PhoneGuide: Museum Guidance Supported by On-Device Object Recognition on Mobile Phones

We present PhoneGuide – an enhanced museum guidance approach that uses camera-equipped mobile phones and on-device object recognition. Our main technical achievement is a simple and light-weight object recognition approach that is realized with single-layer perceptron neuronal networks. In contrast to related systems which perform computational intensive image processing tasks on remote servers, our intention is to carry out all computations directly on the phone. This ensures little or even no network traffic and consequently decreases cost for online times. Our laboratory experiments and field surveys have shown that photographed museum exhibits can be recognized with a probability of over 90%. We have evaluated different feature sets to optimize the recognition rate and performance. Our experiments revealed that normalized color features are most effective for our method. Choosing such a feature set allows recognizing an object below one second on up-to-date phones. The amount of data that is required for differentiating 50 objects from multiple perspectives is less than 6KBytes

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Validation of downward surface radiation derived from MSG data by in-situ observations over the Atlantic ocean

The present work investigates the quality of the shortwave and and longwave downward radiation (DSR, DLR) at the sea surface over the Atlantic Ocean as retrieved from Meteosat Second Generation (MSG) measurements and EUMETSAT's Climate Monitoring - Satellite Application Facility (CM-SAF) algorithms. The observations taken at two transatlantic research cruises have been an ideal basis to be compared with the MSG products for DSR and DLR derived from Meteosat-8 and Meteosat-9. Onboard the research vessels "Akademik Ioffe" and "Polarstern" high quality in situ measurements of both radiation fluxes have been performed. Continuous full sky imagery and standard meteorological observations enable a comprehensive evaluation of the skills of MSG DSR- and DLR-retrievals in different climate zones and under various cloud and weather conditions. The DSR was retrieved by MSG with a positive bias of 2.77 Wm−2 during the Ioffe cruise, and 22.23 Wm−2 during the Polarstern cruise. The bias for the DLR was −1.73 Wm−2 and 2.76 Wm−2, respectively. The differences between the two cruises mainly arise from the different weather conditions. No significant differences between the satellite products from Meteosat-8 and Meteosat-9 were found. In general DSR and DLR for clear sky conditions are captured with a high accuracy. Largest retrieval errors occur for fast fluctuating broken cloud conditions, though on average the MSG algorithm match the in-situ observations well. Semitransparent cirrus was found to cause a negative bias for the retrieved DSR. In tropics and subtropics the errors for DLR are smaller compared to higher latitudes. Most importantly, no significant dependencies of the satellite retrieval errors for both the DSR and the DLR on the solar elevation, near-surface humidity, cloud cover, SST and the shift of day and night were found, indicating that the CM-SAF radiation products are not subject to significant systematic errors. Diese Arbeit evaluiert die Qualität der abwärtsgerichteten kurzwelligen Einstrahlung (DSR) und der abwärtsgerichteten langwelligen Gegenstrahlung (DLR) an der Meeresoberfläche des Atlantischen Ozeans, berechnet aus Fernerkundungsdaten von Meteosat Second Generation (MSG) mit Hilfe der EUMETSAT Climate Monitoring - Satellite Application Facility (CM-SAF) - Algorithmen. Die auf zwei transatlantischen Forschungsfahrten gewonnenen Beobachtungsdaten stellen eine ideale Basis für den Vergleich mit den MSG-Produkten DSR und DLR dar, die aus Daten des Meteosat-8 und Meteosat-9 abgeleitet wurden. An Bord der Forschungsschiffe Akademik Ioffe und Polarstern wurden hochwertige in situ Messungen beider Strahlungsflüsse durchgeführt. Kontinuierliche Sequenzen der Wolkenkamera in Verbindung mit meteorologischen Standardmessungen ermöglichen diese Vergleichsstudie mit den Ergebnissen der MSG-Algorithmen für DSR und DLR in unterschiedlichen Klimazonen und unter verschiedensten Wolken- und Wetterbedingungen. Für die Fahrt der Ioffe zeigte die DSR abgeleitet aus MSG-Daten eine Überschätzung von 2.77 Wm−2, für die Fahrt der Polarstern wurden 22.23 Wm−2 ermittelt. Der systematische Fehler der DLR war −1.73 Wm−2 bzw. 2.76 Wm−2. Die unterschiedlichen Werte der beiden Fahrten resultieren hauptsächlich aus den verschiedenen Wetterbedingungen. Durch den zeitlichen Überlapp konnten Satellitenprodukte von Meteosat-8 und Meteosat-9 verglichen werden, die keine signifikanten Unterschiede zeigten. Im Allgemeinen werden DSR und DLR im wolkenfreien Fall mit hoher Genauigkeit wiedergegeben. Die größten Fehler im Algorithmus kommen bei sich schnell ändernder Cumulusbedeckung vor, wobei die berechneten Einstrahlungen im Mittel gut mit den in situ Messungen übereinstimmen. Semitransparenter Cirrus verursacht Unterschätzungen in der abgeleiteten DSR. In Tropen und Subtropen sind die Fehler in der DLR geringer als in hohen Breiten. Wichtig ist die Tatsache, dass der Fehler für den Satellitenalgorithmus sowohl für DSR als auch für DLR keine signifikanten Abhängigkeiten von dem Sonnenstand, von der Luftfeuchtigkeit in Bodennähe, vom Wolkenbedeckungsgrad, von der SST und vom Tag-Nacht-Unterschied zeigen. Dies weißt darauf hin, dass die CM-SAF Strahlungsprodukte keinen signifikanten systematischen Fehlern unterliegen

