Vehicle flow prediction through probabilistic modeling

Within the area of wireless and mobile communications, ad hoc vehicular networks have generated the interest of different organizations, which has generated a topic of study and analysis for the increase of applications, devices, technology integration, security, standards, and quality of service in different areas (Zhu et al. in IEEE Trans Veh Technol 64(4):1607–1619, [1]) and (Tian et al. in A self-adaptive V2V communication system with DSRC, pp 1528–1532, [2]). This study on vehicle networks shows a great deal of opportunity and motivation to deepen the aspects that involve it, which have emerged due to the advance of wireless technologies, as well as research in the automotive industry. This allows the development of networks with spontaneous topologies with vehicles in constant movement in several simulations (Mir and Filali in LTE and IEEE 802.11p for vehicular networking: a performance evaluation, pp 1–15, [3]), with reliable vehicle flows, through the share of traffic information, considering that continuous mobility is an essential characteristic of a VANET vehicle network, which can have short changes in terms of groups of vehicles in the same direction (Lokhande and Khamitkar, 9(12):30–33, [4]). The following paper uses a road scenario called VANET to obtain a predictive characterization of vehicle flow using a probabilistic model

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

© 2020 Elsevier B.V. Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the un-monitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. AmDrs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in sever atmospheric conditions. In this paper, we propose an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM)and K Nearest Neighbour (KNN)to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM

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

Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the un-monitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. AmDrs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in sever atmospheric conditions. In this paper, we propose an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM)and K Nearest Neighbour (KNN)to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM