The rapid proliferation of drones across various industries has introduced significant challenges related to privacy, security, and noise pollution. Current drone detection systems, primarily based on visual and radar technologies, face limitations under certain conditions, highlighting the need for effective acoustic-based detection methods. This paper presents a unique and comprehensive dataset of drone acoustic signatures, encompassing 32 different categories differentiated by brand and model. The dataset includes raw audio recordings, spectrogram plots, and Mel-frequency Cepstral Coefficient (MFCC) plots for each drone. Additionally, we introduce an interactive web application that allows users to explore this dataset by selecting specific drone categories, listening to the associated audio, and viewing the corresponding spectrogram and MFCC plots. This tool aims to facilitate research in drone detection, classification, and acoustic analysis, supporting both technological advancements and educational initiatives. The paper details the dataset creation process, the design and implementation of the web application, and provides experimental results and user feedback. Finally, we discuss potential applications and future work to expand and enhance the project.

The rapid proliferation of drones across various industries has introduced significant challenges related to privacy, security, and noise pollution. Current drone detection systems, primarily based on visual and radar technologies, face limitations under certain conditions, highlighting the need for effective acoustic-based detection methods. This paper presents a unique and comprehensive dataset of drone acoustic signatures, encompassing 32 different categories differentiated by brand and model. The dataset includes raw audio recordings, spectrogram plots, and Mel-frequency cepstral coefficient (MFCC) plots for each drone. Additionally, we introduce an interactive web application that allows users to explore this dataset by selecting specific drone categories, listening to the associated audio, and viewing the corresponding spectrogram and MFCC plots. This tool aims to facilitate research in drone detection, classification, and acoustic analysis, supporting both technological advancements and educational initiatives. The paper details the dataset creation process, the design and implementation of the web application, and provides experimental results and user feedback. Finally, we discuss potential applications and future work to expand and enhance the project.

As models based in artificial intelligence increase in sophistication, there is a higher demand for the integration of hardware components to heighten real-world implementations. Both facial feature tracking and shape-from-focus are known techniques in computer vision. However, the combination of these two elements, particularly in a cost-effective configuration, has not been extensively explored. In this study, a 64 megapixel (MP) autofocus Arducam camera module collected images of participants at various focal lengths and the Laplacian of the Gaussian (LoG) identified the frame of maximum sharpness each second. The image data was then processed by two convolutional neural networks (CNNs) from Google MediaPipe that identified the bounding box for the face and the coordinates of facial features. These coordinates, in conjunction with a shape-from-focus calculations, were fused to measure facial feature depths relative to the camera system. The depths, aggregated across a working period contributed another metric for total participant effort in a Human Robot Collaboration (HRC) experiment without introducing significant additional costs or logistical modifications. Inheriting the constraints of an existing HRC configuration, this methodology achieved consistent 2D tracking of facial features and coarse 3D facial motion trends from a singular, static imaging system.

This study examined the effects of robot speed, number, and spatial orientation on operator stress, mental workload, attention, and excitement during human–robot collaboration. As collaborative robots (cobots) become increasingly prevalent in modern manufacturing, operator safety concerns persist. Despite Industry 5.0’s focus on human-centered production, the impact of robot configuration on operators’ cognitive and emotional states remains underexplored. Thirty participants completed eight sessions of a Lego block stacking task using a mixed-factor (3 × 2 × 2) within-subjects design that varied robot speed (slow, fast, or mixed), robot number (one or two), and left–right spatial orientation. Physiological data were collected continuously via an Empatica E4 wristband and analyzed using linear mixed models; subjective data were collected via a post-session questionnaire and analyzed using non-parametric tests. Slower robot speeds reduced stress, workload, attention, and excitement relative to higher speeds. Two robots increased stress, mental workload, attention, and excitement relative to one robot, with consistent elevations in electrodermal activity, shorter inter-beat intervals, and lower skin temperature confirming sympathetic arousal. The fast two-robot condition produced the highest subjective demand across all measures. Notably, one robot at fast speed and two robots at slow speed produced statistically comparable subjective workload, suggesting both configurations offer productive alternatives with manageable operator strain. Robot spatial orientation did not significantly affect any outcome measure. These findings suggest that, under the conditions of this laboratory paradigm, a configuration of one robot at fast speed may offer a favorable balance of workload and engagement for safe and effective collaboration, with two robots at slow speed as a viable secondary alternative; direct objective productivity data were not collected, and these recommendations should be validated against task throughput in future work. Design implications for collaborative workstations are discussed.