Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication technology.Unlike other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental platform.Based on such a platform,we first construct a real signal dataset with ten modulation methods.Then,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature classification.We also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated signals.Finally,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other algorithms.It also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

An improved initial rotor position estimation method using high-frequency pulsating voltage injection for PMSM

High frequency pulsating voltage injection method is a good candidate for detecting the initial rotor position of permanent magnet synchronous motor.However,traditional methods require a large number of filters,which leads to the deterioration of system stability and dynamic performance.In order to solve these problems,a new signal demodulation method is proposed in this paper.The proposed new method can directly obtain the amplitude of high-frequency current,thus eliminating the use of filters,improving system stability and dynamic performance and saving the work of adjusting filter parameters.In addition,a new magnetic polarity detection method is proposed,which is robust to current measurement noise.Finally,experiments verify the effectiveness of the method.

Lensless complex amplitude demodulation based on deep learning in holographic data storage

To increase the storage capacity in holographic data storage(HDS),the information to be stored is encoded into a complex amplitude.Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in HDS.In this study,we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase.By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages,the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks(CNNs)to demodulate amplitude and phase respectively.The experimental system is simple,stable,and robust,and it only needs a single diffraction image to realize the direct demodulation of both amplitude and phase.To our investigation,this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations.

A Fault Feature Extraction Model in Synchronous Generator under Stator Inter-Turn Short Circuit Based on ACMD and DEO3S

This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.

Accurate Phase Detection for ZigBee Using Artificial Neural Network

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh networking.This standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 MHz.The frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at receiver.Therefore,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less error.Therefore,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor networks.Moreover,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ZigBee.Mean-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification purposes.This paper develops a neural network predictive detection method to enhance the performance of BPSK modulation.First,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)standard.Then,Gaussian noise was injected into the BPSK simulation model.To reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase signal.We evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score metrics.The result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)values.In addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

超小型气象卫星云图接收系统

超小型气象卫星云图接收系统于东海，孙敏松，齐宁华（东南大学毫米波国家重点实验室，南京２１００１８）卫星云图已广泛应用于天气预报、洪涝监测、海洋捕捞、森林防火、农业估产、植被覆盖观测等多种领域，但原有设备庞大复杂，造价高，操作过于专业，不利于普及使用，...

数字移动通信中的均衡技术

数字移动通信中的均衡技术林福华曹基荣邢松（东南大学移动通信国家重点实验室，南京２１００１８）模拟蜂窝移动通信系统因其话音采用模拟制调频，通信质量及系统容量受到较大限制．而数字蜂窝通信系统因其频谱利用率高、质量性能优良、可支持多种业务等优点，正日益显示...

Demodulation Based on Harmonic Wavelet and Its Application into Rotary Machinery Fault Diagnosis

The harmonic wavelet transform（HWT） and its fast realization based on fast Fourier transform（FFT） are introduced. Its ability to maintain the same amplitude-frequency feature is revealed. A new method to construct the time-frequency（TF） spectrum of HWT is proposed, which makes the HWT TF spectrum able to correctly reflect the time-frequency-amplitude distribution of the signal. A new way to calculate the HWT coefficients is proposed. By zero padding the data taken out, the non-decimated coefficients of HWT are obtained. Theoretical analysis shows that the modulus of the coefficients obtained by the new calculation way and living at a certain scale are the envelope of the component in the corresponding frequency band. By taking the cross section of the new TF spectrum, the demodulation for the component at a certain frequency band can be realized. A comparison with the Hilbert demodulation combined with band-pass filtering is done, which indicates for multi-components, the method proposed here is more suitable since it realizes ideal band-pass filtering and avoids pass band selecting. In the end, it is applied to bearing and gearbox fault diagnosis, and the results reflect that it can effectively extract the fault features in the signal.

Advances in phase-sensitive optical time-domain reflectometry

Phase-sensitive optical time-domain reflectometry(Φ-OTDR)has attracted numerous attention due to its superior performance in detecting the weak perturbations along the fiber.Relying on the ultra-sensitivity of light phase to the tiny deformation of optical fiber,Φ-OTDR has been treated as a powerful technique with a wide range of applications.It is fundamental to extract the phase of scattering light wave accurately and the methods include coherent detection,I/Q demodulation,3 by 3 coupler,dual probe pulses,and so on.Meanwhile,researchers have also made great efforts to improve the performance ofΦ-OTDR.The frequency response range,the measurement accuracy,the sensing distance,the spatial resolution,and the accuracy of event discrimination,all have been enhanced by various techniques.Furthermore,lots of researches on the applications in various kinds of fields have been carried out,where certain modifications and techniques have been developed.Therefore,Φ-OTDR remains as a booming technique in both researches and applications.

Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences

It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of computational accuracy and e ciency, or dependence on the tachometer. Hence, a new fault diagnosis strategy is proposed to remove gear interferences and spectrum smearing phenomenon without the tachometer and angular resampling technique. In this method, the instantaneous dominant meshing multiple(IDMM) is firstly extracted from the time-frequency representation(TFR) of the raw signal, which can be used to calculate the phase functions(PF) and the frequency points(FP). Next, the resonance frequency band excited by the faulty bearing is obtained by the band-pass filter. Furthermore, based on the PFs, the generalized demodulation transform(GDT) is applied to the envelope of the filtered signal. Finally, the target bearing is diagnosed by matching the peaks in the spectra of demodulated signals with the theoretical FPs. The analysis results of simulated and experimental signal demonstrate that the proposed method is an e ective and reliable tool for bearing fault diagnosis without the tachometer and the angular resampling.

Research and test of the adaptive quadrature demodulation technology for silicon micro-machined gyroscope

A program of adaptive quadrature demodulation is proposed to supply the gaps in the traditional analog detection technology of a silicon micro-machined gyroscope (SMG). This program is suitable for digital phase locked loop (DPLL) drive technology that proposed in other papers. In addition the program adopts an adaptive filtering algorithm, which selects the in-phase and quadrature components that are outputs of the DPLL of the SMG's drive mode as reference signals to update the amplitude of the in-phase and quadrature components of the input signal by iteratively. An objective of the program is to minimize the mean square error of the accurate amplitudes and the estimated amplitudes of SMG's detection mode. The simulation and test results prove the feasibility of the program that lays the foundation for the further improvement of the SMG's system performance and the implementation of the SMG system's self-calibration and self-demarcation in future.

Digital biomedical electrical impedance tomography based on FPGA

A digital biomedical electrical impedance tomography (EIT) system is developed with the aid of FPGA. The key elements of EIT system are described specifically in the paper. The functions are realized to generate excitation source, switch electrode channels, deal collected signals, demodulate measured voltages etc. The system is tested by a circular tank with 16 stainless electrodes attached around the boundary. The adjacent incentive adjacent measurement mode is adapted to collect boundary voltages of the interesting field. By testing, the system works with 36 dB signal-to-noise ratio (SNR) when 1 mA 100 KHz current is applied into a homogenous tank.

Research on Remote Monitoring and Fault Diagnosis Technology of Numerical Control Machine

Based on the internet technology,it has become possible to complete remote monitoring and fault diagnosis for the numerical control machine.In order to capture the micro-shock signal induced by the incipient fault on the rotating parts,the reso- nance demodulation technology is utilized in the system.As a subsystem of the remote monitoring system,the embedded data acquisi- tion instrument not only integrates the demodulation board but also complete the collection and preprocess of monitoring data from different machines.Furthermore,through connecting to the internet,the data can be transferred to the remote diagnosis center and data reading and writing function can be finished in the database.At the same time,the problem of the IP address floating in the dial-up of web server is solved by the dynamic DNS technology.Finally,the remote diagnosis software developed on the Lab VIEW platform can analyze the monitoring data from manufacturing field.The research results have indicated that the equipment status can be monitored by the system effectively.

Signal processing for all fiber optical current transducer

The work principle of all fiber optical current transducer (AFOCT) was introduced. By analyzing the characteristic of photo-detector's output, a measurement and signal processing scheme based on sine wave modulation and demodulation was put forward for eliminating the influence of light intensity change and modulation degree change. A digital signal processing system and a calibration scheme were also advanced. The experimental data show that the mean ratio error is 0.016 74% for direct current and 0.035% for alternating current, and the correlation coefficient of linearity is up to 0.999 982 4, meeting the precision requirement of 0.2 grade. Stability experiments and temperature drift experiments show the AFOCT has a better stable capability.

