Shadow Images Research Articles

Rotating black holes surrounded by a dark matter halo in the galactic center of M87 and Sgr A[formula omitted]

In this paper, we construct the solutions of modified Schwarzschild spacetime with three kinds of dark matter profiles, i.e. the Beta profiles, Moore profiles and Fermionic halos profiles. In order to make the models more realistic, by using the modified Newman–Janis algorithm (MNJA), we extend our solution to obtain three rotating black holes surrounded by dark matter halo and discuss the black hole properties such as the event horizon, ergoregion, geodesics of null particles and the black hole shadow. We consider two specific examples, i.e. using the present values for the parameters of the dark matter density profiles for the Sgr A∗ black hole and M87 central black hole, it shows that the effects of the dark matter on the size of shadows are almost negligible compared to the shadow of Kerr black hole. Besides, in specific condition having dark matter with very high density, we show that the shadow images of Beta model rotating black hole increase, but the shadow images of Moore model and Fermionic model rotating black holes decrease, furthermore, the Moore profile has the largest deviation, the Beta profile has the least deviation and the deviation of Fermionic halo model is in the middle. These phenomena may provides us with a possibility: the effect of dark matter on the apparent shadow shape could shed some light on mass density profiles of dark matter halo of a galaxy, by studying black holes in dark matter, it may provide an inspiration for us to indirectly detect dark matter.

Fluorescence microscopy shadow imaging for neuroscience.

Fluorescence microscopy remains one of the single most widely applied experimental approaches in neuroscience and beyond and is continuously evolving to make it easier and more versatile. The success of the approach is based on synergistic developments in imaging technologies and fluorophore labeling strategies that have allowed it to greatly diversify and be used across preparations for addressing structure as well as function. Yet, while targeted labeling strategies are a key strength of fluorescence microscopy, they reciprocally impose general limitations on the possible types of experiments and analyses. One recent development that overcomes some of these limitations is fluorescence microscopy shadow imaging, where membrane-bound cellular structures remain unlabeled while the surrounding extracellular space is made to fluoresce to provide a negative contrast shadow image. When based on super-resolution STED microscopy, the technique in effect provides a positive image of the extracellular space geometry and entire neuropil in the field of view. Other noteworthy advantages include the near elimination of the adverse effects of photobleaching and toxicity in live imaging, exhaustive and homogeneous labeling across the preparation, and the ability to apply and adjust the label intensity on the fly. Shadow imaging is gaining popularity and has been applied on its own or combined with conventional positive labeling to visualize cells and synaptic proteins in their parenchymal context. Here, we highlight the inherent limitations of fluorescence microscopy and conventional labeling and contrast these against the pros and cons of recent shadow imaging approaches. Our aim is to describe the brief history and current trajectory of the shadow imaging technique in the neuroscience field, and to draw attention to its ease of application and versatility.

Ultrafast Process Characterization of Laser-Induced Damage in Fused Silica Using Pump-Probe Shadow Imaging Techniques.

This study delves into the intricate dynamics of laser-induced damage in fused silica using a time-resolved pump-probe (TRPP) shadowgraph. Three typical ultra-fast processes, laser-induced plasma evolution, shockwave propagation and material fracture splashing, were quantitatively investigated. The results indicate that the diameter of plasma is proportional to the pulse laser energy and increases linearly during the pulse laser duration with an expansion rate of approximately 6 km/s. The maximum shockwave velocity on the air side is 9 km/s, occurring at the end of the pulse duration, and then rapidly decreases due to air resistance, reaching approximately 1 km/s around a 300 ns delay. After hundreds of nanoseconds, there is a distinct particle splashing phenomenon, with the splashing particle speed distribution ranging from 0.15 km/s to 2.0 km/s. The particle sizes of the splashing particles range from 4 μm to 15 μm. Additionally, the smaller the delay, the faster the speed of the splashing particles. Overall, TRPP technology provides crucial insights into the temporal evolution of laser-induced damage in fused silica, contributing to a comprehensive understanding essential for optimizing the performance and safety of laser systems.

Effects of liquid properties on the development of nanosecond-pulsed plasma inside of liquid: comparison of water and liquid nitrogen

In this manuscript, we report on observations of the development of nanosecond-pulsed plasma in liquids and examine liquids with two drastically different properties: water and liquid nitrogen. Here, we compare the discharge appearance using high-speed imaging, examine bubble formation using shadow imaging, and measure the time-averaged optical emission spectra of these plasmas. Because the liquid nitrogen plasma is ignited in a liquid that is at boiling temperature, we also study the water discharge at various temperatures, up to boiling. We demonstrate that the discharge development appears not to be affected by this type of liquid. Optical emission, however, is strikingly different: in water, we observe continuum emission in the UV region only and no black-body continuum or atomic lines, whereas the liquid nitrogen spectrum is populated by molecular and longer wavelength broadband emissions.

