Wilson Ayyanthole Nelson

• Nelson, Wilson Ayyanthole; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Estimation of UAV Count Using Thermal Imaging and Lightweight CNN, 2023 The 11th International Conference on Control, Mechatronics and Automation (ICCMA 2023). IEEE conference proceedings. ISSN 978-1-6654-9048-1. s. 92–96. doi: 10.1109/ICCMA59762.2023.10374791.
• Nelson, Wilson Ayyanthole; Yeduri, Sreenivasa Reddy; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). RL-Based Energy-Efficient Data Transmission Over Hybrid BLE/LTE/Wi-Fi/LoRa UAV-Assisted Wireless Network. IEEE/ACM Transactions on Networking. ISSN 1063-6692. doi: 10.1109/TNET.2023.3332296.
• Nelson, Wilson Ayyanthole; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Estimation of number of unmanned aerial vehicles in a scene utilizing acoustic signatures and machine learning. Journal of the Acoustical Society of America. ISSN 0001-4966. 154(1), s. 533–546. doi: 10.1121/10.0020292. Vis sammendrag

  With the exponential growth in unmanned aerial vehicle (UAV)-based applications, there is a need to ensure safe and secure operations. From a security perspective, detecting and localizing intruder UAVs is still a challenge. It is even more challenging to accurately estimate the number of intruder UAVs on the scene. In this work, we propose a simple acoustic-based technique to detect and estimate the number of UAVs. Our method utilizes acoustic signals generated from the motion of UAV motors and propellers. Acoustic signals are captured by flying an arbitrary number of ten UAVs in different combinations in an indoor setting. The recorded acoustic signals are trimmed, processed, and arranged to create an UAV audio dataset. The UAV audio dataset is subjected to time-frequency transformations to generate audio spectrogram images. The generated spectrogram images are then fed to a custom lightweight convolutional neural network (CNN) architecture to estimate the number of UAVs in the scene. Following training, the proposed model achieves an average test accuracy of 93.33% as compared to state-of-the-art benchmark models. Furthermore, the deployment feasibility of the proposed model is validated by running inference time calculations on edge computing devices, such as the Raspberry Pi 4, NVIDIA Jetson Nano, and NVIDIA Jetson AGX Xavier.

• Nelson, Wilson Ayyanthole; Gupta, Khushi Anil; Koduru, Balu Harshavardan; Kumar, Abhinav; Jha, Ajit & Cenkeramaddi, Linga Reddy (2023). Recent Advances in Thermal Imaging and its Applications Using Machine Learning: A Review. IEEE Sensors Journal. ISSN 1530-437X. 23(4), s. 3395–3407. doi: 10.1109/JSEN.2023.3234335. Vis sammendrag

  Recent advancements in thermal imaging sensor technology have resulted in the use of thermal cameras in a variety of applications, including automotive, industrial, medical, defense and space, agriculture, and other related fields. Thermal imaging, unlike RGB imaging, does not rely on background light, and the technique is nonintrusive while also protecting privacy. This review article focuses on the most recent advancements in thermal imaging technology, key performance parameters, an overview of its applications, and machine-learning techniques applied to thermal images for various tasks. This article begins with the most recent advancements in thermal imaging, followed by a classification of thermal cameras and their key specifications, and finally a review of machine-learning techniques used on thermal images for various applications. This detailed review article is highly useful for designing thermal imaging-based applications using various machine-learning techniques.

• Nelson, Wilson Ayyanthole; Kumar, Abhinav; Jha, Ajit & Cenkeramaddi, Linga Reddy (2023). Multitarget Angle of Arrival Estimation Using Rotating mmWave FMCW Radar and Yolov3. IEEE Sensors Journal. ISSN 1530-437X. 23(3), s. 3173–3182. doi: 10.1109/JSEN.2022.3231790. Vis sammendrag

  It is still challenging to accurately localize unmanned aerial vehicles (UAVs) from a ground control station (GCS) using various sensors. The mmWave frequency-modulated continuous wave (FMCW) radars offer excellent performance for target detection and localization in harsh environments and low lighting conditions. However, the estimated angle of arrival (AoA) of targets in the captured scene is quite poor. This article focuses on improving AoA estimation by combining the cutting-edge machine learning (ML) algorithms with a mechanical radar rotor setup. An mmWave FMCW radar system is mounted on a programmable rotor to capture range–angle maps of targets at various locations. The range–angle images are then labeled and trained further with the Yolov3 algorithm. Subsequent testing reveals that for detected target objects, the centroid of the bounding boxes from the detected objects provides accurate AoA estimation with very low root mean square error (RMSE). The results show that the proposed approach outperforms traditional methods in terms of performance and estimation accuracy.

