# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 1 Introduction  
According to [1], the statistical graph titled \"Number of accident reports by type, 1970 to 2019\" shows that natural disasters are on the rise. In the 20 th century, large-scale natural disasters steadily occurred, including the Great Earthquake in Haiti in 2010, the tsunami caused by the Great East Japan Earthquake in Japan in 2011, and the Maria Hurricane in the United States in 2017. These large-scale natural disasters inevitably result in a huge number of casualties. To rescue more survivors and minimize the use of human and monetary resources, it is very important to accurately detect the existence of survivors in a certain coverage. Existing wireless infrastructures could be of great help in confirming the existence of survivors. However, conventional wireless infrastructures are likely to be destroyedor out of order at a disaster site, and thus it is necessary to develop a cost-effective but powerful system that can detect the existence of survivors at such a site. Due to its flexible operation capability and cost-effectiveness, UAVs can be a perfect candidate for an autonomous survivor search system. Specifically, rescue UAVs can be used for two purposes: (1) recovery, and (2) search and rescue. Recently, for the recovery of the telecommunications infrastructure, several major telecommunications companies such as AT&T and Verizon started to evaluate the possible benefits of using UAV base stations [2; 3]. These attempts have confirmed that UAV base stations can recover damaged infrastructure costeffectively. In contrast, despite its high potential, there have been few efforts using UAVs for survivor search and rescue purposes. A problem that always arises in operating an UAV is the UAV's power consumption. If power is supplied by wire, there is no problem with the operating time, but the operating radius is limited due to the power wire. It is exactly the opposite when UAVs are operated through batteries. Despite limited operating time, battery use is essential in a disaster site as a rescue UAV has to investigate a large disaster area. To reduce the power consumption of battery-powered UAVs, the payload should also be minimized. Therefore, a computationally lightweight survivor-detection algorithm should be developed to avoid the use of high-end companion computers, which are usually heavy and also consume a lot of power. Furthermore, since the global market size for small UAVs is expected to reach USD 22.55 billion by 2026 and to grow at a CAGR of 15.92% [4], it is more likely that survivor detection UAVs will be small, with minimal payload. To find survivors, an UAV can utilize a high-resolution camera to perform object-detection algorithms. However, this method has the limitation that if survivors are obscured by obstacles, an UAV cannot find them. Alternatively, it is possible to estimate the number of survivors by obtaining communication history from the telecommunications companies. However, as rescue time increases, this information becomes outdated, since survivors can move around the disaster area, which can confuse rescuers. Therefore, there is a need for a technology that can continuously update the number of survivors in real time without being disturbed by obstacles. Therefore, it is necessary to develop a lightweight and effective search scheme for survivors. In this paper, we develop a survivor search system that consists of a machine learning-based multi-source detection scheme that detects the number of simultaneously transmitted signals from survivors' mobile devices and a simple survivor discovery protocol. For the survivor discovery protocol, there is a challenge that an UAV should detect the number of signals that are transmitted _asynchronously_. For the solution, a machine learning-based asynchronous stream-sensing technique is proposed. The contribution of this work is summarized as follows:  
* Since the proposed scheme uses wireless signals from mobile devices of survivors, our proposal is relatively unaffected by obstacles.