Effects of viscosity on hydrodynamics and mass transfer under a wire mesh-coupled solid particles method

Many practical industrial processes require gas–liquid mass transfer in highly viscous liquids, and liquid viscosity affects bubble characteristics and gas–liquid mass transfer. The current study investigated the effects of liquid viscosity on bubble dynamics and gas–liquid mass transfer via shadow imaging and dynamic oxygen dissolution methods, and the influence of fluid viscosity on the hydrodynamic effect when using a wire mesh-coupled solid particles method. The coupling strategy was associated with a bubble size regulation effect, with greater viscosity increasing the gas–liquid interface area by 27%–55% compared with unreinforced gas–liquid flow, which was superior to embedded wire mesh and added solid particles methods. Increased viscosity weakened the mass transfer enhancement effect of the coupling method, but the coupling method still effectively enhanced the gas–liquid mass transfer process, increasing the volumetric mass transfer coefficient (KLa) by 80%–130% compared to non-enhanced gas–liquid flow. Novel empirical KLa correlation equations were developed to predict the effects of the coupling method on gas–liquid mass transfer processes, and those equations exhibited good reliability and predictive capacity.

Imaging ultracompact objects with radiatively inefficient accretion flows

Context. Recent Event Horizon Telescope observations of M 87* and Sgr A* strongly suggest the presence of a supermassive black hole at their respective cores. We use the semi-analytic radiatively inefficient accretion flows (RIAF) model to investigate the resulting images of the Joshi–Malafarina–Narayan (JMN-1) naked singularity and the Schwarzschild black hole. Aims. We chose the JMN-1 naked singularity model and compared the synchrotron images with the Schwarzschild solution to search for any distinct features that can distinguish the two objects and to find an alternative to the solution with a black hole. Methods. We performed general relativistic ray-tracing and radiative transfer simulations using the Brahma code to generate synchrotron-emission images using the thermal distribution function for emissivity and absorptivity. We investigated effects in the images by varying the inclination angle, the disk width, and the frequency. Results. The shadow images simulated with the JMN-1 model closely resemble those generated by the Schwarzschild black hole. The disparities between the two images are very small. We conducted simulations using various plasma parameters, but the resulting images remained largely consistent for both scenarios. This similarity is evident in the horizontal cross-sectional brightness profiles of the two scenarios. Notably, the JMN-1 model exhibits a slightly higher intensity than the Schwarzschild black hole. Conclusions. We conclude that JMN-1 is a viable substitute for the black hole scenario. This conclusion is not solely grounded in the fact that the two scenarios are indistinguishable from their respective shadow observations, but also in the consideration that JMN-1 emerges as an end state of a continual gravitational collapse. This paradigm not only allows for constraints on spacetime, but also provides a good probe for the nature of the central compact object.

Hyperspectral Image Shadow Enhancement Using Three-Dimensional Dynamic Stochastic Resonance and Classification Based on ResNet

Classification is an important means of extracting rich information from hyperspectral images (HSIs). However, many HSIs contain shadowed areas, where noise severely affects the extraction of useful information. General noise removal may lead to loss of spatial correlation and spectral features. In contrast, dynamic stochastic resonance (DSR) converts noise into capability that enhances the signal in a way that better preserves the image’s original information. Nevertheless, current one-dimensional and 2D DSR methods fail to fully utilize the tensor properties of hyperspectral data and preserve the complete spectral features. Therefore, a hexa-directional differential format is derived in this paper to solve the system’s output, and the iterative equation for HSI shadow enhancement is obtained, enabling 3D parallel processing of HSI spatial–spectral information. Meanwhile, internal parameters are adjusted to achieve optimal resonance. Furthermore, the residual neural network 152 model embedded with the convolutional block attention module is proposed to diminish information redundancy and leverage data concealed within shadow areas. Experimental results on a real-world HSI demonstrate the potential performance of 3D DSR in enhancing weak signals in HSI shadow regions and the proposed approach’s effectiveness in improving classification.

Synthetic Document Images with Diverse Shadows for Deep Shadow Removal Networks.

