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Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We consider a signal composed of several periods of a periodic function, of which we observe a noisy reparametrization. The phase estimation problem consists of finding that reparametrization and, in particular, the number of observed periods. Existing methods are well suited to the setting where the periodic function is known or, at least, simple. We consider the case when it is unknown, and we propose an estimation method based on the shape of the signal. We use the persistent homology of sublevel sets of the signal to capture the temporal structure of its local extrema. We infer the number of periods in the signal by counting points in the persistence diagram and their multiplicities. Using the estimated number of periods, we construct an estimator of the reparametrization. It is based on counting the number of sufficiently prominent local minima in the signal. This work is motivated by a vehicle positioning problem, on which we evaluated the proposed method. [ABSTRACT FROM AUTHOR]

Low scattering terrain areas introduce complex phase interference, which reduces the accuracy of deformation signal estimation in InSAR(Interferometric Synthetic Aperture Radar) techniques. Existing covariance matrix-based InSAR phase calculation methods often fail to account for translational offset relations between scatterers leading to inaccuracies, and pixels with zero spatial coherence exist. To address this issue, this paper proposes a distributed scatterer InSAR phase estimation method based on the Cross-Correlation complex coherence matrix. The effectiveness and superiority of the algorithm are verified through simulation and actual data. The results show that: (i) The simulation analysis shows that, compared to the traditional covariance matrix method, the optimal Cross-Correlation matrix improves the interferometric phase, coherence, and accuracy by 21.51%, 15.24%, and 6.52%, respectively. (ii) The actual experimental data show that the interferometric phase optimal by the Cross-Correlation matrix can effectively overcome the pseudo-signal caused by spatial hopping and make the phase more continuous. Compared with the traditional covariance matrix, the average a posteriori coherence and average coherence of arbitrary interference combinations in the Cross-Correlation matrix are improved by 18.12% and 58.10%, respectively. (iii) The number of DS points selected by the Cross-Correlation matrix algorithm is more than that of the covariance matrix algorithm. PS-InSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) achieved more accurate deformation rates compared to the covariance and correlation matrices, with errors of 9.34, 17.21, and 16.28 mm∙a-1 when compared against GNSS data, respectively. (iv) The Cross-Correlation matrix reduces the deformation rate error by 5.43 % relative to the covariance matrix. The algorithm provides reliable phase estimation for accurate monitoring of surface deformation in low-scattering regions, supporting geological disaster early warning and resource and environmental management.

The two-site Heisenberg model has an extraordinarily simple analytical solution and is traditionally used as a benchmark against numerical methods, such as exact diagonalization and Monte Carlo methods. In the same spirit, we benchmark three quantum algorithms that are implemented in a quantum computer against the analytical solution of this model. In particular, this presentation includes a description of the standard and iterative quantum phase estimation algorithms, as well as the variational quantum eigensolver. These quantum algorithms are introduced in a pedagogical fashion allowing newcomers to the subject, familiar with only the most basic quantum mechanical systems, to easily reproduce the presented results and apply the methods to other problems, thus building a seemingly under-appreciated path towards useful quantum algorithms through the lens of simulating and computing properties of physical quantum systems.

Abstract Quantum algorithms have shown their superiority in many application fields. However, a general quantum algorithm for numerical integration, an indispensable tool for processing sophisticated science and engineering issues, is still missing. Here, we first proposed a quantum integration algorithm suitable for any continuous functions that can be approximated by polynomials. More impressively, the algorithm achieves quantum encoding of any integrable functions through polynomial approximation, then constructs a quantum oracle to mark the number of points in the integration area and finally converts the statistical results into the phase angle in the amplitude of the superposition state. The quantum algorithm introduced in this work exhibits quadratic acceleration over the classical integration algorithms by reducing computational complexity from O(N) to O(√N). Our work addresses the crucial impediments for improving the generality of quantum integration algorithm, which provides a meaningful guidance for expanding the superiority of quantum computing.

Evaluating quantum Fisher information is an essential task in the parameter estimation and quantum metrology. It quantifies the sensitivity of a quantum state to probe and capture variations in an unknown parameter, which is aimed to be estimated. In this context, the amount of quantum Fisher information measures the operational nonclassicality of a given state, regarded as a quantifiable resource for quantum metrology. We construct su(1, 1) coherent states, using the Perelomov formalism, and present their various optical realizations forming a general class of su(1, 1) algebraic squeezed states. We analyze the nonclassicality of these states and evaluate the corresponding Fisher information. Also, we find that su(1, 1) algebraic squeezed states surpass the standard quantum limit, thereby exhibiting a quantum metrological advantage. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The performance enhancement of an inverter-based grid-connected system necessitates a fast and accurate dynamic response in terms of estimating three-phase grid voltage attributes. The synchronous reference frame phase-locked loop (PLL) and/or the frequency-locking (i.e., frequency-locked loop) approaches are widely used in practical applications. However, due to the phase/frequency feedback loops, the aforementioned parameter estimation schemes may experience instability and provide a slow dynamic response. This work presents a PLL-less grid synchronization solution for three-phase applications to counter the slower dynamic response and demonstrate better immunity against the nonideality of a three-phase grid. In order to remove even and odd-order harmonics and extract the fundamental frequency positive sequence (FFPS), the proposed method employs a combination of band pass filters (CBPFs). Additionally, a novel frequency estimation algorithm is developed, which accurately estimates the angular three-phase grid frequency. Furthermore, the phase angle and amplitude are adaptively estimated using an off-line error-resolving approach, which is derived from the transfer function of the proposed prefiltering solution. Finally, the experimental findings validate the robustness of the current proposal.

Maximum likelihood estimation and Bayesian analysis are exploited to study the quantum phase estimation in a twin-Fock state input Mach-Zehnder (M-Z) interferometer. After the theoretical and numerical calculation of particle-number difference measurement and parity measurement at the output of M-Z interferometer, the Bayesian analysis combined with particle-number difference measurement is found to be the best scheme for the optimal phase estimation, and the precision attains the quantum measurement limit given by quantum Cramer-Rao Lower Bound in full phase space. Meanwhile, the number of sample needed in Bayesian analysis is less than Maximum likelihood estimation. In the study of parity measurement, Bayesian analysis is found unsuitable for phase estimation, but Maximum likelihood estimation is good. With the Monte Carlo simulation of the specific phase estimation, the theoretical result is verified, i.e., the precision of phase estimation will change with the change of estimated θ0. Compared with the result of particle-number difference measurement, we further confirm the superiority of the particle-number difference measurement. [ABSTRACT FROM AUTHOR]

Abstract Clock synchronization is indispensable for numerous applications of wireless sensor networks (WSNs). When no common reference clock is available, the nodes must employ distributed synchronization techniques. This paper proposes, a distributed pulse‐based clock synchronisation approach, wherein the propagation delay is eliminated through signal ping‐pongs between neighbouring nodes. Such an approach can jointly estimate the clock skew and offset without requiring any reference clock. The whole synchronization process is completed at the physical (PHY) layer, effectively avoiding the random delay caused by packet queuing and retransmission. Simulation results show that the proposed approach can achieve higher synchronization accuracy compared with other existing methods.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Clock synchronization is indispensable for numerous applications of wireless sensor networks (WSNs). When no common reference clock is available, the nodes must employ distributed synchronization techniques. This paper proposes, a distributed pulse‐based clock synchronisation approach, wherein the propagation delay is eliminated through signal ping‐pongs between neighbouring nodes. Such an approach can jointly estimate the clock skew and offset without requiring any reference clock. The whole synchronization process is completed at the physical (PHY) layer, effectively avoiding the random delay caused by packet queuing and retransmission. Simulation results show that the proposed approach can achieve higher synchronization accuracy compared with other existing methods. [ABSTRACT FROM AUTHOR]

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.

Quantum state tomography (QST) is a fundamental tool requiring privacy in future distributed systems where unknown states are measured for extracting information. Gentle measurement and differential privacy (DP)-based privacy solutions minimize the damage on unknown state and leakage about the quantum information, respectively. In this article, we propose a fundamentally different design for privacy-preserving QST in a multi-party setting. We assume that Alice delegates QST task of a distant source for which she has no access to a third-party player Bob accessing to the source while preserving the source privacy against the operations realized by Bob. Encrypted QST algorithm is proposed which encodes or maps source computational basis states by exploiting phase estimation and feature mapping concept of quantum machine learning (QML). Bob maps basis states to eigenvalues of a specially designed unitary operator in an entangled manner with his ancillary qubits while teleporting the source qubits back to Alice before applying conventional QST. Encoding mechanism is conjectured as having NP-hard decoding complexity based on difficulty of subset-sum problem combined with Hadamard transform. Linear optical design and quantum circuit implementations are presented for future experiments in noisy intermediate-scale quantum (NISQ) devices. Theoretical and numerical supporting evidences are proposed supporting the proposed eigenstructure. EQST promises further applications for multiple source classification tasks and as a novel feature mapping method for future data embedding tasks in QML. [ABSTRACT FROM AUTHOR]

