Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors.The designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training.Consequently,this method allows unknown classes to occupy a larger space in the feature space.This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct.Additionally,the feature comparator threshold can be used to reject unknown samples.For signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter.The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment.Specifically,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

A Support Data-Based Core-Set Selection Method for Signal Recognition

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

Wireless distributed test system based on transient pressure signal detection and recognition

During the test on transient pressure signal in explosion field,false trigger caused by field interference can lead to test failure.To improve the stability of test system,a signal detection and recognition technology is proposed for transient pressure test system.In the process of signal acquisition,firstly,electrical levels are monitored in real time to find effective abrupt changes and mark them;then the effective data segments are detecdted totected;thus the effective signals can be acquired in turn finally.The experimental results show that the shock wave signal can be collected effectively and the reliability of the test system can be improved after removal of interferences.

Monitoring and Recognition of Debris Flow Infrasonic Signals

Low frequency infrasonic waves are emitted during the formation and movement of debris flows, which are detectable in a radius of several kilometers, thereby to serve as the precondition for their remote monitoring.However, false message often arises from the simple mechanics of alarms under the ambient noise interference.To improve the accuracy of infrasound monitoring for early-warning against debris flows, it is necessary to analyze the monitor information to identify in them the infrasonic signals characteristic of debris flows.Therefore, a large amount of debris flow infrasound and ambient noises have been collected from different sources for analysis to sum up their frequency spectra, sound pressures, waveforms, time duration and other correlated characteristics so as to specify the key characteristic parameters for different sound sources in completing the development of the recognition system of debris flow infrasonic signals for identifying their possible existence in the monitor signals.The recognition performance of the system has been verified by simulating tests and long-term in-situ monitoring of debris flows in Jiangjia Gully,Dongchuan, China to be of high accuracy and applicability.The recognition system can provide the local government and residents with accurate precautionary information about debris flows in preparation for disaster mitigation and minimizing the loss of life and property.

Modulation recognition of communication signals based on SCHKS-SSVM

A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.

Radar Signal Recognition by CWD Picture Features

In this paper a system for automatic recognition of radar waveform is introduced. This technique is used in many spectrum management, surveillance, and cognitive radio and radar applications. For instance the transmitted radar signal is coded into six codes based on pulse compression waveform such as linear frequency modulation (LFM), Frank code, P1, P2, P3 and P4 codes, the latter four are poly phase codes. The classification system is based on drawing Choi Willliams Distribution (CWD) picture and extracting features from it. In this study, various new types of features are extracted from CWD picture and then a pattern recognition method is used to recognize the spectrum. In fact, signals from CWD picture are defined using biometric techniques. We also employ false reject rate (FRR) and false accept rate (FAR) which are two types of fault measurement criteria that are deploy in biometric papers. Fairly good results are obtained for recognition of Signal to Noise Ratio (-11 dB).

Modulation recognition of MIMO radar signal based on joint HOS and SNR algorithm

This paper presents a joint high order statistics （HOS） and signal-to-noise ratio （SNR） algorithm for the recognition of multiple-input multiple-output （MIMO） radar signal without a priori knowledge of the signal parameters. This method is capable of recognizing the MIMO radar signal as well as discriminating it from single-carrier signal adopted by conventional radar. Meanwhile, the sub-carrier number of the none-coding MIMO radar signal is estimated. Extensive simulations are carried out in different operating conditions. Simulation results prove the feasibility and indicate that the recognition probability could reach over 90% when the value of SNR is above 0 dB.

Pattern recognition of surface electromyography signal based on wavelet coefficient entropy

This paper introduced a novel, simple and ef-fective method to extract the general feature of two surface EMG (electromyography) signal patterns: forearm supination (FS) surface EMG signal and forearm pronation (FP) surface EMG signal. After surface EMG (SEMG) signal was decomposed to the fourth resolution level with wavelet packet transform (WPT), its whole scaling space (with frequencies in the interval (0Hz, 500Hz]) was divided into16 frequency bands (FB). Then wavelet coefficient entropy (WCE) of every FB was calculated and corre-spondingly marked with WCE(n) (from the nth FB, n=1,2,…16). Lastly, some WCE(n) were chosen to form WCE feature vector, which was used to distinguish FS surface EMG signals from FP surface EMG signals. The result showed that the WCE feather vector consisted of WCE(7) (187.25Hz, 218.75Hz) and WCE(8) (218.75Hz, 250Hz) can more effectively recog-nize FS and FP patterns than other WCE feature vector or the WPT feature vector which was gained by the combination of WPT and principal components analysis.

