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.

Single channel source separation of radar fuze mixed signal based on phase difference analysis

A new method based on phase difference analysis is proposed for the single-channel mixed signal separation of single-channel radar fuze.This method is used to estimate the mixing coefficients of de-noised signals through the cumulants of mixed signals,solve the candidate data set by the mixing coefficients and signal analytical form,and resolve the problem of vector ambiguity by analyzing the phase differences.The signal separation is realized by exchanging data of the solutions.The waveform similarity coefficients are calculated,and the time鈥攆requency distributions of separated signals are analyzed.The results show that the proposed method is effective.

A search-free near-field source localization method with exact signal model

Most of the near-field source localization methods are developed with the approximated signal model,because the phases of the received near-field signal are highly non-linear.Nevertheless,the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures.In this paper,a search-free near-field source localization method is proposed with the exact signal model.Firstly,the approximative estimates of the direction of arrival(DOA)and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations.Then,the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations.The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance.Numerical simulations are provided to demonstrate the effectiveness of the proposed method.

THE NATURE AND SOURCE OF ESR SIGNAL IN BILIRUBIN

The ESR signals of bilirubin-IXα were studied including the samples treated with free radical generating and inhibiting systems,i.e.X-X0., Fe/EDTA,SOD,mannitol/ascorbate,DTPA,KCN et al.These stable signals all comprise those originated from a semiquinone radical(g=2.0012)and superoxide radical(g_=2.041,g_=2.0040).The latter is shown to be bound with metal ions especially iron,chelated by bilirubin.The iron probably comes from bilirubin precursor——hemootobin.Active oxygen free radical scavengers may destroy these radicals.Kinetic curves of regeneration of the bilirubin radicals have been determined.Bilirubin is discussed as'active oxygen trap'in mammatians.

Cycle-by-Cycle Queue Length Estimation for Signalized Intersections Using Multi-Source Data

In order to estimate vehicular queue length at signalized intersections accurately and overcome the shortcomings and restrictions of existing studies especially those based on shockwave theory,a new methodology is presented for estimating vehicular queue length using data from both point detectors and probe vehicles. The methodology applies the shockwave theory to model queue evolution over time and space. Using probe vehicle locations and times as well as point detector measured traffic states,analytical formulations for calculating the maximum and minimum( residual) queue length are developed. The proposed methodology is verified using ground truth data collected from numerical experiments conducted in Shanghai,China. It is found that the methodology has a mean absolute percentage error of 17. 09%,which is reasonably effective in estimating the queue length at traffic signalized intersections. Limitations of the proposed models and algorithms are also discussed in the paper.

Effects of Root Pruning on Non-Hydraulic Root-Sourced Signal, Drought Tolerance and Water Use Efficiency of Winter Wheat

Two pot experiments were conducted to study the effects of root pruning at the stem elongation stage on non-hydraulic root-sourced signals （nHRS）, drought tolerance and water use efficiency of winter wheat （Triticum aestivum）. The root pruning significantly reduced the root weight of wheat, but had no effect on root/shoot ratio at the two tested stages. At booting stage, specific root respiration of root pruned plants was significantly higher than those with intact roots （1.06 and 0.94 mmol g-1 s-1, respectively）. The soil water content （SWC） at which nHRS for root pruned plants appeared was higher and terminated lower than for intact root plants, the threshold range of nHRS was markedly greater for root pruned plants （61.1-44.6% field water capacity） than for intact root plants （57.9-46.1% field water capacity）. At flowering stage, while there was no significant difference in specific root respiration. The SWCs at which nHRS appeared and terminated were both higher for root pruned plants than for intact root plants. The values of chlorophyll fluorescence parameters, i.e., the effective photosystem II quantum yield （F PS II ）, the maximum photochemical efficiency of PS II （F v /F m ）, coefficient of photochemical quenching （qP）, and coefficient of non-photochemical quenching （NPQ）, in root pruned plants were significantly higher than in intact root plants, 7 d after withholding of water. Root pruned plants had significantly higher water use efficiency （WUE） than intact root plants in well-watered and medium drought soil, but not in severe drought condition. In addition, root pruning had no significant effect on grain yield in well-watered and medium drought soil, but significantly decreased grain yield in severe drought condition. In conclusion, the current study showed that root pruning significantly altered nHRS sensitivity and improved WUE of winter wheat in well-watered and medium drought soil, but lowered drought tolerance of winter wheat in severe drought soil. This suggests a possible direction of drought- resistance breeding and potential agricultural measure to improve WUE of winter wheat under semiarid conditions.

For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

Measuring Acoustic Wave Transit Time in Furnace Based on Active Acoustic Source Signal

Accurate measurement of transit time for acoustic wave between two sensors installed on two sides of a furnace is a key to implementing the temperature field measurement technique based on acoustical method. A new method for measuring transit time of acoustic wave based on active acoustic source signal is proposed in this paper, which includes the followings： the time when the acoustic source signal arrives at the two sensors is measured first; then, the difference of two arriving time arguments is computed, thereby we get the transit time of the acoustic wave between two sensors installed on the two sides of the furnace. Avoiding the restriction on acoustic source signal and background noise, the new method can get the transit time of acoustic wave with higher precision and stronger ability of resisting noise interference.

Reliability Evaluation on Weak Signal Extraction for Airgun Source Surveys

Based on the data synthesis simulation and the actual processing of the airgun seismic source signal,three quantitative indicators of signal-to-noise ratio,waveform correlation coefficient and phase offset,are superimposed. We systematically evaluate the functions of the following three stack methods including linearity,phase weighting and S-transform in the extraction of weak signals under strong background noise and quantitatively estimate the reliability of the stack results. Through the comprehensive discussion of the above three methods of stack results,the preliminary comparative analysis believes that the linear stack signal-to-noise ratio is low,but the waveform distortion is minimal; the phase-weighted superimposed signal-to-noise ratio is high and the phase offset is small,but the results of the waveform quality and linear stack are larger than the deviation; the S-transform stack has a relatively higher signal-to-noise ratio and a small loss of waveform amplitude,but there is a certain phase shift phenomenon. It is therefore suggested that linear stack technology should be used when the requirements of both waveform quality and time precision are high. However,the selection of the stack method when the airgun source excitation is limited should be emphasized. If high fidelity is required, the S-transform stack method should be selected; if the required time is high,accuracy can be selected by phase-weighted stack method to achieve reasonable extraction of weak signals.

Demonstration of the Source of Motor Program Signal: Study on the Correlation between Muscle Strength and sEMG Signal in Normal Children and Adults

To investigate the relationship between muscle strength and sEMG of biceps brachii during elbow flexion by measuring the maximum muscle strength and sEMG value of normal children and adults, and to analyze their sources, so as to lay a theoretical foundation for the method of motor program reconstruction to restore the function after brain injury, 30 healthy children aged 9 - 10 years and 30 adults aged 20 - 30 years were randomly selected. The muscle strength and sEMG of biceps brachii during elbow flexion were detected and recorded, and the data were statistically analyzed. The muscle strength of children was significantly lower than that of adults (P < 0.001), and the sEMG value of biceps brachii was significantly lower than that of adults (P < 0.001), but the sEMG value per kilogram force of children was significantly higher than that of adults (P < 0.01). The results show that there was a very significant difference in pull (efficiency) between adults and children when there was no significant difference in SEMG signal intensity. This is because although children’s central nervous system has matured, the muscle tissue has not been well trained, resulting in insufficient muscle strength. The muscle strength of adults is significantly higher than that of children, because they have been exercising for a long time after the development of the central nervous system. It is proved that sEMG signal is not produced by muscle contraction itself, but comes from the motor program signal of central nervous system which drives muscle contraction, and it is produced before muscle contraction.

