Phase Information Of Signal Research Articles

Efficient Design of Chirp Spread Spectrum Modulation for Low-Power Wide-Area Networks

LoRa is an abbreviation for low power and long range and it refers to a communication technology developed for low-power wide-area networks (LPWANs). Based on the principle of chirp spread spectrum (CSS), LoRa technology is very attractive to provide low bit-rate wireless connections over an extended communication range and under very low power consumption. While the medium access control (MAC) layer of LoRa specifications is open for developers, the physical layer is not. In particular, LoRa modulation and demodulation techniques are patented by Semtech and have not been mathematically described in detail. This paper presents novel approaches to modulate and demodulate LoRa signals with very high implementation efficiency, great flexibility, and excellent performance. In particular, compared to the commercially available receiver made by Semtech, the proposed design is shown to yield a saving of transmitted power from 0.9 to 2.5 dB over the spreading factor (SF) range of 6–12. Moreover, this paper suggests a method to exploit the phase information of CSS signals to encode extra information bits, leading to throughput improvement over the conventional CSS system, for example, by 33%, 25%, 20%, and 17% for SFs of 6, 8, 10, and 12, respectively.

Examination and prediction of drivers’ reaction when provided with V2I communication-based intersection maneuver strategies

Connected vehicle technology provides promising opportunities to improve road safety, enhance traffic efficiency, and reduce fuel consumption and emissions. It has been suggested that if drivers comply with suggested recommendations, connected vehicle technology can introduce huge benefits. However, whether drivers will accept suggestions and what factors will influence their likelihood of accepting the suggestions in a connected environment have not been studied. In addition, few models have been developed to predict drivers’ reactions under such conditions. This paper aims to fill the research gap by examining and modeling drivers’ acceptance and behavior when receiving energy- and safety-related speed recommendations through vehicle-to-infrastructure communications. A mixed-subject-design experiment was conducted in a closed-loop test track, Mcity, with seven intersection maneuver scenarios. A generally high compliance rate to the recommended speed strategies was observed that during 72% of the events, drivers changed their intersection-approaching behavior to follow the recommendations. Mixed models were conducted to explore the impacting factors while Principal Component Analysis was used to classify subjective (i.e., self-reported) data into four categories. To predict drivers’ reactions when offered a speed suggestion, Random Forests were built with 13 independent variables, derived from four categories: vehicle kinematic features, device information, driver characteristics, and subjective data. Using this model, drivers’ reactions during each intersection maneuver could be predicted with a reasonably high accuracy about 87.4 m away from the intersection, where the vehicle started to receive signal phase and timing information. Findings in this study can contribute to the optimization of energy-saving algorithms and the improvement of driving safety by using connected vehicle technologies.

The study of time domain denoising for the time-frequency electromagnetic method prospecting data

The time-frequency electromagnetic prospecting system has both the characteristics of frequency sounding and transient electromagnetic sounding system, can obtain apparent resistivity both in frequency domain and time domain, therefore, thus, more accurate and credible geoelectric information can be obtained. After analyzing, there are mainly three typical types of noise in time-frequency electromagnetic observed data, they are 50Hz power frequency and its harmonics interference noise, high-frequency random impulse noise and low frequency interference noise. In view of characteristics of these three types of noise and the time-frequency electromagnetic prospecting signal, we adopted frequency domain band stop filtering method remove the 50 Hz power frequency and its harmonics interference noise, proposed a segmentation and extension median filtering method and a fitting fixed extreme EMD method to remove the high frequency random impulse noise and the low frequency interference noise separately, and proposed a median filtering window size test selection method based on variance and skewness coefficient of sample signal. Moreover, we designed a denoising processing fl ow as handle 50Hz power frequency and its harmonics interference noise at fist step, handle high frequency random impulse noise at second step, handle low frequency interference noise at last step, it can give full advantages of the denoising handling methods. By theoretical analysis and verification by practical observation data experiments, the methods what we adopted and proposed and the processing flow we designed can effectively remove the three types of interference noises effectively and avoid method error, can preserve phase and amplitude information of effective signal accurately, finally, high quality and satisfactory time domain signal waveform can obtained as a result, and lays a good foundation for extracting accurate transient electromagnetic attenuation curve in time domain for the Time-Frequency electromagnetic prospecting data.

