English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Owing to small size, sensing capabilities and autonomous nature, the UnmannedAir Vehicles (UAVs) have enormous applications in various areas, e.g., remotesensing, navigation, archaeology, journalism, environmental science, andagriculture. However, the unmonitored deployment of UAVs called the amateurdrones (AmDr) can lead to serious security threats and risk to human life andinfrastructure. Therefore, timely detection of the AmDr is essential for theprotection and security of sensitive organizations, human life and other vitalinfrastructure. AmDrs can be detected using different techniques based onsound, video, thermal, and radio frequencies. However, the performance of thesetechniques is limited in sever atmospheric conditions. In this paper, wepropose an efficient unsupervise machine learning approach of independentcomponent analysis (ICA) to detect various acoustic signals i.e., sounds ofbird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario.After unmixing the signals, the features like Mel Frequency CepstralCoefficients (MFCC), the power spectral density (PSD) and the Root Mean SquareValue (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSDsignals are extracted by first passing the signals from octave band filterbanks. Based on the above features the signals are classified using SupportVector Machines (SVM) and K Nearest Neighbor (KNN) to detect the presence orabsence of AmDr. Unique feature of the proposed technique is the detection of asingle or multiple AmDrs at a time in the presence of multiple acousticinterfering signals. The proposed technique is verified through extensivesimulations and it is observed that the RMS values of PSD with KNN performsbetter than the MFCC with KNN and SVM.

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference