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Relevant bibliographies by topics / Signal complexity

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Academic literature on the topic 'Signal complexity'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Signal complexity"

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Iqbal, Jameel, Li Sun, and Mone Zaidi. "Complexity in signal transduction." Annals of the New York Academy of Sciences 1192, no. 1 (2010): 238–44. http://dx.doi.org/10.1111/j.1749-6632.2010.05388.x.

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Warr, Paul A., and Alan M. Potter. "A Reduced-Complexity Mixer Linearization Scheme." Research Letters in Communications 2009 (2009): 1–4. http://dx.doi.org/10.1155/2009/541084.

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Measurement results of the signals emanating from both IF and LO ports of a double balanced mixer are presented, and, thus, it is shown that the linearization of the output in a down-converting mixer by the summation of the IF signal and the signal emanating from the LO or RF port is feasible. Feedforward-based architectures for the linearization of down-conversion mixers are introduced that exploit this phenomenon, and linearity performance results of the frequency translation of both two-tone and TETRA-modulated signals are presented. This technique employs only a single mixer and hence overcomes the complexity of other mixer linearization schemes. The overall processing gain of the system is limited by the level of wanted signal present in the error signal.

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RAGHAVENDRA, B. S., and D. NARAYANA DUTT. "SIGNAL CHARACTERIZATION USING FRACTAL DIMENSION." Fractals 18, no. 03 (2010): 287–92. http://dx.doi.org/10.1142/s0218348x10004968.

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Fractal Dimensions (FD) are one of the popular measures used for characterizing signals. They have been used as complexity measures of signals in various fields including speech and biomedical applications. However, proper interpretation of such analyses has not been thoroughly addressed. In this paper, we study the effect of various signal properties on FD and interpret results in terms of classical signal processing concepts such as amplitude, frequency, number of harmonics, noise power and signal bandwidth. We have used Higuchi's method for estimating FDs. This study may help in gaining a better understanding of the FD complexity measure itself, and for interpreting changing structural complexity of signals in terms of FD. Our results indicate that FD is a useful measure in quantifying structural changes in signal properties.

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Bruni, Vittoria, Michela Tartaglione, and Domenico Vitulano. "A Signal Complexity-Based Approach for AM–FM Signal Modes Counting." Mathematics 8, no. 12 (2020): 2170. http://dx.doi.org/10.3390/math8122170.

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Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise.

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Klonowski, Włodzimierz, Pawel Stepien, and Robert Stepien. "Complexity Measures of Brain Electrophysiological Activity." Journal of Psychophysiology 24, no. 2 (2010): 131–35. http://dx.doi.org/10.1027/0269-8803/a000024.

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Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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Liu, Jizhen, Chao Cui, Qingwei Meng, Yali Shen, and Fang Fang. "IAE performance based signal complexity measure." Measurement 75 (November 2015): 255–62. http://dx.doi.org/10.1016/j.measurement.2015.07.038.

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Hegde, Ramanujan S., and Harris D. Bernstein. "The surprising complexity of signal sequences." Trends in Biochemical Sciences 31, no. 10 (2006): 563–71. http://dx.doi.org/10.1016/j.tibs.2006.08.004.

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Cho, Young-Hee, and Sang-Dong Yoo. "Emerging Complexity of Ethylene Signal Transduction." Journal of Plant Biology 52, no. 4 (2009): 283–88. http://dx.doi.org/10.1007/s12374-009-9038-6.

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Ay, Nihat, Jessica Flack, and David C. Krakauer. "Robustness and complexity co-constructed in multimodal signalling networks." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1479 (2007): 441–47. http://dx.doi.org/10.1098/rstb.2006.1971.

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In animal communication, signals are frequently emitted using different channels (e.g. frequencies in a vocalization) and different modalities (e.g. gestures can accompany vocalizations). We explore two explanations that have been provided for multimodality: (i) selection for high information transfer through dedicated channels and (ii) increasing fault tolerance or robustness through multichannel signals. Robustness relates to an accurate decoding of a signal when parts of a signal are occluded. We show analytically in simple feed-forward neural networks that while a multichannel signal can solve the robustness problem, a multimodal signal does so more effectively because it can maximize the contribution made by each channel while minimizing the effects of exclusion. Multimodality refers to sets of channels where within each set information is highly correlated. We show that the robustness property ensures correlations among channels producing complex, associative networks as a by-product. We refer to this as the principle of robust overdesign . We discuss the biological implications of this for the evolution of combinatorial signalling systems; in particular, how robustness promotes enough redundancy to allow for a subsequent specialization of redundant components into novel signals.

