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arXiv 2023年

作者： Kymn, Christopher J. Kleyko, Denis Frady, E. Paxon Bybee, Connor Kanerva, Pentti Sommer, Friedrich T. Olshausen, Bruno A. Redwood Center for Theoretical Neuroscience University of California BerkeleyCA United States Centre for Applied Autonomous Sensor Systems Örebro University Sweden Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Neuromorphic Computing Lab Intel Santa ClaraCA United States

We introduce Residue Hyperdimensional Computing, a computing framework that unifies residue number systems with an algebra defined over random, high-dimensional vectors. We show how residue numbers can be represented as high-dimensional vectors in a manner that allows algebraic operations to be performed with component-wise, parallelizable operations on the vector elements. The resulting framework, when combined with an efficient method for factorizing high-dimensional vectors, can represent and operate on numerical values over a large dynamic range using vastly fewer resources than previous methods, and it exhibits impressive robustness to noise. We demonstrate the potential for this framework to solve computationally difficult problems in visual perception and combinatorial optimization, showing improvement over baseline methods. More broadly, the framework provides a possible account for the computational operations of grid cells in the brain, and it suggests new machine learning architectures for representing and manipulating numerical data. © 2023, CC BY-SA.

关键词： Combinatorial optimization

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Gas Source Localization Based on Binary Sensing with a UAV

Gas Source Localization Based on Binary Sensing with a UAV

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ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)

作者： Thomas Wiedemann Marius Schaab Juan Marchal Gomez Dmitriy Shutin Monika Scheibe Achim J. Lilienthal Institute of Communications and Navigation German Aerospace Center (DLR) Oberpfaffenhofen Germany Institute of Atmospheric Physics German Aerospace Center (DLR) Oberpfaffenhofen Germany Center for Applied Autonomous Sensor Systems &#x00D6 rebro University Sweden

Precise gas concentration measurements are often difficult, especially by in-situ sensors mounted on an Unmanned Aerial Vehicle (UAV). Simple gas detection, on the other hand, is more robust and reliable, yet brings significantly less information for gas source localization. In this paper, we compensate for the lack of information by a physical model of gas propagation based on the advection-diffusion Partial Differential Equation (PDE). By linking binary gas detection measurements to computed gas concentration using the physical model and an appropriately designed likelihood function, it becomes possible to identify the most likely gas source distribution. The approach was validated in two experiments with ethanol and smoke as “toy” gasses. It is shown that the method is able to successfully localize the source locations in experiments based on gas detection measurements taken by a UAV.

关键词： Location awareness Current measurement Computational modeling Toy manufacturing industry Position measurement autonomous aerial vehicles Mathematical models

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From statistical relational to neural-symbolic artificial intelligence 29

From statistical relational to neural-symbolic artificial in...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： de Raedt, Luc Dumancic, Sebastijan Manhaeve, Robin Marra, Giuseppe KU Leuven Department of Computer Science and Leuven.AI Belgium Örebro University Center for Applied Autonomous Sensor Systems Sweden

ISBN: (纸本)9780999241165

Neural-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. This survey identifies several parallels across seven different dimensions between these two fields. These cannot only be used to characterize and position neural-symbolic artificial intelligence approaches but also to identify a number of directions for further research. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Artificial intelligence

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Mathematical Model Validation of Search Protocols in MP2P Networks

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Computers, Materials & Continua 2021年第8期68卷 1807-1829页

作者： Ajay Arunachalam Vinayakumar Ravi Moez Krichen Roobaea Alroobaea Saeed Rubaiee Centre for Applied Autonomous Sensor Systems(AASS) Örebro UniversityÖrebroSweden Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Faculty of CSIT Al-Baha UniversitySaudi Arabia ReDCAD LaboratoryUniversity of SfaxTunisia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringUniversity of JeddahJeddahSaudi Arabia