Shadow removal for document images is an essential task for digitized document applications. Recent shadow removal models have been trained on pairs of shadow images and shadow-free images. However, obtaining a large, diverse dataset for document shadow removal takes time and effort. Thus, only small real datasets are available. Graphic renderers have been used to synthesize shadows to create relatively large datasets. However, the limited number of unique documents and the limited lighting environments adversely affect the network performance. This paper presents a large-scale, diverse dataset called the Synthetic Document with Diverse Shadows (SynDocDS) dataset. The SynDocDS comprises rendered images with diverse shadows augmented by a physics-based illumination model, which can be utilized to obtain a more robust and high-performance deep shadow removal network. In this paper, we further propose a Dual Shadow Fusion Network (DSFN). Unlike natural images, document images often have constant background colors requiring a high understanding of global color features for training a deep shadow removal network. The DSFN has a high global color comprehension and understanding of shadow regions and merges shadow attentions and features efficiently. We conduct experiments on three publicly available datasets, the OSR, Kligler's, and Jung's datasets, to validate our proposed method's effectiveness. In comparison to training on existing synthetic datasets, our model training on the SynDocDS dataset achieves an enhancement in the PSNR and SSIM, increasing them from 23.00 dB to 25.70 dB and 0.959 to 0.971 on average. In addition, the experiments demonstrated that our DSFN clearly outperformed other networks across multiple metrics, including the PSNR, the SSIM, and its impact on OCR performance.

Semantic-aware Transformer for shadow detection

Shadow detection is significant for scene understanding. Ambiguities in a shadow image, such as shadow-like non-shadow regions and shadow regions with non-shadow patterns, are still very challenging for prevalent CNN-based methods. This work attempts to alleviate this problem from a new perspective of shape semantics, and then proposes a Semantic-aware Transformer (SaT) in a multi-task learning manner. Concretely, we first propose a shadow detection network based on the recent progress of Transformer architecture, allowing us to capture significant global interactions between contexts. Next, we design a multi-task learning framework, combining shadow supervision and semantic supervision to perform a semantic-aware shadow detection. Finally, we introduce a simple yet effective information buffer unit to overcome the gradient signal conflict from multi-task learning. Experimental results on three public benchmark datasets (i.e., ISTD, SBU, and UCF) show that our SaT can effectively detect ambiguous cases and achieve state-of-the-art results.

Polarization imaging shadow removal based on attention conditional generative adversarial networks

Polarization imaging shadow removal based on attention conditional generative adversarial networks

The size distribution of the agitated saline microbubbles for contrast transcranial Doppler generated using standard manual methods.

Agitated saline microbubbles (MBs) are a common contrast agent for determining right-to-left shunt (RLS) by the contrast transcranial Doppler (c-TCD). The size of the generated bubbles is not standardized in clinical practice. MBs were generated using the recommended manual method by reciprocating motion through two syringes. The bubble size distributions (BSD) were measured using the microscopic shadow imaging technique. The results show that the diameter of MBs is mainly distributed between 10 and 100 μm, the mean bubble size is between 21 and 34 μm, the Sauter mean diameter (D32) is primarily between 50 and 300 μm, and the standard deviation (SD) is between 6 and 17 μm in 80 experiments. It provides a more accurate basis for the recommended manual method instability. The high variance values of the BSD indicate that the manual method has low stability and repeatability. The results of this study can be useful for further improvement of the reliability of c-TCD in detecting RLS. RESEARCH HIGHLIGHTS: This study provided the first detailed descriptions of the MBs size distribution in a flowing contrast agent by the microscopic shadow imaging technique. It reveals significant differences in the bubble size of manual foaming during repeated manipulations for each individual and between individuals.

Verifiable visually meaningful image encryption algorithm based on compressive sensing and (t, n)-threshold secret sharing

Abstract A verifiable visually meaningful image encryption algorithm based on compressive sensing and (t, n)-threshold secret sharing is proposed. Firstly, the plain image is compressed and encrypted by 2D block compressive sensing to obtain the pre-encrypted image. During this process, the enhanced logistic map and the enhanced tent map are used to generate the measurement matrix and the permutation sequence, respectively. Secondly, multiple shadow images of the pre-encrypted image are generated by using the (t, n)-threshold secret sharing scheme, and then each shadow image is further encrypted by using the encryption matrix generated by the chaotic system. Meanwhile, the signatures of the shadow images are obtained by using RSA signature algorithm. Finally, the shadow images and their corresponding signatures are embedded into the carrier image to generate the cipher image with high visual quality by using the LSB method. Additionally, the present algorithm can resist known-plaintext and chosen-plaintext attacks by incorporating the hash value of the plain image as part of the key. Meanwhile, the use of 2D block compressive sensing significantly reduces the reconstruction time. Simulation results demonstrate that the proposed algorithm achieves excellent decryption quality and operational efficiency.

