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Abstract

The work considers a one-dimensional time series protocol packet intensity, measured on the city backbone network. The intensity of the series is uneven. Scattering diagrams are constructed. The Dickie Fuller test and Kwiatkowski-Phillips Perron-Shin-Schmitt test were applied to determine the initial series to the class of stationary or nonstationary series. Both tests confirmed the involvement of the original series in the class of differential stationary. Based on the Dickie Fuller test and Private autocorrelation function graphs, the Integrated Moving Average Autoregression Model model is created. The results of forecasting network traffic showed the adequacy of the selected model.
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Bibliography

[1] V. S. Maraev, “Time series visualization Tools in space research. Volume 1”, Research of science city, vol. 4, no. 22, 2017
[2] G.G. Kantorovich, “Analysis of temporal rows. Lecture and methodical materials”, Economic Journal of the Higher School of Economics, no. 3, 2002, pp. 379-701.
[3] M. S. Vershinina, “Analysis of assumptions about the stationarity of some temporal series”, Collection of the all-Russian conference on mathematics with international participation "IAC-2018", Barnaul: AltSU University, 2018, pp. 172-176.
[4] R. M. De Jong, C. Amsler, and P. Schmidt, “A robust version of the KPSS test, based on indicators”, J. Econometrics, vol. 137, no. 2, 2007, pp. 311–333.
[5] W. Wojcik, T. Bieganski, A, Kotyra, and A, Smolarz, "Application of forcasting algorithms in the optical fiber coal dust burner monitoring system", Proc. SPIE 3189, Technology and Applications of Light Guides, (5 August 1997); https://doi.org/10.1117/12.285618
[6] K. O. Kizbikenov, “Prognostication and temporary series: textbook by K. O. Kizbikenov”, Barnaul: AltSPU, 2017.
[7] V. S. Korolyuk, N. I. Portenko, A. V. Skorokhod, A. F. Turbin (eds.) “Handbook of probability theory and mathematical statistics”, Moscow: Nauka, 2005.
[8] G. Box, G. Jenkins, “Time Series Analysis: Forecasting and Control,” San Francisco: Holden-Day, 1970.
[9] I Rizkya, K Syahputri, R. M.Sari, I. Siregar and J. Utaminingrum, “Autoregressive Integrated Moving Average (ARIMA) Model of Forecast Demand in Distribution Centre,” Department of Industrial Engineering, Faculty of Engineering, Universitas Sumatera Utara in IOP Conf. Series: Materials Science and Engineering 598, 2019, 012071.
[10] N.Albanbay, B.Medetov, M. A. Zaks, “Statistics of Lifetimes for Transient Bursting States in Coupled Noisy Excitable Systems,” Journal of Computational and Nonlinear Dynamics. vol. 15, no. 12, 2020, https://doi.org/10.1115/1.4047867
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Authors and Affiliations

Tansaule Serikov
1
Аinur Zhetpisbayeva
1
Ainur Аkhmediyarova
2
Sharafat Mirzakulova
3
Aigerim Kismanova
1
Aray Tolegenova
1
Waldemar Wójcik
4

  1. S.Seifullin Kazakh AgroTechnical University, Nur-Sultan, Kazakhstan
  2. Institute of Information and Computational Technologies, Almaty, Kazakhstan
  3. Turan University, Almaty, Kazakhstan
  4. Lublin University of Technology, Poland
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Abstract

Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too. So, an efficient technique is required to compress the data. This problem arises in Magnetic Resonance Imaging (MRI), Electrocardiogram (ECG), Electroencephalogram (EEG), and other medical signal processing domains. In this paper, we demonstrate Block Sparse Bayesian Learning (BSBL) based compressive sensing technique on an Electroencephalogram (EEG) signal. The efficiency of the algorithm is described using the Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM) value. Apart from this analysis we also use different combinations of sensing matrices too, to demonstrate the effect of sensing matrices on MSE and SSIM value. And here we got that the exponential and chi-square random matrices as a sensing matrix are showing a significant change in the value of MSE and SSIM. So, in real-time body sensor networks, this scheme will contribute a significant reduction in power requirement due to its data compression ability as well as it will reduce the cost and the size of the device used for real-time monitoring.
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Bibliography

