Camera Data Research Articles

Assessment of a space and energy resolved diagnostic based on GEM technology on MAST-U

Abstract A Gas Electron Multiplier (GEM)-based detector was utilized for the first
time on a spherical tokamak, MAST-U, during the 2023 campaign to investigate Soft
X-Ray (SXR) radiation (1-20 keV) emitted from the plasma. GEM detectors, chosen
for their resilience to harsh fusion environments and their ability to provide energy-
resolved SXR emission images with sub-millisecond time resolution, are a relatively
new diagnostic compared to standard semiconductor diodes. In this study, the GEM
detector features a pinhole geometry outside the vacuum chamber and observes the
plasma through a beryllium window. Filled with an ArCO2 mixture, the detector
consists of an Aluminized Mylar cathode, three Aluminum-coated GEM foils, and an
anode made of a 16x16 matrix of 6mm2 pads for 2D readout. It employs custom
GEMINI ASICs (Application Specific Integrated Circuits) for signal readout, enabling
photon-counting techniques with Time over Threshold (ToT) analysis on each detector
channel, reaching a total maximum rate of 256 MHz.
Preliminary findings from the 2023 campaign demonstrate the effectiveness of the
GEM detector in complementing existing SXR camera data in terms of spatial and
temporal resolution. Case studies include the identification of Magnetohydrodynamic
(MHD) instabilities, such as the Snake instability, and the exploitation of energy
capabilities for plasma event energy characterization, as shown in the case of Internal
Reconnection Events. Additionally, the GEM detector allows for estimation of the
Electron Temperature of Maxwellian plasmas. These results underscore the potential
of the GEM-based diagnostic system in advancing tokamak research for comprehensive
plasma characterization and control.

Scene Measurement Method Based on Fusion of Image Sequence and Improved LiDAR SLAM

To address the issue that sparse point cloud maps constructed by SLAM cannot provide detailed information about measured objects, and image sequence-based measurement methods have problems with large data volume and cumulative errors, this paper proposes a scene measurement method that integrates image sequences with an improved LiDAR SLAM. By introducing plane features, the positioning accuracy of LiDAR SLAM is enhanced, and real-time odometry poses are generated. Simultaneously, the system captures image sequences of the measured object using synchronized cameras, and NeRF is used for 3D reconstruction. Time synchronization and data registration between the LiDAR and camera data frames with identical timestamps are achieved. Finally, the least squares method and ICP algorithm are employed to compute the scale factor s and transformation matrices R and t between different point clouds from LiDAR and NeRF reconstruction. Then, the precise measurement of the objects could be implemented. Experimental results demonstrate that this method significantly improves measurement accuracy, with an average error within 10 mm and 1°, providing a robust and reliable solution for scene measurement.

High-performance surface defect detection of aluminum substrate based on event camera

Abstract Traditional industrial surface defect detection often employs CCD/CMOS cameras, but they are difficult to detect the minute defects on aluminum substrates in highly dynamic industrial scenes due to their nature. Event camera is a novel high-resolution vision sensor which measures per-pixel brightness changes in an asynchronous manner and outputs as event information flow (EIF). Small and weak defects on aluminum substrate can be captured by event camera effectively, but the EIF contain a large amount of noise, making it difficult to perform accurate and high-precision defect detection. To address this problem, we propose a frame aggregation method to realize good event information flow processing (EIFP), and then use an improved circle detection method to locate the aluminum substrate in each frame, removing abundant event information outside the aluminum substrate. Subsequently, we enhance the event signals under different frames based on optical flow tracking using multiple features, and construct a semi-supervised detector based on pseudo-labels to achieve high-precision defect localization. Finally, considering the small inter-class differences in defects on the surface of aluminum substrates, we construct a defect class corrector based on ensemble learning to enhance the ability to determine defect classes, achieving high-precision automatic quality inspection of aluminum substrate surfaces. The performance of our method is compared with other advanced methods based on event camera data of aluminum substrates in real industrial scenarios. The experimental results show that our method has improved the detection accuracy by about 10% and the classification accuracy by about 25% compared to the original state-of-the-art methods.

Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning

Eight-band one-shot MSFA (multispectral filter array) cameras are innovative technologies used to capture multispectral images by capturing multiple spectral bands simultaneously. They thus make it possible to collect detailed information on the spectral properties of the observed scenes economically. These cameras are widely used for object detection, material analysis, and agronomy. The evolution of one-shot MSFA cameras from 8 to 32 bands makes obtaining much more detailed spectral data possible, which is crucial for applications requiring delicate and precise analysis of the spectral properties of the observed scenes. Our study aims to develop models based on deep learning to estimate the spectral response of this type of camera and provide images close to the spectral properties of objects. First, we prepare our experiment data by projecting them to reflect the characteristics of our camera. Next, we harness the power of deep super-resolution neural networks, such as very deep super-resolution (VDSR), Laplacian pyramid super-resolution networks (LapSRN), and deeply recursive convolutional networks (DRCN), which we adapt to approximate the spectral response. These models learn the complex relationship between 8-band multispectral data from the camera and 31-band multispectral data from the multi-object database, enabling accurate and efficient conversion. Finally, we evaluate the images’ quality using metrics such as loss function, PSNR, and SSIM. The model evaluation revealed that DRCN outperforms others in crucial performance. DRCN achieved the lowest loss with 0.0047 and stood out in image quality metrics, with a PSNR of 25.5059, SSIM of 0.8355, and SAM of 0.13215, indicating better preservation of details and textures. Additionally, DRCN showed the lowest RMSE 0.05849 and MAE 0.0415 values, confirming its ability to minimize reconstruction errors more effectively than VDSR and LapSRN.

KOALA: A Modular Dual-Arm Robot for Automated Precision Pruning Equipped with Cross-Functionality Sensor Fusion

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.

Neural Approach to Coordinate Transformation for LiDAR–Camera Data Fusion in Coastal Observation

The paper presents research related to coastal observation using a camera and LiDAR (Light Detection and Ranging) mounted on an unmanned surface vehicle (USV). Fusion of data from these two sensors can provide wider and more accurate information about shore features, utilizing the synergy effect and combining the advantages of both systems. Fusion is used in autonomous cars and robots, despite many challenges related to spatiotemporal alignment or sensor calibration. Measurements from various sensors with different timestamps have to be aligned, and the measurement systems need to be calibrated to avoid errors related to offsets. When using data from unstable, moving platforms, such as surface vehicles, it is more difficult to match sensors in time and space, and thus, data acquired from different devices will be subject to some misalignment. In this article, we try to overcome these problems by proposing the use of a point matching algorithm for coordinate transformation for data from both systems. The essence of the paper is to verify algorithms based on selected basic neural networks, namely the multilayer perceptron (MLP), the radial basis function network (RBF), and the general regression neural network (GRNN) for the alignment process. They are tested with real recorded data from the USV and verified against numerical methods commonly used for coordinate transformation. The results show that the proposed approach can be an effective solution as an alternative to numerical calculations, due to process improvement. The image data can provide information for identifying characteristic objects, and the obtained accuracies for platform dynamics in the water environment are satisfactory (root mean square error—RMSE—smaller than 1 m in many cases). The networks provided outstanding results for the training set; however, they did not perform as well as expected, in terms of the generalization capability of the model. This leads to the conclusion that processing algorithms cannot overcome the limitations of matching point accuracy. Further research will extend the approach to include information on the position and direction of the vessel.

The Possibility of Improving the Range and Quality of Air-to-Ground WebRTC-Based IoT Communication in Emergency Situations by Employing an External Transceiver.

The proliferation of new services, either interpersonal or machine-oriented, has generated new demands concerning the flexibility and efficiency of transmission. The ubiquity of multimedia communication in the current internet is seamlessly and successfully supported by the WebRTC concept. This paper reports on the study of the usage of a solution employing a proxy transmission unit for air-to-ground delivery of video streaming multiplexed with sensor data in the UAV-IoT system when using the WebRTC protocol stack. The comparative experiments were carried out for two cases employing the 802.11ac network with WebRTC: the first scenario (S1) without an external transceiver and the second scenario (S2) with an external transceiver working as a proxy of the ground receiver. The presented results compare the transmission conditions without (scenario S1) and with (scenario S2) the external transceiver in terms of the RSSI, the available data rate, and total throughput of transmission of multimedia data (video stream from the UAV camera and bursty data coming from employed sensors. The usefulness of the external transceiver used in a wide range of transmission conditions is clearly proven.