A VIBRATION RECOGNITION METHOD BASED ON DEEP LEARNING AND SIGNAL PROCESSING

Effective vibration recognition can improve the performance of vibration control and structural damage detection and is in high demand for signal processing and advanced classification.Signal-processing methods can extract the potent time-frequency-domain characteristics of signals;however,the performance of conventional characteristics-based classification needs to be improved.Widely used deep learning algorithms(e.g.,convolutional neural networks(CNNs))can conduct classification by extracting high-dimensional data features,with outstanding performance.Hence,combining the advantages of signal processing and deep-learning algorithms can significantly enhance vibration recognition performance.A novel vibration recognition method based on signal processing and deep neural networks is proposed herein.First,environmental vibration signals are collected;then,signal processing is conducted to obtain the coefficient matrices of the time-frequency-domain characteristics using three typical algorithms:the wavelet transform,Hilbert-Huang transform,and Mel frequency cepstral coefficient extraction method.Subsequently,CNNs,long short-term memory(LSTM)networks,and combined deep CNN-LSTM networks are trained for vibration recognition,according to the time-frequencydomain characteristics.Finally,the performance of the trained deep neural networks is evaluated and validated.The results confirm the effectiveness of the proposed vibration recognition method combining signal preprocessing and deep learning.

Open World Recognition of Communication Jamming Signals

To improve the recognition ability of communication jamming signals,Siamese Neural Network-based Open World Recognition(SNNOWR)is proposed.The algorithm can recognize known jamming classes,detect new(unknown)jamming classes,and unsupervised cluseter new classes.The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset.On the one hand,the network is required to have the ability to distinguish whether two samples are from the same class.On the other hand,the latent distribution of known class is forced to approach their own unique Gaussian distribution,which is prepared for the subsequent open set testing.During the test,the unknown class detection process based on Gaussian probability density function threshold is designed,and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes.The simulation results show that when the jamming-to-noise ratio is more than 0d B,the accuracy of SNN-OWR algorithm for known jamming classes recognition,unknown jamming detection and unsupervised clustering of unknown jamming is about 95%.This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.

Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

ANALYSIS OF AFFECTIVE ECG SIGNALS TOWARD EMOTION RECOGNITION

Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.

Key Radar Signal Sorting and Recognition Method Based on Clustering Combined with PRI Transform Algorithm

In this paper,we investigate the problem of key radar signal sorting and recognition in electronic intelligence(ELINT).Our major contribution is the development of a combined approach based on clustering and pulse repetition interval(PRI)transform algorithm,to solve the problem that the traditional methods based on pulse description word(PDW)were not exclusively targeted at tiny particular signals and were less time-efficient.We achieve this in three steps:firstly,PDW presorting is carried out by the DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering algorithm,and then PRI estimates of each cluster are obtained by the PRI transform algorithm.Finally,by judging the matching between various PRI estimates and key targets,it is determined whether the current signal contains key target signals or not.Simulation results show that the proposed method should improve the time efficiency of key signal recognition and deal with the complex signal environment with noise interference and overlapping signals.

An Introduction to Convex Optimization Theory in Communication Signals Recognition

In this paper,convex optimization theory is introduced into the recognition of communication signals. The detailed content contains three parts. The first part gives a survey of basic concepts,main technology and recognition model of convex optimization theory. Special emphasis is placed on how to set up the new recognition model of communication signals with multisensor reports. The second part gives the solution method of the recognition model,which is called Logarithmic Penalty Barrier Function. The last part gives several numeric simulations,in contrast to D-S evidence inference method,this new method can also generate reasonable recognition results. Moreover,this new method can deal with the form of sensor reports which is more general than that allowed by the D-S evidence inference method,and it has much lower computation complexity than that of D-S evidence inference method. In addition,this new method has better recognition result,stronger anti-interference and robustness. Therefore,the convex optimization methods can be widely used in the recognition of communication signals.

