In this paper, we investigate machine learning methods for enabling high performance noncooperative spectrum sensing, for future cognitive radio systems. The fulfillment of sensing requirements is crucial for ensuring an efficient reuse of the scarce spectrum by unlicensed users, without causing harmful interference to primary users. In this work, we propose a deep convolutional neural network-based transfer learning framework for non-cooperative spectrum sensing in TV bands, applicable across various locations, wireless environments and even frequency assignments. Specifically, we design a four-layer convolutional neural network for limiting the computational costs while satisfying the sensing requirements, and apply transfer learning by freezing the first two convolutional layers. The performance of the proposed method is evaluated against benchmarks, based on over 29,000 spectrograms collected in UHF TV band from a recent measurement campaign. The experiments show that thanks to transfer learning, the proposed method is able to detect TV signals with high accuracy despite a significantly reduced amount of data, thereby providing a high adaptability to various locations, environments, and frequencies. Furthermore, the proposed method with transfer learning not only guarantees the sensing requirements but also realizes up to 94% reduction of training time of the network, as well as 20% reduction of the required sensing time, compared to the case without transfer learning.
Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.
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