Signal Demodulation Research Articles

Identification of Analogue Modulated Signal via Artificial Intelligence

Referring to a huge usage of the analogue modulated signal in both civilian and military applications, and impotency of identification it, the importance is came from many reasons also to how to get an algorithm to recognize an analogue modulated signal via AI for high performance of identification and simple to use. The identification is a middle-step between recognizing and signal demodulation. Selecting the most suitable variables are for input modulated signal and with (AI) assistant to identify some of analogue modulated signals such as (AM, DSB, SSB, and FM). Using simulation with Gaussian noise (0 dB to 10 dB) and AI. Classification system consisting from 3 parts: 1-Pre-processing stage which is talking about Feature keys extraction did. The element keys extraction did so as to get input include keys for the AAMR classifier, 2-Training stage which talked about the preparation, and Analog programmed adjustment acknowledgment (AAMR) classifier advancement., and 3-Testing stage which talked on the classifier performance evaluated using 25% of all generated data as a test data. Using two algorithms (SCG and CONJGRAD), In the SCG, the possibility of each single key feature for each single modulation scheme with variance SNR results got. A couple of methods compared and the results show that the performance of the AI based recognition system is superior to the decision classifiers based on way with less SNR values. Both of the suggested identification systems are capable to discriminate (AM, DSB, SSB, and FM) at SNR>5 dB with a success rate 100%.

Designing a system to synchronize the input signal in a telecommunication network under the condition for reducing a transitional component of the phase error

The quality of reception, as well as the processing and demodulation of the input signal in the telecommunication systems' networks, are closely related to the quality indicators in the functioning of one of the subsystems of these networks, namely a phase synchronization system. This work has directly considered the issues related to improving the performance and reducing the transitional component of the phase error generated by transition processes in the combined synchronization system. A mathematical model has been built that makes it possible to synthesize a disrupted link in the synchronization system of a telecommunication network meeting the condition for a decrease in the transitional component of the phase error. It is shown that a simple disrupted link, synthesized under the condition of suppressing a slow-fading transition component, makes it possible to shorten the time of the transition process in the system while maintaining the initial order of astatism. When a complex link is synthesized, the transition process becomes oscillatory. It was established that under the conditions of a phase jump or a frequency jump, it is possible to improve the dynamics of the system and reduce the transitional component of the phase error variance by making the parameters for the disrupted communication link influence the roots of the characteristic equation of the transition process. The features in synthesizing disrupted communication have been considered for the intervals of movement corresponding to areas with the positive and negative inclination of the phase discriminator's static characteristic. Such conditions have been devised that make it possible to determine the value and sign of the root in the characteristic equation of the transition process, which is introduced by the parameter of a disrupted communication link separately for areas of the stable and non-steady movement of the phase discriminator's static characteristic. The reported mathematical model of disrupted link synthesis has made it possible to derive reference results. They indicated that in order to suppress the slowly fading component of the phase error characteristic equation to "0", it is necessary to provide for a significant advantage, up to 10 times, of the roots introduced by the disrupted communication link over the roots of the specified component. By changing the value for a disrupted communication parameter, one can significantly, up to 5 times or larger, shorten the time of the transition process in the combined synchronization system at a simultaneous decrease of 18‒25 % in the initial value of the transition error

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

Marine current energy as a kind of renewable energy has gradually attracted more and more attention from many countries. However, the blade imbalance fault of marine current turbines (MCTs) will have an effect on the power production efficiency and cause damage to the MCT system. It is hard to classify the severity of an MCT blade imbalance fault under the condition of the current instability and seafloor noise. This paper proposes a fault classification method based on the combination of variational mode decomposition denoising (VMD denoising) and screening linear discriminant analysis (S-LDA). The proposed method consists of three parts. Firstly, phase demodulation of the collected stator current signal is performed by the Hilbert transform (HT) method. Then, the obtained demodulation signal is denoised by variational mode decomposition denoising (VMD denoising), and the denoised signal is analyzed by power spectral density (PSD). Finally, S-LDA is employed on the power signal to determine the severities of fault classification. The effectiveness of the proposed method is verified by experimental results under different severities of blade imbalance fault. The stator current signatures of experiments with different severities of blade imbalance fault are used to validate the effectiveness of the proposed method. The fault classification accuracy is 92.04% based on the proposed method. Moreover, the experimental results verify that the influence of velocity fluctuation on fault classification can be eliminated.

