Search

Integrating deformable mirrors within the optical train of an adaptive telescope was one of the major innovations in astronomical observation technology, distinguished by its high optical throughput, reduced optical surfaces, and the incorporation of the deformable mirror. Typically, voice-coil actuators are used, which require additional position sensors, internal control electronics, and cooling systems, leading to a very complex structure. Piezoelectric deformable secondary mirror technologies were proposed to overcome these problems. Recently, a high-order piezoelectric deformable secondary mirror has been developed and installed on the 1.8-m telescope at Lijiang Observatory in China to make it an adaptive telescope. The system consists of a 241-actuator piezoelectric deformable secondary mirror, a 192-sub-aperture Shack-Hartmann wavefront sensor, and a multi-core-based real-time controller. The actuator spacing of the PDSM measures 19.3 mm, equivalent to approximately 12.6 cm when mapped onto the primary mirror, significantly less than the voice-coil-based adaptive telescopes such as LBT, Magellan and VLT. As a result, stellar images with Strehl ratios above 0.49 in the R band have been obtained. To our knowledge, these are the highest R band images captured by an adaptive telescope with deformable secondary mirrors. Here, we report the system description and on-sky performance of this adaptive telescope.

In the natural light imaging scene, it is often illuminated by a variety of wavelength light sources. To obtain images with better robustness and richer information, in this paper, we propose a method of visible and infrared image fusion based on ghost imaging, which fully combines the advantages of visible and infrared imaging. In fact, two single wavelength illumination information and detection values are used for correlation reconstruction, and then the target information is obtained, using the New Sum of Modified Laplacian. Due to the combination of the advantages of dual-wavelength imaging, as a result, complementary information on the same object can be collected to comprehensively reconstruct the information on the object and reproduce the complete detailed information, thus promoting the development of multi-wavelength fusion technology in the field of imaging. [ABSTRACT FROM AUTHOR]

The proposed research aims to enhance the capabilities of information fusion-based object tracking using thermal and visible imaging. An essential motivation behind this technique is to carve out a framework that may utilize many attributes from several sensors. The built-in particle filter is adapted with the granular computing concept for weighing the particles. Furthermore, the algorithm is extended to obtain source-level fusion after the attribute weight adaptation. After comparing with the state-of-the-art methods, the results obtained suggest that it outperforms the compared trackers from the literature. [ABSTRACT FROM AUTHOR]

Multi-sensor defect detection technology is a research hotspot for monitoring the powder bed fusion (PBF) processes, of which the quality of the captured defect images and the detection capability is the vital issue. Thus, in this study, we utilize visible information as well as infrared imaging to detect the defects in PBF parts that conventional optical inspection technologies cannot easily detect. A multi-source image acquisition system was designed to simultaneously acquire brightness intensity and infrared intensity. Then, a multi-sensor image fusion method based on finite discrete shearlet transform (FDST), multi-scale sequential toggle operator (MSSTO), and an improved pulse-coupled neural networks (PCNN) framework were proposed to fuse information in the visible and infrared spectra to detect defects in challenging conditions. The image fusion performance of the proposed method was evaluated with different indices and compared with other fusion algorithms. The experimental results show that the proposed method achieves satisfactory performance in terms of the averaged information entropy, average gradient, spatial frequency, standard deviation, peak signal-to-noise ratio, and structural similarity, which are 7.979, 0.0405, 29.836, 76.454, 20.078 and 0.748, respectively. Furthermore, the comparison experiments indicate that the proposed method can effectively improve image contrast and richness, enhance the display of image edge contour and texture information, and also retain and fuse the main information in the source image. The research provides a potential solution for defect information fusion and characterization analysis in multi-sensor detection systems in the PBF process. [ABSTRACT FROM AUTHOR]

