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  • 1.
    Al-Rawi, Mohammed
    et al.
    Universidade de Aveiro, Portugal.
    Elmgren, Fredrik
    DeepVision AB, Linköping, Sweden.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Yuan, Xin
    Universidad Politecnica de Madrid, Spain.
    Martínez, José-Fernán
    Universidad Politecnica de Madrid, Spain.
    Bastos, Joaquim
    Instituto de Telecomunicações - Pólo de Aveiro, Portugal.
    Rodriguez, Jonathan
    Universidade de Aveiro, Portugal.
    Pinto, Marc
    ECA Robotics, France.
    Algorithms for the Detection of First Bottom Returns and Objects in the Water Column in Side-Scan Sonar Images2017In: OCEANS '17 A Vision for our Marine Future OCEANS '17, Aberdeen, United Kingdom, 2017Conference paper (Refereed)
    Abstract [en]

    Underwater imaging has become an active research area in recent years as an effect of increased interest in underwater environments and is getting potential impact on the world economy, in what is called blue growth. Since sound propagates larger distances than electromagnetic waves underwater, sonar is typically used for underwater imaging. One interesting sonar image setting is comprised of using two parts (left and right) and is usually referred to as sidescan sonar. The image resulted from sidescan sonars, which is called waterfall image, usually has to distinctive parts, the water column and the image seabed. Therefore, the edge separating these two parts, which is called the first bottom return, is the real distance between the sonar and the seabed bottom (which is equivalent to sensor primary altitude). The sensory primary altitude can be measured if the imaging sonar is complemented by interferometric sonar, however, simple sonar systems have no way to measure the first bottom returns other than signal processing techniques. In this work, we propose two methods to detect the first bottom returns; the first is based on smoothing cubic spline regression and the second is based on a moving average filter to detect signal variations. The results of both methods are compared to the sensor primary altitude and have been successful in 22 images out of 25.

  • 2.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering.
    Collaborative Adaptive Autonomous Agents2018Licentiate thesis, comprehensive summary (Other academic)
  • 3.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Analysis of perceived helpfulness in adaptive autonomous agent populations2018In: Transactions on Computational Collective Intelligence XXVIII, Springer Verlag , 2018, Vol. 10780, p. 221-252Conference paper (Refereed)
    Abstract [en]

    Adaptive autonomy allows agents to change their autonomy levels based on circumstances, e.g. when they decide to rely upon one another for completing tasks. In this paper, two configurations of agent models for adaptive autonomy are discussed. In the former configuration, the adaptive autonomous behavior is modeled through the willingness of an agent to assist others in the population. An agent that completes a high number of tasks, with respect to a predefined threshold, increases its willingness, and vice-versa. Results show that, agents complete more tasks when they are willing to give help, however the need for such help needs to be low. Agents configured to be helpful will perform well among alike agents. The second configuration extends the first by adding the willingness to ask for help. Furthermore, the perceived helpfulness of the population and of the agent asking for help are used as input in the calculation of the willingness to give help. Simulations were run for three different scenarios. (i) A helpful agent which operates among an unhelpful population, (ii) an unhelpful agent which operates in a helpful populations, and (iii) a population split in half between helpful and unhelpful agents. Results for all scenarios show that, by using such trait of the population in the calculation of willingness and given enough interactions, helpful agents can control the degree of exploitation by unhelpful agents. © Springer International Publishing AG, part of Springer Nature 2018.

  • 4.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Comparison Between Static and Dynamic Willingness to Interact in Adaptive Autonomous Agents2018In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence, 2018, Vol. 1, p. 258-267Conference paper (Refereed)
    Abstract [en]

    Adaptive autonomy (AA) is a behavior that allows agents to change their autonomy levels by reasoning on their circumstances. Previous work has modeled AA through the willingness to interact, composed of willingness to ask and give assistance. The aim of this paper is to investigate, through computer simulations, the behavior of agents given the proposed computational model with respect to different initial configurations, and level of dependencies between agents. Dependency refers to the need for help that one agent has. Such need can be fulfilled by deciding to depend on other agents. Results show that, firstly, agents whose willingness to interact changes during run-time perform better compared to those with static willingness parameters, i.e. willingness with fixed values. Secondly, two strategies for updating the willingness are compared, (i) the same fixed value is updated on each interaction, (ii) update is done on the previous calculated value. The maximum number of completed tasks which need assistance is achieved for (i), given specific initial configurations.