[1] Zou, Xiuming, Lei Feng, and Huaijiang Sun. "Compressive Sensing of Multichannel EEG Signals Based on Graph Fourier Transform and Cosparsity." Neural Processing Letters (2019): 1-10.
[2] Tayyib, Muhammad, Muhammad Amir, Umer Javed, M. Waseem Akram, Mussyab Yousufi, Ijaz M. Qureshi, Suheel Abdullah, and Hayat Ullah. "Accelerated sparsity-based reconstruction of compressively sensed multichannel EEG signals." PLoS One 15, no. 1 (2020): e0225397.
[3] Şenay, Seda, Luis F. Chaparro, Mingui Sun, and Robert J. Sclabassi. "Compressive sensing and random filtering of EEG signals using Slepian basis." In 2008 16th European Signal Processing Conference, pp. 1-5. IEEE, 2008.
[4] Gurve, Dharmendra, Denis Delisle-Rodriguez, Teodiano Bastos-Filho, and Sridhar Krishnan. "Trends in Compressive Sensing for EEG Signal Processing Applications." Sensors 20, no. 13 (2020): 3703.
[5] Amezquita-Sanchez, Juan P., Nadia Mammone, Francesco C. Morabito, Silvia Marino, and Hojjat Adeli. "A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals." Journal of Neuroscience Methods 322 (2019): 88-95.
[6] R. DeVore, "Nonlinear approximation." Acta Numerica, 7, 51-150. (1998).
[7] A. Einstein, B. Podolsky, N. Rosen, 1935, “Can quantum-mechanical description of physical reality be considered complete?”, Phys. Rev. 47, 777-780.
[8] R. G. Baraniuk, "Compressive sensing, IEEE Signal Proc." Mag 24, no. 4 (2007): 118-120.
[9] Upadhyaya, Vivek, and Mohammad Salim. "Basis & Sensing Matrix as key effecting Parameters for Compressive Sensing." In 2018 International Conference on Advanced Computation and Telecommunication (ICACAT), pp. 1-6. IEEE, 2018.
[10] E. Candes, “Compressive sampling”, In Proc. Int. Congress of Math., Madrid, Spain, Aug. 2006.
[11] E. Candes, J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions”, Found. Compute. Math., 6(2): 227-254, 2006.
[12] E. Candes, J. Romberg, T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information”. IEEE Trans. Inform. Theory, 52(2):489-509, 2006.
[13] E. Candes, J. Romberg, and T. Tao. Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math., 59(8): 1207-1223, 2006.
[14] E. Candes and T. Tao. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inform. Theory, 52(12): 5406-5425, 2006.
[15] D. Donoho. Compressed sensing. IEEE Trans. Inform. Theory, 52(4):1289-1306, 2006.
[16] S. Kirolos, J. Laska, M. Wakin, M. Duarte, D. Baron, T. Ragheb, Y. Massoud, and R.G. Baraniuk, “Analog-to-information conversion via random demodulation,” in Proc. IEEE Dallas Circuits Systems Workshop, Oct. 2006, pp. 71-74.
[17] Zhang, Zhilin, Tzyy-Ping Jung, Scott Makeig, Bhaskar D. Rao. "Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning." IEEE Transactions on Biomedical Engineering 60, no. 2 (2012): 300-309.
[18] https://sccn.ucsd.edu/eeglab/download.php.
[19] Joshi, Amit Mahesh, Vivek Upadhyaya. "Analysis of compressive sensing for non-stationary music signal." In 2016 International Conference on Advances in Computing, Communications, and Informatics (ICACCI), pp. 1172-1176. IEEE, 2016.
[20] Wang, Zhou, Alan C. Bovik, Hamid R. Sheikh, Eero P. Simoncelli. "Image quality assessment: from error visibility to structural similarity." IEEE transactions on image processing 13, no. 4 (2004): 600-612.
[21] Nibheriya, Khushboo, Vivek Upadhyaya, Ashok Kumar Kajla. "To Analysis the Effects of Compressive Sensing on Music Signal with variation in Basis & Sensing Matrix." In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1121-1126. IEEE, 2018.
[22] Zhang, Zhilin, Tzyy-Ping Jung, Scott Makeig, and Bhaskar D. Rao. "Compressed sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware." IEEE Transactions on Biomedical Engineering 60, no. 1 (2012): 221-224.
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Authors and Affiliations

Vivek Upadhyaya
1
ORCID: ORCID
Mohammad Salim
1

  1. Malaviya National Institute of Technology, India
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Abstract

The quick leakage alarm and the accurate concentration prediction are two important aspects of natural gas safety monitoring. In this paper, a rapid monitoring method of sensor data sharing, rapid leakage alarm and simultaneous output of concentrations prediction is proposed to accelerate the alarm speed and predict the possible impact of leakage. In this method, the Dempster-Shafer evidence theory is used to fuse the trend judgment and the CUSUM (cumulative sum) and the Gauss-Newton iteration is used to predict the concentration. The experiment system based on the TGS2611 natural gas sensor was built. The results show that the fusion method is significantly better than the single monitoring method. The alarm time of fusion method was more advanced than that of the CUSUM method and the trend method (being averagely, 10.4% and 7.6% in advance in the CUSUM method and the trend method respectively). The relative deviations of the predicted concentration were the maximum (13.3%) at 2000 ppm (parts per million) and the minimum (0.8%) at 6000 ppm, respectively.
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Bibliography

