scholarly journals Filtering Techniques for Chaotic Signal Processing

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 450 ◽  
Author(s):  
Denis Butusov ◽  
Timur Karimov ◽  
Alexander Voznesenskiy ◽  
Dmitry Kaplun ◽  
Valery Andreev ◽  
...  
Keyword(s):  
Signal Processing ◽  
Communication Systems ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Chaotic Signal ◽  
Denoising Method ◽  
Practical Applications ◽  
Iir Filter ◽  
Chaotic Signals ◽  
Processing Techniques

The vulnerability of chaotic communication systems to noise in transmission channel is a serious obstacle for practical applications. Traditional signal processing techniques provide only limited possibilities for efficient filtering broadband chaotic signals. In this paper, we provide a comparative study of several denoising and filtering approaches: a recursive IIR filter, a median filter, a wavelet-based denoising method, a method based on empirical modes decomposition, and, finally, propose the new filtering algorithm based on the cascade of driven chaotic oscillators. Experimental results show that all the considered methods make it possible to increase the permissible signal-to-noise ratio to provide the possibility of message recognition, while the new proposed method showed the best performance and reliability.

Signal-processing techniques for low signal-to-noise ratio laser Doppler velocimetry signals

1992 ◽  
Vol 12-12 (4-5) ◽  
pp. 319-328 ◽  
Author(s):  
K. A. Shinpaugh ◽  
R. L. Simpson ◽  
A. L. Wicks ◽  
S. M. Ha ◽  
J. L. Fleming
Keyword(s):  
Signal Processing ◽  
Laser Doppler Velocimetry ◽  
Signal To Noise Ratio ◽  
Laser Doppler ◽  
Doppler Velocimetry ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Processing Techniques ◽  
Signal Processing Techniques

Adaptive MMSE Multiuser Detection in MIMO OFDM Wireless Communication System

2011 ◽  
Vol 367 ◽  
pp. 233-240 ◽  
Author(s):  
T. Eneh ◽  
P. Rapajic ◽  
K. Anang ◽  
Bello Lawal
Keyword(s):  
Signal Processing ◽  
Wireless Communication ◽  
Multiuser Detection ◽  
Communication Systems ◽  
Power Transmission ◽  
Signal To Noise Ratio ◽  
Mobile Station ◽  
Signal To Noise ◽  
Mimo Ofdm ◽  
Noise Ratio

The combination of MIMO signal processing with OFDM is a solution to achieving high data rates for next generation wireless communication systems operating in frequency selective fading environments. To realize the extension of the MIMO with OFDM, a number of changes are required in the baseband signal processing. The developed adaptive Multiuser Detection in MIMO OFDM(AMUD) scheme performs better compared to non adaptive MIMO OFDM, at low Signal to noise ratio (SNR), it shows good performance in computational complexity, bit error rate (BER) and capacity. Simulation results show that the developed algorithm sum rate capacity is very close to MIMO theoretical upper bound (21.5 bits/s/Hz at signal to noise ratio of 20dB) which strongly indicate it’s applicability to the uplink channel where power transmission at the mobile station is a constraint. The BER performance of the developed scheme shows that, as the number of antenna increases, the 8 x 8 AMUD provides a 2dB gain compared to known non adaptive MIMO OFDMO at low SNR.

scholarly journals Preface

1995 ◽  
Vol 353 (1701) ◽  
pp. 2-2
Keyword(s):  
Integrated Circuit ◽  
Secure Communication ◽  
Communication Systems ◽  
Nonlinear Models ◽  
Great Accuracy ◽  
Experimental Investigations ◽  
Electronic Circuits ◽  
Physical Systems ◽  
Practical Applications ◽  
Chaotic Signals

Chaos is an ubiquitous phenomenon that arises in many natural and artificial systems where nonlinearity is present (Thompson & Stewart 1986; Moon 1992). Nowhere is this important and robust phenomenon more easily observed, studied and exploited than in electronic circuits. Three reasons for this can be identified. First, there exist exceedingly simple and inexpensive circuits costing less than a paperback, which are ideal for heuristic experimental investigations of the diverse chaotic phenomena that have been identified in the more complex systems of solid and fluid dynamics, chemical kinetics, etc. Second, the physics of the electronic devices used in these circuits is a well-understood and mature branch of electrical engineering. Excellent mathematical models exist, allowing the experimental observations to be reproduced by computer simulation (Parker & Chua 1989) with great accuracy; and the bifurcational structure of these nonlinear models can be analysed by using the new topological techniques of dynamical systems theory. It is indeed the case that no other chaotic physical systems are known which are amenable simultaneously to experimental, numerical and analytical studies, giving correlations which are not only qualitative but often quantitative to within 5%. Third, for applications which call for a source of real chaotic signals (such as secure communication systems and neural networks), electronic circuits provide the simplest and cheapest source of such physical signals. Moreover, such circuits can be readily mass-produced in practical applications as inexpensive integrated circuit chips

