Jamming Detection and Classification in OFDM-based UAVs via Feature- and Spectrogram-tailored Machine Learning

In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers with applications to unmanned aerial vehicles (UAVs). Using software-defined radio (SDR), four types of jamming attacks; namely, barrage, protocol-aware, single-tone, and successive-pulse are launched and investigated. Each type is qualitatively evaluated considering jamming range, launch complexity, and attack severity. Then, a systematic testing procedure is established by placing an SDR in the vicinity of a UAV (i.e., drone) to extract radiometric features before and after a jamming attack is launched. Numeric features that include signal-to-noise ratio (SNR), energy threshold, and key OFDM parameters are used to develop a feature-based classification model via conventional ML algorithms. Furthermore, spectrogram images collected following the same testing procedure are exploited to build a spectrogram-based classification model via state-of-the-art deep learning algorithms (i.e., convolutional neural networks). The performance of both types of algorithms is analyzed quantitatively with metrics including detection and false alarm rates. Results show that the spectrogram-based model classifies jamming with an accuracy of 99.79% and a false-alarm of 0.03%, in comparison to 92.20% and 1.35%, respectively, with the feature-based counterpart

Faster inference from state space models via GPU computing

Funding: C.F.-J. is funded via a doctoral scholarship from the University of St Andrews, School of Mathematics and Statistics.Inexpensive Graphics Processing Units (GPUs) offer the potential to greatly speed up computation by employing their massively parallel architecture to perform arithmetic operations more efficiently. Population dynamics models are important tools in ecology and conservation. Modern Bayesian approaches allow biologically realistic models to be constructed and fitted to multiple data sources in an integrated modelling framework based on a class of statistical models called state space models. However, model fitting is often slow, requiring hours to weeks of computation. We demonstrate the benefits of GPU computing using a model for the population dynamics of British grey seals, fitted with a particle Markov chain Monte Carlo algorithm. Speed-ups of two orders of magnitude were obtained for estimations of the log-likelihood, compared to a traditional ‘CPU-only’ implementation, allowing for an accurate method of inference to be used where this was previously too computationally expensive to be viable. GPU computing has enormous potential, but one barrier to further adoption is a steep learning curve, due to GPUs' unique hardware architecture. We provide a detailed description of hardware and software setup, and our case study provides a template for other similar applications. We also provide a detailed tutorial-style description of GPU hardware architectures, and examples of important GPU-specific programming practices.Publisher PDFPeer reviewe

DEVELOPMENT AND CONTROL OF PERSONAL MOBILITY PLATFORM FOR MULTI-HIERARCHY LAST MILE TRANSPORTATION

Ph.DDOCTOR OF PHILOSOPH

Improving the accuracy of weed species detection for robotic weed control in complex real-time environments

Alex Olsen applied deep learning and machine vision to improve the accuracy of weed species detection in real time complex environments. His robotic weed control prototype, AutoWeed, presents a new efficient tool for weed management in crop and pasture and has launched a startup agricultural technology company

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

Computer Vision Algorithms for Mobile Camera Applications

Wearable and mobile sensors have found widespread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, including accelerometer, gyroscope, magnetometer, microphone and camera, it has become more feasible to develop algorithms for activity monitoring, guidance and navigation of unmanned vehicles, autonomous driving and driver assistance, by using data from one or more of these sensors. In this thesis, we focus on multiple mobile camera applications, and present lightweight algorithms suitable for embedded mobile platforms. The mobile camera scenarios presented in the thesis are: (i) activity detection and step counting from wearable cameras, (ii) door detection for indoor navigation of unmanned vehicles, and (iii) traffic sign detection from vehicle-mounted cameras. First, we present a fall detection and activity classification system developed for embedded smart camera platform CITRIC. In our system, the camera platform is worn by the subject, as opposed to static sensors installed at fixed locations in certain rooms, and, therefore, monitoring is not limited to confined areas, and extends to wherever the subject may travel including indoors and outdoors. Next, we present a real-time smart phone-based fall detection system, wherein we implement camera and accelerometer based fall-detection on Samsung Galaxy S™ 4. We fuse these two sensor modalities to have a more robust fall detection system. Then, we introduce a fall detection algorithm with autonomous thresholding using relative-entropy within the class of Ali-Silvey distance measures. As another wearable camera application, we present a footstep counting algorithm using a smart phone camera. This algorithm provides more accurate step-count compared to using only accelerometer data in smart phones and smart watches at various body locations. As a second mobile camera scenario, we study autonomous indoor navigation of unmanned vehicles. A novel approach is proposed to autonomously detect and verify doorway openings by using the Google Project Tango™ platform. The third mobile camera scenario involves vehicle-mounted cameras. More specifically, we focus on traffic sign detection from lower-resolution and noisy videos captured from vehicle-mounted cameras. We present a new method for accurate traffic sign detection, incorporating Aggregate Channel Features and Chain Code Histograms, with the goal of providing much faster training and testing, and comparable or better performance, with respect to deep neural network approaches, without requiring specialized processors. Proposed computer vision algorithms provide promising results for various useful applications despite the limited energy and processing capabilities of mobile devices