Phase Information Of Signal Research Articles

How to Build the “Optical Inverse” of a Multimode Fibre

When light propagates through multimode optical fibres (MMFs), the spatial information it carries is scrambled. Wavefront shaping reverses this scrambling, typically one spatial mode at a time—enabling deployment of MMFs as ultrathin microendoscopes. Here, we go beyond sequential wavefront shaping by showing how to simultaneously unscramble all spatial modes emerging from an MMF in parallel. We introduce a passive multiple-scattering element—crafted through the process of inverse design—that is complementary to an MMF and undoes its optical effects. This “optical inverter” makes possible single-shot widefield imaging and super-resolution imaging through MMFs. Our design consists of a cascade of diffractive elements, and can be understood from the perspective of both multi-plane light conversion, and as a physically inspired diffractive neural network. This physical architecture outperforms state-of-the-art electronic neural networks tasked with unscrambling light, as it preserves the phase and coherence information of optical signals flowing through it. We show, in numerical simulations, how to efficiently sort and tune the relative phase of up to ~ 400 step-index fibre modes, reforming incoherent images of scenes at arbitrary distances from the fibre facet. Our optical inverter can dynamically adapt to see through experimentally realistic flexible fibres—made possible by moulding optical memory effects into its design. The scheme is based on current fabrication technology so could be realised in the near future. Beyond imaging, these concepts open up a range of new avenues for optical multiplexing, communications, and computation in the realms of classical and quantum photonics.

High Temporal Resolution Pyrometry and Velocimetry Based on Acoustic Frequency Division Multiplexing

Real-time and accurate flue gas temperature and velocity measurement is crucial for the safety, efficiency, and low emission of a power plant operation. This article describes a new technique for measuring flue gas temperature and velocity based on acoustic frequency division multiplexing (FDM) in a simultaneous way. The FDM method provides a technique that allows the simultaneous utilization of multiple acoustic signals that leads to a greatly reduced measurement cycle time required; therefore, the measurement system’s temporal resolution is significantly improved. With this new FDM method, the composite signal formed of two channels’ is decomposed by using a Butterworth filter for the extraction of the amplitude and phase information of signals passing each of the individual channels. From the derived frequency signals, the time of flight (TOF) on each acoustic path can be obtained. Demonstrated through both a theoretical analysis and experimental results, by applying the new technique, the measurement period can be reduced to 0.083 times (or say less than 10%) of that by using a traditional method. At the same time, the experimental results indicate that the new technique also improves the accuracy of the measurements. The sensitivity of the FDM method is 3.3, and 2.6 times higher than that of the HWA and traditional methods for velocity, and 1.6 and 1.4 times higher for temperature measurement. Moreover, its optimal robustness and precision are verified by several combined experiments with and without wind with background noise.

Real-time dynamic predictive cruise control for enhancing eco-driving of electric vehicles, considering traffic constraints and signal phase and timing (SPaT) information, using artificial-neural-network-based energy consumption model

This paper proposes a real-time dynamic predictive cruise control (PCC) system to minimize the energy consumption for electric vehicles (EVs) under integrated traffic situations with synthetic driving scenarios, considering both constraints from the preceding vehicle and the influence of traffic signal lights. The proposed PCC system is working based on the bi-level model predictive control (MPC) algorithm. The Signal Phase and Timing (SPaT)-oriented MPC calculates a desired acceleration command as the optimal control signal at each sampling step based on the forthcoming SPaT information with the purpose of passing the nearest signalized intersection during the green light interval without stop. The car-following-oriented MPC executes preceding vehicle tracking task through maintaining a safe inter-distance using a customized variable time headway (VTH) strategy. The instantaneous energy consumption for EV in different traffic scenarios was quantified by a data-driven model. The developed system was validated through comparison with IDM and human driver's maneuver in both suburban and urban areas road in the city of Fukuoka, Japan, during off-peak and peak hours, using the real traffic system and SPaT data. To further evaluate the performance of the proposed PCC system in high speed driving situation, another case study with transitions from highway to urban road was conducted. The simulative results showed that the proposed PCC system can realize the energy-saving rates by 8.5%–15.6%. And it was working well and robustly under high speed driving situation.

