Phase Of Signal Research Articles

A multipath error cancellation method based on antenna jitter

Global Navigation Satellite System signals are often affected by multipath errors, which impact the accuracy of positioning measurements. Traditional methods frequently fail to effectively mitigate multipath errors across different environments, primarily due to their inherent sensitivity to varying conditions. Here, we propose a multipath error cancellation method that utilizes antenna jitter, which mitigates multipath errors by rapidly changing the relative phases of direct and multipath signals without requiring changes to the receiver structure. The model that combines theoretical analysis with experimental verification is conducted to identify the minimum jitter amplitude required for effective error reduction in straight-line jitter scenarios. Moreover, extensive satellite data collection and verification were performed in Changsha, China, from December 2023 to August 2024. The results indicate that the proposed method enhances robustness and applicability across various environments compared to traditional approaches. Notably, it enables a vehicle-mounted antenna, priced at just a few dollars, to achieve positioning accuracy comparable to that of high-precision antennas costing thousands of dollars, making advanced positioning technology more accessible.

Understanding the biochemistry of hormones - message in a bottle.

Hormones play pivotal roles in our well-being, and even more so in times of stress or disease. They determine body composition and govern reproductive processes. Hormonal compounds tend to be evolutionarily very old compounds, but only coevolved receptor systems make up powerful biological signals. We will discuss what makes some metabolites good building materials for hormones and how information may be encoded, using these scaffolds. Starting with hormone biosynthesis and regulated release from secreting cells, we will look at different stages of the whole hormone signaling process: the distribution of the hormonal "message-in-a-bottle" throughout the body, the passing of some hormones through membranes, and pre-receptor metabolism. Binding to different classes of receptors is not the end of hormone signaling, but the beginning of a second phase of signaling via second messengers, before hormonal messages are switched off again. Studying hormone biochemistry will produce exciting new findings in the future.

Synthetic adaptive matched field processing for moving source range estimation in deep water.

Adaptive matched field processing (AMFP) has proven effective for source localization in deep-water environments. However, when the target is in motion, the need for numerous snapshot samples can lead to distortion in covariance estimation, degrading AMFP performance. A synthetic AMFP method has been proposed to compensate for the phase of multi-snapshot signals, enhancing AMFP performance. Additionally, a rough estimation of target velocity is obtained. The efficacy of the method has been validated through numerical simulations and experimental data, with results showing that, within a 9 km range, the average localization error is reduced by 1.45 km compared to traditional AMFP.

Investigation of the Fresnel Scale From Ionospheric Scintillation Spectra

AbstractTrans‐ionospheric radio signals recorded on the ground exhibit random amplitude and phase fluctuations attributed to irregularities in the ionospheric electron density. Studying the ground‐based measurements of trans‐ionospheric radio signals can contribute to understanding plasma instability mechanisms leading to the development of ionospheric structures. In this regard, radio signals emitted from satellites, making up the Global Positioning System (GPS), and recorded by the Canadian High Arctic Ionospheric Network (CHAIN) GPS receivers, are analyzed to study the physical signatures of both amplitude and phase fluctuations. The current ionospheric scintillation paradigm posits that amplitude fluctuations arise from diffraction caused by Fresnel scale ionospheric structures, while refraction is responsible for signal phase variations. The amplitude power spectrum profile consistently displays a rollover frequency, which is not equal to the Fresnel frequency under the Taylor hypothesis. Phase screen theory is used to investigate this phenomenon further and identify an empirical relation between the rollover and Fresnel frequencies. Notably, we have found that the rollover frequency is consistently smaller than the Fresnel frequency. Furthermore, the Fresnel frequency extracted from two‐component phase spectra tends to be larger than the rollover frequency. Based on our results, we have concluded that the identified Fresnel frequencies are directly linked to the ionospheric irregularities causing scintillation.

Diffusion coefficient measurements for moving samples under strong magnetic field gradients.