Hierarchical Event-RGB Interaction Network for single-eye expression recognition

The Single-eye Expression Recognition task stands as a crucial vision task, aimed at decoding human emotional states through careful examination of the eye region. Nevertheless, traditional cameras face challenges in detecting and capturing relevant biological information, especially under demanding lighting conditions such as dim environments, high exposure scenarios, or when other radiation sources are present. In this regard, we use a new type of sensor data that can resist extreme lighting conditions, namely event camera data, to improve the performance of single-eye expression recognition. To this end, we propose a novel Hierarchical Event-RGB Interaction Network (HI-Net), to fully integrate RGB and event data to overcome the extreme lighting challenges faced by the single-eye expression recognition task. The HI-Net contains two novel designs: Event-RGB Semantic Interaction Mechanism (ER-SIM) and Hierarchical Semantics Modeling (HSM) Scheme. The former aims to achieve interaction between Event and RGB modality semantics, while the latter aims to obtain high-quality modality semantic representations. In the ER-SIM, we employ an effective cross-attention mechanism to facilitate information fusion, to adaptively integrate and complement multi-scale Event and RGB semantics to cope with extreme lighting conditions. In HSM Scheme, we first explore multi-scale contextual semantics for the event modality and the RGB modality respectively. Then, we perform a semantics interaction strategy for these multi-scale contextual semantics, to enhance each modality's semantic representation. Extensive experiments demonstrate that our HI-Net significantly outperforms many state-of-the-art methods on the single-eye expression recognition task, especially under degraded lighting conditions.

Deep Learning for Generating Time-of-Flight Camera Artifacts.

Time-of-Flight (ToF) cameras are subject to high levels of noise and errors due to Multi-Path-Interference (MPI). To correct these errors, algorithms and neuronal networks require training data. However, the limited availability of real data has led to the use of physically simulated data, which often involves simplifications and computational constraints. The simulation of such sensors is an essential building block for hardware design and application development. Therefore, the simulation data must capture the major sensor characteristics. This work presents a learning-based approach that leverages high-quality laser scan data to generate realistic ToF camera data. The proposed method employs MCW-Net (Multi-Level Connection and Wide Regional Non-Local Block Network) for domain transfer, transforming laser scan data into the ToF camera domain. Different training variations are explored using a real-world dataset. Additionally, a noise model is introduced to compensate for the lack of noise in the initial step. The effectiveness of the method is evaluated on reference scenes to quantitatively compare to physically simulated data.

Assessing the health and yield of argan trees in Morocco’s unique ecosystem: a multispectral and machine learning approach

ABSTRACT This research is focused on developing an AI model that utilizes multispectral camera data from the Souss-Massa region. The model aims to estimate the yield and identify a range of diseases in Argan trees through meticulous on-field investigations. Initially, the work is focused on understanding the resistance of Argan plants against different diseases, based on non-irrigated and irrigated Argan trees as well as planted ones. The results show that disease resistance is high in the case of non-irrigated Argan trees and low in the case of irrigated trees. In addition, we conducted a detailed study of the Argan trees to provide a comprehensive view of the plant’s health. Utilizing machine-learning techniques, the yield estimation model suggests that it is possible to achieve up to 97% accuracy in yield estimation, processing data at an impressive rate of 33 images per second. After careful consideration and analysis, we have concluded that machine counting is the most suitable technique for Argan plants. Machine counting and disease detection offer high precision, fast and efficient data processing and cost-effectiveness. Additionally, it is less labour-intensive and can be easily integrated into the existing production process.