Dilated convolutional neural networks for fiber Bragg grating signal demodulation.

In quasi-distributed fiber Bragg grating (FBG) sensor networks, challenges are known to arise when signals are highly overlapped and thus hard to separate, giving rise to substantial error in signal demodulation. We propose a multi-peak detection deep learning model based on a dilated convolutional neural network (CNN) that overcomes this problem, achieving extremely low error in signal demodulation even for highly overlapped signals. We show that our FBG demodulation scheme enhances the network multiplexing capability, detection accuracy and detection time of the FBG sensor network, achieving a root-mean-square (RMS) error in peak wavelength determination of < 0.05 pm, with a demodulation time of 15 ms for two signals. Our demodulation scheme is also robust against noise, achieving an RMS error of < 0.47 pm even with a signal-to-noise ratio as low as 15 dB. A comparison on our high-performance computer with existing signal demodulation methods shows the superiority in RMS error of our dilated CNN implementation. Our findings pave the way to faster and more accurate signal demodulation methods, and testify to the substantial promise of neural network algorithms in signal demodulation problems.

Theoretical study of methods of finding signals and synchronization of the earth stations of satellite communications

When using spread-spectrum signals, the problem of searching by frequency and delay must be solved before the signal is processed. For demodulation of simple signals with AM, CHM and FM, it is fundamentally necessary to provide only a frequency search, and clock synchronization can be carried out after the first decision circuit, i.e. in the signal conversion equipment, usually placed in the interface equipment or channel-forming equipment. The paper presents a theoretical analysis of the methods of the search of signals and synchronization in earth stations - search and tracking of singaporemetro signals with an expanded range and features auto scan and auto-tracking frequency when receiving simple signals.

A Machine Learning Based Signal Demodulator in NOMA-VLC

Non-orthogonal multiple access (NOMA) is a promising scheme to improve the spectral efficiency, user fairness, and overall throughput in visible light communication (VLC) systems. However, the error propagation problem together with linear and nonlinear distortions induced by multipath, limited modulation bandwidth, and nonlinearity of light emitting diode significantly limit the transmission performance of NOMA-VLC systems. In addition, having an accurate channel state information, which is important in the recovery of NOMA signal, in mobile wireless VLC is challenging. In this work, we propose a convolutional neural network (CNN) based demodulator for NOMA-VLC, in which signal compensation and recovery are jointly realized. Both simulation and experiment results show that, the proposed CNN based demodulator can effectively compensate for both linear and nonlinear distortions, thus achieving improved bit error ratio performance compared with the successive interference cancellation and joint detection based receivers. Compared to SIC, the performance gains are 1.9, 2.7, and 2.7 dB for User1 for the power allocation ratios (PARs) of 0.16, 0.25, and 0.36, respectively, which are 4, 4 and 2.6 dB for User2 for PARs of 0.16, 0.25, and 0.36, respectively.

A modified convolutional neural network-based signal demodulation method for direct detection OFDM/OQAM-PON

Orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) is a promising modulation candidate for passive optical network (PON) due to its high flexibility, better time–frequency focusing characteristic, great resistance to the inter symbol and inter carrier interferences and higher spectrum efficiency compared to OFDM. However, the intrinsic imaginary interference together with linear and nonlinear distortions make it more difficult to recover the transmitted OFDM/OQAM signal at the receiver side. To mitigate the transmission impairments, a modified convolutional neural network (CNN) is utilized to learn the channel state information and the constellation demapping mechanism for OFDM/OQAM-PON. The distorted received signals are equalized implicitly to obtain the transmitted binary bits directly. The simulation results show that the CNN based receiver (Rx) can compensate the linear and nonlinear distortions more effectively compared to traditional pilot-based Rx, especially for the high order modulation long-reach PON.