Consensus and optimal speed advisory model for mixed traffic at an isolated signalized intersection

Due to the accessibility for connected vehicle (CV) to obtain the V2X covered traffic conditions and signal phase and timing (SPaT) information, the CV becomes able to follow its predecessors and adjust its speed in a different way with comparison to human driving vehicle (HDV). Aiming at promoting energy economy and keeping safety, a consensus and optimal speed advisory model(SAM) is proposed for CV platoon at an isolated signalized intersection in the presence of mixed traffic scenario. To ensure that CV followers track their leader’s speed and time headway spacing with uniform performance being achieved, a consensus based CV platoon model is proposed with V2X network topology being taken into account. To determine average desired speed of the leader and prevent itself from red light running, two kinds of intersection passing decisions considering amber light duration time are presented for CV and HDV, respectively. To plan a speed trajectory for CV leader, a safety and energy conservation oriented SAM based on trigonometric-curve and logistic-curve is employed to guide CV leader approaching and departing from signalized intersection in a smooth driving behavior at the moment of queue discharging entirely at green light. Numerical simulations show that taking speed profile governed by consensus and optimal SAM as CV platoon’s recommended speed can improve safety and fuel economy performance of the mixed traffic on signal controlled highways.

Phase-sensitive imaging of microwave currents in superconductive circuits

We propose an improved measuring technique for microwave currents in superconductive circuits, demonstrate it experimentally, and validate the experimental data with numerical simulations. Contemporary superconductive electronics widely uses planar circuits with micrometer-scale elements for a variety of applications. With the rise of complexity of a circuit and increase in the number of its components, a simple impedance measurement is often not efficient for the diagnosis of problems, nor for clarifying the physics underlying the circuit response. The established Laser Scanning Microscopy (LSM) technique generates micrometer-scale images of the amplitude of the microwave currents in a planar superconductive circuit, but not the phase of the oscillating currents. Here, we present a more powerful type of LSM imaging containing signal phase information. We employ a fast-optical modulator in order to synchronize the phase of the laser intensity oscillation with the phase of the probing microwave signal. The loss induced in a laser illuminated area strongly depends on the phase difference between the RF probing signal and the laser beam modulation. As a proof of concept, we present the phase-sensitive LSM measurements of the inner mode currents in a planar superconductive resonator and confirm the data with numerical simulation of the resonator's electrodynamics.

Signal Timing Parameters Estimation for Intersections using Floating Car Data

Signal phase and timing (SPaT) information is critical for many in-vehicle applications. However, it is challenging and time-consuming to acquire city-wide SPaT information from local traffic management agencies directly. A significant limitation of existing SPaT information estimation methods in the literature is that they can only be applied to a specified time-of-day (TOD) period. In the real-world, however, different TOD timing plans are used to accommodate fluctuations in traffic demands. In this paper, we propose a novel method for traffic light parameter estimation based on floating car data, which features recognizing TOD breakpoints and can thus be applied to intersections with multi-TOD timing plans. Also, good estimation of TOD breakpoints leads to more data availability for estimation of other parameters. The proposed method is tested with real-world data collected from the DiDi on-line hailing platform in China. The filed test results show promising accuracy. The absolute error of green duration is within 3 s in daytime and the estimation error of TOD breakpoints is within 15 min.

Safety Assessment of Ecodriving Vehicles on Following Traffic

Ecodriving aims to achieve the best fuel efficiency by guiding vehicles to travel at a planned speed trajectory, which is developed by making use of topography or signal phasing and timing (SPaT) information. Previous work has mainly focused on algorithm development or the assessment of ecodriving efficiency. However, there has been little research regarding the safety issues of ecodriving. This study addresses safety concerns for normal driving vehicles (FNV) following ecodriving vehicles (EV). Time to collision (TTC) was used as the dependent variable to quantify the critical situation. The effects on TTC of three network-related factors: initial signal status, ambient vehicles, and speed limit, are considered and investigated. Six testing scenarios (experimental group) where FNV (subjects) followed an EV were generated with JMP (statistical software), and six baseline scenarios with conventional cars only were used as the control group. The driving simulation study was designed and implemented with a drive safety DNS-250 simulator. Twenty-nine volunteer drivers participated in the driving simulation study. The results show that the initial signal status has a significant influence on TTC of FNV following EV in the initial signal part, deceleration part, and overall optimization horizon. In addition, the speed limits have a significant impact the TTC of FNV following EV in the acceleration part. Overall, following an EV is safer than following a non-EV, with the exception of FNV in the initial signal part and deceleration part when the initial signal is green.