[1] P. Lustenberger, F. Schumacher, M. Spada, P. Burgherr, and B. Stojadinovic, ”Assessing the performance of the European natural gas network for selected supply disruption scenarios using open-source information”, Energies 12, 1–28 (2019). https://doi.org/10.3390/en12244685
[2] S. Kakuma, and K. Noda, ”Practical and Sensitive Measurement of Methane Gas Concentration Using a 1.6 μm Vertical–Cavity–Surface– Emitting–Laser Diode”, Sensors and Materials 22 (7), 365–375 (2010).
[3] K. L. Su, Y. L. Liao, S. W. Shiau, and J. H. Guo, ”Bayesian-Estimation– Algorithm-Based Gas Detection Modules”, Sensors and Materials 25 (6), 397–402 (2013).
[4] Y. Xia, J. Wang, L. Xu, X. Li, and S. J. Huang, ”A roomtemperature methane sensor based on Pd-decorated ZnO/rGO hybrids enhanced by visible light photocatalysis”, Sensors and Actuators B:Chemical 304, DOI: 10.1016/j.snb.2019.127334 (2020). https://doi.org/10.1016/j.snb.2019.127334
[5] T. Long, E. Li, L. Yang, J. F. Fan, and Z. Z. Lian, ”Analysis and design of an effective light interference methane sensor based on threedimensional optical path model”, Journal of Sensors Article ID 1342593, 1-11 (2018). https://doi.org/10.1155/2018/1342593
[6] Q. Xiao, J. Li, Z. Bai, J. Sun, N. Zhou, and Z. Zeng, ”A small leak detection method based on VMD adaptive de-noising and ambiguity correlation classification intended for natural gas pipelines”, Sensors 12, 1-16 (2016). https://doi.org/10.3390/s16122116
[7] Z. M. Zhou, J. Zhang, X. S. Huang, and X. S. Guo, ”Experimental study on distributed optical-fiber cable for high-pressure buried natural gas pipeline leakage monitoring”, Optical Fiber Technology 53, Article ID 102028 (2019). https://doi.org/10.1016/j.yofte.2019.102028
[8] T. Jia, T. Guo, X. Wang, D. Zhao, C. Wang, Z. Zhang, S. Lei, W. Liu, H. Liu, and X. Li, ”Mixed Natural Gas Online Recognition Device Based on a Neural Network Algorithm Implemented by an FPGA”, Sensors 19 (9), 1-16 (2019). https://doi.org/10.3390/s19092090
[9] A. X. He, J. Yu, G. F. Wei, Y. Chen, H. Wu, and Z. A. Tang, ”A. Short-time fourier transform and decision tree-based pattern recognition for gas identification using temperature modulated microhotplate gas sensors”, Journal of Sensors Article ID 7603931, 1-12 (2016). https://doi.org/10.1155/2016/7603931
[10] Z. Y. Yang, M. Yin, J. G. Xu, and W. Lin, ”Spatial evolution model of tourist destinations based on complex adaptive system theory: a case study of Southern Anhui, China”, Journal of Geographical Sciences 29 (8), 1411–1434 (2019). https://doi.org/10.1007/s11442-019-1669-z
[11] L. W. Tian, D. J. Moschandreas, J. H. Hao, ”The impact of kitchen activities on indoor pollutant concentrations”, Indoor and Built Environment 17 (4), 377–383 (2008). https://doi.org/10.1177/1420326x08094626
[12] N. Hu, S. D. Liu, Y. Q. Gao, J. P. Xu, X. Zhang, Z. Zhang, and X. H. Lee, ”Large methane emissions from natural gas vehicles in Chinese cities”, Atmospheric Environment 187, 374–380 (2018). https://doi.org/10.1016/j.atmosenv.2018.06.007
[13] Y. Li, C. Hu, C. Huang, and L. Duan, ”The concept of smart tourism in the context of tourism information services”, Tourism Management 58, 293-300 (2016). https://doi.org/10.1016/j.tourman.2016.03.014
[14] P. Zhao, R. Zhuo, S. Li, C. Shu, B. Laiwang, Y. Jia, Y. Shi, and L. Suo, ”Analysis of advancing speed effect in gas safety extraction channels and pressure-relief gas extraction”, Fuel 265, Article ID 116825 (2020). https://doi.org/10.1016/j.fuel.2019.116825
[15] E. R. S. Jaclyn, N. N. A. Franz, C. M. J. Fernando, and M. D. Fabian, ”Developing a chemical and hazardous waste inventory system”, Journal of Chemical Health and Safety 18 (6), 15–18 (2011). https://doi.org/10.1016/j.jchas.2011.05.012
[16] F. Mehraliyev, Y. Choi, and M. A. Koseoglu, ”Progress on smart tourism research”, Journal of Hospitality and Tourism Technology 10, 522-538 (2019). https://doi.org/10.1108/jhtt-08-2018-0076
[17] F. Mehraliyev, I. C. C. Chan, Y. Choi, and M. A. Koseoglu, ”A state-of-the-art review of smart tourism research”, Journal of Travel and Tourism Marketing 37 (1), 78–91 (2020). https://doi.org/10.1080/10548408.2020.1712309
[18] M. Pastell, J. Hietaoja, J. Yun, J. Tiusanen, and A. Valros, ”Predicting farrowing of sows housed in crates and pens using accelerometers and CUSUM charts”, Computers and Electronics in Agriculture 127, 197–203 (2016). https://doi.org/10.1016/j.compag.2016.06.009
[19] S. C. Chapra, ”Applied Numerical Methods with MATLAB for Engineers and Scientists”, McGraw-Hill, New York, 2001.
[20] Q. Liu, Y. Tian, and B. Kang, ”Derive knowledge of Znumber from the perspective of Dempster–Shafer evidence theory”, Applications of Artificial Intelligence 85, 754–764 (2019). https://doi.org/10.1016/j.engappai.2019.08.005
[21] D. Su, Q. Shi, H. Xu, ”Nonintrusive Load Monitoring Based on Complementary Features of Spurious Emissions”, Electronics 8 (9), 1-13 (2019). https://doi.org/10.3390/electronics8091002
[22] Y. S. Chen, ”Research on Self-Validating Methods for Metal Oxide Semiconductor Gas Sensor Arrays”, Doctor’s Thesis, Harbin Institute of Technology, Harbin, China, (2017).
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Authors and Affiliations