Broadcasting is a basic technique in Mobile ad-hoc network(MANET),and it refers to sending a packet from one node to every other node within the transmission *** is a type of broadcast where the received packet is retransmitted once by every *** naive flooding technique,floods the network with query messages,while the random walk technique operates by contacting the subsets of every node’s neighbors at each step,thereby restricting the search *** of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried *** earlier works have mainly focused on the simulation-based analysis of flooding,and its variants under a wired ***,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of P2P systems running over *** this paper,we describe how P2P resource discovery protocols perform badly over *** address the limitations,we propose a new protocol named ABRW(Address Broadcast Random Walk),which is a lightweight search approach,designed considering the underlay topology aimed to better suit the unstructured *** provide the mathematical model,measuring the performance of our proposed search scheme with different widely popular benchmarked search ***,we also derive three relevant search performance metrics,i.e.,mean *** steps needed to find a resource,the probability of finding a resource,and the mean *** message *** validated the analytical expressions through *** simulation results closely matched with our analyticalmodel,justifying our *** proposed search algorithm under such highly dynamic self-evolving networks performed better,as it reduced the search latency,decreased the overall message overhead,and still equally had a good success rate.

关键词： Search protocols random walk MANET P2P networks P2P MANET mathematical model peer-to-peer wireless ad hoc network flooding mobile P2P unstructured P2P NS-2 network simulator

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DARKO-Nav: Hierarchical Risk and Context-Aware Robot Navigation in Complex Intralogistic Environments 16th

DARKO-Nav: Hierarchical Risk and Context-Aware Robot Naviga...

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16th European Robotics Forum, ERF 2025

作者： Stracca, Elena Rudenko, Andrey Palmieri, Luigi Salaris, Paolo Castri, Luca Mazzi, Nicolò Rakcevic, Vasilije Vaskevicius, Narunas Linder, Timm Bellotto, Nicola Schreiter, Tim Zhu, Yufei Castellano Quero, Manuel Napolitano, Olga Stefanini, Elisa Heuer, Lukas Magnusson, Martin Swikir, Abdalla J. Lilienthal, Achim Research Center "E. Piaggio" Department of Information Engineering University of Pisa Pisa Italy Corporate Sector Research and Advance Engineering Robert Bosch GmbH Gerlingen Germany University of Lincoln Lincoln United Kingdom Spindox Labs Povo Italy Technical University of Munich Munich Germany University of Padua Padua Italy Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden

ISBN: (纸本)9783031894701

We propose a flexible hierarchical navigation stack for a mobile robot in complex dynamic environments. Addressing the growing need for reliable navigation in real-world scenarios, where dynamic agents and environmental uncertainties pose significant challenges, our solution decomposes this complexity into task planning, navigation, control, and safe velocity components. In contrast to the prior art, our system at every level incorporates diverse contextual information about the environment, anticipates navigation risks and proactively avoids collisions with dynamic agents. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Risk management

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Decision explanation: Applying contextual importance and contextual utility in affect detection

Decision explanation: Applying contextual importance and con...

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2020 Italian Workshop on Explainable Artificial Intelligence, XAI.it 2020

作者： Fouladgar, Nazanin Alirezaie, Marjan Främling, Kary Department of Computing Science Umeå University Sweden Center for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Aalto University School of Science and Technology Finland

Explainable AI has recently paved the way to justify decisions made by black-box models in various areas. However, a mature body of work in the field of affect detection is still limited. In this work, we evaluate a black-box outcome explanation for understanding humans’ affective states. We employ two concepts of Contextual Importance (CI) and Contextual Utility (CU), emphasizing on a context-aware decision explanation of a non-linear model, mainly a neural network. The neural model is designed to detect the individual mental states measured by wearable sensors to monitor the human user’s well-being. We conduct our experiments and outcome explanation on WESAD and MAHNOB-HCI, as multimodal affect computing datasets. The results reveal that in the first experiment the electrodermal activity, respiration as well as accelorometer and in the second experiment the electrocardiogram and respiration signals contribute significantly in the classification task of mental states for a specific participant. To the best of our knowledge, this is the first study leveraging the CI and CU concepts in outcome explanation of an affect detection model. © 2020 CEUR-WS. All rights reserved.

