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  • 1.
    Valieva, Inna
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Spectrum Sensing for Cognitive Radio2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This work focuses on the improvement of spectrum utilization by evaluating and proposing a subset of radio scene analysis algorithms for opportunistic spectrum access deployment in a cognitive radio network. The proposed algorithms aim to solve two problems: detecting vacant frequency channels and estimating the waveform, including modulation type, symbol rate, and central frequency. To test and prove the hypothesis three research questions related to radio scene observation, classification, and estimation have been formulated, studied, and answered. A two-step spectrum sensing algorithm has been proposed. The first step covers the coarse classification of the observed band into three broad categories: white, gray, or black space, commonly used in the literature to describe spectrum occupancy. Various machine learning algorithms were applied and tested for the coarse classification step. Fine decision trees demonstrated the highest classification accuracy and speed. The second step covers the detailed gray space analysis performed to detect vacant channels and waveforms of the signals present in the observed band. Algorithms such as cyclostationary, energy detection, and wavelet transform were employed for solving the vacant channel detection. The hypothesis has been proven by demonstrating the possibility of blind real-time vacant frequency channel detection using discrete wavelet transform and energy detection within the time compatible with real-time operation and 5G latency requirements on the test hardware.

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  • 2.
    Valieva, Inna
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Spectrum Sensing for Dynamic Spectrum Access in Cognitive Radio2020Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Abstract. The number of mobile devices is constantly growing, and the exclusivestatic spectrum allocation approach is leading to the spectrum scarcity problem whensome of the licensed bands are heavily occupied and others are nearly unused.Spectrum sharing and opportunistic spectrum access allow achieving more efficientspectrum utilization. Radio scene analysis is a first step in the cognitive radiooperation required to employ opportunistic spectrum access scenarios such as thedynamic spectrum access or frequency hopping spread spectrum. The objective of thiswork is to develop and virtual prototype the subset of radio scene analysis algorithmsintended to be used for deployment of opportunistic spectrum access in our targetapplication: a cognitive radio network consisting of multiple software-defined radionodes BitSDR. The proposed radio scene analysis algorithms are devoted to solvingtwo radio scene analysis problems: 1. detection of vacant frequency channels toimplement spectrum sharing scenarios; 2. waveform estimation including modulationtype, symbol rate, and central frequency estimation. From the subset of two radioscene analysis problems two hypotheses are formulated: the first is related to thevacant band identification and the second to waveform estimation. Then sevenresearch questions related to the trade-off between the sensing accuracy and real-time operation requirement for the proposed radio scene analysis algorithms, the nature of the noise, and assumptions used to model the radio scene environment such as the AWGN channel. In the scope of this work, Hypothesis 1, dedicated to vacant frequency band detection, has been proven. Research questions related to the selection of the observation bandwidth, vacant channels detection threshold, and the optimal algorithm have been answered. We have proposed, prototyped, and tested a vacant frequency channels detection algorithm based on wavelet transform performing multichannel detection in the wide band of 56 MHz based on the received signal observed during500 microseconds. Detection accuracy of 91 % has been demonstrated. Detection has been modeled as a binary hypothesis testing problem. Also, energy detection and cyclostationary feature extraction algorithms have been prototyped and tested, however, they have shown lower classification accuracy than wavelets. Answering research question 7 revealed the advantage of using wavelets due to the potential of the results of wavelet transform to be applied for solving the waveform estimation problem including symbol rate and modulation type. Test data samples have been generated during the controlled experiment by the hardware signal generator and received by proprietary hardware based on AD9364 Analog Devices transceiver. To test Hypothesis 2 research questions related to the waveform estimation have been elaborated. We could not fully prove Hypothesis 2 in the scope of this work. The algorithm and features that have been chosen for modulation type classification have not met the required classification accuracy to classify between five studied modulation classes 2FSK, BPSK, QPSK, 8PSK, and 16PSK. To capture more of the fine differences between the received signal modulated into different linear modulations it has been suggested to use the spectral features derived from the time-series signal observed during 500 microseconds or less observation time in the scope of the future work. However, the binary classification between 2FSK and BPSKpresented in Paper 1 could be performed based on instantaneous values and SNRinput: ensemble boosted trees and decision trees have shown an average classification accuracy of 86.3 % and 86.0 % respectively and classification speed of 1200000objects per second, what is faster than required 2000 objects per second.3The prototyping and testing of the proposed algorithm for symbol rate estimation based on deep learning have been performed to answer research question 2. Wavelet transform feature extraction has been proposed to be applied as a preprocessing step for deep learning-based estimation of the symbol rate for 2FSK modulated signals. This algorithm has shown an improvement in the accuracy of the symbol rate estimation in comparison with cyclostationary based detection. The validation accuracy of the symbol rate classification has reached 99.7 %. During testing, the highest average classification accuracy of 100 % has been observed for the signals with SNR levels 25-30 dB, while for signals with SNR 20-25 dB it was 96.3 %.

