Diagnostic accuracy of voluntary and stimulated neuromuscular jitter studies in ocular myasthenia gravis.

INTRODUCTION/AIMS: There is a lack of studies comparing the accuracy of neuromuscular jitter analysis during voluntary activation (v-jitter study) versus axonal stimulation (s-jitter study). The study aimed to compare these two techniques in the same population of patients with suspected ocular myasthenia gravis (OMG). METHODS: Fourteen control subjects (mean age: 55.5 ± 15.2 years) and 34 patients with suspected OMG (mean age: 59 ± 13.9 years) were prospectively evaluated. Twenty spike pairs and 30 individual spikes were analyzed during v-jitter and s-jitter study, respectively. Two different criteria for abnormal individual jitter values were evaluated: ≥ or > than 10% values exceeding the upper normal limit. RESULTS: OMG was diagnosed in 19 patients based on clinical and laboratory findings, without considering jitter measurements. In most patients, v-jitter and s-jitter analyses provided comparable results. The maximum sensitivity (89%) was achieved with s-jitter study using the ≥10% criterion, while the maximum specificity (93%) was found with v-jitter study using the >10% criterion. DISCUSSION: Both v-jitter and s-jitter studies showed good to very good accuracy for the diagnosis of OMG, in the absence of any statistically significant difference. Therefore, the patient's cooperation level and examiner's experience should guide the choice of performing v-jitter or s-jitter analysis in patients with suspected OMG.

Efficacy of platelet-rich plasma (PRP) in benign vocal fold lesions: a systematic review and meta-analysis.

OBJECTIVES: To evaluate the efficacy of platelet-rich plasma (PRP) in benign vocal fold lesions. METHODS: MEDLINE, Cochrane Central, Web of Science, and Scopus databases were searched in April 2023 for relevant clinical trials. Inclusion criteria were clinical trials evaluating the efficacy of PRP in benign vocal fold lesions. We conducted a comparative double-arm analysis using the pooled mean difference (MD) and 95% confidence interval (CI). Outcomes of interest included the vocal handicap index (VHI), the Jitter and Shimmer percentages, and the noise-to-harmonic ratio (NHR). RESULTS: Six studies matched the inclusion criteria. The pooled analysis shows that PRP was associated with significantly lower VHI scores compared with the control (MD = - 5.06, p < 0.01). Regarding the Jitter percentage, the PRP group was not superior to the control group at 2 and 4 weeks. However, the results revealed that PRP significantly reduced the Jitter percentage at 3 months (MD = - 0.61, p = 0.0008). The overall analysis favored the PRP arm significantly (p < 0.001). As for the Shimmer percentage, the combined effect estimate favored the PRP group (MD = - 1.22, p = 0.002). Subgroup analysis according to the time did not reveal any significant differences between studies at 2 weeks, 4 weeks, and 3 months. The analysis of the NHR outcome revealed a significant difference between both groups (MD = -1.09, p = 0.01). However, at 4 weeks, the treatment group had a significantly lower NHR % compared to the control group (MD = - 0.61, p = 0.02). There was no significant difference at 3 months (MD = - 2.14, p = 0.14). CONCLUSIONS: Platelet-rich plasma is effective in reducing VHI scores, Jitter and Shimmer percentages, and NHR values. This effect is more evident after follow-up, especially 3 months.

Study of the Bias of the Initial Phase Estimation of a Sinewave of Known Frequency in the Presence of Phase Noise.

The estimation of the parameters of a sinusoidal signal is of paramount importance in various applications in the fields of sensors, signal processing, parameter estimation, and device characterization, among others. The presence, in the measurement system, of non-ideal phenomena such as additive noise in the signals, phase noise in the stimulus generation, jitter in the sampling system, frequency error in the experimental setup, among others, leads to increased uncertainty and bias in the estimated quantities obtained by least squares methods and those derived from them. Therefore, from a metrological point of view, it is important to be able to theoretically predict and quantify those uncertainties in order to properly design the measurement system and its parameters, such as the number of samples to acquire or the stimulus signal amplitude to use to minimize the uncertainty in the estimated values. Previous works have shown that the presence of these non-ideal phenomena leads to increased uncertainty and bias in the estimation of the sinewave amplitude. The present work complements this knowledge by focusing specifically on the effect of phase noise and sampling jitter in the bias of the initial phase estimation of a sinusoidal signal of known frequency (three­parameter sine fitting procedure). A theoretical derivation of the bias of initial phase estimation that takes into consideration the presence of phase noise in the sinewave is presented. Since a Taylor series approximation was used where only the first term was retained, it was necessary to validate the analytical derivations with numerical simulations using a Monte Carlo type of procedure. This process was applied to different conditions regarding the phase noise standard deviation, initial phase value, and number of samples. It is concluded that, in most scenarios, initial phase estimation using sine fitting is unbiased in the presence of phase noise or jitter. It is shown, however, that in cases of extremely high phase noise standard deviation and a very low number of samples, a bias occurs.