Among the numerous measurements carried out during a well-logging procedure, the Nuclear Magnetic Resonance (NMR) assessment is one of the fundamental analyses in determining the economic viability of a well for the oil industry. Nowadays, two reliable approaches, Wireline Logging (WL) and Logging While Drilling (LWD), stand out. WL comprises the acquisition of NMR data under static conditions. On the other hand, in LWD, the NMR measurements happen simultaneously with the drilling process, while the NMR tool experiences translation, rotation, and vibration motions relative to the rock formation. In order to comprehend better the NMR response acquired under LWD conditions, a setup emulating an LWD tool was developed, consisting of a single-sided magnet, rf probes, and a mechanical device that emulates a relative sinusoidal movement between the sample and the applied magnetic field. A bulk sample and three representative rocks, Fontainebleau Sandstone, Berea Sandstone, and Indiana Limestone, were investigated. Even though the diffusion coefficient measurements remain neglected for their intrinsic characteristics of data acquisition, the findings demonstrate that the diffusion coefficient parameter of a fluid in a bulk sample or confined in a porous rock can be precise and accurately predicted. In strong magnetic field gradients, the Hahn spin echo is predominantly weighted by the diffusion process, an effect used to measure diffusion coefficients. Under LWD conditions, the diffusion coefficient measurement is considerably affected by signal phase modulation due to sample movement in the presence of strong magnetic field gradients, making this measurement difficult. This article present solutions for correct diffusion coefficient measurements, synchronizing Hahn spin echo experiments with sample movement.

Nonlinear real-time correction in homodyne interferometers by multi-area spatial sampling and dimensionality-reduced ellipse fitting

Homodyne interferometers are susceptible to signal instability, including the amplitude, relative phase, and DC bias of interference signals, which lead to dynamic nonlinear errors that require real-time correction to ensure full-range displacement measurement accuracy. To address these issues, this paper proposes a real-time, non-iterative FPGA-based nonlinear correction method, designed to balance accuracy and computational efficiency. The method employs peak detection to simplify the elliptical fitting matrix and utilizes feature-based segmented sampling to perform reduced-order correction. Experimental results show that when elliptical signals are unstable, this method reduces residual error from 1.14 nm to 0.12 nm, effectively compensating for nonlinear errors. In a 25 µm displacement test, this method maintains nonlinear error deviation within the sub-nanometer range compared to traditional correction methods, while reducing the computational load by two orders of magnitude, achieving a balance between correction accuracy and efficiency.

Simultaneous acquisition of water, fat and velocity images using a phase-contrast 3p-Dixon method.

Phase-contrast MRI (2D PC-MRI) and Dixon techniques share the characteristic that the difference in frequency between water and fat, as well as the velocity, are encoded in the phase of the MR signal. We propose to take advantage of this characteristic to obtain both sets of images simultaneously. Such an acquisition will improve efficiency by obtaining both types of images in the same scan and will provide co-registered images of water-fat species and velocity images. This, in turn, will correct fat artifacts due to chemical shift in PC-MRI based measurements. This study presents a novel PC multi-echo (PCME-MRI) sequence jointly with a 3-point (3p-) Dixon pipeline that enables reconstruction of water, fat, and velocity images simultaneously. The proposed 3p-Dixon approach preserves the phase information of water-fat images, while velocity images are obtained from the resulting water components. Numerical phantom tests and 2D MR axial images of the neck acquired in 12 healthy volunteers demonstrated the feasibility of the PC 3p-Dixon method, showing comparable performance to standard techniques. In volunteers the median and range MAE comparing PC 3p-Dixon, and standard 3p-Dixon fat fraction were 0.06 and [0.03, 0.09]. The median and range of velocity for PC 3p-Dixon were 6.15 ml and [3.86, 7.21]ml, compared to 6.43 ml and [4.62, 8.27]ml obtained by 2D PC-MRI. Numerical phantom experiments and acquisitions from healthy volunteers showed promising results in fat fraction and velocity estimation of PC 3p-Dixon compared with standard 3p-Dixon and 2D PC-MRI, obtaining both data sets in similar times as standard 3p Dixon.