Robotic frameless brain biopsy system enhanced by facial mesh registration

AbstractIn this study, a new approach is presented that eliminates stereotactic frameworks and the use of markers, offering an alternative to traditional brain biopsy systems. The classical biopsy operation involves the registration of magnetic resonance (MR) and computed tomography (CT) information taken from the patient at different times. Typically, the surgeon's planning information, which takes an average of 4 h on MR, is transferred to CT, and the surgical operation commences. However, this approach necessitates two separate acquisitions (MR and CT), adversely affecting patient comfort and increasing the workload. In the proposed system, it is recommended to register MR‐Depth camera data instead of MR‐CT registration. To achieve this, a 3D face pattern is obtained from the data received from the depth camera attached to the robot arm and overlapped with the mesh obtained by segmentation of the MR. It was observed that registration with sub‐millimeter precision was achieved using the CMFreg surface registration technique.

Target Tracking for TarkBot Intelligent Robots by Fusing Lidar and Camera Data

Target Tracking for TarkBot Intelligent Robots by Fusing Lidar and Camera Data

PV-LaP: Multi-sensor fusion for 3D Scene Understanding in intelligent transportation systems

Intelligent transportation systems are pivotal in modern urban development, aiming to enhance traffic management efficiency, safety, and sustainability. However, existing 3D Visual Scene Understanding methods often face challenges of robustness and high computational complexity in complex traffic environments. This paper proposes a Multi-Sensor Signal Fusion method based on PV-RCNN and LapDepth (PV-LaP) to improve 3D Visual Scene Understanding. By integrating camera and LiDAR data, the PV-LaP method enhances environmental perception accuracy. Evaluated on the KITTI and WHU-TLS datasets, the PV-LaP framework demonstrated superior performance. On the KITTI dataset, our method achieved an Absolute Relative Error (Abs Rel) of 0.079 and a Root Mean Squared Error (RMSE) of 3.014, outperforming state-of-the-art methods. On the WHU-TLS dataset, the method improved 3D reconstruction precision with a PSNR of 19.15 and an LPIPS of 0.299. Despite its high computational demands, PV-LaP offers significant improvements in accuracy and robustness, providing valuable insights for the future development of intelligent transportation systems.

Charting the Lyman-α escape fraction in the range 2.9 < z < 6.7 and consequences for the LAE reionisation contribution

Context. The escape of Lyman-α photons at redshifts greater than two is an ongoing subject of study and an important quantity to further understanding of Lyman-α emitters (LAEs), the transmission of Lyman-α photons through the interstellar medium and intergalactic medium, and the impact these LAEs have on cosmic reionisation. Aims. This study aims to assess the Lyman-α escape fraction, fesc, Lyα, over the redshift range 2.9 < z < 6.7, focusing on Very Large Telescope/Multi Unit Spectroscopic Explorer (VLT/MUSE) selected, gravitationally lensed, intrinsically faint LAEs. These galaxies are of particular interest as the potential drivers of cosmic reionisation. Methods. We assessed fesc, Lyα in two ways: through an individual study of 96 LAEs behind the A2744 lensing cluster, with James Webb Space Telescope/Near-Infrared Camera (JWST/NIRCam) and HST data, and through a study of the global evolution of fesc, Lyα using the state-of-the-art luminosity functions for LAEs and the UV-selected ‘parent’ population (dust-corrected). We compared these studies to those in the literature based on brighter samples. Results. We find a negligible redshift evolution of fesc, Lyα for our individual galaxies; it is likely that it was washed out by significant intrinsic scatter. We observed a more significant evolution towards higher escape fractions with decreasing UV magnitude and fit this relation. When comparing the two luminosity functions to derive fesc, Lyα in a global sense, we saw agreement with previous literature when integrating the luminosity functions to a bright limit. However, when integrating using a faint limit equivalent to the observational limits of our samples, we observed enhanced values of fesc, Lyα, particularly around z ∼ 6, where fesc, Lyα becomes consistent with 100% escape. This indicates for the faint regimes we sampled that galaxies towards reionisation tend to allow very large fractions of Lyman-α photons to escape. We interpret this as evidence of a lack of any significant dust in these populations; our sample is likely dominated by young, highly star-forming chemically unevolved galaxies. Finally, we assessed the contribution of the LAE population to reionisation using our latest values for fesc, Lyα and the LAE luminosity density. The dependence on the escape fraction of Lyman continuum photons is strong, but for values similar to those observed recently in z ∼ 3 LAEs and high-redshift analogues, LAEs could provide all the ionising emissivity necessary for reionisation.

