Fire incidents are responsible for severe damage and thousands of deaths every year all over the world. Extreme temperatures, low visibility, toxic gases, and unknown locations of victims create difficulties and delays in rescue operations, escalating the risk of injury or death. It is time-critical to detect the victims trapped inside the burning sites for facilitating the rescue operations. This research work presents an audio-based automated system for victim detection in fire emergencies, investigating two machine learning (ML) methods: support vector machines (SVM) and long short-term memory (LSTM). The performance of these two ML techniques has been evaluated based on a variety of performance metrics. Our analyses show that both ML methods provide superior scream detection performance, with SVM slightly overperforming LSTM. Because of its lower complexity, SVM is a better candidate for real-time implementation in our autonomous embedded system vehicle (AESV).

Using a 5 µm-diameter X-ray beam, we collected scanning X-ray microdiffraction in both the small-angle (SAXS) and the wide-angle (WAXS) regimes from thin sections of fixed human brain tissue from Alzheimer's subjects. The intensity of scattering in the SAXS regime of these patterns exhibits essentially no correlation with the observed intensity in the WAXS regime, indicating that the structures responsible for these two portions of the diffraction patterns, which reflect different length scales, are distinct. SAXS scattering exhibits a power-law behavior in which the log of intensity decreases linearly with the log of the scattering angle. The slope of the log–log curve is roughly proportional to the intensity in the SAXS regime and, surprisingly, inversely proportional to the intensity in the WAXS regime. We interpret these observations as being due to the presence of sub-micrometre-sized voids formed during dehydration of the fixed tissue. The SAXS intensity is due largely to scattering from these voids, while the WAXS intensity derives from the secondary structures of macromolecular material surrounding the voids. The ability to detect and map the presence of voids within thin sections of fixed tissue has the potential to provide novel information on the degradation of human brain tissue in neurodegenerative diseases.

This work presents and experimentally tests the framework used by our context-aware, distributed team of small Unmanned Aerial Systems (SUAS) capable of operating in real time, in an autonomous fashion, and under constrained communications. Our framework relies on a three-layered approach: (1) an operational layer, where fast temporal and narrow spatial decisions are made; (2) a tactical layer, where temporal and spatial decisions are made for a team of agents; and (3) a strategical layer, where slow temporal and wide spatial decisions are made for the team of agents. These three layers are coordinated by an ad hoc, software-defined communications network, which ensures sparse but timely delivery of messages amongst groups and teams of agents at each layer, even under constrained communications. Experimental results are presented for a team of 10 small unmanned aerial systems tasked with searching for and monitoring a person in an open area. At the operational layer, our use case presents an agent autonomously performing searching, detection, localization, classification, identification, tracking, and following of the person, while avoiding malicious collisions. At the tactical layer, our experimental use case presents the cooperative interaction of a group of multiple agents that enables the monitoring of the targeted person over wider spatial and temporal regions. At the strategic layer, our use case involves the detection of complex behaviors, i.e., the person being followed enters a car and runs away, or the person being followed exits the car and runs away, which require strategic responses to successfully accomplish the mission.