Artificial Intelligence for Predictive Maintenance of Armoured Fighting Vehicles Engine

Armoured Fighting Vehicles (AFVs) also called as Tanks play a critical role in modern warfare, providing mobility, protection and firepower on the battlefield. However, maintaining these complex machines and ensuring their operational readiness is a significant challenge for military organizations. Traditional maintenance practices are often reactive, resulting in unexpected failures, increased downtime, and operational inefficiencies. This paper focuses on the application of Artificial Intelligence (AI) for predictive maintenance of Armoured Fighting Vehicles. By harnessing the power of AI algorithms and advanced data analytics, predictive maintenance aims to anticipate and address potential equipment failures before they occur. This proactive approach enables military organizations to optimize resource allocation, improve operational planning and extend the lifespan of AFVs. The integration of AI in predictive maintenance involves collecting and analysing data from various sensors installed on the AFV engine. These sensors monitor key parameters, such as engine performance, temperature, vibration and fluid levels to detect anomalies and deviations from normal operating conditions. AI algorithms process this data, utilizing machine learning techniques to identify patterns, correlations, and potential failure indicators. The benefits of AI-based predictive maintenance for AFVs are multifaceted. Firstly, it enhances equipment readiness by reducing unexpected failures and maximizing operational availability. Secondly, it enables optimized resource allocation, ensuring that maintenance activities are scheduled efficiently, minimizing downtime, and improving overall operational efficiency. Thirdly, the predictive capabilities of AI help military planners in better decision-making allowing for improved mission planning and execution. However, the successful implementation of AI for predictive maintenance of AFV engine requires overcoming several challenges. These include data collection and integration from diverse sensors, ensuring data accuracy and quality, establishing robust communication infrastructure, and addressing cyber security concerns to protect sensitive vehicle data. This paper underscores the growing importance of AI in revolutionizing maintenance practices for Armoured Fighting Vehicles. By shifting from reactive maintenance to predictive strategies, military organizations can enhance their operational capabilities, reduce costs, and ensure the optimal performance and longevity of their AFV fleet.Lattice Science Publication (LSP) © Copyright: All rights reserved

Smart territories

The concept of smart cities is relatively new in research. Thanks to the colossal advances in Artificial Intelligence that took place over the last decade we are able to do all that that we once thought impossible; we build cities driven by information and technologies. In this keynote, we are going to look at the success stories of smart city-related projects and analyse the factors that led them to success. The development of interactive, reliable and secure systems, both connectionist and symbolic, is often a time-consuming process in which numerous experts are involved. However, intuitive and automated tools like “Deep Intelligence” developed by DCSc and BISITE, facilitate this process. Furthermore, in this talk we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems, as well as the use of edge platforms or fog computing