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Bhattacharya, Joydeep, and Eun-Jeong Lee. "Modulation of EEG Theta Band Signal Complexity by Music Therapy." International Journal of Bifurcation and Chaos 26, no. 01 (2016): 1650001. http://dx.doi.org/10.1142/s0218127416500012.

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The primary goal of this study was to investigate the impact of monochord (MC) sounds, a type of archaic sounds used in music therapy, on the neural complexity of EEG signals obtained from patients undergoing chemotherapy. The secondary goal was to compare the EEG signal complexity values for monochords with those for progressive muscle relaxation (PMR), an alternative therapy for relaxation. Forty cancer patients were randomly allocated to one of the two relaxation groups, MC and PMR, over a period of six months; continuous EEG signals were recorded during the first and last sessions. EEG signals were analyzed by applying signal mode complexity, a measure of complexity of neuronal oscillations. Across sessions, both groups showed a modulation of complexity of beta-2 band (20–29[Formula: see text]Hz) at midfrontal regions, but only MC group showed a modulation of complexity of theta band (3.5–7.5[Formula: see text]Hz) at posterior regions. Therefore, the neuronal complexity patterns showed different changes in EEG frequency band specific complexity resulting in two different types of interventions. Moreover, the different neural responses to listening to monochords and PMR were observed after regular relaxation interventions over a short time span.

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Dissertations / Theses on the topic "Signal complexity"

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Bull, David R. "Signal processing techniques with reduced computational complexity." Thesis, Cardiff University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388006.

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Lallo, Madeline M. "Good Vibrations: Signal Complexity in Schizocosa Ethospecies." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1554215678769319.

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Shah, Kushal Yogeshkumar. "Computational Complexity of Signal Processing Functions in Software Radio." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1292854939.

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Wang, Tong. "Low-complexity signal processing algorithms for wireless sensor networks." Thesis, University of York, 2012. http://etheses.whiterose.ac.uk/2844/.

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Recently, wireless sensor networks (WSNs) have attracted a great deal of research interest because of their unique features that allow a wide range of applications in the areas of military, environment, health and home. One of the most important constraints on WSNs is the low power consumption requirement as sensor nodes carry limited, generally irreplaceable, power sources. Therefore, low complexity and high energy efficiency are the most important design characteristics for WSNs. In this thesis, we focus on the development of low complexity signal processing algorithms for the physical layer and cross layer designs for WSNs. For the physical layer design, low-complexity set-membership (SM) channel estimation algorithms for WSNs are investigated. Two matrix-based SM algorithms are developed for the estimation of the complex matrix channel parameters. The main goal is to reduce the computational complexity significantly as compared with existing channel estimators and extend the lifetime of the WSN by reducing its power consumption. For the cross layer design, strategies to jointly design linear receivers and the power allocation parameters for WSNs via an alternating optimization approach are proposed. We firstly consider a two-hop wireless sensor network with multiple relay nodes. Two design criteria are considered: the first one minimizes the mean-square error (MMSE) and the second one maximizes the sum-rate (MSR) of the wireless sensor network. Then, in order to increase the applicability of our investigation, we develop joint strategies for general multihop WSNs. They can be considered as an extension of the strategies proposed for the two-hop WSNs and more complex mathematical derivations are presented. The major advantage is that they are applicable to general multihop WSNs which can provide larger coverage than the two-hop WSNs.

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Paraskevas, Alexandros. "Organisational crisis signal detection from a complexity thinking perspective." Thesis, Oxford Brookes University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.515276.

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Perry, Russell. "Low complexity adaptive equalisation for wireless applications." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389138.

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Ma, Hannan. "Iterative row-column algorithms for two-dimensional intersymbol interference channel equalization complexity reduction and performance enhancement /." Pullman, Wash. : Washington State University, 2010. http://www.dissertations.wsu.edu/Thesis/Summer2010/h_ma_062110.pdf.

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Thesis (M.S. in electrical engineering)--Washington State University, August 2010.<br>Title from PDF title page (viewed on July 28, 2010). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 51).

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Kale, Kaustubh R. "Low complexity, narrow baseline beamformer for hand-held devices." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0001223.

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Sepehr, H. "Advanced adaptive signal processing techniques for low complexity speech enhancement applications." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1306808/.

