Delay Estimation Research Articles

A Collaborative Transit Signal Priority Method Considering Gaming among Traffic Participants

How to realize the game equilibrium between bus and nontransit vehicle is a hot topic in the field of transit signal priority (TSP). To this end, a collaborative transit signal priority (Co-TSP) method is proposed. The core of Co-TSP is a two-objective optimization problem which takes the expected delays of buses and the average delays of nontransit vehicles as the objectives. Different from previous studies, Co-TSP uses game theory to realize collaborative optimization, instead of transforming the problem into a single objective optimization problem by weighting. A finite state machine-based algorithm is developed to estimate the average delays of nontransit vehicles. The stochasticity of bus arrival time is also considered in the estimation of bus delays to improve the robustness. Candidate timing plans obtained by the nondominated sorting genetic algorithm (NSGA) are divided into three priority levels based on the delays of buses. The final timing plans can be picked intuitively from the candidates by rules representing expert knowledge and demands to control the priority level. Co-TSP guarantees theoretically by preliminary screening that the expected delays of bus after optimization must be no higher than that before optimization. Simulation experiments are conducted in Shanghai, China, to verify the performance. Results show that Co-TSP reduces the delays of buses by 27.7%∼41.0% and still performs well under low and high congestion levels, while the conventional TSP (CTSP) fails in some cases. Priority control proves to be effective at last. The research provides a new idea for the benefit allocation among participants at intersections.

Impact of ambiguity resolution on phase center offsets and hardware delay estimation for BDS-3 inter-satellite links

The Chinese BeiDou navigation satellite system (BDS) has already completed its three phases and developed into a global navigation satellite system for open positioning, navigation, and timing services. The BDS-3 satellites feature the inter-satellite link (ISL). The ISL observation-related error model and ambiguity resolution for L-band observation are the crucial factors in precise data processing. In this study, we present for the first time the impact of ambiguity resolution on phase center offsets (PCOs) and hardware delay estimation of BDS-3 inter-satellite links. Two weeks’ L-band observations from 99 globally distributed ground stations and Ka-band ISL observations are collected for experimental validation and analysis. First, network solutions with and without ISL observations are conducted to investigate the role of ISL observation in ambiguity resolution. Afterward, ISL observation-related errors, mainly PCOs and hardware delays, are estimated by processing L-band with ISL observations with and without ambiguity resolution to analyze the impact of ambiguity resolution on these two factors. Finally, orbit accuracy in the network solution is assessed to further validate the effectiveness of the estimated PCOs and hardware delays in our experiment. The result indicates that introducing the ISL can slightly improve the fixing rate compared to only L-band observations from 83.7% to 84.3%. Furthermore, ambiguity resolution has a positive influence on the stability of estimated PCOs and hardware delays in turn, although the root mean square (RMS) values basically remain unchanged. The standard deviation (STD) of the x-offset is reduced from 0.021 m to 0.012 m, a significant improvement of about 43%, and 0.022 m–0.016 m, with an improvement of about 27%, for the y-offset. There is a slight improvement of about 8% for z-offset. Similarly, around 10% improvement in the STD for hardware delays can be achieved while the RMS values almost stay the same except for C40. Orbit determination from network solutions shows high accuracy compared to the public products for the satellite with good geometry configuration, which further validates our estimates for ISL PCOs and hardware delays.

Dual Sagnac Interferometer Distributed Optical Fiber Localization Method Based on Hilbert–Huang Transform

In order to solve the problem that the dual Mach Zehnder (M-Z) interferometer system is easily affected by external environmental noise, a data signal-processing scheme based on Hilbert–Huang transform (HHT) is proposed to achieve high-precision location with distributed optical fibers. The polarization compensation module has a built-in dual Sagnac interferometer system which is used to stabilize the polarization state of the Sagnac interferometer. The eigenmode function is obtained by empirical mode decomposition of the received two optical signals, and then the Hilbert spectrum is obtained by superimposing the Hilbert transform, so that the high-similarity curve caused by the vibration signal can be clearly and intuitively extracted. The optical signal information can be calculated based on the cross-correlation and delay estimation algorithm to accurately obtain the vibration position information. The experimental results show that the positioning accuracy can reach ±11 m, with a sensing fiber length of 15 km and a sampling rate of 10 MHz. It is proven that the distributed optical fiber sensing technology based on the dual Sagnac interferometer system has high practical application value.

