Irregular Background Research Articles

Bayesian electron density determination from sparse and noisy single-molecule X-ray scattering images.

Single molecule x-ray scattering experiments using free-electron lasers hold the potential to resolve biomolecular structures and structural ensembles. However, molecular electron density determination has so far not been achieved because of low photon counts, high noise levels, and low hit rates. Most approaches therefore focus on large specimen like entire viruses, which scatter sufficiently many photons to allow orientation determination of each image. Small specimens like proteins, however, scatter too few photons for the molecular orientations to be determined. Here, we present a rigorous Bayesian approach to overcome these limitations, additionally taking into account intensity fluctuations, beam polarization, irregular detector shapes, incoherent scattering, and background scattering. We demonstrate using synthetic scattering images that electron density determination of small proteins is possible in this extreme high noise Poisson regime. Tests on published virus data achieved the detector-limited resolution of 9 nm, using only 0.01% of the available photons per image.

Performance of Binarization Algorithms on Tamizhi Inscription Images: An Analysis

Binarization of Tamizhi (Tamil-Brahmi) inscription images are highly challenging, as it is captured from very old stone inscriptions that exists around 3rd century BCE in India. The difficulty is due to the degradation of these inscriptions by environmental factors and human negligence over ages. Though many works have been carried out in the binarization of inscription images, very little research was performed for inscription images and no work has been reported for binarization of inscriptions inscribed on irregular medium. The findings of the analysis hold true to all writings that are carved in irregular background. This article reviews the performance of various binarization techniques on Tamizhi inscription images. Since no previous work was performed, we have applied the existing binarization algorithms on Tamizhi inscription images and analyzed the performance of these algorithms with proper reasoning. In the future, we believe that this reasoning on the results will help a new researcher to adapt or combine or devise new binarization techniques.

Manufacture of Bagasse Activated Carbon Using H2SO4 Activator

Manufacture activated carbon bagasse waste has been prepared using H2SO4 catalyst in successful research. Bagasse is made from yellow bagasse. First, carbon C is obtained by drying carbon using an oven at temperature of 300°C for 2.5 hours. Active carbon of bagasse is obtained from activation of 5% H2SO4 (1M) in a ratio of 1:10. The measurements performed in this study were the water absorption test and activated carbon. When measuring the water content, Evaporated substance content, ash content, and bound activated carbon level were measured according to SNI 06-3730-1995. FTIR results showed a sharp and wide peak around 3387 cm−1, 2922.16 cm−1 which shows the vibration of the O-H group on inactivated bagasse carbon, but after activation there is a peak around 3410.15 cm−1. Comparison of the diffraction patterns of carbon before activation and carbon after activation shows that all solids have broad peaks in the 20° - 45° region which is a characteristic peak of carbon and there are also pointed peaks in the 23° and 43° regions. The XRD results showed broadened peaks and absence of pointed peaks, irregular background intensity and showed an amorphous structure.

Research on defect detection of bottle cap interior based on low-angle and large divergence angle vision system.

During the machine vision inspection of the inner section of bottle caps within pharmaceutical packaging, the unique conca bottom and convex side walls often create obstructions to the illumination. Consequently, this results in challenges such as irregular background and diminished feature contrast in the image, ultimately leading to the misidentification of defects. As a solution, a vision system characterized by a Low-Angle and Large Divergence Angle (LALDA) is presented in this paper. Using the large divergence angle of LED, combined with low-angle illumination, a uniform image of the side wall region with bright-field characteristics and a uniform image of inner circle region at the bottom with dark-field characteristics are obtained, thus solving the problems of light being obscured and brightness overexposure of the background. Based on the imaging characteristics of LALDA, a multi-channel segmentation (MCS) algorithm is designed. The HSV color space has been transformed, and the image is automatically segmented into multiple sub-regions by mutual calculation of different channels. Further, image homogenization and enhancement are used to eliminate fluctuations in the background and to enhance the contrast of defects. In addition, a variety of defect extraction methods are designed based on the imaging characteristics of different sub-regions, which can avoid the problem of over-segmentation in detection. In this paper, the LALDA is applied to the defect detection inside the cap of capsule medicine bottle, the detection speed is better than 400 pcs/min and the detection accuracy is better than 95%, which can meet the actual production line capacity and detection requirements.

