Mr. Dirk Pallmer Profile

Dirk Pallmer

at Fraunhofer IOSB

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Publications (4)

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Proceedings Article | 13 June 2023 Presentation + Paper

A prototype architecture of smart sensor network

Wilmuth Müller, Dirk Pallmer, Frank Reinert, Andrea Masini, Stefano Firpi

Proceedings Volume 12544, 125440T (2023) https://doi.org/10.1117/12.2663150

KEYWORDS: Sensors, Smart sensors, Sensor networks, Surveillance, Reconnaissance, Situational awareness sensors, Network architectures, Data transmission, Data storage, C4ISR

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Experiences from recent conflicts show the strong need for providing timely a precise situation picture in order to improve the situational awareness of commanders. Situational Awareness requires comprehensive information delivered at the right time and at the right place, which must be adapted to the user, his role and current task; it must be conforming to the communication and visualisation devices currently in use. Therefore, a lot of sensor data and corresponding information have to be considered, exploited and combined in a flexible way. Smart sensor suites comprising different multi-spectral imaging sensors as core elements as well as additional non-imaging sensors may contribute decisively to the needed complete situation picture. The smart sensor suites should be part of a smart sensor network – a network of sensors, databases, evaluation stations and user terminals. Its goal is to optimize the use of various information sources for military operations like situation assessment, intelligence, reconnaissance, target recognition and tracking. Such a smart sensor network will enable commanders to achieve higher levels of situational awareness. Such a smart sensor network will enable commanders to achieve higher levels of situational awareness based on increased flexibility in using combined smart sensors. This paper presents a prototype of an Open System Architecture based on a system-of-systems approach. The open system architecture enables combining different sensors in multiple physical configurations, like distributed sensors, co-located sensors combined in a single package, sensors mounted on a tower, sensors integrated in a mobile platform, and use of trigger sensors. The mode of operation is adaptable to a series of scenarios with respect to relevant objects of interest, activities to be observed, available transmission bandwidth, etc.

Proceedings Article | 21 April 2020 Paper

Reasoning with small data samples for organised crime

Wilmuth Müller, Dirk Pallmer, Dirk Mühlenberg, Ioannis Loumiotis, Konstantina Remoundou, Pavlos Kosmides, Konstantinos Demestichas

Proceedings Volume 11413, 114130D (2020) https://doi.org/10.1117/12.2557543

KEYWORDS: Data modeling, Logic, Data communications, Taxonomy, Computing systems, Data processing, Systems modeling, Machine learning

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Building upon the possibilities of technologies like big data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions and reasoning tools, even with only small data samples. Due to the fact that the MAGNETO tools have to operate on highly sensitive data from criminal investigations, the data samples provided to the tool developers have been small, scarce, and often not correlated. The project team had to overcome these drawbacks. The developed reasoning tools are based on the MAGNETO ontology and knowledge base and enables LEA officers to uncover derived facts that are not expressed in the knowledge base explicitly, as well as discover new knowledge of relations between different objects and items of data. Two reasoning tools have been implemented, a probabilistic reasoning tool based on Markov Logic Networks and a logical reasoning tool. The design of the tools and their interfaces will be presented, as well as the results provided by the tools, when applied to operational use cases.

Proceedings Article | 21 April 2020 Paper

Machine learning for discovery analytics to support criminal investigations

Wilmuth Müller, Dirk Pallmer, Dirk Mühlenberg, Ioannis Loumiotis, Konstantina Remoundou, Pavlos Kosmides, Konstantinos Demestichas

Proceedings Volume 11395, 1139504 (2020) https://doi.org/10.1117/12.2557541

KEYWORDS: Machine learning, Data fusion, Analytics, Legal, Data conversion, Information fusion, Databases, Computer programming, Visualization

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Over the last decades, criminal activities have progressively expanded into the information technology (IT) world, adding to the “traditional” criminal activities, ignoring political boundaries and legal jurisdictions. Building upon the possibilities of technologies like Big Data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support LEAs in their critical need to exploit all available resources and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions for information fusion and classification tools intended to support LEA’s investigations. The Person Fusion Tool will be responsible for finding in an underlying knowledge graph different person instances that refer to the same person and fuse these instances. The general approach, the similarity metrics, the architecture of the tool and design choices as well as measures to improve the efficiency of the tool will be presented. The tool for classifying money transfer transactions uses decision trees. This is due to a requirement of easy explainability of the classification results, which is demanded from the ethical and legal perspective of the MAGNETO project. The design of the tool, the selected implementation and an evaluation based on anonymized financial data records will be presented.

Proceedings Article | 9 May 2018 Paper

Open architecture of a counter UAV system

W. Müller, F. Reinert, D. Pallmer

Proceedings Volume 10651, 1065106 (2018) https://doi.org/10.1117/12.2305606

KEYWORDS: Unmanned aerial vehicles, Sensors, Video, Control systems, Laser range finders, Databases, Optical tracking, Cameras, Information fusion

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Government agencies and military are seeing a rise in drones used for terrorism, destruction and espionage. As recently as a decade ago, UAV technology has been used only by US military. Today, dozens of countries manufacture and operate military-grade drones. Drone technology has been made available around the world. Anyone can purchase a drone from an online retailer. Western military operations as well as critical infrastructure protection agencies experienced multiple drone incidents in the last years, ranging from the use of weaponized drones by ISIS to drones flying over airports or drones breaching airspace of other critical infrastructure. The emergence of threats caused by unfriendly or hostile drones requires a proactive drone detection in order to decide on appropriate defence actions. In this contribution, an open architecture of a UAV detection system including decision support for counter-action is presented. The system is composed of multiple deployable sensor stations, an operation center comprising the operational picture display and the decision support component, and a communication bus consisting of a message-oriented middleware connecting the sub-systems and components. The architectural design specifies the sub-systems, their constitutive components, the information and control flow between the components, the protocols used for data and information exchange, the functionality and responsibility of each component, and the functional parameters. The designed architecture provides a blueprint for a UAV detection and defence actions decision-making system which will allow public protection agencies and military to react timely against threats caused by hostile drones.

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