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Background Cardiovascular magnetic resonance (CMR) phase contrast (PC) flow measurements suffer from phase offset errors. Background subtraction based on stationary phantom measurements can most reliably be used to overcome this inaccuracy. Stationary tissue correction is an alternative and does not require additional phantom scanning. The aim of this study was 1) to compare measurements with and without stationary tissue correction to phantom corrected measurements on different GE Healthcare CMR scanners using different software packages and 2) to evaluate the clinical implications of these methods. Methods CMR PC imaging of both the aortic and pulmonary artery flow was performed in patients on three different 1.5 T CMR scanners (GE Healthcare) using identical scan parameters. Uncorrected, first, second and third order stationary tissue corrected flow measurement were compared to phantom corrected flow measurements, our reference method, using Medis QFlow, Circle cvi42 and MASS software. The optimal (optimized) stationary tissue order was determined per scanner and software program. Velocity offsets, net flow, clinically significant difference (deviation > 10% net flow), and regurgitation severity were assessed. Results Data from 175 patients (28 (17–38) years) were included, of which 84% had congenital heart disease. First, second and third order and optimized stationary tissue correction did not improve the velocity offsets and net flow measurements. Uncorrected measurements resulted in the least clinically significant differences in net flow compared to phantom corrected data. Optimized stationary tissue correction per scanner and software program resulted in net flow differences (> 10%) in 19% (MASS) and 30% (Circle cvi42) of all measurements compared to 18% (MASS) and 23% (Circle cvi42) with no correction. Compared to phantom correction, regurgitation reclassification was the least common using uncorrected data. One CMR scanner performed worse and significant net flow differences of > 10% were present both with and without stationary tissue correction in more than 30% of all measurements. Conclusion Phase offset errors had a significant impact on net flow quantification, regurgitation assessment and varied greatly between CMR scanners. Background phase correction using stationary tissue correction worsened accuracy compared to no correction on three GE Healthcare CMR scanners. Therefore, careful assessment of phase offset errors at each individual scanner is essential to determine whether routine use of phantom correction is necessary. Trial registration Observational Study

In the assessment of pulmonary regurgitation (PR) using phase contrast MRI, phase offset errors affect the accuracy of flow. This study evaluated the use of automated background correction for phase offset in the quantification of PR fraction and volume in patients with repaired tetralogy of Fallot (TOF), and to assess its clinical impact. We retrospectively analyzed 203 cardiac MRI studies, performed on 1.5-T scanner. Pulmonary flow (Q P ) and systemic flow (Q S ) was assessed both with and without background correction. Non-corrected and corrected Q P was correlated with Q S . PR was correlated with (1) indexed right ventricular end-diastolic volume (RVEDVi) and (2) with differential right and left ventricular stroke volumes (PR SV ). Both PR fraction and volume showed major change after correction (−43 to +36 % and −13 to +13 ml/m 2 ). Corrected Q P and Q S were stronger correlated with each other than non-corrected Q P and Q S [r = 0.78 vs. 0.73 ( p  < 0.001)]. Both PR fraction and volume were stronger correlated with RVEDVi, compared to their non-corrected counterparts ( p  < 0.001). PR volume was stronger correlated with RVEDVi, compared to PR fraction [r = 0.74 vs. 0.69 ( p  < 0.001)]. When patients were divided according to PR severity, 12 % of patients reclassified after correction. Background correction for phase offset significantly changed the quantification of PR. Non-corrected assessment of PR may result in the misclassification of patients. Our data suggest that the use of PR volume is favourable in the follow-up of patients with repaired TOF.

