Frame Interval Research Articles

Single-shot ultrafast imaging of plasma dynamics induced by dual-angle ultrashort laser pulses with subpicosecond delay

Spatiotemporal manipulation of ultrashort laser pulses is crucial for enhancing laser processing and phonon generation. Optimization of these applications requires ultrafast visualization of the underlying processes. In this study, we induced laser ablation using spatiotemporally manipulated double pulses focused from two angles onto a glass surface with a 0.7 ps interval, and captured the images of its dynamics with 5 sequential frames at a frame interval of 0.8 ps. The observed dynamics suggest that the laser profile reflected on the glass surface is influenced by its topography, which in turn affects the behavior of air breakdown plasmas.

An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)

AbstractDetailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image‐based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image‐based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)‐based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image‐derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications.

Sub-frame timestamping of a camera network using a coded light signal

To extract the 3D displacements of structures using a camera network, it is essential to know the frame timestamps. However, technical constraints in outdoor conditions can make synchronous frame acquisition impossible. This study aims to reconstruct the timestamps of frames during post-processing to employ cameras without special camera feature. To achieve this, a LED panel that emits a coded light signal is placed in the field of view common to all cameras. The pixel intensity of the LED region in the sequential images is processed to reconstruct each frame’s timestamp. The uncertainty of the timestamp reconstruction is numerically estimated to encode the light signal. Validation was conducted through indoor experiments and outdoor applications to monitor a tower crane. The uncertainty of the obtained timestamp is 1.65% of the frame interval under uncontrolled conditions. The proposed method can be used with cameras, even when synchronization is unattainable.

Enhancing human behavior recognition with spatiotemporal graph convolutional neural networks and skeleton sequences

ObjectivesThis study aims to enhance supervised human activity recognition based on spatiotemporal graph convolutional neural networks by addressing two key challenges: (1) extracting local spatial feature information from implicit joint connections that is unobtainable through standard graph convolutions on natural joint connections alone. (2) Capturing long-range temporal dependencies that extend beyond the limited temporal receptive fields of conventional temporal convolutions.MethodsTo achieve these objectives, we propose three novel modules integrated into the spatiotemporal graph convolutional framework: (1) a connectivity feature extraction module that employs attention to model implicit joint connections and extract their local spatial features. (2) A long-range frame difference feature extraction module that captures extensive temporal context by considering larger frame intervals. (3) A coordinate transformation module that enhances spatial representation by fusing Cartesian and spherical coordinate systems.FindingsEvaluation across multiple datasets demonstrates that the proposed method achieves significant improvements over baseline networks, with the highest accuracy gains of 2.76%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} on the NTU-RGB+D 60 dataset (Cross-subject), 4.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} on NTU-RGB+D 120 (Cross-subject), and 4.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} on Kinetics (Top-1), outperforming current state-of-the-art algorithms. This paper delves into the realm of behavior recognition technology, a cornerstone of autonomous systems, and presents a novel approach that enhances the accuracy and precision of human activity recognition.

Dual-camera off-axis holographic particle tracking velocimetry: Development and application to air-blast swirl spray measurement

Quantifying the droplet field near the atomizer exit poses a challenge due to its three-dimensional (3D), multiscale and turbulent characteristics. An optical measurement method named dual-camera high-resolution holographic particle tracking velocimetry (DCHR-HPTV) is proposed. This method enables the extraction of near-field spatial Sauter mean diameter (SMD) distribution and three-dimensional two-component (3D-2C) velocity field, from off-axis hologram pairs with a frame interval of 6 μs, recorded using two synchronized industrial cameras and pulsed lasers. The system achieves a resolution of 9344 × 7000 pixels with an individual pixel size (dpix) of 3.2 μm. The droplet size measurement range is from 10 μm to above 500 μm with an absolute error within 3 μm. The velocity measurement range is adjustable and is set to be 0 to 38.4 m/s in this study, with errors of 0.23 m/s and 0.15 m/s for horizontal and axial components, respectively. The method is applied to investigate a spray region of 29 mm × 20 mm with a depth of 35 mm (equivalent to 20.3 cm3) at the exit of an air-blast atomizer. The spatial distribution of circumferentially integrated SMD along radial and axial directions is obtained. Statistical analysis is performed on both spatial and quantity distributions of droplet 3D-2C velocities. Concurrently acquired droplet size and velocity in the central toroidal recirculation zone (CTRZ) indicate that both the increasing air pressure and enlarged size range lead to a weakened droplet backflow motion. This method, distinguished by its large field of view and high resolution for particle velocimetry, is suited for dynamic analysis of near-nozzle droplet fields and can be employed in various testing scenarios involving 3D motion of micron-sized particles within multiscale flow fields.