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This thesis research is focused on using subband and multi rate adaptive signal processing techniques in order to develop practical speech enhancement algorithms. This thesis comprises of research on three different speech enhancement applications. Firstly, design of a novel method for attenuation of a siren signal in an emergency telephony system (by use of single source siren noise reduction algorithms) is investigated. The proposed method is based on wavelet filter banks and series of adaptive notch filters in order to detect and attenuate the siren noise signal with minimal effect on quality of speech signal. Results of my testing show that this algorithm provides superior results in comparison to prior art solutions. Secondly, effect of time and frequency resolution of a filter bank used in a statistical single source noise reduction algorithm is investigated. Following this study, a novel method for improvement of time domain noise reduction algorithm is presented. The suggested method is based on detection of transient elements of speech signal followed by a time varying signal dependent filter bank. This structure provides a high time resolution at points of transient in a noisy speech signal hence temporal smearing of the processed signal is avoided. Additionally, this algorithm provides high frequency resolution at other times which results in a good performing noise reduction algorithm and benchmarking results against a prior art algorithm and a commercially available noise reduction solution show better performance of proposed algorithm. The time domain nature of algorithm provides a low processing delay algorithm that is suitable for applications with low latency requirement such as hearing aid devices. Thirdly, a low footprint delayless subband adaptive filtering algorithm for applications with low processing delay requirement such as echo cancellation (EC) in telephony networks is proposed. The suggested algorithm saves substantial memory and MIPS and provides significantly faster convergence rate in comparison with prior art algorithms. Finally, challenges and issues for implementation of real-time audio signal processing algorithms on DSP chipsets (especially low power DSPs) are briefly explained and some applications of research conducted in this thesis are presented.

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Page, Kevin J. "Reduced complexity interconnection and computation for digital signal processing in communications /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9804035.

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Books on the topic "Signal complexity"

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Sabadini, Irene, Daniele C. Struppa, and David F. Walnut, eds. Harmonic Analysis, Signal Processing, and Complexity. Birkhäuser Boston, 2005. http://dx.doi.org/10.1007/0-8176-4416-4.

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Bai, Lin. Low Complexity MIMO Detection. Springer US, 2012.

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Achilleas, Anastasopoulos, and Chen Xiaopeng, eds. Iterative detection: Adaptivity, complexity reduction, and applications. Kluwer Academic Publishers, 2001.

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Chugg, Keith M. Iterative detection: Adaptivity, complexity reduction, and applications. Kluwer Academic Publishers, 2001.

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Yarlagadda, R. K. Rao. Hadamard Matrix Analysis and Synthesis: With Applications to Communications and Signal/Image Processing. Springer US, 1997.

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Mitkowski, Wojciech. Advances in the Theory and Applications of Non-integer Order Systems: 5th Conference on Non-integer Order Calculus and Its Applications, Cracow, Poland. Springer International Publishing, 2013.

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Robert A. Welch Foundation Conference on Chemical Research (50th 2005 Houston, Tex.). Exploring the complexity of signaling pathways: The Robert A. Welch Foundation 50th Conference on Chemical Research : October 23-24, 2006, the Wyndam Greenspoint Hotel, Houston, Texas. Robert A. Welch Foundation, 2006.

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Houghton, A. Error Coding for Engineers. Springer US, 2001.

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Andrzej, Cichocki, Yeredor Arie, Zibulevsky Michael, and SpringerLink (Online service), eds. Latent Variable Analysis and Signal Separation: 10th International Conference, LVA/ICA 2012, Tel Aviv, Israel, March 12-15, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Salazar, Addisson. On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling. Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Signal complexity"

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Losada, Ricardo A., and Richard G. Lyons. "Reducing CIC Filter Complexity." In Streamlining Digital Signal Processing. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118316948.ch6.

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Caulfield, H. J. "Space Time Complexity in Optical Computing." In Optical Signal Processing. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4006-9_4.

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Monteil, Thierry. "A Universal Oracle for Signal Machines." In Unveiling Dynamics and Complexity. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58741-7_30.

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Bai, Lin, Jinho Choi, and Quan Yu. "Signal Processing at Receivers: Detection Theory." In Low Complexity MIMO Receivers. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04984-7_2.

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Bai, Lin, Jinho Choi, and Quan Yu. "MIMO Detection: Vector Space Signal Detection." In Low Complexity MIMO Receivers. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04984-7_3.

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Brandejsky, Tomas. "Evolutionary Systems in Complex Signal Analysis." In Emergence, Complexity and Computation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45438-7_10.