Ultrafast Transient Holographic Microscopy.

Nanotechnology is increasingly being applied in many emerging technologies, ranging from metamaterials to next-generation nanodrugs. A key ingredient for its success is the ability to specifically tailor ultrafast nanoscale light–matter interactions over very large areas. Unfortunately, dynamic imaging by ultrafast nanoscopy so far remains limited to very small 2D areas. This shortcoming prevents connecting single-particle observations with large-scale functionality. Here, we address this experimental challenge by combining concepts of ultrafast spectroscopy, wide-field nanoscopy, and digital holography. We introduce an ultrafast holographic transient microscope for wide-field transient nanoscale imaging with high frequency all-optical signal demodulation. We simultaneously record ultrafast transient dynamics of many individual nano-objects and demonstrate time-resolved spectroscopy of gold nanoparticles over a large volume irrespective of their x–y–z position. Our results pave the way to single-shot 3D microscopy of 2D and 3D materials on arbitrary time scales from femtosecond carrier dynamics in optoelectronic materials to millisecond dynamics in complex tissues.

Homodyne Solid-State Biased Coherent Detection of Ultra-Broadband Terahertz Pulses with Static Electric Fields.

We present an innovative implementation of the solid-state-biased coherent detection (SSBCD) technique, which we have recently introduced for the reconstruction of both amplitude and phase of ultra-broadband terahertz pulses. In our previous works, the SSBCD method has been operated via a heterodyne scheme, which involves demanding square-wave voltage amplifiers, phase-locked to the THz pulse train, as well as an electronic circuit for the demodulation of the readout signal. Here, we demonstrate that the SSBCD technique can be operated via a very simple homodyne scheme, exploiting plain static bias voltages. We show that the homodyne SSBCD signal turns into a bipolar transient when the static field overcomes the THz field strength, without the requirement of an additional demodulating circuit. Moreover, we introduce a differential configuration, which extends the applicability of the homodyne scheme to higher THz field strengths, also leading a two-fold improvement of the dynamic range compared to the heterodyne counterpart. Finally, we demonstrate that, by reversing the sign of the static voltage, it is possible to directly retrieve the absolute THz pulse polarity. The homodyne configuration makes the SSBCD technique of much easier access, leading to a vast range of field-resolved applications.

Pilot Tone Power Limits of Brillouin Amplified Carrier Recovery for Optical Communications

The benefit of Brillouin amplification for optical carrier recovery from pilot tones is evaluated in application to coherent signal detection without a conventional local oscillator (LO) laser. The focus being on the narrow gain bandwidth suppressing broadband spectral noise power relative to the pilot tone. Calculations indicate its use at the receiver with realistic 30 dB gain and 30 MHz gain bandwidth permit lowering the minimum pilot tone to signal power ratio (PSR) for low distortion signal demodulation by up to ≈24 dB in the case of a 10 GHz wide signal channel, and even larger for broader channels. The ultimate limit being its ratio to the narrowness of the Brillouin gain bandwidth. Low noise Brillouin gain is also accounted for from characterization by a coherent receiver and digital signal processing approach for an optical fiber used as the gain medium in experiments. Its use in optical carrier recovery from a 3 channel WDM 48 Gb/s 64-QAM signal with polarization multiplexed pilot tones demonstrates improvement consistent with predictions in reducing the impact of pilot tone noise to near that for a conventional LO. The estimated low PSR limit for a bit error rate below the hard decision FEC limit was extrapolated as ≈-38 dB. This rose to ≈-30 dB with inclusion of 80 km link transmission due to the restricted signal launch power for avoiding nonlinear distortion. The insight highlights the wide-ranging capabilities for enhancing performance in advanced coherent communications.

OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection

The fast estimation of synchronization parameters plays an extremely important role in the demodulation of burst signals. In order to solve the problem of high computational complexity in the implementation of traditional algorithms, a synchronization parameter (frequency offset, phase offset, and timing error) estimation algorithm based on Offset Quadrature Phase Shift Keying (OQPSK) burst signal detection is proposed in this article. We first use the Data-Aided (DA) method to detect where the burst signal begins by taking the segment correlation between the receiving signals and the known local Unique Word (UW). In the sequel, the above results are adopted directly to estimate the synchronization parameters, which is obviously different from the conventional algorithms. In this way, the complexity of the proposed algorithm is greatly reduced, and it is more convenient for hardware implementation. The simulation results show that the proposed algorithm has high accuracy and can track the Modified Cramer–Rao Bound (MCRB) closely.