Symmetry Feature Estimation Over STFT Transform to Improve Radar Classification Based on the Wisard Weightless Neural Network

Automated early recognition of enemy radar emissions are essential for the survival of a warship. Radar signals intercepted by a passive digital Electronic Support Measure (ESM) receiver can be classified based on the type of intrapulse modulation. The modulation classification is typically based on features extracted from the preprocessed radar’s signal. Low Probability of Intercept (LPI) radars can use phase or frequency modulated signals to make radar emissions difficult for the enemy to detect. This paper proposes the use of a new feature, the symmetry measured in a Time-Frequency (TF) matrix, to improve intrapulse radar modulation classification. The analysis of the symmetry in a STFT matrix is characterized by an image processing problem, where the matrix is interpreted as a grayscale image. This paper also proposes the use of Weightless Neural Network WiSARD in identifying symmetry patterns in the Short Time Fourier Transform (STFT) matrix, a characteristic present in signals with Barker and Polytime phase modulations and which can be used by classifiers to discriminate them from signals with other types of modulations such as polyphase modulations (P1, P2, P3, P4 and Frank), linear frequency modulation (LFM), and Non Linear Frequency Modulation.

Multi-band reconfigurable microwave photonic transceiver towards high-performance integrated radar.

As an effective approach to overcome the electronic bottlenecks of conventional electrical radars, microwave photonic radars have demonstrated significant superiority in their perception and recognition capabilities. However, trade-offs exist among the reconfigurability, signal time-bandwidth product (TBWP), linearity, and phase coherence of current photonic radars, which ultimately weaken the overall performance. To address these challenges, a photonic transceiver based on electrically assisted synchronized lasers is proposed and demonstrated, which combines high resolution and multi-band reconfigurability. Optical coherent heterodyne linear frequency-modulated (LFM) radar signal generation and photonic dechirping reception are implemented through the synchronized lasers at the transmitter and receiver, respectively. In a proof-of-concept experiment, reconfigurable LFM signals covering the L- to Ka-band with improved linearity and phase coherence are generated. Furthermore, the proposed photonic transceiver operates in the Ka-band with an ultra-large signal TBWP of 4 × 106, enabling high-resolution ranging and inverse synthetic aperture radar (ISAR) imaging. A range resolution of 1.92 cm and an imaging resolution of 1.92 cm × 1.89 cm are obtained, which require a receiver sampling rate of only 5 MSa/s. Featuring a simple structure, flexible reconfiguration, and integration compatibility, the demonstrated photonic transceiver opens new opportunities for next-generation miniaturized radar application scenarios.

Design and fabrication of integrator using adiabatic quantum-flux-parametron circuit

Abstract We believe that the Adiabatic Quantum-Flux-Parametron (AQFP) circuit, as a new type of low-power superconducting circuit, can be applied in various fields, such as using its high precision to determine the phase of input signals. To improve the precision of AQFP, it is necessary to reduce the gray zone of the circuit. In this paper, we investigate a digital integration method to reduce the gray zone of the AQFP circuit system and enhance its precision. Theoretical results show that the gray zone of the AQFP circuit system becomes narrower and the precision of the system increases as more data points are integrated.
Additionally, we establish the relationship between the reduction in gray zone and the number of integrated data points. To validate our findings, we fabricate integrator circuits using
AQFP and conduct experimental verification. Furthermore, we estimate the relationship between the scale of the integration circuit and the chip area, providing possibilities for various application scenarios in the future.various application scenarios in the future.

A Transformer Encoder Approach for Localization Reconstruction During GPS Outages from an IMU and GPS-Based Sensor.

Accurate localization is crucial for numerous applications. While several methods exist for outdoor localization, typically relying on GPS signals, these approaches become unreliable in environments subject to a weak GPS signal or GPS outage. Many researchers have attempted to address this limitation, primarily focusing on real-time solutions. However, for applications that do not require real-time localization, these methods remain suboptimal. This paper presents a novel Transformer-based bidirectional encoder approach to address, in postprocessing, the localization challenges during GPS weak signal phases or GPS outages. Our method predicts the velocity during periods of weak or lost GPS signals and calculates the position through bidirectional velocity integration. Additionally, it incorporates position interpolation to ensure smooth transitions between active GPS and GPS outage phases. Applied to a dataset tracking horse positions-which features velocities up to 10 times those of pedestrians and higher acceleration-our approach achieved an average trajectory error below 3 m, while maintaining stable relative distance errors regardless of the GPS outage duration.

