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Pignaton de Freitas, EdisonORCID iD iconorcid.org/0000-0003-4655-8889
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Publications (10 of 62) Show all publications
Oss Boll, H., Amirahmadi, A., Ghazani, M. M., Ourique de Morais, W., Pignaton de Freitas, E., Soliman, A., . . . Recamonde-Mendoza, M. (2024). Graph neural networks for clinical risk prediction based on electronic health records: A survey. Journal of Biomedical Informatics, 151, Article ID 104616.
Open this publication in new window or tab >>Graph neural networks for clinical risk prediction based on electronic health records: A survey
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2024 (English)In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 151, article id 104616Article, review/survey (Refereed) Published
Abstract [en]

Objective: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. Methods: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. Results: Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. Conclusion: GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care. © 2024 The Authors

Place, publisher, year, edition, pages
Maryland Heights, MO: Academic Press, 2024
Keywords
Artificial intelligence, Deep learning, Electronic health records, Graph neural networks, Graph representation learning, Keyword
National Category
Computer Sciences
Research subject
Health Innovation, IDC; Health Innovation, IDC
Identifiers
urn:nbn:se:hh:diva-53018 (URN)10.1016/j.jbi.2024.104616 (DOI)38423267 (PubMedID)2-s2.0-85186598720 (Scopus ID)
Note

Funding: This work was financed in part by the Swedish Council for Higher Education through the Linnaeus-Palme Partnership, Sweden (3.3.1.34.16456), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil - Finance Code 001, and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil through grants nr. 309505/2020-8 and 308075/2021-8. We also acknowledge the support from Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS), Brazil through grants nr. 22/2551-0000390-7 (Project CIARS) and 21/2551-0002052-0.

Available from: 2024-03-28 Created: 2024-03-28 Last updated: 2024-04-02Bibliographically approved
Vicenzi, J. C., Korol, G., Jordan, M. G., Ourique de Morais, W., Ali, H., Pignaton de Freitas, E., . . . Beck, A. C. (2023). Dynamic Offloading for Improved Performance and Energy Efficiency in Heterogeneous IoT-Edge-Cloud Continuum. In: 2023 IEEE Computer Society Annual Symposium on VLSI (ISVLSI): . Paper presented at 26th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023, Iguazu Falls, Brazil, 20-23 June, 2023. IEEE, 2023-June
Open this publication in new window or tab >>Dynamic Offloading for Improved Performance and Energy Efficiency in Heterogeneous IoT-Edge-Cloud Continuum
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2023 (English)In: 2023 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), IEEE, 2023, Vol. 2023-JuneConference paper, Published paper (Refereed)
Abstract [en]

While machine learning applications in IoT devices are getting more widespread, the computational and power limitations of these devices pose a great challenge. To handle this increasing computational burden, edge, and cloud solutions emerge as a means to offload computation to more powerful devices. However, the unstable nature of network connections constantly changes the communication costs, making the offload process (i.e., when and where to transfer data) a dynamic trade-off. In this work, we propose DECOS: a framework to automatically select at run-time the best offloading solution with minimum latency based on the computational capabilities of devices and network status at a given moment. We use heterogeneous devices for edge and Cloud nodes to evaluate the framework's performance using MobileNetV1 CNN and network traffic data from a real-world 4G bandwidth dataset. DECOS effectively selects the best processing node to maintain the minimum possible latency, reducing it up to 29% compared to Cloud-exclusive processing while reducing the energy consumption by 1.9times compared to IoT-exclusive execution. © 2023 IEEE.

Place, publisher, year, edition, pages
IEEE, 2023
Series
VLSI, IEEE Computer Society Annual Symposium on, ISSN 2159-3469, E-ISSN 2159-3477
Keywords
Cloud, Edge, IoT, Neural Networks, Offloading
National Category
Communication Systems
Identifiers
urn:nbn:se:hh:diva-52059 (URN)10.1109/ISVLSI59464.2023.10238564 (DOI)001066014800020 ()2-s2.0-85172134973 (Scopus ID)979-8-3503-2769-4 (ISBN)979-8-3503-2770-0 (ISBN)
Conference
26th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2023, Iguazu Falls, Brazil, 20-23 June, 2023
Note

Funding: This study was financed in part by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Brazil - Finance Code 001, São Paulo Research Foundation (FAPESP) grant #2021/06825-8, FAPERGS and CNPq. 