Many navigation, sensing, and time-transfer applications call for the high-precision timestamping of events that occur in different digital clock domains. However, state-of-the-art techniques such as digital dual mixer time difference (DDMTD) typically require that the measured clocks be syntonized, i.e., derived from a common reference oscillator and identical in frequency. This article introduces a novel, all-digital technique that can provide accurate collinear timestamps for asynchronous clock domains of arbitrary frequencies. The vernier-referenced digital asynchronous collinear timestamp (VERDACT) system allows timestamps to be generated and compared across any number of arbitrary clock domains with sub-picosecond precision. The novel circuit can be implemented on most field programmable gate arrays (FPGAs) without the need for external components. Under laboratory conditions, an open-source prototype has demonstrated end-to-end precision better than 0.8 ps-rms on a Zynq UltraScale+ FPGA. Potential applications include sub-nanosecond two-way time-transfer using precision time protocol (PTP / IEEE-1588) over an asynchronous multiport Ethernet switch.

We introduce a two-mode hybrid entangled state (NAAN) which is constructed by two n-photon Fock states and two coherent states with an arbitrary relative phase. We show that the NAAN can be considered as the superpositions of NOON states when α ≠ 0 . In the special case, when α = 0 , the NAAN degenerates to the general NOON. The most interesting nonclassical properties of this state are its strong violations of the CHSH inequality. In addition, we show explicitly some typical nonclassical properties of the NAAN state, such as entanglement, sub-Poissonian distribution, phase fluctuation and squeezing. These findings suggest that the even NAAN states exhibit a high degree of entanglement, while the odd NAAN states have a distinct sub-Poissonian distribution and the optimal phase sensitivity. [ABSTRACT FROM AUTHOR]

We discuss the accuracy of phase estimation for single-qubit and multi-qubit systems under energy conservation constraints. For the case where both the target and the auxiliary system are qubits, we analytically maximize the FI over all possible energy-conserving joint measurements and auxiliary states, and derive the form of optimal measurement and state. Then, we generalize the results to the case where the auxiliary system is d-dimensional and discover that the achievable FI can approximate quantum Fisher information for large d. Finally, we discuss the difference between global and local measurements when considering multi-qubit target systems. Our results can contribute to the study of restrictions of conservation laws on quantum measurements. [ABSTRACT FROM AUTHOR]

Using wearable devices to realize the mining and application of human behavior patterns has become a hotspot in the field of intelligent positioning. Wearable devices provide an analyzable data foundation for indoor spatial distribution and human behavior pattern prediction. The development of the intelligent positioning system based on RSSI has encountered a bottleneck that it is difficult to improve the positioning accuracy. Therefore, some research works started emphasizing location technology based on channel state information (CSI). In this paper, the principle used by Wi-Fi channel state information to realize intelligent positioning is described, the characteristics of CSI are analyzed, and an intelligent positioning algorithm based on CSI is proposed. Specifically, the algorithm first estimates the angle of arrival (AoA) based on the MUSIC algorithm, separates the reflected paths in the multipath components, and accurately estimates the AoA of each path. Second, phase estimation with channel state information is achieved by forming different antenna subarray measurements under the consideration of a subset of antennas and subcarriers. Then, the phase response linear fitting of the data packet CSI is eliminated using the ToF purification algorithm to obtain the corrected phase response and realize the elimination of the STO noise of the channel state information. Finally, the target position is calculated by effectively filtering the reflection path through the likelihood value, and the accurate target positioning function is achieved. The experimental results demonstrate that the intelligent positioning algorithm proposed in this paper can achieve decimeter-level positioning accuracy under the condition of a fixed number of APs, and the average error is better than that of deep learning-based and SVM-based positioning algorithms. In other words, the accuracy of intelligent positioning is improved. [ABSTRACT FROM AUTHOR]