Investigating and modeling crash risk for interactions between motorized and non-motorized in intersection center areas

Objective Motorized vehicles (MV) and non-motorized vehicles (NMV) are mixed in the intersection center area (ICA). This mixing leads to complicated interactions between vehicles, which seriously affects traffic safety, especially at mixed intersections of high density. To deep understanding of the interaction course between motorized and non-motorized vehicles in ICAs. Methods Two intersections with a high density of interaction behavior between motorized and non-motorized vehicles were investigated through high-resolution traffic video. Firstly, to extract high-precision trajectories from roadside video, we proposed a new trajectory extraction framework that integrates Yolov7, Deepsort, and the trajectory reconstruction algorithm, which integrated the social force model and particle filtering (SFPF) proposed in our previous research. Second, 183 complete interaction events between motorized and non-motorized vehicles were extracted based on the surrogate safety indicator TTC, and latent variables affecting the course of interaction behavior between motorized and non-motorized vehicles were defined based on turning direction, kinetic state, surrounding environment, signal light, vehicle action behavior, and types of NMV. Third, an ordered logit model was built to study the interactions. Results Analyzing the significance of the model showed that the following variables have a significant effect on the severity of the conflict (p < 0.05 or lower): the turning directions of the two vehicles, their speeds, steering behaviors, the distance between the conflict point and the vehicle, and the surrounding environment. The vehicles entering the ICA 10 s before the end of the signal phase have a higher probability of having a serious crash event while making the interaction. Conclusions The study contributes to developing active safety control and driver assistance strategies.

Bioinspired activation strategies for Peano-HASEL artificial muscle.

Human muscles perform many functions during activities of daily living producing a wide range of force outputs, displacements, and velocities. This versatile ability is believed to be associated with muscle activation strategies, such as the number and position of activated motor units within the muscle, as well as the frequency, magnitude and shape of the activation signal. Activation strategies similar to those in the human neuromuscular system could increase the functionality of artificial muscles. Activation in an artificial muscle is the contraction of a single actuator or multiple actuators within the muscle. The number of activated actuators, timing and magnitude of activation (the activation strategy) will enable modulation of the artificial muscles force, displacement and contraction velocity. These activation strategies will mean that an artificial muscle will be able to change its performance to modulate its displacement, length (maximal contractile strain) and velocity for various loading conditions without altering its hardware-making it more versatile in a range of applications or tasks. This study aims to investigate the effect of activation strategies on the displacement-time response, force-length relationship, and force-velocity relationship of a Peano-hydraulically amplified self-healing electrostatic (HASEL) artificial muscle. This study developed a finite element model of an artificial muscle consisting of four Peano-HASEL actuators arranged in three parallel groups in a diamond pattern (two actuators in series in the middle-middle actuators, with one actuator in parallel either side-side actuators). Bioinspired activation strategies were applied to the artificial muscle. Specifically, the number of activated actuators (i.e., activation level), the position of activated actuators, the profile, frequency, and phase of the activation signal were investigated. Activating more actuators resulted in increased displacement (106%) and increased average contraction velocity (128%), but overall energy efficiency was sacrificed by 47%. The distortion of inactivated actuators was mitigated by symmetric and phased activation. Phased activation refers to activating middle actuators before side actuators. In addition, displacement patterns of the Peano-HASEL artificial muscle changed with activation signal frequency. The ramp activation signal with low frequencies (less than 5 Hz) is suitable for applications favouring controllable displacement, while the step activation signal produces greater average contraction velocity (325%) which would be advantageous for applications requiring a fast response. This paper demonstrates that activation strategies can enhance multi-actuator artificial muscle function without changing the physical hardware configuration. Specifically, activation strategy can, improve displacement control, contraction velocity and output force. Future work should focus on more complex artificial muscle arrangements and test activation strategies in practical experiments.

The development of deep convolutional generative adversarial network to synthesize odontocetes' clicks.

