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Dust, L., Persson, E., Ekström, M., Mubeen, S., Seceleanu, C. & Gu, R. (2023). Experimental Evaluation of Callback Behavior in ROS 2 Executors. In: IEEE Int. Conf. Emerging Technol. Factory Autom., ETFA: . Paper presented at IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Experimental Evaluation of Callback Behavior in ROS 2 Executors
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2023 (English)In: IEEE Int. Conf. Emerging Technol. Factory Autom., ETFA, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Robot operating system 2 (ROS 2) is increasingly popular both in research and commercial robotic systems. ROS 2 is designed to allow real-time execution and data communication, enabling rapid prototyping and deployment of robotic systems. In order to predict and calculate execution times in ROS 2, one needs to analyze its internal scheduler, called executor. The executor has been updated in various distributions of ROS 2, which is shown to impact significantly the periodic execution invoked by the underlying operating system's timers, potentially causing unexpected latencies. To expose the mentioned impact due to executor differences, in this paper, we present an experimental evaluation of the execution behavior of ROS 2's schedulable entities, namely callbacks, among the existing versions of the executor. We visualize the differences of callback execution order via simulation, and we create design-level scenarios that impact the execution of periodically scheduled callbacks, negatively. Moreover, we show how such negative impact can be mitigated by using multi-threaded executors. Finally, we illustrate the observed behavior on a real-world centralized multi-agent robot system. Our work aims to raise awareness within the ROS 2 developer community, regarding possible problems of timer blocking, and propose a mitigation solution of the latter.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Multi agent systems, Real time systems, Data-communication, Design levels, Experimental evaluation, Multithreaded, Rapid deployments, Rapid-prototyping, Real time execution, Real-time data, Real-world, Robotic systems, Robot Operating System
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-64706 (URN)10.1109/ETFA54631.2023.10275668 (DOI)2-s2.0-85175439932 (Scopus ID)9798350339918 (ISBN)
Conference
IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Available from: 2023-11-09 Created: 2023-11-09 Last updated: 2023-11-09Bibliographically approved
Valieva, I., Shashidhar, B., Björkman, M., Åkerberg, J., Ekström, M. & Voitenko, I. (2023). Machine Learning-Based Coarse Frequency Bands Classification For Cognitive Radio Applications. In: Int. Conf. Electr. Eng./Electron., Comput., Telecommun. Inf. Technol., ECTI-CON: . Paper presented at 2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Machine Learning-Based Coarse Frequency Bands Classification For Cognitive Radio Applications
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2023 (English)In: Int. Conf. Electr. Eng./Electron., Comput., Telecommun. Inf. Technol., ECTI-CON, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper is focused on multiple supervised machine learning algorithms' performance evaluation in terms of classification accuracy and speed for the blind frequency bands classification into three occupancy classes: white, gray, and black spaces for potential implementation in cognitive radio application. Training and validation data sets consisting of 180 000 samples, including 60 000 samples per class, have been collected in the controlled experiment. Data samples have been generated using a hardware signal generator and recorded on the receiver's front end as the time-domain complex signals. Gray space data samples contain one, two, or three signals modulated into 2FSK, BPSK, or QPSK with symbol rates 10, 100, or 1000 kSymbol/s. White space data samples contain no own generated signals. Black space data samples contain two signals with the symbol rate of 22.5 MSymbol/s and offset +14 MHz and -14 MHz from the central frequency occupying the entire observation band. Training and validation of twenty supervised machine learning algorithms have been performed offline in the Matlab Classification Learner application using the collected data set. Fine decision trees have demonstrated the highest classification accuracy of 87.8 %, the observed classification speed of 630000 Objects/s is also higher than the required 2000 Objects/s. Medium decision trees and ensemble boosted trees have demonstrated 87.5 % and 87.7 % accuracy and classification speeds of 950000 and 230000 Objects/s respectively. Therefore, ensemble boosted trees, and fine and medium decision trees have been selected for the deployment on the target radio application in the scope of future work.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
cognitive radio, decision trees, machine learning, vacant frequency channels, Classification (of information), Learning algorithms, Signal receivers, Classification accuracy, Data sample, Data set, Frequency channels, Machine learning algorithms, Machine-learning, Radio applications, Space data, Supervised machine learning, Vacant frequency channel
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-63918 (URN)10.1109/ECTI-CON58255.2023.10153155 (DOI)2-s2.0-85164912117 (Scopus ID)9798350310467 (ISBN)
Conference
2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023
Available from: 2023-07-26 Created: 2023-07-26 Last updated: 2023-07-26Bibliographically approved
Miloradović, B., Curuklu, B., Ekström, M. & Papadopoulos, A. (2023). Optimizing Parallel Task Execution for Multi-Agent Mission Planning. IEEE Access, 11, 24367-24381
Open this publication in new window or tab >>Optimizing Parallel Task Execution for Multi-Agent Mission Planning
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 24367-24381Article in journal (Refereed) Published
Abstract [en]