High-capacity medical information hiding scheme with P-tensor product secret image sharing for wireless body area networks

Given the escalating demand for information transmission in the resource-restricted wireless body area networks (WBANs), securing medical data and minimizing energy consumption are paramount. We present an innovative information hiding scheme that employs P-tensor product secret image sharing (PTP-SIS) and multiple information-hiders for WBANs. Considering the remarkable compression performance demonstrated by compressed sensing (CS), as well as its link to Shamir's (k, n) secret sharing method, using CS rather than traditional secret sharing techniques for image processing purposes could potentially help alleviate transmission burden of sensor nodes. However, utilizing CS to process high-dimensional signals poses a significant challenge due to the large dimensionality of the measurement matrix. The P-tensor product (PTP) is a tool that offers outstanding universality and can overcome the dimension limitations of traditional matrix multiplication, it is applied in the secret sharing stage. Moreover, applying the ideas of secret sharing leads to less stress on an individual device as well as lower communication spending, and multiple hiders have the ability to independently embed additional secrets into shadow images for efficient information backup. Theoretical analysis and experiments validate that the proposed scheme ensures information security and transmission efficiency while achieving superior image recovery performance and higher information embedding rates compared to other approaches.

Blockchain-Based Secure and Efficient Secret Image Sharing With Outsourcing Computation in Wireless Networks

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> shadow images in the way that any subset of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k, n</i> ) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks.

Shadows of loop quantum black holes: semi-analytical simulations of loop quantum gravity effects on Sagittarius A* and M87*

In this study, we delve into the observational implications of rotating Loop Quantum Black Holes (LQBHs) within an astrophysical framework. We employ semi-analytical General Relativistic Radiative Transfer (GRRT) computations to study the emission from the accretion flow around LQBHs. Our findings indicate that the increase of Loop Quantum Gravity (LQG) effects results in an enlargement of the rings from LQBHs, thereby causing a more circular polarization pattern in the shadow images. We make comparisons with the Event Horizon Telescope (EHT) observations of Sgr A* and M87*, which enable us to determine an upper limit for the polymetric function P in LQG. The upper limit for Sgr A* is 0.2, while for M87* it is 0.07. Both black holes exhibit a preference for a relatively high spin (a ≳ 0.5 for Sgr A* and 0.5 ≲ a ≲ 0.7 for M87*). The constraints for Sgr A* are based on black hole spin and ring diameter, whereas for M87*, the constraints are further tightened by the polarimetric pattern. In essence, our simulations provide observational constraints on the effect of LQG in supermassive black holes (SMBH), providing the most consistent comparison with observation.

Саади и Бунин под тенью Хумая

The article examines the significance of the bird Humay (Khoma) in the culture and mystical literature of Iran and, through them, in Russian literature. The Quran mentions birds associated with the prophets, such as the hoopoe, raven, and quail. In Persian mystical literature, formed on the basis of Islamic culture, symbolic images of light, darkness and shadow occupy a significant place. With their help, poets expressed the spiritual path by which the mystic goes to self-perfection, comprehending the essence of God. The most important work of Iranian mystical literature is “Mantik-ut-tayr” by the poet of the 12th century. Faridaddin Attar, where in an allegorical form a spiritual journey in the name of the search for Truth is described, the result of which is self-knowledge – an important step in the knowledge of God. Saadi Shirazi (13th century) is one of the classics of Iranian literature, whose poems and views greatly influenced a number of Russian writers. It was most noticeable on Ivan Bunin. In his works, Bunin repeatedly mentions the name of Saadi and, imitating him, uses the image of Humay. Saadi Shirazi in his poems presented Humay as a sacred bird, foreshadowing happiness and good. She is a symbol of liberation from the shackles of the material world through spiritual renewal and transcendental transition to the world of the Absolute. “The shadow of the bird Humay, giving good luck and power”, fell on Bunin, who lived 700 years later than Saadi.