A blind source separation algorithm based on negentropy and signal noise ratio

A novel blind source separation （BSS） algorithm based on the combination of negentropy and signal noise ratio （SNR） is presented to solve the deficiency of the traditional independent component analysis （ICA） algorithm after the introduction of the principle and algorithm of ICA. The main formulas in the novel algorithm are elaborated and the idiographic steps of the algorithm are given. Then the computer simulation is used to test the performance of this algorithm. Both the traditional FastlCA algorithm and the novel ICA algorithm are applied to separate mixed signal data. Experiment results show the novel method has a better performance in separating signals than the traditional FastlCA algorithm based on negentropy. The novel algorithm could estimate the source signals from the mixed signals more precisely.

The Improved Design of Multi-channel Thin Gap Chamber Simulation Signal Source for the ATLAS Detector Upgrade

We develop an improved design of thin gap chamber （TGC） simulation signal source. To further simulate the feature of TGC detector, a novel thought is proposed. The TGC source has 256 channels. Every channel can randomly output the signal in 25 ns. The design is based on true random number generator （TRNG）. Considering the electrical connection between the TGC source and the developing trigger electronics, the GFZ connector is used. The experimental results show that the improved TGC simulation signal source can uniformly output the random signal in every channel. The output noise is less than 3 mVrms.

An algorithm for ^(252)Cf-Source-Driven neutron signal denoising based on Compressive Sensing

As photoelectrically detected ^(252)Cf-source-driven neutron signals always contain noise, a denoising algorithm is proposed based on compressive sensing for the noised neutron signal. In the algorithm, Empirical Mode Decomposition(EMD) is applied to decompose the noised neutron signal and then find out the noised Intrinsic Mode Function(IMF) automatically. Thus, we only need to use the basis pursuit denoising(BPDN) algorithm to denoise these IMFs. For this reason, the proposed algorithm can be called EMDCSDN(Empirical Mode Decomposition Compressive Sensing Denoising). In addition, five indicators are employed to evaluate the denoising effect. The results show that the EMDCSDN algorithm is more effective than the other denoising algorithms including BPDN. This study provides a new approach for signal denoising at the front-end.

Source Separation of Diesel Engine Vibration Based on the Empirical Mode Decomposition and Independent Component Analysis

Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis（ICA）.However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition（EMD） and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.

ESTIMATION OF THE NUMBER OF CORRELATED SOURCES WITH COMMON FREQUENCIES BASED ON POWER SPECTRAL DENSITY

Blind source separation and estimation of the number of sources usually demand that the number of sensors should be greater than or equal to that of the sources, which, however, is very difficult to satisfy for the complex systems. A new estimating method based on power spectral density （PSD） is presented. When the relation between the number of sensors and that of sources is unknown, the PSD matrix is first obtained by the ratio of PSD of the observation signals, and then the bound of the number of correlated sources with common frequencies can be estimated by comparing every column vector of PSD matrix. The effectiveness of the proposed method is verified by theoretical analysis and experiments, and the influence of noise on the estimation of number of source is simulated.

Identification of diesel front sound source based on continuous wavelet transform

Acoustic signals from diesel engines contain useful information but also include considerable noise components To extract information for condition monitoring purposes, continuous wavelet transform (CWT) is used for the characterization of engine acoustics. This paper first reviews CWT characteristics represented by short duration transient signals. Wavelet selection and CWT are then implemented and wavelet transform is used to analyze the major sources of the engine front's exterior radiation sound. The research provides a reliable basis for engineering practice to reduce vehicle sound level. Comparison of the identification results of the measured acoustic signals with the identification results of the measured surface vibration showed good agreement.