The smart grid control applications necessitate real-time communication systems with time efficiency for real-time monitoring, measurement, and control. Time-efficient communication systems should have the ability to function in severe propagation conditions in smart grid applications. The data/packet communications need to be maintained by synchronized timing and reliability through equally considering the signal deterioration occurrences, which are propagation delay, phase errors and channel conditions. Phase synchronization plays a vital part in the digital smart grid to get precise and real-time control measurement information. IEEE C37.118 and IEC 61850 had implemented for the synchronization communication to measure as well as control the smart grid applications. Both IEEE C37.118 and IEC 61850 experienced a huge propagation and packet delays due to synchronization precision issues. Because of these delays and errors, measurement and monitoring of the smart grid application in real-time is not accurate. Therefore, it has been investigated that the time synchronization in real-time is a critical challenge in smart grid applications, and for this issue, other errors raised consequently. The existing communication systems are designed with the phasor measurement unit (PMU) along with communication protocol IEEE C37.118 and uses the GPS timestamps as the reference clock stamps. The absence of GPS increases the clock offsets, which surely can hamper the synchronization process and the full control measurement system that can be imprecise. Therefore, to reduce this clock offsets, a new algorithm is needed which may consider any alternative reference timestamps rather than GPS. The revolutionary Artificial Intelligence (AI) enables the industrial revolution to provide a significant performance to engineering solutions. Therefore, this article proposed the AI-based Synchronization scheme to mitigate smart grid timing issues. The backpropagation neural network is applied as the AI method that employs the timing estimations and error corrections for the precise performances. The novel AIFS scheme is considered the radio communication functionalities in order to connect the external timing server. The performance of the proposed AIFS scheme is evaluated using a MATLAB-based simulation approach. Simulation results show that the proposed scheme performs better than the existing system.

Quadrature phase shift keying (QPSK) is a digital modulation technique that transmits data at a constant frequency whilst varying the phases of the carrier signal. QPSK is one of the fundamental modulation schemes for orthogonal frequency division multiplexing systems (OFDM). It is a stable modulation technique with good spectral efficiency. However, during transmission, the carrier signal can undergo numerous phase changes. This creates phase ambiguity problems at the receiver end. This results in inter-symbol interference (ISI) and a high bit error rate (BER). In this paper, the wind-driven optimization was incorporated into the genetic algorithm (GA) as its population selection function. This hybrid algorithm was used to determine the phase assignments for the QPSK. The developed QPSK was implemented on an OFDM network and the message signal was recovered at more than 92% accuracy in a noisy Rayleigh fading channel and 100% accuracy in a noiseless channel. The enhancements greatly mitigated phase ambiguity and bit errors.

Background A velocity offset error in phase contrast cardiovascular magnetic resonance (CMR) imaging is a known problem in clinical assessment of flow volumes in vessels around the heart. Earlier studies have shown that this offset error is clinically relevant over different systems, and cannot be removed by protocol optimization. Correction methods using phantom measurements are time consuming, and assume reproducibility of the offsets which is not the case for all systems. An alternative previously published solution is to correct the in-vivo data in post-processing, interpolating the velocity offset from stationary tissue within the field-of-view. This study aims to validate this interpolation-based offset correction in-vivo in a multi-vendor, multi-center setup. Methods Data from six 1.5 T CMR systems were evaluated, with two systems from each of the three main vendors. At each system aortic and main pulmonary artery 2D flow studies were acquired during routine clinical or research examinations, with an additional phantom measurement using identical acquisition parameters. To verify the phantom acquisition, a region-of-interest (ROI) at stationary tissue in the thorax wall was placed and compared between in-vivo and phantom measurements. Interpolation-based offset correction was performed on the in-vivo data, after manually excluding regions of spatial wraparound. Correction performance of different spatial orders of interpolation planes was evaluated. Results A total of 126 flow measurements in 82 subjects were included. At the thorax wall the agreement between in-vivo and phantom was − 0.2 ± 0.6 cm/s. Twenty-eight studies were excluded because of a difference at the thorax wall exceeding 0.6 cm/s from the phantom scan, leaving 98. Before correction, the offset at the vessel as assessed in the phantom was − 0.4 ± 1.5 cm/s, which resulted in a − 5 ± 16% error in cardiac output. The optimal order of the interpolation correction plane was 1st order, except for one system at which a 2nd order plane was required. Application of the interpolation-based correction revealed a remaining offset velocity of 0.1 ± 0.5 cm/s and 0 ± 5% error in cardiac output. Conclusions This study shows that interpolation-based offset correction reduces the offset with comparable efficacy as phantom measurement phase offset correction, without the time penalty imposed by phantom scans. Trial registration The study was registered in The Netherlands National Trial Register (NTR) under TC 4865 . Registered 19 September 2014. Retrospectively registered.

In 2016, an application programming interface was added to the Android operating systems, which enables the access of GNSS raw observations. Since then, an in-depth evaluation of the performance of smartphone GNSS chips is very much simplified. We analyzed the quality of the GNSS observations, especially the carrier phase observations, of the dual-frequency GNSS chip Kirin 980 built into Huawei P30 and other smartphones. More than 80 h of static observations were collected at several locations. The code and carrier phase observations were processed in baseline mode with reference to observations of geodetic-grade equipment. We were able to fix carrier phase ambiguities for GPS L1 observations. Furthermore, we performed an antenna calibration for this frequency, which revealed that the horizontal phase center offsets from the central vertical axis of the smartphone and also the phase center variations do not exceed 1–2 cm. After successful ambiguity fixing, the 3D position errors (standard deviations) are smaller 4 cm after 5 min of static observation session and 2 cm for long observation session.

