Research of the Phase Sensitive Detection Property of Magnetic Sensor Based on Hall Effect

Aim to detect the characteristic weak magnetic field signal against the strong noises background. Methods In combination with a low-pass-filter, the correlation output of magne-* tic sensors between the magnetic field and reference current was utilized to provide a DC output voltage proportional to the applied magnetic induction, computer simulation was* done to investigate the correlation output of the Hall-effect sensors. Results Some analysis results concerning the noise property, harmonic supppression and the sensitivity were given. Conclsion The minimum detection signal of the equipment evolved from the mentioned cor-* relation theory can be 10-6 T. In addition to the DC output, such sensors can also measure the phase of the detected magnetic induction and has good harmonic suppression as well as* noise elimination.

Advances in Phase Detection Technique of Surface Plasmon Resonance Biosensor

Surface plasmon resonance (SPR) sensing is an optical method based on evanescent wave.SPR biosensor can detect interaction of label-free biomolecules in real-time.With further development,it can become a research instrument in proteomics.SPR biosensor can be divided intensity measurement and phase measurement,and the latter possesses higher sensitivity than the former one.This paper attempts to summarize the SPR phase detection theory,discuss the major developments,compare the merits and deficiencies of various methods,and look forward to future prospects.

Line spectrum detection algorithm based on the phase feature of target radiated noise

In order to improve the performance of line spectrum detection,according to the feature that the underwater target radiated noise containing stable line spectrum,the differences of the phase difference between line spectrum and background noise,a weighted line spectrum detection algorithm based on the phase variance is proposed in frequency domain.After phase difference alignment,the phase variance of line spectrum and the phase of background noise,respectively,are small and big in frequency domain,this method utilizes the weighted statistical algorithm to cumulate the frequency spectrum based on the phase variance,which can restrain the background noise disturbance,and enhance the signal to noise ratio（SNR）.The theory analysis and experimental results both verify that the proposed method can well enhance the energy of line spectrum,restrain the energy of background noise,and have better detection performance under lower SNR.

General seismic wave and phase detection software driven by deep learning

We developed an automatic seismic wave and phase detection software based on PhaseNet,an efficient and highly generalized deep learning neural network for P-and S-wave phase picking.The software organically combines multiple modules including application terminal interface,docker container,data visualization,SSH protocol data transmission and other auxiliary modules.Characterized by a series of technologically powerful functions,the software is highly convenient for all users.To obtain the P-and S-wave picks,one only needs to prepare threecomponent seismic data as input and customize some parameters in the interface.In particular,the software can automatically identify complex waveforms(i.e.continuous or truncated waves)and support multiple types of input data such as SAC,MSEED,NumPy array,etc.A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software.The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data,thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.

A Simple Model for On-Sensor Phase-Detection Autofocusing Algorithm

A simple model of the phase-detection autofocus device based on the partially masked sensor pixels is described. The cross-correlation function of the half-images registered by the masked pixels is proposed as a focus function. It is shown that—in such setting—focusing is equivalent to searching of the cross-correlation function maximum. Application of stochastic approximation algorithms to unimodal and non-unimodal focus functions is shortly discussed.

NON-AQUEOUS REVERSED PHASE HPLC WITH LOW-WAVELENGTH UV DETECTION FOR TRIGLYCERIDE ANALYSIS

Glycerides are first separated to classes of triglycerides(TGs), diglycerides(DGs) and monoglycerides(MGs) by normal phase HPLC on silica gel column. Individual triglyceride separation is then achieved by non-aqueous reversed phase(NARP) HPLC on C_(18) column with UV detection at 215nm.

Iterative Noncoherent Block Detection of Coded MPSK for Cooperative Relay Systems

Maximum likelihood(ML) noncoherent block detection techniques are investigated for block-coded MPSK modulation in cooperative decode-and-forward relay systems over slow fading channels.A decision-directed iterative Viterbi algorithm(IVA) is derived for a suboptimal ML noncoherent detection.Simulation results show that the IVA can approach the error performances of the exhaustive detection method but at a lower complexity.