Rongli Li
1
Yuexin Fan
2

  1. Faculty of Sanjiang University, Nanjing, China
  2. Faculty of Fujian Normal University, Fuzhou, China
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Abstract

A novice advanced architecture of 8-bit analog to digital converter is introduced and analyzed in this paper. The structure of proposed ADC is based on the sub-ranging ADC architecture in which a 4-bit resolution flash-ADC is utilized. The proposed ADC architecture is designed by employing a comparator which is equipped with common mode current feedback and gain boosting technique (CMFD-GB) and a residue amplifier. The proposed 8 bits ADC structure can achieve the speed of 140 megasamples per second. The proposed ADC architecture is designed at a resolution of 8 bits at 10 MHz sampling frequency. DNL and INL values of the proposed design are -0.94/1.22 and -1.19/1.19 respectively. The ADC design dissipates a power of 1.24 mW with the conversion speed of 0.98 ns. The magnitude of SFDR and SNR from the simulations at Nyquist input is 39.77 and 35.62 decibel respectively. Simulations are performed on a SPICE based tool in 90 nm CMOS technology. The comparison shows better performance for this proposed ADC design in comparison to other ADC architectures regarding speed, resolution and power consumption.
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Bibliography

[1] Y. Zhou, B. Xu and Y. Chiu, “A 12-b 1-GS/s 31.5-mW Time-Interleaved SAR ADC With Analog HPF-Assisted Skew Calibration and Randomly Sampling Reference ADC,” IEEE Journal of Solid-State Circuits 54, 8, 2207-2218, (2019). https://doi.org/10.1109/JSSC.2019.2915583.
[2] D. Oh, J. Kim, D. Jo, W. Kim, D. Chang and S. Ryu, “A 65-nm CMOS 6-bit 2.5-GS/s 7.5-mW 8 x Time-Domain Interpolating Flash ADC With Sequential Slope-Matching Offset Calibration,” IEEE Journal of Solid-State Circuits 54, 1, 288- 297,(2019). https://doi.org/10.1109/JSSC.2018.2870554.
[3] A. Wu, J. Wu, and J. Huang, “Energy-efficient switching scheme for ultra-low voltage SAR ADC.”, Analog Integr Circ Sig Process 90, 507–511, (2017). https://doi.org/10.1007/s10470-016-0892-0
[4] M. Guo, J. Mao, S. Sin, H. Wei and R. P. Martins, “A 1.6- GS/s 12.2-mW Seven-/Eight-Way Split Time-Interleaved SAR ADC Achieving 54.2-dB SNDR With Digital Background Timing Mismatch Calibration,”IEEE Journal of Solid-State Circuits 55, 3,693-705, (2020). https://doi.org/10.1109/JSSC.2019.2945298.
[5] M. Davidovic, G. Zach, H. Zimmermann, “An 11-bit successive approximation analog-to-digital converter based on a combined capacitor-resistor network.”, Elektrotech. Inftech. 127, 98–102, (2010). https://doi.org/10.1007/s00502-010-0704-7
[6] D. Chang, W. Kim, M. Seo, H. Hong, and S. Ryu, “Normalized- Full-Scale-Referencing Digital-Domain Linearity Calibration for SAR ADC.”, IEEE Transactions on Circuits and Systems I: Regular Papers. 64, 2, 322-332 (2017). https://doi.org/10.1109/TCSI.2016.2612692
[7] M. Shim et al.,“Edge-Pursuit Comparator: An Energy-Scalable Oscillator Collapse-Based Comparator With Application in a 74.1 dB SNDR and 20 kS/s 15 b SAR ADC”, IEEE Journal of Solid-State Circuits 52, 4, 1077-1090, (2017). https://doi.org/10.1109/JSSC.2016.2631299
[8] D. Zhang and A. Alvandpour, “A 12.5-ENOB 10-kS/s Redundant SAR ADC in 65-nm CMOS”, IEEE Transactions on Circuits and Systems II: Express Briefs 63, 3, 244-248, (2016). https://doi.org/10.1109/TCSII.2015.2482618.
[9] S.A. Zahrai, M. Onabajo, “ Review of Analog-To-Digital Conversion Characteristics and Design Considerations for the Creation of Power- Efficient Hybrid Data Converters.”, J. Low Power Electron. Appl. 8, 12, (2018). https://doi.org/10.3390/jlpea8020012
