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Journal articles on the topic 'Signal complexity'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

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 over

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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 b

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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 Entro

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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

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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 s

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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 sig

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Xu, Fei, Guozheng Yan, Kai Zhao, Li Lu, Zhiwu Wang, and Jinyang Gao. "Quantifying the complexity of human colonic pressure signals using an entropy measure." Biomedical Engineering / Biomedizinische Technik 61, no. 1 (2016): 127–32. http://dx.doi.org/10.1515/bmt-2015-0026.

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Abstract Studying the complexity of human colonic pressure signals is important in understanding this intricate, evolved, dynamic system. This article presents a method for quantifying the complexity of colonic pressure signals using an entropy measure. As a self-adaptive non-stationary signal analysis algorithm, empirical mode decomposition can decompose a complex pressure signal into a set of intrinsic mode functions (IMFs). Considering that IMF2, IMF3, and IMF4 represent crucial characteristics of colonic motility, a new signal was reconstructed with these three signals. Then, the time entr

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12

Vittal, M. V. R., and K. Rama Naidu. "Modified Alternative-signal Technique for Sequential Optimisation for PAPR Reduction in OFDM-OQAM Systems." Defence Science Journal 67, no. 3 (2017): 298. http://dx.doi.org/10.14429/dsj.67.10642.

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<p>A modified alternative signal technique for reducing peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing systems employing offset quadrature amplitude modulation (OFDM-OQAM) is proposed. Lower PAPR reduces the complexity of digital to analog converters and results in increasing the efficiency of power amplifiers. The main objective of the algorithm is to decrease PAPR with low complexity. The alternative signal method involves the individual alternative signal (AS-I) and combined alternative signal (AS-C) algorithms. Both the algorithms decrease the peak t

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13

Ahamed, Mohamed Rasmi Ashfaq, Mohammad Hossein Babini, and Hamidreza Namazi. "Complexity-based decoding of the relation between human voice and brain activity." Technology and Health Care 28, no. 6 (2020): 665–74. http://dx.doi.org/10.3233/thc-192105.

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BACKGROUND: The human voice is the main feature of human communication. It is known that the brain controls the human voice. Therefore, there should be a relation between the characteristics of voice and brain activity. OBJECTIVE: In this research, electroencephalography (EEG) as the feature of brain activity and voice signals were simultaneously analyzed. METHOD: For this purpose, we changed the activity of the human brain by applying different odours and simultaneously recorded their voices and EEG signals while they read a text. For the analysis, we used the fractal theory that deals with t

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NAMAZI, HAMIDREZA, and SAJAD JAFARI. "DECODING OF WRIST MOVEMENTS’ DIRECTION BY FRACTAL ANALYSIS OF MAGNETOENCEPHALOGRAPHY (MEG) SIGNAL." Fractals 27, no. 02 (2019): 1950001. http://dx.doi.org/10.1142/s0218348x19500014.

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Analysis of human movements is an important category of research in biomedical engineering, especially for the rehabilitation purpose. The human’s different movements are usually investigated by analyzing the movement signals. Based on the literatures, fewer efforts have been made in order to investigate how human movements are represented in the brain. In this paper, we decode the movements’ directions of wrist by complexity analysis of Magnetoencephalography (MEG) signal. For this purpose, we employ fractal theory. In fact, we investigate how the complexity of MEG signal changes in case of d

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15

Matsui, Norio, Dileep Divekar, Neven Orhanovic, and Hiroshi Wabuka. "Chip complexity requires signal and power integrity." IEEE Potentials 25, no. 6 (2006): 24–29. http://dx.doi.org/10.1109/p-m.2006.248053.

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16

Volinsky, N., and B. N. Kholodenko. "Complexity of Receptor Tyrosine Kinase Signal Processing." Cold Spring Harbor Perspectives in Biology 5, no. 8 (2013): a009043. http://dx.doi.org/10.1101/cshperspect.a009043.

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17

Stoddard, Philip K. "The evolutionary origins of electric signal complexity." Journal of Physiology-Paris 96, no. 5-6 (2002): 485–91. http://dx.doi.org/10.1016/s0928-4257(03)00004-4.