AUV instantaneous velocity estimation method based on azimuth and Doppler shift

Autonomous underwater vehicle (AUV), as a new underwater operation platform, has been widely used in the field of ocean engineering. AUV underwater condition monitoring is an important guarantee for the safety and effective execution of AUV operations. To meet the requirement of real-time and high-precision underwater instantaneous velocity monitoring of AUV, this paper proposed an AUV instantaneous velocity estimation method. The method takes the target azimuth and Doppler shift as the observation information. It comprehensively uses the geometric relationship among the target azimuth, heading angle, radial velocity angle, and the physical relationship between Doppler and radial velocity to construct an AUV instantaneous velocity estimation model based on distributed dual acoustic observation nodes. We derive the analytical solution of AUV speed and heading angle and analyze the influence of each observation error on the estimation precision of the algorithm. The research and practical data processing results show that the proposed method can effectively estimate the instantaneous velocity of AUV. Compared with the traditional method based on position difference, it has the advantages of high estimation precision, is not limited by frame interval, and is adaptive to many kinds of motion modes. When AUV moves in a straight line at a uniform speed, the precision of speed estimation and heading angle estimation of the algorithm proposed in the paper is 0.506 m/s and 6.146°, which is equivalent to that of the traditional method based on position difference with 50 frame intervals.

Single-shot optical imaging with spectrum circuit bridging timescales in high-speed photography.

Single-shot optical imaging based on ultrashort lasers has revealed nonrepetitive processes in subnanosecond timescales beyond the recording range of conventional high-speed cameras. However, nanosecond photography without sacrificing short exposure time and image quality is still missing because of the gap in recordable timescales between ultrafast optical imaging and high-speed electronic cameras. Here, we demonstrate nanosecond photography and ultrawide time-range high-speed photography using a spectrum circuit that produces interval-tunable pulse trains while keeping short pulse durations. We capture a shock wave propagating through a biological cell with a 1.5-ns frame interval and 44-ps exposure time while suppressing image blur. Furthermore, we observe femtosecond laser processing over multiple timescales (25-ps, 2.0-ns, and 1-ms frame intervals), showing that the plasma generated at the picosecond timescale affects subsequent shock wave formation at the nanosecond timescale. Our technique contributes to accumulating data of various fast processes for analysis and to analyzing multi-timescale phenomena as a series of physical processes.

Noncontact remote sensing of abnormal blood pressure using a deep neural network: a novel approach for hypertension screening.

As the global burden of hypertension continues to increase, early diagnosis and treatment play an increasingly important role in improving the prognosis of patients. In this study, we developed and evaluated a method for predicting abnormally high blood pressure (HBP) from infrared (upper body) remote thermograms using a deep learning (DL) model. The data used in this cross-sectional study were drawn from a coronavirus disease 2019 (COVID-19) pilot cohort study comprising data from 252 volunteers recruited from 22 July to 4 September 2020. Original video files were cropped at 5 frame intervals to 3,800 frames per slice. Blood pressure (BP) information was measured using a Welch Allyn 71WT monitor prior to infrared imaging, and an abnormal increase in BP was defined as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. The PanycNet DL model was developed using a deep neural network to predict abnormal BP based on infrared thermograms. A total of 252 participants were included, of which 62.70% were male and 37.30% were female. The rate of abnormally high HBP was 29.20% of the total number. In the validation group (upper body), precision, recall, and area under the receiver operating characteristic curve (AUC) values were 0.930, 0.930, and 0.983 [95% confidence interval (CI): 0.904-1.000], respectively, and the head showed the strongest predictive ability with an AUC of 0.868 (95% CI: 0.603-0.994). This is the first technique that can perform screening for hypertension without contact using existing equipment and data. It is anticipated that this technique will be suitable for mass screening of the population for abnormal BP in public places and home BP monitoring.

