Spatiotemporal Image Research Articles

The visual experience dataset: Over 200 recorded hours of integrated eye movement, odometry, and egocentric video.

We introduce the Visual Experience Dataset (VEDB), a compilation of more than 240hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 56 observers ranging from 7 to 46 years of age. This article outlines the data collection, processing, and labeling protocols undertaken to ensure a representative sample and discusses the potential sources of error or bias within the dataset. The VEDB's potential applications are vast, including improving gaze-tracking methodologies, assessing spatiotemporal image statistics, and refining deep neural networks for scene and activity recognition. The VEDB is accessible through established open science platforms and is intended to be a living dataset with plans for expansion and community contributions. It is released with an emphasis on ethical considerations, such as participant privacy and the mitigation of potential biases. By providing a dataset grounded in real-world experiences and accompanied by extensive metadata and supporting code, the authors invite the research community to use and contribute to the VEDB, facilitating a richer understanding of visual perception and behavior in naturalistic settings.

Brain and eye movement dynamics track the transition from learning to memory-guided action.

Learning never stops. As we navigate life, we continuously acquire and update knowledge to optimize memory-guided action, with a gradual shift from the former to the latter as we master our environment. How are these learning dynamics expressed in the brain and in behavioral patterns? Here, we devised a spatiotemporal image learning task ("Memory Arena") in which participants learn a set of 50 items to criterion across repeated exposure blocks. Critically, brief task-free periods between successive image presentations allowed us to assess multivariate electroencephalogram (EEG) patterns representing the previous and/or upcoming image identity, as well as anticipatory eye movements toward the upcoming image location. As expected, participants eventually met the performance criterion, albeit with different learning rates. During task-free periods, we were able to readily decode representations of both previous and upcoming image identities. Importantly though, decoding strength followed opposing slopes for previous vs. upcoming images across time, with a gradual decline of evidence for the previous image and a gradual increase of evidence for the upcoming image. Moreover, the ratio of upcoming vs. previous image evidence directly followed behavioral learning rates. Finally, eye movement data revealed that participants increasingly used the task-free period to anticipate upcoming image locations, with target-precision slopes paralleling both behavioral performance measures as well as EEG decodability of the upcoming image across time. Together, these results unveil the neural and behavioral dynamics underlying the gradual transition from learning to memory-guided action.

B-flow/spatiotemporal image correlation M-mode ultrasound provides novel method to quantify spiral artery remodeling during normal human pregnancy.

During human pregnancy, placental extravillous trophoblasts replace vascular smooth muscle and elastic tissue within the walls of the uterine spiral arteries, thereby remodeling them into distensible low-resistance vessels to promote placental perfusion. The present study determined whether B-flow/spatiotemporal image correlation (STIC) M-mode ultrasonography provides an in-vivo imaging method able to digitally quantify spiral artery luminal distensibility as a physiological index of spiral artery remodeling during the advancing stages of normal human pregnancy. A prospective, longitudinal, observational study was conducted to quantify spiral artery distensibility (i.e. vessel luminal diameter at systole minus diameter at diastole) by B-flow/STIC M-mode ultrasonography during the first, second and third trimesters in 290 women exhibiting a normal pregnancy. Maternal serum levels of placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1), growth factors that modulate important events in spiral artery remodeling, were quantified in a subset of the women in the first, second and third trimesters of pregnancy. Median (interquartile range (IQR)) spiral artery distensibility increased progressively between the first (0.17 (0.14-0.21) cm), second (0.23 (0.18-0.28) cm) and third (0.26 (0.21-0.35) cm) trimesters of pregnancy (P < 0.0001 for all). Median (IQR) spiral artery volume flow increased progressively between the first (2.49 (1.38-4.99) mL/cardiac cycle), second (3.86 (2.06-6.91) mL/cardiac cycle) and third (7.79 (3.83-14.98) mL/cardiac cycle) trimesters (P < 0.001 for all). In accordance with the elevation in spiral artery distensibility, the median (IQR) ratio of serum PlGF/sFlt-1 × 103 levels increased between the first (7.2 (4.5-10.0)), second (22.7 (18.6-42.2)) and third (56.2 (41.9-92.5)) trimesters (P < 0.001 for all). The present study shows that B-flow/STIC M-mode ultrasonography provides an in-vivo imaging technology to quantify digitally the structural and physiological expansion of the walls of the spiral arteries during the cardiac cycle as a consequence of their transformation into compliant vessels during advancing stages of normal human pregnancy. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Earthquake migration characteristics and triggering mechanisms in the Baihetan Reservoir Area based on machine-learning microseismic detection

