Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected

Land Parcel Identification System (LPIS) Anomalies' Sampling and Spatial Pattern: Towards convergence of ecological methodologies and GIS technologies

To date, the Land Parcel Identification System (LPIS) has often been proposed as the foundation for effective spatial management of agriculture and the environment and many land managers have suggested incorporating it in most of the instruments for sustainable agriculture. The LPIS is originally used for registration of agricultural reference parcels considered eligible for annual payments of European Common Agricultural Policy (CAP) subsidies to farmers. Its intrinsic quality depends on the frequency and magnitude of the discrepancies in area, since some parcels can be under- or over-declared by farmers compared with reference registered within the LPIS. General application of the LPIS therefore depends on our capacity to ¿ first identify and explain the causes of these area discrepancies perceived as anomalies by national CAP payment agencies ¿second, to propose future improvements in its overall quality. From a set of images used during the 2005 Control with Remote Sensing (CwRS) campaign, using the geographic information system (GIS) and ecological methodologies we assessed the quality of the LPIS by identifying the diversity of the existing anomalies. To that end, the ecological sampling method was adapted to the specific case of image-based detection of anomalies. The observed anomalies assemblages obtained from a set of European Member States representing the four types of LPIS were analysed to establish the spatial pattern of the anomalies. We showed that the twelve zones surveyed can be grouped into four different clusters, each individually correlated with the presence of certain categories of LPIS anomaly. Some clusters were more particularly related to the presence of natural and anthropogenic landscape features, whereas others were typified by anomalies which stemmed from the process for creating and updating the LPIS, which accounted for 20% of the anomalies detected. Finally, we also showed that, even if useful for establishing procedures to manage the LPIS, the LPIS typology used in the European Union had no effect on the anomalies assemblage or on the spatial pattern; consequently, the type of LPIS no longer needs to be considered and LPIS anomalies assemblages could be pooled across Europe. In the light of the results obtained, different proposals are made to improve LPIS quality by: ¿ identifying the critical points along the LPIS management chain; ¿ using landscape ecological methodologies to explain the causes of the clusters observed; and ¿ extrapolating the whole results in the CwRS risk analysis to perform ex-ante LPIS anomalies risk map. Keywords: Land Parcel Identification System, Control with Remote Sensing, orthophoto, quality assessment, diversity, spatial pattern, landscape structureJRC.G.3-Agricultur

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

An interpretable artificial intelligence based smart agriculture system

With increasing world population the demand of food production has increased exponentially. Internet of Things (IoT) based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time. Interpretability can be an important factor to make such systems trusted and easily adopted by farmers. In this paper, we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production. The strength of the proposed system is in its interpretability which makes it easy for farmers to understand, trust and use it. The use of fuzzy logic makes the system customisable in terms of types/number of sensors, type of crop, and adaptable for any soil types and weather conditions. The proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning algorithms. To ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type, soil moisture, soil temperature, plant life cycle, irrigation requirement and water application timing for Maize as our target crop. The experimental results show that our proposed system is interpretable, can detect anomalous data, and triggers actions accurately based on crop requirements

Detecting anomalies in remotely sensed hyperspectral signatures via wavelet transforms

An automated subpixel target detection system has been designed and tested for use with remotely sensed hyperspectral images. A database of hyperspectral signatures was created to test the system using a variety of Gaussian shaped targets. The signal-to-noise ratio of the targets varied from -95dB to -50dB. The system utilizes a wavelet-based method (discrete wavelet transform) to extract an energy feature vector from each input pixel signature. The dimensionality of the feature vector is reduced to a one-dimensional feature scalar through the process of linear discriminant analysis. Signature classification is determined by nearest mean criterion that is used to assign each input signature to one of two classes, no target present or target present. Classification accuracy ranged from nearly 60% with target SNR at -95dB without any a priori knowledge of the target, to 100% with target SNR at -50dB and a priori knowledge about the location of the target within the spectral bands of the signature