Multi-sensor defect detection technology is a research hotspot for monitoring the powder bed fusion (PBF) processes, of which the quality of the captured defect images and the detection capability is the vital issue. Thus, in this study, we utilize visible information as well as infrared imaging to detect the defects in PBF parts that conventional optical inspection technologies cannot easily detect. A multi-source image acquisition system was designed to simultaneously acquire brightness intensity and infrared intensity. Then, a multi-sensor image fusion method based on finite discrete shearlet transform (FDST), multi-scale sequential toggle operator (MSSTO), and an improved pulse-coupled neural networks (PCNN) framework were proposed to fuse information in the visible and infrared spectra to detect defects in challenging conditions. The image fusion performance of the proposed method was evaluated with different indices and compared with other fusion algorithms. The experimental results show that the proposed method achieves satisfactory performance in terms of the averaged information entropy, average gradient, spatial frequency, standard deviation, peak signal-to-noise ratio, and structural similarity, which are 7.979, 0.0405, 29.836, 76.454, 20.078 and 0.748, respectively. Furthermore, the comparison experiments indicate that the proposed method can effectively improve image contrast and richness, enhance the display of image edge contour and texture information, and also retain and fuse the main information in the source image. The research provides a potential solution for defect information fusion and characterization analysis in multi-sensor detection systems in the PBF process.

• Photosynthetic traits of potato were successfully estimated using image analysis. • Visible and thermal imaging features were fused to build the prediction model. • The study offered a fast and handy tool to monitor photosynthetic traits of crops. Accurate determination of photosynthetic parameters is critical to evaluate crop physiological and growth processes. This study aimed to estimate photosynthetic and fluorescence variables of potatoes by visible and thermal imaging fusion analysis. Two-years pot experiments were conducted in a climate chamber with different irrigation treatments. Multi-modal image features of crop canopy were extracted from both visible and thermal images by color component extraction, discrete wavelet transformation, gray level co-occurrence matrix, and local binary pattern algorithms. Extracted features were subsequently employed to build partial least squares regression (PLSR) models for estimation of transpiration rate (Tr), net photosynthetic rate (An), stomatal conductance (GSW), electron transport rate (ETR), and maximum photochemical efficiency under Photosystem II (Fv'/Fm'). Results showed that Mask Region-Convolutional Neural Network (Mask R-CNN) performed satisfactorily on canopy segmentation with intersection over union of 87.29 % and 86.93 % in visible and thermal images, respectively. Three different types of models that either using only visible image features (PLSR RGB), or only thermal image features (PLSR T) or both visible and thermal image features (PLSR RGB+T) as inputs were compared. Results showed that PLSR RGB+T had superior estimation performance in terms of R2 and RMSE. It achieved the highest R2 of 0.85 with An and the lowest R2 of 0.66 with GSW for Zhongshu 5 , while it had the highest R2 of 0.86 with Fv'/Fm', and the lowest R2 of 0.71 with Tr and An for D681. This implied the potential of visible and thermal image-driven method for quick and accurate estimation of photosynthetic traits of crops grown in controlled environment. [ABSTRACT FROM AUTHOR]

Abstract Background Phenotyping technologies are expected to provide predictive power for a range of applications in plant and crop sciences. Here, we use the disease pressure of Beet Cyst Nematodes (BCN) on sugar beet as an illustrative example to test the specific capabilities of different methods. Strong links between the above and belowground parts of sugar beet plants have made BCN suitable targets for use of non-destructive phenotyping methods. We compared the ability of visible light imaging, thermography and spectrometry to evaluate the effect of BCN on the growth of sugar beet plants. Results Two microplot experiments were sown with the nematode susceptible cultivar Aimanta and the nematode tolerant cultivar BlueFox under semi-field conditions. Visible imaging, thermal imaging and spectrometry were carried out on BCN infested and non-infested plants at different times during the plant development. Effects of a chemical nematicide were also evaluated using the three phenotyping methods. Leaf and beet biomass were measured at harvest. For both susceptible and tolerant cultivar, canopy area extracted from visible images was the earliest nematode stress indicator. Using such canopy area parameter, delay in leaf growth as well as benefit from a chemical nematicide could be detected already 15 days after sowing. Spectrometry was suitable to identify the stress even when the canopy reached full coverage. Thermography could only detect stress on the susceptible cultivar. Spectral Vegetation Indices related to canopy cover (NDVI and MCARI2) and chlorophyll content (CHLG) were correlated with the final yield (R = 0.69 on average for the susceptible cultivar) and the final nematode population in the soil (R = 0.78 on average for the susceptible cultivar). Conclusion In this paper we compare the use of visible imaging, thermography and spectrometry over two cultivars and 2 years under outdoor conditions. The three different techniques have their specific strengths in identifying BCN symptoms according to the type of cultivars and the growth stages of the sugar beet plants. Early detection of nematicide benefit and high yield predictability using visible imaging and spectrometry suggests promising applications for agricultural research and precision agriculture.