SUMMARY The Baihetan Reservoir, the second largest in the world, is located at the intersection of multiple large active fault zones on the eastern boundary of the Sichuan-Yunnan rhombic block. After impoundment on 6 April 2021, many earthquakes occurred around the reservoir area submerged by water. The largest ML 4.7 earthquake in the reservoir area occurred after the water level reached its highest point. But the seismogenic structures and mechanisms of earthquakes in the reservoir area are still unclear. Based on dense array data from the reservoir area, this paper uses the experimental site submodel of USTC-Pickers, transfer learned with ‘DiTing’ data set of China to obtain a high-precision earthquake catalogue that is twice as large as that the manual catalogue. This study show that earthquakes in the reservoir region primarily occur on secondary faults of pre-existing ones, characterized by a prominent feature of high dip angles trending northwest to southeast. Combined with the spatiotemperal migration characteristics of earthquakes and the relationship between earthquakes and water levels, we infer that most earthquakes are rapid response type and may be induced by rapid increase in elastic stress. Only the spatiotemporal distribution image of the ML 3.2 earthquakes sequence in the dam site-Toudaogou section conforms to the law of pore pressure diffusion, and belongs to the fast response type, which may be induced by the poroelasiticity coupling mechanism. The ML 3.0 earthquake swarm with deep depths in the Heishui River section belongs to the delayed response type and may be induced by the poroelasiticity coupling mechanism.

Prenatal Diagnosis of Persistent Left Superior Vena Cava Using High-Definition Flow Render Mode and Spatiotemporal Image Correlation.

This study aimed to assess the use of two-dimensional (2D) ultrasound combined with high-definition flow (HD-flow) render mode and spatiotemporal image correlation (STIC) in diagnosing and classifying fetal persistent left superior vena cava (PLSVC). Overall, 114 cases of fetal PLSVC were diagnosed using 2D ultrasound combined with STIC, and 114 normal fetuses of the same gestational week were selected. These cases were retrospectively analyzed to evaluate the effectiveness of the diagnostic approach. All 114 PLSVC cases were diagnosed using 2D ultrasound combined with STIC. Although the diagnostic coincidence rate of PLSVC in the HD-flow combined with STIC was similar to that in the 2D ultrasound combined with HD-flow (96.8 vs 96.2%), 2D ultrasound with STIC enabled dynamic visualization of the PLSVC, furthering prenatal diagnosis. These cases were classified as type I PLSVC: 80 cases of type Ia, 29 cases of type Ib, and 5 cases of type Ic. Seventy isolated PLSVC cases (61.4%) were noted, whereas 44 cases (35.6%) were associated with concomitant structural abnormalities. Intracardiac structural malformations accounted for the highest proportion (n = 53, 58.89%), followed by single umbilical artery and facial/bodily abnormalities (n = 10, 11.11%). Combining HD-flow and STIC complements 2D ultrasound in diagnosing and classifying fetal PLSVC, demonstrating significant clinical relevance.

Tailoring Space-Time Nonlocality for Event-Based Image Processing Metasurfaces.

Analog computation with passive optical components can enhance processing speeds and reduce power consumption, recently attracting renewed interest thanks to the opportunities enabled by metasurfaces. Basic image processing tasks, such as spatial differentiation, have been recently demonstrated based on engineered nonlocalities in metasurfaces, but next-generation computational schemes require more advanced capabilities. Here, by simultaneously tailoring the nonlocal electromagnetic response of a metasurface in space and time, we demonstrate a passive ultrathin silicon-based device that performs mixed spatiotemporal differentiation of input images, realizing event-based edge detection. The metasurface performs spatial differentiation only when the input image is evolving in time, resulting in spatiotemporal image processing on subpicosecond timescales. Moreover, the metasurface design can be tailored to selectively enhance objects moving at desired speeds. Our results point towards fully passive processing of spatiotemporal signals, for highly compact neuromorphic cameras.