A robust clustering algorithm for underdetermined blind separation of sparse sources

In underdetermined blind source separation, more sources are to be estimated from less observed mixtures without knowing source signals and the mixing matrix. This paper presents a robust clustering algorithm for underdetermined blind separation of sparse sources with unknown number of sources in the presence of noise. It uses the robust competitive agglomeration （RCA） algorithm to estimate the source number and the mixing matrix, and the source signals then are recovered by using the interior point linear programming. Simulation results show good performance of the proposed algorithm for underdetermined blind sources separation （UBSS）.

Passive Localization of Multiple Sources Using Joint RSS and AOA Measurements in Spectrum Sharing System

In spectrum sharing systems,locating mul-tiple radiation sources can efficiently find out the in-truders,which protects the shared spectrum from ma-licious jamming or other unauthorized usage.Com-pared to single-source localization,simultaneously lo-cating multiple sources is more challenging in prac-tice since the association between measurement pa-rameters and source nodes are not known.More-over,the number of possible measurements-source as-sociations increases exponentially with the number of sensor nodes.It is crucial to discriminate which measurements correspond to the same source before localization.In this work,we propose a central-ized localization scheme to estimate the positions of multiple sources.Firstly,we develop two computa-tionally light methods to handle the unknown RSS-AOA measurements-source association problem.One method utilizes linear coordinate conversion to com-pute the minimum spatial Euclidean distance sum-mation of measurements.Another method exploits the long-short-term memory(LSTM)network to clas-sify the measurement sequences.Then,we propose a weighted least squares(WLS)approach to obtain the closed-form estimation of the positions by linearizing the non-convex localization problem.Numerical re-sults demonstrate that the proposed scheme could gain sufficient localization accuracy under adversarial sce-narios where the sources are in close proximity and the measurement noise is strong.

A Modal Identification Algorithm Combining Blind Source Separation and State Space Realization

A modal identification algorithm is developed, combining techniques from Second Order Blind Source Separation (SOBSS) and State Space Realization (SSR) theory. In this hybrid algorithm, a set of correlation matrices is generated using time-shifted, analytic data and assembled into several Hankel matrices. Dissimilar left and right matrices are found, which diagonalize the set of nonhermetian Hankel matrices. The complex-valued modal matrix is obtained from this decomposition. The modal responses, modal auto-correlation functions and discrete-time plant matrix (in state space modal form) are subsequently identified. System eigenvalues are computed from the plant matrix to obtain the natural frequencies and modal fractions of critical damping. Joint Approximate Diagonalization (JAD) of the Hankel matrices enables the under determined (more modes than sensors) problem to be effectively treated without restrictions on the number of sensors required. Because the analytic signal is used, the redundant complex conjugate pairs are eliminated, reducing the system order (number of modes) to be identified half. This enables smaller Hankel matrix sizes and reduced computational effort. The modal auto-correlation functions provide an expedient means of screening out spurious computational modes or modes corresponding to noise sources, eliminating the need for a consistency diagram. In addition, the reduction in the number of modes enables the modal responses to be identified when there are at least as many sensors as independent (not including conjugate pairs) modes. A further benefit of the algorithm is that identification of dissimilar left and right diagonalizers preclude the need for windowing of the analytic data. The effectiveness of the new modal identification method is demonstrated using vibration data from a 6 DOF simulation, 4-story building simulation and the Heritage court tower building.

In-Solid Acoustic Source Localization Using Likelihood Mapping Algorithm

The significant challenge in human computer interaction is to create tangible interfaces that will make digital world accessible through augmented physical surfaces like walls and windows. In this paper, various acoustic source localization methods are proposed which have the potential to covert a physical object into a tracking sensitive interface. The Spatial Likelihood method has been used to locate acoustic source in real time by summing the spatial likelihood from all sensors. The source location is obtained from searching the maximum in the likelihood map. The data collected from the sensors is pre-processed and filtered for improvement of the accuracy of source localization. Finally a sensor fusion algorithm based on least squared error is presented to minimize the error while positioning the source. Promising results have been achieved experimentally for the application of acoustic tangible interfaces.