With the deepening application of wearable devices in the field of indoor positioning, the mining and application of human behavior patterns based on new technologies such as mobile terminals, channel technologies and intelligent algorithms have gradually become a research hotspot in the field of intelligent positioning. At this stage, outdoor positioning satellite technology has been gradually improved, but it cannot work effectively in indoor environment, and indoor positioning technology has not formed a set of standard and effective scheme at this stage, which leads to a scene of a hundred contentions for indoor positioning technology. This paper proposes an indoor intelligent localization algorithm based on channel state information, which first forms the measurements of antenna subarrays by the fixed antenna subset arrays located in different paths to achieve the phase estimation based on channel state; then determines the AoA of the direct path from the target to the AP using ToF information and eliminates the STO noise of channel state information by unfolding the best linear fit of CSI phase; again the AoA and ToF estimates from multiple measurements are plotted in two-dimensional space, and the direct path possibility estimation is achieved by identifying the estimates through five clusters of Gaussian-averaged clustering algorithm; finally the coordinates of the target to be located are obtained by coupling direct propagation path, estimation clustering and least-squares estimation using three APs as the receiver and the target to be located as the transmitter. The simulation results show that the intelligent localization algorithm proposed in this paper is able to lack the localization accuracy at the decimeter level in the indoor localization environment with three fixed APs, and the average error is better than the localization algorithms of support vector machine, deep learning and limit learning. [ABSTRACT FROM AUTHOR]

Sensing and metrology are crucial in both fundamental science and practical applications. They meet the constant demand for precise data, enabling more dependable assessments of theoretical models’ validity. Sensors, now a common feature in many fields, play a vital role in applications like gravity imaging, geology, navigation, security, timekeeping, spectroscopy, chemistry, magnetometry, healthcare, and medicine. The advancements in quantum technologies have sparked interest in employing quantum systems as sensors, offering enhanced capabilities and new possibilities. This article describes the optimization of the quantum-enhanced sensing of magnetic fluxes with a Kitaev phase estimation algorithm based on frequency tunable transmon qubits. It provides the optimal flux biasing point for sensors with different qubit transition frequencies and gives an estimation of decoherence rates and achievable sensitivity. The use of 2- and 3-qubit entangled states are compared in simulation with the single-qubit case. The flux sensing accuracy reaches $10^{-8}\cdot\Phi_0$ and scales inversely with time, which proves the speed-up of sensing with high ultimate accuracy.

To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers. [ABSTRACT FROM AUTHOR]

Human gait phase estimation has been studied in the field of robotics due to its importance in controlling wearable devices (e.g., robotic prostheses or exoskeletons) in a synchronized manner with the user. Researchers have attempted to estimate the user’s gait phase using a learning-based method, as data-driven approaches have recently emerged in the field. In this study, we propose a new labeling method (i.e., a piecewise linear label) to have the estimator learn the ground truth based on variable toe-off onset at different walking speeds. Using whole-body marker data, we computed the angular positions and velocities of thigh and torso segments and utilized them as input data for model training. Three models (i.e., general, slow, and normal-fast) were obtained based on long short-term memory (LSTM). These models are compared in order to identify the effect of the piecewise linear label at various walking speeds. As a result, when the proposed labeling method was used while training the general model, the estimation accuracy was significantly improved. This fact was also found when estimating the user’s gait phase during the mid-stance phase. Furthermore, the proposed method maintained good performance in detecting the heel-strike and toe-off. According to the findings of this study, the newly proposed labeling method could improve speed-adaptability in gait phase estimation, resulting in outstanding accuracy for both gait phase, heel-strike, and toe-off estimation. [ABSTRACT FROM AUTHOR]

Phase estimation for a single-mode photon is investigated when the photon is influenced by two completely thermalizing channels with indefinite causal order, where a phase shift takes place between the two channels. Accuracy of the phase estimation is evaluated in terms of the quantum Fisher information. It is shown that although the channels completely destruct the phase information of the photon, the phase estimation is possible with the assistance of the indefinite causal order. • Phase estimation is studied for a single-mode photon influenced by completely thermalizing channels. • The channels are applied to the photon in indefinite causal order. • The phase estimation is shown to be possible with the assistance of the indefinite causal order. [ABSTRACT FROM AUTHOR]

Position, navigation, and timing information are critical to today’s infrastructures; as a result, the possibility of estimating ranges is being explored in more and more radio systems. One way to achieve this is to extend the modulation with time-synchronised aiding carriers and to estimate their phase at the receiver side. In this paper, we present two ways to minimise the negative influence of the modulation on the phase estimation. We show that the classical maximum likelihood estimator is still an efficient estimator for our problem, using a medium-frequency R-Mode signal as an example, and is therefore used in receiver designs. We then describe two possible ways to precondition the signal to increase the accuracy for short observations. As a first approach, we describe how window functions can positively change the signal-to-noise ratio for our estimation. As a second approach, we show the use of a narrowband bandpass filter. Finally, we show that these approaches, applied to real measurements, improve the variance of the estimate by up to two orders of magnitude.