Metamaterials (MMs) are subwavelength-structured materials that have been rapidly developed in this century and have various potentials to realize novel phenomena, such as negative refraction, cloaking and super-resolution. Theoretical proposals for super-resolution image transfer using metallic thin films were experimentally demonstrated at ultraviolet and violet wavelengths from 365 to 405 nm. However, the most preferred wavelengths of optical imaging are green wavelengths around 500 nm, because optical microscopy is most extensively exploited in the area of biotechnology. In order to make the super-resolution techniques using MMs more practical, we propose the design of a stratified metal-insulator MM that has super-resolution image transfer modes at green wavelengths, which we here call hyper modes. The design assumed only Ag and SiO2 as constituent materials and was found employing Bloch-state analysis, which is based on a rigorous transfer-matrix method for the metal-insulator MMs. It is numerically substantiated that the designed stratified metal-insulator metamaterial (SMIM) is capable of forming super-resolution images at the green wavelengths, and optical loss reduction is also studied. We discuss the results derived by the Bloch-state analysis and by effective medium models usually used for the metal-insulator MMs and show that the Bloch-state analysis is more suitable to reproduce the experimental data.

Background: Phenotyping technologies are expected to provide predictive power for a range of applications in plant and crop sciences. Here, we use the disease pressure of Beet Cyst Nematodes (BCN) on sugar beet as an illustrative example to test the specific capabilities of different methods. Strong links between the above and belowground parts of sugar beet plants have made BCN suitable targets for use of non-destructive phenotyping methods. We compared the ability of visible light imaging, thermography and spectrometry to evaluate the effect of BCN on the growth of sugar beet plants. Results: Two microplot experiments were sown with the nematode susceptible cultivar Aimanta and the nematode tolerant cultivar BlueFox under semi-field conditions. Visible imaging, thermal imaging and spectrometry were carried out on BCN infested and non-infested plants at different times during the plant development. Effects of a chemical nematicide were also evaluated using the three phenotyping methods. Leaf and beet biomass were measured at harvest. For both susceptible and tolerant cultivar, canopy area extracted from visible images was the earliest nematode stress indicator. Using such canopy area parameter, delay in leaf growth as well as benefit from a chemical nematicide could be detected already 15 days after sowing. Spectrometry was suitable to identify the stress even when the canopy reached full coverage. Thermography could only detect stress on the susceptible cultivar. Spectral Vegetation Indices related to canopy cover (NDVI and MCARI2) and chlorophyll content (CHLG) were correlated with the final yield (R = 0.69 on average for the susceptible cultivar) and the final nematode population in the soil (R = 0.78 on average for the susceptible cultivar). Conclusion: In this paper we compare the use of visible imaging, thermography and spectrometry over two cultivars and 2 years under outdoor conditions. The three different techniques have their specific strengths in identifying BCN symptoms according to the type of cultivars and the growth stages of the sugar beet plants. Early detection of nematicide benefit and high yield predictability using visible imaging and spectrometry suggests promising applications for agricultural research and precision agriculture. [ABSTRACT FROM AUTHOR]

Skinning injury on potato tubers is a kind of superficial wound that is generally inflicted by mechanical forces during harvest and postharvest handling operations. Though skinning injury is pervasive and obstructive, its detection is very limited. This study attempted to identify injured skin using two CCD (Charge Coupled Device) sensor-based machine vision technologies, i.e., visible imaging and biospeckle imaging. The identification of skinning injury was realized via exploiting features extracted from varied ROIs (Region of Interests). The features extracted from visible images were pixel-wise color and texture features, while region-wise BA (Biospeckle Activity) was calculated from biospeckle imaging. In addition, the calculation of BA using varied numbers of speckle patterns were compared. Finally, extracted features were implemented into classifiers of LS-SVM (Least Square Support Vector Machine) and BLR (Binary Logistic Regression), respectively. Results showed that color features performed better than texture features in classifying sound skin and injured skin, especially for injured skin stored no less than 1 day, with the average classification accuracy of 90%. Image capturing and processing efficiency can be speeded up in biospeckle imaging, with captured 512 frames reduced to 125 frames. Classification results obtained based on the feature of BA were acceptable for early skinning injury stored within 1 day, with the accuracy of 88.10%. It is concluded that skinning injury can be recognized by visible and biospeckle imaging during different stages. Visible imaging has the aptitude in recognizing stale skinning injury, while fresh injury can be discriminated by biospeckle imaging. [ABSTRACT FROM AUTHOR]