Odontocetes are capable of dynamically changing their echolocation clicks to efficiently detect targets, and learning their clicking strategy can facilitate the design of man-made detecting signals. In this study, we developed deep convolutional generative adversarial networks guided by an acoustic feature vector (AF-DCGANs) to synthesize narrowband clicks of the finless porpoise (Neophocaena phocaenoides sunameri) and broadband clicks of the bottlenose dolphins (Tursiops truncatus). The average short-time objective intelligibility (STOI), spectral correlation coefficient (Spe-CORR), waveform correlation coefficient (Wave-CORR), and dynamic time warping distance (DTW-Distance) of the synthetic clicks were 0.975, 0.968, 0.877, and 0.992, respectively. AF-DCGAN outperformed the minimum phase signal reconstruction (MPSR) method and variational quantized variational autoencoders (VQ-VAE) by 5.9% and 3.7% in STOI, 5.2% and 3.5% in Spe-CORR, and 5.8% and 2.8% in Wave-CORR, respectively. In addition, AF-DCGAN reduced DTW-Distances by 29.9% and 9.4% compared to MPSR and VQ-VAE, respectively. Results showed that AF-DCGAN was robust in synthesizing both narrowband and broadband clicks that can produce a substantial number of high-fidelity odontocetes' clicks with flexibility in modulating parameters. Employing AF-DCGAN to synthesize odontocete-like clicks could advance the development of a click database, offering promising applications in the research of biomimetic target detection and recognition.

Low-Power Retentive True Single Phase Clocked(TSPC) D-Flip-Flop with Redundant Precharge Free Operation

Abstract: In this paper, optimizing power consumption of flip-flops (FFs) can significantly reduce the power of digital systems. In this article, an energy-efficient retentive true single- phase-clocked (TSPC) FF is proposed. With the employment of inputaware precharge scheme, the proposed TSPC FF precharges only when necessary. In addition, floating node analysis and transistor level optimization are employed to further ensure the high energy efficiency of the FF without significantly increasing the area. As The proposed Low power Retentive TSPC FF consumes less power which can be used in application like PLL (phased Lock loop) The main objective of a PLL is to generate a signal in which the phase is the same as the phase of a reference signal. The main Block For this is To design Phase frequency Circuit, Which is designed by using Low power Retentive TSPC FF To Give the better Results compare to conventional Circuit with a power Supply of 1.2 V by using Tanner EDA Tool

Deep Learning Training Data for Phase Unwrapping of SAR Interferograms

Phase unwrapping is an essential process in synthetic aperture radar interferometry that restores phase signals constrained within the range of (-π, π) to their true phase values. Traditional algorithm-based methods can introduce significant errors due to rapid and steep phase gradient or noise, which negatively impact terrain elevation and surface displacement analyses. To overcome these limitations, deep learningbased phase unwrapping techniques have been proposed; however, there has been insufficient previous studies due to the lack of accurate training and test data. This paper aims to share the training data generated using the phase unwrapping simulation method with locally-different phase noise. The data were generated by the simulation of topographic phases and phase noise, atmospheric and orbital distortions. Additionally, data augmentation for phase variation and noise levels was applied to address data imbalance issues. The shared data consists of two types: one with a constant phase noise level for each patch, and another that simulates locally different phase noise based on augmented coherence data. This data is primarily effective for unwrapping topographic phase components and holds significance as the first phase unwrapping training data of synthetic aperture radar interferograms shared in Korea. We expect this resource to serve as foundational data for future phase unwrapping technology research, including applications for upcoming satellites like KOMPSAT-6 and water resource satellites.

On the origin of MTF reduction in grating-based x-ray differential phase contrast CT imaging.

The complementary absorption contrast CT (ACT) and differential phase contrast CT (DPCT) can be generated simultaneously from an x-ray computed tomography (CT) imaging system incorporated with grating interferometer. However, it has been reported that ACT images exhibit better spatial resolution than DPCT images. By far, the primary cause of such discrepancy remainsunclear. The purpose of this study is to investigate the underlying cause of the resolution discrepancy between ACT and DPCT in a grating interferometer CT imagingsystem. In this study, theoretical derivations were performed with a -phase Talbot-Lau grating interferometer system to model the signal formation mechanism of absorption imaging and phase imaging, respectively. In addition, physical, and numerical experiments were conducted to verify the theoretical findings and assess the resolution discrepancy between ACT and DPCT under various conditions. Herein, the ACT and DPCT images were reconstructed from the filtered-back-projection algorithm using a standard Ramp filter and a standard Hilbert filter,respectively. Experiments demonstrated that the spatial resolution of ACT and DPCT images are primarily impacted by the beam diffraction induced signal splitting. In particular, lower modulation transfer function (MTF) was observed for DPCT than ACT due to the opposite-superposition of phase signals. In addition, factors such as focal spot size, beam spectra, object composition, sample size, and detector pixel size were found to have minor impacts on the MTFs of both ACT and DPCT. In conclusion, this study reveals that the opposite-superposition of split phase signals causes the spatial resolution reduction in DPCTimaging.

