Properties of Cirrus Clouds over the European Arctic (Ny-Ålesund, Svalbard)

Cirrus is the only cloud type capable of inducing daytime cooling or heating at the top of the atmosphere (TOA) and the sign of its radiative effect highly depends on its optical depth. However, the investigation of its geometrical and optical properties over the Arctic is limited. In this work the long-term properties of cirrus clouds are explored for the first time over an Arctic site (Ny-Ålesund, Svalbard) using lidar and radiosonde measurements from 2011 to 2020. The optical properties were quality assured, taking into account the effects of specular reflections and multiple-scattering. Cirrus clouds were generally associated with colder and calmer wind conditions compared to the 2011–2020 climatology. However, the dependence of cirrus properties on temperature and wind speed was not strong. Even though the seasonal cycle was not pronounced, the winter-time cirrus appeared under lower temperatures and stronger wind conditions. Moreover, in winter, geometrically- and optically-thicker cirrus were found and their ice particles tended to be more spherical. The majority of cirrus was associated with westerly flow and westerly cirrus tended to be geometrically-thicker. Overall, optically-thinner layers tended to comprise smaller and less spherical ice crystals, most likely due to reduced water vapor deposition on the particle surface. Compared to lower latitudes, the cirrus layers over Ny-Ålesund were more absorbing in the visible spectral region and they consisted of more spherical ice particles.

Specular Reflections Detection and Removal Based on Deep Neural Network for Endoscope Images

Specular reflections have always been undesirable when processing endoscope vision for clinical purpose. Scene afflicted with strong specular reflection could result in visual confusion for the operation of surgical robot. In this paper, we propose a novel model based on deep learning framework, known as Surgical Fix Deep Neural Network (SFDNN). This model can effectively detect and fix the reflection points in different surgical videos hence opening up a whole new approach in handling undesirable specular reflections.

Specular Reflections Detection and Removal Based on Deep Neural Network for Endoscope Images

Specular reflections have always been undesirable when processing endoscope vision for clinical purpose. Scene afflicted with strong specular reflection could result in visual confusion for the operation of surgical robot. In this paper, we propose a novel model based on deep learning framework, known as Surgical Fix Deep Neural Network (SFDNN). This model can effectively detect and fix the reflection points in different surgical videos hence opening up a whole new approach in handling undesirable specular reflections.

Impact of multiple reflections on urban acoustics

Understanding how the urban form contributes to noise is important for the successful acoustic design of cities. The amplification of sound is mainly due to the multiple reflections that occur between the high and parallel walls of urban canyons. This study explores the use of ray tracing at the urban scale through the measurement and simulation of three configurations. These are referred to as “1D”, “2D” and “3D”. Impulse response measurements performed at points located on the top of the façades show an increase of 6 dB for the “2D” case and 11 dB for the “3D” case. These results are consistent with ray tracing simulations. This kind of simulation is useful to determine the influence of the street aspect ratio on the sound level. Since specular reflections are related to geometry, a spatialized representation is proposed and discussed.

Refined karst feature and fault identification through integrated wave field separation and imaging of Diffraction energy

In this abstract, a case study from offshore Indonesia is showcased with examples emphasizing integrated wave field separation methods with the objective of diffraction imaging towards refined karst feature and fault identification. For imaging optimally all diffraction energy, pre-migration and post-migration methods have been integrated. The dataset and examples in this abstract are in a complex geological setting in a very shallow water environment, with a subsurface that is characterized by large carbonate pinnacles containing large amount of karst features with thinning and thickening carbonate layers. For the purpose of refined imaging of diffraction energy only, the total wave field has been separated into specular reflections and diffractions prior to migration and these have been integrated with existing post-migration wave field separation methods. Both the pre-migration and post-migration wave field separation methods have their advantages and disadvantages and is discussed later in this abstract. Diffraction energy, in general is much lower in amplitude than the specular reflections and separately imaging these, unveils higher resolution small scale geological features such as karst features and faults complementing the total wave field PSDM data. With existing industry available methods applying wave field separation in either pre-migration or post-migration stage, limitations have been observed, and therefore we propose in this abstract to integrate both methods and take advantage of the improvements showcased with examples throughout the abstract.

Acoustic Prediction Modeling and Sound Mapping of public transport users' exposure at a bus stop.