Prediction-Based Eco-Approach and Departure at Signalized Intersections With Speed Forecasting on Preceding Vehicles

Using connected vehicle technology, a number of eco-approach and departure (EAD) strategies have been designed to guide vehicles through signalized intersections in an eco-friendly way. Most of the existing EAD applications have been developed and tested in traffic-free scenarios or in a fully connected environment, where the presence and behavior of all surrounding vehicles are detectable. In this paper, we describe a prediction-based EAD strategy that can be applied toward more realistic scenarios, where the surrounding vehicles can be either a connected or non-connected. Unlike highway scenarios, predicting speed trajectories along signalized corridors is much more challenging due to disturbances from signals, traffic queues, and pedestrians. Based on vehicle activity data available via inter-vehicle communication or onboard sensing (e.g., by radar), we evaluate three state-of-the-art nonlinear regression models to perform short-term speed forecasting of the preceding vehicle. It turns out radial basis function neural network outperformed both Gaussian process and multi-layer perceptron network in terms of prediction accuracy and computational efficiency. Using signal phase and timing information and the predicted state of the preceding vehicle, our prediction-based EAD algorithm achieved better fuel economy and emissions reduction in urban traffic and queues at intersections. Results from the numerical simulation using the next generation simulation data set show that the proposed prediction-based EAD system achieve 4.0% energy savings and 4.0% – 41.7% pollutant emission reduction compared with a conventional car following strategy. Prediction-based EAD saves 1.9% energy and reduces criteria pollutant emissions by 1.9% – 33.4% compared with an existing EAD algorithm without prediction in urban traffic.

Multiobjective predictive cruise control for connected vehicle systems on urban conditions with InPA‐SQP

SummaryThe connected vehicle (CV) system is one of the most effective core technologies in intelligent transportation systems. In order to solve the optimal velocity prediction problem for a CV system on urban roads, a multiobjective predictive cruise controller (MOPCC) for vehicles in the CV system is proposed to coordinate multiple performances including safety, tracking ability, ride comfort, and fuel economy. Firstly, with the ad hoc wireless communication technology, the signal phase and timing information is obtained to calculate the feasible velocity range for improving mobility. Then, the optimal target velocity of vehicles is computed by minimizing the fuel economic polynomial models of the vehicle system. Secondly, in order to systematically cope with those multiple performances, the Utopia point method is applied to change the multiobjective optimization problem into Utopia tracking problem. Furthermore, the MOPCC problem is formulated and solved by a fast numerical algorithm, ie, integrated perturbation analysis and sequential quadratic programming. Finally, simulations are presented to demonstrate the effectiveness of the proposed method in terms of improved the multiple performances.

PS Selection Method for and Application to GB‐SAR Monitoring of Dam Deformation

Ground‐based synthetic aperture radar (GB‐SAR) is a relatively new technique that can be used to monitor the deformation of large‐volume targets, such as dams, slopes, and bridges. In this study, the permanent scatterer (PS) technique is used to address the issues encountered in the continuous monitoring of the external deformation of an arch‐gravity dam in a hydraulic and hydropower engineering structure in Hubei, China; the technique includes large image data sizes, high accuracy requirements, a susceptibility of the monitoring data to atmospheric disturbances, complex phase unwrapping, and pronounced decoherence. Through an in‐depth investigation of PS extraction methods, a combined PS selection (CPSS) method is proposed by fully taking advantage of the signal amplitude and phase information in the monitored scene. The principle and implementation of CPSS are primarily studied. In addition, preliminarily selected PS candidates are directly used to construct and update a triangular irregular network (TIN) to maintain the stability of the subsequent Delaunay TIN. To implement this method, a differential‐phase standard‐deviation threshold method is proposed to extract PSs that are highly spatially coherent and consistent. Finally, the proposed CPSS was applied to the safety monitoring of the dam. The monitoring results are compared with conventional inverted plumb line monitoring results, and the proposed CPSS is found to be effective and reliable.