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Paradisi, Paolo, and Paolo Allegrini. "Intermittency-Driven Complexity in Signal Processing." In Complexity and Nonlinearity in Cardiovascular Signals. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58709-7_6.

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Gorin, A. L., D. B. Roe, and A. G. Greenberg. "On the Complexity of Pattern Recognition Algorithms on a Tree-Structured Parallel Computer." In Signal Processing. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4684-6393-4_8.

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Azzini, Antonia, Mauro Dragoni, and Andrea G. B. Tettamanzi. "A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification." In Evolution, Complexity and Artificial Life. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-37577-4_13.

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Zhang, Meng, Hao Wu, Jinwei Cai, and Wenshi Li. "Signal Complexity Measures Based on Ising Model." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5887-0_39.

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Conference papers on the topic "Signal complexity"

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Roztocki, Piotr, Michael Kues, Christian Reimer, et al. "Quantum photonic circuits for optical signal processing." In 2015 Spatiotemporal Complexity in Nonlinear Optics (SCNO). IEEE, 2015. http://dx.doi.org/10.1109/scno.2015.7324001.

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Li, Ling, and Ruiping Wang. "Complexity Analysis of Sleep EEG Signal." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5515699.

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Tsumura, K. "Signal complexity in cyclic consensus systems." In 2010 American Control Conference (ACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/acc.2010.5530987.

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Kurths, J., U. Schwarz, A. Witt, R. Th Krampe, and M. Abel. "Measures of complexity in signal analysis." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51037.

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Schmieder, L., D. Mellon, and M. Saquib. "Signal direction finding for low complexity radar." In 2009 International Waveform Diversity and Design Conference. IEEE, 2009. http://dx.doi.org/10.1109/wddc.2009.4800304.

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Picone, J. "Managing software complexity in signal processing research." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319430.

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Shafivulla, Sayyed, Aaqib Patel, and Mohammed Zafar Ali Khan. "Low Complexity Signal Detection in MIMO Systems." In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall). IEEE, 2018. http://dx.doi.org/10.1109/vtcfall.2018.8690912.

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Dumenil, Arnaud, Elie Awwad, and Cyril Méasson. "Low-Complexity PDL-Resilient Signaling Design." In Signal Processing in Photonic Communications. OSA, 2019. http://dx.doi.org/10.1364/sppcom.2019.spth2e.3.

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Savory, Seb J., Md Saifuddin Faruk, and Xiang Li. "Low Complexity Coherent for Access Networks." In Signal Processing in Photonic Communications. OSA, 2020. http://dx.doi.org/10.1364/sppcom.2020.spw1i.3.

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Кутепов, Илья, Ilya Kutepov, Вадим Крысько, et al. "Complexity of EEG Signals in Schizophrenia Syndromes." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-140-143.

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In the present study, 45 patients with schizophrenia syndromes and 39 healthy subjects are studied with electroencephalogram (EEG) signals. The study groups were of different genders. For each of the two groups, the signals were analyzed using 16 EEG channels. Multiscale entropy, Lempel-Ziv complexity and Lyapunov exponent were used to study the chaotic signals. The data were compared for two groups of subjects. Entropy was compared for each of the 16 channels for all subjects. As a result, topographic images of brain areas were obtained, illustrating the entropy and complexity of Lempel-Ziv. Lempel-Ziv complexity was found to be more representative of the classification problem. The results will be useful for further development of EEG signal classification algorithms for machine learning. This study shows that EEG signals can be an effective tool for classifying participants with symptoms of schizophrenia and control group. It is suggested that this analysis may be an additional tool to help psychiatrists diagnose patients with schizophrenia.

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Reports on the topic "Signal complexity"

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Crispin, Darla. Artistic Research as a Process of Unfolding. Norges Musikkhøgskole, 2018. http://dx.doi.org/10.22501/nmh-ar.503395.