This paper presents an approach to the problems of detection and frequency estimation of a frequency modulated narrow-band signal in additive complex Gaussian noise. The signal is assumed to have an unknown amplitude, initial phase and frequency trajectory over time, while a priori information regarding random frequency variability is taken to be available. The proposed approach operates in the frequency domain and uses the magnitude and phase of the discrete-time Fourier transforms computed over nonoverlapping signal segments. Robustness at low signal-to-noise ratios is achieved by suppressing the segment-related likelihoods for which the phase estimation error is large. The approach utilises unthresholded transform data and thus works in a track-before-detect manner, and the frequency trajectory is estimated by applying a search over the frequency-time bins. The results of a simulation study involving two signal types with different frequency variability are described.

Objective: In-phase stimulation of EEG slow waves (SW) during deep sleep has shown to improve cognitive function. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration. However, existing algorithms to estimate the up-phase of EEG suffer from a poor phase accuracy at low amplitudes and when SW frequencies are not constant. Methods: We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices, a phase-locked loop (PLL) and, for the first time, a phase vocoder (PV). We compared these phase tracking algorithms with a simple amplitude threshold approach. The optimized algorithms were benchmarked for phase accuracy, the capacity to estimate phase at SW amplitudes between 20 and 60 μV, and SW frequencies above 1 Hz on 324 home-based recordings from healthy older adults and Parkinson disease (PD) patients. Furthermore, the algorithms were implemented on a wearable device and the computational efficiency and the performance was evaluated in simulation and with a PD patient. Results: All three algorithms delivered more than 70% of the stimulation triggers during the SW up-phase. The PV showed the highest capacity on targeting low-amplitude SW and SW with frequencies above 1 Hz. The hardware testing revealed that both PV and PLL have marginal impact on microcontroller load, while the efficiency of the PV was 4% lower. Active stimulation did not influence the phase tracking. Conclusion: This work demonstrated that phase-accurate auditory stimulation can also be delivered during fully remote sleep interventions in populations with low-amplitude SW. [ABSTRACT FROM AUTHOR]

Purpose: 4D reconstruction based on radiation-free ultrasound can provide valuable information about the anatomy. Current 4D US technologies are either faced with limited field-of-view (FoV), technical complications, or cumbersome setups. This paper proposes a spatiotemporal US reconstruction framework to enhance its ability to provide dynamic structure information. Methods: We propose a spatiotemporal US reconstruction framework based on freehand sonography. First, a collecting strategy is presented to acquire 2D US images in multiple spatial and temporal positions. A morphology-based phase extraction method after pose correction is presented to decouple the compounding image variations. For temporal alignment and reconstruction, a robust kernel regression model is established to reconstruct images in arbitrary phases. Finally, the spatiotemporal reconstruction is demonstrated in the form of 4D movies by integrating the US images according to the tracked poses and estimated phases. Results: Quantitative and qualitative experiments were conducted on the carotid US to validate the feasibility of the proposed pipeline. The mean phase localization and heart rate estimation errors were 0.07 ± 0.04 s and 0.83 ± 3.35 bpm, respectively, compared with cardiac gating signals. The assessment of reconstruction quality showed a low RMSE (<0.06) between consecutive images. Quantitative comparisons of anatomy reconstruction from the generated US volumes and MRI showed an average surface distance of 0.39 ± 0.09 mm on the common carotid artery and 0.53 ± 0.05 mm with a landmark localization error of 0.60 ± 0.18 mm on carotid bifurcation. Conclusion: A novel spatiotemporal US reconstruction framework based on freehand sonography is proposed that preserves the utility nature of conventional freehand US. Evaluations on in vivo datasets indicated that our framework could achieve acceptable reconstruction performance and show potential application value in the US examination of dynamic anatomy. [ABSTRACT FROM AUTHOR]

