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Publications (10 of 124) Show all publications
Ilie, D., Grahn, H., Lundberg, L., Westerhagen, A., Granbom, B. & Höök, A. (2023). Avoiding Detection by Hostile Nodes in Airborne Tactical Networks. Future Internet, 15(6), Article ID 204.
Open this publication in new window or tab >>Avoiding Detection by Hostile Nodes in Airborne Tactical Networks
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2023 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 15, no 6, article id 204Article in journal (Refereed) Published
Abstract [en]

Contemporary airborne radio networks are usually implemented using omnidirectional antennas. Unfortunately, such networks suffer from disadvantages such as easy detection by hostile aircraft and potential information leakage. In this paper, we present a novel mobile ad hoc network (MANET) routing protocol based on directional antennas and situation awareness data that utilizes adaptive multihop routing to avoid sending information in directions where hostile nodes are present. Our protocol is implemented in the OMNEST simulator and evaluated using two realistic flight scenarios involving 8 and 24 aircraft, respectively. The results show that our protocol has significantly fewer leaked packets than comparative protocols, but at a slightly higher cost in terms of longer packet lifetime.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
mobile ad hoc networks, routing, protocol
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25214 (URN)10.3390/fi15060204 (DOI)001017172700001 ()2-s2.0-85163779771 (Scopus ID)
Projects
NFFP7 (Call 2)-Riktad luftdatalänk
Funder
Vinnova
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-08-07Bibliographically approved
Sundstedt, V., Boeva, V., Zepernick, H.-J., Goswami, P., Cheddad, A., Tutschku, K., . . . Arlos, P. (2023). HINTS: Human-Centered Intelligent Realities. In: Håkan Grahn, Anton Borg and Martin Boldt (Ed.), 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023: . Paper presented at 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023, Karlskrona, June 12-13, 2023 (pp. 9-17). Linköping University Electronic Press
Open this publication in new window or tab >>HINTS: Human-Centered Intelligent Realities
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2023 (English)In: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023 / [ed] Håkan Grahn, Anton Borg and Martin Boldt, Linköping University Electronic Press, 2023, p. 9-17Conference paper, Published paper (Refereed)
Abstract [en]

During the last decade, we have witnessed a rapiddevelopment of extended reality (XR) technologies such asaugmented reality (AR) and virtual reality (VR). Further, therehave been tremendous advancements in artificial intelligence(AI) and machine learning (ML). These two trends will havea significant impact on future digital societies. The vision ofan immersive, ubiquitous, and intelligent virtual space opensup new opportunities for creating an enhanced digital world inwhich the users are at the center of the development process,so-calledintelligent realities(IRs).The “Human-Centered Intelligent Realities” (HINTS) profileproject will develop concepts, principles, methods, algorithms,and tools for human-centered IRs, thus leading the wayfor future immersive, user-aware, and intelligent interactivedigital environments. The HINTS project is centered aroundan ecosystem combining XR and communication paradigms toform novel intelligent digital systems.HINTS will provide users with new ways to understand,collaborate with, and control digital systems. These novelways will be based on visual and data-driven platforms whichenable tangible, immersive cognitive interactions within realand virtual realities. Thus, exploiting digital systems in a moreefficient, effective, engaging, and resource-aware condition.Moreover, the systems will be equipped with cognitive featuresbased on AI and ML, which allow users to engage with digitalrealities and data in novel forms. This paper describes theHINTS profile project and its initial results. ©2023, Copyright held by the authors   

Place, publisher, year, edition, pages
Linköping University Electronic Press, 2023
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 199
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:bth-25413 (URN)10.3384/ecp199001 (DOI)9789180752749 (ISBN)
Conference
35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023, Karlskrona, June 12-13, 2023
Funder
Knowledge Foundation, 20220068
Available from: 2023-09-22 Created: 2023-09-22 Last updated: 2023-12-28Bibliographically approved
van Dreven, J., Boeva, V., Abghari, S., Grahn, H., Al Koussa, J. & Motoasca, E. (2023). Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities. Electronics, 12(6), Article ID 1448.
Open this publication in new window or tab >>Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities
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2023 (English)In: Electronics, E-ISSN 2079-9292, Vol. 12, no 6, article id 1448Article in journal (Refereed) Published
Abstract [en]

