Asymptotically optimal one-bit quantizer design for weak-signal detection in generalized Gaussian noise and lossy binary communication channel

Wang, Guanyu; Zhu, Jiang; Xu, Zhiwei

doi:10.1016/j.sigpro.2018.09.005

Cited by 18 publications

(11 citation statements)

References 34 publications

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“…The conventional method of analysing the state space diagrams of the Duffing oscillator is effective in detecting weak periodic signals in noise with low SNR as demonstrated in Fig. 2 [7, 34–44]. However, this method is manual; it does not have a graphical representation of the frequency spectrum such as FFT.…”

Section: Discussionmentioning

confidence: 99%

Automated system for weak periodic signal detection based on Duffing oscillator

Akıllı

Yılmaz

Akdeniz³

2020

IET signal process.

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The periodic signals that have predictable and deterministic characteristics are used in the analysis and modelling of dynamical systems in diverse fields. These signals can be detected as the weak signals within the time series obtained from the measurable processes of dynamical systems. The Duffing oscillator is effective in detecting weak periodic signals with a very low signal‐to‐noise ratio. In this study, the authors present a method to automate the weak periodic signal detection of the Duffing oscillator using a quantitative index for the classification of the periodic and non‐periodic signals. In this method, the authors use the wavelet scale index as the quantitative index in the classification of signals. Thus, they are able to plot the wavelet scale index spectrum of the Duffing oscillator where the frequency values of the weak periodic signals correspond to near‐zero wavelet scale index parameters. First, the authors perform simulations using the method and detect weak periodic signals embedded in noise. Then, they employ two electroencephalogram signals to demonstrate the feasibility of the proposed method in the empirical data. Lastly, they compare the method to the periodogram power spectral density estimate based on fast Fourier transform.

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Section: Discussionmentioning

confidence: 99%

Automated system for weak periodic signal detection based on Duffing oscillator

Akıllı

Yılmaz

Akdeniz³

2020

IET signal process.

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“…For the context of SAR images, the pixel magnitudes usually follow an asymmetric distribution and have nonnegative values; hence, aiming at having a good characterization of image pixel magnitude, we selected some well-known distributions with nonnegative support, such as Burr, Gamma, Log-normal, Rayleigh, Rician, and Weibull. Additionally, we considered the Gaussian distribution as a preliminary study to fit the targets related to the small size data, since this distribution is widely used in signal and image processing [18], [19].…”

Section: Discussionmentioning

confidence: 99%

Computer-Simulated UWB VHF SAR Targets Based on Statistics of Real Data

Palm¹,

Alves²,

Machado³

et al. 2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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In this paper, we performed a statistical study of the targets of wavelength-resolution synthetic aperture radar (SAR) images and addressed the problem of limited training samples. Based on suitable statistical models for the real targets of the evaluated SAR image, we considered a sample generator method to create computer-simulated targets. For such, well-known statistical models, namely, Burr, Gamma, Gaussian, Log-normal, Rayleigh, Rician, and Weibull, are considered for the Anderson-Darling (AD) goodness-of-fit test. This test is a statistical nonparametric hypothesis test generally used to investigate whether a given probability distribution yields a good fit for a given data set. The images used in this study were obtained from the Swedish ultrawideband (UWB) very-high-frequency (VHF) SAR CARABAS II system whose consists of 24 magnitude single-look SAR images. The obtained computer-simulated targets followed similar statistical characteristics when compared to the real ones.

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“…where ⎾(. ) is the Gamma Function, and −1 > 0 and > 0 represent the scale and shape parameters [43]. In this case, notably, the value of ranges from 1, 2 to ∞.…”

Section: (Iii) Quantisationmentioning

confidence: 99%

AMC2-Pyramid: Intelligent Pyramidal Feature Engineering and Multi-Distance Decision Making for Automatic Multi-Carrier Modulation Classification

et al. 2021

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Automatic modulation classification (AMC) is a method that supported different wireless communication systems for modulation type classification. Currently, orthogonal frequency division multiplexing, multiple-input, multiple-output systems are widely using this technique. Recent AMC methods are designed for a single-carrier system identifying a few modulation types. To motivate the AMC for the current communication systems, we present an intelligent pyramid model for automatic multi-carrier modulation classification (AMC2pyramid) with three significant operations, namely, signal fortification, feature engineering and modulation classification. First, signal quality is estimated to reduce the complexity in classification because some signals are affected by noise and other environmental or channel artefacts. Hence, before preprocessing the signal, the quality is assessed according to the channel state information, signal to inference plus noise ratio, received signal strength indicator and spectral efficiency. For low quality, quality augmentation is applied. Then, quality augmentation is implemented with noise elimination, equalisation, quantisation and channel frequency offset compensation. In the feature engineering step, feature extraction and clustering are presented using the gated feature response pyramid network, and a twin-functioned human mental search algorithm is used. The modulation classification is implemented using amulti-distance-based nearest centroid classifier, and improved Q-learning is used to identify signals as any of 16QAM, 32QAM, 64QAM, 128QAM, QPSK, BPSK, DPSK, ASK and FSK. The performance of the proposed AMC2-pyramid is implemented using MatlabR2017b, where accuracy, precision, recall, F-score, error rate and computational time is computed for the proposed work including previous well-known methods. The proposed work proves that this method outperforms the previous ones.

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Asymptotically optimal one-bit quantizer design for weak-signal detection in generalized Gaussian noise and lossy binary communication channel

Cited by 18 publications

References 34 publications

Automated system for weak periodic signal detection based on Duffing oscillator

Automated system for weak periodic signal detection based on Duffing oscillator

Computer-Simulated UWB VHF SAR Targets Based on Statistics of Real Data

AMC2-Pyramid: Intelligent Pyramidal Feature Engineering and Multi-Distance Decision Making for Automatic Multi-Carrier Modulation Classification

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