Automatic modulation classification (AMC) is the key technique in both military and civilian wireless communication. However, performance unsatisfactory, even several deep learning-based methods are involved. Targeting its low accuracy at SNR, high computational cost label overdependence, we propose a novel AMC framework, where autoencoder (AE) serves as backbone Convolution-AE LSTM-AE combined parallel way temporal spatial feature extractors. The comparisons with serval algorithms on...

This study constructs an acupuncture knowledge graph (AcuKG) to systematically organize and represent acupuncture-related in a structured scalable format. By extracting integrating from diverse data sources, covering indication, treatment efficacy, practice guidelines, clinical research etc., AcuKG enhances discovery utilization while improving interoperability the field of acupuncture. To achieve this, we employ multiple methods, including entity recognition, term normalization, semantic...

Much research has focused on classification within a closed set of emitters, while emitters outside this are misclassified. This paper proposes an open-set recognition model based prototypical networks and extreme value theory to solve the problem specific emitter identification in scenes further improve accuracy robustness. Firstly, one-dimensional convolutional neural network was designed for recognizing I/Q signals, squeeze-and-excitation block with attention mechanism added increase...

Specific emitter identification (SEI) aims to distinguish different individuals based on the subtle differences in signals. The technology can be widely applied various wireless communication systems. Existing individual methods often ignore radio frequency (RF) fingerprint information carried by waveforms and thus interfered unreliable RF features. To alleviate these issues, we devise deep bidirectional long short-term memory (DBi-LSTM) one-dimensional residual convolution network with...

Jamming against frequency-hopping spread spectrum (FHSS) in wireless sensor networks (WSNs) has been primarily investigated with the follower jamming mode. However, implementing practical applications encounters manifold challenges, such as stringent requirements on hardware performance and difficulties attaining accurate synchronization signals. Diverging from existing works, this article, we propose a novel partial-band noise (PBNJ) decision-making algorithm based asynchronous deep...

?The advancement of deep learning (DL) techniques has led to significant progress in Automatic Modulation Classification (AMC). However, most existing DL-based AMC methods require massive training samples, which are difficult obtain non-cooperative scenarios. The identification modulation types under small sample conditions become an increasingly urgent problem. In this paper, we present a novel few-shot model named the Spatial Temporal Transductive Classifier (STTMC), comprises two modules:...

In modulation classification domain, handcrafted feature based method can fit well from a few labeled samples, while deep learning require large amount of samples to achieve the superior performance. order improve accuracy under constraint limited this paper proposes few-shot on dimension reduction and pseudo-label training (FDRPLT), which combines with method. First, an optimal low-dimensional subset is created by combination features autoencoder-extracted post-processed selection...

Due to the nonconvexity feature of optimal controlling such as jamming link selection and power allocation issues, obtaining resource strategy in communication countermeasures scenarios is challenging. Thus, we propose a novel decentralized algorithm based on multiagent deep reinforcement learning (MADRL) improve efficiency battlefield countermeasures. We first model problem fully cooperative task, considering interrelationship equipment (JE). Then, alleviate nonstationarity high decision...

In order to solve the optimization of interference resource allocation in communication network countermeasures, an method based on maximum policy entropy deep reinforcement learning (MPEDRL) was proposed. The introduced idea into countermeasures allocation, it could enhance exploration and accelerate convergence global optimum with adding criterion adaptively adjusting coefficient. modeled as Markov decision process, then established strategy output scheme, constructing effect evaluation...

This paper investigates non-data-aided (NDA) SNR estimation for envelope-based QAM transmission. It proposes a new technique to estimate the signal-to-noise ratio (SNR) using well-known moment-based M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> method over flat-fading channel. is non-data-aided, estimator that can be applied...

The Cramer-Rao lower bounds (CRLB), data-aided maximum-likelihood (ML) signal-to-noise ratio (SNR) estimation algorithm (DAML), decision-directed ML SNR (DDML) and an iterative for QAM signals are presented in this paper. performance analysis, computer simulation comparing with another blind <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[10,</sup> xmlns:xlink="http://www.w3.org/1999/xlink">11]</sup> CRLB completed. And then the corresponding...