[10] S.Taheri, J. Lin, J. S. Yuan,“Security Interrogation and Defense for SAR Analog to Digital Converter.”, Electronics 6, 48, (2017). https://doi.org/10.3390/electronics6020048
[11] J. Kim, B. Sung, W. Kim and S. Ryu, “A 6-b 4.1-GS/s Flash ADC With Time-Domain Latch Interpolation in 90-nm CMOS”, IEEE Journal of Solid-State Circuits 48, 6, 1429-1441, (2013). https://doi.org/10.1109/JSSC.2013.2252516
[12] S. Danesh, J. Hurwitz, K. Findlater, D. Renshaw and R. Henderson, “A Reconfigurable 1 GSps to 250 MSps, 7-bit to 9-bit Highly Time-Interleaved Counter ADC with Low Power Comparator Design”, IEEE Journal of Solid-State Circuits 48, 3, 733-748, (2013). https://doi.org/10.1109/JSSC.2013.2237672
[13] L. Wang, M. LaCroix and A. C. Carusone, “A 4-GS/s Single Channel Reconfigurable Folding Flash ADC for Wireline Applications in 16-nm FinFET.”, IEEE Transactions on Circuits and Systems II: Express Briefs 64, 12, 1367-1371, (2017). https://doi.org/10.1109/TCSII.2017.2726063
[14] F. M´arquez, et al., “A novel autozeroing technique for flash Analog-to-Digital converters.”, Integration 47, 1, 23-29, (2014). https://doi.org/10.1016/j.vlsi.2013.06.002
[15] Masumeh Damghanian, Seyed Javad Azhari, “A low-power 6-bit MOS CML flash ADC with a novel multi-segment encoder for UWB applications.”, Integration 57, 158-168, (2017). https://doi.org/10.1016/j.vlsi.2017.01.006
[16] Y. Wang, M. Yao, B. Guo, Z. Wu, W. Fan and J. J. Liou, “A Low-Power High-Speed Dynamic Comparator With a Transconductance-Enhanced Latching Stage,” IEEE Access 7, 93396- 93403,(2019). https://doi.org/10.1109/ACCESS.2019.2927514.
[17] A. Khatak, M. Kumar, S. Dhull, “An Improved CMOS Design of Op-Amp Comparator with Gain Boosting Technique for Data Converter Circuits.”, J. Low Power Electron. Appl. 8, 33, (2018). https://doi.org/10.3390/jlpea8040033.
[18] B. Hershberg et al., “3.6 A 6-to-600MS/s Fully Dynamic Ringamp Pipelined ADC with Asynchronous Event-Driven Clocking in 16nm,” 2019 IEEE International Solid- State Circuits Conference - (ISSCC), San Francisco, CA, USA 68-70, (2019). https://doi.org/10.1109/ISSCC.2019.8662319.
[19] U. Chio et al., “Design and Experimental Verification of a Power Effective Flash-SAR Sub ranging ADC.”, IEEE Transactions on Circuits and Systems II: Express Briefs 57, 8, 607-611, (2010). https://doi.org/10.1109/TCSII.2010.2050937
[20] Young-Deuk Jeon et al., “A dual-channel pipelined ADC with sub-ADC based on flash-SAR architecture.”, Circuits and Systems II: Express Briefs 59, 741-745. (2012). https://doi.org/10.1109/TCSII.2012.2222837
[21] Y. Lin et al.,“ A 9-Bit 150-MS/s Subrange ADC Based on SAR Architecture in 90-nm CMOS.”, IEEE Transactions on Circuits and Systems I: Regular Papers 60, 3, 570-581, (2013). https://doi.org/10.1109/TCSI.2012.2215756
[22] J.I. Lee, J. Song, “Flash ADC architecture using multiplexers to reduce a preamplifier and comparator count.”, 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013) 1-4, (2013). https://doi.org/10.1109/TENCON.2013.6718487
[23] A. Esmailiyan, F. Schembari and R. B. Staszewski, “A 0.36-V 5-MS/s Time-Mode Flash ADC With Dickson-Charge-Pump- Based Comparators in 28-nm CMOS,”IEEE Transactions on Circuits and Systems I: Regular Papers 67, 6, 1789-1802, (2020). https://doi.org/10.1109/TCSI.2020.2969804.
[24] J. Xu, et al., “Low-leakage analog switches for low-speed sample-and-hold circuits”, Microelectronics Journal 76, 22–27, (2018). https://doi.org/10.1016/j.mejo.2018.04.008
[25] M. Nazari, L. Sharifi,A. Aghajani, and O. Hashemipour, “A 12-bit high performance current-steering DAC using a new binary to thermometer decoder.”, 2016 24 Iranian Conference on Electrical Engineering (ICEE), Shiraz 2016 1919-1924, (2016). https://doi.org/10.1109/IranianCEE.2016.7585835
