rate sensor devices dealing with highdimensional signals. In this paper, we propose a selective sensing framework that adopts the novel concept of data-driven nonuniform subsampling to reduce the dimensionality of acquired signals while retaining the information of interest in a computation-free fashion. Selective sensing adopts a co-optimization methodology to co-train a selective sensing operator with a subsequent information decoding neural network. We take image as the sensing modality and reconstruction as the information decoding task to demonstrate the 1st proof-ofconcept of selective sensing. The experiment results on CIFAR10, Set5 and Set14 datasets show that selective sensing can achieve an average reconstruction accuracy improvement in terms of PSNR/SSIM by 3.73dB/0.07 and 9.43dB/0.16 over compressive sensing and uniform subsampling counterparts across the dimensionality reduction ratios of 4-32x, respectively. Source code is available at https://figshare.com/s/519a923fae8f386d7f5b.

1. INTRODUCTION

In the era of Internet-of-things (IoT) data explosion (Biookaghazadeh et al., 2018) , efficient information acquisition and on-sensor data dimensionality reduction techniques are in great need. Compressive sensing is the state-of-the-art signal sensing technique that is applicable to on-sensor data dimensionality reduction. However, directly performing compressive sensing in the digital domain as a linear transformation of signals can be computationally costly, especially when the signal dimension n is high and/or a data-driven sensing matrixMousavi et al. 2018) is used. To mitigate this problem, several approaches have been proposed to reduce the computational complexity of compressive sensing by constraining the sensing matrices to be sparse, binary, or ternary (Wang et al., 2016; Nguyen et al., 2017; Zhao et al., 2018; Hong et al., 2019) . While these approaches can reduce the computational complexity by a constant factor (O(cn 2 ), where c can be as low as 10 -2 ), such reduced computational complexity can be still too high to be affordable for resource-constrained sensor devices, e.g., low-cost IoT sensors (Djelouat et al., 2018) , or high-data-rate sensor devices dealing with high-dimensional signals, e.g., LiDAR and depth map (Chodosh et al., 2019) . Other approaches (Duarte et al., 2008; Robucci et al., 2010) propose to implement compressive sensing in the analog domain instead, eliminating or reducing the computation cost of compressive sensing through custom hardware implementation. However, such custom hardware implementation inevitably increases the cost of the sensor and is often specific to the sensor design, thereby cannot be generally applied to other sensors or applications. In this paper, we propose a selective sensing framework to address the above-mentioned problem by adopting the novel concept of data-driven nonuniform subsampling to reduce the dimensionality of acquired signals while retaining the information of interest in a computation-free fashion. Specifically, the data dimensionality reduction in selective sensing is a nonuniform subsampling (or



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