Bayesian decision-based pattern recognition on spectrum signal of metal transfer modes

A software system of pattern recognition on spectrum signal of metal transfer mode has been developed using Visual Basic under Windows environment. On the basis of the coincidence relation between the spectrum signal and metal transfer mode, according to the geometrical pattern feature of the spectrum signal, several key characteristic parameters are extracted. The correspondent recognition function and a minimum distance classifier have been constructed based on Bayesian decision theory. The results show that using this system, the metal transfer mode of MIG, MAG, CO2 welding can be recognized automatically which provides the basis for automatically controlling of the metal gas arc welding metal transfer.

G protein signalling involved in host recognition and mycoparasitism-related chitinase expression in Trichoderma atroviride

Mycoparasitic species of Trichoderma are commercially applied as biological control agents against various fungal pathogens. The mycoparasitic interaction is host specific and includes recognition, attack and subsequent penetration and killing of the host. Investigations on the underlying events revealed that Trichoderma responds to multiple signals from the host (e.g. lectins or other ligands such as low molecular weight components released from the host’s cell wall) and host attack is accompanied by morphological changes and the secretion of hydrolytic enzymes and antibiotics. Degradation of the cell wall of the host fungus is-besides glucanases and proteases-mainly achieved by chitinases. In vivo studies showed that the ech42 gene (encoding endochitinase 42) is expressed before physical contact of Trichoderma with its host, probably representing one of the earliest events in mycoparasitism, whereas Nag1 (N-acetylglucosaminidase) plays a key role in the general induction of the chitinolytic enzyme system of T. atroviride . Investigations on the responsible signal transduction pathways of T. atroviride led to the isolation of several genes encoding key components of the cAMP and MAP kinase signaling pathways, as alpha and β subunits of heterotrimeric G proteins, the regulatory subunit of cAMP-dependent protein kinase, adenylate cyclase, and three MAP kinases. Analysis of knockout mutants, generated by Agrobacterium-mediated transformation, revealed that at least two alpha-subunits of heterotrimeric G proteins are participating in mycoparasitism-related signal transduction. The Tga1 G alpha subunit was shown to be involved in mycoparasitism-related processes such as chitinase expression and overproduction of toxic secondary metabolites, whereas Tga3 was found to be completely avirulent showing defects in chitinase formation and host recognition.

Clinical detection and movement recognition of neuro signals

Neuro signal has many more advantages than myoelectricity in providing information for prosthesis control, and can be an ideal source for developing new prosthesis. In this work, by implanting intrafascicular electrode clinically in the amputee’s upper extremity, collective signals from fascicules of three main nerves (radial nerve, ulnar nerve and medium nerve) were suc- cessfully detected with sufficient fidelity and without infection. Initial analysis of features under different actions was performed and movement recognition of detected samples was attempted. Singular value decomposition features (SVD) extracted from wavelet coefficients were used as inputs for neural network classifier to predict amputee’s movement intentions. The whole training rate was up to 80.94% and the test rate was 56.87% without over-training. This result gives inspiring prospect that col- lective signals from fascicules of the three main nerves are feasible sources for controlling prosthesis. Ways for improving accu- racy in developing prosthesis controlled by neuro signals are discussed in the end.

Mood States Recognition of Rowing Athletes Based on Multi-Physiological Signals Using PSO-SVM

Athletes have various emotions before competition, and mood states have impact on the competi- tion results. Recognition of athletes’ mood states could help athletes to have better adjustment before competition, which is significant to competition achievements. In this paper, physiological signals of female rowing athletes in pre- and post-competition were collected. Based on the multi-physiological signals related to pre- and post-competition, such as heart rate and respiration rate, features were extracted which had been subtracted the emotion baseline. Then the particle swarm optimization (PSO) was adopted to optimize the feature selection from the feature set, and combined with the least squares support vector machine (LS-SVM) classifier. Positive mood states and negative mood states were classified by the LS-SVM with PSO feature optimization. The results showed that the classification accuracy by the LS-SVM algorithm combined with PSO and baseline subtraction was better than the condition without baseline subtraction. The combination can contribute to good classification of mood states of rowing athletes, and would be informative to psychological adjustment of athletes.