[26] H.S. Bindra et al., “A 1.2-V Dynamic Bias Latch-Type Comparator in 65-nm CMOS With 0.4-mV Input Noise.”, IEEE Journal of Solid-State Circuits 53, 7, 1902-1912, (2018). https://doi.org/10.1109/JSSC.2018.2820147
[27] A. Taghizadeh, Z.D. Koozehkanani, J. Sobhi, “A new high-speed lowpower and low-offset dynamic comparator with a current-mode offset compensation technique.”, AEU - Int. J. Electron. Commun. 81, 163–170, (2018). https://doi.org/10.1016/j.aeue.2017.07.018.
[28] M. Saberi and R. Lotfi,“ Segmented Architecture for Successive Approximation Analog-to-Digital Converters.”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems 22, 3, 593-606, (2014). https://doi.org/10.1109/TVLSI.2013.2246592
[29] Y. Haga et al., “Design of a 0.8 Volt fully differential CMOS OTA using the bulk-driven technique.”, 2005 IEEE International Symposium on Circuits and Systems 1, 220-223, (2005). https://doi.org/10.1109/ISCAS.2005.1464564.
[30] J. Lagos, B. P. Hershberg, E. Martens, P. Wambacq and J. Craninckx, “A 1-GS/s, 12-b, Single-Channel Pipelined ADC With Dead-Zone- Degenerated Ring Amplifiers,” IEEE Journal of Solid-State Circuits 54, 3, 646-658, (2019). https://doi.org/10.1109/JSSC.2018.2889680.
[31] Y. Lim and M. P. Flynn, “A 1 mW 71.5 dB SNDR 50 MS/s 13 bit Fully Differential Ring Amplifier Based SAR-Assisted Pipeline ADC,” IEEE Journal of Solid-State Circuits 50, 12, 2901-2911, (2015). https://doi.org/10.1109/JSSC.2015.2463094
[32] B. Murmann, “The successive approximation register ADC: a versatile building block for ultra-low- power to ultra-high-speed applications.”, IEEE Communications Magazine 54, 4, 78-83, (2016). https://doi.org/10.1109/MCOM.2016.7452270
[33] T. Ogawa et al., “Non-binary SAR ADC with digital error correction for low power applications,” 2010 IEEE Asia Pacific Conference on Circuits and Systems, Kuala Lumpur196-199, (2010). https://doi.org/10.1109/APCCAS.2010.5774747.
[34] M. Hotta et al., “SAR ADC Architecture with Digital Error Correction.”. IEEJ Trans Elec Electron Eng 5, 651-659, (2010). https://doi.org/10.1002/tee.20588
[35] S. Lee, A.P. Chandrakasan and H. Lee, “A 1 GS/s 10b 18.9 mW Time-Interleaved SAR ADC with Background Timing Skew Calibration.”, IEEE Journal of Solid-State Circuits 49, 12, 2846-2856, (2014). https://doi.org/10.1109/JSSC.2014.2362851
[36] M. Damghanian and S.J. Azhari, “A novel three-section encoder in a low-power 2.3 GS/s flash ADC.”, Microelectronics J 82, 71–80, (2018). https://doi.org/10.1016/j.mejo.2018.10.009
[37] Yi. Shen and Z. Zhu, “Analysis and optimization of the twostage pipelined SAR ADCs.”, Microelectronics Journal 47, 1–5, (2016). https://doi.org/10.1016/j.mejo.2015.10.018.
[38] Rui Ma, Lisha Wang, Dengquan Li, Ruixue Ding, Zhangming Zhu,“A 10-bit 100-MS/s 5.23 mW SAR ADC in 0.18 μm CMOS.”,Microelectronics Journal 78, 63-72, (2018). https://doi.org/10.1016/j.mejo.2018.06.007
[39] X. Xin et al.,“A 0.4-V 10-bit 10-KS/s SAR ADC in 0.18 μm CMOS for low energy wireless senor network chip.”,Microelectronics Journal 83, 104–116, (2019). https://doi.org/10.1016/j.mejo.2018.11.017
[40] W. Guo, S. Liu, and Z. Zhu, “ An asynchronous 12-bit 50MS/s rail-torail Pipeline-SAR ADC in 0.18 μm CMOS.”, Microelectronics Journal 52, 23–30, (2016). https://doi.org/10.1016/j.mejo.2016.03.003
[41] B. Samadpoor Rikan et al.,“A 10-bit 1 MS / s segmented Dual-Sampling SAR ADC with reduced switching energy.”, Microelectronics Journal 70, 89–96, (2017). https://doi.org/10.1016/j.mejo.2017.11.005
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Authors and Affiliations