HYBRID WAVELET PACKET-TEAGER ENERGY OPERATOR ANALYSIS AND ITS APPLICATION FOR GEARBOX FAULT DIAGNOSIS

Based on wavelet packet decomposition （WPD） algorithm and Teager energy operator （TEO）, a novel gearbox fault detection and diagnosis method is proposed. Its process is expatiated after the principles of WPD and TEO modulation are introduced respectively. The preprocessed sigaaal is interpolated with the cubic spline function, then expanded over the selected basis wavelets. Grouping its wavelet packet components of the signal based on the minimum entropy criterion, the interpolated signal can be decomposed into its dominant components with nearly distinct fault frequency contents. To extract the demodulation information of each dominant component, TEO is used. The performance of the proposed method is assessed by means of several tests on vibration signals collected from the gearbox mounted on a heavy truck. It is proved that hybrid WPD-TEO method is effective and robust for detecting and diagnosing localized gearbox faults.

DESIGN OF DIGITAL IMPACTING FILTER IN CP-EBPSK WITH RANDOM-POLAR COMMUNICATION SYSTEM

To solve the difficulty of designing digital impacting filter in the receiver of random-polar modulated Extended Binary Phase Shift Keying with Continuous Phase (CP-EBPSK), a design method based on Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is proposed. Firstly, QPSO is introduced elaborately, and the basic flow of QPSO is also given. Then, the demodulation principle of digital impacting filter in the communication system of CP-EBPSK with random-polar is demonstrated, and QPSO is utilized to design the digital impacting filter, which also takes the effect of finite word length into consideration when implemented by hardware. Finally, the proposed method is simulated. Simulation results show that the digital impacting filter designed by new method can derive satisfied demodulation performance.

LDPC Coded DQPSK Modulation with Iterative Detection Method for Underwater Acoustic Communications

Aimed at the conventional serial communication system fails when the channel was frequency-selective,long delay and multi-paths effect,resulting in inter-symbol-interference( ISI),an iterative system for underwater acoustic communication( UWAC) was proposed,which employed iterative differential detection and spread spectrum technique. The proposed scheme adopts π /4-DQPSK soft demodulation to overcome the difficulty of phase ambiguity. For suppression of ISI in UWAC,joint demodulation and decoding iteration was used. The communication scheme has the characteristic of long communication range at low SNR,and the maximum range is up to 100 km. The theoretic analysis and simulation results show that the proposed scheme outperforms traditional serial one. When SNR is- 6 dB,after four joint iterations,compared with serial scheme,BER can decrease from 10- 2to 10- 5.

Using Walsh-m Composite Sequence and LDPC-QPSK Joint Iteration to Reduce ISI in Long-range Underwater Acoustic Systems

To reduce inter-symbol-interference (ISI) in underwater acoustic (UWA) communication systems, a method based on LDPC-QPSK joint iteration and Walsh-m composite sequence is proposed in this paper. The method is intended for use in long-range and low signal-to-noise ratio (SNR) UWA communications. At the transmitter, Walsh-m composite sequence is introduced to resist multipath effect. At the receiver, a soft-input soft-output (SISO) module is implemented in a joint iterative process between QPSK demodulator and LDPC decoder. This method is demonstrated in three types of UWA channel models: positive, negative and invariable sound velocity gradients channels. It is shown that through contrastive simulation experiments, this method is more efficient than conventional methods based on independent decoding and demodulation. After two rounds of joint iteration, the proposed method can obtain 2.5 dB over conventional method at BER of 10-5. Numerical results verify that the proposed method is a good candidate for long-range underwater acoustic communication systems.

Demodulation of acoustic telemetry binary phase shift keying signal based on high-order Duffing system

In order to grasp the downhole situation immediately, logging while drilling(LWD) technology is adopted. One of the LWD technologies, called acoustic telemetry, can be successfully applied to modern drilling. It is critical for acoustic telemetry technology that the signal is successfully transmitted to the ground. In this paper, binary phase shift keying(BPSK) is used to modulate carrier waves for the transmission and a new BPSK demodulation scheme based on Duffing chaos is investigated. Firstly, a high-order system is given in order to enhance the signal detection capability and it is realized through building a virtual circuit using an electronic workbench(EWB). Secondly, a new BPSK demodulation scheme is proposed based on the intermittent chaos phenomena of the new Duffing system. Finally, a system variable crossing zero-point equidistance method is proposed to obtain the phase difference between the system and the BPSK signal. Then it is determined that the digital signal transmitted from the bottom of the well is ‘0’ or ‘1’. The simulation results show that the demodulation method is feasible.

A Method of Coding and Decoding in Underwater Image Transmission

A new method of coding and decoding in the system of underwater image transmission is introduced, including the rapid digital frequency synthesizer in multiple frequency shift keying, image data generator, image grayscale decoder with intelligent fuzzy algorithm, image restoration and display on microcomputer.