Quantifying superimposed protein flow dynamics in live cells using spatial filtering and spatiotemporal image correlation spectroscopy.

Flow or collective movement is a frequently observed phenomenon for many cellular components including the cytoskeletal proteins actin and myosin. To study protein flow in living cells, we and others have previously used spatiotemporal image correlation spectroscopy (STICS) analysis on fluorescence microscopy image time series. Yet, in cells, multiple protein flows often occur simultaneously on different scales resulting in superimposed fluorescence intensity fluctuations that are challenging to separate using STICS. Here, we exploited the characteristic that distinct protein flows often occur at different spatial scales present in the image series to disentangle superimposed protein flow dynamics. We employed a newly developed and an established spatial filtering algorithm to alternatively accentuate or attenuate local image intensity heterogeneity across different spatial scales. Subsequently, we analysed the spatially filtered time series with STICS, allowing the quantification of two distinct superimposed flows within the image time series. As a proof of principle of our analysis approach, we used simulated fluorescence intensity fluctuations as well as time series of nonmuscle myosin II in endothelial cells and actin-based podosomes in dendritic cells and revealed simultaneously occurring contiguous and noncontiguous flow dynamics in each of these systems. Altogether, this work extends the application of STICS for the quantification of multiple protein flow dynamics in complex biological systems including the actomyosin cytoskeleton.

In Operando Imaging Electrostatic-Driven Disassembly and Reassembly of Collagen Nanostructures.

Collagen is the most abundant protein in tissue scaffolds in live organisms. Collagen can self-assemble in vitro, which has led to a number of biotechnological and biomedical applications. To understand the dominant factors that participate in the formation of collagen nanostructures, here we study in real time and with nanoscale resolution the disassembly and reassembly of collagens. We implement a high-speed force microscope, which provides in situ high spatiotemporal resolution images of collagen nanostructures under changing pH conditions. The disassembly and reassembly are dominated by the electrostatic interactions among amino-acid residues of different molecules. Acidic conditions favor disassembly by neutralizing negatively charged residues. The process sets a net repulsive force between collagen molecules. A neutral pH favors the presence of negative and positively charged residues along the collagen molecules, which promotes their electrostatic attraction. Molecular dynamics simulations reproduce the experimental behavior and validate the electrostatic-based model of the disassembly and reassembly processes.

Visual neurons recognize complex image transformations.

Natural visual scenes are dominated by sequences of transforming images. Spatial visual information is thought to be processed by detection of elemental stimulus features which are recomposed into scenes. How image information is integrated over time is unclear. We explored visual information encoding in the optic tectum. Unbiased stimulus presentation shows that the majority of tectal neurons recognize image sequences. This is achieved by temporally dynamic response properties, which encode complex image transitions over several hundred milliseconds. Calcium imaging reveals that neurons that encode spatiotemporal image sequences fire in spike sequences that predict a logical diagram of spatiotemporal information processing. Furthermore, the temporal scale of visual information is tuned by experience. This study indicates how neurons recognize dynamic visual scenes that transform over time.

Fragilized Ecologies and Multilayer Violence in Two Oil Films About the Niger Delta

ABSTRACT: Nigeria's oil-rich Niger Delta region has continuously been conceived as a context of terror and political violence. However, this representation of the Niger Delta is deemed too schematic and misleading since it does not account for oil production's complicated local and human issues. In fact, violent mining geographies like the Niger Delta are always permeated by various forms of subtle and imperceptible violence that, if overlooked, may lead to misrepresentations. Rob Nixon has theorized such invisibilized forms of violence as "slow violence." Building on this concept of slow violence as long emergencies that are hard to harness and turn into dramatic stories, this article examines how Black November : Struggle for the Niger Delta (Jeta Amata, 2012) and Oloibiri (Curtis Graham and Samantha Iwowo, 2016), two films set in the vulnerable ecosystem of the Niger Delta oil-producing enclaves, take on the representational challenge posed by the slow violence of ecological destruction. It studies how the filmmakers conceptualize slow violence and explores their strategies to make visible subtle forms of harm that resist representation. More specifically, it analyzes how spatiotemporal relations, images, and narrative sequences are combined to witness the slow violence of oil production and ecological destruction.