Insulator Image Denoising Based on the Simplified PCNN Model

2013 ◽  
Vol 718-720 ◽  
pp. 2092-2098 ◽  
Author(s):  
Dan Li ◽  
Hong Ying He ◽  
Yi Jia Cao ◽  
Dian Sheng Luo
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Infrared Image ◽  
Window Size ◽  
Denoising Method ◽  
Similarity Calculation ◽  
Image Edge ◽  
Filter Noise ◽  
Better Than

A new denoising method was proposed in the paper according to the characteristics of insulator infrared image with impulse noise. First, based on the pulse coupled neural network (PCNN) to detect the location of the impulse noise pixels, while maintaining the same non-noise pixels. and then according to the characteristics of the impulse noise, the window size of the filter was adaptively determined by calculating the noise intensity of the image. The pixels with maximum and minimum gray value in filtering window are excluded, using the left pixels similarity calculation out weights. A new weighted filtering algorithm is used to filter noise pixels. The experiments show that the method is better than the median filter in peak signal-to-noise ratio (PSNR), and has better image edge details protection ability.

Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays

2014 ◽  
Vol 59 (4) ◽  
Author(s):  
Gert Van Dijck ◽  
Marc M. Van Hulle
Keyword(s):  
Signal Processing ◽  
Signal To Noise Ratio ◽  
Cell Types ◽  
Signal Quality ◽  
Quality Signals ◽  
Processing Techniques ◽  
Electrode Selection ◽  
Signal Processing Techniques ◽  
Processing Steps ◽  
Selection Of

AbstractRecently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the “usefulness” of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The ever-increasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing “good quality signals” or “signals of interest.” This article reviews the different criteria for electrode selection as well as the basic signal processing steps to prepare the data to compute those criteria. We discuss three of them. The first two select the electrodes based on “signal quality.” The first criterion computes the penalized signal-to-noise ratio (SNR); the second criterion models the neuroscientist’s appraisal of signal quality. Last, our most recent work allows the selection of electrodes that capture particular anatomical cell types. The discussed algorithms perform what is called in the literature “electronic depth control” in contrast to the mechanical repositioning of the electrode shafts in search of “good quality signals” or “signals of interest.”

scholarly journals Time Delay Estimation Method Used Median Filter in Low Signal to Noise Ratio Pulse Environment

2021 ◽  
Vol 252 ◽  
pp. 02039
Author(s):  
Hang Liu ◽  
Wenhong Liu
Keyword(s):  
Signal Processing ◽  
Time Delay ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Estimation Algorithm ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
P Value ◽  
Signal To Noise ◽  
Second Order Statistics

In practice, the collected signal often contains impulsive noise. The classical time delay estimation algorithm based on the second-order statistics of Gaussian distribution will degrade or even be unreliable, so that it cannot be used. Although the fractional low-order signal processing method can be better adapted to signal processing in the impulse noise environment, the determination of the order p value of the fractional low-order moment depends on the prior knowledge or estimation of the characteristic index α value of the pulse, and when the pulse is stronger or the signal-to-noise ratio is low, the performance cannot meet the requirements well. The paper adopted the method of median filter preprocessing. First, the abnormal points (pulse points) are removed in the noise and return the noise to the Gaussian model distribution; next, use the time delay estimation algorithm under the second-order statistics to avoid the estimate of p-value. Computer simulation experiments show that the method proposed in this paper has better estimation performance in low snr pulse environment.

scholarly journals An Overview of Signal Processing Techniques for Terahertz Communications

2020 ◽  
Author(s):  
Hadi Sarieddeen ◽  
Mohamed-Slim Alouini ◽  
Tareq Y. Al-Naffouri
Keyword(s):  
Signal Processing ◽  
Channel Coding ◽  
Communication Systems ◽  
Channel Model ◽  
Mimo Systems ◽  
Future Generation ◽  
Data Detection ◽  
Wide Range ◽  
Processing Techniques ◽  
Signal Processing Techniques

Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advancements in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and the corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress paves the way for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques with an emphasis on ultra-massive multiple-input multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, which are vital to overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.

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