A Segmented Cross-Correlation Algorithm for Dynamic North Finding Using Fiber Optic Gyroscopes.

Fiber optic gyroscope (FOG)-based north finding is extensively applied in navigation, positioning, and various fields. In dynamic north finding, an accelerated turntable speed shortens the time required for north finding, resulting in a rapid north-finding response. However, with an increase in turntable speed, the turntable's jitter contributes to signal contamination in the FOG, leading to a deterioration in north-finding accuracy. This paper introduces a divide-and-conquer algorithm, the segmented cross-correlation algorithm, designed to mitigate the impact of turntable speed jitter. A model for north-finding error is established and analyzed, incorporating FOG's self-noise and the turntable's speed jitter. To validate the feasibility of our method, we implemented the algorithm on a FOG. The simulation and experimental results exhibited a strong concordance, affirming the validity of our proposed north-finding error model. The experimental findings indicate that, at a turntable speed of 180°/s, the north-finding bias error within a 360 s duration is 0.052°, representing a 64% improvement over the traditional algorithm. These results indicate the effectiveness of the proposed algorithm in mitigating the impact of unstable turntable speeds, offering a solution for north finding with both prompt response and enhanced accuracy.

Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices.

The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm's data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes.

Jitter-Caused Clutter and Drift-Caused Clutter of Staring Infrared Sensor in Geostationary Orbit.

For staring infrared sensors in geostationary orbit, the clutter caused by the high-frequency jitter and low-frequency drift of the sensor line-of-sight (LOS) is the impact of background features, sensor parameters, LOS motion characteristics, and background suppression algorithms. In this paper, the spectra of LOS jitter caused by cryocoolers and momentum wheels are analyzed, and the time-related factors such as the jitter spectrum, the detector integration time, the frame period, and the temporal differencing background suppression algorithm are considered comprehensively; they are combined into a background-independent jitter-equivalent angle model. A jitter-caused clutter model in the form of multiplying the background radiation intensity gradient statistics by the jitter-equivalent angle is established. This model has good versatility and high efficiency and is suitable for the quantitative evaluation of clutter and the iterative optimization of sensor design. Based on satellite ground vibration experiments and on-orbit measured image sequences, the jitter-caused clutter and drift-caused clutter models are verified. The relative deviation between the model calculation and the actual measurement results is less than 20%.

Modeling for Single-Photon Avalanche Diodes: State-of-the-Art and Research Challenges.

With the growing importance of single-photon-counting (SPC) techniques, researchers are now designing high-performance systems based on single-photon avalanche diodes (SPADs). SPADs with high performances and low cost allow the popularity of SPC-based systems for medical and industrial applications. However, few efforts were put into the design optimization of SPADs due to limited calibrated models of the SPAD itself and its related circuits. This paper provides a perspective on improving SPAD-based system design by reviewing the development of SPAD models. First, important SPAD principles such as photon detection probability (PDP), dark count rate (DCR), afterpulsing probability (AP), and timing jitter (TJ) are discussed. Then a comprehensive discussion of various SPAD models focusing on each of the parameters is provided. Finally, important research challenges regarding the development of more advanced SPAD models are summarized, followed by the outlook for the future development of SPAD models and emerging SPAD modeling methods.

Effects of short-term unloading and active recovery on human motor unit properties, neuromuscular junction transmission and transcriptomic profile.