Available from: 2023-11-17 Created: 2023-11-17 Last updated: 2023-11-17Bibliographically approved
Kochenborger Duarte, E., Erneberg, M., Pignaton de Freitas, E., Bellalta, B. & Vinel, A. (2023). SafeSmart 6G: The Future of Emergency Vehicle Traffic Light Preemption. In: 2023 2nd International Conference on 6G Networking (6GNet): . Paper presented at 2nd International Conference on 6G Networking (6GNet 2023), Paris, France, 18-20 October, 2023. IEEE
Open this publication in new window or tab >>SafeSmart 6G: The Future of Emergency Vehicle Traffic Light Preemption
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2023 (English)In: 2023 2nd International Conference on 6G Networking (6GNet), IEEE, 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper delves into the utilization of Vehicular Ad-hoc Networks (VANETs) in emergency vehicle warning systems in the era of 6G. The proposed system, named SafeSmart 6G, will leverage VANET-based vehicle-to-infrastructure Communication powered by 6G to exchange data between traffic lights and emergency vehicles, enhancing safety and reducing response times. SafeSmart 6G will predict the arrival time of emergency vehicles at intersections using historical data and AI-driven analytics, requesting signal preemption for the chosen route. The paper discusses the potential benefits and challenges that might arise from the use of 6G in emergency scenarios. © 2023 IEEE.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
6G, C-ITS, emergency vehicle, traffic light, V2I
National Category
Telecommunications
Identifiers
urn:nbn:se:hh:diva-52320 (URN)10.1109/6GNet58894.2023.10317728 (DOI)2-s2.0-85179756967 (Scopus ID)979-8-3503-0673-6 (ISBN)979-8-3503-0674-3 (ISBN)
Conference
2nd International Conference on 6G Networking (6GNet 2023), Paris, France, 18-20 October, 2023
Projects
SafeSmart
Funder
Knowledge FoundationELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2023-12-22 Created: 2023-12-22 Last updated: 2023-12-22Bibliographically approved
Kochenborger Duarte, E., Da Costa, L. A. F., Erneberg, M., Pignaton de Freitas, E., Bellalta, B. & Vinel, A. (2021). SafeSmart: A VANET System for Faster Responses and Increased Safety in Time-Critical Scenarios. IEEE Access, 9, 151590-151606
Open this publication in new window or tab >>SafeSmart: A VANET System for Faster Responses and Increased Safety in Time-Critical Scenarios
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2021 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 151590-151606Article in journal (Refereed) Published
Abstract [en]

An important use case for Vehicular Ad-hoc Networks (VANETs) is its application in the warning systems of emergency vehicles (EV). VANET-based vehicle-to-infrastructure (V2I) communication can be used to exchange important data and information between traffic lights and EVs, by means of transceivers at both ends. This communication helps in reducing the risks of accidents and also saves valuable time through an optimized orchestration of the traffic lights. This paper outlines the system design of an EV warning system that makes use of V2I communication. The system has been extensively studied in state-of-the-art simulators, such as SUMO and OMNeT++, in a huge variety of scenarios, where metrics for both time and safety have been collected. The results show that SafeSmart is highly effective in reducing trip times as well as increasing the overall safety of EVs in emergency scenarios. © 2013 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2021
Keywords
emergency vehicles, Vehicular networks, wireless networks, wireless transceivers
National Category
Telecommunications
Identifiers
urn:nbn:se:hh:diva-46047 (URN)10.1109/ACCESS.2021.3126334 (DOI)000719552400001 ()2-s2.0-85119719131 (Scopus ID)
Projects
Safety of Connected Intelligent Vehicles in Smart Cities—SafeSmart ProjectEmergency Vehicle Traffic Light Pre-Emption in Cities—EPIC
Funder
Knowledge FoundationVinnovaELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2021-12-06 Created: 2021-12-06 Last updated: 2021-12-06Bibliographically approved
da Costa, L. A. F., Duarte, E. K., Erneberg, M., Pignaton de Freitas, E. & Vinel, A. (2020). Poster: SafeSmart - A VANET system for efficient communication for emergency vehicles. In: IFIP Networking 2020 Conference and Workshops June 22-25, 2020 - Paris, France: . Paper presented at 2020 IFIP Networking Conference and Workshops, Networking 2020, Paris, France, 22-25 June, 2020 (pp. 643-645). Piscataway: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Poster: SafeSmart - A VANET system for efficient communication for emergency vehicles
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2020 (English)In: IFIP Networking 2020 Conference and Workshops June 22-25, 2020 - Paris, France, Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 643-645Conference paper, Published paper (Refereed)
Abstract [en]