Fast and accurate monitoring of the phase, amplitude, and frequency of the grid voltage is essential for single-phase grid-connected converters. The presence of DC offset in the grid voltage is detrimental to not only grid synchronization but also the closed-loop stability of the grid-connected converters. In this paper, a new synchronization method to mitigate the effect of DC offset is presented using arbitrarily delayed signal cancelation (ADSC) in a second-order generalized integrator (SOGI) phase-locked loop (PLL). A frequency-fixed SOGI-based PLL (FFSOGI-PLL) is adopted to ensure better stability and to reduce the complexity compared with other SOGI-based PLLs. A small-signal model of the proposed PLL is derived for the systematic design of proportional-integral (PI) controller gains. The effects of frequency variation and ADSC on the proposed PLL are considered, and correction methods are adopted to accurately estimate grid information. The simulation results are presented, along with comparisons to other single-phase PLLs in terms of settling time, peak frequency, and phase error to validate the proposed PLL. The dynamic performance of the proposed PLL is also experimentally validated. Overall, the proposed PLL has the fastest transient response and better dynamic performance than the other PLLs for almost all performance indices, offering an improved solution for precise grid synchronization in single-phase applications.

This paper proposes a hierarchical multi‐stage framework for carrier frequency offset estimation in terahertz (THz) communications. In heterodyne THz transceivers, MHz‐level local‐oscillator mismatches can be amplified to hundreds of MHz after multi‐stage frequency multiplication, whereas severe phase noise and low signal‐to‐noise ratio restrict coherent integration. The proposed estimator comprises three stages: (i) coarse acquisition based on time‐averaged short‐time Fourier transform spectrum analysis with multi‐peak selection, (ii) a confidence‐guided adaptive grid search that is invoked only when the coarse estimate is unreliable and (iii) fine time‐domain correlation refinement with sub‐grid interpolation. Numerical results under multipath propagation, molecular absorption and Wiener phase noise demonstrate that the proposed method reduces a 300 MHz‐level initial offset to residual errors on the order of several tens of kHz with adaptive complexity, and outperforms FFT‐based and pilot‐correlation baselines by orders of magnitude in mean‐square error.

Tracking ambiguity and multipath are two serious threats in processing binary offset carrier (BOC) signals that are widely used in global navigation satellite systems (GNSS). Three-loop tracking methods, such as dual binary phase-shift keying tracking (DBT), use a delay lock loop (DLL), a phase lock loop (PLL), and a subcarrier PLL (SPLL) to track the code, carrier, and subcarrier, respectively, thus solving the tracking ambiguity problem. However, the multipath remains an important factor in the ranging performance deterioration of these tracking methods. Using the offset correlator (OC) technique in the SPLL can effectively mitigate subcarrier multipath errors, but it substantially raises thermal noise errors. To solve this contradiction, this paper proposes a prompt-assisted-offset correlator (PAOC) technique that combines the prompt and offset correlators in a complementary way to mitigate multipath for BOC signals. Compared with the original OC technique, the PAOC technique has less thermal noise performance loss. Moreover, this paper discovers and quantifies the interaction between carrier and subcarrier multipath errors by analyzing the coupling effect between SPLL and PLL. Most multipath mitigation methods for BOC signals ignore the carrier multipath in the PLL, so their subcarrier multipath performance is not optimal. Thus, this paper further proposes applying the PAOC technique in both PLL and SPLL to mitigate carrier and subcarrier multipath errors simultaneously. Theoretical analysis and experimental results demonstrate that the proposed method can significantly improve the multipath performance with small noise performance loss.

The issue of intrinsic‐type misalignment errors arising from angular offsets between magnets in an undulator is addressed. A random tilt of the magnets or poles generates undesirable magnetic field components in both transverse and longitudinal directions and gives rise to errors in period lengths and amplitudes. These localized errors are carried to the entire undulator segments and are a cause of concern for precision field integral and phase error estimates. A laser interferometer has been designed to read the offsets and to fix the magnets to minimize the offsets. A laser interferometer has been developed to read and fix magnet misalignments in an undulator.