The Cramér-Rao bound (CRB) has been extensively used as a benchmark for estimation performance in both Bayesian and non-Bayesian frameworks. In many practical periodic parameter estimation problems, such as phase, frequency, and direction-of-arrival estimation, the observation model is periodic with respect to the unknown parameters and thus, the appropriate criterion is periodic in the parameter space. Consequently, Bayesian lower bounds on the mean-squared-error (MSE) are not valid bounds for periodic estimation problems. In addition, many Bayesian MSE lower bounds cannot be derived in the periodic case due to their restrictive regularity conditions. For example, the regularity conditions of the Bayesian CRB (BCRB) are not satisfied for parameters with uniform prior distribution. In this letter, we derive a Bayesian Cramér-Rao-type lower bound on the mean-squared-periodic-error (MSPE). The proposed periodic BCRB (PBCRB) is a lower bound on the MSPE of any estimator and has less restrictive regularity conditions than the BCRB. The PBCRB is compared with the MSPE of the minimum MSPE estimator for phase estimation in Gaussian noise and it is shown that the PBCRB is a valid and tight lower bound for this problem. [ABSTRACT FROM AUTHOR]

In recent years, kernel methods have provided an important alternative solution, as they offer a simple way of expanding linear algorithms to cover the non-linear mode as well. In this paper, we propose a novel recursive kernel approach allowing to identify the finite impulse response (FIR) in non-linear systems, with binary value output observations. This approach employs a kernel function to perform implicit data mapping. The transformation is performed by changing the basis of the data in a high-dimensional feature space in which the relations between the different variables become linearized. To assess the performance of the proposed approach, we have compared it with two other algorithms, such as proportionate normalized least-meansquare (PNLMS) and improved PNLMS (IPNLMS). For this purpose, we used three measurable frequency-selective fading radio channels, known as the broadband radio access network (BRAN C, BRAN D, and BRAN E), which are standardized by the European Telecommunications Standards Institute (ETSI), and one theoretical frequency selective channel, known as the Macchi’s channel. Simulation results show that the proposed algorithm offers better results, even in high noise environments, and generates a lower mean square error (MSE) compared with PNLMS and IPNLMS.

In a subcarrier multiplexing 125 Gbaud DP-PCS-64QAM system with a net bitrate of 800 Gbit/s, we numerically and experimentally study the performance of transmission-aware carrier phase estimation (CPE) for long-haul systems. The recently proposed transmission-aware dual-reference subcarrier CPE (DRS-CPE) is assessed against a more conventional per-subcarrier CPE approach and a transmission-agnostic joint CPE in 8- and 16-subcarrier transmission. At laser linewidths of $\sim$ 150 kHz, we achieve an experimental signal-to-noise ratio gain over a single-carrier of up to 0.5 dB after 1800 km transmission, enabled by the DRS-CPE. [ABSTRACT FROM AUTHOR]

We present a solid foundation for the theory and practice of using pilot tones for estimation and compensation of phase noise in a communication link. We analyze several phase estimators and illustrate their pros and cons, and we derive the necessary optimal signal-to-pilot-power relationship when the transmitted power is shared between the communication signal and the pilot tone. We show that the optimal signal-to-pilot-power ratio increases with decreasing phase noise and with increased additive noise. [ABSTRACT FROM AUTHOR]

In recent years, kernel methods have provided an important alternative solution, as they offer a simple way of expanding linear algorithms to cover the non-linear mode as well. In this paper, we propose a novel recursive kernel approach allowing to identify the finite impulse response (FIR) in non-linear systems, with binary value output observations. This approach employs a kernel function to perform implicit data mapping. The transformation is performed by changing the basis of the data in a high-dimensional feature space in which the relations between the different variables become linearized. To assess the performance of the proposed approach, we have compared it with two other algorithms, such as proportionate normalized least-meansquare (PNLMS) and improved PNLMS (IPNLMS). For this purpose, we used three measurable frequency-selective fading radio channels, known as the broadband radio access network (BRAN C, BRAN D, and BRAN E), which are standardized by the European Telecommunications Standards Institute (ETSI), and one theoretical frequency selective channel, known as the Macchi's channel. Simulation results show that the proposed algorithm offers better results, even in high noise environments, and generates a lower mean square error (MSE) compared with PNLMS and IPNLMS. [ABSTRACT FROM AUTHOR]