Electrophysiological alterations of the neuromuscular junction (NMJ) and motor unit potential (MUP) with unloading are poorly studied. We aimed to investigate these aspects and the underlying molecular mechanisms with short-term unloading and active recovery (AR). Eleven healthy males underwent a 10-day unilateral lower limb suspension (ULLS) period, followed by 21-day AR based on resistance exercise. Quadriceps femoris (QF) cross-sectional area (CSA) and isometric maximum voluntary contraction (MVC) were evaluated. Intramuscular electromyographic recordings were obtained during 10% and 25% MVC isometric contractions from the vastus lateralis (VL). Biomarkers of NMJ molecular instability (serum c-terminal agrin fragment, CAF), axonal damage (neurofilament light chain) and denervation status were assessed from blood samples and VL biopsies. NMJ and ion channel transcriptomic profiles were investigated by RNA-sequencing. QF CSA and MVC decreased with ULLS. Increased CAF and altered NMJ transcriptome with unloading suggested the emergence of NMJ molecular instability, which was not associated with impaired NMJ transmission stability. Instead, increased MUP complexity and decreased motor unit firing rates were found after ULLS. Downregulation of ion channel gene expression was found together with increased neurofilament light chain concentration and partial denervation. The AR period restored most of these neuromuscular alterations. In conclusion, the human NMJ is destabilized at the molecular level but shows functional resilience to a 10-day unloading period at least at relatively low contraction intensities. However, MUP properties are altered by ULLS, possibly due to alterations in ion channel dynamics and initial axonal damage and denervation. These changes are fully reversed by 21 days of AR. KEY POINTS: We used integrative electrophysiological and molecular approaches to comprehensively investigate changes in neuromuscular integrity and function after a 10-day unilateral lower limb suspension (ULLS), followed by 21 days of active recovery in young healthy men, with a particular focus on neuromuscular junction (NMJ) and motor unit potential (MUP) properties alterations. After 10-day ULLS, we found significant NMJ molecular alterations in the absence of NMJ transmission stability impairment. These findings suggest that the human NMJ is functionally resilient against insults and stresses induced by short-term disuse at least at relatively low contraction intensities, at which low-threshold, slow-type motor units are recruited. Intramuscular electromyography analysis revealed that unloading caused increased MUP complexity and decreased motor unit firing rates, and these alterations could be related to the observed changes in skeletal muscle ion channel pool and initial and partial signs of fibre denervation and axonal damage. The active recovery period restored these neuromuscular changes.

Single fiber electromyography and measuring jitter with concentric needle electrodes.

This monograph contains descriptions of the single fiber electromyography (SFEMG) method and of the more recently implemented method of recording jitter with concentric needle electrodes (CNEs). SFEMG records action potentials from single muscle fibers (SFAPs), which permits measuring fiber density (FD), a sensitive measure of reinnervation, and jitter, a sensitive measure of abnormal neuromuscular transmission (NMT). With voluntary activation, jitter is measured between two SFAPs with acceptable amplitude and rise time. With activation by axon stimulation, jitter is measured between the stimulus and individual SFAPs. Pitfalls due to unstable triggers and inconstant firing rates during voluntary activation and subliminal stimulation during axon stimulation should be identified and avoided. In CNE recordings, spikes with shoulders or rising phases that are not parallel are produced by summation of SFAPS; these should be excluded and reference values for CNE jitter should be used. CNE and SFEMG have similar and very high sensitivity in detecting increased jitter, as in myasthenia gravis and other myasthenic conditions. However, jitter is also seen in ongoing reinnervation and some myopathic conditions. With SFEMG, these can be identified by increased FD; however, FD cannot be measured with CNE, and conventional electromyography should be performed in muscles with increased jitter to detect neurogenic or myogenic abnormalities. Jitter is abnormal after injections of botulinum toxin, even in muscles remote from the injection site, and can persist for 6 mo or more. This can complicate the detection or exclusion of abnormal NMT.

Single fiber EMG and measuring jitter with concentric needle electrodes.

This monograph contains descriptions of the single-fiber electromyography (SFEMG) method and of the more recently implemented method of recording jitter with concentric needle electrodes (CNE). SFEMG records action potentials from single muscle fibers (SFAPs), which permits measuring fiber density (FD), a sensitive measure of reinnervation, and jitter, a sensitive measure of abnormal neuromuscular transmission (NMT). With voluntary activation, jitter is measured between two SFAPs with acceptable amplitude and rise time. With activation by axon stimulation, jitter is measured between the stimulus and individual SFAPs. Pitfalls due to unstable triggers and inconstant firing rates during voluntary activation and subliminal stimulation during axon stimulation should be identified and avoided. In CNE recordings, spikes with shoulders or rising phases that are not parallel are produced by summation of SFAPS; these should be excluded and reference values for CNE jitter should be used. CNE and SFEMG have similar and very high sensitivity in detecting increased jitter, as in myasthenia gravis and other myasthenic conditions. However, jitter is also seen in ongoing reinnervation and some myopathic conditions. With SFEMG, these can be identified by increased FD; however, FD cannot be measured with CNE, and conventional EMG should be performed in muscles with increased jitter to detect neurogenic or myogenic abnormalities. Jitter is abnormal after injections of botulinum toxin, even in muscles remote from the injection site, and can persist for 6 mo or more. This can complicate the detection or exclusion of abnormal NMT.