Three visible imaging diagnostic systems have been installed on EAST tokamak for several different diagnostic purposes. One is a high-speed camera system that can be operated to study edge plasma phenomena with a maximum recording speed of 16 kfps and a minimum exposure time of 1 µs. The other two systems have color cameras that are used by machine operators to monitor the discharge process including plasma shape, position, movement, and interaction with the device wall. A newly developed wide-view optics subsystem has been introduced into the imaging diagnostic system to transmit the plasma visible light to the outside of the device so that the camera can be installed outside, ensuring the safe and reliable functioning of the imaging diagnostic system. To match the optics, upgrades of the imaging acquisition and storage subsystem have been made to obtain both high temporal and high spatial resolutions in plasma imaging. In this paper, the design and results obtained during operations of the new visible imaging diagnostic systems are described using images of EAST tokamak plasma discharges during the recent plasma campaign. [ABSTRACT FROM AUTHOR]

This article proposes a computer-vision based protocol, useful to contribute to high-throughput automated phenotyping of seedlings during elongation, the stage following germination. Radicle and hypocotyl are two essential organs which start to develop at this stage, with the hypocotyl growing towards the soil surface and the radicle exploring deeper layers for nutrient absorption. Early identification and measurement of these two organs are important to the characterization of the plant emergence and to the prognosis of the adult plant. In normal conditions, this growth process of radicle and hypocotyl takes place in the soil, in the dark. Identification and measurement of these two organs are therefore challenging, because they need to be achieved with no light that could alter normal growth conditions. We propose here an original protocol exploiting an inactinic green light, produced by a controlled LED source, coupled to a standard low-cost gray-level camera. On the resulting digital images, we devise a simple criterion based on gravitropism and amenable to direct computer implementation. The automated criterion, through comparison with the performance of human experts, is demonstrated to be efficient for the detection and separation of radicle and hypocotyl, and generic for various species of seedlings. Our protocol especially brings improvement in terms of cost reduction over the current method found in the recent literature which resorts to higher-cost passive thermal imaging to perform the same task in the dark, and that we also consider here for comparison. Our protocol connected to automation of image acquisition, can serve to improve high-throughput phenotyping equipments for analysis of seed quality and genetic variability. [ABSTRACT FROM AUTHOR]

We experimentally demonstrate and image the hybrid nature of surface plasmon polaritons (SPPs) at visible wavelengths excited in low aspect ratio 1D and 2D periodic plasmonic structures with periods nearly equal to the resonance wavelengths, fabricated on thin homogeneous metal-coated glass substrates. A thin homogeneous metal layer was sandwiched between the substrate and the periodic metal patterns to out-couple the SPPs as leakage radiation through the substrate. This resulted in the detection of SPP excitation as transmission peaks in a dark background, as opposed to transmission dips in a bright background in the structures without the metal layer reported earlier. Due to the periods being almost equal to the resonance wavelengths, the transmission peaks were not due to extraordinary transmission phenomenon, but because of the radiative leakage of wavelength selective SPPs excited on the surface. A cross-axis polarizer-analyzer was used in broadband leakage radiation microscopy to diminish the direct zeroth -order transmission and image the real and Fourier plane characteristics of the SPP transmission. The bright emission of different colors against a dark background corresponding to the transmission plasmonic resonances for different periods, in both real and Fourier plane revealed the hybrid nature of excited SPPs, when the polarizer was positioned at 45$^\circ$ with respect to the grating vector. The fabricated plasmonic substrates present interesting opportunities for imaging and sensing applications. [ABSTRACT FROM PUBLISHER]