An ultra-robust accurate gait phase estimator is developed by training a time-delay neural network (D67) on data collected from the hip and knee joint angles of 14 participants during treadmill and overground walking. Collected data include normal gait at speeds ranging from 0.1m/s to 1.9m/s and conditions such as long stride, short stride, asymmetric walking, stop-start, and abrupt speed changes. Spatial analysis of our method indicates an average RMSE of 1.74±0.23% and 2.35±0.52% in gait phase estimation of test participants in the treadmill and overground walking, respectively. The temporal analysis reveals that D67 detects heel-strike events with an average MAE of 1.70±0.54% and 2.74±0.92% of step duration on test participants in the treadmill and overground walking, respectively. Both spatial and temporal performances are uniform across participants and gait conditions. Further analyses indicate the robustness of the D67 to smooth and abrupt speed changes, limping, variation of stride length, and sudden start or stop of walking. The performance of the D67 is also compared to the state-of-the-art techniques confirming the superior and comparable performance of the D67 to techniques without and with a ground contact sensor, respectively. The estimator is finally tested on a participant walking with an active exoskeleton, demonstrating the robustness of D67 in interaction with an exoskeleton without being trained on any data from the test subject with or without an exoskeleton. [ABSTRACT FROM AUTHOR]

The performanceof previous machine learning models for gait phase is only satisfactory under limited conditions. First, they produce accurate estimations only when the ground truth of the gait phase (of the target subject) is known. In contrast, when the ground truth of a target subject is not used to train an algorithm, the estimation error noticeably increases. Expensive equipment is required to precisely measure the ground truth of the gait phase. Thus, previous methods have practical shortcoming when they are optimized for individual users. To address this problem, this study introduces an unsupervised domain adaptation technique for estimation without the true gait phase of the target subject. Specifically, a domain-adversarial neural network was modified to perform regression on continuous gait phases. Second, the accuracy of previous models can be degraded by variations in stride time. To address this problem, this study developed an adaptive window method that actively considers changes in stride time. This model considerably reduces estimation errors for walking and running motions. Finally, this study proposed a new method to select the optimal source subject (among several subjects) by defining the similarity between sequential embedding features. [ABSTRACT FROM AUTHOR]

In fringe projection profilometry, high-order harmonics information of distorted fringe will lead to errors in the phase estimation. In order to solve this problem, a point-wise phase estimation method based on a neural network (PWPE-NN) is proposed in this paper. The complex nonlinear mapping relationship between the gray values and the phase under non-sinusoidal distortion is constructed by using the simple neural network model. It establishes a novel implicit expression for phase solution without complicated measurement operations. Compared with the previous method of combining local image information, it can accurately calculate each phase value by point. The comparison results show that the traditional method is with periodic phase errors, while the proposed method can effectively eliminate phase errors caused by non-sinusoidal phase shifting. [ABSTRACT FROM AUTHOR]

In this study, the use of boosting techniques is empirically examined to determine to what extent quantum weak learners can be improved for binary classification tasks. In classical or quantum boosting, weak learners are treated as hybrid quantum–classical learners when realized on quantum simulators or NISQ devices. For hybrid quantum–classical learners, classical algorithms are used to train the learners. In the quantum boosting method proposed in this study, quantum mean estimation using quantum amplitude estimation is applied and conditions for ensuring a strong learner are presented. In addition, a quantum perceptron using quantum phase estimation is proposed as an easy-to-implement weak learner. Experiments were conducted on two binary classification tasks (binary and continuous feature spaces), the results of which showed that the resultant learners behaved as strong learners. [ABSTRACT FROM AUTHOR]

Blocking the grid voltage imbalance through the $\alpha \beta$ -frame delayed signal cancellation ($\alpha \beta$ DSC) operator requires a relatively high delay value of $1/4$ cycle. Since the dynamic behavior of the $\alpha \beta$ -frame cascaded delayed signal cancellation based phase-locked loop ($\alpha \beta$ CDSC-PLL) depends on the total delay length of the cascaded operators, this value can slow down the dynamic response. In this paper, however, inspired by the delayed signal cancelation (DSC) concept, an enhanced $\alpha \beta$ -frame DSC operator, which removes the grid voltage imbalance with a delay length of only $1/12$ cycle, is proposed. The small-signal model of the suggested frequency adaptive operator is derived, making the tuning procedure and stability analysis easy yet effective. Due to the shorter delay length, using the proposed operator in the $\alpha \beta$ CDSC-PLL structure leads to a higher speed-of-response and, therefore, a less phase-angle error during grid voltage scenarios. Moreover, when the total delay length decreases, the filtering capability will be improved using more operators at the chain of the cascaded DSC operators. The performance of the $\alpha \beta$ CDSC-PLL and its suggested enhanced structure, benefiting from the proposed operator, are compared through theoretical analysis and experimental tests, which verify that the proposed structure offers a better dynamic response and filtering capability. [ABSTRACT FROM AUTHOR]

