Phase Of Signal Research Articles

Enhancing Urban Intersection Efficiency: Utilizing Visible Light Communication and Learning-Driven Control for Improved Traffic Signal Performance

This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management.

Investigating a defect width identification based on half-peak width for LDC-based eddy current testing

ABSTRACT The eddy current testing (ECT) technology based on the Inductance-to-Digital Converter (LDC) has the advantages of low power consumption and simple signal conditioning circuitry, which contributes to the miniaturisation of the instrument and low power consumption design. Existing defect identification methods primarily focus on the conventional Analog-to-Digital Converter (ADC)-based ECT. These methods rely on complex analytical models to analyze the phase and amplitude information of voltage signal. However, these approaches do not apply to the LDC-based ECT. The paper proposes a method to identify defects employing the R p -Inductance 2D plane, and use the half-peak width of the inductance signal to evaluate the defect width for symmetric slots. Using finite element simulation and experimental verification, the half-peak widths of the inductive signal wave crests at the defects are the same for the four slots with 1 mm width and different depths on the aluminum specimen; for four wire-cut slots with 4 mm depth and different widths, the half-peak widths are positively correlated with the defect widths. These experimental results demonstrate that the relationship between crack width, wave crest, and half-peak width can serve as the foundation for width identification and classification. This also offers a fresh perspective for further quantifying crack widths.

Field Evaluation of Yellow Change and Red Clearance Intervals with Subsequent Permissive Left-Turn Flashing Yellow Arrow Indication

The flashing yellow arrow (FYA) indication has become commonly used for communicating permissive left-turn operations to road users. A conventional signal phasing communicated to left-turning drivers consists of a leading protected phase followed by a permissive phase using a FYA indication, often referred to as protected-permissive. To transition from the protected to the permissive phase, change (steady yellow arrow) and clearance (steady red arrow, all-red) intervals, or only a change interval, may be used. There is no specific guidance on the use of clearance intervals for left turns with protected-permissive phase and FYA indication. In this study, field data were collected from different geographical regions in the United States to evaluate change and clearance intervals with the objective of developing guidance. Video data were recorded at 37 intersections during peak hours, for approximately 142 h, across eight states. Overall, 4,001 observations of vehicles turning left during the change or clearance interval were analyzed. Field observations were evaluated at the approach level to assess left-turn violation rate and violations per cycle as a function of change and clearance interval configuration. At the individual vehicle level, logistic regression was implemented to evaluate the effect of change interval and clearance interval duration, delayed onset of FYA, and regional variation. The results of the analysis at the approach and individual vehicle level were consistent and indicate that: (i) a clearance interval should be included, (ii) delaying the onset of FYA indication with an extension beyond the all-red clearance interval would reduce left-turn signal violations, and (iii) duration of change intervals has a marginal impact on left-turn signal violations.

Reliable Identification of Binary Supermassive Black Holes from Rubin Observatory Time-domain Monitoring

Periodic signatures in time-domain observations of quasars have been used to search for binary supermassive black holes (SMBHs). These searches, across existing time-domain surveys, have produced several hundred candidates. The general stochastic variability of quasars, however, can masquerade as a false-positive periodic signal, especially when monitoring cadence and duration are limited. In this work, we predict the detectability of binary SMBHs in the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST). We apply computationally inexpensive sinusoidal curve fits to millions of simulated LSST Deep Drilling Field light curves of both single, isolated quasars and binary quasars. The period and phase of simulated binary signals can generally be disentangled from quasar variability. Binary amplitude is overestimated and poorly recovered for two-thirds of potential binaries due to quasar accretion variability. Quasars with strong intrinsic variability can obscure a binary signal too much for recovery. We also find that the most luminous quasars mimic current binary candidate light curves and their properties: The false-positive rates are 60% for these quasars. The reliable recovery of binary period and phase for a wide range of input binary LSST light curves is promising for multi-messenger characterization of binary SMBHs. However, pure electromagnetic detections of binaries using photometric periodicity with amplitude greater than 0.1 mag will result in samples that are overwhelmed by false positives. This paper represents an important and computationally inexpensive way forward for understanding the true and false-positive rates for binary candidates identified by Rubin.

