A photonic-assisted approach for instantaneous frequency measurement (IFM) based on frequency-power mapping is proposed and demonstrated. The principle of the technique is based on frequency-to-optical power mapping. In this scheme, in order to obtain the amplitude comparison function (ACF), two counter-propagation optical channels are constructed by four intensity modulators (IM) and two RF time delay lines. The RF signal-under-test (SUT) is divided evenly into four parts: two of them are time-delayed compared to other two. For the clockwise channel, the optical carrier is firstly intensity modulated by SUT in a manner of double sideband signal with suppression central carrier (CS-DSB). When traveling to the next IM, the CS-DSB optical light is further CS-DSB modulated by a time-delayed replica of SUT. After output from the two cascaded IMs, the RF modulated optical signal is launched into the last two IMs used in a reverse direction and filtered by an optical band-pass filter (OBPF) before received by an optical power meter. Here, it should note that the IM worked in a reverse direction has weak intensity modulation on the transmitted optical wave and can be regarded as a transmission waveguide. Similarly, the optical power traveled in anticlockwise channel can also be detected. For the anticlockwise direction, the optical carrier is CS-DSB modulated by another SUT and time-delayed SUT in sequence. Hence, the ACF can be established to convert the frequency into optical power ratio between two optical signals propagated in opposite directions. Experimental results show that RF signals varying from 0 to 14 GHz can be measured with an acceptable error.
Phase sensitive optical time domain reflectometer (φ-OTDR) can retrieve vibration waveforms based on linear relationship between phase change and external events. Yet, it is difficult to identify different events due to the complexity of working environment. How to accurately determine the type of vibration events and thus reduce false alarm rate is important in many practical engineering applications. The existing deep learning (DL) algorithm can directly extract the original data feature, without manual extraction. Hence, DL is usually used to classify and recognize multiple events in φ-OTDR. In this work, a dual input deep convolutional neural network (Di-DCNN) is applied to distinguish six kinds of actual vibration events (including walking, tapping, blowing and raining, vehicle passing, digging and background noise). The features of these two inputs are extracted, respectively, and fused together to identify six vibration events. For comparison, network models with other five inputs are employed for event recognition, including single input of 1D time-demain or 2D image of phase (amplitude) data, and dual input of 1D time-demain and 2D image of amplitude data. Here, the 2D image denotes the transformation of 1D data by Markov Transition Field (MTF). Experimental results show that the Di-DCNN with 1D time-domain phase waveforms and its 2D MTF image being the two inputs considerably improve the recognition accuracy. The average recognition rate of six kinds of vibration events is higher than 94%.
KEYWORDS: Signal detection, Ferroelectric materials, Signal processing, Pulse signals, Digital signal processing, Bragg cells, Repetition frequency, Reflectometry
Distributed vibration sensors by amplitude-demodulated phase sensitive optical time-domain reflectometers (φ-OTDR) have been widely used in many applications like safety monitoring of large-scale infrastructures and fence security. In order to locate vibrations, every two Rayleigh backscattering traces (RBTs) spaced apart by a certain interval (i.e., differential step k) are traditionally subtracted over a certain measurement time, and then all the subtractions are summed together and used to detect external vibration events. This method is denoted as amplitude-based accumulative differential method (ADM). Yet, the ADM has been demonstrated that the superimposing differential signal shows highly dependent on k which is closely related to vibration frequency (i.e., fs). Inappropriate k values would fail to locate the vibration. It is thus possible to envisage measuring fs by taking advantage of this strong dependence of the ADM on fs. In this manuscript, a simple method of estimating fs is proposed by measuring the relationship between the ADM signal and k. The validity of this method is demonstrated by our experiment, in which a vibration event induced by a sinusoidal-driven PZT is detected.
Reducing false alarming of vibration and shortening data processing time are one of the key problems of phase sensitive optical time-domain reflectometer (φ-OTDR). Generally speaking, there are two demodulation methods to locate vibrations: phase demodulation and amplitude demodulation. At present, an often-used method is phase-based crosscorrelation, which shows a comparatively reliable detection performance. Compared with phase cross-correlation, energy/power cross-correlation between different positions is simpler and has certain advantages in practical applications. In this paper, we use φ-OTDR to collects periodic vibration signals (power signals) and transient vibration signals (energy signals). Amplitude differentiation is firstly calculated along slow time axis for the Rayleigh backscattering trajectories. For periodic vibration, power spectrum is then obtained at each position, and cross-correlation coefficients between any two spectrums are computed. If the vibration is transient, average energy is calculated along fast time axis and average energy cross-correlation is performed between any two locations. With the cross-correlation values, we are able to determine whether there is vibration on the optical fiber. In the experiment, periodic vibration is simulated by a sine-driven PZT and transient vibration is mimicked by pencil-break. These results demonstrate that power (energy) cross-correlation coefficients work well to locate periodic (transient) vibrations.