Cluster-Wise Cooperative Eco-Approach and Departure Application for Connected and Automated Vehicles Along Signalized Arterials

In recent years, various versions of the Eco-Approach and Departure (EAD) application have been developed and evaluated. This application utilizes Signal Phase and Timing (SPaT) information to allow connected and automated vehicles (CAVs) to approach and depart from a signalized intersection in an energy-efficient manner. To date, most existing work have studied the EAD application from an ego-vehicle perspective (Ego-EAD) using Vehicle-to-Infrastructure (V2I) communication, while relatively limited research takes into account cooperation among vehicles at intersections via Vehicle-to-Vehicle (V2V) communication. In this research, we developed a cluster-wise cooperative EAD (Coop-EAD) application for CAVs to further reduce energy consumption compared to existing Ego-EAD applications. Instead of considering CAVs traveling through signalized intersections one at a time, our approach strategically coordinates CAVs' maneuvers to form clusters using various operating modes: initial vehicle clustering, intra-cluster sequence optimization, and cluster formation control. The novel Coop-EAD algorithm is applied to the cluster leader, and CAVs in the cluster follow the cluster leader to conduct EAD maneuvers. A preliminary simulation study with a given scenario shows that, compared to an Ego-EAD application, the proposed Coop-EAD application achieves 11% reduction on energy consumption, up to 19.9% reduction on pollutant emissions, and 50% increase on traffic throughput, respectively.

Hierarchical energy management control strategies for connected hybrid electric vehicles considering efficiencies feedback

This paper presents a closed-loop hierarchical energy management control strategy for a group of connected hybrid electric vehicles (HEVs) considering the feedback of propulsion and recuperation efficiencies. The higher level controller utilizes model predictive control (MPC) incorporating signal phase and timing (SPAT) information for the prediction of the optimal velocity profile over a finite time horizon, with periodically updated efficiencies from the lower level controller. The adaptive equivalent consumption minimization strategy (A-ECMS)-based lower level controller then makes use of the optimal velocity profile for the energy management control of the HEVs. Meanwhile, the lower level controller calculates and feeds back the average propulsion and recuperation efficiencies of the current time window to the higher level controller, which updates the efficiencies with the average values and corrects the fuel consumption model for the calculation of the velocity profile of the next time window. Simulation results validate the control effectiveness of the proposed control strategy.

Closed‐loop hierarchical control strategies for connected and autonomous hybrid electric vehicles with random errors

This study presents a closed-loop hierarchical control strategy for a group of connected and autonomous hybrid electric vehicles (HEVs) with the purpose of optimising fuel economy while guaranteeing traffic mobility and vehicle safety. In the hierarchical control architecture, a decentralised stochastic model predictive control (SMPC)-based higher level controller incorporating signal phase and timing information is formulated to optimise the velocity profile of each vehicle, and an adaptive equivalent consumption minimisation strategy based lower level controller is employed for the energy management control of the HEVs. Creatively, random errors of the control variable are considered in the SMPC framework. The errors are discretised and modelled as a Markov process and the state transition matrix of the errors is generated randomly to capture the error transition dynamics. To solve the SMPC problem more efficiently, a scenario-based SMPC is employed. Moreover, the propulsion and recuperation efficiencies of the lower level controller are calculated at each time step with measurable variables, and fed back to the higher level controller for velocity optimisation in next time step. Simulation results validate the control effectiveness and advantages of the proposed control architecture.

All-Optical Half Adder Based on a 2x2 Multimode Interference Coupler

In this paper, an all-optical logic gate based on 2x2 (MMI) multimode interference coupler is theoretically designed and simulated using three-dimensional beam propagation method (3D-BPM) and effective index method (EIM) to analyze and evaluate the performance of the device. The proposed device is used to convert phase information of input optical signals to amplitude at the output ports of MMI. With this mechanism, the device can operates as a logical half adder. Simulation results show that the optical half adder archives with insertion loss and extinction ratio from ON to OFF logic–level below 0.7 dB and over 40 dB, respectively. Furthermore, wide bandwidth of 100nm is also an advantage of this devices. Therefore, it can be applied to all optical signal processing in next generation optical networks as well as in photonics integrated circuits.

A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection

The advancements in communication and sensing technologies can be exploited to assist the drivers in making better decisions. In this paper, we consider the design of a real-time cooperative eco-driving strategy for a group of vehicles with mixed automated vehicles (AVs) and human-driven vehicles (HVs). The lead vehicles in the platoon can receive the signal phase and timing information via vehicle-to-infrastructure (V2I) communication and the traffic states of both the preceding vehicle and current platoon via vehicle-to-vehicle (V2V) communication. We propose a receding horizon model predictive control (MPC) method to minimise the fuel consumption for platoons and drive the platoons to pass the intersection on a green phase. The method is then extended to dynamic platoon splitting and merging rules for cooperation among AVs and HVs in response to the high variation in urban traffic flow. Extensive simulation tests are also conducted to demonstrate the performance of the model in various conditions in the mixed traffic flow and different penetration rates of AVs. Our model shows that the cooperation between AVs and HVs can further smooth out the trajectory of the latter and reduce the fuel consumption of the entire traffic system, especially for the low penetration of AVs. It is noteworthy that the proposed model does not compromise the traffic efficiency and the driving comfort while achieving the eco-driving strategy.