The Larmor frequency shift of a white matter magnetic microstructure model with multiple sources.

Magnetic susceptibility imaging may provide valuable information about chemical composition and microstructural organization of tissue. However, its estimation from the MRI signal phase is particularly difficult as it is sensitive to magnetic tissue properties ranging from the molecular to the macroscopic scale. The MRI Larmor frequency shift measured in white matter (WM) tissue depends on the myelinated axons and other magnetizable sources such as iron-filled ferritin. We have previously derived the Larmor frequency shift arising from a dense medium of cylinders with scalar susceptibility and arbitrary orientation dispersion. Here, we extend our model to include microscopic WM susceptibility anisotropy as well as spherical inclusions with scalar susceptibility to represent subcellular structures, biologically stored iron, and so forth. We validate our analytical results with computer simulations and investigate the feasibility of estimating susceptibility using simple iterative linear least squares without regularization or preconditioning. This is done in a digital brain phantom synthesized from diffusion MRI measurements of an ex vivo mouse brain at ultra-high field.

A large-scale traffic signal control algorithm based on multi-layer graph deep reinforcement learning

Due to its capability in handling complex urban intersection environments, deep reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control (ATSC). However, most existing algorithms are designed for specific road networks or traffic conditions, making it difficult to transfer them to new environments. Moreover, current graph-based algorithms do not fully capture the geometric and spatial features of intersections, leading to incomplete embedding of the agent's environment depiction. Additionally, the actions adopted by these algorithms are inherently based on fixed-cycle phases, limiting the flexibility of traffic signal control. To address the aforementioned issues, this paper proposes a Multi-layer Graph Mask Q-Learning (MGMQ) algorithm for multi-intersection ATSC to optimize traffic and reduce delay. Unlike previous graph-based algorithms, this paper introduces a method for computing multi-layer graphs, dividing the traffic environment into upper-level traffic network-layer graphs and lower-level intersection-layer graphs, and employs the graph attention algorithm and an improved GraphSAGE algorithm for computation. This method not only enables the generation of embedded state for intersections that include geometric and spatial features, but also allows the algorithm to adapt to different traffic conditions and road networks. Additionally, we introduce an action masking mechanism, allowing this algorithm can be adapted to different action spaces. As a result, the algorithm uses arbitrary signal phases as actions to achieve flexible traffic flow control, and can be directly applied to intersections with arbitrary geometry. The final test results demonstrate that a model trained solely on synthetic road networks can be directly transferred to other synthetic network configurations or real-world urban road networks, outperforming current state-of-the-art algorithms.

Achieving the Fundamental Quantum Limit of Linear Waveform Estimation.

Sensing a classical signal using a linear quantum device is a pervasive application of quantum-enhanced measurement. The fundamental precision limits of linear waveform estimation, however, are not fully understood. In certain cases, there is an unexplained gap between the known waveform-estimation quantum Cramér-Rao bound and the optimal sensitivity from quadrature measurement of the outgoing mode from the device. We resolve this gap by establishing the fundamental precision limit, the waveform-estimation Holevo Cramér-Rao bound, and how to achieve it using a nonstationary measurement. We apply our results to detuned gravitational-wave interferometry to accelerate the search for postmerger remnants from binary neutron-star mergers. If we have an unequal weighting between estimating the signal's power and phase, then we propose how to further improve the signal-to-noise ratio by a factor of sqrt[2] using this nonstationary measurement.