Algorithms for Processing Measuring Signals in the Integrating Solid-State Wave Gyroscope

Different variants of algorithms for processing the internal signals of the measuring device of integrating solid-state wave gyroscopes are synthesized. The main attention is paid to the consideration of subtle factors affecting the accuracy of calculation of gyroscope output signals and measurement signals of internal circuits to ensure their effective operation. For this purpose, different models of capacitive sensor signals in the gyroscope measuring device and the measured structure of standing waves in its resonator are preliminarily considered. The traditional aggregation of sensor signals into measuring device signals is described. First, variants of algorithms for capacitive direct-current operating measuring devices are systematized and thoroughly analyzed. They are divided into algorithms with tuning for periodicity of resonant oscillations and without tuning for periodicity. Variants of calculations differing in accuracy are presented for: the angle of the operating standing wave, proportional to the measured rate of the gyroscope base rotation; the amplitude of the operating quadrature standing wave for the internal circuit of its assigned value maintenance; the amplitude of the quadrature standing wave for the internal circuit of its suppression; the frequency of the operating standing wave for tuning of the excitation system of resonator oscillations and for implementation of the digital quadrature coherent demodulator of the measuring device. It is shown that in the majority of practical applications algorithms with tuning to periodicity of resonant oscillations are more preferable from the point of view of the computational complexity. And computationally labor-intensive algorithms without periodicity tuning are intended for measuring increased angular velocities with the reduced time of "freezing" of measured parameters. Further the given algorithms for the direct-current measuring device are generalized on algorithms of processing of measuring signals when operating with the alternating current. The variants of computational algorithms reduction to the algorithms previously synthesized for the direct current are described. They mainly use demodulation of AC voltage signals, including digital processing of modulated signals using a high-speed analog-to-digital converter. In addition, algorithms for information extraction from AC measuring device signals with demodulation at the combined frequency of modulated signals are synthesized. The different variants of single-band demodulation of measurement signals and also algorithm of double-band demodulation of measuring device signals with the extended list of measured quantities are considered. The choice of the necessary algorithm depends on the requirements to the measured angular velocities, on the constructive implementation of the gyroscope measuring device and on the choice of technical characteristics of its components.

Signal Demodulation for Surface Plasmon Resonance Tilted Fiber Bragg Grating Based on Root Sum Squared Method

There is an increasing interest in the use of surface plasmon resonance tilted fiber Bragg grating (SPR-TFBG) as a sensor for various chemical applications. Signal demodulation is an important process for extracting useful parameters from the unique optical spectra for an accurate measurement. In this work, we proposed a global-based estimator for SPR-TFBG based on a spectral subtraction approach. The estimator is generated based on the root sum squared (RSS) error of the spectral intensity difference between the tested spectrum and the reference spectrum. In the comparison with the other established methods, the proposed method has the lowest standard deviation, the maximum absolute deviation, the finest measurement resolution, and the fastest processing speed. Besides, the proposed method is immune to the interference of white noise in the transmission spectra and capable of producing a high-quality signal with the lowest noise level.

Demodulation and Recognition of Image Transmission Signals for Small Uav Under Non-Cooperative Conditions

According to the characteristics of CP-OFDM signals, analyzed and studied the subcarrier number estimation, timing synchronization and frequency offset compensation algorithms of CP-OFDM signals under non-cooperative reception conditions, and verified the feasibility of these algorithms based on UAV image transmission signals. As the results show, the algorithm proposed in the article can complete blind demodulation of the CP-OFDM signal and finally complete the sensing and recognition of small UAV signals under the condition of unknown CP-OFDM signal format by means of analysis and estimation algorithms.