A suggestion-based fuel efficient control framework for connected and automated vehicles in heterogeneous urban traffic

This paper proposes a novel robust suggestion-based control framework based on model predictive control (MPC) to optimize the fuel efficiency of connected and automated vehicles (CAVs) in heterogeneous urban traffic (i.e., including both CAVs and human-driven vehicles (HDVs)). In this suggestion-based control framework, the CAVs are considered to provide suggestion commands to the HDVs to follow. These suggestion commands are the velocities the CAVs want the HDVs to follow to improve their own, as well as the group’s fuel economy. The host CAV thus needs to find its own fuel-efficient control solution, as well as the suggestion commands to be provided to its preceding HDV. We assume the CAVs can communicate with the HDVs via Vehicle to Vehicle (V2V) communication, and the Signal Phase and Timing (SPaT) information are available to the CAVs through Vehicle to Infrastructure (V2I) communication. The suggested velocity commands are held constant for a predefined time to allow the driver in the HDV to reach the suggested velocity. We also consider that the suggested velocity commands are non binding, i.e., a driver can choose not to follow the suggested velocity. For this control framework to function, we present a velocity prediction model based on experimental data that captures the response of a HDV to different suggested-commands, and a robust approach to ensure collision avoidance. The velocity prediction’s accuracy is also validated with the experimental data (on a table-top drive simulator), and the results are presented in this paper. Simulation studies show the proposed control strategy’s efficacy compared with existing baseline methods.

Overlapped speech detection using phase features.

Simultaneous speech of multiple speakers is known as overlapped speech, which causes problems for speech recognition and speaker diarization systems. The present work uses previously less utilized signal phase information in the task of overlapped speech detection. In this context, Instantaneous Frequency Cosine Coefficient (IFCC) and Modified Group Delay Cepstral Coefficient (MGDCC) features are explored. IFCC captures the time-varying phase characteristics, while MGDCC represents the frequency-varying information of the phase spectrum. A Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based classifier is used for the classification. The present work uses synthetically generated overlapped speech from the GRID corpus. The proposed method is benchmarked against three baseline approaches that use magnitude spectrum features. It is observed that the combination of IFCC and MGDCC features with CNN-LSTM classifier provides better performance than the baselines. The combination of phase features with magnitude-based MFCC feature provides the best performance, indicating the importance of complementary information. The present study also investigates the effect of segment duration, genders, and number of simultaneous speakers on the overlapped speech detection system. Finally, the proposed method is also evaluated on real overlapped data from the AMI corpus.

Phase Retrieval of Complex OFDM Signal by Solving Temporal Transport-of-Intensity Equation

We simulate the complex signal reconstruction by solving the temporal transport-of-intensity equation (TIE) in a direct detection orthogonal frequency division multiplexing (OFDM) system. At the receiver, by measuring two intensities before and after a dispersive element, we construct a TIE and retrieve the phase information of the complex OFDM signal by solving it. We investigate the influence of the carrier-signal power ratio (CSPR), the value of the dispersion, signal baud rate, sampling rate, laser linewidth, and frequency offset on the performance of phase retrieval (PR). In a 20 Gbaud 16 quadrature-amplitude-modulation (16QAM) OFDM system, the TIE-based direct-detection requires a 12 dB CSPR, which is comparable to Kramers–Kronig (KK)-based receiver and the single carrier TIE scheme. A proof-of-concept adaptive modulation scheme has also been applied to the TIE-based OFDM system.