A cross-polarized antenna sensor based on U-shaped resonator for crack sensing

To address the insufficient strain transfer ratio of monolithic antenna sensors and signal self-jamming in wireless interrogation, this paper presents a cross-polarized antenna sensor based on a U-shaped resonator for crack sensing. The sensor comprises an Rx antenna, a Tx antenna, and a sensing element. Employing cross polarization between Rx and Tx antennas mitigates ambient reflection, and the channel model of the sensing system based on cross polarization technique is demonstrated to reveal the working mechanism. Within the sensing element, a U-shaped resonator and movable shorted unit form an unstressed resonator, encoding the crack width in both the amplitude and phase in the retransmission signal, which are utilized to extract the sensor’s resonant frequency. The equivalent circuit model is established to analyze the monitoring mechanism comprehensively. The operational performance of the sensor is simulated in CST and validated by experiment. Results demonstrate a good linear relationship between the sensor’s resonant frequency and the crack width.

Phase screen prediction using deep phase network for FSO links.

Due to the presence of air turbulence in free-space optical (FSO) links, random fluctuations in wavefront phase and amplitude of the optical signal are reduced after it propagates through the air channel, which degrades the performance of free-space optical communication (FSOC) systems. Phase screen reflects the phase distortions resulting from air turbulence. Accordingly, accurate prediction with respect to phase screen is of significance for the FSOC. In this paper, we propose a phase screen prediction method based on the deep phase network (DPN). The advantages of the proposed method include strong robustness against air turbulence, low model depth, and fewer parameters as well as low complexity. The results reveal that our DPN enables desired inference accuracy and faster inference speed compared with the existing models, by combining the mean square deviation loss function with the pixel penalty terms. More concretely, the accuracy of phase screen prediction can reach up to 95%; further, the average time consumed to predict the phase screen is in the order of milliseconds only under various turbulence conditions. Also, our DPN outperforms the traditional Gerchberg-Saxton algorithm in convergence speed.

Pulse Accumulation Approach Based on Signal Phase Estimation for Doppler Wind Lidar.

Coherent Doppler wind lidar (CDWL) uses transmitted laser pulses to measure wind velocity distribution. However, the echo signal of CDWL is easily affected by atmospheric turbulence, which can decrease the signal-to-noise ratio (SNR) of lidar. To improve the SNR, this paper proposes a pulse accumulation method based on the cross-correlation function to estimate the phase of the signal. Compared with incoherent pulse accumulation, the proposed method significantly enhances the correlation between signals from different periods to obtain high SNR gains that arise from pulse accumulation. Using simulation, the study evaluates the effectiveness of this phase estimation method and its robustness against noise in algorithms which analyze Doppler frequency shifts. Furthermore, a CDWL is developed for measuring the speed of an indoor motor turntable and the outdoor atmospheric wind field. The phase estimation method yielded SNR gains of 28.18 dB and 32.03 dB for accumulation numbers of 500 and 1500, respectively. The implementation of this method in motor turntable speed measurements demonstrated a significant reduction in speed error-averaging 9.18% lower than that of incoherent accumulation lidar systems. In experiments that measure atmospheric wind fields, the linear fit curve slope between the measured wind speed and the wind speed measured via a commercial wind-measuring lidar can be reduced from 1.146 to 1.093.

Line-Spacing-Multiplied Optical Frequency Comb Generation Using an Electro-Optic Talbot Laser and Cross-Phase Modulation in a Fiber

An optical frequency comb (OFC) generator based on an electro-optic Talbot laser and cross-phase modulation (XPM) in a high nonlinear fiber (HNLF) is designed and demonstrated. The Talbot laser is an electro-optic frequency shifting loop that is used to produce repetition rate-multiplied pulses, and these pulses work as a pump signal that induces the XPM process in the HNLF to modulate the phase of a probe signal. At the output of the HNLF, OFCs with a multiplied line spacing can be generated. The effects of the pump power and the HNLF length on the performance of the generated OFCs are theoretically analyzed. In the experiments, the line spacing of the generated OFCs is multiplied to be 10 GHz, 15 GHz, and 20 GHz with a factor of 2, 3, and 4, respectively. The center of the OFCs is tuned in a 4 nm range by adjusting the wavelength of the probe signal.

