Strong Edge Research Articles

Joint strong edge and multi-stream adaptive fusion network for non-uniform image deblurring

Non-uniform motion deblurring has been a challenging problem in the field of computer vision. Currently, deep learning-based deblurring methods have made promising achievements. In this paper, we propose a new joint strong edge and multi-stream adaptive fusion network to achieve non-uniform motion deblurring. The edge map and the blurred map are jointly used as network inputs and Edge Extraction Network (EEN) guides the Deblurring Network (DN) for image recovery and to complement the important edge information. The Multi-stream Adaptive Fusion Module (MAFM) adaptively fuses the edge information and features from the encoder and decoder to reduce feature redundancy to avoid image artifacts. Furthermore, the Dense Attention Feature Extraction Module (DAFEM) is designed to focus on the severely blurred regions of blurry images to obtain important recovery information. In addition, an edge loss function is added to measure the difference of edge features between the generated and clear images to further recover the edges of the deblurred images. Experiments show that our method outperforms currently public methods in terms of PSNR, SSIM and VIF, and generates images with less blur and sharper edges.

Research on surface defect detection of glass wafer based on visual inspection

Glass wafer (GW) is used in a variety of integrated circuit (IC) packaging applications and as substrates to provide better performance and cost-effectiveness. Glass wafer (GW) protects the IC from impact and corrosion while maintaining the contract pins and leads that connect it to the external circuit. In the process of technology or production, this kind of structure is continuously working for a long time. Due to the inherent defects such as bubbles generation, starvation and the structure is often suffered from acid, alkali, moisture, vibration and other factors, which makes its internal structure gradually form corrosion stains. These defects have posed a serious threat to the quality and performance of equipment. Based on these disadvantages, this paper analyzes the defect detection principle of Glass wafer, then designed a method of determine the defect region. The edge signal processing method of visual image defects is studied, the edge detection and defect feature extraction model is established, and the principle of defining strong and weak edges is clarified. A defect feature classifier based on multi-layer perceptron (MLP) is created, and a segmentation algorithm of the classifier is implemented. Finally, a multi-channel image detection experimental platform is built to verify the typical unit structure. The experimental results show that the rate of defective features recognition is high, the detection rate is fast, and it has practical application value in engineering. The research of this recognition method has positive theoretical significance for accurately evaluating the overall reliability of GW structure and ensuring the safe operation of equipment.

Interface Edge Mode Confinement in Dielectric-Based Quasi-Periodic Photonic Crystal Structure

In this paper, the localization of optical interface edge-states is analyzed for a dielectric material-based quasi-periodic photonic crystal (QPhC) structure. The design comprises a bilayer PhC structure, where layers are arranged in a Fibonacci configuration to introduce quasi-periodicity. The impact of local symmetric sub-structures on Eigenstate coupling is considered over a wider wavelength range. This confirms the localization of interface edge modes for different wavelengths at the structural local resonators, where the number of local resonators depends on the length of the QPhC. The proposed seven-element QPhC structure shows a strong Tamm-like top interface edge mode localization for a 45.04° incident angle at 750 nm operating wavelength, whereas a bulk interface guided mode is also excited for a higher incident angle of around 79°. The investigation facilitates the development of reconfigurable devices to excite both bulk interface and surface interface edge modes with improved field intensities for spectroscopy and sensing applications.

Network analysis of psychiatric symptoms in schizophrenia: Findings from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics (REAP-AP).

