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.

A Nondestructive Instrumented Wheelset System for Contact Forces Measurements

A nondestructive continuous instrumented wheelset design is proposed based on strain gauges placing inside of the wheel web and wireless telemetry system. The signal feature analysis including frequency contents and high order harmonic ripples is also carried out. The strain gradient decoupling method for vertical and lateral force identification is proposed based on the strain distributions under respective loads. The method implements minimum crosstalk effects and insensitive to the varying contact points. The KMT telemetry system is adopted for wireless inductive powering and signal transferring. The drilling holes on the wheel and axles are avoidable to ensure the integrity and long-term using of the wheelset. Bridging and demodulating schemes for lateral and vertical force are designed respectively as they have dramatic differences at the dynamic signal features. High order harmonic ripple analysis and error estimation are gotten by independent waveforms. Based on the data form calibration test rig, it is indicated that the high order ripple amplitudes are below 10% of the demodulation amplitudes and fulfill designed requirements.

A novel demodulation method for transmission using nitrogen–vacancy-based solid-state quantum sensor

Diamond based quantum sensing is a fast-emerging field with both scientific and technological significance.The nitrogen–vacancy(NV)center,a crystal defect in diamond,has become a unique object for microwave sensing applications due to its excellent stability,long spin coherence time,and optical properties at ambient condition.In this work,we use diamond NV center as atomic receiver to demodulate on–off keying(OOK)signal transmitted in broad frequency range(2 GHz–14 GHz in a portable benchtop setup).We proposed a unique algorithm of voltage discrimination and demonstrated audio signal transceiving with fidelity above 99%.This diamond receiver is attached to the end of a tapered fiber,having all optic nature,which will find important applications in data transmission tasks under extreme conditions such as strong electromagnetic interference,high temperatures,and high corrosion.

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.

Autoencoder with Fitting Network for Terahertz Wireless Communications:A Deep Learning Approach

Terahertz wireless communication has been regarded as an emerging technology to satisfy the ever-increasing demand of ultra-high-speed wireless communications.However,affected by the imperfections of cheap and energy-efficient Terahertz devices,Terahertz signals suffer from serve hybrid distortions,including in-phase/quadrature imbalance,phase noise and nonlinearity,which degrade the demodulation performance significantly.To improve the robustness against these hybrid distortions,an improved autoencoder is proposed,which includes coding the transmitted symbols at the transmitter and decoding the corresponding signals at the receiver.Moreover,due to the lack of information of Terahertz channel during the training of the autoencoder,a fitting network is proposed to approximate the characteristics of Terahertz channel,which provides an approximation of the gradients of loss.Simulation results show that our proposed autoencoder with fitting network can recover the transmitted symbols under serious hybrid distortions,and improves the demodulation performance significantly.

Micro EEG/ECG signal's chopper-stabilization amplifying chip for novel drycontact electrode

Facing the body＇s EEG（electroencephalograph, 0.5–100 Hz, 5–100 μV） and ECG＇s（electrocardiogram,〈 100 Hz, 0.01–5 mV） micro signal detection requirement, this paper develops a pervasive application micro signal detection ASIC chip with the chopping modulation/demodulation method. The chopper-stabilization circuit with the RRL（ripple reduction loop） circuit is to suppress the ripple voltage, which locates at the single-stage amplifier＇s outputting terminal. The single-stage chopping core＇s noise has been suppressed too, and it is beneficial for suppressing noises of post-circuit. The chopping core circuit uses the PFB（positive feedback loop） to increase the inputting resistance, and the NFB（negative feedback loop） to stabilize the 40 dB intermediate frequency gain. The cascaded switch-capacitor sample/hold circuit has been used for deleting spike noises caused by non-ideal MOS switches, and the VGA/BPF（voltage gain amplifier/band pass filter） circuit is used to tune the chopper system＇s gain/bandwidth digitally. Assisted with the designed novel dry-electrode, the real test result of the chopping amplifying circuit gives some critical parameters： 8.1 μW/channel, 0.8 μVrms（@band-widthD100 Hz）, 4216–11220 times digitally tuning gain range, etc. The data capture system uses the NI CO＇s data capturing DAQmx interface,and the captured micro EEG/ECG＇s waves are real-time displayed with the PC-Labview. The proposed chopper system is a unified EEG/ECG signal＇s detection instrument and has a critical real application value.

Pressure Sensor Based on the Fiber-Optic Extrinsic Fabry-Perot Interferometer

Pressure sensors based on fiber-optic extrinsic Fabry-Perot interferometer(EFPI)have been extensively applied in various industrial and biomedical fields.In this paper,some key improvements of EFPI-based pressure sensors such as the controlled thermal bonding technique,diaphragm-based EFPI sensors,and white light interference technology have been reviewed.Recent progress on signal demodulation method and applications of EFPI-based pressure sensors has been introduced.Signal demodulation algorithms based on the cross correlation and mean square error(MSE)estimation have been proposed for retrieving the cavity length of EFPI.Absolute measurement with a resolution of 0.08 nm over large dynamic range has been carried out.For downhole monitoring,an EFPI and a fiber Bragg grating(FBG)cascade multiplexing fiber-optic sensor system has been developed,which can operate in temperature 300℃with a good long-term stability and extremely low temperature cross-sensitivity.Diaphragm-based EFPI pressure sensors have been successfully used for low pressure and acoustic wave detection.Experimental results show that a sensitivity of 31 mV/Pa in the frequency range of 100 Hz to 12.7 kHz for aeroacoustic wave detection has been obtained.