SPECTRAL ANALYSIS AND FORECASTING OF THE 25TH SOLAR CYCLES

In the present work, we propose an extrapolation method, developed on the basis of spectral analysis, digital filtering, and the principle of demodulation of a complex signal, for predicting the beginning of cycle 25 of solar activity. The Wolf number and other measured characteristics of solar activity have a very complex spectral composition. The Sun, by the nature of its radiation, contributes a significant stochastic component to the observational data. The experimental data are known only up to the present, and the prediction is about bridging the gap in our data set. Mathematically, the prediction problem boils down to extrapolation of discontinuous functions, which leads to a Gibbs phenomenon that occurs at the point of discontinuity and makes prediction into the future impossible. To overcome this discontinuity, additional physical models describing a continuous process are most often used. This paper uses only the Wolf series of numbers from 1818 to 2020. The authors developed an original forecasting technique using Fourier series, digital filtering and representation of the complex process as modulated and subsequent demodulation. As a result of decomposing the complex signal by Fourier series into separate components, the spectral ranges characteristic of the Wolf number were singled out. Taylor's series was used for construction of prediction or extrapolation algorithms. The extraction of spectral ranges, characteristic for the investigated process, is carried out by means of sequential digital filtering methods and information compression in accordance with the cut-off frequency of the digital filter. For example, when selecting eleven-year cycles of solar activity, we have to compress the information by a factor of 160. With such a processing scheme, the forecasting starts with the ultralow-frequency component with a period of more than 11 years, successively moving to the ranges of higher frequencies. The use of spectral analysis and Chebyshev filtering showed the possibility to predict the low-frequency component for the full cycle period. The eleven-year component forecast obtained by the authors is in good agreement with the data of the Brussels Royal Center.

A Blind Demodulation Algorithm for Underwater Acoustic MPSK Signal

Multiple phase-shift keying (MPSK) is a common carrier modulation in underwater acoustic (UWA) communication. This paper proposes a robust blind demodulation algorithm to improve the performance and practicability of blind demodulation of MPSK signals under impulsive noise and UWA sparse multipath channels. The proposed algorithm adopts a $T$ /2-spaced blind equalizer based on quasi-affine projection, so it has strong adaptability to sparse UWA multipath channels. First, the received signal is preprocessed to suppress the impulsive interference, and the parameters are estimated. Then, timing synchronization is performed point by point based on sliding discrete Fourier transform (SDFT) to avoid equalizer performance degradation or even lock-loss caused by large clock frequency deviation and long-time error accumulation. Based on this, the over-sampling signal with two fixed sampling points per symbol is obtained. Meanwhile, following the idea of memory improved proportionate affine projection algorithm and normalized factor, the cost function of the blind equalizer is constructed by using the $l_{0}$ norm penalty on the tap coefficients. In this way, the updating formula of the tap coefficients is derived, and the blind equalization is then conducted on the signal after timing synchronization to eliminate or weaken the channel influence. Finally, the residual frequency offset and phase offset of the carrier are removed through the M-power transform and the second-order decision feedback digital phase-locked loop. Simulation experiments and practical signal demodulation results indicate that the proposed algorithm achieves higher BER performance and modulation parameter robustness for impulsive noise and UWA multipath channel.

A Modular Magnetic Induction Tomography System for Low-Conductivity Medium Imaging

Due to the noncontact, nonradiative, and low-cost features, magnetic induction tomography (MIT) is highly applicable in conductive medium imaging. To meet the demands of low-conductivity medium imaging in industrial and biomedical applications, a portable, flexible, and extensible MIT system with high-phase detection precision is proposed. The proposed system is designed with different modules. Therefore, it is flexible to extend and meet different applications. The sensor modules can be used as both the excitation and the detection. The detected signal is directly demodulated at the sensor end, which avoids the external interference that may be introduced into the transmission of the detected signal. A phase demodulation part is designed which can achieve 0.0068° phase detection precision. Furthermore, the designed system does not need to rely on other instruments to realize the signal generation and demodulation, which makes the MIT system independent and portable in the practical application. The sensing coils have high consistency, and the signal-to-noise ratio (SNR) of the demodulated signal is over 60 dB. To illustrate the performance of the system, the capability of reconstructing low-conductivity saline solutions is studied. The images of conductivity distribution are obtained using the Tikhonov regularization method. The minimum detectable volume of saline solution is 10 mL, with a spatial resolution of 16.6%. The minimum detectable conductivity is 0.1 S/m, and the maximum detectable depth is 40 mm. The reconstructed results indicate the feasibility of low-conductivity medium imaging in the industrial and biomedical applications by the proposed MIT system.

