Imagerie

Bridge detection in remote sensing images (RSIs) plays a crucial role in various applications, but it poses unique challenges compared to the detection of other objects. In RSIs, bridges exhibit considerable variations in terms of their spatial scales and aspect ratios. Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with large-size VHR RSIs limits the deep learning algorithms' performance on bridge detection...

With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist's task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets...

In the art of conjuring, as well as in cognitive science, possibilities for designing magic tricks that are suitable for people who are blind or visually impaired have only rarely been considered. In this article, we argue that many magic tricks which are normally presented in a visual way, are not inherently based on visual processes, but instead on systematic illusions and limitations in visual imagery and mental simulations. Accordingly, it should be possible to adapt these tricks for presentation in a non-visual format, which should be suitable for people who are blind or visually impaired...

Human cooperation relies on key features of social interaction in order to reach desirable outcomes. Similarly, human-robot interaction may benefit from integration with human-human interaction factors. In this paper, we aim to investigate brain-to-brain coupling during motor imagery (MI)-based brain-computer interface (BCI) training using eye-contact and hand-touch interaction. Twelve pairs of friends (experimental group) and 10 pairs of strangers (control group) were recruited for MI-based BCI tests concurrent with electroencephalography (EEG) hyperscanning...

Cyanobacteria are major contributors to algal blooms in inland waters, threatening ecosystem function and water uses, especially when toxin-producing strains dominate. Here, we examine 140 hyperspectral (HS) images of five representatives of the widespread, potentially toxin-producing and bloom-forming genera Microcystis, Planktothrix, Aphanizomenon, Chrysosporum and Dolichospermum, to determine the potential of utilizing visible and near-infrared (VIS/NIR) reflectance for their discrimination. Cultures were grown under various light and nutrient conditions to induce a wide range of pigment and spectral variability, mimicking variations potentially found in natural environments...

This study compared two different methods, the satellite altimetry-based and DEM (digital elevation model)-based, for estimating lake water volume changes. We focused on 34 lakes in China as the testing sites to compare the two methods for lake water volume changes from 2005 to 2020. The satellite altimetry-based method used water levels provided by the DAHITI (Database for Hydrological Time Series of Inland Waters) data and surface areas derived from Landsat imagery. The DEM-based method used the SRTM DEM data in combination with Landsat-derived lake extents...

The identification of maritime targets plays a critical role in ensuring maritime safety and safeguarding against potential threats. While satellite remote-sensing imagery serves as the primary data source for monitoring maritime targets, it only provides positional and morphological characteristics without detailed identity information, presenting limitations as a sole data source. To address this issue, this paper proposes a method for enhancing maritime target identification and positioning accuracy through the fusion of Automatic Identification System (AIS) data and satellite remote-sensing imagery...

In the pursuit of sustainable agriculture, efficient water management remains crucial, with growers relying on advanced techniques for informed decision-making. Cotton yield prediction, a critical aspect of agricultural planning, benefits from cutting-edge technologies. However, traditional methods often struggle to capture the nuanced complexities of crop health and growth. This study introduces a novel approach to cotton yield prediction, leveraging the synergy between Unmanned Aerial Vehicles (UAVs) and scale-aware convolutional neural networks (CNNs)...

For the development of an idyllic rural landscape, an accurate survey of rural buildings is essential. The extraction of rural structures from unmanned aerial vehicle (UAV) remote sensing imagery is prone to errors such as misclassifications, omissions, and subpar edge detailing. This study introduces a multi-scale fusion and detail enhancement network for rural building extraction, termed the Multi-Attention-Detail U-shaped Network (MAD-UNet). Initially, an atrous convolutional pyramid pooling module is integrated between the encoder and decoder to enhance the main network's ability to identify buildings of varying sizes, thereby reducing omissions...

BACKGROUND: Pseudomyxoma peritonei (PMP) is a rare, progressive, slowly growing, inadequately understood neoplasm with a 5-year progression-free survival rate of as low as 48%. It is characterized by varying degrees of malignancy and the production of mucinous and gelatinous structures. Typically, the development of pseudomyxoma peritonei is associated with the rupture of appendiceal mucinous tumors and other gastrointestinal or ovarian mucinous tumors. The goal of our literature review was to identify various aspects that characterize the ovarian causes of pseudomyxoma peritonei...

Being able to represent and remember verbally-encoded information about spatial environments from different perspectives is important for numerous daily activities. The present study examined how frequently individuals spontaneously engage in visual mental imagery and verbal rehearsal strategies in memory recall of spatial descriptions, and whether using certain strategies is associated with better recall performance. Memory recall for route (person-centred) and survey (object-centred) spatial descriptions was examined in a sample of 105 neurotypical individuals, who also reported to what extent they used route- and survey-based mental imagery and verbal rehearsal strategies in each description...

Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time-frequency maps and employed a DCGAN-GP network to generate synthetic time-frequency representations resembling real data...

Brain imaging studies have recently provided some evidence in favor of covert cognitive processes that are ongoing in patients with disorders of consciousness (DoC) (e.g., a minimally conscious state and vegetative state/unresponsive wakefulness syndrome) when engaged in passive sensory stimulation or active tasks such as motor imagery. In this exploratory study, we used transcranial magnetic stimulation (TMS) of the motor cortex to assess modulations of corticospinal excitability induced by action observation in eleven patients with DoC...

Similar to traditional imaging, virtual reality (VR) imagery encompasses nonstereoscopic (VR-2D) and stereoscopic (VR-3D) modes. Currently, Russell's emotional model has been extensively studied in traditional 2D and VR-3D modes, but there is limited comparative research between VR-2D and VR-3D modes. In this study, we investigate whether Russell's emotional model exhibits stronger brain activation states in VR-3D mode compared to VR-2D mode. By designing an experiment covering four emotional categories (high arousal-high pleasure (HAHV), high arousal-low pleasure (HALV), low arousal-low pleasure (LALV), and low arousal-high pleasure (LAHV)), EEG signals were collected from 30 healthy undergraduate and graduate students while watching videos in both VR modes...

Action observation and motor imagery (AOMI) are commonly delivered through a laptop screen. Immersive virtual reality (VR) may enhance the observer's embodiment, a factor that may boost AOMI effects. The study aimed to investigate the effects on manual dexterity of AOMI delivered through immersive VR compared to AOMI administered through a laptop. To evaluate whether VR can enhance the effects of AOMI, forty-five young volunteers were enrolled and randomly assigned to the VR-AOMI group, who underwent AOMI through immersive VR, the AOMI group, who underwent AOMI through a laptop screen, or the control group, who observed landscape video clips...

Monitoring burned areas in Thailand and other tropical countries during the post-harvest season is becoming increasingly important. High-resolution remote sensing data from Sentinel-2 satellites, which have a short revisit time, is ideal for accurately and efficiently mapping burned regions. However, automating the mapping of agriculture residual on a national scale is challenging due to the volume of information and level of detail involved. In this study, a Sentinel-2A Level-1C Multispectral Instrument image (MSI) from February 27, 2018 was combined with object-based image analysis (OBIA) algorithms to identify burned areas in Mae Chaem, Chom Thong, Hod, Mae Sariang, and Mae La Noi Districts in Chiang Mai, Thailand...

Red Noctiluca scintillans (RNS), a prominent species of dinoflagellate known for its conspicuous size and ability to form blooms, exhibits heterotrophic behavior and functions as a microzooplankton grazer within the marine food web. In this study, a straightforward technique referred to as the blue-green index (BGI) has been introduced for the purpose of distinguishing and discerning RNS from neighboring waters, owing to its pronounced absorption in the blue-green spectral range. This method has been applied across a range of satellite imagery, encompassing both multi-spectral and hyperspectral sensors...

Advanced nighttime light (NTL) remote sensing techniques enable the large-scope epidemiological investigations of people's exposure to outdoor artificial light at night (ALAN) and its health effects. However, multiple uncertainties remain in the measurements of people's exposure to outdoor ALAN, including the representations of outdoor ALAN, the contextual settings of exposure measurements, and measurement approaches. Non-exposed but included outdoor ALAN and causally irrelevant outdoor ALAN may manifest as contextual errors, and these uncertain contextual errors may lead to biased measurements and erroneous interpretations when modeling people's health outcomes...

Mental imagery and relaxation are noninvasive methods and can easily be used by the patient. Accordingly, this study aimed to detect the effect of guided mental imagery (GI) and progressive muscle relaxation (PMR) on sedation in patients undergoing noninvasive mechanical ventilation (NIV). This clinical trial study was carried out in the Shahid Modarres Hospital in 2022 and encompassed 80 patients with COVID-19 undergoing NIV, who were assigned to two intervention and control groups. The sedation survey scale was used to collect the required data...

BACKGROUND: Infective endocarditis (IE) is a serious condition which is difficult to diagnose and to treat, both medically and surgically. OBJECTIVES: The objective of this study was to evaluate the impact of the SARS-CoV-2 pandemic on the management of patients with IE. METHODS: We conducted a single-centre retrospective study including patients hospitalized for IE during the pandemic (Group 2) compared with the same period the year before (Group 1)...