The Use of the Normalized Difference Red Edge (NDRE) Vegetation Index from Multispectral Cameras Mounted on Unmanned Aerial Vehicle to Estimate the Nutrient Content in Oil Palm Leaves

This study aimed to develop a prediction model for the nutrient content of N, P, K, Mg, and Ca in oil palm leaves using the Normalized Difference RedEdge (NDRE) vegetation index derived from multispectral camera data. Data acquisition was carried out by an unmanned aerial vehicle (UAV), which was correlated to leaf sample analysis of the 17th frond number. Results showed that simple regression analysis successfully represented nutrient content (N, P, K, Mg, and Ca) based on NDRE values. Based on the MAPE and Correctness values, the nutrient content prediction model for N and P yields reliable results, while for K, Mg, and Ca, they are considered good, with Correctness values of 95.5%, 96.6%, 88.8%, 87.3%, and 90.0% for N, P, K, Mg, and Ca, respectively. The study found that the NDRE vegetation index can be used to predict the nutrient content of oil palm leaves with reliable results in accuracy for N and P, and good accuracy for K, Mg, and Ca. This is a promising finding, as it could lead to the development of a non-destructive and rapid method for monitoring the nutrient status of oil palm trees, with the validation models for N, P, K, Mg, and Ca are yN = 1.1089x - 0.2497, yP = 0.99x + 0.002, yK = 1.204x - 0.1576, yMg = 0.9149x + 0.0183, and yCa = 1.0418x - 0.0218. Keywords: Multispectral Cameras, Oil Palm, Leaf Nutrient Contents, Prediction.

Comparison of thermal cameras and human observers to estimate population density of fallow deer (Dama dama) from aerial surveys in Tasmania, Australia

Context The population of introduced fallow deer (Dama dama) is thought to have increased exponentially across much of the island of Tasmania, Australia, since 2000. Historically, deer management decisions have relied on population trend data from vehicular spotlight surveys. Renewed focus on the contemporary management of the species requires development of more robust and precise population estimation methodology. Aims This study demonstrates two aerial survey methods – conventional counts by trained human observers, and thermal imaging footage recorded during the same flights – to inform future survey practices. Methods Conventional counts were carried out by three observers, two seated on the left side of the helicopter, and one on the right. A high-resolution thermal camera was fitted to the helicopter and was orientated to meet the assumptions of distance sampling methodologies. Both survey methods were used to generate deer population density estimates. Spatial distribution of deer was also analysed in relation to patches of remnant native vegetation across an agricultural landscape. Mark–recapture distance sampling was used to estimate density from human observer counts and provide a comparison to the distance sampling estimates derived from the thermal camera. Key results Human observer counts gave a density estimate of 2.7 deer per km2, while thermal camera counts provided an estimate of 2.8 deer per km2. Deer population density estimates calculated via both methods were similar, but variability of the thermal camera estimate (coefficient of variation (CV) of 36%) was unacceptably high. Human observer data was within acceptable bounds of variability (CV, 19%). The estimated population size in central and north-eastern Tasmania for 2019 approximated 53,000 deer. Deer were primarily congregated within 200 metres of the interface between canopy cover and open pasture. Conclusions The population density estimate provides a baseline for monitoring and managing the Tasmanian deer population. Human observer data was more precise than thermal camera data in this study, but thermal counts could be improved by reducing sources of variability. Implications Improvements for the collection of thermal imagery are recommended. Future control efforts may be more efficient if they preferentially target habitat edges at this time of year, paired with random or grid-based searches where population density is lower.