Research on security encryption mechanism of physical Layer based on iterative quantization method

Third-party eavesdropping is a unsolved problem in the process of data transmission in the physical layer of IoT (Internet of Things) in Power Systems. The security encryption effect is affected by channel noise and the half-duplex nature of the wireless channel, which leads to low key consistency and key generation rate. To address this problem, a reliable solution for physical layer communication security is proposed in this paper. First, the solution improved the key consistency by dynamically adjusting the length of the training sequence during feature extraction; Second, using an iterative quantization method to quantify the RSS (Received Signal Strength) measurements to improve generation rate of the key. Finally, based on the short-time energy method for the extraction of wireless frame interval features, by monitoring the change of inter-frame interval features, we can quickly determine whether there is an eavesdropping device into the link. Simulation results show that the reciprocity of legitimate channels R (R will be explained in detail in the following) is improved by 0.1, the key generation rate is increased by about 70%, and the beacon frames are extracted from the wireless link with good results compared to the methods that do not use dynamic adjustment of the pilot signal during the channel probing phase. The result shows that this method can effectively prevents third-party eavesdropping, effectively improves the key consistency and generation rate, and effectively implements beacon frame detection.

Testing the Dynamics of Flight for the Products of Explosion for a Warhead with a Weight of 250 kg

This study presents the results of the testing of the explosion process of a warhead with a weight of 250 kg, filled with 87 kg of TNT with 20% of aluminium dust, in two configurations: with horizontal and vertical alignment of the warheads longitudinal axis, and with the centre of length of the warhead body located at a height of approx. 1 m above the ground. Four warheads were detonated in each configuration. The horizontal configuration allowed the collection of some amount of the fragments from the ground, with sizes and spatial distribution of the fragments corresponding to the location on the body from which they came, with the largest fragments from the central part of the shell measuring approximately 9 30 280 mm. For the vertical configuration, the warheads nose was pointed downwards, with an up-down excitation. In both configurations, the explosion process was recorded from a distance of 300 m using a PHANTOM fast camera with a time resolution (frame interval) of 55 s to 133 s: for the horizontal configuration along the bodys longitudinal axis, for the vertical configuration perpendicular to this axis. In the vertical configuration, the bodys expansion process was recorded using short-circuit sensors spaced every 5 mm along the flight radius. The sensors sent short-circuit signals to the time meter, whereas the first sensor was installed at a distance of approx. 1 mm from the body surface and was used to initiate the processes of time counting and recording the overpressure diagrams over time at the front of the explosion/shock (FU) wave. The recorded expansion velocity was approx. 1300 m/s, with the shell radius increasing by 20 mm. Overpressure at the front of the FU was measured by PCB pencil-tip piezoelectric sensors (CzP). Every sensor had two active surfaces arranged in tandem at a distance of 100 mm, which made it possible to determine the local FU velocity. Signals from CzP were recorded every 200 ns using a DEWETRON recorder with software allowing their initial and further processing. Three sensors were spaced 8 m from each other, whereas the first was located 8 m to 10 m from the warheads longitudinal axis. Under a row of the sensors a thick-wall steel pipe was placed to protect the sensors from destruction by the fragments. The determined local FU velocities varied from approx. (590 m/s to 740 m/s) at a distance of approx. 8 m from the epicentre up to approx. 370 m/s at a distance of approx. 26 m from the epicentre; the overpressure measured values varied from approx. (230 kPa to 550 kPa) at a distance of approx. 8 m to approx. 22 kPa at a distance of approx. 26 m from the epicentre; satisfying conformance of the velocity and pressure values under the flat FU model was found. The FU trajectory was also taken from the video recording the velocities measured varied from approx. 2,650 m/s at a distance of 0.3 m to approx. 670 m/s at a distance of 6 m from the epicentre, which corresponds to the CzP data. The fragments flying next to the CzP, generally with the highest mass to effective transverse surface ratio, left traces of their conical FU on the CzP overpressure records, which allowed the determination of average velocities for some of them across the access path to the CzP, whereas these velocities ranged from approx. 1700 m/s at a distance of approx. 8 m and (1500 m/s to 1600 m/s) at a distance of 16 m to approx. 1300 to 1400 m/s at a distance of 26 m from the epicentre. Average access velocities of the selected fragments to the field marks were determined on the basis of the video recording ranged from approx. 1800 m/s at a distance of 5 m to approx. 1500 m/s at a distance of 20 m from the explosion epicentre.