Hemodynamic timing in resting-state and breathing-task BOLD fMRI

The blood flow response to a vasoactive stimulus demonstrates regional heterogeneity across both the healthy brain and in cerebrovascular pathology. The timing of a regional hemodynamic response is emerging as an important biomarker of cerebrovascular dysfunction, as well as a confound within fMRI analyses. Previous research demonstrated that hemodynamic timing is more robustly characterized when a larger systemic vascular response is evoked by a breathing challenge, compared to when only spontaneous fluctuations in vascular physiology are present (i.e., in resting-state data). However, it is not clear whether hemodynamic delays in these two conditions are physiologically interchangeable, and how methodological signal-to-noise factors may limit their agreement. To address this, we generated whole-brain maps of hemodynamic delays in nine healthy adults. We assessed the agreement of voxel-wise gray matter (GM) hemodynamic delays between two conditions: resting-state and breath-holding. We found that delay values demonstrated poor agreement when considering all GM voxels, but increasingly greater agreement when limiting analyses to voxels showing strong correlation with the GM mean time-series. Voxels showing the strongest agreement with the GM mean time-series were primarily located near large venous vessels, however these voxels explain some, but not all, of the observed agreement in timing. Increasing the degree of spatial smoothing of the fMRI data enhanced the correlation between individual voxel time-series and the GM mean time-series. These results suggest that signal-to-noise factors may be limiting the accuracy of voxel-wise timing estimates and hence their agreement between the two data segments. In conclusion, caution must be taken when using voxel-wise delay estimates from resting-state and breathing-task data interchangeably, and additional work is needed to evaluate their relative sensitivity and specificity to aspects of vascular physiology and pathology.

Low sampling rate digital dechirp for Inverse Synthetic Aperture Ladar imaging processing

The application of digital dechirp technology to Inverse Synthetic Aperture Ladar (ISAL) can greatly reduce the sampling rate burden on analog-to-digital converters. We address the disadvantage of signal sampling and imaging processing in linear frequency-modulated continuous waveform (LFMCW) ISAL systems and suggest a delay estimation method based on a reference channel to reduce the impact of echo delay error. Our method uses the strongest point in the scene as the scene center to estimate the delay and target distance via dechirped pulse compression. The reference channel data are also used to align the dechirped pulse signal and, in turn, reduce clock errors and ensure azimuth signal quality. To our knowledge, our experiment represents the first successful application of digital dechirp to ISAL systems. Experimental results confirm the applicability of our digital dechirp method to ISAL system in order to reduce sampling requirements.

Model-free adaptive control based on prescribed performance and time delay estimation for robotic manipulators subject to backlash hysteresis

Aim to solve the problem of over-dependence on dynamics model and low tracking accuracy of robotic manipulators under uncertainties and backlash hysteresis, an adaptive continuous nonsingular fast terminal sliding mode controller combining time delay estimation (TDE) and prescribed performance control (PPC) is formulated. Firstly, TDE is applied to estimate the model information and external disturbances, so that the dynamics model of robotic manipulators is simplified into a local model. Then, an adaptive terminal sliding mode controller is established based on the local model and PPC, which solves the singularity problem of traditional terminal sliding mode and improves the transient and steady-state performance of robotic manipulators. Next, by combining the reaching law with the adaptive term, robotic manipulators achieve favorable tracking performance, fast convergence time, and chattering suppression under complex disturbances. And the Lyapunov theory is applied to prove that the robotic manipulators can reach the steady state in finite time. Meanwhile, a saturation compensator is formulated to settle actuator saturation and the compensator can converge within a finite time. Finally, the simulation is applied to verify the formulated control strategy can effectively improve the tracking performance and enhance the robustness against disturbances.

Ultra-short baseline positioning delay estimation based on signal preprocessing and fourth-order cumulant

To address the problem that the traditional generalized cross correlation (GCC) method in ultra-short baseline (USBL) positioning systems has a poor delay estimation accuracy in a low signal-to-noise ratio environment or complex noise background, a generalized quadratic cross correlation (GQCC) time delay estimation algorithm based on signal preprocessing and fourth-order cumulants is proposed. The noisy signal was first preprocessed using singular value decomposition and wavelet denoising. Then, the delay was calculated using an algorithm that combined the GQCC and the fourth-order cumulant. The results of the simulation and sea trial demonstrate that the proposed method is superior to the GCC method and the GQCC method and may significantly increase the positioning accuracy of the USBL system. This technique can offer a fresh technological perspective for weak signal detection and passive positioning of small targets in ocean detection.