A arte da fotografia na China: algumas observações sobre sua conotação estética

Abstract: Photography, as a tool, mainly presents the relationship between people and objects through the use of artistic elements, such as light, shadow and color in images. The creation of photography is an activity that requires inspiration and skills. It requires photographers to carefully and deeply observe and think about the overall structure of the work, in order to take good photos that are both artistic and thoughtful. The development and popularization of photography in today’s society cannot be underestimated for their impact on public aesthetics and humanistic thinking. This paper aims to analyze the aesthetic connotation of Chinese photography and explore its aesthetic standards and values. The philosophical thoughts in Chinese photography are explored, and their reflections and perspectives on life and society are analyzed. The relationship and mutual influence between aesthetic connotations and philosophical thoughts in Chinese photography are explored, and their significance and influence on photography works are analyzed, so as to better understand and appreciate the uniqueness of Chinese photography. Chinese photography is a unique and diverse form of artistic expression that reflects China’s rich cultural heritage and social development. By emphasizing harmony, the integration of painting elements, storytelling and appreciation of nature, Chinese photography provides a profound aesthetic experience and conveys deeper meanings of unity, tranquility, and the relationship between people and the environment.

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

Side-scan sonar (SSS) is an essential acoustic sensor device for obtaining underwater information. The instance segmentation of sonar images can effectively locate and detect underwater objects. Although various CNN-based frameworks have achieved promising results in natural image instance segmentation, the noise interference, highlight shadow, and blurred edge in sonar images bring more significant challenges for sonar image instance segmentation. To solve these problems, we propose a novel recurrent pyramid frequency feature fusion network (RPFNet), which mainly consists of the pyramid frequency feature fusion network (PFN), recurrent residual attention mechanism (RRAM), mask prediction module, and semantic segmentation module. By enhancing and fusing different frequency features of SSS images, PFN can effectively extract fine-grained features and reduce background information interference. The RRAM uses residual structure and attention mechanism to enhance the correlation of different frequency features and improve nonlinear feature representation ability. The mask prediction and semantic segmentation modules are used to discriminate object instance categories and generate corresponding semantic segmentation results. We extensively evaluate the proposed framework on the real-scenes sonar image dataset. The evaluation results demonstrate that the proposed method outperforms the state-of-the-art methods in various evaluation metrics. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/darkseid-arch/SonarRPFNet</uri> .

Experimental study on dribbling characteristics of gasoline/biodiesel blends after the end-of-injection

The effects of biodiesel addition on gasoline fuel dribbling process and characteristics after the end-of-injection (EOI) were investigated in a constant volume chamber at different injection pressures and ambient temperatures. The fuel dribbling process was recorded with high spatial and time resolution by using the microscopic shadow imaging technique, and the corresponding dribble velocity, dribble volume and droplet size were extracted. The results showed that the fuel dribbling experienced four typical morphological structures (spray-like, membrane-type, ligament-type, droplets) after the EOI under all the tested conditions. Additionally, increased biodiesel proportion in the blends caused decreased dribble velocity, longer dribbling duration, and increased droplet size, indicating deteriorated breakup and atomization processes of the dribbling flow. Moreover, the higher injection pressure increased the dribble amount but reduced the dribbling duration and droplet size. The increased ambient temperature caused faster evaporation of the dribbling fuel, resulting in a significant decrease in the dribble size. So, these two strategies were believed to be overall favorable to restrain the adverse effect on gasoline fuel dribbling characteristics caused by the addition of biodiesel.

The Design and Development of Traditional Elements in Visual Communication Design under the Perspective of New Media

Abstract This paper takes visual communication design as the entry point and reduces the complexity of the network model and the number of parameters by a convolutional neural network. The Faster R-CNN algorithm has been improved and is now used as the main algorithm for traditional element extraction and detection. The global detection and segmentation extraction algorithm based on U2-Net is utilized to reduce the depth of the network model, and the VGG16 algorithm is improved according to the characteristics of traditional elements, migration learning and its network structure in order to increase the sensory field and solve the problem of loss of accuracy of the feature maps on the basis of not changing the feature layer. An empirical study of traditional element extraction is conducted by using shadow costume patterns as an example. The results show that in multiple shadow images, the range of hues used for the five character types, namely, small Dan, Sheng, Jingsheng, Chou and old Dan, is H ∈ [0°, 210°] ∪ [345°, 360°], and medium-high saturation is dominant, and the range of brightness of the colors used is also basically the same, which is between 0.5-1. This research experiment verifies the feasibility of the element extraction algorithm, which can realize the feature extraction and classification of traditional elements, and its application in visual communication design can improve the visual impact of the works, provide consumers with more diversified design solutions, and can also effectively realize the innovative application and development of traditional elements.