Analyzing differences of highway lane-changing behavior using vehicle trajectory data

Analyzing fine-resolution lane-changing behavior is of great significance in the traffic flow modeling and safety assessment. The fact that lane-changing behavior varies over different environment and operation factors poses great challenges for lane-changing behavior modeling and analysis. To explore the differences of highway lane-changing behavior in complicated scenarios, the analysis of this study stems from a large volume of lane-changing trajectories (i.e., 6506 lane-changing trajectories) collected from the Highway Drone (HighD) dataset. In real-world scenarios, to identify and extract lane-changing start and end points of lane-changing behavior from trajectories, we present a fusion method by using frame interval and lateral displacement variation in trajectory data, which can be calibrated by the visualization. The empirical results show that: (a) the relative error between the extracted lane-changing time and real one is less than 4%; (b) the lane-changing time is ranged from 2.88 s to 7.32 s, while the lane-changing speed is ranged from 18.83 m/s to 43.30 m/s. For the difference analysis, we use the tests of statistical significance and heatmaps to analyze the differences of lane-changing behavior under complicated scenarios that involve different lane numbers, lane-changing directions, vehicle types, and speed limit condition. The results demonstrate that the lane-changing behavior varies over different scenarios significantly. Overall, the systematical analysis of lane-changing behavior in this study provides insight into the microscopic traffic flow modeling.

Short-Time Remote Heart Rate Measurement Based on mmWave FMCW Radar Frame Structure

This paper presents a novel method to reduce measurement time for remote heart rate measurement using mmWave FMCW radar. The proposed method is based on the sampling theory and the frame structure of recent FMCW radar systems. The range displacement from heartbeat is measured by 60GHz FMCW radar with a fixed frame interval, 50ms. With additional chirp repetition in each frame, the heartbeat signal can be sampled with increased or decreased interval. By adopting three different sampling rates from a single radar measurement, the resolution of heart rate measurement is improved in a short time, 2.5s. The proposed method is evaluated with a commercial off-the-shelf FMCW radar system and three different human subject, for no respiration and natural status. To evaluate the performance of the proposed method, the heart rate result is compared with ECG sensor and conventional signal processing method. The results show that the proposed method estimates the heart rate with lower error and shorter measurement time.

Single-shot spatiotemporal plasma density diagnosis using an arbitrary time-wavelength-encoded biprism interferometer

Single-shot ultrafast imaging reveals the transient dynamics of ultrafast phenomena. Herein, we introduce a new approach for capturing ultrafast micron-scale dynamics using an arbitrary time-wavelength-encoded biprism interferometer (TWEBI). In TWEBI, a time-wavelength-encoded pulse train is generated using a set of cascaded frequency-doubling crystals with slightly different phase-matching angles and independent delay lines, which have the advantages of independent temporal resolution, time window, and frame interval parameters. Phase measurement of TWEBI is achieved using a plug-and-play biprism interferometer. In the experiment to capture the transient dynamic of femtosecond laser-induced plasma filament, we demonstrated that the TWEBI setup can capture a total of 12 frames in a single shot, and its effective frame rate is 5 trillion frames per second (Tfps), corresponding to a temporal resolution of 200 fs and a spatial resolution of 4 μm. Moreover, the time window of the TWEBI setup can be adjusted from sub-picosecond to nanosecond timescales. Thus, TWEBI has the advantages of high-precision temporal-spatial resolution, high frame rate, adjustable time window, and no reference arm, thus providing a practical and feasible diagnostic scheme for complex transient dynamics.