A Novel Demodulation Algorithm for Micro-Displacement Measurement Based on FMCW Sinusoidal Modulation

Frequency-modulated continuous wave (FMCW) interferometry, an emerging laser interferometry technology, can be applied in the field of fibre-optic sensing to achieve high-precision micro-displacement measurements. To address nonlinearity issues in laser frequency modulation and localisation deviations of feature points in traditional algorithms, this paper proposes a demodulation algorithm suitable for sinusoidal frequency modulation schemes, incorporating the principle of orthogonal phase-locked amplification. The algorithm includes signal preprocessing, phase-locked amplification, error correction, and phase calculation. Experimental results show that the system achieves a measurement error standard deviation of 3.23 nanometres for static targets. The displacement measurement error at 100 μm is 0.057% F.S., and the linearity between the measured values and the actual displacement values is 0.99997. Compared with conventional methods, the approach introduced in this paper eliminates the need for separate nonlinear corrections of the current-to-optical frequency relationship and minimises the issue of feature point localization deviations, showing significant potential for practical applications.

Integration of progression maximization and delay minimization controls for commuting arterials accommodating highly asymmetric directional traffic flows

Many commuting arterials often suffer from queue spillovers and excessive delays in the commuting direction of heavy flows but underuse the roadway capacity in the opposing direction. Existing models for arterial signal control, despite their effectiveness, are not customized for such congested traffic systems with highly asymmetric flows. As such, this study proposes a customized signal optimization model with the objective of minimizing delays for the congested direction while ensuring the maximum progression for the opposite low-volume traffic. With the embedded formulations to reflect the impacts of upstream traffic streams’ arriving rates and sequence on each intersection approach’s resulting queue formation pattern, the proposed model can produce the optimal signal offsets and phase sequence to minimize the total delay in the high-volume direction. The proposed model’s explicit optimization of the arriving sequence for upstream traffic streams with the optimal phasing plan can also hold back the turning bay blockage’s onset time and duration, if inevitable, and thus reduce the total incurred delay. Performance comparison results with three benchmark models under extensive experimental scenarios have confirmed the promising properties of the proposed model, customized to contend with congested arterials plagued by saturated flows, excessive delays, and queue overflows for traffic in the commuting direction.

Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections.

Cooperative control of intersection signals and connected automated vehicles (CAVs) possess the potential for safety enhancement and congestion alleviation, facilitating the integration of CAVs into urban intelligent transportation systems. This research proposes an innovative deep reinforcement learning-based (DRL) cooperative control framework, including signal and speed modules, to dynamically adapt signal timing and CAV velocities for traffic safety and efficiency optimization. Among the DRL-based signal modules, a traffic state prediction model is merged with the current state to augment characteristics and the agent-learning process. A multi-objective reward function is designed to evaluate safety and efficiency using a traffic conflict prediction model and vehicle waiting time. The double deep Q network (DDQN) model is used to design the agent observing the traffic state, learning the optimal signal control policy, and then inputting the signal phase into the speed module. Based on the green duration analysis and constraints of mixed traffic flow of CAVs and human-driven vehicles, a speed planning model is constructed to optimize CAVs' speed and alter traffic state, which in turn affects the agent's next signal decisions. The proposed framework is tested at isolated intersections simulated by two real-world intersections in Changsha, China. The results reveal the superiority of the proposed method over DRL-based traffic signal control (DRL-TSC) in terms of coverage speed and computation time. Compared to actuated signal control, adaptive traffic signal control, and DRL-TSC, the proposed method significantly optimizes traffic safety and efficiency across diverse intersections, temporal spans, and traffic demands. Furthermore, the advantage of the proposed method substantially amplifies with the increased CAV penetration, regardless of the intersection types.