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  • 3.
    Valieva, Inna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Shashidhar, Bharath
    Voitenko, Iurii
    Electronics Development Wireless P2P Technologies, Falun, Sweden.
    AUTONOMOUS DETECTION OF VACANT FREQUENCY BANDS FOR COGNITIVE RADIOManuscript (preprint) (Other academic)
    Abstract [en]

    This This paper is focused on thea utonomous detection of the vacant frequency channels in the wide observation band of 60MHz. Vacant channel detection has been modeled as a binary hypothesis testing problem. Three signal detection algorithms including energy detection, wavelets, and cyclostationary have been tested and evaluated in terms of accuracy.Testing has been performed offline on the data samples collected during the controlled experiment. Data samples consisting of AWGN noise and FSK, BPSK, QPSK modulated signals have been generated using thehardware signal generator and received on our targetapplication's receiver (AD9364) front end as a time domain complex signal. The optimal threshold value hasbeen determined as an optimal value between the hitrate and the false positive rate. The highest accuracy of 91.0% has been reached the wavelet transform feature extraction, energy detection has shown 86.4% accuracy. Cyclostationary detection has shown no distinguishable difference in the spectrum correlation values calculatedfor the AWGN noise sample and samples containing BPSK and 2FSK modulated signals captured with -20dB power.

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  • 4.
    Valieva, Inna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Voitenko, Iurii
    Electronics Development Wireless P2P Technologies Falun, Sweden .
    Blind symbol rate estimation for cognitive radio using wavelet transform and deep learning for fsk modulated digital signalsManuscript (preprint) (Other academic)
    Abstract [en]

    This paper is focused on the blind symbol rate estimation for the digital FSK modulated signals. Symbol rate estimation is based on the classification between three symbol rate classes: 10, 100 and 1000K Symbol/second using the scalogram images obtained from time-frequency analysis performed using the 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 from 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. 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. Proposed algorithm has been compared with cyclostationary and has shown improved classification accuracy especially in conditions of low SNR. Central frequency estimation has been performed using a modified periodogram estimate of the power spectral density with a rectangular window. 

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  • 5.
    Valieva, Inna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Voitenko, Iurii
    Electronics Development Wireless P2P Technologies AB, Falun, Sweden.
    Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio2019In: MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), Norfolk, USA: IEEE, 2019, article id 9020735Conference paper (Refereed)
    Abstract [en]

    In this paper the potential of improving channel utilization by signal modulation type classification based on machine learning algorithms has been studied. The classification has been performed between two popular digital modulations: BPSK and FSK in target application. Classification was based on three features available on a popular software defined radio transceiver AD9361: In-phase and quadrature components of the digital time domain signal and signal-to-noise ratio (SNR), measured as RSSI value. Data used for network training, validation and testing was generated by the Simulink model consisting mainly of modulator, transceiver AD9361 and AWGN to generate the signal with SNR ranging from 1 to 30 dB. Twenty-three supervised machine learning algorithms including K-nearest neighbor, Support Vector Machines, Decision Trees and Ensembles have been studied, evaluated and verified against the target application's requirements in terms of classification accuracy and speed. The highest average classification accuracy of 86.9% was achieved by Support Vector Machines with Fine Gaussian kernel, however with demonstrated classification speed of 790 objects per second it was considered unable to meet target application's real-time operation requirement of 2000 objects per second. Fine Decision Trees and Ensemble Boosted Trees have shown optimal performance in terms of both reaching classification speed of 1200000 objects per second and average classification accuracy of 86.0% and 86.3% respectively. Classification accuracy has been also studied as a function of SNR to determine the most accurate classifier for each SNR level. At the target application's demodulation threshold of 12 dB 87.0% classification accuracy has been observed for the Fine Decision Trees, 87.5% for both Fine Gaussian SVM and Coarse KNN. At SNR higher than 27 dB Fine Trees, Coarse KNN have reached 97.5% classification accuracy. The effects of data set size and number of classification features on classification speed and accuracy have been studied too.