Dylematy interpretacyjne stosowania sygnałów drogowych ze szczególnym uwzględnieniem sygnalizatora S-2 w „bezpiecznym” zarządzaniu ruchem drogowym. Część II/III

Interpretative dilemmas of the use of road signals with particular emphasis on the S-2 signal in “safe” traffic management. Part II/III In this part of the article, the authors focus their attention on the formal and legal issues related to the design of traffic signals on roads and intersections in Poland. A synthesis of the characteristics of the necessary content of the design of traffic organization with traffic lights, approved by the competent authority managing traffic on the road is given. A historical outline of the guidelines for the implementation of the signal allowing turning in the direction indicated by the green arrow is provided. The most important aspect of this part of the article is a commentary on the current legal provisions regarding the permissibility and inadmissibility of allowing simultaneous movement of individual collision streams – with particular emphasis on vehicle streams controlled by the S-2 traffic signal. The commentary includes separate references to the legal status from the original version of the requirements made in 2004 and from the 2008 and 2015 amendments to the regulations. In the authors’ opinion, these descriptions provide key points explaining the complexity of the problem for designers, reviewers, and approvers of signals controlled traffic organization. An effort has been made to explain comprehensively all possible situations since there is currently a non-uniform application of signal phases at intersections and different opinions on the questions of interpreting the correct application of the "green arrow" signal. The results of the study in this regard are included in part III of the article.

Novel motion parameter estimation and coherent integration algorithm for high maneuvering target with jerk motion

The high maneuvering target with jerk motion detection suffers from range migration (RM) and Doppler frequency migration (DFM). In this paper, the fourth order polynomial signal model is used to model the radar echo signal, and a novel high maneuvering target detection method is proposed. The RM is eliminated with Keystone transform (KT) and acceleration searching, and then the aligned echoes are extracted. With two defined symmetric autocorrelation functions (SAF) and the scaled Fourier transform, the third and fourth order motion parameters are estimated. Based on the estimated high order motion parameters, the fourth order polynomial phase signal is turned into linear frequency modulated (LFM) signal. To estimate the chirp rate of the LFM signal, a fast implementation method of second order Wigner Ville Distribution (SoWVD), named frequency domain SoWVD (FDSoWVD) is proposed. Based on the triple estimated motion parameters, the DFM is compensated and then the coherent integration can be achieved by fast Fourier transform, followed which the detection process is performed. Compared with other representative algorithms, the proposed method achieves a good balance between the computation complexity and detection ability. The effectiveness of the proposed method is verified by experiment with synthetic and raw data.

Method for detecting and locating cracks in aluminum plates based on balanced field electromagnetic technique

Aluminum plates are susceptible to environmental and fatigue loads producing randomly distributed cracks and thus compromising safety performance. This paper proposes a detection and location method based on the balanced field electromagnetic technique (BFET) for the detection of cracks on the upper and backside surfaces. The self-zero nature of BFET and the principle of high-sensitivity crack detection are analyzed. The signal characteristics and variation laws are clarified theoretically. The results show that the amplitude and phase of the upper surface crack signal both vary linearly with the crack depth. The signal amplitude of the backside crack decays exponentially with depth and the phase lags linearly. The two-dimensional amplitude phase diagram is rotated at a fixed angle with increasing crack depth and can locate the crack. BFET can detect cracks below the skin depth and provides a reference to increase the detection penetration depth for other electromagnetic detection methods.