The recognition of modulation schemes for communication signals is an important part surveillance and spectrum monitoring. An algorithm based on deep learning texture proposed to recognize schemes. Based imperceptible differences among various spectrums schemes, the uses Convolution Neural Network capture features image thus classify with a SOFTMAX classifier. experiment shows performs better than traditional feature parameters, while captured can reveal signal detail reduces effort parameter design.

We present the use of a computer-generated hologram (CGH) to test mid-spatial frequency error large aperture long-focal-length lens. In order verify this approach, 450 mm × reflective CGH is designed and fabricated for testing 440 spatial filter Both 0th 1st diffraction wavefront are measured, used calibrate substrate error. The caused by fabrication errors evaluated using binary linear grating model power spectral density (PSD) theory. Experimental results analysis show that approach also...

Abstract With the continuous improvement of China’s national economy and unprecedented innovation development modern science technology, artificial intelligence technology has been widely used in industrial production, power transportation, aerospace other fields. As an extension pattern recognition plays important role computer image processing by virtue its own automation. It fundamentally guarantees accuracy greatly improves information processing. Overall work efficiency. This paper...

Based on end effects of empirical mode decomposition (EMD) Hilbert-Huang Transform (HHT) in detecting ultra-wideband (UWB) signal, the non-equidistance grey model (NGM) is analyzed to mitigate EMD by predicting uncertainly data. Since some extreme points can hardly be detected particular situation, modified NGM(1,1)model using Fourier series (TFNGM(1,1)) at time domain proposed. Furthermore, new criterion terminate iteration process also proposed and this paper, which effectively improve...

The estimation of carrier frequency communication signals with high precision and wide range is required in lots applications. A algorithm based on maximum-likelihood principle iterative process proposed this paper. performance the compared other two widely used classic algorithms by computer simulations. In simulation results, has better than others'.

The performances of several signal-to-noise ratio (SNR) estimation techniques reported in the literature are compared to identify "best" estimator. SNR estimators investigated by computer simulation quartenary phase-shift keying (QPSK) signals complex additive white Gaussian noise (AWGN).The mean square error is used as a measure performance. In addition comparing relative performances, absolute levels performance also established, simulated for AWGN that derived here. Computer simulations...

Internet of Vehicles (IoVs) provides communication and computing resources, which makes the on-board diagnosis vehicle faults possible. However, those resources need to be expanded support accurate analysis diagnosis. Vehicular Cloud Computing (VCC) can solve pressure local processing but will cause an unavoidable delay. Thus, accuracy timeliness cannot guaranteed. To address issue, we propose a Mobile Edge Caching based Resource Scheduling (MECRS) mechanism for faults. According urgency...

Deep-learning is widely used in modulation classification to reduce labor and improve the efficiency. Graph convolutional network (GCN) a type of feature extraction for graph data. Considering signals as nodes similarity each signal an edge, GCN propagates node information similar along edges. extracts more features achieves better results, particularly characterless examples. In this paper, we propose algorithm based on feature-embedding (FE-GCN). It comprises three parts: (FEN), adjacent...

Deep Learning models have ushered in leapfrog development Automatic Modulation Classification (AMC). However, existing AMC frequently fail to generalize well testing data with different distribution. In this letter, we propose a simple and efficient baseline: incorporating self-distillation (SD) training strategy into an advanced backbone network. SD constructs series of tasks continuously retrain on source dataset generates generalized backbone. Then the trained model is served as...

Abstract Traditional segmentation methods can only segment grayscale images, which limits their application; The process often depends on the doctor’s experience, lead to subjective factors affecting results; Therefore, accuracy and efficiency of are difficult achieve practical application results. deep learning model is a structural that mimics neural connections within human brain. accurately extract multi-level features key information in images from low-level high-level, provide feedback...