Seismic radiation from wind turbines: observations and analytical modeling of frequency-dependent amplitude decays

Abstract. In this study, we determine spectral characteristics and amplitude decays of wind turbine induced seismic signals in the far field of a wind farm (WF) close to Uettingen, Germany. Average power spectral densities (PSDs) are calculated from 10 min time segments extracted from (up to) 6 months of continuous recordings at 19 seismic stations, positioned along an 8 km profile starting from the WF. We identify seven distinct PSD peaks in the frequency range between 1 and 8 Hz that can be observed to at least 4 km distance; lower-frequency peaks are detectable up to the end of the profile. At distances between 300 m and 4 km the PSD amplitude decay can be described by a power law with exponent b. The measured b values exhibit a linear frequency dependence and range from b=0.39 at 1.14 Hz to b=3.93 at 7.6 Hz. In a second step, the seismic radiation and amplitude decays are modeled using an analytical approach that approximates the surface wave field. Since we observe temporally varying phase differences between seismograms recorded directly at the base of the individual wind turbines (WTs), source signal phase information is included in the modeling approach. We show that phase differences between source signals have significant effects on the seismic radiation pattern and amplitude decays. Therefore, we develop a phase shift elimination method to handle the challenge of choosing representative source characteristics as an input for the modeling. To optimize the fitting of modeled and observed amplitude decay curves, we perform a grid search to constrain the two model parameters, i.e., the seismic shear wave velocity and quality factor. The comparison of modeled and observed amplitude decays for the seven prominent frequencies shows very good agreement and allows the constraint of shear velocities and quality factors for a two-layer model of the subsurface. The approach is generalized to predict amplitude decays and radiation patterns for WFs of arbitrary geometry.

Investigating the operational performance of connected and autonomous vehicles on signalized superstreets

ABSTRACT With the development of artificial intelligence and wireless communication technology, connected and autonomous vehicles (CAVs) have been treated as a promising strategy to increase road capacity and mitigate traffic congestion. Besides the technology of CAVs, innovative intersection design was also originally introduced as a countermeasure for dealing with traffic congestion at intersections. Though many studies have been conducted to explore the benefits of CAVs under various transportation scenarios, few have been implemented to explore the impact of CAVs on traffic flow at innovative intersections. Hence, to achieve a better understanding of the impacts of CAVs on existing transportation infrastructure, this study conducts a simulation-based study to investigate the operational performance of CAVs with available Signal Phase and Timing (SPaT) information in the environment of typical innovative intersection design, i.e. superstreets. The impact of CAVs with different market penetration rates on the operational performance of a superstreet is identified. The operational performance of the superstreet increases as the market penetration rate increases overall. Average speed and average traffic delay for vehicles in the superstreet system can be improved with the increase of market penetration rates.

Amplitude-Phase Information Measurement on Riemannian Manifold for Motor Imagery-Based BCI

Phase synchronization phenomena are directly connected with the underlying neural mechanisms of certain cognitive processes. However, only the amplitude information is utilized in most electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Few of the existing methods can simultaneously measure the amplitude and phase information required for classification. In this study, a novel common amplitude-phase measurement (CAPM) method is proposed. This method is capable of jointly measuring the phase and amplitude information of EEG signals on the Riemannian manifold. The proposed CAPM method comprises a two-step approach. First, a novel Riemannian graph embedding is proposed for dimensionality reduction while performing spatial-spectral filtering. The graph embedding is excellent in capturing the intrinsic features contained by the physiological signal. Second, to enhance robustness, a novel classifier is designed to incorporate the regularized linear regression in the computation of Riemannian distance. Experimental results on two BCI competition datasets demonstrate CAPM can yield high classification performance. The proposed CAPM method is a promising tool in analyzing EEG amplitude-phase characteristics and exhibits great potential in BCI applications.

Swept-sine integration method for complex amplitude extraction of swept-sine signal

Swept-sine vibration test plays a significant role in product validations. As its basic step, the accuracy and efficiency of complex amplitude (which includes the amplitude and phase information of swept-sine signal) extraction have an important impact on the performance of swept-sine vibration test. In order to improve the efficiency while ensuring its accuracy, a swept-sine integration method is proposed. The proposed swept-sine integration method, which is inspired by tracking filter method and tracking integration method, utilizes a weighted integral process to extract the complex amplitude. A numeral example is presented to compare the performance of the proposed method with several other methods for complex amplitude extraction which are also reviewed in this paper. By comparing the results of the numerical example, some light is shed on the strength of the method proposed. Afterwards, the swept-sine integration method is applied in a real test, further validating the appliance value of this method.