Multi-agent systems have received a tremendous amount of attention in many areas of research and industry, especially in robotics and computer science. With the increased number of agents in missions, the problem of allocation of tasks to agents arose, and it is one of the most fundamental classes of problems in robotics, formally known as the Multi-Robot Task Allocation (MRTA) problem. MRTA encapsulates numerous problem dimensions, and it aims at providing formulations and solutions to various problem configurations, i.e., complex multi-agent missions. One dimension of the MRTA problem has not caught much of the research attention. In particular, problem configurations including Multi-Task (MT) robots have been neglected. However, the increase in computational power, in robotic systems, has allowed the utilization of parallel task execution. This in turn had the benefit of allowing the creation of more complex robotic missions; however, it came at the cost of increased problem complexity. Our contribution to the aforementioned domain can be grouped into three categories. First, we model the problem using two different approaches, Integer Linear Programming and Constraint Programming. With these models, we aim at filling the gap in the literature related to the formal definition of MT robot problem configuration. Second, we introduce the distinction between physical and virtual tasks and their mutual relationship in terms of parallel task execution. This distinction allows the modeling of a wider range of missions while exploiting possible parallel task execution. Finally, we provide a comprehensive performance analysis of both models, by implementing and validating them in CPLEX and CP Optimizer on the set of problems. Each problem consists of the same set of test instances gradually increasing in complexity, while the percentage of virtual tasks in each problem is different. The analysis of the results includes exploration of the scalability of both models and solvers, the effect of virtual tasks on the solvers' performance, and overall solution quality.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023
Keywords
Task analysis, Robots, Planning, Taxonomy, Resource management, Complexity theory, Analytical models, Multi-agent mission planning, multi-robot task allocation, parallel task execution, integer linear programming, constraint programming
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-62204 (URN)10.1109/ACCESS.2023.3254900 (DOI)000953721300001 ()2-s2.0-85149859205 (Scopus ID)
Available from: 2023-04-12 Created: 2023-04-12 Last updated: 2023-04-12Bibliographically approved
Dust, L., Gu, R., Seceleanu, C., Ekström, M. & Mubeen, S. (2023). Pattern-Based Verification of ROS 2 Nodes Using UPPAAL. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): . Paper presented at 28th International Conference on Formal Methods in Industrial Critical Systems, FMICS 2023, Antwerp, Belgium, 20 September - 22 September 2023 (pp. 57-75). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Pattern-Based Verification of ROS 2 Nodes Using UPPAAL
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2023 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Science and Business Media Deutschland GmbH , 2023, p. 57-75Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a pattern-based modeling and Uppaal-based verification of latencies and buffer overflow in distributed robotic systems that use ROS 2. We apply pattern-based modeling to simplify the construction of formal models for ROS 2 systems. Specifically, we propose Timed Automata templates for modeling callbacks in Uppaal, including all versions of the single-threaded executor in ROS 2. Furthermore, we demonstrate the differences in callback scheduling and potential errors in various versions of ROS 2 through experiments and model checking. Our formal models of ROS 2 systems are validated in experiments, as the behavior of ROS 2 presented in the experiments is also exposed by the execution traces of our formal models. Moreover, model checking can reveal potential errors that are missed in the experiments. The paper demonstrates the application of pattern-based modeling and verification in distributed robotic systems, showcasing its potential in ensuring system correctness and uncovering potential errors.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 14290 LNCS
Keywords
Model Checking, Pattern-Based Modeling, Robot Operating System 2, Errors, Robot Operating System, Buffer overflows, Distributed robotic systems, Execution trace, Formal modeling, Models checking, Pattern-based models, Potential errors, Single-threaded, Timed Automata
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-64592 (URN)10.1007/978-3-031-43681-9_4 (DOI)2-s2.0-85174439033 (Scopus ID)9783031436802 (ISBN)
Conference
28th International Conference on Formal Methods in Industrial Critical Systems, FMICS 2023, Antwerp, Belgium, 20 September - 22 September 2023
Available from: 2023-10-30 Created: 2023-10-30 Last updated: 2023-10-30Bibliographically approved
Valieva, I., Voitenko, I., Björkman, M., Åkerberg, J. & Ekström, M. (2022). Blind Symbol Rate Estimation Using Wavelet Transform and Deep Learning for FSK Modulated Signals. In: 2022 International Conference on Advanced Technologies for Communications (ATC): . Paper presented at 2022 International Conference on Advanced Technologies for Communications (ATC), 20-22 October 2022.
Open this publication in new window or tab >>Blind Symbol Rate Estimation Using Wavelet Transform and Deep Learning for FSK Modulated Signals
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2022 (English)In: 2022 International Conference on Advanced Technologies for Communications (ATC), 2022Conference paper, Published paper (Refereed)
Abstract [en]