A novel phase-sensitive scanning near-field optical microscope

Phase is one of the most important parameters of electromagnetic waves. It is the phase distribution that determines the propagation, reflection, refraction, focusing, divergence, and coupling features of light, and further affects the intensity distribution. In recent years, the designs of surface plasmon polariton （SPP） devices have mostly been based on the phase modulation and manipulation. Here we demonstrate a phase sensitive multi-parameter heterodyne scanning near-field opti- cal microscope （SNOM） with an aperture probe in the visible range, with which the near field optical phase and amplitude distributions can be simultaneously obtained. A novel architecture combining a spatial optical path and a fiber optical path is employed for stability and flexibility. Two kinds of typical nano-photonic devices are tested with the system. With the phase-sensitive SNOM, the phase and amplitude distributions of any nano-optical field and localized field generated with any SPP nano-structures and irregular phase modulation surfaces can be investigated. The phase distribution and the interference pattern will help us to gain a better understanding of how light interacts with SPP structures and how SPP waves generate, localize, convert, and propagate on an SPP surface. This will be a significant guidance on SPP nano-structure design and optimization.

Fingerprint Liveness Detection Based on Multi-Scale LPQ and PCA

Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.

Arrival picking method for microseismic phases based on curve fi tting

Microseismic phase detection and arrival picking are critical steps in the processing of hydraulic fracturing microseismic monitoring data.As the signal-to-noise ratios of P-and S-waves diff er because of the infl uence of focal mechanisms,geometry,and background noise,diffi culties are introduced in the eff ective discrimination of seismic phases and the accurate acquisition of arrivals in conventional processing methods.In this paper,we propose a method for identifying microseismic phase arrival time by comprehensively analyzing the variation of moveout curves and combining the intra-event waveform similarities with the energy ratio of multitrace signals.First,a curve-fi tting formula is constructed with perforation arrivals,and event detection is achieved by adopting an energy-weighted similarity coeffi cient that seeks plausible fi tting curves with a sliding time window in continuous microseismic recordings.Then,the P-and S-waves are separated by the fitting parameters.The known arrival time trend of the microseismic phase is employed to calculate residual time corrections.Finally,the accurate arrival results of the microseismic phases can be obtained by picking the arrivals of stacked traces.The reliability and eff ectiveness of the proposed method for microseismic phase detection and arrival picking were determined through tests using field data.Arrival results indicate that the proposed method can improve accuracy compared with the traditional energy ratio method.

TOPOLOGICAL STRUCTURE OF THE SINGULAR POINTS OF THE THIRD ORDER PHASE LOCKED LOOP EQUATIONS WITH THE CHARACTER OF DETECTED PHASE BEING g(φ)=(1+k)sinφ/(1+kcosφ)

In this paper, we study the topological structure of the singular points of the third order phase locked loop equations with the character of detected phase being g(?) =(1+k)sin?/1+kcos?.

A new measuring technique in detecting of high voltage three phase induction motor for single phasing

Open phase in three phase induction motors is a common fault that can occur as a result of a fuse blowing or a pro- tective device failing on one phase of the motor. This paper introduces a new method,which is based on the transient mea- surement and can distinguish the fault of one phase connecting ground. The method has been proved to be in correspondence with the simulation results by Matlab and LabVIEW in practice, The method has merits of simplicity, accuracy and ease of USe.

FRT Control Method Including Islanding Detection in PCS Model for Photovoltaic Generation

The experimental results by the simulated PV （photovoltaic） system for understanding the cause of disconnection when instantaneous voltage sag occurs are reported in this study. Moreover, it is understood that the PCS with the voltage phase jump detection method which is one of the main causes of the disconnection. We modeled it in the simulation. When fault occurs, even though the residual voltage is more than 20% and the voltage sag time is within 1.0 s, the PCS disconnected by the voltage phase jump detection was confirmed in the simulation results. Ifa large number of PV systems are interconnected in the power system, this study is verified through the simulation that the interaction caused by the disconnection which generated by the voltage phase jump detection. This study is proposed threshold limit value of islanding detection. The validity of proposed method was verified through the simulation in this study.

Time Synchronous Averaging Based on Cross-power Spectrum

Periodic components are of great significance for fault diagnosis and health monitoring of rotating machinery.Time synchronous averaging is an effective and convenient technique for extracting those components.However,the performance of time synchronous averaging is seriously limited when the separate segments are poorly synchronized.This paper proposes a new averaging method capable of extracting periodic components without external reference and an accurate period to solve this problem.With this approach,phase detection and compensation eliminate all segments'phase differences,which enables the segments to be well synchronized.The effectiveness of the proposed method is validated by numerical and experimental signals.