关键词： Knowledge management

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Usability Testing for the Operation of a Mobile Robotic Telepresence System by Older Adults

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2021年第1期64卷 1191 - 1195页

作者： Samuel Olatunji Andre Potenza Tal Oron-Gilad Andrey Kiselev Amy Loutfi Yael Edan Dept. of industrial Engineering and Management Ben-Gurion University of the Negev Beer-Sheva Israel Center for Applied Autonomous Sensor Systems (AASS) Örebro University 70182 Örebro Sweden

Mobile robotic telepresence (MRP) systems feature a video conferencing interface on a mobile robot, enabling pilot users to remotely control the robot while communicating with a local user. For older adults in an assisted living facility, the operators are mostly caregivers or remote family members. This small-sample usability testing aimed to evaluate the use of MRP by the older adult. Participants navigated the robot to locations in the home, e.g., to check if the front-door is closed. Two levels of automation were introduced; assisted teleoperation and autonomous. Observations revealed that the older adults enjoyed the dexterity with which the robot could be teleoperated in the assisted teleoperation mode. Yet, they preferred the operation of the MRP at the autonomous mode where the robot navigated autonomously towards the locations the user indicated. Usability, preference and objective findings raise awareness regarding elder care assistive robot developmental factors. Future experimental plans are discussed.

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Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

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Computers, Materials & Continua 2021年第7期68卷 983-1001页

作者： Ajay Arunachalam Vinayakumar Ravi Moez Krichen Roobaea Alroobaea Jehad Saad Alqurni Centre for Applied Autonomous Sensor Systems(AASS) Örebro UniversityÖrebroSweden Center for Articial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Faculty of CSIT Al-Baha UniversitySaudi Arabia ReDCAD LaboratoryUniversity of SfaxTunisia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Education Technologies College of EducationImam Abdulrahman Bin Faisal UniversitySaudi Arabia

One of the key challenges in ad-hoc networks is the resource discovery *** efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),and it refers to sending a packet from one node to every other node within the transmission *** is a type of broadcast where the received packet is retransmitted once by every *** naive ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search *** earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network ***,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P *** this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT *** provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed *** analytically derive 3 relevant key performance measures,i.e.,the *** of steps needed to nd a resource,the probability of locating a resource,and the *** of messages generated during the entire search process.

关键词： Mathematical model MANET P2P networks P2P MANET unstructured search algorithms Peer-to-Peer ad-hoc ooding random walk resource discovery content discovery mobile peer-to-peer broadcast peer

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Composing complex and hybrid AI solutions

arXiv

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arXiv 2022年

作者： Schüller, Peter Costeira, João Paulo Crowley, James Grosinger, Jasmin Ingrand, Félix Köckemann, Uwe Saffotti, Alessandro Welss, Martin Knowledge-based Systems Group Technische Universität Wien Vienna Austria Institute for Systems and Robotics Técnico Lisboa Lisbon Portugal Univ. Grenoble Alpes CNRS Inria Grenoble France Center for Applied Autonomous Sensor Systems Örebro University Sweden LAAS-CNRS University of Toulouse France Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Germany

Progress in several areas of computer science has been enabled by comfortable and efficient means of experimentation, clear interfaces, and interchangable components, for example using OpenCV for computer vision or ROS for robotics. We describe an extension of the Acumos system towards enabling the above features for general AI applications. Originally, Acumos was created for telecommunication purposes, mainly for creating linear pipelines of machine learning components. Our extensions include support for more generic components with gRPC/Protobuf interfaces, automatic orchestration of graphically assembled solutions including control loops, sub-component topologies, and event-based communication, and provisions for assembling solutions which contain user interfaces and shared storage areas. We provide examples of deployable solutions and their interfaces. The framework is deployed at http://***/ and its source code is managed as an open source Eclipse project. © 2022, CC BY.

关键词： User interfaces

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Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data

Ensemble of Sparse Gaussian Process Experts for Implicit Sur...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Johannes A. Stork Todor Stoyanov Center for Applied Autonomous Sensor Systems (AASS) and Autonomous Mobile Manipulation Lab Örebro University Sweden

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Creating maps is an essential task in robotics and provides the basis for effective planning and navigation. In this paper, we learn a compact and continuous implicit surface map of an environment from a stream of range data with known poses. For this, we create and incrementally adjust an ensemble of approximate Gaussian process (GP) experts which are each responsible for a different part of the map. Instead of inserting all arriving data into the GP models, we greedily trade-off between model complexity and prediction error. Our algorithm therefore uses less resources on areas with few geometric features and more where the environment is rich in variety. We evaluate our approach on synthetic and real-world data sets and analyze sensitivity to parameters and measurement noise. The results show that we can learn compact and accurate implicit surface models under different conditions, with a performance comparable to or better than that of exact GP regression with subsampled data.

关键词： Surface treatment Data models Predictive models Covariance matrices Gaussian processes Computational modeling Measurement uncertainty

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