This paper presents a comprehensive survey of state-of-the-art intelligent fault detection and diagnosis in district heating systems. Maintaining an efficient district heating system is crucial, as faults can lead to increased heat loss, customer discomfort, and operational cost. Intelligent fault detection and diagnosis can help to identify and diagnose faulty behavior automatically by utilizing artificial intelligence or machine learning. In our survey, we review and discuss 57 papers published in the last 12 years, highlight the recent trends, identify current research gaps, discuss the limitations of current techniques, and provide recommendations for future studies in this area. While there is an increasing interest in the topic, and the past five years have shown much advancement, the absence of open-source high-quality labeled data severely hinders progress. Future research should aim to explore transfer learning, domain adaptation, and semi-supervised learning to improve current performance. Additionally, a researcher should increase knowledge of district heating data using data-centric approaches to establish a solid foundation for future fault detection and diagnosis in district heating.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
artificial intelligence, data mining, machine learning, review
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-24457 (URN)10.3390/electronics12061448 (DOI)000958374200001 ()2-s2.0-85152400101 (Scopus ID)
Funder
Knowledge Foundation, 20220068
Available from: 2023-04-20 Created: 2023-04-20 Last updated: 2023-04-28Bibliographically approved
Nordahl, C., Boeva, V. & Grahn, H. (2023). MultiStream EvolveCluster. In: The 36th Canadian Conference on Artificial Intelligence: . Paper presented at The 36th Canadian Conference on Artificial Intelligence, Montreal, 5-9 June 2023.
Open this publication in new window or tab >>MultiStream EvolveCluster
2023 (English)In: The 36th Canadian Conference on Artificial Intelligence, 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a novel multi-stream clustering algorithm, MultiStream EvolveCluster (MS-EC), that can be used for continuous and distributed monitoring and analysis ofevolving time series phenomena. It can maintain evolving clustering solutions separatelyfor each stream/view and consensus clustering solutions reflecting evolving interrelationsamong the streams. Each stream behavior can be analyzed by different clustering techniques using a distance measure and data granularity that is specially selected for it. Theproperties of the MultiStream EvolveCluster algorithm are studied and evaluated withrespect to different consensus clustering techniques, distance measures, and cluster evaluation measures in synthetic and real-world smart building datasets. Our evaluation resultsshow a stable algorithm performance in synthetic data scenarios. In the case of real-worlddata, the algorithm behavior demonstrates sensitivity to the individual streams’ data quality and the used consensus clustering technique.

Keywords
evolve clustering, data stream mining, consensus clustering
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25534 (URN)
Conference
The 36th Canadian Conference on Artificial Intelligence, Montreal, 5-9 June 2023
Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2023-11-03Bibliographically approved
Ahlstrand, J., Boldt, M., Borg, A. & Grahn, H. (2023). Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles. In: Håkan Grahn, Anton Borg and Martin Boldt (Ed.), 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023: . Paper presented at The 35th Swedish Artificial Intelligence Society (SAIS'23) annual workshop, Karlskrona, 12-13 June, 2023 (pp. 68-76). Linköping University Electronic Press
Open this publication in new window or tab >>Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles
2023 (English)In: 35th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2023 / [ed] Håkan Grahn, Anton Borg and Martin Boldt, Linköping University Electronic Press, 2023, p. 68-76Conference paper, Published paper (Refereed)
Abstract [en]

During the last decade we have witnessed how artificial intelligence (AI) have changed businesses all over the world. The customer life cycle framework is widely used in businesses and AI plays a role in each stage. However,implementing and generating value from AI in the customerlife cycle is not always simple. When evaluating the AI against business impact and value it is critical to consider both themodel performance and the policy outcome. Proper analysis of AI-derived policies must not be overlooked in order to ensure ethical and trustworthy AI. This paper presents a comprehensive analysis of the literature on AI in customer lifecycles (CLV) from an industry perspective. The study included 31 of 224 analyzed peer-reviewed articles from Scopus search result. The results show a significant research gap regardingoutcome evaluations of AI implementations in practice. This paper proposes that policy evaluation is an important tool in the AI pipeline and empathizes the significance of validating bothpolicy outputs and outcomes to ensure reliable and trustworthy AI.