Improving construction site efficiency through automated progress monitoring of underground pipe installation sites using image color analysis of iPhone LiDAR camera data

This study presents an innovative method utilizing smartphone for automated progress monitoring at underground pipe installation sites. Leveraging the LiDAR iPhone camera, the method captures detailed point cloud data of construction sites. Sophisticated color analysis of images accurately distinguishes between areas with and without pipes within excavations. Key aspects of the proposed workflow include segmentation of the excavation area, differentiation between main and side excavations, and application of an earth color mask in the RGB space to isolate pipes. The research focuses on enhancing measurement precision for excavation width, depth, and pipe burial depth, significantly reducing the manual labor traditionally required at construction sites, thereby offering an efficient and cost-effective solution. We further demonstrated the robustness of the proposed algorithm by applying it to two types of data acquired at actual construction sites. This approach is expected to contribute significantly to the digital transformation in the construction industry.

Fear of nothing to hide? How do Chinese people feel about privacy when facing facial recognition cameras?

ABSTRACT Facial-recognition cameras are becoming increasingly important for governments around the world to help maintain public security and improve public services. Despite these benefits, previous studies have shown that facial-recognition cameras may also cause risks such as privacy violations. This study explores how citizens respond to government-sponsored facial-recognition cameras in terms of privacy concerns in China, which is a country with a well-established digital system. By using a national survey combined with facial-recognition camera data, this study reveals that the presence of government-sponsored facial-recognition cameras does not lead to increased information privacy concerns among Chinese citizens. Instead, citizens in China are generally very willing to accept facial-recognition cameras built by governments, and this acceptance increases when citizens’ trust in companies and government institutions is greater. Our study suggests that the application of facial-recognition cameras has probably been normalised because people are more likely to consider the social and economic advantages of such application over the downside, specifically that related to privacy intrusion.

Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering.

Hyperspectral cameras capture spectral information at each pixel in an image. Acquired spectra can be analyzed to estimate quantities of absorbing and scattering components, but the use of traditional fitting algorithms over megapixel images can be computationally intensive. Deep learning algorithms can be trained to rapidly analyze spectral data and can potentially process hyperspectral camera data in real time. A hyperspectral camera was used to capture wide-field reflectance images of in vivo human tissue at 205 wavelength bands from 420 to 830nm. The optical properties of oxyhemoglobin, deoxyhemoglobin, melanin, and scattering were used with multi-layer Monte Carlo models to generate simulated diffuse reflectance spectra for 24,000 random combinations of physiologically relevant tissue components. These spectra were then used to train an artificial neural network (ANN) to predict tissue component concentrations from an input reflectance spectrum. The ANN achieved low root mean square errors in a test set of 6000 independent simulated diffuse reflectance spectra while calculating concentration values more than 4000× faster than a conventional iterative least squares approach. In vivo finger occlusion and gingival abrasion studies demonstrate the ability of this approach to rapidly generate high-resolution images of tissue component concentrations from a hyperspectral dataset acquired from human subjects.

Data streaming infra-red video bolometer (IRVB) of Korea Superconducting Tokamak Advanced Research (KSTAR).

Due to the increasing demands for active plasma control operations, in situ diagnostics are highly sought after. Tungsten plasma-facing components have been utilized in the Korea Superconducting Tokamak Advanced Research (KSTAR) lower divertor since the 2023 campaign. Plasma radiation is a key parameter for plasma control, especially in radiation front control experiments. Therefore, the KSTAR infra-red video bolometer (IRVB) needs to be reconfigured into an in situ data streaming diagnostic. This requires comprehensive changes in both infra-red camera control and data analysis compared to the previous system. To ensure the stability of the reconfigured system, functional parts are grouped and separated into individual processes to protect camera acquisitions from errors in other processes. In addition, to enhance the speed of data streaming analysis, the analysis code has been optimized and converted into graphics processing unit (GPU) code. Besides the data streaming analysis, the system is also designed to support post-shot analysis with the entire frame data for the same shot to address frame drop issues encountered during data streaming. Radiation front control experiments with N2 gas seeding are successful results of the data streaming IRVB for its commissioning. This paper focuses on the development of the data streaming IRVB system.