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This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on sound event detection. We recorded the sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental sound data to remedy the scarcity of drone sound data in diverse environments. We investigated the effectiveness of state-of-the-art event sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone sound detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time detection systems.

BackgroundThe purpose of this study was to examine the relationship between Korean pharmacy students’ empathy and psychological need satisfaction and their levels of burnout and psychological well-being, using structural equation modeling.MethodsThe participants were 452 pharmacy students from five South Korean universities. The Jefferson Scale of Empathy (Health Professions Students version), the Activity-Feeling States Scale, and the Maslach Burnout Inventory-Student Survey were used to assess empathy, psychological need satisfaction, and burnout, respectively. Psychological well-being was measured with the Mood Rating Scale, Self-Esteem Scale, and Satisfaction With Life Scale. The fits of the measurement and structural regression (SR) models with data on the four variables were evaluated using the Tucker-Lewis index (TLI), incremental fit index (IFI), comparative fit index (CFI), and root mean-square error of approximation (RMSEA) using AMOS 18.0.ResultsA total of 447 students (98.9%) completed the survey. The measurement model showed adequate fit indices; all hypothesized factor loadings were significant. The proposed SR model also showed an acceptable fit (TLI = 0.92, IFI = 0.94, CFI = 0.94, RMSEA = 0.072); each path was supported except the path from empathy to burnout (β = 0.005). Empathy was positively associated with psychological well-being (β = 0.18). Perceived satisfaction of psychological needs was positively related to psychological well-being (β = 0.59), but strongly and negatively related to burnout (β = − 0.71). The model explained 50 and 44% of variances in burnout and psychological well-being, respectively.ConclusionsPharmacy students’ empathy and psychological needs should be considered in pharmacy education systems to promote psychological adjustment.

When the low power wide area network (LPWAN) was developed for the internet of things (IoT), it attracted significant attention. LoRa, which is one of the LPWAN technologies, provides low-power and long-range wireless communication using a frequency band under 1 GHz. A long-range wide area network (LoRaWAN) provides a simple star topology network that is not scalable; it supports multi-data rates by adjusting the spreading factor, code rate, and bandwidth. This paper proposes an adaptive spreading factor selection scheme for corresponding spreading factors (SFs) between a transmitter and receiver. The scheme enables the maximum throughput and minimum network cost, using cheap single channel LoRa modules. It provides iterative SF inspection and an SF selection algorithm that allows each link to communicate at independent data rates. We implemented a multi-hop LoRa network and evaluated the performance of experiments in various network topologies. The adaptive spreading factor selection (ASFS) scheme showed outstanding end-to-end throughput, peaking at three times the performance of standalone modems. We expect the ASFS scheme will be a suitable technology for applications requiring high throughput on a multi-hop network.