Anil Khatak
1
ORCID: ORCID
Manoj Kumar
2
Sanjeev Dhull
3

  1. Faculty of Biomedical Engineering, GJUS&T, Hisar, Haryana, India
  2. Faculty of USICT, Guru Gobind Singh Indraprastha University, New Delhi, India
  3. Faculty of ECE, GJUS&T, Hisar, Haryana, India
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Abstract

The purpose of the article is a comparison between DC/DC topologies with a wide input voltage range. The research also explains how the implementation of GaN E‑HEMT transistors influences the overall efficiency of the converter. The article presents a process of selection of the most efficient topology for stabilization of the battery storage voltage (9 V – 36 V) at the level of 24 V, which enables the usage of ultracapacitor energy storage in a wide range of applications, e.g., in automated electric vehicles. In order to choose the most suitable topology, simulation and laboratory research were conducted. The two most promising topologies were selected for verification in the experimental model. Each of the converters was constructed in two versions: with Si and with GaN E-HEMT transistors. The paper presents experimental research results that consist of precise power loss measurements and thermal analysis. The performance with an increased switching frequency of converters was also examined.
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Bibliography

[1] M. Nowak and R. Barlik, „Poradnik inżyniera energoelektronika,” in WNT, Warszawa, pp.161-194, 1998. (in Polish)
[2] N. Mohan, W. P. Robbins, T. M. Undeland, and N. Mohan, “Solutions manual: power electronics: converters, applications, and design,” New York: Wiley, 1989.
[3] L. Wuidart, “Topologies For Switched Mode Power Supplies,” STMicroelectronics, 1999.
[4] M. Zehendner and M. Ulmann, “Power Topologies Handbook,” Texas Instrument, pp.23-171, 2016.
[5] X. Weng, X. Xiao, W. He, Y. Zhou, Y. Shen, W. Zhao, and Z. Zhao, "Comprehensive comparison and analysis of non-inverting buck boost and conventional buck boost converters" The Journal of Engineering, vol. 2019, no. 16, pp. 3030–3034, 2019. DOI: 10.1049/joe.2018.8373
[6] M. Luthfansyah, S. Suyanto, and A. Bakarr Momodu Bangura, "Evaluation and Comparison of DC-DC Power Converter Variations in Solar Panel Systems Using Maximum Power Point Tracking (MPPT) Flower Pollination Algorithm (FPA) Control" E3S Web of Conferences, vol. 190, p. 00026, 2020. DOI: 10.1051/e3sconf/202019000026
[7] B. Amri and M. Ashari, "The comparative study of Buck-boost, Cuk, Sepic and Zeta converters for maximum power point tracking photovoltaic using P&O method" 2015 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 327-332, 2015. DOI: 10.1109/ICITACEE.2015.7437823
[8] M. V. D. de Sá and R. L. Andersen, "Dynamic modeling and design of a Cúk converter applied to energy storage systems" 2015 IEEE 13th Brazilian Power Electronics Conference and 1st Southern Power Electronics Conference (COBEP/SPEC), pp. 1-6. DOI: 10.1109/COBEP.2015.7420080, 2015
[9] B. M. M. Mwinyiwiwa and J. Dunia, "Performance Comparison between ĆUK and SEPIC Converters for Maximum Power Point Tracking Using Incremental Conductance Technique in Solar Power Applications," World Academy of Science, Engineering and Technology International Journal of Computer and Systems Engineering , vol. 7, no. 12. DOI: 10.5281/zenodo.1089293, 2013.
[10] Y. Attia and M. Youssef, "GaN on silicon E-HEMT and pure silicon MOSFET in high frequency switching of EV DC/DC converter: A comparative study in a nissan leaf," 2016 IEEE International Telecommunications Energy Conference (INTELEC), pp. 1-6, 2016. DOI: 10.1109/INTLEC.2016.7749112
[11] S. K. Pullabhatla, P. B. Bobba, and S. Yadlapalli, "Comparison of GAN, SIC, SI Technology for High Frequency and High Efficiency Inverters," E3S Web of Conferences, vol. 184, p. 01012, 2020. DOI: 10.1051/e3sconf/202018401012
[12] A. Deihimi and M. E. Mahmoodieh, "Analysis and control of battery‐integrated dc/dc converters for renewable energy applications" IET Power Electronics, vol. 10, no. 14, pp. 1819–1831, 2017. DOI: 10.1049/iet-pel.2016.0832
[13] R. Nowakowski and N. Tang, "Efficiency of synchronous versus nonsynchronous buck converters, " Texas Instruments, 2009. [14] Gan Systems, “GS61008T datasheet, ”, 2021 online: www.gansystems.com (2021).
[15] Infineon, “IPP030N10N5 datasheet”, Rev.2.3,2016-10-03, 2021. online: www.infineon.com.
[16] P. Grzejszczak , A. Czaplicki , M. Szymczak , R. Barlik „The impact of snubber circuits on switching energy losses in high frequency converters” Przeglad Elektrotechniczny, vol. 96, no. 06, pp 93-97, 2020, (in Polish). DOI: 10.15199/48.2020.06.17
[17] GN012 Application Guide Design with GaN Enhancement Mode HEMT, , 2021 online: www.gansystems.com (2021).
[18] M. Koszel and P. Grzejszczak, "Power loss estimating in GaN E-HEMT based synchronous buck-boost converter," 2020 Progress in Applied Electrical Engineering (PAEE), 2020, pp. 1-6. DOI: 10.1109/PAEE50669.2020.9158576
[19] D. Craig, "Common misconceptions about the MOSFET body diode," GaN Systems, 23-Oct-2019. online: https://gansystems.com/newsroom/common-misconceptions-about-the-mosfet-body-diode/ (2021)
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Authors and Affiliations