Jitter-Robust Phase Retrieval Wavefront Sensing Algorithms.

Phase retrieval wavefront sensing methods are now of importance for imaging quality maintenance of space telescopes. However, their accuracy is susceptible to line-of-sight jitter due to the micro-vibration of the platform, which changes the intensity distribution of the image. The effect of the jitter shows some stochastic properties and it is hard to present an analytic solution to this problem. This paper establishes a framework for jitter-robust image-based wavefront sensing algorithm, which utilizes two-dimensional Gaussian convolution to describe the effect of jitter on an image. On this basis, two classes of jitter-robust phase retrieval algorithms are proposed, which can be categorized into iterative-transform algorithms and parametric algorithms, respectively. Further discussions are presented for the cases where the magnitude of jitter is unknown to us. Detailed simulations and a real experiment are performed to demonstrate the effectiveness and practicality of the proposed approaches. This work improves the accuracy and practicality of the phase retrieval wavefront sensing methods in the space condition with non-ignorable micro-vibration.

Analysis of Pre-Driver and Last-Stage Power-Ground-Induced Jitter at Different PVT Corners.

This paper presents the study of power/ground (P/G) supply-induced jitter (PGSIJ) on a cascaded inverter output buffer. The PGSIJ analysis covers the IO buffer transient simulation under P/G supply voltage variation at three process, voltage, and temperature (PVT) corners defined at different working temperatures and distinct P/G DC supply voltages at the pre-driver (i.e., VDD/VSS) and last stage (i.e., VDDQ/VSSQ). Firstly, the induced jitter contributions by the pre-driver, as well as the last, stage are compared and studied. Secondly, the shared and decoupled P/G supply topologies are investigated. The outcomes of these simulation analyses with respect to worst case jitter corners are determined, while highlighting the importance of modeling the pre-driver circuit behavior to include the induced jitter in the input-output buffer information specification (IBIS)-like model. Accordingly, the measured PGSIJ depends on the corners to be analyzed and, therefore, the designer needs to explore the worst-case corner for the driver's technology node and the most supply voltage noise affecting the jitter output for signal and power integrity (SiPI) simulations. Finally, the jitter transfer function sensitivity to the amplitude and frequency/phase variations of the separate and combined impacts of the pre-driver and last stage are explored, while discussing the superposition of the power supply induced jitter (PSIJ) induced by both the driver's IO stages under small signal and large signal supply voltage variations. The linear superposition of the separate PSIJ effects by the pre-driver and last stage depends on the amplitude of the variation of the supply voltage that can drive the transistor to their nonlinear working regions.

Discriminative Method for Crack Detection Signals in Balanced-Field Electromagnetic Technique Based on Amplitude-Phase Composite Figure.

The balanced-field electromagnetic technique is an effective in-line inspection method for pipeline cracks. To address the problem that the interference signal generated by the tilt jitter of the sensor during the detection process affects the judgment of cracks, this paper proposes a method to differentiate the crack detection signal from the sensor jitter signal by using an amplitude-phase composite figure. The generation principle of the detection signal was analyzed by using the mutual inductance model, and the amplitude-phase composite figure was constructed by using the components of the detection signal after quadrature demodulation. The feasibility of using the phase as a signal discrimination feature was illustrated by finite element simulations, and the characteristics of the amplitude-phase composite figure were determined. The validity of the proposed method was verified experimentally. The results show that the crack detection signal and the signal generated by the sensor jitter are of the same frequency with similar waveforms and significantly different phases. The phase base value of the crack detection signal ranges from 35° to 55°, and the phase base value of the jitter signal is -4°. In terms of the characteristics of the amplitude-phase composite figure, the crack detection signal distribution is symmetrical about the origin in the first and third quadrants, and the axial crack is closer to the Y-axis than the circumferential crack; the jitter signal is distributed in the second and fourth quadrants and has a very small angle to the X-axis. In addition, the proposed method effectively weakens the observation of the phase noise region in the detection signal of the balanced-field electromagnetic technique.

Speech as an indicator for psychosocial stress: A network analytic approach.