Place, publisher, year, edition, pages
Linköping University Electronic Press, 2023
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 199
Keywords
artificial intelligence, customer life cycle, machine learning, policy evaluation
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25419 (URN)10.3384/ecp199007 (DOI)9789180752749 (ISBN)
Conference
The 35th Swedish Artificial Intelligence Society (SAIS'23) annual workshop, Karlskrona, 12-13 June, 2023
Note

This work was funded by Telenor Sverige AB.

Available from: 2023-09-27 Created: 2023-09-27 Last updated: 2023-09-27Bibliographically approved
Ilie, D., Grahn, H., Lundberg, L. & Westerhagen, A. (2023). Topology Control for Directed DataLinks between Airborne Platforms: Directed Air Data Link: WP3 report. Karlskrona: Blekinge Tekniska Högskola
Open this publication in new window or tab >>Topology Control for Directed DataLinks between Airborne Platforms: Directed Air Data Link: WP3 report
2023 (English)Report (Other academic)
Abstract [en]

Contemporary airborne radio networks are usually implemented using omnidirectional antennas. Unfortunately, such networks suffer from disadvantages such as easy detection by hostile aircraft and potential information leakage. In addition, tactical links used for military communication rely on NATO-specific standards such as Link 16, which are becoming outdated. 

To this end we are investigating the feasibility of replacing omnidirectional communication with directed communication, which will address the disadvantages mentioned above. In addition, we definine a communication architecture based on the conventional Ethernet and TCP/IP protocol stack, which will ease management and interoperability with existing Internet-based system 

In this report, we briefly review the TCP/IP stack and the services offerd at each layer of the stack. Furthermore, we review existing litterature involving mobile ad hoc network (MANET) protocols used for airborne networks along with various performance studies in the same area. Finally, we propose a novel MANET routing protocol based on directional antennas and situation awareness data that utilizes adaptive multihop routing to avoid sending information in directions where hostile nodes are present. 

Our protocol is implemented in the OMNEST simulator and evaluated using two realistic flight scenarios involving 8 and 24 aircraft, respectively. The results show that our protocol has significantly fewer leaked packets than comparative protocols, but at a slightly higher cost in terms of longer packet lifetime. 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2023
Keywords
routing, MANET, directed communication, FANET
National Category
Communication Systems
Research subject
Computer Science; Telecommunication Systems
Identifiers
urn:nbn:se:bth-25288 (URN)
Projects
Directed Air Data Link (Vinnova)
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2023-08-22Bibliographically approved
Nordahl, C., Boeva, V., Grahn, H. & Netz Persson, M. (2022). EvolveCluster: an evolutionary clustering algorithm for streaming data. Evolving Systems (4), 603-623
Open this publication in new window or tab >>EvolveCluster: an evolutionary clustering algorithm for streaming data
2022 (English)In: Evolving Systems, ISSN 1868-6478, E-ISSN 1868-6486, no 4, p. 603-623Article in journal (Refereed) Published
Abstract [en]

Data has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.

Place, publisher, year, edition, pages
SPRINGER HEIDELBERG, 2022
Keywords
Evolving data stream; Clustering; Data stream clustering
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-22395 (URN)10.1007/s12530-021-09408-y (DOI)000717906700001 ()2-s2.0-85119001929 (Scopus ID)
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2021-11-26 Created: 2021-11-26 Last updated: 2023-11-03Bibliographically approved
Åleskog, C., Grahn, H. & Borg, A. (2022). Recent Developments in Low-Power AI Accelerators: A Survey. Algorithms, 15(11), Article ID 419.
Open this publication in new window or tab >>Recent Developments in Low-Power AI Accelerators: A Survey
2022 (English)In: Algorithms, E-ISSN 1999-4893, Vol. 15, no 11, article id 419Article in journal (Refereed) Published
Abstract [en]

