Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network

Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed.

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Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism

Sensors ◽

10.3390/s20216350 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6350

Author(s):

Bin Wu ◽

Shibo Yuan ◽

Peng Li ◽

Zehuan Jing ◽

Shao Huang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Attention Mechanism ◽

Superior Performance ◽

Radar Signal ◽

Signal Recognition ◽

Electromagnetic Environment ◽

One Dimensional ◽

The Given ◽

And Storage

As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.

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Radiation Curable Polysiloxane: Synthesis to Applications

Soft Matter ◽

10.1039/d1sm00269d ◽

2021 ◽

Author(s):

Selvan T. Muthamil ◽

Titash Mondal

Keyword(s):

Structural Features ◽

Superior Performance ◽

Operational Conditions ◽

Different Types

Among the different types of specialty polymers, polysiloxane finds its position in the pyramid's apex in performance attributes. Unique structural features result in superior performance benefits over wide operational conditions....

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2D Self-assembly and Electronic Characterization of Oxygen-Boron-Oxygen-Doped Chiral Graphene Nanoribbons

Chemical Communications ◽

10.1039/d1cc01901e ◽

2021 ◽

Author(s):

Lei Jin ◽

Nerea Bilbao ◽

Yang Lv ◽

Xiao-Ye Wang ◽

Soltani Paniz ◽

...

Keyword(s):

Edge Effects ◽

Self Assembly ◽

Quantum Confinement ◽

Graphene Nanoribbons ◽

One Dimensional ◽

The Past ◽

Different Types

Graphene nanoribbons (GNRs), quasi-one-dimensional strips of graphene, exhibit a nonzero bandgap due to quantum confinement and edge effects. In the past decade, different types of GNRs with atomically precise structures...

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DISSIPATIVE QUANTUM DISORDERED MODELS

International Journal of Modern Physics B ◽

10.1142/s0217979206035308 ◽

2006 ◽

Vol 20 (19) ◽

pp. 2795-2804 ◽

Cited By ~ 2

Author(s):

LETICIA F. CUGLIANDOLO

Keyword(s):

Glassy Phase ◽

Quantum Critical Point ◽

Mean Field ◽

Static Properties ◽

One Dimensional ◽

Time Dynamics ◽

Long Range Interactions ◽

Disordered Models ◽

Different Types ◽

Dissipative Environments

This article reviews recent studies of mean-field and one dimensional quantum disordered spin systems coupled to different types of dissipative environments. The main issues discussed are: (i) The real-time dynamics in the glassy phase and how they compare to the behaviour of the same models in their classical limit. (ii) The phase transition separating the ordered – glassy – phase from the disordered phase that, for some long-range interactions, is of second order at high temperatures and of first order close to the quantum critical point (similarly to what has been observed in random dipolar magnets). (iii) The static properties of the Griffiths phase in random king chains. (iv) The dependence of all these properties on the environment. The analytic and numeric techniques used to derive these results are briefly mentioned.

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Collision patterns on mollusc shells

Discrete Dynamics in Nature and Society ◽

10.1155/s1026022697000071 ◽

1997 ◽

Vol 1 (1) ◽

pp. 57-76 ◽

Cited By ~ 2

Author(s):

P. J. Plath ◽

J. K. Plath ◽

J. Schwietering

Keyword(s):

Reaction Diffusion ◽

Growth Front ◽

Cell System ◽

One Dimensional ◽

Reaction Diffusion Systems ◽

Diffusion Systems ◽

Different Types ◽

Classical Waves ◽

Cellular Behaviour ◽

Mollusc Shells

On mollusc shells one can find famous patterns. Some of them show a great resemblance to the soliton patterns in one-dimensional systems. Other look like Sierpinsky triangles or exhibit very irregular patterns. Meinhardt has shown that those patterns can be well described by reaction–diffusion systems [1]. However, such a description neglects the discrete character of the cell system at the growth front of the mollusc shell.We have therefore developed a one-dimensional cellular vector automaton model which takes into account the cellular behaviour of the system [2]. The state of the mathematical cell is defined by a vector with two components. We looked for the most simple transformation rules in order to develop quite different types of waves: classical waves, chemical waves and different types of solitons. Our attention was focussed on the properties of the system created through the collision of two waves.

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The Solvent Effect on Composition and Dimensionality of Mercury(II) Complexes with Picolinic Acid

Molecules ◽

10.3390/molecules26165002 ◽

2021 ◽

Vol 26 (16) ◽

pp. 5002

Author(s):

Željka Soldin ◽

Boris-Marko Kukovec ◽

Dubravka Matković-Čalogović ◽

Zora Popović

Keyword(s):

Coordination Compounds ◽

Picolinic Acid ◽

Chloride Ion ◽

13C Nmr Spectroscopy ◽

Thermal Methods ◽

1H And 13C Nmr ◽

Coordination Environment ◽

One Dimensional ◽

X Ray ◽

Different Types

Three new mercury(II) coordination compounds, {[HgCl(pic)]}n (1), [HgCl(pic)(picH)] (2), and [HgBr(pic)(picH)] (3) (picH = pyridine-2-carboxylic acid, picolinic acid) were prepared by reactions of the corresponding mercury(II) halides and picolinic acid in an aqueous (1) or alcohol–methanol or ethanol (2 and 3) solutions. Two different types of coordination compounds were obtained depending on the solvent used. The crystal structures were determined by the single-crystal X-ray structural analysis. Compound 1 is a one-dimensional (1-D) coordination polymer with mercury(II) ions bridged by chelating and bridging N,O,O′-picolinate ions. Each mercury(II) ion is four-coordinated with a bidentate picolinate ion, a carboxylate O atom from the symmetry-related picolinate ion and with a chloride ion; the resulting coordination environment can be described as a highly distorted tetrahedron. Compounds 2 and 3 are isostructural mononuclear coordination compounds, each mercury(II) ion being coordinated with the respective halide ion, N,O-bidentate picolinate ion, and N,O-bidentate picolinic acid in a highly distorted square-pyramidal coordination environment. Compounds 1–3 were characterized by IR spectroscopy, PXRD, and thermal methods (TGA/DSC) in the solid state and by 1H and 13C NMR spectroscopy in the DMSO solution.