Schizophrenia is a major mental disorder with a wide range of psychiatric symptoms. This study explored the structure of psychiatric symptoms of schizophrenia using network analysis in a large representative Asian sample based on a survey of clinical features and treatment used in schizophrenia patients across 15 countries/territories in Asia. Data on the demographic characteristics and psychiatric symptoms in schizophrenia patients were extracted from the dataset of the fourth Research on Asia Psychotropic Prescription for Antipsychotics (REAP-AP) project. The presence of the following psychiatric symptoms including delusions, hallucinations, disorganized speech, grossly disorganized or catatonic behavior, negative symptoms, social/occupational dysfunction, verbal aggression, physical aggression, and affective symptoms were analyzed. A total of 3681 patients were included. The network analysis revealed that verbal aggression, hallucinations, and social/occupational dysfunction were the most central symptoms, while the connections between social/occupational dysfunction and verbal aggression, and between hallucinations and disorganized speech were the two strongest edges. There were significant gender differences in the network structure based on the network structure invariance test (M=0.74, P=0.03) and invariant edge strength test. The positive correlation between verbal aggression and hallucinations was significantly stronger in the female network than that in the male network (P=0.03), while a negative correlation between affective symptoms and negative symptoms was found in the female, but not the male network (P<0.01). Central symptoms including verbal aggression, hallucinations, and socio-occupational dysfunction should be addressed in developing targeted treatment strategy for schizophrenia patients.

Mapping post-traumatic stress disorder symptoms and quality of life among residents of Wuhan, China after the COVID-19 outbreak: A network perspective

ObjectivesThe Coronavirus Disease 2019 (COVID-19) outbreak may have a long-term impact on mental health in the general population. This study examined inter-relationships between post-traumatic stress disorder symptoms (PTSS) and quality of life (QOL) in Wuhan residents after the COVID-19 outbreak using network approach. MethodsA cross-sectional survey was conducted between May 25 and June 18, 2020. PTSS and QOL were measured using Chinese versions of the Post -Traumatic Stress Disorder Checklist - Civilian Version and the World Health Organization Quality of Life Questionnaire - brief version, respectively. ResultsA total of 2598 participants were included. A network analysis revealed “Avoiding reminders”, “Feeling emotionally numb”, “Avoiding thoughts”, “Hypervigilance”, and “Reliving experiences” as the most central (influential) nodes in PTSS network models both before and after controlling for covariates. The connection between “Avoiding thoughts” and “Avoiding reminders” had the strongest edge. Three symptom communities were detected and can be summarized as “re-experiencing and avoidance”, “negative changes in thinking and mood”, and “hyperarousal”. The bridge symptoms connecting PTSS and QOL were “Sleep disturbances”, “Irritability”, and “Loss of interest”. LimitationsLimitations included the cross-sectional study design, self-report measures in data collection, and lack of follow-ups beyond the initial phase of the pandemic. ConclusionsPTSS were common among Wuhan residents even after the initial COVID-19 outbreak had passed. Attention should be paid to lingering symptoms of avoiding reminders, emotional numbness, avoiding thoughts, hypervigilance, and reliving experiences in treating PTSS related to the COVID-19 outbreak.

Photoluminescent and photocatalytic behaviour of zinc oxide nanostructures synthesized through the room temperature ultrasonication method.

In the present article we report the synthesis of zinc oxide (ZnO) nanostructures at room temperature using an ultrasonication technique to study their photoluminescent and photocatalytic behaviour. Synthesized nanomaterial showed a strong near band edge ultraviolet (UV) light emission and red emission, thereby finding its use in photoluminescent materials. We developed a UV/ZnO/O2 /H2 O2 system for the photodegradation of organic pollutants in an aqueous system. We used synthesized nanostructures to photodegrade phenol red (PR) dye to check their photocatalytic activity. The ZnO nanostructures photodegraded more than 90% of the PR dye under UV light irradiation in which photonic energy is converted to chemical energy (photocatalytic energy conversion), thereby exploitable for water purification applications. Synthesized ZnO nanostructures were characterized using powder X-ray diffraction, Fourier transform infrared spectroscopy, UV-visible light spectroscopy, scanning electron microscopy, and energy dispersive X-ray spectroscopy to investigate their structural, optical, morphological, and compositional properties, respectively.