• Nelson, Wilson Ayyanthole; Yeduri, Sreenivasa Reddy; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2022). Hybrid BLE/LTE/Wi-Fi/LoRa Switching Scheme for UAV-Assisted Wireless Networks, 2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE conference proceedings. ISSN 978-1-6654-4893-2. s. 78–83. doi: 10.1109/ANTS52808.2021.9936962. Vis sammendrag

  The unmanned aerial vehicles are deployed in multiple layers to monitor an area and report the information to the ground control station. When we use a single communication protocol such as Bluetooth Low Energy (BLE)/Wi-Fi with low range, the data has to pass through multiple hops for data transfer. This, in turn, increases the delay for data transmission. Even though LoRa protocol supports longer distances, the delay is more due to the limited bandwidth. Thus, in this work, we propose a hybrid BLE/LTE/Wi-Fi/LoRa switching scheme that consumes lower energy in addition to reducing the average delay in the network. The proposed scheme switches between the communication technologies based on the lower energy consumption. The performance of the proposed hybrid switching scheme is compared with the individual communication protocols in terms of both energy consumption and average delay. Through extensive numerical results, we show that the proposed hybrid switching scheme performs better in comparison to the individual communication technologies.

• Nelson, Wilson Ayyanthole; Kumar, Abhinav; Jha, Ajit & Cenkeramaddi, Linga Reddy (2022). Embedded Sensors, Communication Technologies, Computing Platforms and Machine Learning for UAVs: A Review. IEEE Sensors Journal. ISSN 1530-437X. 22, s. 1807–1826. doi: 10.1109/JSEN.2021.3139124. Vis sammendrag

  Unmanned aerial vehicles (UAVs) are increasingly becoming popular due to their use in many commercial and military applications, and their affordability. The UAVs are equipped with various sensors, hardware platforms and software technologies which enable them to support the diverse application portfolio. Sensors include vision-based sensors such as RGB-D cameras, thermal cameras, light detection and ranging (LiDAR), mmWave radars, ultrasonic sensors, and an inertial measurement unit (IMU) which enable UAVs for autonomous navigation, obstacle detection, collision avoidance, object tracking and aerial inspection. To enable smooth operation, UAVs utilize a number of communication technologies such as wireless fidelity (Wi-Fi), long range (LoRa), long-term evolution for machine-type communication (LTE-M), etc., along with various machine learning algorithms. However, each of these different technologies come with their own set of advantages and challenges. Hence, it is essential to have an overview of the different type of sensors, computing and communication modules and algorithms used for UAVs. This paper provides a comprehensive review on the state-of-the-art embedded sensors, communication technologies, computing platforms and machine learning techniques used in autonomous UAVs. The key performance metrics along with operating principles and a detailed comparative study of the various technologies are also studied and presented. The information gathered in this paper aims to serve as a practical reference guide for designing smart sensing applications, low-latency and energy efficient communication strategies, power efficient computing modules and machine learning algorithms for autonomous UAVs. Finally, some of the open issues and challenges for future research and development are also discussed.

Se alle arbeider i Cristin

• Nelson, Wilson Ayyanthole; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2023). Estimation of UAV Count Using Thermal Imaging and Lightweight CNN.
• Nelson, Wilson Ayyanthole; Yeduri, Sreenivasa Reddy; Jha, Ajit; Kumar, Abhinav & Cenkeramaddi, Linga Reddy (2021). Hybrid BLE/LTE/Wi-Fi/LoRa Switching Scheme for UAV-Assisted Wireless Networks. Vis sammendrag

  The unmanned aerial vehicles are deployed in multiple layers to monitor an area and report the information to the ground control station. When we use a single communication protocol such as Bluetooth Low Energy (BLE)/Wi-Fi with low range, the data has to pass through multiple hops for data transfer. This, in turn, increases the delay for data transmission. Even though LoRa protocol supports longer distances, the delay is more due to the limited bandwidth. Thus, in this work, we propose a hybrid BLE/LTE/Wi-Fi/LoRa switching scheme that consumes lower energy in addition to reducing the average delay in the network. The proposed scheme switches between the communication technologies based on the lower energy consumption. The performance of the proposed hybrid switching scheme is compared with the individual communication protocols in terms of both energy consumption and average delay. Through extensive numerical results, we show that the proposed hybrid switching scheme performs better in comparison to the individual communication technologies.

Se alle arbeider i Cristin