Real-time urban regional route planning model for connected vehicles based on V2X communication

Advancement in the novel technology of connected vehicles has presented opportunities and challenges for smart urban transport and land use. To improve the capacity of urban transport and optimize land-use planning, a novel real-time regional route planning model based on vehicle to X communication (V2X) is presented in this paper. First, considering the traffic signal timing and phase information collected by V2X, road section resistance values are calculated dynamically based on real-time vehicular driving data. Second, according to the topology structure of the current regional road network, all predicted routes are listed based on the Dijkstra algorithm. Third, the predicted travel time of each alternative route is calculated, while the predicted route with the least travel time is selected as the optimal route. Finally, we design the test scenario with different traffic saturation levels and collect 150 sets of data to analyze the feasibility of the proposed method. The numerical results have shown that the average travel times calculated by the proposed optimal route are 8.97 seconds, 12.54 seconds, and 21.85 seconds, which are much shorter than the results of traditional navigation routes. This proposed model can be further applied to the whole urban traffic network and contribute to a greater transport and land-use efficiency in the future.

Eco-Driving of Autonomous Vehicles for Nonstop Crossing of Signalized Intersections

This article is devoted to the development of an optimal speed profile for autonomous vehicles in order to cross a signalized intersection without stopping. The design objective is to achieve both a short travel time and low energy consumption by taking full advantage of the traffic light information based on vehicle-to-infrastructure communication. The eco-driving problem is formulated as an optimal control problem. For the case where the vehicles are in free-flow mode, we derive a real-time on-line analytical solution, distinguishing our method from most existing approaches based on numerical calculations. Under mild assumptions, the optimal eco-driving algorithm is readily extended to cases where the free-flow mode does not apply due to the presence of interfering traffic. Extensive simulations are provided to compare the performance of autonomous vehicles under the proposed speed profile and human-driven vehicles. The results show quantitatively the advantages of the proposed algorithm in terms of energy consumption and travel time. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the requirements for increased safety, increased efficiency in energy consumption, and lower congestion in signalized intersections. We take advantage of the traffic signal phase and timing information based on vehicle to infrastructure communication, and use the information to plan the vehicle’s trajectory to avoid the red traffic signal. An optimal speed profile is developed to achieve a trade-off between minimizing trip time and avoiding unnecessary braking and acceleration which corresponds to minimizing energy consumption. We then show how such a speed profile can be efficiently computed and control the motion of an autonomous vehicle (or serve as an intelligent speed advisory system for human-driven vehicles) leading to a safe, time-efficient, and energy-efficient trip. A video of a real autonomous vehicle test implementing our control algorithm can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.youtube.com/watch?v=x-ao4szeLYo</uri> .

6D phase-difference attributes for wide-azimuth seismic data interpretation

Phase information of seismic signals is sensitive to subsurface discontinuities. However, 1D phase attributes are not robust when dealing with noisy data. In addition, variations of seismic phase attributes with azimuth are seldom explored. To address these issues, we have developed 6D phase-difference attributes (PDAs) derived from azimuthal phase-frequency spectra. For the seismic volume of a certain azimuth and frequency, we first construct stacked phase traces at each common-depth point along a certain decomposed trending direction. Then, the 6D PDA is extracted by calculating the complex-valued covariance at a 6D phase space. The proposed method enables characterization of the subsurface discontinuities and indicates seismic anisotropy. Moreover, we provide one q-value attribute obtained by singular value decomposition to describe the variation intensity of PDA along different azimuths. Simulated and field wide-azimuth seismic data sets are used to illustrate the performance of the proposed 6D PDA and the derived q-value attribute. The results show that PDA at different frequencies can image various geologic features, including faults, fracture groups, and karst caves. Our field data example shows that PDA is able to discern the connectivity of karst caves using large-azimuth data. In addition, PDA along different azimuths and the q-value attribute provide a measurement of azimuthal variations, as well as the anisotropy. Our 6D PDA method can be used as a potential tool for wide-azimuth seismic data interpretation.