Progressive downsampling and adaptive guidance networks for dynamic scene deblurring

The existing learning-based dynamic scene deblurring methods have made good progress to some extent. However, these methods are usually based on multiscale strategy, which has the following shortcomings: (1) The bilinear downsampling operation will cause some loss of important high-frequency information, e.g., strong edges, which also further affects the network learning a better deblurring mapping. (2) Existing methods only use a single activation function, which limits the ability of the network model to fit data and causes the network performance to be easily saturated. Therefore, we propose an end-to-end progressive downsampling and adaptive guidance network called PDAG-Net for solving above problems. The proposed PDAG-Net can retain more strong edges and other high-frequency information of a blurry image so as to make the network learn a more effective deblurring mapping between the input and label images. In the proposed PDAG-Net, we implement a multiscale blended activation residual block called MSBA-ResBlock for learning the nonlinear characteristics of dynamic scene blur, which can also alleviate the performance saturation problem caused by a single activation function and improve multiscale feature extraction ability. Finally, we propose a multisupervision strategy for obtaining more robust and effective features and making the network possess more stable trainging and faster convergence. Extensive experimental results on a public dataset indicate that the proposed network outperforms the state-of-the-art image deblurring methods.

Strong Vertex Coloring in Bipolar Fuzzy Graphs

Bipolar fuzzy graph (BFG) coloring techniques are used to solve many complex real world problems. The chromatic number of complement of BFG is obtained and compared with the chromatic number of the corresponding BFGs. This paper is an attempt to define coloring in a BFG based on strong edges. The strong chromatic number of complete BFG and BF tree are obtained.

Geoid Reveals the Density Structure of Cratonic Lithosphere

AbstractGeoid is a key observable for understanding the dynamics of the deep Earth, but has been considered largely transparent to long‐wavelength shallow density structures, especially those of the cratonic lithosphere. Here, we demonstrate that the observed flat craton‐ocean geoid pattern, traditionally interpreted as reflecting neutrally buoyant cratonic keels, provides critical constraints on both the net buoyancy and the depth‐dependent density distribution of cratonic mantle lithosphere. Using both simple theoretical calculations and quantitative numerical models, we show that the recent seismic data on lithospheric structure require the existence of a dense cratonic mantle lithosphere to explain the observed topography and geoid. In practice, topography reveals the net buoyancy of the cratonic lithosphere, while geoid further delineates the depth‐dependence of excess density. We find that the mantle lithosphere below large cratons bears net negative buoyancy close to that of a pure‐thermal lithosphere, with most of the excess density distributed within the lower half of the lithosphere. Density profiles of small cratons, due to strong edge effects from surrounding orogenic belts, are harder to constrain, except that their mantle lithosphere is also negatively buoyant.

Interrelationships between childhood maltreatment, depressive symptoms, functional impairment, and quality of life in patients with major depressive disorder: A network analysis approach

BackgroundChildhood maltreatment continues to pose a great challenge to psychiatry. Although there is growing evidence demonstrating that childhood maltreatment is an important risk factor for depressive disorders, it remains to be elucidated which specific symptoms occur after exposure to different kinds of childhood maltreatment, and whether certain pathways may account for these associations. Participants and settingsA total of 203 adult patients (18–53 years old) with MDD, diagnosed by Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria, were recruited from the outpatient clinic of Beijing Anding Hospital, Capital Medical University. MethodsChildhood maltreatment, depressive symptoms, functional impairment, and quality of life were evaluated by the Childhood Trauma Questionnaire - Short Form (CTQ-SF), 17-item Hamilton Depression Rating Scale (HAMD-17), Sheehan Disability Scale (SDS), and Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF). Undirected network analysis was used to explore the most relevant connections between them. Bayesian network analysis was used to estimate a directed acyclic graph (DAG) while investigating the most likely direction of the putative causal association. ResultsIn network analysis, the strongest edges were a positive correlation between emotional abuse and suicidal behavior as well as a negative association between emotional neglect and age of onset. In DAG analysis, emotional abuse emerged as the most pivotal network node, triggering both suicidal behaviors and depression symptoms. ConclusionsEmotional abuse appears to be an extremely harmful form of childhood maltreatment in the clinical presentation of depression. This study has promise in informing the clinical intervention of depression.