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18

Rezek, I. A., and S. J. Roberts. "Stochastic complexity measures for physiological signal analysis." IEEE Transactions on Biomedical Engineering 45, no. 9 (1998): 1186–91. http://dx.doi.org/10.1109/10.709563.

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19

B Whitchurch, Cynthia. "Complexity in ?2-component? signal transduction systems." Microbiology Australia 27, no. 3 (2006): 128. http://dx.doi.org/10.1071/ma06128.

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The ?2-component? regulatory systems of bacteria are the predominant signal transduction mechanisms that bacteria utilise to modulate behaviours and metabolism in response to environmental changes. These systems classically involve two proteins ? a membrane bound sensor histidine kinase and a soluble response regulator.

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20

Zappasodi, Filippo, Laura Marzetti, Elzbieta Olejarczyk, Franca Tecchio, and Vittorio Pizzella. "Age-Related Changes in Electroencephalographic Signal Complexity." PLOS ONE 10, no. 11 (2015): e0141995. http://dx.doi.org/10.1371/journal.pone.0141995.

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21

Wen Jiang, Ye Li, and Xingxing Yu. "Truncation for Low-Complexity MIMO Signal Detection." IEEE Transactions on Information Theory 53, no. 4 (2007): 1564–71. http://dx.doi.org/10.1109/tit.2007.892785.

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22

Hlavacek, William S., James R. Faeder, Michael L. Blinov, Alan S. Perelson, and Byron Goldstein. "The complexity of complexes in signal transduction." Biotechnology and Bioengineering 84, no. 7 (2003): 783–94. http://dx.doi.org/10.1002/bit.10842.

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23

Huang, L., S. Wu, D. Feng, and L. Zhang. "Low complexity method for signal subspace fitting." Electronics Letters 40, no. 14 (2004): 847. http://dx.doi.org/10.1049/el:20040579.

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24

Taylor, Steven R., and Dale N. Anderson. "Rediscovering Signal Complexity as a Teleseismic Discriminant." Pure and Applied Geophysics 166, no. 3 (2009): 325–37. http://dx.doi.org/10.1007/s00024-008-0449-y.

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25

Xiao, Hu, Shao, and Li. "A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition." Sensors 19, no. 23 (2019): 5330. http://dx.doi.org/10.3390/s19235330.

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Biometric systems allow recognition and veriﬁcation of an individual through his or her physiological or behavioral characteristics. It is a growing ﬁeld of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on

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26

Sun, Jie, Bin Wang, Yan Niu, et al. "Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review." Entropy 22, no. 2 (2020): 239. http://dx.doi.org/10.3390/e22020239.

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Alzheimer’s disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000–2019 was performed in the Web of Science a

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27

Li, Gao, and Wang. "Reverse Dispersion Entropy: A New Complexity Measure for Sensor Signal." Sensors 19, no. 23 (2019): 5203. http://dx.doi.org/10.3390/s19235203.

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Permutation entropy (PE), as one of the powerful complexity measures for analyzing time series, has advantages of easy implementation and high efficiency. In order to improve the performance of PE, some improved PE methods have been proposed through introducing amplitude information and distance information in recent years. Weighted-permutation entropy (W-PE) weight each arrangement pattern by using variance information, which has good robustness and stability in the case of high noise level and can extract complexity information from data with spike feature or abrupt amplitude change. Dispers

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28

NAMAZI, HAMIDREZA, TIRDAD SEIFI ALA, and VLADIMIR KULISH. "DECODING OF UPPER LIMB MOVEMENT BY FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAM (EEG) SIGNAL." Fractals 26, no. 05 (2018): 1850081. http://dx.doi.org/10.1142/s0218348x18500810.