Power Measurement Using Stockwell Transform

The spread of harmonics in electric power systems causes deviations in voltage and current waveforms from the ideal sinusoidal form and, consequently, the traditional power calculation is unsuitable. One of the power theories defined for non-sinusoidal conditions is the IEEE Standard 1459-2010, based on Fourier Transform (FT). However, when applied to non-stationary signals, the FT leads to miscalculation due to spectral leakage, being unsuitable for non-stationary waveforms. In this paper, a new approach for the IEEE Standard 1459-2010 definitions is proposed using Stockwell Transform (ST). The ST provides a time-frequency resolution which retains magnitude and phase information of signals over time. The performance of the proposed method was evaluated in three scenarios including stationary waveforms, a non-linear circuit, and real measured data. The results were compared to the IEEE Standard 1459-2010 using the FT and the Stationary Discrete Wavelet Packet Transform (SDWPT) approaches. In the three scenarios studied, the proposed method presented a good estimation for each quantity defined in IEEE Standard 1459-2010 and provided more accurate results than FT-based and SDWPT-based methods.

An intelligent algorithm based on evolutionary strategy and clustering algorithm for Lamb wave defect location

Imaging algorithms for visualization of defects play a significant role in Lamb wave–based research of nondestructive testing and structural health monitoring. In classical algorithms, the position or distribution of defects is located by mapping the amplitude or phase information of signals from the time domain to every discrete spatial grid of the structure. It is time-consuming. In this study, the diversity, statistical, and fuzzy characteristics of the elliptic imaging algorithm are analyzed first; then, an intelligent defect location algorithm is proposed based on the evolutionary strategy and the K-means algorithm. The position of defects can be identified by observing the distribution of individuals. There are six parts in the proposed algorithm, including the data structure design, adaptive population screening, adaptive population reproduction, diversity maintenance mechanism, and cutoff criterion. Considering the statistical and fuzzy characteristics in the detection, several specific input parameters are defined in our algorithm, such as the distance-dependent screening threshold, path-dependent residual vector, and path-independent residual. To maintain the diversity of individuals in the analysis, we have made two adjustments to the evolutionary strategy: one is to optimize the population screening and reproduction steps with the K-means algorithm, and the other is to add a diversity maintenance method into the evolutionary strategy. The effectiveness of the proposed intelligent defect location algorithm is verified by numerical simulations and experiments. Numerical studies indicate that the proposed algorithm has a reliable performance in the detection of defects with different shapes and sizes. In the experimental research, we demonstrate that the efficiency of the proposed algorithm is about 200 times faster than the elliptic imaging algorithm. And the optimum parameter setting of the algorithm is investigated by analyzing the influence of parameter setting on the detection.

A Defects Localization Algorithm Based on the Lamb Wave of Plate Structure

Abstract Defect imaging algorithms play an important role in Lamb waves based researches of nondestructive testing (NDT) and structural health monitoring (SHM). In classical algorithms, the location or distribution of defects is visualized through mapping the amplitude or phase information of signals gotten by multiple inspection pairs from the time domain to every discrete spatial grid of plates. It is time-consuming in the detection of plates with large size and many transducers. Transforming the defect imaging problem into a scattering source search problem, an intelligent defect localization algorithm was proposed for NDT and SHM with the Lamb waves and sparse array. In the algorithm, the elliptic trajectory-dependent individuals of every inspection pair were extracted first, then the defect position was identified by analyzing the distribution of individuals these located at the intersection of multiply elliptic trajectories. Considering the fuzzy and diversity characteristics in the detection of defects, a fuzzy control parameter and an adaptive individual updating strategy based on the k-means algorithm were introduced to ensure the robustness of the algorithm. The effectiveness of the proposed algorithm was verified by numerical models and experiments. The influences of the fuzzy control parameter and the individual updating strategy on the performance of the algorithm were analyzed furthermore.