Delay Prediction for ASIC HLS: Comparing Graph-Based and Nongraph-Based Learning Models

While high-level synthesis (HLS) tools offer faster design of hardware accelerators with different area versus delay tradeoffs, HLS-based delay estimates often deviate significantly from results obtained from ASIC logic synthesis (LS) tools. Current HLS tools rely on simple additive delay models which fail to capture the downstream optimizations performed during LS and technology mapping. Inaccurate delay estimates prevent fast and accurate design-space exploration without performing time-consuming LS tasks. In this work, we exploit different machine learning models which automatically learn to map the different downstream optimizations onto the HLS critical paths. In particular, we compare graph-based and nongraph-based learning models to investigate their efficacy, devise hybrid models to get the best of the both worlds. To carry out our learning-assisted methodology, we create a dataset of different HLS benchmarks and develop an automated framework, which extends a commercial HLS toolchain, to extract essential information from LS critical path and automatically matches this information to HLS path. This is a nontrivial task to perform manually due to difference in level of abstractions. Finally, we train the proposed hybrid models through inductive learning and integrate them in the commercial HLS toolchain to improve delay prediction accuracy. Experimental results demonstrate significant improvements in delay estimation accuracy across a wide variety of benchmark designs. We demonstrate that the graph-based models can infer essential structural features from the input design, while incorporating them into traditional nongraph-based models can significantly improve the model accuracy. Such “hybrid” models can improve delay prediction accuracy by 93% compared to simple additive models and provide <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$175\times $ </tex-math></inline-formula> speedup compared to LS. Furthermore, we discuss key insights from our experiments, identifying the influence of different HLS features on model performance.

Disrupted multi-scale topological organization of directed functional brain networks in patients with disorders of consciousness.

Disorders of consciousness are impaired states of consciousness caused by severe brain injuries. Previous resting-state functional magnetic resonance imaging studies have reported abnormal brain network properties at different topological scales in patients with disorders of consciousness by using graph theoretical analysis. However, it is still unclear how inter-regional directed propagation activities affect the topological organization of functional brain networks in patients with disorders of consciousness. To reveal the altered topological organization in patients with disorders of consciousness, we constructed whole-brain directed functional networks by combining functional connectivity analysis and time delay estimation. Then we performed graph theoretical analysis based on the directed functional brain networks at three topological scales, from the nodal scale, the resting-state network scale to the global scale. Finally, the canonical correlation analysis was used to determine the correlations between altered topological properties and clinical scores in patients with disorders of consciousness. At the nodal scale, we observed decreased in-degree and increased out-degree in the precuneus in patients with disorders of consciousness. At the resting-state network scale, the patients with disorders of consciousness showed reorganized motif patterns within the default mode network and between the default mode network and other resting-state networks. At the global scale, we found a lower global clustering coefficient in the patients with disorders of consciousness than in the controls. The results of the canonical correlation analysis showed that the abnormal degree and the disrupted motif were significantly correlated with the clinical scores of the patients with disorders of consciousness. Our findings showed that consciousness impairment can be revealed by abnormal directed connection patterns at multiple topological scales in the whole brain, and the disrupted directed connection patterns may serve as clinical biomarkers to assess the dysfunction of patients with disorders of consciousness.

HOLISMOKES

Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. With the large number of detections in current and upcoming surveys, such as the Rubin Legacy Survey of Space and Time (LSST), it is pertinent to investigate automated and fast analysis techniques beyond the traditional and time-consuming Markov chain Monte Carlo sampling methods. Building upon our (simple) convolutional neural network (CNN), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a singular isothermal ellipsoid (SIE) profile (lens center x and y, ellipticity ex and ey, Einstein radius θE) and the external shear (γext, 1, γext, 2) from ground-based imaging data. In contrast to our previous CNN, this ResNet further predicts the 1σ uncertainty for each parameter. To train our network, we use our improved pipeline to simulate lens images using real images of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find very good recoveries overall for the SIE parameters, especially for the lens center in comparison to our previous CNN, while significant differences remain in predicting the external shear. From our multiple tests, it appears that most likely the low ground-based image resolution is the limiting factor in predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to quickly predict the next appearing image and time delays of lensed transients. Therefore, we use the network-predicted mass model to estimate these quantities and compare to those values obtained from our simulations. Unfortunately, the achieved precision allows only a first-order estimate of time delays on real lens systems and requires further refinement through follow-up modeling. Nonetheless, our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU, meaning that we are able to efficiently process the huge amount of galaxy-scale lenses expected in the near future.