Quantum phase estimation is an important component in diverse quantum algorithms. However, it suffers from spectral leakage, when the reciprocal of the record length is not an integer multiple of the unknown phase, which incurs an accuracy degradation. For the existing single-sample estimation scheme, window-based methods have been proposed for spectral leakage mitigation. As a further advance, we propose a dual-frequency estimator, which asymptotically approaches the Cramér-Rao bound, when multiple samples are available. Numerical results show that the proposed estimator outperforms the existing window-based methods, when the number of samples is sufficiently high. [ABSTRACT FROM AUTHOR]

Abstract A low‐complexity method to reduce the offset voltage of dynamic comparators employed as samplers in decision feedback equalisers (DFE) is introduced. The authors propose the phase‐domain offset reduction technique (PORT), which leverages an all‐digital phase estimation of output data for offset compensation, without setting the comparator input to a common‐mode voltage (VCM). While traditional techniques might break the data link for offset adjustment, the proposed technique allows calibrating the comparator on‐the‐fly. Measurements from a 26‐dB‐loss on‐chip emulated channel with chip‐scope capability validates PORT through eye‐diagrams at sampler input. A prototype was implemented in a TSMC 130 nm 1.2 V process, and experimental results show the possibility of extending PORT to state‐of‐the‐art technology nodes for multi‐gigabit operation.

• Non-ideal sensing matrix is implemented in the MWC scheme for signal reconstruction. • Phase mismatches of reconstructed signals in the MWC are detailed. • Average phase calibration method based on a 0/1 mixing sequence has been proposed to reduce phase errors of the sensing matrix. • Several comparative simulations are carried out to validate the proposed method. As a sub-Nyquist sampling structure based on compressed sensing (CS), a modulated wideband converter (MWC) is used for spectral blind sampling and reconstruction of multiband sparse signals. Sinusoidal signals with known phases have been theoretically used to study the calibration of the sensing matrix of MWC, but in practice, the initial phase of sinusoidal signals is unfortunately difficult to know in advance. Consequently, phase error will exist in the calibrated sensing matrix, which reduces the reconstruction signal-to-noise ratio (RSNR) of the reconstructed signal. In this paper, we first analyze the phase error uncertainty caused by the DC component of the periodic pseudo-random sequence. In addition, an average-phase calibration method based on a 0/1 mixing sequence is proposed to calibrate the sensing matrix. Each column of the sensing matrix is calibrated according to its average phase, which avoids introducing phase error when using sinusoidal signals with arbitrary unknown phases. Comparative experiments are conducted to evaluate the performance of the proposed method in terms of the RSNR and the reconstruction mean squared error (RMSE). The results show that the RSNR obtained by the proposed method is 40 dB, which is close to the signal-to-noise (SNR) of the input RF signal. [ABSTRACT FROM AUTHOR]

Sub-terahertz (THz)-band communication system has drawn much attention as a promising technology to provide future-proof high data rate services. In a photonics-based sub-THz communication system, the generation of THz wave using free running lasers enables us to make a simple, cost effective and frequency tunable implementation. On the other hand, commercially available lasers have relatively broader linewidth and a large carrier frequency offset (CFO). To mitigate the performance degradations due to phase noise and CFO, a carrier recovery digital signal processing (DSP) algorithm is studied for a sub-THz transmission system. We propose a novel phase estimation algorithm to avoid cyclic slips while minimizing phase estimation error to improve BER performance. Our proposed phase recovery DSP algorithm is demonstrated in a 16-quadrature amplitude modulation (QAM) in a 0.3 THz band photonics-based transmission system. Experimental results show that the measured BER are improved from 8.8×10−3 to 3.6×10−3 in a 120 Gb/s 16-QAM transmission using the proposed algorithm. A wide range of CFO estimation is also supported for a sub-THz wireless transmission system using off-the-shelf lasers. Recovery of a CFO between −5 GHz and 5 GHz was also successfully demonstrated. [ABSTRACT FROM AUTHOR]