This paper is focused on the blind symbol rate estimation for the digital FSK modulated signals, based on the classification between three symbol rate classes: 10, 100, and 1000 KSymbol/second using the scalogram images obtained from continuous wavelet transform with Morse wavelet. Pretrained deep learning AlexNet has been transfer learned to classify between symbol rate classes. Training, testing, and validation data sets have been composed of the artificial data generated using Bernoulli binary random signal generator modulated into FSK signal corrupted by additive white Gaussian noise (AWGN) noise with SNR ranging from 1 to 30 dB. Training and validation data sets have been augmented to obtain twice more extensive data set i.e 1800 scalogram images, compared to the original size of 900 samples. The average classification accuracy during validation has reached 99.7 % and during testing 100 % and 96.3 % for the data sets with SNR 25–30 dB and 20–25 dB respectively. The proposed algorithm has been compared with cyclostationary and has shown improved classification accuracy, especially in conditions of low SNR.

National Category
Telecommunications
Identifiers
urn:nbn:se:mdh:diva-61136 (URN)10.1109/atc55345.2022.9943051 (DOI)2-s2.0-85142741268 (Scopus ID)
Conference
2022 International Conference on Advanced Technologies for Communications (ATC), 20-22 October 2022
Available from: 2022-12-07 Created: 2022-12-07 Last updated: 2023-03-10Bibliographically approved
Valieva, I., Shashidhar, B., Björkman, M., Åkerberg, J., Ekström, M. & Voitenko, I. (2022). Blind Vacant Frequency Channels Detection for Cognitive Radio. In: 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022: . Paper presented at 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Blind Vacant Frequency Channels Detection for Cognitive Radio
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2022 (English)In: 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Conference paper, Published paper (Refereed)
Abstract [en]

This paper is focused on the blind vacant frequency channels detection in 56 MHz observation band divided into 56 channels (1 MHz each) implemented on in-house developed hardware based on the AD9364 transceiver operating in automatic gain control (AGC) mode. Vacant channel detection has been modeled as a binary hypothesis testing problem. Three signal detection algorithms widely used in the literature including energy detection, wavelets, and cyclostationary have been tested and evaluated for potential use in our target application. Primary, offline testing has been performed in the Matlab environment using the data samples captured on the target receiver's front end as a time-domain complex signal. Data samples containing one, two, or three signals generated by hardware signal generator and modulated into 2FSK, BPSK, or QPSK with symbol rate 10, 100, or 1000 kSymbol/s. The highest accuracy of 91.0 % has been observed in the offline detection for continuous wavelet transform, while energy detection has demonstrated 86.4 % accuracy. Cyclostationary detection has shown no distinguishable difference in the spectrum correlation values calculated for the AWGN noise sample and the sample containing BPSK and 2FSK modulated signals. Energy detection and discrete wavelet transform have been implemented on our target hardware and tested in the office environment in conditions that could be approximated by AWGN channel. Test sequences containing one or two signals have been generated by the signal generator and received and processed by our target radio node. Discrete wavelet transform has demonstrated 85.73 % and energy detection 85.25 % accuracy in real-time testing. © 2022 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2022
Keywords
cognitive radio, energy detection, vacant frequency channels, wavelet transform
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-58205 (URN)10.1109/ICEIC54506.2022.9748704 (DOI)000942023400111 ()2-s2.0-85128807098 (Scopus ID)9781665409346 (ISBN)
Conference
2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
Available from: 2022-05-11 Created: 2022-05-11 Last updated: 2023-03-22Bibliographically approved
Trinh, L., Ekström, M. & Curuklu, B. (2022). Dependable Navigation for Multiple Autonomous Robots with Petri Nets Based Congestion Control and Dynamic Obstacle Avoidance. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 104(4), Article ID 69.
Open this publication in new window or tab >>Dependable Navigation for Multiple Autonomous Robots with Petri Nets Based Congestion Control and Dynamic Obstacle Avoidance
2022 (English)In: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, ISSN 0921-0296, Vol. 104, no 4, article id 69Article in journal (Refereed) Published
Abstract [en]