One important use case for Vehicular Ad-hoc Networks (VANETs) are applications related to emergency vehicles (EV). V2I (Vehicle-to-Infrastructure) communication can provide the infrastructure and protocol stack necessary to establish a communication channel between the transceivers in the EVs and the ones in the traffic lights, reducing accident risks and also help save valuable time. This paper outlines the system design of an EV warning system that makes use of V2I communication. A prototype of the system has been tested in a traffic simulation environment including EVs and traffic lights. To evaluate the system we performed a simulation and conducted a performance comparison between the travel times for EVs in normal traffic and when the system is in use. © 2020 IFIP.

Place, publisher, year, edition, pages
Piscataway: Institute of Electrical and Electronics Engineers (IEEE), 2020
Keywords
Vehicular networks, wireless networks, emergency vehicles, wireless transceivers
National Category
Communication Systems Telecommunications
Identifiers
urn:nbn:se:hh:diva-42890 (URN)2-s2.0-85090032479 (Scopus ID)978-3-903176-28-7 (ISBN)978-1-7281-6710-7 (ISBN)
Conference
2020 IFIP Networking Conference and Workshops, Networking 2020, Paris, France, 22-25 June, 2020
Funder
Knowledge Foundation, 2019-2023Swedish Foundation for Strategic Research , 2019-2020
Note

Funding text: ACKNOWLEDGMENT: The research leading to the results reported in this work has received funding from the Knowledge Foundation in the framework of SafeSmart ”Safety of Connected Intelligent Vehicles in Smart Cities” Synergy project (2019-2023), Swedish Foundation for Strategic Research (SSF) in the framework of Strategic Mobility Program (2019-2020), and the ELLIIT Strategic Research Network. This support is greatly acknowledged.

Available from: 2020-07-25 Created: 2020-07-25 Last updated: 2020-11-25Bibliographically approved
Agnoletto, D., Jonsson, M. & Pignaton de Freitas, E. (2020). Time slot transmission scheme with packet prioritization for Bluetooth low energy devices used in real-time applications. International Journal of Wireless Information Networks, 27(4), 518-534
Open this publication in new window or tab >>Time slot transmission scheme with packet prioritization for Bluetooth low energy devices used in real-time applications
2020 (English)In: International Journal of Wireless Information Networks, ISSN 1068-9605, E-ISSN 1572-8129, Vol. 27, no 4, p. 518-534Article in journal (Refereed) Published
Abstract [en]

Bluetooth Low Energy (BLE) is one of the most important technologies that feed the growing field of Internet of Things and Wireless Sensor Networks. Due to its flexibility and unique low power-consumption, an increasing number of industrial devices, household appliances and wearables are being designed using it. However, the real-time demands of these networks such as timing and Quality of Service are not fully covered by the protocol itself. To help improve and offer some control over these characteristics, this paper presents a time slot transmission scheme with packet prioritization. It is based on the division and allocation of the connection interval to two types of messages: real-time and ordinary. The goal is to offer the lowest packet loss and time guarantees for real-time messages, while providing acceptable throughput for ordinary ones. Since the probability of a BLE connection to close increases with the number of packets sent through it, the position where a real-time packet is being sent as well as the number of ordinary messages in a connection represent key factors. The use of the first and last slot for real-time packets with ordinary flow restricted to the space between them decreases the transmission delay uncertainty and allows probability tuning based on the number of ordinary messages. Simulations were performed using the proposed scheme and a reduction of more than 100 times in the delay variance was observed for real-time transmissions. Regarding reliability, around 5% of the packets were lost for a bit error rate of 10−3. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Place, publisher, year, edition, pages
New York, NY: Springer Science+Business Media B.V., 2020
Keywords
Bluetooth low energy, Real-time communication requirements, Time slot management, Packet prioritization
National Category
Communication Systems
Identifiers
urn:nbn:se:hh:diva-43593 (URN)10.1007/s10776-020-00494-4 (DOI)000572984100001 ()2-s2.0-85091488688 (Scopus ID)
Note