Responsive friction modulation of 3D printed elastomeric bioinspired structures

Elastomeric bioinspired structures with desirable friction performances are attractive for engineering applications. However, friction-tuning mechanisms through hybrid designs and shape fabrication techniques remain challenging. Herein, we developed a scalable elastomeric bioinspired structure using 3D printing inspired by claws, toe pads and overhang granules found in nature. Friction modulations are demonstrated with pillar and dimple hybrid structures in dry and wet conditions, including different structure arrangements, mechanical properties (strength ~13.8 MPa, elongation ~410%) and numerical simulations. We identified pillar hybrid design has strong friction features (~2.5 times higher than smooth) on wet surfaces, by evaluating through strong edge boundary and spontaneous liquid splitting effects. Pillar structures are used to mimic grip experiments and simulations to determine gripping strategies.

Development of Zn1−xBaxO nanoparticles reinforced poly(vinyl alcohol) macromolecular nanocomposite films: Eco‐friendly integrated materials for optical systems

AbstractIn this paper, we report the synthesis of Ba doped ZnO (Zn1−xBaxO, [x = 0.05]) nanoparticles (NPs) with a polycrystalline hexagonal wurtzite structure using chemical co‐precipitation method and prepared optically transparent, high refractive index polymer nanocomposite films by incorporating Zn1−xBaxO into poly(vinyl alcohol) (PVA) matrix. The average crystallite size of 104 nm was estimated by Scherrer's formula from X‐ray diffraction (XRD) patterns. The crystallinity of the composite film is significantly increased when Zn1−xBaxO NPs are added. The basic principles and theoretical aspects of scattering, transparency and refractive index of the composite structures are discussed. The formation of ZnBaO inter metallic bond was confirmed by the IR peaks that appear at the spectral wavenumbers (⁓596–608) cm−1. ATR‐FTIR spectral transmittance strongly proves the intermolecular interactions that occur in the composite film with molecular modification and various functional group impressions. UV–Vis absorption spectrum demonstrates a strong absorption edge around 379 nm owing to oxygen species corresponding to ZnBaO nanostructures in the composite films. The developed polymer composite films yielded strikingly higher RI value of ⁓(n = 2.5) having Abbe's number (υD = 10) with 57% plus transparency for 5 wt% NPs (ZBP‐3) with sufficiently low extinction coefficient values (k⁓10−4). Optical conductivity (σopt) was found to elevate from σopt ⁓ (4.465 × 1010) s−1 for pristine PVA to a σopt ⁓ (1.309 × 1012) s−1 for 5 wt% Zn0.95Ba0.05O filled composite films. Differential scanning calorimetry analysis showed a significant increase in the Tg value. The significant increase in Tm and Td &gt; 311°C values indicates excellent thermal stability. Hence, the nanocomposite films demonstrated an integrated system of excellent refractive index, considerable visual transparency and excellent thermal stability, establishing their potential for use as a wider spectrum of optical system requirements.

Cubic Planar Graph and Its Application to Road Network

In this research article, we present the notion of a cubic planar graph and investigate its related properties. The cubic graphs are more effective than both interval-valued and fuzzy graphs as it represents the level of participation (membership degree) of vertices and edges both in interval form and as a fuzzy number. Moreover, it handles the uncertainty and vagueness more efficiently than both interval-valued fuzzy graph and fuzzy graph. The interval indicates a continuous process, whereas the point indicates a specific process. We introduce the terms cubic multigraph, cubic strong and weak edges, and degree of planarity for cubic planar graphs. Some fundamental theorems based on these concepts are also elaborated. We also propose the idea of a cubic strong and weak fuzzy faces and cubic dual graph. Some results related to these concepts are also established. Comparison with the existing method shows the worth of our proposed work.