Comparison of Different Ultrasonic Screening Methods and Analysis of High Risk Factors for Fetal Cardiac Malformation in Second Trimester of Pregnancy.

Current study aims to compare the application of two-dimensional (2D) color doppler ultrasound (CDU) and four-dimensional (4D) ultrasound spatiotemporal image correlation (STIC) in fetal congenital heart disease in the second trimester of pregnancy and to analyze the high risk factors of the disease. From August 2019 to July 2021, 135 second-trimester patients with highly suspected congenital heart malformations were selected who underwent prenatal screening at South Taihu Hospital Affiliated to Huzhou University. 2D-CDU, 4D STIC, and postnatal examination were completed in all patients. 2D-CDU, 4D STIC and 2D-CDU combined with 4D STIC were used to detect fetal cardiac malformations and classify cardiac malformations. The sensitivity, specificity, positive predictive value, negative predictive value and coincidence rate of 2D-CDU, 4D STIC and 2D-CDU combined with 4D STIC were compared. The results of 2D-CDU, 4D STIC and 2D-CDU combined with 4D STIC screening were analyzed for consistency using the results of postpartum diagnosis as the gold standard. Moreover, effects of maternal gestational factors on fetal cardiac malformations by univariate and multivariate analysis. 2D-CDU combined with 4D STIC showed significantly higher section display number than 2D-CDU or 4D STIC in the view of ductal arch, aortic arch, and aortic short-axis. A total of 45 cases of fetal congenital heart malformation were detected in 135 patients in the second trimester, 40, 38 or 42 cases were detected by 2D-CDU, 4D STIC or 2D-CDU combined with 4D STIC, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and coincidence rate of 2D-CDU combined with 4D ultrasound in congenital heart malformation screening were higher than those of 2D-CDU or 4D STIC. Kappa agreement analysis showed that the diagnostic results of 4D STIC and 2D-CDU combined with 4D ultrasound in fetuses with suspected congenital heart malformation were in excellent agreement (κ > 0.75), while 2D-CDU was in good agreement with postpartum diagnosis (κ < 0.75). Univariate and multivariate regression analysis revealed that maternal age ≥ 35, drinking during pregnancy, and history of adverse pregnancy and childbirth were all independent risk factors for fetal cardiac malformations, while folic acid supplementation was an independent protective factor for fetal cardiac malformations. 2D-CDU combined with 4D echocardiography may be superior to single 2D-CDU or 4D STIC in the screening of fetal congenital heart malformation in the second trimester. In order to reduce the incidence of fetal heart anomalies, we should strengthen the screening of pregnancy anomalies in high-risk pregnant women and control the risk factors.

Evaluating fetal tricuspid and mitral annular plane systolic excursion (TAPSE and MAPSE) using spatiotemporal image correlation (STIC) M-mode.

This study aimed to evaluate the feasibility andreliability of tricuspid and mitral annular plane systolic excursion measurements (TAPSE and MAPSE) applying M-mode spatiotemporal image correlation (STIC) technology in low-risk pregnancies. An initial retrospective pilot study was carried out to assess repeatability, followed by a larger mixed cross-sectional and prospective longitudinal study, both evaluating low-risk singleton pregnancies between 22+6 and 39+1 weeks. As only annuli capture was necessary, STIC acquisition parameters were set to the minimum volume angle of 15° and acquisition time of 7.5 s. A total of 330 volumes were analysed offline applying STIC M-mode. Acquisition rates were 96.9 % for TAPSE and 93.7 % MAPSE in the pilot study (n=32) and 98.0 % for both in the longitudinal study (n=102). Both study designsrevealed good repeatability for both sides of the heart, withhigher intraclass correlation coefficients (ICCs) for TAPSE (0.84-0.94) compared to MAPSE (0.80-0.88). Goodrepeatability was demonstrated for both sides of the heart, more so for TAPSE than MAPSE, with ICCs for TAPSE 0.84-0.91 and MAPSE 0.75-0.76, comparable to prior ICCs forSTIC repeatability. Modified STIC acquisition settings specifically tailored for capturing the longitudinal annular displacement may improve STIC TAPSE and MAPSE acquisition rates, optimising image quality for precise measurement and potentially bringing these modalities closer to clinical application.