A study on application of machine-learning on DBI soot diagnostics

Nowadays, Diffused Background-illumination Extinction Imaging technique (DBI) has been widely applied to quantify the in-flame soot formation of diesel-like sprays. In order to eliminate the errors on soot KL value brought from flame radiation, the soot radiation images are usually needed to be recorded between every two successive back light-on pulses. Consequently, it is necessary to do the frame interpolation for the missing flame radiation images and back light-on images. In this study, a Super SloMo machine-learning method was applied to generate the missing frames and the accuracy of interpolated frames was evaluated from three aspects, namely the frame interval length, prediction region and number of intermediate frames. Meanwhile, Gunner Farnebäck method was applied here as a reference. The results show that the Super SloMo method consistently outperformed the Gunner Farnebäck method in terms of accuracy, regardless of the variations on frame intervals, soot regions, as well as interpolated frame numbers. Consequently, the application of the Super SloMo method in conjunction with the DBI technique significantly improved the accuracy of soot KL value, resulting in a 26.4% reduction in average KL error compared to the Gunner Farnebäck method in the studied cases.

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

CenterTube: Tracking Multiple 3D Objects With 4D Tubelets in Dynamic Point Clouds

3D Multi-Object Tracking (MOT) in dynamic point cloud sequences is a fundamental research problem for several downstream tasks such as motion planning and action recognition. Existing methods usually rely on the traditional tracking-by-detection (TBD) paradigm, which performs the tracking based on the results achieved by dedicated detectors. However, this two-stage framework usually cannot sufficiently exploit spatial-temporal information and end-to-end optimization, leading to sub-optimal tracking performance, especially when the object is partially or completely occluded. In this paper, we propose a joint detection and tracking framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CenterTube</i> for dynamic point cloud sequences. The key to our approach is to formulate the problem of multiple object trajectory predictions as 4D tubelet detections. In particular, the proposed CenterTube is composed of three head branches, including a center branch, a regression branch, and a movement branch for the estimation of object center, object size, instance movement, and frame interval, respectively. Additionally, a Tube BEV-IoU (TB-IoU) is also presented to link the generated clip-level tubelets and form the final tracks. Extensive experiments conducted on the KITTI-MOT and nuScenes datasets demonstrate that our model achieves competitive performances even if no ready-made detection results is adopted.

Influence of Ocular Surface Hydration on Intra-Operative Aberrometry Measurement and Toric Intraocular Lens Recommendation.

The purpose of this study was to examine the effect of intra-operative live refraction stability as a surrogate marker of ocular surface hydration on intra-operative aberrometry (IA) results and to quantify the minimum duration of stable refraction needed to achieve accurate intraocular lens (IOL) selection. In this nonrandomized consecutive retrospective chart review, 18,000 data points from 45 live refraction runs of 15 patients were digitized and analyzed. An objective automated moving average method of frames lengths of 88ms, 110ms, and 132ms with less than 0.5 diopters (D) of fluctuation was compared to raw IA capture data. The difference-vector (DV) of the predicted toric powers was compared among these groups. Subjectively, traces were classified as stable or unstable if the live refraction fluctuated less than 0.5D for 5 seconds. The DV based on the stable period was compared with the IA capture data. The DVs for all frame intervals showed no significant difference when compared with IA readings. In 15 of 45 (33.3%) cases, IA active refraction traces were stable and the DV (0.27D ± 0.15D) was significantly less than unstable traces (0.49D ± 0.28D). IOL recommendations from 14 (93.3%) of stable runs led to a <0.5D of postoperative cylinder compared to 14 (47.7%) in unstable runs. Intra-operative live refraction stability is affected by ocular hydration. Surgeons should look for periods of refractive stability for at least 5 seconds to better assess measurement quality. This can be facilitated by capturing and including the active refraction trace, which is currently unavailable for review. Graphing live refractive IA readings and determining refractive stability translates to more accurate IOL selection.

SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow

Multiple human tracking is a fundamental problem for scene understanding. Although both accuracy and speed are required in real-world applications, recent tracking methods based on deep learning have focused on accuracy and require substantial running time. This study aims to improve running speed by performing human detection at a certain frame interval because it accounts for most of the running time. The question is how to maintain accuracy while skipping human detection. In this paper, we propose a method that complements the detection results with optical flow, based on the fact that someone's appearance does not change much between adjacent frames. To maintain the tracking accuracy, we introduce robust interest point selection within human regions and a tracking termination metric calculated by the distribution of the interest points. On the MOT20 dataset in the MOTChallenge, the proposed SDOF-Tracker achieved the best performance in terms of the total running speed while maintaining the MOTA metric. Our code is available at https://github.com/hitottiez/sdof-tracker.

Branching Morphology of Negative Leaders with Different Propagation Directions in Natural Lightning

Comparing the branching features of negative leaders with different propagation directions could provide insight into the common tendency of development pathways and the formation pattern of branches in natural lightning. This paper reports an upward negative leader (UNL) and a downward negative leader (DNL), and their branching features are analyzed and compared. The UNL is classified into vertical (UNL-V) and horizontal (UNL-H) segments based on propagation directions at different stages. The downward negative leader (DNL) is classified into main (DNL-M) and secondary (DNL-S) channels based on whether the channel is ultimately connected to the upward connecting leader. The vital angle parameters characterizing the branching morphology are investigated. For the strong branch eventually forming a section of the main channel, its deflection angle conforms to the lognormal distribution with a mean range of 22–36°. The included angle between the branches and the deflection angle of weak branches conform to the normal distribution with means close to 40° and 60°, respectively. Moreover, the velocity for four categories of negative leaders decreases noticeably by two or more branching behaviors in a frame interval of about 80 μs. In particular, similarities in branching morphology have been found in UNL-H, UNL-V, and DNL-S, with a semblable distribution in deflection and included angles. Statistical results indicate that branches of DNL-M tend to follow the previous direction of leader development, and the branching behavior has minimal impact on its velocity.

Alzheimer’s disease pattern derived from relative cerebral flow as an alternative for the metabolic pattern using SSM/PCA

Background2-Deoxy-2-[18F]fluoroglucose (FDG) PET is an important tool for the identification of Alzheimer’s disease (AD) patients through the characteristic neurodegeneration pattern that these patients present. Regional cerebral blood flow (rCBF) images derived from dynamic 11C-labelled Pittsburgh Compound B (PIB) have been shown to present a similar pattern as FDG. Moreover, multivariate analysis techniques, such as scaled subprofile modelling using principal component analysis (SSM/PCA), can be used to generate disease-specific patterns (DP) that may aid in the classification of subjects. Therefore, the aim of this study was to compare rCBF AD-DPs with FDG AD-DP and their respective performances. Therefore, 52 subjects were included in this study. Fifteen AD and 16 healthy control subjects were used to generate four AD-DP: one based on relative cerebral trace blood (R1), two based on time-weighted average of initial frame intervals (ePIB), and one based on FDG images. Furthermore, 21 subjects diagnosed with mild cognitive impairment were tested against these AD-DPs.ResultsIn general, the rCBF and FDG AD-DPs were characterized by a reduction in cortical frontal, temporal, and parietal lobes. FDG and rCBF methods presented similar score distribution.ConclusionrCBF images may provide an alternative for FDG PET scans for the identification of AD patients through SSM/PCA.