Funding: This study was financed in part by the Conselho Nacional de Pesquisa-Brasil (CNPq) and by the CoordenacAo de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES)-Finance Code 001.

Available from: 2020-12-03 Created: 2020-12-03 Last updated: 2020-12-03Bibliographically approved
Zacarias, I., Schwarzrock, J., Gaspary, L. P., Kohl, A., Fernandes, R. Q. A., Stocchero, J. M. & Pignaton de Freitas, E. (2018). Enhancing Mobile Military Surveillance Based on Video Streaming by Employing Software Defined Networks. Wireless Communications & Mobile Computing, 2018, 1-12, Article ID 2354603.
Open this publication in new window or tab >>Enhancing Mobile Military Surveillance Based on Video Streaming by Employing Software Defined Networks
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2018 (English)In: Wireless Communications & Mobile Computing, ISSN 1530-8669, E-ISSN 1530-8677, Vol. 2018, p. 1-12, article id 2354603Article in journal (Refereed) Published
Abstract [en]

Situation awareness in surveillance systems benefits from high-quality video streaming service. This is even more important considering military systems, in which delays in image transmission may have a significant impact on the decision-making process. However, in order to deliver high-quality video streaming service, the required network infrastructure may be prohibitively complex, or even completely impossible to deploy, if mobile data providers are considered. Moreover, the demand for high network throughput poses extra requirements on the network. Considering this context, this paper addresses the problem of highly mobile networks composed of unmanned aerial vehicles (UAVs) as data providers of a military surveillance system. The proposed approach to tackle the problem is based on a Software Defined Networking (SDN) approach aiming at providing the best routes to deliver the data, enhancing the end-user quality of experience. An extensive experimental campaign was performed by means of simulations and the acquired results provide solid evidence of the usefulness of this proposal.© 2018 Iulisloi Zacarias et al.

Place, publisher, year, edition, pages
Oxford: John Wiley & Sons, 2018
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-38320 (URN)10.1155/2018/2354603 (DOI)000448537000001 ()
Note

Funding Agency:

State of Rio Grande do Sul Research Foundation (FAPERGS)

Brazilian National Council for Scientific and Technological Development (CNPq)  

Brazilian Army

Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2018-11-15Bibliographically approved
Marinho, M., Antreich, F., da Costa, J. P. .., Caizzone, S., Vinel, A. & Pignaton de Freitas, E. (2018). Robust Nonlinear Array Interpolation for Direction of Arrival Estimation of Highly Correlated Signals. Signal Processing, 144, 19-28
Open this publication in new window or tab >>Robust Nonlinear Array Interpolation for Direction of Arrival Estimation of Highly Correlated Signals
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2018 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 144, p. 19-28Article in journal (Refereed) Published
Abstract [en]

Important signal processing techniques need that the response of the different elements of a sensor array has specific characteristics. For physical systems this often is not achievable as the array elements’ responses are affected by mutual coupling or other effects. In such cases, it is necessary to apply array interpolation to allow the application of ESPRIT, Forward Backward Averaging (FBA), and Spatial Smoothing (SPS). Array interpolation provides a model or transformation between the true and a desired array response. If the true response of the array becomes more distorted with respect to the desired one or the considered region of the field of view of the array increases, nonlinear approaches becomes necessary. This work presents two novel methods for sector discretization. An Unscented Transform (UT) based method and a principal component analysis (PCA) based method are discussed. Additionally, two novel nonlinear interpolation methods are developed based on the nonlinear regression schemes Multivariate Adaptive Regression Splines (MARS) and Generalized Regression Neural Networks (GRNNs). These schemes are extended and applied to the array interpolation problem. The performance of the proposed methods is examined using simulated and measured array responses of a physical system used for research on mutual coupling in antenna arrays. © 2017 The Author(s). Published by Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2018
Keywords
Array Interpolation, Array Mapping, Antenna Arrays, Direction of Arrival Estimation
National Category
Signal Processing Communication Systems
Identifiers
urn:nbn:se:hh:diva-35082 (URN)10.1016/j.sigpro.2017.09.025 (DOI)000419412000003 ()2-s2.0-85030321519 (Scopus ID)
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