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Analysis of human movements is an important category of research in biomedical engineering, especially for the rehabilitation purpose. The movement of limbs is investigated usually by analyzing the movement signals. Less efforts have been made to investigate how neural that correlate to the movements, are represented in the human brain. In this research, for the first time we decode the limb movements by fractal analysis of Electroencephalogram (EEG) signals. We investigated how the complexity of EEG signal changes in different limb movements in motor execution (ME), and motor imagination (MI)

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29

Nguyen, Huu Minh, Xuan Nam Tran, Van Bien Pham, and Cong Dinh Nguyen. "Low-Complexity Estimation for Spatially Modulated Physical-Layer Network Coding Systems." Wireless Communications and Mobile Computing 2018 (October 1, 2018): 1–13. http://dx.doi.org/10.1155/2018/6310519.

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This paper proposes a low-complexity signal estimator at the relay node for a spatially modulated physical-layer network coding system. In the considered system, the two terminal nodes use spatial modulation to transmit their signals to the relay node during the multiple access phase. Based on the channel quantization method, we propose a low-complexity estimator which can detect both antenna indices and M-QAM symbols using successive interference cancellation (SIC). Moreover, we design signal constellations for a combined signal component at the relay for arbitrary M-QAM modulation. The obtai

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Hu, Gang, Kejun Wang, and Liangliang Liu. "Detection Line Spectrum of Ship Radiated Noise Based on a New 3D Chaotic System." Sensors 21, no. 5 (2021): 1610. http://dx.doi.org/10.3390/s21051610.

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This paper proposes a new detection model of a weak signal based on a third-order chaotic system. Using a dynamic analysis tool, such as the Lyapunov exponent and the bifurcation diagram, variations of dynamic behavior can be observed, and the weak signal underwater can be picked up. In order to improve the observability of detection signals in the time domain and frequency domain, the spectral entropy complexity algorithm (SE) and C0 complexity algorithms are used to analyze and extract the weak signal. The experimental results show that the spectrum extraction based on the complexity algorit

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Herberstein, M. E., A. E. Wignall, E. A. Hebets, and J. M. Schneider. "Dangerous mating systems: Signal complexity, signal content and neural capacity in spiders." Neuroscience & Biobehavioral Reviews 46 (October 2014): 509–18. http://dx.doi.org/10.1016/j.neubiorev.2014.07.018.

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32

Kuske, Jan, and Stefania Petra. "Performance Bounds For Co-/Sparse Box Constrained Signal Recovery." Analele Universitatii "Ovidius" Constanta - Seria Matematica 27, no. 1 (2019): 79–106. http://dx.doi.org/10.2478/auom-2019-0005.

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Abstract The recovery of structured signals from a few linear measurements is a central point in both compressed sensing (CS) and discrete tomography. In CS the signal structure is described by means of a low complexity model e.g. co-/sparsity. The CS theory shows that any signal/image can be undersampled at a rate dependent on its intrinsic complexity. Moreover, in such undersampling regimes, the signal can be recovered by sparsity promoting convex regularization like ℓ1- or total variation (TV-) minimization. Precise relations between many low complexity measures and the sufficient number of

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Tang, Youfu, Feng Lin, and Qian Zou. "Complexity Analysis of Time-Frequency Features for Vibration Signals of Rolling Bearings Based on Local Frequency." Shock and Vibration 2019 (July 10, 2019): 1–13. http://dx.doi.org/10.1155/2019/7190568.

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The multisource impact signal of rolling bearings often represents nonlinear and nonstationary characteristics, and quantitative description of the complexity of the signal with traditional spectrum analysis methods is difficult to be obtained. In this study, firstly, a novel concept of local frequency is defined to develop the limitation of traditional frequency. Then, an adaptive waveform decomposition method is proposed to extract the time-frequency features of nonstationary signals with multicomponents. Finally, the normalized Lempel–Ziv complexity method is applied to quantitatively measu

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delBarco-Trillo, Javier, Caitlin R. Sacha, George R. Dubay, and Christine M. Drea. "Eulemur , me lemur: the evolution of scent-signal complexity in a primate clade." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1597 (2012): 1909–22. http://dx.doi.org/10.1098/rstb.2011.0225.