An LSTM based DNN Model for Neurological Disease Prediction Using Voice Characteristics

INTRODUCTION: A neurological condition known as Parkinson's disease (PD); it affected millions of individuals worldwide. An early diagnosis can help enhance the quality of life for those who are affected with this disease. This paper presents a novel Deep neural network model based on Long Short-Term Memory (LSTM) design for the identification of PD using voice features.
 OBJECTIVES: This research work aims to Identify the presence of PD using voice features of individuals. To achieve this, a Deep neural Network with LSTM is to be designed. Objective of the work is to analyse the voice data and implement the model with good accuracy.
 METHODS: The proposed model is a Deep Neural Network with LSTM.
 RESULTS: The proposed method uses the features gleaned from voice signals for training phase of LSTM model which achieved an accuracy of 89.23%, precision value as 0.898, F1-score of 0.965, and recall value as 0.931and is observed as best when compared to existing models.
 CONCLUSION: Deep Neural Networks are more powerful than ANNs ahd when associated with LSTM , the model outperformed the job of identifying PD using voice data.

MI-MZI based distributed optical fiber sensor for location and pattern recognition.

This research addressed the drawbacks of the conventional hybrid structure and processing technique by presenting a novel distributed fiber optic sensor based on a hybrid Michelson and Mach-Zehnder interferometer. The sensor can achieve blind spot free positioning and has a wide response frequency, additionally its structure is not complex. It can obtain two phase signals from each of the two interferometers by using a demodulation method that uses a 3 × 3 optical coupler. To determine the position of the disturbance, we computed cross-correlations on the two signals following basic mathematical techniques. Markov Transition Field was used to transform the phase signals-which had been filtered by a band pass filter-into two-dimensional images. Tagged photos built a dataset, which is then fed into a neural network to identify patterns. Experiments have shown that the frequency response capacity of the structure was verified, and it was able to achieve location within 0-30 km with location errors of ±85 m. In a six-category pattern recognition, the testing set accuracy was 98.74%.

Left-Turn Lane Capacity Estimation based on the Vehicle Yielding Maneuver Model to Pedestrians at Signalized Intersections

Crossing pedestrians may significantly affect the capacity of the left-turn (LT) lane at signalized intersections while sharing the same signal phase in the left-hand traffic system, the quantitative estimation method is still not intensively discussed when considering the vehicle yielding maneuver. Despite the Road Traffic Act in Japan mandating vehicles to yield to pedestrians, instances of vehicles crossing in front of pedestrians are frequent. This study aims to refine the evaluation of LT lane capacity by introducing a novel vehicle yielding maneuver model, considering factors such as pedestrian numbers, crosswalk length, and signal timing. The model, developed using data from various Japanese crosswalks, is subjected to Monte Carlo simulation for validation. Comparative analysis with existing methods in Japanese and U.S. manuals, along with observed data, highlights the effectiveness of our model. This innovative approach has the potential to mitigate vehicle–pedestrian conflicts and reduce air pollution. By incorporating techniques such as signal optimization and two-stage crossing, our model contributes to sustainability while maintaining efficient traffic flow.

Temporal Properties of Self-Prioritization.

Using electroencephalogram (EEG), we tested the hypothesis that the association of a neutral stimulus with the self would elicit ultra-fast neural responses from early top-down feedback modulation to late feedforward periods for cognitive processing, resulting in self-prioritization in information processing. In two experiments, participants first learned three associations between personal labels (self, friend, stranger) and geometric shapes (Experiment 1) and three colors (Experiment 2), and then they judged whether the shape/color-label pairings matched. Stimuli in Experiment 2 were shown in a social communicative setting with two avatars facing each other, one aligned with the participant's view (first-person perspective) and the other with a third-person perspective. The color was present on the t-shirt of one avatar. This setup allowed for an examination of how social contexts (i.e., perspective taking) affect neural connectivity mediating self-related processing. Functional connectivity analyses in the alpha band (8-12 Hz) revealed that self-other discrimination was mediated by two distinct phases of neural couplings between frontal and occipital regions, involving an early phase of top-down feedback modulation from frontal to occipital areas followed by a later phase of feedforward signaling from occipital to frontal regions. Moreover, while social communicative settings influenced the later feedforward connectivity phase, they did not alter the early feedback coupling. The results indicate that regardless of stimulus type and social context, the early phase of neural connectivity represents an enhanced state of awareness towards self-related stimuli, whereas the later phase of neural connectivity may be associated with cognitive processing of socially meaningful stimuli.