Recently, the possibilities of detecting psychosocial stress from speech have been discussed. Yet, there are mixed effects and a current lack of clarity in relations and directions for parameters derived from stressed speech. The aim of the current study is - in a controlled psychosocial stress induction experiment - to apply network modeling to (1) look into the unique associations between specific speech parameters, comparing speech networks containing fundamental frequency (F0), jitter, mean voiced segment length, and Harmonics-to-Noise Ratio (HNR) pre- and post-stress induction, and (2) examine how changes pre- versus post-stress induction (i.e., change network) in each of the parameters are related to changes in self-reported negative affect. Results show that the network of speech parameters is similar after versus before the stress induction, with a central role of HNR, which shows that the complex interplay and unique associations between each of the used speech parameters is not impacted by psychosocial stress (aim 1). Moreover, we found a change network (consisting of pre-post stress difference values) with changes in jitter being positively related to changes in self-reported negative affect (aim 2). These findings illustrate - for the first time in a well-controlled but ecologically valid setting - the complex relations between different speech parameters in the context of psychosocial stress. Longitudinal and experimental studies are required to further investigate these relationships and to test whether the identified paths in the networks are indicative of causal relationships.

Corticokinematic coherence is stronger to regular than irregular proprioceptive stimulation of the hand.

Proprioceptive afference can be investigated using corticokinematic coherence (CKC), which indicates coupling between limb kinematics and cortical activity. CKC has been quantified using proprioceptive stimulation (movement actuators) with fixed interstimulus interval (ISI). However, it is unclear how regularity of the stimulus sequence (jitter) affects CKC strength. Eighteen healthy volunteers (16 right-handed, 27.8 ± 5.0 yr, 7 females) participated in magnetoencephalography (MEG) session in which their right index finger was continuously moved at ∼3 Hz with Constant 333 ms ISI or with 20% Jitter (ISI 333 ± 66 ms) using a pneumatic-movement actuator. Three minutes of data per condition were collected. Finger kinematics were recorded with a three-axis accelerometer. CKC strength was defined as the peak coherence value in the Rolandic MEG gradiometer pair contralateral to the movement at 3 Hz. Both conditions resulted in significant coherence peaking in the gradiometers over the primary sensorimotor cortex. Constant stimulation yielded stronger CKC at 3 Hz (0.78 ± 0.11 vs. 0.66 ± 0.13, P < 0.001) and its first harmonic (0.60 ± 0.19 vs. 0.27 ± 0.11, P < 0.001) than irregular stimulation. Similarly, the respective sustained-movement evoked field was also stronger for constant stimulation. The results emphasize the importance of temporal stability of the proprioceptive stimulation sequence when quantifying CKC strength. The weaker CKC during irregular stimulation can be explained with temporal and thus spectral scattering of the paired peripheral and cortical events beyond the mean stimulation frequency. This impairs the signal-to-noise ratio of respective MEG signal and thus CKC strength. When accurately estimating and following changes in CKC strength, we suggest using precise movement actuators with constant stimulation sequence.NEW & NOTEWORTHY Cortical proprioceptive processing can be investigated using corticokinematic coherence (CKC). The findings show that CKC method is sensitive to temporal stability in the stimulation sequence. Although both regular and irregular sequences resulted in robust coherence, the regular stimulation sequence with pneumatic movement actuator is recommended to maximize coherence strength and reproducibility to allow better comparability between groups or populations.

Reference jitter values for the sternocleidomastoid muscle with concentric needle electrodes.

BACKGROUND: The aim of this study was to establish reference jitter values for the voluntary activated sternocleidomastoid (SCM) muscle using a concentric needle electrode (CNE). METHODS: The study included 39 healthy participants (20 female and 19 male) aged 18-77 y. Jitter was expressed as the mean consecutive difference (MCD) of 80-100 consecutive discharges. Filters were set at 1 and 10 kHz. The mean MCDs for all participants were pooled, and the mean value +2.5 SD was accepted as the upper limit for the mean MCD. The upper limit for individual MCD was calculated using +2.5 SD of the upper 10th percentile MCD for individual participants. RESULTS: Mean age of the participants was 45 ± 14.5 y. Mean MCD was 16.20 ± 2.23 µs (range: 12-21 µs), and the upper limit of normal for mean MCD was 21.8 µs. The mean value for 823 individual jitters was 23.3 ± 4.61 µs (range: 6.6-36.9 µs), and the upper limit of normal for each individual jitter was 34.6 µs. CONCLUSIONS: The present findings indicate that upper normal limit for mean MCD is 22 µs and for individual data it is 35 µs.