Funding: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under the PVE grant number 88881.030392/2013-01 and by the ELLIIT Strategic Research Network.

Available from: 2017-09-28 Created: 2017-09-28 Last updated: 2020-02-03Bibliographically approved
Schwarzrock, J., Zacarias, I., Bazzan, A. L. .., Fernandes, R. Q., Moreira, L. H. & Pignaton de Freitas, E. (2018). Solving task allocation problem in multi Unmanned Aerial Vehicles systems using Swarm intelligence. Engineering applications of artificial intelligence, 72, 10-20
Open this publication in new window or tab >>Solving task allocation problem in multi Unmanned Aerial Vehicles systems using Swarm intelligence
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2018 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 72, p. 10-20Article in journal (Refereed) Published
Abstract [en]

The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. © 2018 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2018
Keywords
Unmanned Aerial Vehicles, Task allocation, Multi-agent systems, Swarm intelligence
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-38317 (URN)10.1016/j.engappai.2018.03.008 (DOI)000434239000002 ()2-s2.0-85044458179 (Scopus ID)
Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2018-11-15Bibliographically approved
Marinho, M. A. M., Vinel, A., Antreich, F., da Costa, J. P. & Pignaton de Freitas, E. (2017). Antenna Array Based Localization Scheme for Vehicular Networks. In: Lisa O’Conner (Ed.), 2017 IEEE International Conference on Computer and Information Technology (CIT): . Paper presented at 17th IEEE International Conference on Computer and Information Technology (IEEE CIT-2017), Helsinki, Finland, 21-23 August, 2017 (pp. 142-146). Los Alamitos: IEEE Computer Society
Open this publication in new window or tab >>Antenna Array Based Localization Scheme for Vehicular Networks
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2017 (English)In: 2017 IEEE International Conference on Computer and Information Technology (CIT) / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2017, p. 142-146Conference paper, Published paper (Refereed)
Abstract [en]

Vehicular ad hoc networks (VANETs) are emerging as the possible solution for multiple concerns in road traffic such as mobility and safety. One of the main concerns present in VANETs is the localization and tracking of vehicles. This work presents a passive vehicle localization and tracking method based on direction of arrival (DOA) estimation. The proposed method does not rely on external sources of information such as Global Navigation Satellite Systems (GNSS) and can be used to mitigate the possibility of spoofing or to provide a second independent source of position estimation for integrity purposes. The proposed algorithm uses array signal processing techniques to estimate not only the position but also the direction of other vehicles in network. Furthermore, it is a fully passive method and can alleviate the network load since it does not require any location based data exchange and can be performed by any listening vehicle using the signal of any data transmission. A set of numerical simulations is used to validate the proposed method and the results are shown to be more precise than the average accuracy of Global Position System (GPS) receivers. © Copyright 2017 IEEE

Place, publisher, year, edition, pages
Los Alamitos: IEEE Computer Society, 2017
Keywords
Localization, VANETs, MIMO, Spoofing
National Category
Communication Systems Telecommunications
Identifiers
urn:nbn:se:hh:diva-34996 (URN)10.1109/CIT.2017.64 (DOI)000426119400021 ()2-s2.0-85032330318 (Scopus ID)978-1-5386-0958-3 (ISBN)978-1-5386-0959-0 (ISBN)
Conference
17th IEEE International Conference on Computer and Information Technology (IEEE CIT-2017), Helsinki, Finland, 21-23 August, 2017
Available from: 2017-09-17 Created: 2017-09-17 Last updated: 2020-02-03Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4655-8889

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