As machine learning and AI continue to rapidly develop, and with the ever-closer end of Moore’s law, new avenues and novel ideas in architecture design are being created and utilized. One avenue is accelerating AI as close to the user as possible, i.e., at the edge, to reduce latency and increase performance. Therefore, researchers have developed low-power AI accelerators, designed specifically to accelerate machine learning and AI at edge devices. In this paper, we present an overview of low-power AI accelerators between 2019–2022. Low-power AI accelerators are defined in this paper based on their acceleration target and power consumption. In this survey, 79 low-power AI accelerators are presented and discussed. The reviewed accelerators are discussed based on five criteria: (i) power, performance, and power efficiency, (ii) acceleration targets, (iii) arithmetic precision, (iv) neuromorphic accelerators, and (v) industry vs. academic accelerators. CNNs and DNNs are the most popular accelerator targets, while Transformers and SNNs are on the rise.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
survey; hardware accelerator; low-power; performance; machine learning; artificial intelligence; neural networks
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-24171 (URN)10.3390/a15110419 (DOI)000930705100001 ()
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications, C05Knowledge Foundation, 20170236
Note

open access

Available from: 2023-01-09 Created: 2023-01-09 Last updated: 2023-03-29Bibliographically approved
Lundberg, L. & Grahn, H. (2022). Research Trends, Enabling Technologies and Application Areas for Big Data. Algorithms, 15(8), Article ID 280.
Open this publication in new window or tab >>Research Trends, Enabling Technologies and Application Areas for Big Data
2022 (English)In: Algorithms, E-ISSN 1999-4893, Vol. 15, no 8, article id 280Article in journal (Refereed) Published
Abstract [en]

The availability of large amounts of data in combination with Big Data analytics has transformed many application domains. In this paper, we provide insights into how the area has developed in the last decade. First, we identify seven major application areas and six groups of important enabling technologies for Big Data applications and systems. Then, using bibliometrics and an extensive literature review of more than 80 papers, we identify the most important research trends in these areas. In addition, our bibliometric analysis also includes trends in different geographical regions. Our results indicate that manufacturing and agriculture or forestry are the two application areas with the fastest growth. Furthermore, our bibliometric study shows that deep learning and edge or fog computing are the enabling technologies increasing the most. We believe that the data presented in this paper provide a good overview of the current research trends in Big Data and that this kind of information is very useful when setting strategic agendas for Big Data research.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
survey, Big Data, telecommunication, image processing, smart cities, manufacturing, parallel processing, storage systems, cloud computing, deep learning
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-23617 (URN)10.3390/a15080280 (DOI)000846415100001 ()2-s2.0-85137271059 (Scopus ID)
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

open access

Available from: 2022-09-12 Created: 2022-09-12 Last updated: 2023-03-29Bibliographically approved
Petersson, S., Grahn, H. & Rasmusson, J. (2021). Blind Correction of Lateral Chromatic Aberration in Raw Bayer Data. IEEE Access, 9
Open this publication in new window or tab >>Blind Correction of Lateral Chromatic Aberration in Raw Bayer Data
2021 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 9Article in journal (Refereed) Published
Abstract [en]

Chromatic aberration is an error that occurs in color images due to the fact that camera lenses refract the light of different wavelengths in different angles. The common approach today to correct the error is to use a lookup table for each camera-lens combination, e.g., as in Adobe PhotoShop Lightroom or DxO Optics Pro. In this paper, we propose a method that corrects the chromatic aberration error without any priot knowledge of the camera-lens combination, and does the correction already on the bayer data, i.e., before the Raw image data is interpolated to an RGB image. We evaluate our method in comparison to DxO Optics Pro, a state-of-the-art tool based on lookup tables, using 25 test images and the variance of the color differences (VCD) metric. The results show that our blind method has a similar error correction performance as DxO Optics Pro, but without prior knowledge of the camera-lens setup. CCBYNCND