Polar Decomposition of Jones Matrix and Mueller Matrix of Coherent Rayleigh Backscattering in Single-Mode Fibers.

The Jones matrix and the Mueller matrix of the coherent Rayleigh backscattering (RB) in single-mode fibers (SMFs) have been derived recently. It has been shown that both matrices depict two polarization effects-birefringence and polarization-dependent loss (PDL)-although the SMF under investigation is purely birefringent, having no PDL. In this paper, we aim to perform a theoretical analysis of both matrices using polar decomposition. The derived sub-Jones/Mueller matrices, representing birefringence and PDL, respectively, can be used to investigate the polarization properties of the coherent RB. As an application of the theoretical results, we use the derived formulas to investigate the polarization properties of the optical signals in phase-sensitive optical time-domain reflectometry (φ-OTDR). For the first time, to our knowledge, by using the derived birefringence-Jones matrix, the common optical phase of the optical signal in φ-OTDR is obtained as the function of the forward phase and birefringence distributions. By using the derived PDL-Mueller matrix, the optical intensity of the optical signal in φ-OTDR is obtained as the function of the forward phase and birefringence distributions as well as the input state of polarization (SOP). Further theoretical predictions show that, in φ-OTDR, the common optical phase depends on only the local birefringence in the first half of the fiber section, which is occupied by the sensing pulse, irrelevant of the input SOP. However, the intensity of the φ-OTDR signal is not a local parameter, which depends on the input SOP and the birefringence distribution along the entire fiber section before the optical pulse. Moreover, the PDL measured in φ-OTDR is theoretically proven to be a local parameter, which is determined by the local birefringence and local optical phase distributions.

Insomnia disorder characterized by probabilistic metastable substates using blood-oxygenation-level-dependent (BOLD) phase signals.

From a clinical point of view, how to force a transition from insomnia brain state to healthy brain state by external driven stimulation is of great interest. This needs to define brain state of insomnia disorder as metastable substates. The current study was to identify recurrent substates of insomnia disorder in terms of probability of occurrence, lifetime, and alternation profiles by using leading eigenvector dynamics analysis (LEiDA) method. We enrolled 32 patients with insomnia disorder and 30 healthy subjects. We firstly obtained the BOLD phase coherence matrix from Hilbert transform of BOLD signals and then extracted all the leading eigenvectors from the BOLD phase coherence matrix for all subjects across all time points. Lastly, we clustered the leading eigenvectors using a k-means clustering algorithm to find the probabilistic metastable substates (PMS) and calculate the probability of occurrence and associated lifetime for substates. The resulting 3 clusters were optimal for brain state of insomnia disorder and healthy brain state, respectively. The occurred probabilities of the PMS were significantly different between the patients with insomnia disorder and healthy subjects, with 0.51 versus 0.44 for PMS-1 (p < 0.001), 0.25 versus 0.27 for PMS-2 (p = 0.051), and 0.24 versus 0.29 for PMS-3 (p < 0.001), as well as the lifetime (in TR) of 36.65 versus 33.15 for PMS-1 (p = 0.068), 14.36 versus 15.43 for PMS-2 (p = 0.117), and 14.80 versus 16.34 for PMS-3 (p = 0.042). The values of the diagonal of the transition matrix were much higher than the probabilities of switching states, indicating the metastable nature of substates. The resulted probabilistic metastable substates hint the characteristic brain dynamics of insomnia disorder. The results may lay a foundation to help determine how to force a transition from insomnia brain state to healthy brain state by external driven stimulation.