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Number of results: 6
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Abstract

This paper presents an overview of algorithms for one-phase active power estimation using digital signal processing in the time domain and in the frequency domain, and compares the properties of these algorithms for a sinusoidal test signal. The comparison involves not only algorithms that have already been published, but also a new algorithm. Additional information concerning some known algorithms is also included. We present the results of computer simulations in MATLAB and measurement results gained by means of computer plug-in boards, both multiplexed and using simultaneous signal sampling. The use of new cosine windows with a recently published iterative algorithm is also included, and the influence of additive noise in the test signal is evaluated.

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Authors and Affiliations

Milos Sedlacek
Zdenek Stoudek
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Abstract

A new ultrasound digital transcranial Doppler system (digiTDS) is introduced. The digiTDS enables diagnosis of intracranial vessels which are rather difficult to penetrate for standard systems. The device can display a color map of flow velocities (in time-depth domain) and a spectrogram of a Doppler signal obtained at particular depth. The system offers a multigate processing which allows to display a number of spectrograms simultaneously and to reconstruct a flow velocity profile.

The digital signal processing in digiTDS is partitioned between hardware and software parts. The hardware part (based on FPGA) executes a signal demodulation and reduces data stream. The software part (PC) performs the Doppler processing and display tasks. The hardware-software partitioning allowed to build a flexible Doppler platform at a relatively low cost.

The digiTDS design fulfills all necessary medical standards being a new useful tool in the transcranial field as well as in heart velocimetry research.

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Authors and Affiliations

Marcin Lewandowski
Mateusz Walczak
Piotr Karwat
Beata Witek
Paweł Karłowicz
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Abstract

The paper presents a novel implementation of a time-to-digital converter (TDC) in field-programmable gate array (FPGA) devices. The design employs FPGA digital signal processing (DSP) blocks and gives more than two-fold improvement in mean resolution in comparison with the common conversion method (carry chain-based time coding line). Two TDCs are presented and tested depending on DSP configuration. The converters were implemented in a Kintex-7 FPGA device manufactured by Xilinx in 28 nm CMOS process. The tests performed show possibilities to obtain mean resolution of 4.2 ps but measurement precision is limited to at most 15 ps mainly due to high conversion nonlinearities. The presented solution saves FPGA programmable logic blocks and has an advantage of a wider operation range when compared with a carry chain-based time coding line.

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Authors and Affiliations

Paweł Kwiatkowski
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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.
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[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.
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[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

Generalized Rademacher functions, constructed as a sequence of elements of Galois fields are intended to find the spectral representation of signals with levels. These functions form a complete basis on the interval corresponding to -1 discrete time intervals and for passing into the classical Rademacher functions. The advantage of such spectra obtained using Galois Fields Fourier Transform is that the range of variation of the spectrum amplitudes remains the same as the range of variation of the original signal, which is modeled on discrete time functions taking values in the Galois field.
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Authors and Affiliations

Elizaveta S. Vitulyova
1
Dinara K. Matrassulova
2
Ibragim E. Suleimenov
3

  1. Almaty University of Power Engineering and Telecommunications named after Gumarbek Daukeyev, Almaty, Republic of Kazakhstan
  2. Almaty Universityof Power Engineering and Telecommunications named after GumarbekDaukeyev, Almaty, Republic of Kazakhstan
  3. National Engineering Academy of Republic of Kazakhstan, Almaty, Kazakhstan

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