Photonic multiple frequency measurement with phase identification based on frequency conversion

In this paper, a very simple multiple frequency and phase identification structure is proposed and demonstrated. The main device is a dual-parallel Mach–Zehnder modulator (DPMZM). Adjusting the DC voltages to make this modulator works at the minimum transmission point (MITP) and satisfy the power cancellation condition. Then sweeping the LO signal frequency, the frequency and phase information of the received unknown microwave signals are extracted out by the created mapping between the total output power of the system and the LO frequency. The simulation results show that the frequency measurement range can be larger than 40 GHz, the accuracy is about 4 MHz, the resolution is better than 10 MHz. In addition, it also possesses the phase identification ability with the range from 0 to π, and the accuracy can up to 0.03 rad. This approach supports an effective way to implement the multiple-frequency measurement and phase identification. It has the potential to be integrated into a receiver.

Optical Signal Phase Retrieval With Low Complexity DC-Value Method

We propose a novel method to reconstruct the optical signal phase information using direct detection. The method is suitable for minimum phase signals and it enables low complexity, low latency, and low tone power operation. Moreover, the proposed method offers low optical complexity solution for the short-reach links compared with the concurrent phase retrieval techniques. We apply the method to M-ary signals with the transmitted power of as low as 3 dBm, and we are able to reach 70 km for 100 Gb/s quadrature phase shift keying (QPSK) system without optical amplification. Our method is based on the single sideband (SSB) and DC-Value property of the minimum phase signal. The SSB and DC-Value properties are iteratively imposed on the amplitude signal in the frequency domain to recover the full complex field from a directly detected optical signal. The normalized mean square error (NMSE) value between the available amplitude information and reconstructed minimum phase signal amplitude decreases after each iteration, providing global minimum convergence. A constant scaling factor is exploited to improve the convergence speed. The scaling factor provides 6 dB, 4.5 dB, and 2.5 dB error vector magnitude (EVM) gains with 4, 5, and 8 iterations, respectively.

Enhancing Bus Holding Control Using Cooperative ITS

We introduce a novel hybrid controller, which aims to maintain regularity on bus operations while reducing the number of stops at signalized intersections. The controller combines a rule-based holding criterion with a cooperative intelligent transportation systems based strategy, namely green light optimal dwell time adaptation (GLODTA), which exploits signal phase and timing information to modify dwell times at stops. In order to address the bi-objective criterion, holding control is integrated with transit signal priority (TSP) and an additional driving advisory system, which adjusts speed to reduce stopping at signals. The different controllers are evaluated using simulation of major bus line in Luxembourg City. The results show that buses are efficiently operated without necessarily providing additional priority via TSP, hence without negatively affecting the capacity of the private vehicles system. The new controller improves further the performance in terms of regularity and reduces the number of stops at traffic lights.

Modified high-resolution wavenumber analysis for detection of pulse wave velocity using coefficient of variation of arterial wall acceleration waveforms.

In high-resolution wavenumber analysis for detection of pulse wave velocity (PWV), phase information of analytic signals is used to estimate the wavenumber. However, the phase information could be affected by the adjacent signals in the temporal direction. Therefore, we propose a modified high-resolution wavenumber analysis technique using real acceleration waveforms of the arterial wall. In the modified wavenumber analysis, we propose a new evaluation function that corresponds to the inverse of the squared coefficient of variation. The accuracy of estimation of PWV was investigated by performing simulations, and the feasibility was also examined in an in vivo experiment. In the simulation experiments, the estimation accuracy using the proposed method was comparable to that using the previous method using phase information. However, when the pulse wave included the reflection components, the PWV estimated using the proposed method was more stable than that estimated using the previous method. Also, in the in vivo experiments, at opening of the aortic valve, the velocity estimated by the proposed method was almost equal to that estimated by the previous method (previous: 2.97 ± 1.2m/s, proposed: 4.82 ± 1.4m/s). Meanwhile, when the reflection components were present, the estimated PWV values yielded by the previous and proposed methods were - 1.13 and - 3.50 ± 0.9m/s, respectively. The PWVs at those two time points estimated by the previous method were quite different, and the PWV estimate was considered to be more affected by the reflected waves. The results of the simulations and in vivo experiments indicated that the modified high-resolution wavenumber analysis method was less affected by the reflected waves and more accurate in estimation of PWVs of both the forward and reflected waves.