The objective of this research is to develop a mathematical model to predict the road traffic noise level at the bus stop, to assess the level of noise that users of these urban facilities are exposed daily. To help assess the exposure and the environmental impact, sound mapping will be performed using the IMMI software. In the model, the calculation of direct paths and specular reflections and diffuse was adopted. The study was applied in three sections in the city of Maringá, Brazil. At each point, the user was simulated standing and sitting. The sound source was positioned on the axis of each strip, every five meters. In total, 5124 readings of source positions were evaluated in 84 measured points. For the validation, the Anderson-Kurze, Kang, Yang and Zhang, Bistafa and Naish model were applied, and then the t-Student test were applied. The results showed a correspondence between the developed model, the data of the measurements and the reference models in the range of 25 Hz to 10000 Hz, there was a greater variance between the models applied in the high frequencies. It is concluded that the model was able to estimate the sound level of the stretches evaluated.

Continuous-capture microwave imaging

Light-in-flight sensing has emerged as a promising technique in image reconstruction applications at various wavelengths. We report a microwave imaging system that uses an array of transmitters and a single receiver operating in continuous transmit-receive mode. Captures take a few microseconds and the corresponding images cover a spatial range of tens of square meters with spatial resolution of 0.1 meter. The images are the result of a dot product between a reconstruction matrix and the captured signal with no prior knowledge of the scene. The reconstruction matrix uses an engineered electromagnetic field mask to create unique random time patterns at every point in the scene and correlates it with the captured signal to determine the corresponding voxel value. We report the operation of the system through simulations and experiment in a laboratory scene. We demonstrate through-wall real-time imaging, tracking, and observe second-order images from specular reflections.

Spiral sound-diffusing metasurfaces based on holographic vortices

In this work, we show that scattered acoustic vortices generated by metasurfaces with chiral symmetry present broadband unusual properties in the far-field. These metasurfaces are designed to encode the holographic field of an acoustical vortex, resulting in structures with spiral geometry. In the near field, phase dislocations with tuned topological charge emerge when the scattered waves interference destructively along the axis of the spiral metasurface. In the far field, metasurfaces based on holographic vortices inhibit specular reflections because all scattered waves also interfere destructively in the normal direction. In addition, the scattering function in the far field is unusually uniform because the reflected waves diverge spherically from the holographic focal point. In this way, by triggering vorticity, energy can be evenly reflected in all directions except to the normal. As a consequence, the designed metasurface presents a mean correlation-scattering coefficient of 0.99 (0.98 in experiments) and a mean normalized diffusion coefficient of 0.73 (0.76 in experiments) over a 4 octave frequency band. The singular features of the resulting metasurfaces with chiral geometry allow the simultaneous generation of broadband, diffuse and non-specular scattering. These three exceptional features make spiral metasurfaces extraordinary candidates for controlling acoustic scattering and generating diffuse sound reflections in several applications and branches of wave physics as underwater acoustics, biomedical ultrasound, particle manipulation devices or room acoustics.

Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness

Distorted medical images can significantly hamper medical diagnosis, notably in the analysis of Computer Tomography (CT) images and organ segmentation specifics. Therefore, improving diagnostic imagery accuracy and reconstructing damaged portions are important for medical diagnosis. Recently, these issues have been studied extensively in the field of medical image inpainting. Inpainting techniques are emerging in medical image analysis since local deformations in medical modalities are common because of various factors such as metallic implants, foreign objects or specular reflections during the image captures. The completion of such missing or distorted regions is important for the enhancement of post-processing tasks such as segmentation or classification. In this paper, a novel framework for medical image inpainting is presented by using a multi-task learning model for CT images targeting the learning of the shape and structure of the organs of interest. This novelty has been accomplished through simultaneous training for the prediction of edges and organ boundaries with the image inpainting, while state-of-the-art methods still focus only on the inpainting area without considering the global structure of the target organ. Therefore, our model reproduces medical images with sharp contours and exact organ locations. Consequently, our technique generates more realistic and believable images compared to other approaches. Additionally, in quantitative evaluation, the proposed method achieved the best results in the literature so far, which include a PSNR value of 43.44 dB and SSIM of 0.9818 for the square-shaped regions; a PSNR value of 38.06 dB and SSIM of 0.9746 for the arbitrary-shaped regions. The proposed model generates the sharp and clear images for inpainting by learning the detailed structure of organs. Our method was able to show how promising the method is when applying it in medical image analysis, where the completion of missing or distorted regions is still a challenging task.