No abstract

Bio-inspired Event-Based (EB) cameras are a promising new technology that outperforms standard frame-based cameras in extreme lighted and fast moving scenes. Already, a number of EB corner detection techniques have been developed; however, the performance of these EB corner detectors has only been evaluated based on a few author-selected criteria rather than on a unified common basis, as proposed here. Moreover, their experimental conditions are mainly limited to less interesting operational regions of the EB camera (on which frame-based cameras can also operate), and some of the criteria, by definition, could not distinguish if the detector had any systematic bias. In this paper, we evaluate five of the seven existing EB corner detectors on a public dataset including extreme illumination conditions that have not been investigated before. Moreover, this evaluation is the first of its kind in terms of analysing not only such a high number of detectors, but also applying a unified procedure for all. Contrary to previous assessments, we employed both the intensity and trajectory information within the public dataset rather than only one of them. We show that a rigorous comparison among EB detectors can be performed without tedious manual labelling and even with challenging acquisition conditions. This study thus proposes the first standard unified EB corner evaluation procedure, which will enable better understanding of the underlying mechanisms of EB cameras and can therefore lead to more efficient EB corner detection techniques.

Dementia is a major health and social care challenge of today and the near future as a result of increased human lifespan. Currently, there is no therapeutic solution for dementia, but a solution for managing the wandering behavior of dementia patients can be provided by an ambient assisted living system. In this paper, the design and implementation of iCarus, which is an intelligent ambient assisted living system for dealing with wandering behavior in early stages of dementia, is described. The aim of iCarus is to provide independent living for elderly people and a cost-effective way of monitoring them. iCarus is a zone-based system that forms a safety net. When a wandering episode occurs, rule-based context reasoning is employed to determine the actions that are to be executed. These actions include warning the patient, navigating the patient to his home, sending notifications to the caregiver(s), and initiating a real-time tracking session for the caregiver and the emergency service. Also, caregivers are able to construct their own rules and extend the functionality of the system according to their own needs. Constructing new rules is done by an innovative user interface. As a case study, iCarus is described and evaluated with a scenario. In order to evaluate the usability of iCarus, a questionnaire was administered after the users tried the system. The results were then statistically analyzed and reported.

In this paper, SAMS, a novel health information system architecture for developing intelligent health information systems is proposed and also some strategies for developing such systems are discussed. The systems fulfilling this architecture will be able to store electronic health records of the patients using OWL ontologies, share patient records among different hospitals and provide physicians expertise to assist them in making decisions. The system is intelligent because it is rule-based, makes use of rule-based reasoning and has the ability to learn and evolve itself. The learning capability is provided by extracting rules from previously given decisions by the physicians and then adding the extracted rules to the system. The proposed system is novel and original in all of these aspects. As a case study, a system is implemented conforming to SAMS architecture for use by dentists in the dental domain. The use of the developed system is described with a scenario. For evaluation, the developed dental information system will be used and tried by a group of dentists. The development of this system proves the applicability of SAMS architecture. By getting decision support from a system derived from this architecture, the cognitive gap between experienced and inexperienced physicians can be compensated. Thus, patient satisfaction can be achieved, inexperienced physicians are supported in decision making and the personnel can improve their knowledge. A physician can diagnose a case, which he/she has never diagnosed before, using this system. With the help of this system, it will be possible to store general domain knowledge in this system and the personnel's need to medical guideline documents will be reduced.

In space, visual based relative navigation systems suffer from dynamic illumination conditions of the target (Eclipse conditions, solar glare...etc.) where most of these issues are addressed by advanced mission planning techniques. However, such planning would not be always feasible or even if it is, it would not be straightforward for Active Debris Removal (ADR) missions. On the other hand, using an infrared based system would overcome this problem, if a guideline to predict infrared signature of space debris based on the target thermal profile could be provided for algorithm design and testing. Spacecraft thermal design is unique to every platform. This means every ADR target will have a different infrared signature which changes over time not just only due to orbital dynamics but also due to its thermal surface coatings. In order to provide a space debris infrared signature guideline for most of the possible ADR targets, we introduce an innovative grouping system for thermal surface coatings based on their behaviour in Space environment. Through the use of this grouping system, we propose a space debris infrared signature estimation method which was extensively verified by our simulations and experiments. During our verifications, we have also found out very important problem so called "Signature Ambiguity" that is unique to Infrared Based Active Debris Removal (IR-ADR) systems which we have also discussed in our work.

No abstract

No abstract

Abstract:In this paper, the design and development of an artificial neural network (ANN) for similarity value calculation in a context-aware system is proposed. This neural network is used by the neural agent of the iConAwa system. Since iConAwa is an intelligent, context-aware, multiagent system, it provides mobile users with context-aware information and services, and also provides communication with each other. Context and points of interest are modeled in a flexible and extensible way by using ontologies. iConAwa derives high-level implicit context from low-level explicit context by inference performed over the context ontology. This approach decouples context reasoning from the source code of the system. With the addition of a neural agent, which uses an ANN, the system has learning capability. By using a neural network for similarity value calculation, the system can adapt to the needs of different people. System owners can introduce their own similarity metric considering their own requirements, which further improves the iConAwa system. Thus, the extended iConAwa system combines expert system characteristics with the capability to learn.