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2021
Keywords
Blind correction, Cameras, Chromatic aberration, Color, GPGPU, Image color analysis, Image edge detection, Image enhancement, Interpolation, Lenses, Optics, Structural instability, Camera lenses, Error correction, Table lookup, Adobe Photoshop, Color difference, Correction performance, Lens combination, Prior knowledge, Raw image data, State of the art, Aberrations
National Category
Media Engineering
Identifiers
urn:nbn:se:bth-22007 (URN)10.1109/ACCESS.2021.3096201 (DOI)000675195700001 ()2-s2.0-85110877841 (Scopus ID)
Funder
Knowledge Foundation, 20140032
Note

open access

Available from: 2021-08-12 Created: 2021-08-12 Last updated: 2021-09-02Bibliographically approved
Projects
Bigdata@BTH- Scalable resource-efficient systems for big data analytics [20140032]; Blekinge Institute of Technology; Publications
Yavariabdi, A., Kusetogullari, H., Celik, T., Thummanapally, S., Rijwan, S. & Hall, J. (2022). CArDIS: A Swedish Historical Handwritten Character and Word Dataset. IEEE Access, 10, 55338-55349Nordahl, C., Boeva, V., Grahn, H. & Netz Persson, M. (2022). EvolveCluster: an evolutionary clustering algorithm for streaming data. Evolving Systems (4), 603-623Devagiri, V. M., Boeva, V. & Abghari, S. (2021). A Multi-view Clustering Approach for Analysis of Streaming Data. In: Maglogiannis I., Macintyre J., Iliadis L. (Ed.), IFIP Advances in Information and Communication Technology: . Paper presented at 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021, Virtual, Online, 25 June 2021 - 27 June 2021 (pp. 169-183). Springer Science and Business Media Deutschland GmbHPetersson, S., Grahn, H. & Rasmusson, J. (2021). Blind Correction of Lateral Chromatic Aberration in Raw Bayer Data. IEEE Access, 9García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2021). Energy-Aware Very Fast Decision Tree. International Journal of Data Science and Analytics, 11(2), 105-126Borg, A., Ahlstrand, J. & Boldt, M. (2021). Improving Corporate Support by Predicting Customer e-Mail Response Time: Experimental Evaluation and a Practical Use Case. In: Filipe J., Śmiałek M., Brodsky A., Hammoudi S. (Ed.), Enterprise Information Systems: . Paper presented at 22nd International Conference on Enterprise Information Systems, ICEIS 2020, Virtual, Online, 5 May through 7 May (pp. 100-121). Springer Science and Business Media Deutschland GmbHCheddad, A. (2021). Machine Learning in Healthcare: Breast Cancer and Diabetes Cases. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): . Paper presented at AVI 2020 Workshop on Road Mapping Infrastructures for Artificial Intelligence Supporting Advanced Visual Big Data Analysis, AVI-BDA 2020 and 2nd Italian Workshop on Visualization and Visual Analytics, ITAVIS 2020, Ischia; Italy, 29 September 2020 through 29 September 2020 (pp. 125-135). Springer Science and Business Media Deutschland GmbH, 12585Cheddad, A., Kusetogullari, H., Hilmkil, A., Sundin, L., Yavariabdi, A., Aouache, M. & Hall, J. (2021). SHIBR-The Swedish Historical Birth Records: a semi-annotated dataset. Neural Computing & Applications, 33(22), 15863-15875Sidorova, J., Karlsson, S., Rosander, O., Berthier, M. & Moreno-Torres, I. (2021). Towards disorder-independent automatic assessment of emotional competence in neurological patients with a classical emotion recognition system: application in foreign accent syndrome. IEEE Transactions on Affective Computing, 12(4), 962-973Abghari, S., Boeva, V., Brage, J. & Grahn, H. (2020). A Higher Order Mining Approach for the Analysis of Real-World Datasets. Energies, 13(21), Article ID 5781.
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9947-1088

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