  • 5.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Adaptive Autonomy in a Search and Rescue ScenarioConference paper (Refereed)
  • 6.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Cürüklü, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Towards Collaborative Adaptive Autonomous Agents2017In: ICAART: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1, 2017, p. 78-87Conference paper (Refereed)
    Abstract [en]

    Adaptive autonomy enables agents operating in an environment to change, or adapt, their autonomy levels by relying on tasks executed by others. Moreover, tasks could be delegated between agents, and as a result decision-making concerning them could also be delegated. In this work, adaptive autonomy is modeled through the willingness of agents to cooperate in order to complete abstract tasks, the latter with varying levels of dependencies between them. Furthermore, it is sustained that adaptive autonomy should be considered at an agent's architectural level. Thus the aim of this paper is two-fold. Firstly, the initial concept of an agent architecture is proposed and discussed from an agent interaction perspective. Secondly, the relations between static values of willingness to help, dependencies between tasks and overall usefulness of the agents' population are analysed. The results show that a unselfish population will complete more tasks than a selfish one for low dependency degrees. However, as the latter increases more tasks are dropped, and consequently the utility of the population degrades. Utility is measured by the number of tasks that the population completes during run-time. Finally, it is shown that agents are able to finish more tasks by dynamically changing their willingness to cooperate.

  • 7.
    Frasheri, Mirgita
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Trinh, LanAnh
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. ().
    Failure Analysis for Adaptive Autonomous Agents using Petri Nets2017In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, 2017, p. 293-297Conference paper (Refereed)
    Abstract [en]

    Adaptive autonomous (AA) agents are able to make their own decisions on when and with whom to share their autonomy based on their states. Whereas dependability gives evidence on whether a system, (e.g. an agent team), and its provided services are to be trusted. In this paper, an initial analysis on AA agents with respect to dependability is conducted. Firstly, AA is modeled through a pairwise relationship called willingness of agents to interact, i.e. to ask for and give assistance. Secondly, dependability is evaluated by considering solely the reliability attribute, which presents the continuity of correct services. The failure analysis is realized by modeling the agents through Petri Nets. Simulation results indicate that agents drop slightly more tasks when they are more willing to interact than otherwise, especially when the fail-rate of individual agents increases. Conclusively, the willingness should be tweaked such that there is compromise between performance and helpfulness.

  • 8.
    Rodríguez-Molina, J.
    et al.
    Centro de Investigación en Tecnologías Software y Sistemas Multimedia Para la Sostenibilidad—CITSEM, Madrid, Spain.
    Bilbao, S.
    TECNALIA, Derio, Bizkaia, Spain.
    Martínez, B.
    TECNALIA, Derio, Bizkaia, Spain.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    An optimized, data distribution service-based solution for reliable data exchange among autonomous underwater vehicles2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 8, article id 1802Article in journal (Refereed)
    Abstract [en]

    Major challenges are presented when managing a large number of heterogeneous vehicles that have to communicate underwater in order to complete a global mission in a cooperative manner. In this kind of application domain, sending data through the environment presents issues that surpass the ones found in other overwater, distributed, cyber-physical systems (i.e., low bandwidth, unreliable transport medium, data representation and hardware high heterogeneity). This manuscript presents a Publish/Subscribe-based semantic middleware solution for unreliable scenarios and vehicle interoperability across cooperative and heterogeneous autonomous vehicles. The middleware relies on different iterations of the Data Distribution Service (DDS) software standard and their combined work between autonomous maritime vehicles and a control entity. It also uses several components with different functionalities deemed as mandatory for a semantic middleware architecture oriented to maritime operations (device and service registration, context awareness, access to the application layer) where other technologies are also interweaved with middleware (wireless communications, acoustic networks). Implementation details and test results, both in a laboratory and a deployment scenario, have been provided as a way to assess the quality of the system and its satisfactory performance. 

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