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Signal complexity has been linked to social complexity in vocal, but not chemical, communication. To address this gap, we examined the chemical complexity of male and female glandular secretions in eight species of Eulemur. In this diverse clade of macrosmatic primates, species differ by social or mating system and dominance structure. We applied principal component and linear discriminate analyses to data obtained by gas chromatography/mass spectrometry. Beyond the significant effects on chemical signals of gland type, sex, season and species, we found effects of social variables and phylogen

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35

Kamal, Shahul Mujib, Sue Sim, Rui Tee, Visvamba Nathan, and Hamidreza Namazi. "Complexity-Based Analysis of the Relation between Human Muscle Reaction and Walking Path." Fluctuation and Noise Letters 19, no. 03 (2020): 2050025. http://dx.doi.org/10.1142/s021947752050025x.

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Legs are the contact point of humans during walking. In fact, leg muscles react when we walk in different conditions (such as different speeds and paths). In this research, we analyze how walking path affects leg muscles’ reaction. In fact, we investigate how the complexity of muscle reaction is related to the complexity of path of movement. For this purpose, we employ fractal theory. In the experiment, subjects walk on different paths that have different fractal dimensions and then we calculate the fractal dimension of Electromyography (EMG) signals obtained from both legs. The result of our

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Soundirarajan, Mirra, Mohammad Hossein Babini, Sue Sim, Visvamba Nathan, and Hamidreza Namazi. "Decoding of the Relationship between Brain and Facial Muscle Activities in Response to Dynamic Visual Stimuli." Fluctuation and Noise Letters 19, no. 04 (2020): 2050041. http://dx.doi.org/10.1142/s0219477520500418.

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In this research, for the first time, we analyze the relationship between facial muscles and brain activities when human receives different dynamic visual stimuli. We present different moving visual stimuli to the subjects and accordingly analyze the complex structure of electromyography (EMG) signal versus the complex structure of electroencephalography (EEG) signal using fractal theory. Based on the obtained results from analysis, presenting the stimulus with greater complexity causes greater change in the complexity of EMG and EEG signals. Statistical analysis also supported the results of

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Ai, Qing Song, Ling Chen, and Wei Meng. "Rehabilitation Assessment Based on the Complexity and Fluctuation of EMG Signal." Advanced Engineering Forum 6-7 (September 2012): 530–35. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.530.

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The assessment after rehabilitation is an important part of rehabilitation medicine, and of great significance for the basic research and clinical application for rehabilitation medicine. Physician can make objective analysis about patients’ present conditions, and then appropriate training program can be developed for patients to recover their body function more effectively. Three kinds of EMG signal, that is, the healthy, patient with myopathy, and patient with neuropathy, are involved. The complexity of the three types of signal is compared and the related variables (embedding dimension and

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38

Bergey, Gregory K., and Piotr J. Franaszczuk. "Epileptic seizures are characterized by changing signal complexity." Clinical Neurophysiology 112, no. 2 (2001): 241–49. http://dx.doi.org/10.1016/s1388-2457(00)00543-5.

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39

King, Cynthia, Trent Nicol, Therese McGee, and Nina Kraus. "Thalamic asymmetry is related to acoustic signal complexity." Neuroscience Letters 267, no. 2 (1999): 89–92. http://dx.doi.org/10.1016/s0304-3940(99)00336-5.

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40

ZHENG, Binglian. "Cytokinin signal transduction: Known simplicity and unknown complexity." Chinese Science Bulletin 48, no. 13 (2003): 1309. http://dx.doi.org/10.1360/02wc0534.

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Xiao, Yue, Zongfei Yang, Lilin Dan, Ping Yang, Lu Yin, and Wei Xiang. "Low-Complexity Signal Detection for Generalized Spatial Modulation." IEEE Communications Letters 18, no. 3 (2014): 403–6. http://dx.doi.org/10.1109/lcomm.2013.123113.132586.

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42

Jun, David, and Douglas L. Jones. "Cascading Signal-Model Complexity for Energy-Aware Detection." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 3, no. 1 (2013): 65–74. http://dx.doi.org/10.1109/jetcas.2013.2243632.

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SENGUPTA, R., N. DEY, A. K. DATTA, D. GHOSH, and A. PATRANABIS. "ANALYSIS OF THE SIGNAL COMPLEXITY IN SITAR PERFORMANCES." Fractals 18, no. 02 (2010): 265–70. http://dx.doi.org/10.1142/s0218348x10004816.