Improving spatiotemporal image fusion incorporating unmixing step by considering the point spread function effect

ABSTRACT In this paper, an improving spatiotemporal image fusion (STIF) incorporating unmixing step by considering the point spread function (PSF) effect model (CPSF) is proposed to address the problem that unmixing accuracy in STIF models incorporating unmixing step is vulnerable to the PSF effect. First, an unsupervised clustering algorithm is used to cluster the fine image at previous date into a land cover map, and the coarse class fraction matrix is generated from this land cover map. Second, the fine class fraction matrix uncontaminated by the PSF effect is calculated by upsampling the coarse class fraction matrix using the Area-to-Point Kriging (ATPK) method under the PSF constraint. Third, this fine class fraction matrix is convolved with the ideal square wave filter to downsample, and then normalized to obtain the coarse class fraction matrix without the influence of the PSF effect. Finally, the CPSF-processed class fraction matrix is used to replace the original one in the following unmixing and prediction process to generate the fine resolution image at the prediction date. Experiments on two remote sensing data sets show that the proposed method can effectively improve the accuracy of spatiotemporal image fusion methods incorporating unmixing step.

Image-processing-based ultrasonic velocimetry development with high applicability to flows in microparticle dispersion

This study presents the development of a novel methodology, image-processing-based ultrasonic velocimetry (IPUV). For evaluating flow velocity, we present a new perspective utilizing image processing for spatiotemporal echo images instead of conventional ultrasonic analysis methods such as pulse compression and quadrature detection. In addition to traditional tracer particle size O(100 μm), IPUV has a high applicability for measuring the velocity of microparticle dispersion of the particle size O(1 μm) and dilute concentration O(0.001 wt.%). Velocimetry utilizing microparticles has significant merit: Velocity measurements can be conducted under conditions with less influence on the flow and higher traceability to fluid flow than the size conventionally used as a tracer particle. The accuracy and effectiveness of velocity analysis based on the IPUV principle are verified numerically and experimentally. In the experiments, IPUV was applied to Couette flow and stirring flow. In the former, the validity of the velocity profiling by IPUV was confirmed compared to the theoretical profiles of Couette flow. In the latter, IPUV measured velocity profiles in the stirring flows with different dispersions [spherical particle O(100 μm) and mica O(1 μm)]. Differences in the ability of dispersed particles to follow turbulent fluctuations in stirring water flow appeared in the frequency spectrum of IPUV velocity profiles.

Prenatal diagnosis of interrupted aortic arch using high-definition flow render mode and spatiotemporal image correlation.

To evaluate the clinical utility of two dimensional (2D) ultrasound combined with spatiotemporal image correlation (STIC) in diagnosing interrupted aortic arch (IAA) in fetal life. A total of 53 cases of fetal IAA were diagnosed using 2D ultrasound combined with STIC, and 53 normal fetuses of the same gestational week were selected. These cases were retrospectively analyzed to assess the utility of employing 2D ultrasound combined with STIC in the diagnosis of IAA. 2D ultrasound combined with STIC detected 22 cases of type A IAA, 24 cases of type B IAA, and seven cases of type C IAA. Furthermore, combining 2D ultrasound with STIC enabled dynamic visualization of the IAA, aiding in prenatal diagnosis. The diagnostic coincidence rate of IAA was found to be higher in the HD-flow combined with STIC than that in the 2D combined with HD-flow. HD-flow combined with STIC can assist in diagnosing fetal IAA, and this technique has important clinical value.