  • 6. Valieva, Inna
    et al.
    Shashidhar, B.
    Wireless P2P Technologies, Research and Development, Falun, Sweden.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Voitenko, I.
    Wireless P2P Technologies, Research and Development, Falun, Sweden.
    Blind Vacant Frequency Channels Detection for Cognitive Radio2022In: 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Conference 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.

  • 7.
    Valieva, Inna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Shashidhar, B.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Voitenko, I.
    Frequency Bands Classification Using Machine Learning for Frequency Hopping Spread SpectrumManuscript (preprint) (Other academic)
  • 8.
    Valieva, Inna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Shashidhar, B.
    Research and Development, Wireless P2P Technologies, Falun, Sweden.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Voitenko, I.
    Research and Development, Wireless P2P Technologies, Falun, Sweden.
    Machine Learning-Based Frequency Bands Classification for Efficient Frequency Hopping Spread Spectrum Applications2022In: Proceedings - IEEE Military Communications Conference MILCOM, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 72-77Conference 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. 

  • 9.
    Valieva, Inna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Shashidhar, B.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Voitenko, I.
    Multiple Machine Learning Algorithms Comparison for Coarse Frequency Bands ClassificationManuscript (preprint) (Other academic)
  • 10.
    Valieva, Inna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Shashidhar, B.
    Wireless P2P Technologies, Research and Development, Falun, Sweden.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Voitenko, Iurii
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Machine Learning-Based Coarse Frequency Bands Classification For Cognitive Radio Applications2023In: Int. Conf. Electr. Eng./Electron., Comput., Telecommun. Inf. Technol., ECTI-CON, Institute of Electrical and Electronics Engineers Inc. , 2023Conference 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.

  • 11.
    Valieva, Inna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Voitenko, I.
    Electronics Development, Wireless P2P Technologies, Sweden.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Multiple machine learning algorithms comparison for modulation type classification based on instantaneous values of the time domain signal and time series statistics derived from wavelet transform2021In: Advances in Science, Technology and Engineering Systems, ISSN 2415-6698, Vol. 6, no 1, p. 658-671Article in journal (Refereed)
    Abstract [en]

    Modulation type classification is a part of waveform estimation required to employ spectrum sharing scenarios like dynamic spectrum access that allow more efficient spectrum utilization. In this work multiple classification features, feature extraction, and classification algorithms for modulation type classification have been studied and compared in terms of classification speed and accuracy to suggest the optimal algorithm for deployment on our target application hardware. The training and validation of the machine learning classifiers have been performed using artificial data. The possibility to use instantaneous values of the time domain signal has shown acceptable performance for the binary classification between BPSK and 2FSK: Both ensemble boosted trees with 30 decision trees learners trained using AdaBoost sampling and fine decision trees have shown optimal performance in terms of both an average classification accuracy (86.3 % and 86.0 %) and classification speed (120 0000 objects per second) for additive white gaussian noise (AWGN) channel with signal-to-noise ratio (SNR) ranging between 1 and 30 dB. However, for the classification between five modulation classes demonstrated average classification accuracy has reached only 78.1 % in validation. Statistical features: Mean, Standard Deviation, Kurtosis, Skewness, Median Absolute Deviation, Root-Mean-Square level, Zero Crossing Rate, Interquartile Range and 75th Percentile derived from the wavelet transform of the received signal observed during 100 and 500 microseconds were studied using fractional factorial design to determine the features with the highest effect on the response variables: classification accuracy and speed for the additive white gaussian noise and Rician line of sight multipath channel. The highest classification speed of 170 000 objects/second and 100 % classification accuracy has been demonstrated by fine decision trees using as an input Kurtosis derived from the wavelet coefficients derived from signal observed during 100 microseconds for AWGN channel. For the line of sight fading Rician channel with AWGN demonstrated classification speed is slower 130 000 objects/s.

  • 12.
    Valieva, Inna
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Voitenko, Iurii
    Research and Development, Wireless P2P Technologies, Falun, Sweden.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Johan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Blind Symbol Rate Estimation Using Wavelet Transform and Deep Learning for FSK Modulated Signals2022In: 2022 International Conference on Advanced Technologies for Communications (ATC), 2022Conference 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.

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