In this paper, we develop a privacy-preserving UAV system that does not infringe on the privacy of people in the videos taken by UAVs. Instead of blurring or masking the face parts of the videos, we want to exquisitely modify only the face parts so that the people in the modified videos still look like humans, but they become anonymous. Doing so, the semantic information of the videos can be preserved even with the anonymization. Specifically, based on the latest generative adversarial network architecture, we propose a deep learning-based face-anonymization scheme so that each modified face part looks like the face of a person who does not actually exist. The trained face-anonymizer is then mounted on the UAV system we have implemented. Through experiments, we confirm that the developed privacy-preserving UAV system anonymizes UAV's first-person videos so that the people in the video are not recognized as anyone in the dataset used. In addition, we show that even with such anonymized videos, the perception performance required for performing UAV's essential functions such as simultaneous localization and mapping is not degraded. INDEX TERMSPrivacy infringement, privacy-preserving vision, deep learning, security robot, UAV patrol system I. INTRODUCTION 1 As one of the most important systems in the 4-th industrial 2 era, unmanned aerial vehicles (UAVs) are expanding their 3 use in all directions, ranging from transportation, delivery, 4 surveillance, security, exploration, military, public safety, agriculture, and smart factories. In particular, UAV systems 6 capable of performing missions autonomously have bound-7 less potential in many applications. The recent rapid advance-8 ment of UAV systems is attributed to recent deep learning-9 based computer vision techniques. As UAV's cognitive abil-10 ity has soared, it has become possible to autonomously find 11 paths, avoid obstacles, and perform missions stably in various 12 situations. However, as UAVs become ubiquitous around us, 13 UAV's high-performance vision function may raise serious 14 concerns about privacy breaches by exposing us to unwanted 15
For next-generation Internet-of-Things (IoT) networks, asynchronous instant transmission has attracted increasing research interest with the expectation of achieving near-zero latency without excessive initiation procedure. However, in an asynchronous multiple-access scenario, there exist significant inter-carrier interference between sub-carriers allocated to different users. To suppress out-of-band emission (OOBE) of each sub-carrier, a new generalized frequency division multiplexing (GFDM) has been proposed, which has lower OOBE than the conventional orthogonal frequency division multiplexing (OFDM). In this paper, by using GFDM, two types of receivers are proposed with the aim of reducing latency and improving throughput: a GFDM-based minimum mean square error (MMSE) receiver and a GFDMbased MMSE-successive interference cancellation (SIC) receiver. Then, we develop a lightweight scheme using an -conservative rate control with GFDM-based MMSE receivers and also invent a performancefocused scheme using an advanced rate control with GFDM-based MMSE-SIC receivers. In particular, the latter scheme provides higher throughput with limited increase in computational load of user equipments. Numerical results show that with a high successful transmission probability higher than 99 %, the performance-focused scheme and the lightweight scheme achieve up to 85 % and up to 70 % higher throughput compared to the conventional OFDM-based asynchronous multiple-access scheme, respectively. Furthermore, since our proposal does not require any centralized user scheduling or initiation procedure, it presents a significant reduction in latency compared to the existing low-latency technologies.INDEX TERMS Asynchronous multiple-access, generalized frequency division multiplexing (GFDM), out-of-band emission (OOBE), minimum mean square error (MMSE)
In a disaster site, terrestrial communication infrastructures are often destroyed or malfunctioning, and hence it is very difficult to detect the existence of survivors in the site. At such sites, UAVs are rapidly emerging as an alternative to mobile base stations to establish temporary infrastructure. In this paper, a novel deep-learning-based multi-source detection scheme is proposed for the scenario in which an UAV wants to estimate the number of survivors sending rescue signals within its coverage in a disaster site. For practicality, survivors are assumed to use off-the-shelf smartphones to send rescue signals, and hence the transmitted signals are orthogonal frequency division multiplexing (OFDM)-modulated. Since the line of sight between the UAV and survivors cannot be generally secured, the sensing performance of existing radar techniques significantly deteriorates. Furthermore, we discover that transmitted signals of survivors are unavoidably aysnchronized to each other, and thus existing frequency-domain multi-source classification approaches cannot work. To overcome the limitations of these existing technologies, we propose a lightweight deep-learning-based multi-source detection scheme by carefully designing neural network architecture, input and output signals, and a training method. Extensive numerical simulations show that the proposed scheme outperforms existing methods for various SNRs under the scenario where synchronous and asynchronous transmission is mixed in a received signal. For almost all cases, the precision and recall of the proposed scheme is nearly one, even when users’ signal-to-noise ratios (SNRs) are randomly changing within a certain range. The precision and recall are improved up to 100% compared to existing methods, confirming that the proposal overcomes the limitation of the existing works due to the asynchronicity. Moreover, for Intel(R) Core(TM) i7-6900K CPU, the processing time of our proposal for a case is 31.8 milliseconds. As a result, the proposed scheme provides a robust and reliable detection performance with fast processing time. This proposal can also be applied to any field that needs to detect the number of wireless signals in a scenario where synchronization between wireless signals is not guaranteed.
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