Enhanced wavelet based spatiotemporal fusion networks using cross-paired remote sensing images

Spatiotemporal fusion can provide remote sensing images with both high temporal and high spatial resolution for earth observation applications. Most of the state-of-the-art models require three or even five images as input, which may lead to difficulties in practical applications due to bad weather or data missing. In this paper, the enhanced cross-paired wavelet based spatiotemporal fusion networks (ECPW-STFN) are proposed to improve the accuracy and quality of the fusions with fewer remote sensing images as inputs. Wavelet transform is introduced into spatiotemporal image fusion to achieve separate training of the high and low frequency components of the image to better extract features of different levels. In addition, a compound loss function containing wavelet loss is proposed to facilitate the preservation of details. Also, an enhancement module with convolutional block attention is put forward to further refine the prediction quality. Experiments were conducted to compare the proposed ECPW-STFN with five state-of-the-art methods on the public CIA and Daxing datasets. The results show ECPW-STFN is better than GAN-STFM which also requires two images, and not inferior to the methods requiring at least three inputs, even exceeds the optimal MLFF-GAN in some cases, proving its great superiority and potential. The code will be available at https://github.com/lixinghua5540/ECPW-STFN.

A satellite-field phenological bridging framework for characterizing community-level spring forest phenology using multi-scale satellite imagery

Forest phenology, as a sensitive indicator of a forest’s response to climate change and variability, has long been monitored using remote sensing, yet has seldom been interpreted or validated with spatially compatible, community-level field phenological observations. In temperate deciduous forests, multiple spring phenological phases are critical for modeling carbon storage and biogeochemical cycles. The simultaneous detection of all these critical phenological phases at the community level remains a long-standing challenge. To tackle these challenges, the objective of this study is to develop a novel satellite-field phenological bridging framework for characterizing all key spring phenological phases of a fragmented deciduous forest using multi-scale satellite time series. The framework consists of four key components: deep learning-based spatiotemporal image fusion, satellite-based forest phenology modeling, satellite-based forest phenological metric extraction, and field-based forest community phenological characterization. With the devised framework, we extract a total of 24 satellite phenological metrics of Trelease Woods, a forest fragment near Urbana, IL, USA. From weekly field phenological observations of 148 canopy trees of 15 common species of the forest community, we devise three summative field phenological indices to characterize community-level phenological states in spring. Under the satellite-field bridging framework, events during each key spring forest phenological phase (i.e., bud swell, budburst/leafing out, leaf expansion, and leaf maturity) are successfully detected using the fusion imagery (MAE from 1.1 to 2.9 days and bias from -2.4 to 1.5 days). However, the satellite detection of the earliest field events may be influenced by understory plants, soil background, and snow. The subsequent multi-scale, satellite phenological analysis underscores the importance of taking into account spatial scale and representation from both satellite and field phenological perspectives in building corresponding bridging relationships. Among the extracted pheno-metrics, bridging the threshold-based metrics to field phenological indices results in the highest accuracy (MAE less than 3 days and bias less than 2 days). The strong agreement among the field indices demonstrates the effectiveness of our field phenological surveying approach in generating community-wide forest phenological representation. Our study innovatively scales up the field phenological observations from the individual trees to the species to the community level, and the devised framework enables accurate retrieval of all key phenological events of community-wide, spring canopy development of the forest fragment.