Eco-Approach and Departure (EAD) Application for Actuated Signals in Real-World Traffic

The connected vehicle eco-approach and departure (EAD) application for signalized intersections has been widely studied and is deemed to be effective in terms of reducing energy consumption and both greenhouse gas and other criteria pollutant emissions. Prior studies have shown that tangible environmental benefits can be gained by communicating the driver with the signal phase and timing (SPaT) information of the upcoming traffic signals with fixed time control to the driver. However, similar applications to actuated signals pose a significant challenge due to their randomness to some extent caused by vehicle actuation. Based on the framework previously developed by the authors, a real-world testing has been conducted along the El Camino Real corridor in Palo Alto, CA, USA, to evaluate the system performance in terms of energy savings and emissions reduction. Strategies and algorithms are designed to be adaptive to the dynamic uncertainty for actuated signal and real-world traffic. It turns out that the proposed EAD system can save 6% energy for the trip segments when activated within DSRC ranges and 2% energy for all trips. The proposed system can also reduce 7% of CO, 18% of HC, and 13% of NOx for all trips. Those results are compatible with the simulation results and validate the previously developed EAD framework.

The complete optical oscilloscope

Observing ultrafast transient dynamics in optics is a challenging task. Two teams in Europe have now independently developed ‘optical oscilloscopes’ that can capture both amplitude and phase information of ultrafast optical signals. Their schemes yield new insights into the nonlinear physics that takes place inside optical fibres.

Modified Variable Step Size FLOM-CMA Based on Lorentzian Function under Impulse Noise

Aiming at the problem that the traditional equalization algorithm under impulse noise is difficult to suppress impulse noise and achieve equalization, a new modified variable step size FLOM-CMA algorithm based on Lorentzian function is proposed in this paper. The proposed algorithm has modified the cost function to made full use of the phase information of signal to correct the phase rotation. Also, by adjusting the FLOM of cost function, that impulse noise is restrained effectively. Furthermore, the Lorentzian function is use to update the step size to make sure it is is appropriate for each equalization point. Simulation experiments show that, compared with FLOM-CMA and VS-FLOM-CMA, the proposed algorithm that the proposed algorithm achieves higher convergence accuracy with similar convergence speed. And under different impulse noise conditions, the robustness of the algorithm is better.

Fuel efficient model predictive control strategies for a group of connected vehicles incorporating vertical vibration

This paper presents a decentralized fuel efficient model predictive control (MPC) strategy for a group of connected vehicles incorporating vertical vibration. To capture the vehicle vibration dynamics, the dynamics of the suspension system is integrated with the longitudinal dynamics of the vehicle. Furthermore, a MPC framework with finite time horizon is formulated to calculate the optimal velocity profile that compromises fuel economy, mobility and ride comfort for every individual vehicle with the safety and physical constraints considered. In the MPC framework, the target velocity is calculated using signal phase and timing (SPAT) information to reduce the number of stoppage at red lights, and the vertical acceleration is calculated parallel to the calculation of the fuel consumption. The MPC optimal problem is solved with fast-MPC approach which enhances the computational efficiency via exploiting the structure of the control system and approximate methods. Simulation studies are conducted over different SPATs and connectivity penetration rates and the results validate the advantages of the proposed control architecture.

Audio signal separation through complex tensor factorization: Utilizing modulation frequency and phase information

I propose a complex-valued tensor factorization algorithm for audio-source separation to exploit not only amplitude but phase information of audio signals in the modulation frequency (MF) domain. The proposed algorithm is extended from complex non-negative matrix factorization, which is capable of decomposing an arbitrary complex matrix such as the complex spectrum in the acoustic frequency domain. The proposed method enables us to factorize an arbitrary complex tensor of order 3. The detailed performance of the proposed algorithm for single-channel source separation is investigated through numerical experiments. I examine the quantitative contributions of the MF domain and phase information examined by additionally presenting three tensor factorization algorithms and using five objective indices for source separation.