Phase-sensitive optical time domain reflectometer (φ-OTDR) has been extensively investigated in fields of intrusion detection and structural health monitoring. It should be noted that phase noises would keep accumulating during pulse transmission. By subtracting an initial phase at the input point from demodulated phases at other positions, the noises related to the laser itself except random noises can be considerably reduced. In order to further decrease the impact of random noises on waveform retrieval of external vibrations, it is necessary to eliminate the accumulated noises before vibration position as much as possible. In this work, a sliding root mean square method (SRMS) is firstly applied to locate vibration events. By the SRMS, the demodulated phase at ~10 m before vibration point is regarded as the modified reference. Then, the vibration waveform can be retrieved after phase subtraction. For comparison purpose, both the input and modified references are employed to retrieve temporal vibration signals. Experimental results show that the SRMS shows good noise performance for vibration location. In terms of signal retrieval, the vibration waveform can be recovered with better noise suppression by the modified reference compared to the input one.
In the paper, we discuss the field enhancement effect in terahertz nano metasurfaces. The unit cell of the metasurfaces consists of a metallic split ring resonator (SRR) connecting with a wire. When the gap of SRR varies from micron-scale to nano-scale, the field enhancement factor in the gap achieves an order-of-magnitude increase in nano metasurfaces compared to that of micron metasurfaces. We then apply the nano metasurfaces to electric field sensing by assembling a layer of graphene film. In the simulation, the conductivity of graphene is tuned by varying the scattering time (relaxation time) corresponding to the varying external voltage. Compared to the structure without graphene film, the transmission of the graphene-based metasurfaces will be modulated by graphene conductivity. And the conduction effect of the graphene-based metasurfaces with nanogap under the same voltage is much better than that of the structure with micron gap, due to the extreme high field enhancement of the former. Based on this study, we can further optimize the nano metasurfaces for high sensitivity sensing, which can be applied in biological/chemical sensors or nonlinear devices.
Phase-sensitive optical time-domain reflectometry (φ-OTDR) is highly sensitive to strain changes of sensing fiber caused by external vibration, by which we are able to locate the vibration. In practice, interference fading will inevitably occur in backscattered Rayleigh traces of φ-OTDR due to the use of highly coherent light source, which increase the possibility of failure detection. In order to reduce the influence of interference fading on vibration detection, both frequency-division multiplexing (FDM) and rotated-vector-sum (RVS) over both time-and frequency-domain are employed in our method. Based on the method, we perform φ-OTDR experiment to locate vibrations. By extracting 3 frequency components of the beating signals (~200 MHz) and carrying out dual rotation, interference fading can be suppressed to a large extent, the vibration-induced phase changes are precisely recovered. One point should be noted is that we found that there is a certain correlation between each frequency component extracted from the beating signal, resulting in interference fading points cannot be completely removed.
Phase-sensitive optical time-domain reflectometer (φ-OTDR) is widely used for safety monitoring of large-scale civil objects, by which external vibrations along the sensing fiber can be detected. It has to be noticed that the category of vibration signals should be accurately distinguished for many real applications. At present, an extensively approach of signal recognition is deep convolutional neural network (DCNN). In the work, the one-dimensional DCNN (1D-DCNN) is applied to recognize different sound-induced vibrations based on their time-domain intensity signals detected by an amplitude-demodulated φ-OTDR system. It is turned out that the DCNN successfully shows the capability of recognizing walking, rock drill, explosion, hand hammer, car siren, and background noises with a high accuracy. Additionally, the 1D time-domain intensity vectors are rearranged into 2D matrices and the 2D-DCNN is accordingly employed to identify these vibration signals. The confusion matrices demonstrate that the 1D-DCNN has a higher average recognition accuracy to identify the concerned sounds with respect to the 2D-DCNN.
Four-wave mixing (FWM) in few-mode fibers (FMFs) has been extensively investigated to develop mode-related alloptical signal processing, such as wavelength conversion, parametric amplification and mode conversion. Compared to the FWM processes in single-mode fibers, intermodal FWM in FMFs shows more flexible phase-matching condition by tailoring the modal dispersion of each optical mode. Generally, there are two mainly different types of FWM processes, namely, Bragg scattering (BS) and phase conjugation (PC). In this paper, we focus our interest on the PC-FWM in both graded-index (GI) and step-index (SI) FMFs to probe mode conversion. In the PC-FWM, the energy transfers from pump modes to both signal and idler waves. From the point of phase matching, the modal dispersions of the two FMFs is firstly optimized by genetic algorithm (GA) to design optimal core radius and core-cladding refractive difference. We then investigate the effect of the small deviations of these two parameters from their optimal values upon the phase mismatch. Numerical results show that both SI and GI fibers are able to convert the LP01 mode to the LP02 mode with the phase matching condition of the SI fiber being more sensitive to the changes of fiber parameters. In addition, we analyze the dependence of mode conversion performance (bandwidth and efficiency) on fiber length and pump level. It is shown that the 3dB-bandwidth increases with the pump power in the PC-FWM, which can be attributed to the nonlinear phase shift induced by the high pump power compensate for the linear phase mismatch.