The Suomi-NPP Visible Infrared Imager Radiometer Suite (VIIRS) instrument provides the next generation of visible/infrared imaging including the day/night band (DNB) with nominal bandwidth from 500 to 900 nm. Previous to VIIRS, the Defense Meteorological Satellite Program Operational Linescan System (OLS) measured radiances that spanned over seven orders of magnitude, using an onboard gain adjustment to provide the capability to image atmospheric features across the solar terminator, to observe nighttime light emissions over the globe, and to monitor the global distribution of clouds. The VIIRS DNB detects radiances that span over eight orders of magnitude, and because it has 13-14-b quantization (compared with 6 b for OLS) with three gain stages, the DNB has its full dynamic range at every part of the scan. One process that is applied to the VIIRS DNB radiances is a solar/lunar zenith angle dependent gain adjustment to create near-constant contrast (NCC) imagery. The at-launch NCC algorithm was designed to reproduce the OLS capability and, thus, was constrained to solar and lunar angles from 0° to 105°. This limitation has, in part, lead to suboptimal imagery due to the assumption that DNB radiances fall off exponentially beyond twilight. The VIIRS DNB ultrasensitivity in low-light conditions enables it to detect faint emissions from a phenomenon called airglow, thus invalidating the exponential fall-off assumption. Another complication to the NCC imagery algorithm is the stray light contamination that contaminates the DNB radiances in the astronomical twilight region. We address these issues and develop a solution that leads to high-quality imagery for all solar and lunar conditions. [ABSTRACT FROM PUBLISHER]

Abstract: Titan shows its surface through many methane windows in the 1–5 region. Windows at shorter wavelengths also exist, polluted by scattering off of atmospheric haze that reduces the surface contrast. At visible wavelengths, the surface of Titan has been observed by Voyager I, the Hubble Space Telescope, and ground-based telescopes. We present here global surface mapping of Titan using the visible wavelength channels from Cassini''s Visual and Infrared Mapping Spectrometer (VIMS). We show global maps in each of the VIMS-V channels extending from 0.35 to 1.05 . We find methane windows at 0.637, 0.681, 0.754, 0.827, 0.937, and and apply an RGB color scheme to the 0.754, 0.827 and windows to search for surface albedo variations. Our results show that Titan appears gray at visible wavelengths; hence scattering albedo is a good approximation of the Bond albedo. Maps of this genre have already been made and published using the infrared channels of VIMS. Ours are the first global maps of Titan shortward of . We compare the older IR maps to the new VIMS-V maps to constrain surface composition. For instance Tui Regio and Hotei Regio, referred to as bright spots in previous papers, do not distinguish themselves at all visible wavelengths. The distinction between the dune areas and the bright albedo spots, however, such as the difference between Xanadu and Senkyo, is easily discernible. We employ an empirically derived algorithm to remove haze layers from Titan, revealing a better look at the surface contrast. [Copyright &y& Elsevier]

In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient's consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims' mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies' longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night. [ABSTRACT FROM AUTHOR]

Abstract: The fast-evolving structure of MARFEs and ELMs is observed in the National Spherical Torus Experiment (NSTX) using a fast-framing camera. It is shown that Type V ELMs, the small ELM regime in NSTX, is characterized by a poloidally propagating ionization front close to the separatrix position. The ELMs also interact with the main chamber MARFE observed in NSTX coupling its movement and partial burn-through with that of the ELM cycle. During this cycle a toroidally localized MARFE precursor is seen propagating upward along field lines at speed of the order of 15km/s. This interaction between ELM activity and MARFEs may be responsible for slow heat pulse propagation speeds seen in the divertor region. [Copyright &y& Elsevier]

Abstract: Characteristics of discrete ELMs produced in Alcator C-Mod discharges of low edge collisionality (0.2< ν ∗ <1) and large lower triangularity (δ lower ∼0.75) are examined. The energy lost per ELM from the H-mode pedestal is ∼10% of the pedestal energy. These ELMs exhibit relatively long-lived precursor oscillations, often with two modes of intermediate toroidal mode number present. At the ELM ‘crash’ multiple plasma filament structures are expelled into the scrape-off-layer. A short-lived high frequency (∼0.5MHz) magnetic oscillation is initiated at the ‘crash’. The initial ELM filaments are large perturbations to the SOL with radial extents of 0.5–1cm and typical radial propagation velocities of 1km/s. Velocities of up to 8km/s have been seen. The poloidal extent of the initial filaments is >4.5cm. The initial filaments are followed (at intervals of ∼100μs) by multiple, less perturbing secondary filaments. [Copyright &y& Elsevier]