FPGA-based amplitude and phase detection in DLLRF

The new generation particle accelerator requires a highly stable radio frequency（RF） system. The stability of the RF system is realized by the Low Level RF（LLRF） subsystem which controls the amplitude and phase of the RF signal. The detection of the RF signal＇s amplitude and phase is fundamental to LLRF controls. High-speed ADC（Analog to Digital Converter） ,DAC（Digital to Analog Converter） and FPGA（Field Programmable Gate Array） play very important roles in digital LLRF control systems. This paper describes the implementation of real-time amplitude and phase detection based of the FPGA with an analysis of the main factors that affect the detection accuracy such as jitter,algorithm＇s defects and non-linearity of devices,which is helpful for future work on high precision detection and control.

High-precision time and frequency measurement method combining time-space conversion and different frequency phase detection

According to the time&space conversion relations and different frequency phase detection principle,an ultra-high precision time&frequency measurement method is proposed in this paper.The higher accuracy and stability of the speed of light and electromagnetic signals during the transmission in space or a specific medium enable the measurement of short time interval which uses the coincidence detection of signal’s transmission delay in length.The measurement precision better than 10 picoseconds can be easily obtained.The method develops the length vernier utilizing the stability of signal’s transmission delay,minimizes the fuzzy region of phase coincidence between the standard frequency signal and the measured signal,approaches the best phase coincidences and therefore improves the measurement precision which is higher than the precision provided by the traditional methods based on frequency processing.Besides,the method costs less than the traditional methods and can also solve the problem of the measurement of super-high frequency.Experimental results show the method can improve the measurement precision to 10 12/s in the time&frequency domain.

RNN-Based Demand Awareness in Smart Library Using CRFID

To provide more intelligence service in the smart library, we need to better perceive the reader’s preferences. In addition to perceiving online records based on readers’ search history and borrowing records, advanced information technologies give us more chance to perceive the behavior of readers in the actual reading process and further discover the need for reading. In this paper, we use CRFID and RNN deep learning network to recognize book motions in the reading process, so as to judge readers’ need degree for the book, which can provide a basis for library book purchases and readers personalized service. In order to improve the recognition accuracy, we use the RSS as well as acceleration magnitude gathered from CRFID as the input data for RNN, and design a new encoding scheme. We trained and tested the deep learning network using real-world data, recorded during actual reading in our lab environment which mimics a typical reading room, from the experimental results, we conclude that our approach is feasible to recognize different reading phase to perceiving the needs of the readers.

High-Sensitivity Sensor Based on Surface Plasmon Resonance Enhanced Lateral Optical Beam Displacements

We present a new optical sensor based on surface plasmon resonance （SPIt） enhanced lateral optical beam displacements. Compared with the traditional SPIt methods, the new method provides higher sensitivity to the sensor system. Theoretical simulations show that the refractive index （RI） detection sensitivity of the SPR sensor based on the displacement measurement has a strong dependence on the thickness of the metal film. When the optimal thickness of the metal film is selected, the RI resolutlon of the SPIt sensor is predicted to be 2.2 × 10^-7 refractive index units （RIU）. Furthermore, it is found that the incidence angle can be used as a parameter to adjust the operating range of the sensor to different refractive index ranges.

Photoacoustic viscoelasticity imaging dedicated to mechanical characterization of biological tissues

Since changes in mechanical properties of biological tissues are often closely related to pathology,the viscoelastic properties are important physical parameters for medical diagnosis.A photoacoustic(PA)phase-resolved method for noninvasively characterizing the biological tissue viscoelasticity has been proposed by Gao et al.[G.Gao,S.Yang,D.Xing,\Viscoelasticity imaging of biological tissues with phase-resolved photoacoustic measurement,"Opt.Lett.36,3341–3343(2011)].The mathematical relationship between the PA phase delay and the viscosity–elasticity ratio has been theoretically deduced.Moreover,systems of PA viscoelasticity(PAVE)imaging including PAVE microscopy and PAVE endoscopy were developed,and high-PA-phase contrast images re°ecting the tissue viscoelasticity information have been successfully achieved.The PAVE method has been developed in tumor detection,atherosclerosis characterization and related vascular endoscopy.We reviewed the development of the PAVE technique and its applications in biomedical¯elds.It is believed that PAVE imaging is of great potential in both biomedical applications and clinical studies.