A New Subsample Time Delay Estimation Algorithm for LFM-Based Detection

This paper investigated a Subsample Time delay Estimation (STE) algorithm based on the amplitude of cross-correlation function to improve the estimation accuracy. In this paper, a rough time delay estimation is applied based on traditional cross correlator, and a fine estimation is achieved by approximating the sampled cross-correlation sequence to the amplitude of the theoretical cross-correlation function for linear frequency modulation (LFM) signal. Simulation results show that the proposed algorithm outperforms existing methods and can effectively improve time delay estimation accuracy with the complexity comparable to the traditional cross-correlation method. The theoretical Cramér-Rao Bound (CRB) is derived, and simulations demonstrate that the performance of STE can approach the boundary. Eventually, four important parameters discussed in the simulation to explore the impact on Mean Squared Error (MSE).

Machine learning application for cell delay accuracy improvement at post-placement stage: A case study for combinational cells

In the early VLSI design stages, as most interest is given to timing closure, various techniques are deployed to satisfy the timing constraints and close design timing. Nevertheless, timing still needs to be accurate at the early stages due to the lack of physical design information. Therefore, the challenge for most EDA Logic Synthesis engines is to improve the overall timing accuracy at higher levels of abstraction. In this paper, we shall reduce the error gap in Cell Delay estimation for a placed gate-level netlist. We will generate the post-placed netlist from two different Siemens EDA tools. The purpose is to improve the accuracy of the cell delays of Oasys-RTL Synthesis tool, based on accurate cell delays from Aprisa PnR. We have trained and tested various Machine Learning models on several 16 nm designs. We achieved the highest accuracy using the Random Forest model, where the average accuracy is 91.25%. Furthermore, as the accuracy of the Cell delay is improved, the average Root-Mean-Square-Error (RMSE) is reduced from 16.958 to 4.469.

Network performance estimator with applications to route selection for IoT multimedia applications

Estimating the performance of multimedia (MM) traffic is important in numerous contexts, including routing and forwarding, quality of service (QoS) provisioning, and adaptive video streaming. This paper proposes a network performance estimator which aims at providing, in quasi real-time, network performance estimates for IoT MM traffic in IEEE 802.11 multihop wireless networks. To our knowledge, the proposed MM-aware performance estimator, or MAPE, is the first deterministic simulation-based estimator that provides real-time per-flow throughput, packet loss, and delay estimates while considering inter-flow interference and multirate flows, typical of MM traffic. Our experimental results indicate that MAPE is able to provide network performance estimates that can be used by IoT MM services, notably to inform real-time route selection in IoT video transmission, at a fraction of the execution time when compared to stochastic network simulators. When compared to existing deterministic simulators, MAPE yields higher accuracy at comparable execution times due to its ability to consider multirate flows.

An explicit tuning of the fractional order controller using a novel time delay approximation

An explicit tuning of the fractional order controller using a novel time delay approximation

Ionospheric delay estimation of Loran skywave using simple cosine model

ABSTRACT Ionospheric delay is the most significant source of error affecting the Loran skywave timing. To improve timing service, we propose a new ionospheric delay estimation method in this paper. It is a cosine model that combines the ionospheric physical characteristics and skywave delay variation law. This semi-empirical and semi-measured model has characteristics of accuracy and simplicity compared with the traditional method. The experimental results show that estimation error of the model is less than 1 µs for a single day and less than 2 µs for 3 days in the stable time. It is sufficient to correct more than 90% of delay when skywave passes through the ionosphere and is less affected by the season. This paper also gives some suggestions on the propagation of ionospheric coefficients required for the model. It allows broadcasting only by modifying the data channel message structure under the normal operation of Loran system.