Abstract: Change of Hα intensity profiles depending on magnetic configurations is observed in the divertor plasma. It can be explained by the magnetic field line structures in the ergodic layer and the divertor legs. The behavior of neutral particles in the plasma periphery is investigated by a three-dimensional neutral particle transport simulation code which assumes that the distribution of the plasma flow onto the divertor plates corresponds to that of the strike points calculated by magnetic field line traces. Vertical Hα intensity profiles and polarization resolved Hα spectra are calculated by the simulation code including the effect of Doppler broadening, fine structure splitting and polarization of the Hα emission, which agree well with the measurements in various magnetic configurations. It shows spontaneous formation of high neutral density in inboard side of the torus, which is independent of the magnetic configurations in LHD. [Copyright &y& Elsevier]

Abstract: Detailed results of edge plasma and neutral particle behavior are described in the gas puff imaging experiments performed in the GAMMA 10 tandem mirror for the first time. Visible imaging measurement by using a high-speed camera (Ultima-SE, Photron Inc.) was recently performed in the central-cell midplane of GAMMA 10. In the standard plasma discharge heated by ICRF wave, a short gas puff of hydrogen (3ms) was carried out and the time evolution of light emission was visualized precisely. It was found that the time evolution of the emission cloud was similar to that of Hα line intensity measured nearby and was localized close to the gas puff port. Asymmetry in diffusion of the emission cloud is observed radial and axial direction of plasma column in the central-cell. This localization phenomenon of neutrals is qualitatively well explained by a fully 3-dimensional simulation using DEGAS neutral particle transport code. [Copyright &y& Elsevier]

Abstract: Using gas puff imaging technique (GPI) the filamentary structure in edge plasma was firstly observed in Heliotron J. A combination of GPI and simultaneous movable Langmuir probe measurement shows that filamentary structure identified the turbulent burst in edge plasma, which was reported already in Heliotron J. In low electron density (<1×1019 m−3) the filamentary structure moves very fast, and its direction is the same as the E r × B drift. On the other hand, in high electron density (>2×1019 m−3) the motion of the filamentary structure is relatively slow, and typical lifetime of the filamentary structure is about 300–900μs. [Copyright &y& Elsevier]

Abstract: Migration of pre-characterized carbon dust in a tokamak environment was studied by introduction of ∼30mg of micron-size (∼6μm median diameter) dust in the lower divertor of DIII-D using a DiMES sample holder. Direct exposure of the dust to high particle and heat fluxes in ELMing H-mode lower single-null discharges resulted in part of the dust being injected into the plasma. Following a brief exposure (∼0.1s) at the outer strike point, 1.5–2% of the total dust carbon content (2 –3×1019 carbon atoms, equivalent to a few million dust particles) penetrated the core plasma, raising the core carbon density by a factor of 2–3. Individual dust particles were observed moving at velocities of 10–100m/s, predominantly in the toroidal direction for deuteron flow to the outer divertor target, consistent with the ion drag force. The observed behavior of the dust is in qualitative agreement with modeling by the 3D DustT code. [Copyright &y& Elsevier]

Abstract: A movable carbon target with a head diameter of 9cm was inserted into the peripheral region of a Heliotron J plasma. High-speed, two-dimensional images of filtered visible light emission near this target were taken by a C-MOS video camera. In high-density ECH discharges (70GHz, 0.3MW, m−3) it was observed that a striped pattern of Hα light moved along the poloidal direction. Simultaneously, other diagnostic data (ECE signals, line integral electron density, ion saturation currents of Langmuir probes) showed plasma oscillation at a frequency of 5–6kHz. Individual signals from pixels of the visible camera images indicate that the oscillation phase is approximately constant along the field line, suggesting that coherent structure phenomena along the field line are present in the high-density plasmas of Heliotron J. [Copyright &y& Elsevier]

Abstract: Using high speed movies of edge/Scrape-Off-Layer emission, velocity fields of the turbulent fluctuations have been measured in a small region of the poloidal/radial plane at the plasma’s outboard midplane. Gas-puff-imaging is used to localize the emission in the toroidal dimension. The measured D α line emission responds to the underlying spatio-temporal dynamics of density and temperature. Time-delay cross-correlation analysis is applied to the 64×64 pixel movie images and yields time-averaged velocity fields for the emission perturbations. The spatial resolution of the velocity determinations is a few mm over the viewed region. It is found that the velocities inside the separatrix are almost purely poloidal. In the SOL the radial velocity component is outward, and significant radial acceleration is observed. The poloidal and radial components of the SOL velocities vary with the discharge conditions. The magnitudes of all the velocity components measured this way range from 0 to ∼1000m/s. [Copyright &y& Elsevier]