Mikołaj Koszel
1
Piotr Grzejszczak
1
Bartosz Nowatkiewicz
2
Kornel Wolski
1

  1. Warsaw University of Technology, Institute of Control and Industrial Electronics, Poland
  2. Wibar Technology Ltd., Poland
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Abstract

Drinking fresh water, turning the lights on, travelling by tram, calling our family, or getting a medical treatment are usual activities, but the underlying SCADA (Supervisory Control and Data Acquisition) systems like CIS (Critical Infrastructure Systems), ICS (Industrial Control Systems) or DCS (Distributed Control Systems) were always the target of many types of attacks, endangered the above mentioned simple activities. During the last decades because of the fast spread of the internet based services and the continuous technical development these systems become more vulnerable than ever. Full reconstruction and innovative changes in older SCADA systems has high cost, and it is not always rewarding. Communication protocols as Modbus (1979) serve as a main basis for SCADA systems, so security of Modbus has a major impact of the security of SCADA systems. Our paper raises and answers questions about the security of the Modbus RTU protocol. We focus on the serial Modbus protocol, because in that method we found many unsolved problems, like lack of authentication of the participants, lack of secure channel and so on. The aim of this paper to propose a secure communication alternative for Modbus RTU @ RS485 wire. The main advantage of the proposed method is the coexistence with traditional slaves and bus systems and only software update is necessary
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Authors and Affiliations

Éva Ádámkó
Gábor Jakabóczki
Szemes Péter Tamás
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Abstract

Although self-modifying code has been shyed away from due to its complexity and discouragement due to safety issues, it nevertheless provides for a very unique obfuscation method and a different perspective on the relationship between data and code. The generality of the von Neumann architecture is hardly realized by today’s processor models. A code-only model is shown where every instruction merely modifies other instructions yet achieves the ability to compute and Turing machine operation is easily possible.
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Authors and Affiliations

Gregory Morse
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Abstract

Games are among problems that can be reduced to optimization, for which one of the most universal and productive solving method is a heuristic approach. In this article we present results of benchmark tests on using 5 heuristic methods to solve a physical model of the darts game. Discussion of the scores and conclusions from the research have shown that application of heuristic methods can simulate artificial intelligence as a regular player with very good results.
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Authors and Affiliations

Kamil Książek
Dawid Połap
Marcin Woźniak
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Abstract

This paper tries to get a response to the following question: When can a narrowband power amplifier (PA) be considered to be memoryless and when can it not be considered memoryless? To this end, a thorough and consistent analysis of the notions and definitions related with the above topic is carried out. In the considerations presented, two models of the narrowband PA are exploited interchangeably: the black box model widely used in the literature and a model developed here, which is based on the Volterra series. These two models complement each other. In this paper, the conditions for a linear or nonlinear narrowband PA to be memoryless or approximately memoryless or possessing memory are derived and illustrated. They are formulated in terms of the signal delay as well as in terms of the amplitude-to-phase (AM/PM) conversion of the amplifier. Furthermore, the two possible interpretations of the amplitude-to-amplitude (AM/AM) and AM/PM conversions are given a mathematical framework. That is these conversions are presented through some operations. One set of these operations allows to treat the AM/AM and AM/PM conversions as distortions of the modulating signals. Or equivalently as distortions of a given signal constellation when it passes through the PA. Finally, it is proved that the Saleh’s and Ghorbani’s models of the AM/AM and AM/PM conversions occurring in the PAs, which were published in the literature, are not memoryless ones.
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Authors and Affiliations

Andrzej Borys

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