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The sitar is perhaps the most well known of the Indian plucked string musical instruments. It has a varying number of strings but 16–20 is usual. When played by an expert, the sitar produces a melodious sound effect. The mode of attachment of strings and or the interaction of the large number of strings, some of them being playing strings, some drone and others sympathetic, create the total acoustic environment, which also includes the global resonance structure of the instrument. This creates a sound whose complexity is difficult to model by usual mathematical methods.Fractal dimension is an

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44

Jiang, Yang, Yahui Wu, Xia Wu, Xia Chu, and Zonglin Xie. "Low-Complexity Signal Detection for Quadrature Spatial Modulation." International Journal of Future Generation Communication and Networking 10, no. 7 (2017): 45–58. http://dx.doi.org/10.14257/ijfgcn.2017.10.7.04.

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45

Koc, C. K., Guanrong Chen, and C. K. Chui. "Complexity analysis of wavelet signal decomposition and reconstruction." IEEE Transactions on Aerospace and Electronic Systems 30, no. 3 (1994): 910–18. http://dx.doi.org/10.1109/7.303759.

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46

Shafivulla, Sayyed, Aaqib Patel, and Mohammed Zafar Ali Khan. "Low Complexity Signal Detection for Massive-MIMO Systems." IEEE Wireless Communications Letters 9, no. 9 (2020): 1467–70. http://dx.doi.org/10.1109/lwc.2020.2994058.

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47

Zheng, Binglian, Jiaqiang Sun, Suzhi Zhang, Yan Deng, and Jianru Zuo. "Cytokinin signal transduction: Known simplicity and unknown complexity." Chinese Science Bulletin 48, no. 13 (2003): 1309–15. http://dx.doi.org/10.1007/bf03184168.

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48

Ord, Terry J., and Joan Garcia-Porta. "Is sociality required for the evolution of communicative complexity? Evidence weighed against alternative hypotheses in diverse taxonomic groups." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1597 (2012): 1811–28. http://dx.doi.org/10.1098/rstb.2011.0215.

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Complex social communication is expected to evolve whenever animals engage in many and varied social interactions; that is, sociality should promote communicative complexity. Yet, informal comparisons among phylogenetically independent taxonomic groups seem to cast doubt on the putative role of social factors in the evolution of complex communication. Here, we provide a formal test of the sociality hypothesis alongside alternative explanations for the evolution of communicative complexity. We compiled data documenting variations in signal complexity among closely related species for several ca

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49

Hussain, A., F. Akhtar, Z. H. Khand, A. Rajput, and Z. Shaukat. "Complexity and Limitations of GNSS Signal Reception in Highly Obstructed Enviroments." Engineering, Technology & Applied Science Research 11, no. 2 (2021): 6864–68. http://dx.doi.org/10.48084/etasr.3908.

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Multipath (MP) and/or Non Line-Of-Sight (NLOS) reception remains a potential vulnerability to satellite-based positioning and navigation systems in high multipath environments, such as an urban canyon. In such an environment, satellite signals are reflected, scattered or faded, and sometimes completely blocked by roofs and walls of high-rise buildings, fly-over bridges, complex road structures, etc. making positioning and navigation information inaccurate, unreliable, and largely unavailable. The magnitude of the positioning error depends on the satellite visibility, geometric distribution of

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Cheng, Qian, Kexian Gong, Min Zhang, and Xiaoyan Liu. "An efficient wide-band signal detection and extraction method." MATEC Web of Conferences 336 (2021): 04011. http://dx.doi.org/10.1051/matecconf/202133604011.

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Aiming at the influence of time-varying and frequency-varying of noise on the signal detection performance in the short wave wide-band channel and the large amount of computation in the channelized receiver model of the traditional low pass filter bank, a cross-channel reconfigurable multi-phase high-efficiency channelization method based on morphological processing is proposed in this paper .Firstly, The wide-band signal is coarsely filtered by the multi-phase structure of the uniform filter bank which is determined by the protection interval between signals, and then the bandwidth and positi

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