Identifying jitter outliers in single fiber electromyography: Comparison of four methods.

BACKGROUND: Little is known about how different outlier estimation methods affect cutoff limits for outliers in single fiber electromyography. METHODS: We compared in a prospective fashion the established 18th jitter value (18thjv) method to three, whole-distribution based, outlier detection methods: the interquartile range (IQR), the log-normal, and the Z-score method. The reference limits were probed in a normal cohort and in myasthenia gravis (MG) patients. RESULTS: Differences in outlier cutoff values between the different methods were in the range of 2 µs. The number of abnormal muscles according to the computed criteria was similar for all four methods in the control group. Classification metrics (sensitivity, specificity, Youden's statistic, and predictive values) were also similar among the different methods. In the MG group, however, the Z-score method failed to identify the abnormal jitter values. Accordingly, Kappa agreement was substantial to perfect (0.658 to 1) between the three methods (18thjv, IQR, and log-normal), but was equivalent to chance between the three methods and the Z-score in the MG group. CONCLUSIONS: The established 18thjv method proved largely robust when compared to whole-distribution based methods, and its use in clinical practice is justified. Simple estimation of outlier limits by adding two SDs to the mean of the data, leads to unacceptable deviations from the true cutoff values. Moreover, in a clinical scenario in which the final electrodiagnosis depends only on the number of outliers, it is meaningful to accept a tolerance zone of about 2 µs, which is the approximate variation range among the different methods.

Single-institutional reference values for concentric needle jitter analysis using the extrapolated reference values procedure: Comparison to published reference values.

BACKGROUND: The extrapolated reference values procedure (E-Ref) was used to compare data from a single institution with the recently published reference value (RV) for concentric electrode jitter. METHODS: Data from voluntarily activated concentric needle jitter studies in the frontalis muscle were obtained using retrospective chart review. All measured signals were reviewed for acceptable quality. Cutoff values for increased jitter were calculated using E-Ref, and compared with the published RVs. RESULTS: At total of 1501 apparent single-fiber action potential (ASFAP) pairs were reviewed; 1371 ASFAP pairs were determined to have acceptable quality. The cutoff value identified by E-Ref from all reviewed ASFAP pairs was 36 microseconds and the cutoff for acceptable pairs was 35 microseconds. Using either of these cutoff values (36 or 35 microseconds) did not result in a significant difference in percentage of jitter recordings considered normal when compared with the recently published RV (38 microseconds). DISCUSSION: The single-institution jitter cutoff value obtained by E-Ref gives results that are not significantly different from the reported RV.

A 0.8 V, 5.3-5.9 GHz Sub-Sampling PLL with 196.5 fsrms Integrated Jitter and -251.6 dB FoM.

This paper proposes a hybrid dual path sub-sampling phase-locked loop (SSPLL), including a proportional path (P-path) and an integral path (I-path), with 0.8 V supply voltage. A differential master-slave sampling filter (MSSF), replacing the sub-sampling charge pump (SSCP), composed the P-path to avoid the degraded feature caused by the decreasing of the supply voltage. The I-path is built by a rail-to-rail SSCP to suppress the phase noise of the voltage-controlled oscillator (VCO) and avoid the trouble of locking at the non-zero phase offset (as in type-I PLL). The proposed design is implemented in a 40-nm CMOS process. The measured output frequency range is from 5.3 to 5.9 GHz with 196.5 fs root mean square (RMS) integrated jitter and -251.6 dB FoM.

Jitter Detection and Image Restoration Based on Generative Adversarial Networks in Satellite Images.

High-resolution satellite images (HRSIs) obtained from onboard satellite linear array cameras suffer from geometric disturbance in the presence of attitude jitter. Therefore, detection and compensation of satellite attitude jitter are crucial to reduce the geopositioning error and to improve the geometric accuracy of HRSIs. In this work, a generative adversarial network (GAN) architecture is proposed to automatically learn and correct the deformed scene features from a single remote sensing image. In the proposed GAN, a convolutional neural network (CNN) is designed to discriminate the inputs, and another CNN is used to generate so-called fake inputs. To explore the usefulness and effectiveness of a GAN for jitter detection, the proposed GANs are trained on part of the PatternNet dataset and tested on three popular remote sensing datasets, along with a deformed Yaogan-26 satellite image. Several experiments show that the proposed model provides competitive results. The proposed GAN reveals the enormous potential of GAN-based methods for the analysis of attitude jitter from remote sensing images.