72 research outputs found

    Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals

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    Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar. The proposed methodology exploits the nonlinear mapping capabilities of deep stacked autoencoders in combination with density-based clustering. A comparative experimental evaluation of the approach shows promising results in terms of the proposed methodology’s performance

    Semi-Supervised Fine-Tuning for Deep Learning Models in Remote Sensing Applications

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    A combinatory approach of two well-known fields: deep learning and semi supervised learning is presented, to tackle the land cover identification problem. The proposed methodology demonstrates the impact on the performance of deep learning models, when SSL approaches are used as performance functions during training. Obtained results, at pixel level segmentation tasks over orthoimages, suggest that SSL enhanced loss functions can be beneficial in models' performance

    Recent Advances in Anomaly Detection Methods Applied to Aviation

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    International audienceAnomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance

    Maritime Anomaly Detection using Autoencoders and OPTICS-OF

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    Anomaly detection is an important task in many domains such as maritime where it is used to detect, for example, unsafe, unexpected or criminal behaviour. This thesis studies the use of deep autoencoders for anomaly detection on high dimensional data in an unsupervised manner. The study is performed on a benchmark data set and a real-life AIS (Automatic Tracking System) data set containing actual ship trajectories. The ships’ trajectories in the AIS data set are a form of time-series data, and therefore recurrent layers are used in an autoencoder to allow the model to capture temporal dependencies in the data. An autoencoder is a neural network architecture where an encoder network produces an encoding and decoder network takes the encoding intending to produce the original input. An encoding is a compressed fixed-sized vector presentation of the original input. Since the encoding is used by the decoder to construct the original input, the model learns during the training process to store essential information of the input sequence to the encoding. Autoencoders can be used to detect anomalies using reconstruction error by assuming that a trained autoencoder is able to reconstruct non-anomalius data points more accurately than anomalous data points, and therefore data points with high reconstruction error can be considered anomalies. In addition to reconstruction error, the autoencoders produce encodings. The research of this thesis studies the possibility of calculating an outlier score for the encodings and combining the score with resconstruction error to form a combined outlier score. OPTICS-OF (Ordering Points to Identify the ClusteringStructure with Outlier Factors) is a density based anomaly detection technique which can be used to calculate outlier scores for the encodings. The outlier score of OPTICS-OF for a data point is based on how isolated it is within its neighbourhood. The proposed method is evaluated on a benchmark Musk data set for which anomalies are known. A data set with labelled anomalies provides a setting for analyzing the performance of the method and its properties. The method is then put to the test on the AIS data set where it is used to find new anomalies in the data set from two derived distinct feature sets. The AIS data set contains one known anomaly which is presented both as an example of a maritime anomaly and for which more detailed analysis of the produced outlier scores are presented. The results of the study show potential for the proposed combined score method, and the analysis identifies multiple areas for further research. Deep autoencoders are successfully used to find new anomalies from the AIS data set which show actual behaviour deviating from normal ship movement

    Assessing Wireless Sensing Potential with Large Intelligent Surfaces

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    Sensing capability is one of the most highlighted new feature of future 6G wireless networks. This paper addresses the sensing potential of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the attention received by LIS in terms of communication aspects, it can offer a high-resolution rendering of the propagation environment. This is because, in an indoor setting, it can be placed in proximity to the sensed phenomena, while the high resolution is offered by densely spaced tiny antennas deployed over a large area. By treating an LIS as a radio image of the environment relying on the received signal power, we develop techniques to sense the environment, by leveraging the tools of image processing and machine learning. Once a holographic image is obtained, a Denoising Autoencoder (DAE) network can be used for constructing a super-resolution image leading to sensing advantages not available in traditional sensing systems. Also, we derive a statistical test based on the Generalized Likelihood Ratio (GLRT) as a benchmark for the machine learning solution. We test these methods for a scenario where we need to detect whether an industrial robot deviates from a predefined route. The results show that the LIS-based sensing offers high precision and has a high application potential in indoor industrial environments.Comment: arXiv admin note: text overlap with arXiv:2006.0656

    Intelligent Sensing and Learning for Advanced MIMO Communication Systems

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    Advanced Techniques for Ground Penetrating Radar Imaging

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    Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

    Applying Machine Learning Techniques to Improve Safety and Mobility of Urban Transportation Systems Using Infrastructure- and Vehicle-Based Sensors

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    The importance of sensing technologies in the field of transportation is ever increasing. Rapid improvements of cloud computing, Internet of Vehicles (IoV), and intelligent transport system (ITS) enables fast acquisition of sensor data with immediate processing. Machine learning algorithms provide a way to classify or predict outcomes in a selective and timely fashion. High accuracy and increased volatility are the main features of various learning algorithms. In this dissertation, we aim to use infrastructure- and vehicle-based sensors to improve safety and mobility of urban transportation systems. Smartphone sensors were used in the first study to estimate vehicle trajectory using lane change classification. It addresses the research gap in trajectory estimation since all previous studies focused on estimating trajectories at roadway segments only. Being a mobile application-based system, it can readily be used as on-board unit emulators in vehicles that have little or no connectivity. Secondly, smartphone sensors were also used to identify several transportation modes. While this has been studied extensively in the last decade, our method integrates a data augmentation method to overcome the class imbalance problem. Results show that using a balanced dataset improves the classification accuracy of transportation modes. Thirdly, infrastructure-based sensors like the loop detectors and video detectors were used to predict traffic signal states. This system can aid in resolving the complex signal retiming steps that is conventionally used to improve the performance of an intersection. The methodology was transferred to a different intersection where excellent results were achieved. Fourthly, magnetic vehicle detection system (MVDS) was used to generate traffic patterns in crash and non-crash events. Variational Autoencoder was used for the first time in this study as a data generation tool. The results related to sensitivity and specificity were improved by up to 8% as compared to other state-of-the-art data augmentation methods
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