A two-state Markov chain model for slug flow in horizontal ducts

Intermittent flows are common flow patterns in gas–liquid horizontal flow and attract attention and great research effort due to its importance for industrial and engineering applications. The slug flow is typically modelled based on a unit cell varying from an elongated air bubble with a liquid film in segregated flow pattern and an aerated liquid plug, the slug region, with remarkable stochastic characteristics of its alternating regions. In this paper, a two-state Markov chain model is proposed to represent the stochastic dynamics of developed slug flow in horizontal pipes. Each state represents either the liquid slug or the elongated bubble regions and the transition probabilities dictate the change of the given discrete time measurement to stay at a given state or change. This simple but insightful description of the phenomenon allows an analytical treatment of the statistics of Markov chain stochastic process. Measurement stations with two double wire resistive sensors are used to obtain the void fraction time series and a corresponding two-state representation. Each state is classified by a threshold defined by an unsupervised and non-parametric pattern recognition approach. The state transition matrix is then estimated for each corresponding experimental point. Thus random samples can be synthetically regenerated with the same statistical features of the corresponding measurements. It is shown that the Markov chain model can successfully represent second-order statistics of the measurement, such as the autocorrelation and power spectral density, given an appropriate choice of the chain order. Moreover, a cross correlation-based time delay estimation is proposed for the two-sensor measurement station and additional information is obtained from the slug flow. Subsequently, statistics of some slug flow features are estimated using the proposed approach and their interpretation as random variables derived from the void fraction stochastic process is discussed.

A low complexity loop‐delay estimation algorithm based on sliding window for power amplifier characterization

AbstractIt is well known that accurate estimation of the loop delay between the input and output feedback signals is the key to behavioral modeling and digital predistortion of power amplifier. Nevertheless, most of the traditional loop‐delay compensation algorithms have the disadvantage of high computational complexity. For the purpose of solving this problem, a loop‐delay estimation algorithm based on sliding window has been proposed in this paper, where the first derivative characteristic of the signal is employed to evaluate the correlation between the input and output feedback signals. The simulation and experiment are carried out with different bandwidth signals in this paper. The results show that the proposed algorithm and the traditional cross‐correlation‐based method could align the input and output feedback signals successfully. But in terms of resource consumption, the proposed algorithm could reduce the multiplier nearly by 100% and the adder by approximately 93.2%.

Node Depth Adjustment Based Target Tracking in Sparse Underwater Sensor Networks

Due to the limited energy in underwater sensor networks, underwater nodes need to be deployed sparsely. However, sparse USNs will lead to poor tracking coverage and detection capability. To solve these problems, the mobility of nodes in depth can be utilized to optimize the node topology to achieve data fusion more reliably and effectively. In this paper, for underwater target tracking, a node depth adjustment algorithm is proposed. Firstly, after introducing the sound velocity profile on acoustic signal transmission, the asynchronous particle filter algorithm based on delay estimation is improved, which makes the filter more suitable for an underwater environment. Secondly, the influence of node topology on the tracking accuracy is analyzed, and the optimization problem of node depth adjustment is constructed, in which the depth-related Fisher Information Matrix is designed as the optimization criterion. Thirdly, for scenarios in which the target depth is either known or unknown, the analytical method and the interior point method are employed to solve the problem, respectively, and the optimal depth adjustment strategies in corresponding scenarios are obtained. The simulation results show that the proposed algorithm can fully adjust the node depth and achieve a more accurate tracking performance.

On Band Stitching for Wideband Vector Measurements With Vector Signal Analyzers

As the bandwidth of radio signals increases significantly over the evolution of communication systems, the analysis bandwidth of many current instruments becomes insufficient. In this article, we study analysis bandwidth extension using the so-called band stitching. Specifically, we propose a two-step delay estimation method, which combines the cross correlation and an additional refining step to resolve the timing impairment between different sampling branches in postprocessing. We verify the method with a sequential measurement setup, using vector signal analyzers (VSAs). The result shows the necessity of conducting the refining step for getting a more accurate delay estimate to successfully recover the original wideband signal. Besides, a good agreement is found between the stitched data and the reference data in terms of the waveforms and error vector magnitude (EVM), which shows the effectiveness and robustness of the proposed calibration method in practice.

Time delay estimation via average magnitude differences among multiple microphone signals

Time delay estimation (TDE) plays a significant role in hands-free speech communication systems for localizing and tracking speakers. To boost the robustness of time delay estimators in room acoustic environments, a novel TDE approach is proposed in this paper. This method first exploits the reciprocals of average magnitude difference functions of sound signals captured at a microphone array, instead of cross correlation coefficients, to construct the entries of the parameterized correlation coefficient matrix in the multichannel cross-correlation coefficient algorithm. A multichannel average magnitude difference coefficient is then defined to establish the time delay estimator. Simulation results demonstrate that the proposed TDE strategy can yield better performance as compared to the multichannel cross-correlation coefficient method in noisy and reverberant environment.