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A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals

Mathematical Problems in Engineering ◽

10.1155/2015/891731 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 7

Author(s):

Jingchao Li ◽

Jian Guo

Keyword(s):

Feature Extraction ◽

Cloud Model ◽

Recognition Rate ◽

Signal Recognition ◽

Communication Signals ◽

Low Snr ◽

Feature Extraction Algorithm ◽

Cloud Characteristics ◽

Extraction Algorithm ◽

New Feature

Identifying communication signals under low SNR environment has become more difficult due to the increasingly complex communication environment. Most relevant literatures revolve around signal recognition under stable SNR, but not applicable under time-varying SNR environment. To solve this problem, we propose a new feature extraction method based on entropy cloud characteristics of communication modulation signals. The proposed algorithm extracts the Shannon entropy and index entropy characteristics of the signals first and then effectively combines the entropy theory and cloud model theory together. Compared with traditional feature extraction methods, instability distribution characteristics of the signals’ entropy characteristics can be further extracted from cloud model’s digital characteristics under low SNR environment by the proposed algorithm, which improves the signals’ recognition effects significantly. The results from the numerical simulations show that entropy cloud feature extraction algorithm can achieve better signal recognition effects, and even when the SNR is −11 dB, the signal recognition rate can still reach 100%.

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Textile Performance Properties of Cotton Fabric Treated with Selected Flame-Retarding Finishes

Textile Research Journal ◽

10.1177/004051757704700510 ◽

1977 ◽

Vol 47 (5) ◽

pp. 365-371 ◽

Cited By ~ 6

Author(s):

Stanley P. Rowland ◽

John S. Mason

Keyword(s):

Cotton Fabric ◽

Abrasion Resistance ◽

Breaking Strength ◽

Superior Performance ◽

Disodium Hydrogen Phosphate ◽

Performance Properties ◽

Different Types ◽

The Individual ◽

Medium Weight ◽

Selection Of

Seven different types of flame-retarding finishes were applied to light-to-medium weight cotton fabric at add-ons appropriate to pass the DOC FF 3–71 test. The finishes studied were based on tetrakis(hydroxymethyl)phosphonium chloride (THPC), neutralized THPC (THPOH), Fyrol 76, and Pyrovatex CP. The specific finishes were: THPOH-NH3, THPOH-urea-trimethylolmelamine, Proban (THPC-urea precondensate)-NH3, THPC-urea-disodium hydrogen phosphate, Fyrol 76, Fyrol 76-N-methylolacrylamide, and Pyrovatex CP-methylolmelamine. Textile performance properties are reported as a function of add-on of each type of finish; strengths and abrasion resistance of the finished fabrics are considered and discussed as a function of resilience. General trends of decreasing strength and abrasion resistance with increasing resilience were observed for these flame-retardant fabrics. Within this trend there is latitude for selection of finishes that will provide superior performance in the individual textile property such as abrasion resistance, breaking strength, and tearing strength.

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Characteristics of One Dimensional Photonics Structures Having Different Types of Sequences

Springer Proceedings in Physics - ICOL-2019 ◽

10.1007/978-981-15-9259-1_9 ◽

2021 ◽

pp. 37-40

Author(s):

Rajib Chakraborty ◽

Rajorshi Bandyopadhyay ◽

Rajib Ghosh ◽

Saikat Majumder

Keyword(s):

One Dimensional ◽

Different Types

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Measurement Matrix Optimization via Mutual Coherence Minimization for Compressively Sensed Signals Reconstruction

Mathematical Problems in Engineering ◽

10.1155/2020/7979606 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18

Author(s):

Ziran Wei ◽

Jianlin Zhang ◽

Zhiyong Xu ◽

Yong Liu ◽

Krzysztof Okarma

Keyword(s):

Optimization Methods ◽

Superior Performance ◽

Gram Matrix ◽

Sparse Signals ◽

Two Dimensional ◽

Measurement Matrix ◽

Mutual Coherence ◽

One Dimensional ◽

Two Dimensional Image ◽

Matrix Optimization

For signals reconstruction based on compressive sensing, to reconstruct signals of higher accuracy with lower compression rates, it is required that there is a smaller mutual coherence between the measurement matrix and the sparsifying matrix. Mutual coherence between the measurement matrix and sparsifying matrix can be expressed indirectly by the property of the Gram matrix. On the basis of the Gram matrix, a new optimization algorithm of acquiring a measurement matrix has been proposed in this paper. Firstly, a new mathematical model is designed and a new method of initializing measurement matrix is adopted to optimize the measurement matrix. Then, the loss function of the new algorithm model is solved by the gradient projection-based method of Gram matrix approximating an identity matrix. Finally, the optimized measurement matrix is generated by minimizing mutual coherence between measurement matrix and sparsifying matrix. Compared with the conventional measurement matrices and the traditional optimization methods, the proposed new algorithm effectively improves the performance of optimized measurement matrices in reconstructing one-dimensional sparse signals and two-dimensional image signals that are not sparse. The superior performance of the proposed method in this paper has been fully tested and verified by a large number of experiments.

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