Initiation mechanism of arcing generated in RF capacitively coupled plasma

In our previous study, we established an arcing generation and measurement system and we observed prior light emission before arcing current development. However, we briefly analyzed those light emissions with strong assumptions without detailed experiment evaluations and thus, the investigation of the formation mechanism in the initiation phase with detailed experiment evaluations has yet to be conducted. In this work, we investigated the initiation mechanism of arcing generated on an arcing inducing probe (AIP) in a radio frequency capacitively coupled plasma (CCP) environment. Here, the AIP is an aluminum rod covered by anodized film and its tip edge is partially stripped to localize arcing on this edge. We measured emission light, voltage, and current waveforms induced by arcing. The spatiotemporal image of the emission light revealed that the tip glow is the brightest intensity and has longest lifetime during arcing, meaning that it is the primary process in whole arcing process. The current waveform induced by arcing corresponds to the time evolution of the tip glow and estimations revealed that the electron emission is the predominant component of the current formation. Furthermore, snapshot images with AIPs having enlarged stripping area exhibited that arcing occurs at the boundary between the alnuminum and anodized film (dielectric), where charging of ions from the CCP on the film surface can induce high-electric field. In addition, we found that the energy relaxation length of emitted electrons for collisions with Ar atoms, which are the background gas, is much larger than the tip glow diameter, meaning that the electon-Ar collision cannot maintain tip glow. This result supports additional source of atoms to sustain the tip glow such as the surface evaporation from arcing spot, of which evidence was speculated our previous study. We estimated minimum aluminum vapor density and surface temperature, which is sufficiently high enough to induce surface vaporization. Combining those experiment results and estimations, that are electron emission, high surface temperature, and surface evaporation, we can speculate that the initiation mechanism of arcing near dielectric surface in radio-frequency CCP environment is the thermionic emission and surface evaporation from arcing spot.

Interaction of atmospheric pressure helium plasma jet with non-planar substrates: path selectivity of surface ionization wave

Most surfaces treated by atmospheric pressure plasma jets (APPJs) in practical applications are notably three-dimensional. However, non-planar surfaces exhibit a diverse array of geometries, such as variations in curvature, roughness, and texture, complicating the prediction of surface ionization waves (SIWs) propagation behavior across varied surface shapes, in the absence of sufficient experimental data. In this study, we made measurements of APPJ interactions with the non-planar substrates using the spatio-temporal resolved image method. Non-planar substrates encompassed wavy surfaces, arrayed hemispheres, and randomly textured raised surfaces. We tracked the morphology and velocity of SIW propagation over these surfaces. The results indicate that the SIW propagation on non-planar surfaces is significantly influenced by surface geometry and displays path selectivity, i.e. the SIW tends to propagate along valleys. The average propagation velocity of the SIW increases with the increasing radius of the wavy surface, as well as with the increased height of the arrayed hemispheres. This is attributable to the surface geometry constraining the dispersion of the SIW, causing it to concentrate and propagate in a singular direction. Moreover, the surface geometry markedly affects the distribution of the plasma treatment area, with the plasma inclined to enter valleys (where the light emission is significantly stronger than that of peaks) and to closely adhere to hemispherical surfaces. These patterns suggest a potential positive impact on treating skin surfaces such as pores, reducing bacteria in wrinkles, and addressing pimples.

Spatio-Temporal Image-Based Encoded Atlases for EEG Emotion Recognition.

Emotion recognition plays an essential role in human-human interaction since it is a key to understanding the emotional states and reactions of human beings when they are subject to events and engagements in everyday life. Moving towards human-computer interaction, the study of emotions becomes fundamental because it is at the basis of the design of advanced systems to support a broad spectrum of application areas, including forensic, rehabilitative, educational, and many others. An effective method for discriminating emotions is based on ElectroEncephaloGraphy (EEG) data analysis, which is used as input for classification systems. Collecting brain signals on several channels and for a wide range of emotions produces cumbersome datasets that are hard to manage, transmit, and use in varied applications. In this context, the paper introduces the Empátheia system, which explores a different EEG representation by encoding EEG signals into images prior to their classification. In particular, the proposed system extracts spatio-temporal image encodings, or atlases, from EEG data through the Processing and transfeR of Interaction States and Mappings through Image-based eNcoding (PRISMIN) framework, thus obtaining a compact representation of the input signals. The atlases are then classified through the Empátheia architecture, which comprises branches based on convolutional, recurrent, and transformer models designed and tuned to capture the spatial and temporal aspects of emotions. Extensive experiments were conducted on the Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED) public dataset, where the proposed system significantly reduced its size while retaining high performance. The results obtained highlight the effectiveness of the proposed approach and suggest new avenues for data representation in emotion recognition from EEG signals.