In this work, a five-band metamaterial absorber (MMA) for temperature sensing application in terahertz region is analyzed. The MMA is composed of three layers. The bottom layer is the metallic film, the middle dielectric layer is the indium antimonide (InSb) and the top layer is the metallic pattern, in which five resonance peaks are generated. With utilizing the dielectric thermo sensitive property of InSb, the resonant absorption is tunable by varying temperature. The electric current on the MMA is investigated to better understand the physical mechanism of the resonances, revealing the resonances attributed to the high-order magnetic resonances. The multi-band absorber is insensitive to the polarization angle, and be with ultrathin thickness of structure. This design of the MMA provides a new approach for electromagnetic stealth, sensing and imaging.
Generally, phase-sensitive optical time-domain reflectometer (φ-OTDR) adopts a single-channel sensing structure, which makes it vulnerable to random interferences and increases the probability of vibration misjudgment in practical applications. In this paper, a dual-channel φ-OTDR based on a two-mode fiber (LP01 mode and LP11 mode) is constructed, and a simple demodulation algorithm is designed accordingly to locate pencil-break vibrations. The purpose of using dual-channel scheme is that the probability of false detection, simultaneously happened in double channels at the same position and at the same time, would be greatly reduced. In signal processing, both the conventional amplitude differential accumulation algorithm (DAA) and the standard variance algorithm (SVA) are employed to process the Rayleigh scattering traces of LP01 and LP11 channels to detect the pencil-break. The results show that the DAA is highly dependent on the parameters of the algorithm and not suitable to be directly used in practical. Due to the strong randomness of Rayleigh scattering, it is found that the pencil break cannot be detected just by the SVA. Thus, a simple method of producing two decision signals is proposed for vibration detection by combining the DAA and SVA, in which the DAA signals of one channel are crossmultiplied with the SVA signals in another channel. The results show that this method shows reliable performance of locating the pencil-break.
In this study, we propose a dual-band wide-range tunable terahertz absorber based on graphene and bulk Dirac semimetal (BDS), which consists of a patterned BDS array, dielectric material, continuous graphene layer, and gold mirror. Simulation results show that the absorption at 3.97 and 7.94 THz achieve almost 100%. By changing the Fermi energy of graphene and BDS, the resonance frequency can be tuned between 3.97 and 9.28 THz. In addition, we found that when the background refractive index changes, the absorption is almost the same. This feature will broaden its applications. Finally, the influence of structural parameters and incident angles on device performance is discussed. The proposed absorber may have potential applications in photoelectric sensors and other optoelectronic devices.
In practical application, it is found that single-path phase-sensitive optical time-domain reflectometers (φ-OTDR) is susceptible to noise and random interference, which increases the probability of missing detection over external perturbations by conventional amplitude demodulation. In the work, a dual-channel system based on two fibers extracted from an armored four-core cable is investigated to enhance the robustness of the φ-OTDR. In signal demodulation, by combining the conventional differential accumulation algorithm (DAA) and standard deviation algorithm (SVA) a multipath information fusion algorithm (MIFA) is accordingly proposed to conclude whether the vibration signal is present. The MIFA-based dual-channel φ-OTDR is experimentally demonstrated on a highway of 9 km to position a running vehicle, indicating a considerable performance improvement of vibration identification compared to the DAA and SVA.
Optical frequency-domain reflectance (OFDR) has been widely used in vibration measurement due to its unique advantages over optical time-domain reflectometry (OTDR). It should be noted that, however, OFDR requires long measurement time and shows poor sensitivity when applied to measure vibration signal over long distance. In the work, an algorithm is presented to automatically detect and locate the vibration signals. Firstly, we perform cross-correlation analysis in a moving window between the beating signals without and with vibration, and find the maximum cross-correlation coefficients in all windows to reconstruct them into a cross-correlation curve. Secondly, an automatic decision threshold curve is designed to conclude whether there is any vibration over the sensing fiber. Lastly, the cross-correlation curve is compared with the threshold to locate the vibration. We experimentally test the algorithm in an OFDR system and locate a PZT vibration at 26.96 km, which demonstrates its validity in terms of detecting external disturbances over a relative long distance.
In this paper, we proposed a novel numerical algorithm for nth-order cascaded Raman fiber lasers (CRFLs) with the
combination of genetic algorithm (GA) and shooting method. Although shooting method possesses fast speed in solving
nonlinear two-point boundary-value ordinary differential equations, calculating process may diverge if it is directly
applied in the coupled equations of CRFLs when arbitrarily guessed initial values are out of the domain of convergence.
To overcome the problem, genetic algorithm which has rather strong searching ability in global space is firstly employed
to search for the initial value in convergent domain for each Stokes power; and then, the task of finding the more
accurate initial values is finished by shooting method instead of GA whose searching ability is weak in local region. As
an example, a sixth-order Ge-doped CRFL has been simulated by the novel algorithm. Calculated results show that the
new method can effectively and quickly solve the coupled equations of the CRFL without the problem of divergence.
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