Performance Analysis of Vibration Sensors for Closed‐Loop Feedback Health Monitoring of Mechanical Equipment

In this paper, we analyze the performance of mechanical equipment through a closed‐loop feedback health monitoring vibration sensor, develop an OTDR optical signal reception and the processing module, and realize the reception, amplification, and filtering of the backscattered optical signal. In terms of vibration signal demodulation, the FPGA signal processing module was developed and debugged to realize the intermodulation with OTDR optical signal reception processing module and the preprocessing of the vibration data stream by taking advantage of the FPGA in parallel high‐speed data stream processing. The objective function is constructed based on the dynamic data of the first four vertical frequencies of the modal recognition and the static data of the constant‐load cable force of the inclined cable, and the third‐order response surface method is applied to fit the response surface function of each correction target. The errors between the corrected FEM calculated values and the measured results are within 5%. The results were compared with the results of static and dynamic corrections, and the results showed that the joint static and dynamic corrections using the third‐order response surface could obtain a finite element model that was more comprehensive and closer to the actual engineering response. A 180° feedback gain is set in the mass detection system to reduce the system’s equivalent mass and increase the system resonant frequency. An inverse lock‐in amplifier is used instead of a high‐frequency bandpass filter to spectrally migrate the useful frequencies and better filter out noise interference. A thin‐film microresonant pressure sensor, a cantilever beam microresonant gas sensor, and a microresonant biosensor were designed and developed using the micromachining process. A closed‐loop feedback method was used to design a low‐frequency detection system, a medium‐frequency detection system, and a high‐frequency feedback detection based on a phase‐locked loop system, completed open‐loop and closed‐loop detection experiments of the intrinsic frequency of the sensor, through‐pressure experiments of the pressure sensor, low and medium frequency gas‐sensitive experiments of the gas sensor, and high‐frequency detection experiments of the biosensor oxygen absorption/deoxygenation, and measured the mass of individual oxygen molecules.

Wireless Passive High‐Temperature Sensor Readout System for Rotational‐Speed Measurement

Rotational‐speed measurement in harsh environments is an important topic. However, the high rotation results in rapid frequency variations in the signals of a sensor and changes in physical properties under extreme thermal circumstances cause significant difficulties in reading signals. To address this problem, we adopt wireless passive measurement methods to design a special characteristic signal circuit module that achieves precise measurement of rotational speed at high temperatures. The sensor and the readout system include a variable frequency source, a readout antenna, and a radio frequency demodulation circuit. Herein, a demodulation detector of the signal conversion circuit is designed and used in the envelope detection module of the single sideband demodulation method. In addition, a conversion circuit test platform for high‐temperature environment sensor signal is developed. From the testing, the output signal demodulation of the sensor was observed under a maximum temperature of 700°C with error less than 0.12%. The new sensor and measurement method do not require physical leads and achieve wireless noncontact accurate measurement of rotational speed at high temperature.

Statistical models of fading signals propagated through the ionospheric satellite channels

Statistical models of satellite ionospheric radio channels are considered in the article, the influence of which causes fading of signals during their propagation (amplitude and phase variations of signals) due to random temporal and spatial fluctuations of the electron density of ionospheric irregularities. These models are based on the use of empirical laws regarding the densities of the amplitudes and phases of digital signals from the output of signal demodulators. A technique for estimating the probability of erroneous reception of digital signals with phase shift keying using the considered fading models is presented. Estimates of energy losses end errorperformance degradation during propagation along ionospheric satellite radio channels with scintillation index parameters typical for the P- and L-frequency bands, with respect to propagation in free space, have been made.