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As artistic research work in various disciplines and national contexts continues to develop, the diversity of approaches to the field becomes ever more apparent. This is to be welcomed, because it keeps alive ideas of plurality and complexity at a particular time in history when the gross oversimplifications and obfuscations of political discourses are compromising the nature of language itself, leading to what several commentators have already called ‘a post-truth’ world. In this brutal environment where ‘information’ is uncoupled from reality and validated only by how loudly and often it is voiced, the artist researcher has a responsibility that goes beyond the confines of our discipline to articulate the truth-content of his or her artistic practice. To do this, they must embrace daring and risk-taking, finding ways of communicating that flow against the current norms. In artistic research, the empathic communication of information and experience – and not merely the ‘verbally empathic’ – is a sign of research transferability, a marker for research content. But this, in some circles, is still a heretical point of view. Research, in its more traditional manifestations mistrusts empathy and individually-incarnated human experience; the researcher, although a sentient being in the world, is expected to behave dispassionately in their professional discourse, and with a distrust for insights that come primarily from instinct. For the construction of empathic systems in which to study and research, our structures still need to change. So, we need to work toward a new world (one that is still not our idea), a world that is symptomatic of what we might like artistic research to be. Risk is one of the elements that helps us to make the conceptual twist that turns subjective, reflexive experience into transpersonal, empathic communication and/or scientifically-viable modes of exchange. It gives us something to work with in engaging with debates because it means that something is at stake. To propose a space where such risks may be taken, I shall revisit Gillian Rose’s metaphor of ‘the fold’ that I analysed in the first Symposium presented by the Arne Nordheim Centre for Artistic Research (NordART) at the Norwegian Academy of Music in November 2015. I shall deepen the exploration of the process of ‘unfolding’, elaborating on my belief in its appropriateness for artistic research work; I shall further suggest that Rose’s metaphor provides a way to bridge some of the gaps of understanding that have already developed between those undertaking artistic research and those working in the more established music disciplines.

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African Open Science Platform Part 1: Landscape Study. Academy of Science of South Africa (ASSAf), 2019. http://dx.doi.org/10.17159/assaf.2019/0047.