In this paper, a novel path planning algorithm for multiple robots using congestion analysis and control is presented. The algorithm ensures a safe path planning solution by avoiding collisions among robots as well as among robots and humans. For each robot, alternative paths to the goal are realised. By analysing the travelling time of robots on different paths using Petri Nets, the optimal configuration of paths is selected. The prime objective is to avoid congestion when routing many robots into a narrow area. The movements of robots are controlled at every intersection by organising a one-by-one passing of the robots. Controls are available for the robots which are able to communicate and share information with each other. To avoid collision with humans and other moving objects (i.e. robots), a dipole field integrated with a dynamic window approach is developed. By considering the velocity and direction of the dynamic obstacles as sources of a virtual magnetic dipole moment, the dipole-dipole interaction between different moving objects will generate repulsive forces proportional to the velocity to prevent collisions. The whole system is presented on the widely used platform Robot Operating System (ROS) so that its implementation is extendable to real robots. Analysis and experiments are demonstrated with extensive simulations to evaluate the effectiveness of the proposed approach.

Keywords
Dependable path planning, Dipole field, Obstacle avoidance, Congestion control
National Category
Robotics
Identifiers
urn:nbn:se:mdh:diva-56589 (URN)10.1007/s10846-022-01589-1 (DOI)000777399100001 ()2-s2.0-85127723096 (Scopus ID)
Available from: 2021-11-23 Created: 2021-11-23 Last updated: 2022-11-02Bibliographically approved
Ghaderi, A., Ahlberg, C., Östgren, M., Ekstrand, F. & Ekström, M. (2022). FP-SLIC: A Fully-Pipelined FPGA Implementation of Superpixel ImageSegmentation. In: Proceedings: 2022 25th Euromicro Conference on Digital System Design, DSD 2022. Paper presented at DSD2022: EUROMICRO CONFERENCE ON DIGITAL SYSTEMS DESIGN 2022 (pp. 109-117).
Open this publication in new window or tab >>FP-SLIC: A Fully-Pipelined FPGA Implementation of Superpixel ImageSegmentation
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2022 (English)In: Proceedings: 2022 25th Euromicro Conference on Digital System Design, DSD 2022, 2022, p. 109-117Conference paper, Published paper (Refereed)
Abstract [en]

A superpixel segment is a group of pixels that carry similar information. The Simple Linear Iterative Clustering (SLIC) is a well-known algorithm for generating superpixels that offers a good balance between accuracy and efficiency. Nevertheless, due to its high computational requirements, the algorithm does not meet the demands of real-time embedded applications in terms of speed and resources. This paper proposes a fully-pipelined FPGA architecture of SLIC, dubbed FP-SLIC, that exhibits 1) a simplified and efficient algorithm of reduced computational complexity that facilitates algorithm development for FPGAs, 2) a fully pipelined FPGA design operating at 40MHz with a throughput of one pixel per cycle, and 3) a memory-efficient architecture that eliminates the requirement for external memory. Implementation results achieve 259 fps on the BSDS500 dataset, which is ≈ 8.6× more than the requirement for real-time performance (30 frames per second).