Study on Screening Criteria of Gel-Assisted Polymer and Surfactant Binary Combination Flooding after Water Flooding in Strong Edge Water Reservoirs: A Case of Jidong Oilfield.

Strong edge water reservoirs have sufficient natural energy. After long-term natural water flooding development, it is in the stage of ultrahigh water cut. There is an urgent need to change the development mode and improve the development effect. Taking Jidong Oilfield as an example, the mechanism model of strong edge water reservoirs is established by using the method of numerical simulation. Then, the factors and rules affecting the effects of gel-assisted polymer and surfactant binary combination flooding are studied. The screening criteria of gel-assisted polymer and surfactant binary combination flooding in strong edge water reservoirs are obtained. The results show that the existence of edge water is not conducive to binary combination flooding. Smaller water volumetric multiples and larger oil-bearing areas are more suitable for binary combination flooding. Compared with closed reservoirs, binary combination flooding in strong edge water reservoirs is more difficult to establish a displacement pressure gradient. The reservoir with high crude oil viscosity is not suitable for binary combination flooding. Gel-assisted polymer and surfactant binary combination flooding can be adopted for reservoirs with an oil-bearing area greater than 0.2 km2, a water volumetric multiple less than 200, and oil viscosity less than 100 mPa·s. The research results are of guiding significance for the reservoir selection of gel-assisted polymer and surfactant binary combination flooding after natural water flooding.

A Novel Description of Some Concepts in Interval-Valued Intuitionistic Fuzzy Graph with an Application

Covering, matching, and domination are the basic concepts in graphs that play a decisive role in the properties of graphs. Calculating these parameters is one of the difficulties in fuzzy graphs when it is not possible to accurately determine the values of the vertices of a graph. The interval-valued intuitionistic fuzzy graph (IVIFG) is one of the fuzzy graphs which can play an important role in solving uncertain problems in different sciences such as psychology, biological sciences, medicine, and social networks. The necessity of using a range of value instead of one number caused them to help researchers in optimizing and saving time and cost. In this study, we introduce some of the specific concepts such as covering, matching, and paired domination using strong arc or effective edges by giving appropriate examples. In addition, we have calculated strong node covering number, strong independent number, and other parameters of complete bipartite IVIFGs with several examples. Finally, we have presented an application of IVIFG in social networks.

Exploring bridge symptoms in HIV-positive people with comorbid depressive and anxiety disorders

BackgroundThe prevalence of comorbid depressive and anxiety disorders in people living with HIV (PLWH) is high. However, it is unclear which symptom is the bridge symptom between depression and anxiety in PLWH. This study aimed to develop symptom networks for depression and anxiety and explore the bridge symptoms and interconnectedness between these disorders in PLWH with comorbid depressive and anxiety disorders.MethodsA multisite, hospital-based cross-sectional study was conducted from March 2020 to November 2021. Depression and anxiety were measured with the Hospital Anxiety and Depression Scale. We visualized the symptom network using the qgraph package and computed the bridge expected influence of each node. The GLASSO layout was used to generate undirected association networks.ResultsA total of 2016 individuals were included in the analysis. In the anxiety cluster, “not feeling relaxed” had the highest bridge expected influence and strength (rbridge expected influence = 0.628, rstrength = 0.903). In the depression cluster, “not feeling cheerful” was identified as having a high bridge expected influence (rbridge expected influence = 0.385). “Not feeling cheerful” and “not feeling relaxed” were the strongest edges across the depression and anxiety clusters (r = 0.30 ± 0.02).ConclusionsHealthcare professionals should take note when PLWH report severe bridge symptoms. To enhance the levels of perceived cheerfulness and relaxation, positive psychology interventions could be implemented.

