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John C. Woods was born in a small fishing village near Colchester, U.K., in 1964. He received the B.Eng. (hons.) degree (first class) in 1996 and the Ph.D. degree in 1999 from the University of Essex, Colchester, UK. He has been a Lecturer in the Department of Computer Science and Electronic Systems Engineering, University of Essex, since 1999. Although his field of expertise is image processing, he has a wide range of interests including telecommunications, autonomous vehicles and robotics.
Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS detection and classification systems based on radio frequency (RF) signals can be hindered by other interfering signals present in the same frequency band, such as Bluetooth and Wi-Fi devices. In this paper, we evaluate the effect of real-world interference from Bluetooth and Wi-Fi signals concurrently on convolutional neural network (CNN) feature extraction and machine learning classification of UASs. We assess multiple UASs that operate using different transmission systems: Wi-Fi, Lightbridge 2.0, OcuSync 1.0, OcuSync 2.0 and the recently released OcuSync 3.0. We consider 7 popular UASs, evaluating 2 class UAS detection, 8 class UAS type classification and 21 class UAS flight mode classification. Our results show that the process of CNN feature extraction using transfer learning and machine learning classification is fairly robust in the presence of real-world interference. We also show that UASs that are operating using the same transmission system can be distinguished. In the presence of interference from both Bluetooth and Wi-Fi signals, our results show 100% accuracy for UAV detection (2 classes), 98.1% (+/−0.4%) for UAV type classification (8 classes) and 95.4% (+/−0.3%) for UAV flight mode classification (21 classes).
Carolyn Swinney; John Woods. The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems. Aerospace 2021, 8, 179 .
AMA StyleCarolyn Swinney, John Woods. The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems. Aerospace. 2021; 8 (7):179.
Chicago/Turabian StyleCarolyn Swinney; John Woods. 2021. "The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems." Aerospace 8, no. 7: 179.
Unmanned Aerial Vehicles (UAVs) undoubtedly pose many security challenges. We need only look to the December 2018 Gatwick Airport incident for an example of the disruption UAVs can cause. In total, 1000 flights were grounded for 36 h over the Christmas period which was estimated to cost over 50 million pounds. In this paper, we introduce a novel approach which considers UAV detection as an imagery classification problem. We consider signal representations Power Spectral Density (PSD); Spectrogram, Histogram and raw IQ constellation as graphical images presented to a deep Convolution Neural Network (CNN) ResNet50 for feature extraction. Pre-trained on ImageNet, transfer learning is utilised to mitigate the requirement for a large signal dataset. We evaluate performance through machine learning classifier Logistic Regression. Three popular UAVs are classified in different modes; switched on; hovering; flying; flying with video; and no UAV present, creating a total of 10 classes. Our results, validated with 5-fold cross validation and an independent dataset, show PSD representation to produce over 91% accuracy for 10 classifications. Our paper treats UAV detection as an imagery classification problem by presenting signal representations as images to a ResNet50, utilising the benefits of transfer learning and outperforming previous work in the field.
Carolyn Swinney; John Woods. Unmanned Aerial Vehicle Operating Mode Classification Using Deep Residual Learning Feature Extraction. Aerospace 2021, 8, 79 .
AMA StyleCarolyn Swinney, John Woods. Unmanned Aerial Vehicle Operating Mode Classification Using Deep Residual Learning Feature Extraction. Aerospace. 2021; 8 (3):79.
Chicago/Turabian StyleCarolyn Swinney; John Woods. 2021. "Unmanned Aerial Vehicle Operating Mode Classification Using Deep Residual Learning Feature Extraction." Aerospace 8, no. 3: 79.
Energy Harvesting Systems seek to remove the batteries from electronic devices and replace them with devices that generate directly from the environment around them. This paper presents an intelligent algorithm to manage received wireless power to do useful work even though there is insufficient energy to do the work directly. The example ultralow power microcontroller discussed here is the ATtiny85 although the approach is applicable to a whole family of similar micros. The algorithm used makes intelligent decisions whether to sleep or wake according to the amount of received and stored energy. Using an adaptive strategy of this kind the amount of work can be precisely matched to the resources available to achieve maximum utilization with the objective of keeping the device alive for as long as possible subject to the satisfactory completion of a stated set of tasks.
Michael Walton; John Woods. Intelligent control of micro power – Immortal machine. Nano Energy 2020, 72, 104699 .
AMA StyleMichael Walton, John Woods. Intelligent control of micro power – Immortal machine. Nano Energy. 2020; 72 ():104699.
Chicago/Turabian StyleMichael Walton; John Woods. 2020. "Intelligent control of micro power – Immortal machine." Nano Energy 72, no. : 104699.
The consumer demand for retrieving and delivering visual content through consumer electronic devices has increased rapidly in recent years. The quality of video in packet networks is susceptible to certain traffic characteristics: average bandwidth availability, loss, delay and delay variation (jitter). This paper presents a scheduling algorithm that modifies the stream of scalable video to combat jitter. The algorithm provides unequal look-ahead by safeguarding the base layer (without the need for overhead) of the scalable video. The results of the experiments show that our scheduling algorithm reduces the number of frames with a violated deadline and significantly improves the continuity of the video stream without compromising the average Y Peek Signal-to-Noise Ratio (PSNR).
Atinat Palawan; John C. Woods; Mohammed Ghanbari. Continuity-Aware Scheduling Algorithm for Scalable Video Streaming. Computers 2016, 5, 11 .
AMA StyleAtinat Palawan, John C. Woods, Mohammed Ghanbari. Continuity-Aware Scheduling Algorithm for Scalable Video Streaming. Computers. 2016; 5 (2):11.
Chicago/Turabian StyleAtinat Palawan; John C. Woods; Mohammed Ghanbari. 2016. "Continuity-Aware Scheduling Algorithm for Scalable Video Streaming." Computers 5, no. 2: 11.