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-61226 (URN)10.1109/DSD57027.2022.00024 (DOI)000946536500003 ()2-s2.0-85146718029 (Scopus ID)
Conference
DSD2022: EUROMICRO CONFERENCE ON DIGITAL SYSTEMS DESIGN 2022
Available from: 2022-12-13 Created: 2022-12-13 Last updated: 2023-04-05Bibliographically approved
Miloradović, B., Curuklu, B., Ekström, M. & Papadopoulos, A. (2022). GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications. IEEE Transactions on Cybernetics, 52(10), 10627-10638
Open this publication in new window or tab >>GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications
2022 (English)In: IEEE Transactions on Cybernetics, ISSN 2168-2267, E-ISSN 2168-2275, Vol. 52, no 10, p. 10627-10638Article in journal (Refereed) Epub ahead of print
Abstract [en]

The use of multiagent systems (MASs) in real-world applications keeps increasing, and diffuses into new domains, thanks to technological advances, increased acceptance, and demanding productivity requirements. Being able to automate the generation of mission plans for MASs is critical for managing complex missions in realistic settings. In addition, finding the right level of abstraction to represent any generic MAS mission is important for being able to provide general solution to the automated planning problem. In this article, we show how a mission for heterogeneous MASs can be cast as an extension of the traveling salesperson problem (TSP), and we propose a mixed-integer linear programming formulation. In order to solve this problem, a genetic mission planner (GMP), with a local plan refinement algorithm, is proposed. In addition, the comparative evaluation of CPLEX and GMP is presented in terms of timing and optimality of the obtained solutions. The algorithms are benchmarked on a proposed set of different problem instances. The results show that, in the presence of timing constraints, GMP outperforms CPLEX in the majority of test instances.

Keywords
Extended Colored Traveling Salesperson Problem (ECTSP)Genetic Algorithm (GA)Multirobot Mission PlanningMultirobot Systems
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-54306 (URN)10.1109/TCYB.2021.3070913 (DOI)000733455200001 ()33983890 (PubMedID)2-s2.0-85105850900 (Scopus ID)
Projects
Aggregate Farming in the CloudFIESTA - Federated Choreography of an Integrated Embedded Systems Software Architecture
Available from: 2021-06-01 Created: 2021-06-01 Last updated: 2022-11-17Bibliographically approved
Valieva, I., Shashidhar, B., Björkman, M., Åkerberg, J., Ekström, M. & Voitenko, I. (2022). Machine Learning-Based Frequency Bands Classification for Efficient Frequency Hopping Spread Spectrum Applications. In: Proceedings - IEEE Military Communications Conference MILCOM: . Paper presented at 2022 IEEE Military Communications Conference, MILCOM 2022, Rockville, 28 November 2022 through 2 December 2022 (pp. 72-77). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Machine Learning-Based Frequency Bands Classification for Efficient Frequency Hopping Spread Spectrum Applications
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2022 (English)In: Proceedings - IEEE Military Communications Conference MILCOM, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 72-77Conference paper, Published paper (Refereed)
Abstract [en]

This paper is focused on the performance evaluation of nine supervised machine learning algorithms in terms of classification accuracy applied to perform two radio scene analysis tasks: 1. blind binary frequency band occupancy classification: vacant or occupied; 2. interference type classification: sine wave interference, or modulated signal or additive white Gaussian noise (AWGN) for the frequency hopping spread spectrum cognitive radio application. Twenty-nine features derived from the time-, frequency-domain and RSSI, have been used as classification inputs to the evaluated machine learning classifiers. Classifiers training and validation have been performed offline in Matlab Classification Learner and Neural Networks applications using four data sets, generated in the controlled experiment, covering both classification tasks in AWGN and mixed channel propagation conditions (AWGN and Rician fading). Data samples have been generated using a hardware signal generator and recorded on the target application receivers' front end as the time-domain complex signals. The highest classification accuracy of 98.71 % has been demonstrated by Feed Forward Neural Network (FFNN) for the binary occupancy classification in K-fold validation for the mixed data set containing both AWGN and Rician fading channel samples. For the interference type classification, FFNN has demonstrated classification accuracy of 99.82 % for K-fold validation and 99.71 % for hold-out validation. FFNN has been concluded as an acceptable algorithm for further adaptation and embedded deployment on our target radio application for both binary classification between occupied or vacant frequency bands and interference type classification. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2022
Keywords
decision trees, frequency hopping spread spectrum, neural networks, supervised machine learning, vacant frequency bands
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-61925 (URN)10.1109/MILCOM55135.2022.10017912 (DOI)000968304600013 ()2-s2.0-85147333248 (Scopus ID)9781665485340 (ISBN)
Conference
2022 IEEE Military Communications Conference, MILCOM 2022, Rockville, 28 November 2022 through 2 December 2022
Available from: 2023-02-15 Created: 2023-02-15 Last updated: 2023-05-17Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5832-5452

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