Salt and Pepper Noise Removal Method Based on the Edge-Adaptive Total Variation Model

The traditional median filter can handle the image salt and pepper noise better. However, when the noise intensity is large, it is often necessary to enlarge the filter window to ensure the denoising effect, but the enlarged window may also cause excessive smoothing of the image, loss of texture details, and blurred edges. In view of the strong edge preservation characteristics of variational model denoising, we propose a salt and pepper noise removal method based on the edge-adaptive total variational model. Firstly, the image is segmented into edge regions and non-edge regions by edge detection operators. Secondly, the salt and pepper noise of the image is processed using the median filter and adaptive total variation model, respectively. Lastly, the non-edge regions processed by the median filter and the edge regions processed by the adaptive total variation model are extracted for splicing. The experimental results show that the method cannot only effectively remove salt and pepper noise, but also effectively protect the main edge details of the image.

Multi-Intensity Optimization-Based CT and Cone Beam CT Image Registration

Cancer is a highly lethal disease that is mainly treated by image-guided radiotherapy. Because the low dose of cone beam CT is less harmful to patients, cone beam CT images are often used for target delineation in image-guided radiotherapy of various cancers, especially in breast and lung cancer. However, breathing and heartbeat can cause position errors in images taken during different periods, and the low dose of cone beam CT also results in insufficient imaging clarity, rendering existing registration methods unable to meet the CT and cone beam CT registration tasks. In this paper, we propose a novel multi-intensity optimization-based CT and cone beam CT registration method. First, we use a multi-weighted mean curvature filtering algorithm to preserve the multi-intensity details of the input image pairs. Then, the strong edge retention results are registered using and intensity-based method to obtain the multi-intensity registration results. Next, a novel evaluation method called intersection mutual information is proposed to evaluate the registration accuracy of the different multi-intensity registration results. Finally, we determine the optimal registration transformation by intersection mutual information and apply it to the input image pairs to obtain the final registration results. The experimental results demonstrate the excellent performance of the proposed method, meeting the requirements of image-guided radiotherapy.

Artificial life for segmentation of fusion ultrasound images of breast abnormalities

Segmentation of cancerous tumors in ultrasound (US) images of human organs is one of the critical problems in medical imaging. The US images are characterized by low contrast, irregular shapes, high levels of speckle-noise and acoustic shadows, making it difficult to segment the tumor. Yet, US imaging is considered one of the most inexpensive and safe imaging tests available to detect cancer in its early stages. However, an automatic segmentation method applicable to all types of US imagery does not exist.This paper proposes a novel segmentation method that combines image fusion, artificial life (AL) and a genetic algorithm (GA). The new algorithm has been applied to US images of breast cancer. The method is based on tracing agents (TA), which are artificial organisms with memory and the ability to communicate. They live inside a fusion image generated from the US and the elastography (EL) images.The TA can recognize the patterns of strong edges and boundary gaps allowing to outline the tumor. The new model has been tested against six types of segmentation models, i.e., machine learning, active contours, level set models, superpixel models, edge linking models and selected hybrid methods. The experiments include 16 state-of-the-art methods, which outperform 69 recent and classical segmentation routines. The tests were run on 395 breast cancer images from http://onlinemedicalimages.com and https://www.ultrasoundcases.info/.TA training employs a GA. The model has been verified on “hard” cases (complex shapes, boundary leakage, and noisy edge maps). The proposed algorithm produces more accurate results than the reference methods on high complexity images. A video demo of the algorithm is at http://shorturl.at/htBW9.

New concepts of domination in cubic graphs with application

The cubic set, introduced as a combination of a fuzzy set and an interval-valued fuzzy set, provided researchers with more flexibility than the previous two sets in dealing with complex and uncertain problems. Fuzzy graphs, based on this type of set, are among the emerging fuzzy graphs that have a great potential to model the surrounding phenomena. Consistent with the special role that cubic graphs play in decision-making and selecting superior options, dominating these graphs is of great importance and value. In this paper, we introduce the domination of the cubic graphs in terms of strong edges and examine their properties. In addition, we examine domination in terms of independent sets and since many of the phenomena surrounding us are hybrid, we also discuss the domination concept on its fuzzy operations. Finally, we present an application of this graph on the subject of domination.