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This report maps the African landscape of Open Science – with a focus on Open Data as a sub-set of Open Science. Data to inform the landscape study were collected through a variety of methods, including surveys, desk research, engagement with a community of practice, networking with stakeholders, participation in conferences, case study presentations, and workshops hosted. Although the majority of African countries (35 of 54) demonstrates commitment to science through its investment in research and development (R&D), academies of science, ministries of science and technology, policies, recognition of research, and participation in the Science Granting Councils Initiative (SGCI), the following countries demonstrate the highest commitment and political willingness to invest in science: Botswana, Ethiopia, Kenya, Senegal, South Africa, Tanzania, and Uganda. In addition to existing policies in Science, Technology and Innovation (STI), the following countries have made progress towards Open Data policies: Botswana, Kenya, Madagascar, Mauritius, South Africa and Uganda. Only two African countries (Kenya and South Africa) at this stage contribute 0.8% of its GDP (Gross Domestic Product) to R&D (Research and Development), which is the closest to the AU’s (African Union’s) suggested 1%. Countries such as Lesotho and Madagascar ranked as 0%, while the R&D expenditure for 24 African countries is unknown. In addition to this, science globally has become fully dependent on stable ICT (Information and Communication Technologies) infrastructure, which includes connectivity/bandwidth, high performance computing facilities and data services. This is especially applicable since countries globally are finding themselves in the midst of the 4th Industrial Revolution (4IR), which is not only “about” data, but which “is” data. According to an article1 by Alan Marcus (2015) (Senior Director, Head of Information Technology and Telecommunications Industries, World Economic Forum), “At its core, data represents a post-industrial opportunity. Its uses have unprecedented complexity, velocity and global reach. As digital communications become ubiquitous, data will rule in a world where nearly everyone and everything is connected in real time. That will require a highly reliable, secure and available infrastructure at its core, and innovation at the edge.” Every industry is affected as part of this revolution – also science. An important component of the digital transformation is “trust” – people must be able to trust that governments and all other industries (including the science sector), adequately handle and protect their data. This requires accountability on a global level, and digital industries must embrace the change and go for a higher standard of protection. “This will reassure consumers and citizens, benefitting the whole digital economy”, says Marcus. A stable and secure information and communication technologies (ICT) infrastructure – currently provided by the National Research and Education Networks (NRENs) – is key to advance collaboration in science. The AfricaConnect2 project (AfricaConnect (2012–2014) and AfricaConnect2 (2016–2018)) through establishing connectivity between National Research and Education Networks (NRENs), is planning to roll out AfricaConnect3 by the end of 2019. The concern however is that selected African governments (with the exception of a few countries such as South Africa, Mozambique, Ethiopia and others) have low awareness of the impact the Internet has today on all societal levels, how much ICT (and the 4th Industrial Revolution) have affected research, and the added value an NREN can bring to higher education and research in addressing the respective needs, which is far more complex than simply providing connectivity. Apart from more commitment and investment in R&D, African governments – to become and remain part of the 4th Industrial Revolution – have no option other than to acknowledge and commit to the role NRENs play in advancing science towards addressing the SDG (Sustainable Development Goals). For successful collaboration and direction, it is fundamental that policies within one country are aligned with one another. Alignment on continental level is crucial for the future Pan-African African Open Science Platform to be successful. Both the HIPSSA ((Harmonization of ICT Policies in Sub-Saharan Africa)3 project and WATRA (the West Africa Telecommunications Regulators Assembly)4, have made progress towards the regulation of the telecom sector, and in particular of bottlenecks which curb the development of competition among ISPs. A study under HIPSSA identified potential bottlenecks in access at an affordable price to the international capacity of submarine cables and suggested means and tools used by regulators to remedy them. Work on the recommended measures and making them operational continues in collaboration with WATRA. In addition to sufficient bandwidth and connectivity, high-performance computing facilities and services in support of data sharing are also required. The South African National Integrated Cyberinfrastructure System5 (NICIS) has made great progress in planning and setting up a cyberinfrastructure ecosystem in support of collaborative science and data sharing. The regional Southern African Development Community6 (SADC) Cyber-infrastructure Framework provides a valuable roadmap towards high-speed Internet, developing human capacity and skills in ICT technologies, high- performance computing and more. The following countries have been identified as having high-performance computing facilities, some as a result of the Square Kilometre Array7 (SKA) partnership: Botswana, Ghana, Kenya, Madagascar, Mozambique, Mauritius, Namibia, South Africa, Tunisia, and Zambia. More and more NRENs – especially the Level 6 NRENs 8 (Algeria, Egypt, Kenya, South Africa, and recently Zambia) – are exploring offering additional services; also in support of data sharing and transfer. The following NRENs already allow for running data-intensive applications and sharing of high-end computing assets, bio-modelling and computation on high-performance/ supercomputers: KENET (Kenya), TENET (South Africa), RENU (Uganda), ZAMREN (Zambia), EUN (Egypt) and ARN (Algeria). Fifteen higher education training institutions from eight African countries (Botswana, Benin, Kenya, Nigeria, Rwanda, South Africa, Sudan, and Tanzania) have been identified as offering formal courses on data science. In addition to formal degrees, a number of international short courses have been developed and free international online courses are also available as an option to build capacity and integrate as part of curricula. The small number of higher education or research intensive institutions offering data science is however insufficient, and there is a desperate need for more training in data science. The CODATA-RDA Schools of Research Data Science aim at addressing the continental need for foundational data skills across all disciplines, along with training conducted by The Carpentries 9 programme (specifically Data Carpentry 10 ). Thus far, CODATA-RDA schools in collaboration with AOSP, integrating content from Data Carpentry, were presented in Rwanda (in 2018), and during17-29 June 2019, in Ethiopia. Awareness regarding Open Science (including Open Data) is evident through the 12 Open Science-related Open Access/Open Data/Open Science declarations and agreements endorsed or signed by African governments; 200 Open Access journals from Africa registered on the Directory of Open Access Journals (DOAJ); 174 Open Access institutional research repositories registered on openDOAR (Directory of Open Access Repositories); 33 Open Access/Open Science policies registered on ROARMAP (Registry of Open Access Repository Mandates and Policies); 24 data repositories registered with the Registry of Data Repositories (re3data.org) (although the pilot project identified 66 research data repositories); and one data repository assigned the CoreTrustSeal. Although this is a start, far more needs to be done to align African data curation and research practices with global standards. Funding to conduct research remains a challenge. African researchers mostly fund their own research, and there are little incentives for them to make their research and accompanying data sets openly accessible. Funding and peer recognition, along with an enabling research environment conducive for research, are regarded as major incentives. The landscape report concludes with a number of concerns towards sharing research data openly, as well as challenges in terms of Open Data policy, ICT infrastructure supportive of data sharing, capacity building, lack of skills, and the need for incentives. Although great progress has been made in terms of Open Science and Open Data practices, more awareness needs to be created and further advocacy efforts are required for buy-in from African governments. A federated African Open Science Platform (AOSP) will not only encourage more collaboration among researchers in addressing the SDGs, but it will also benefit the many stakeholders identified as part of the pilot phase. The time is now, for governments in Africa, to acknowledge the important role of science in general, but specifically Open Science and Open Data, through developing and aligning the relevant policies, investing in an ICT infrastructure conducive for data sharing through committing funding to making NRENs financially sustainable, incentivising open research practices by scientists, and creating opportunities for more scientists and stakeholders across all disciplines to be trained in data management.

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