Industrial surface defect detection and localization using multi-scale information focusing and enhancement GANomaly

Recently, deep learning-based methods have been widely applied in identifying and detecting surface defects in industrial products. However, in real industrial scenarios, there are challenges such as limited defect samples, weak defect features, diverse defect types, irregular background textures, and difficulties in locating defect regions. To address these issues, this paper proposes a new industrial surface defect detection and localization method called multi-scale information focusing and enhancement GANomaly (MIFE-GANomaly). Firstly, skip-connection is incorporated between the encoder and decoder to effectively capture the multi-scale feature information of normal sample images to enhance representation ability. Secondly, self-attention is introduced in both the encoder and decoder to further focus on the representative information contained in the multi-scale features. Finally, an improved generator loss function based on structural similarity is designed to address the visual inconsistencies, thereby improving the robustness of detecting irregular textures. Experimental results demonstrate that the proposed method achieves superior robustness and accuracy in anomaly detection and defect localization for complex industrial data. The effectiveness of the proposed approach is fully validated through a series of comparative ablation experiments.

Aircraft segmentation in remote sensing images based on multi-scale residual U-Net with attention

Aircraft segmentation in remote sensing images (RSIs) is an important but challenging problem for both civil and military applications. U-Net and its variants are widely used in RSI detection, but they are not suitable for multi-scale aircraft segmentation in RSIs, due to the aircrafts in RSIs are relatively small with various orientations, different sizes, fuzzy illumination and shadow, obscure boundary and irregular background. To overcome this problem, a multi-scale residual U-Net with attention (MSRAU-Net) model is constructed for multi-scale aircraft segmentation in RSIs. A multi-scale convolutional module, two modified Respaths and two kinds of attention modules are designed and introduced into MSRAU-Net to extract the multi-scale feature and make the feature fusion between the contraction path and the expansion path more efficient. Different from U-Net, MSRAU-Net replaces the convolutional block of U-Net with the Inception residual block to help the U-Net architecture coordinate the features learned from aircrafts with different scales, and the residual module and attention module are introduced into the modified Respath to deepen the network layers and solve the gradient disappearing problem while extracting the more effective feature from RSIs. The experiments on the RSI dataset validate that MSRAU-Net outperforms the other networks, in particular for detecting the small aircrafts. Compared with attention U-Net and MultiMixUNet, the precision of MSRAU-Net is improved by 9.25 and 3.36, respectively.

Infrared Small Target Detection Based on Multidirectional Cumulative Measure

Robustness of small target detection is a researchable hotspot in infrared surveillance system. The residual phenomenon of background clutter is universal in current local comparison methods. Algorithm of sparse low-rank decomposition restoration cannot be applied to the actual situations due to the long time consumption. This letter proposes a multi-directional cumulative measure (MDCM) to enhance saliency and effectiveness of weak-small target detection. Firstly,multi-directional cumulative mean difference is implemented in central layer and background layer to estimate the background, while multi-directional cumulative derivative multiplying is calculated in central-active layer to characterize overall target’s heterogeneity, then technology of image fusion is adopted to eliminate interference of false target. Finally,a simple adjudicative technology is employed toward separated target region from complex scenes. Compared to up to date existing approaches, extensive simulational testing on four public datasets prove that proposed approach is capable of separating small targets efficiently from an irregular background in a single-scale window and achieve a comparable or even better accuracy.

Collaborative Learning Attention Network Based on RGB Image and Depth Image for Surface Defect Inspection of No-Service Rail

Surface defect inspection of no-service rail is important for safety of railway transportation. However, there are several challenges of irregular defect boundary, similar foreground and background for no-service rail surface defect inspection. To deal with the above challenges, depth image is used to provide complementary spatial information to RGB image. In recent years, with the development of deep learning and computer vision technology, intelligent inspection of defect has made great progress. We propose a neural network named collaborative learning attention network (CLANet) for no-service rail surface defect inspection. Our method can inspect the defect object of rail surface and segment the accurate region of that defect. The proposed method consists of three main stages: feature extraction, cross-modal information fusion, and defect location and segmentation. A multimodal attention block is proposed to highlight complex defect object with a new cross-modal fusion strategy. Furthermore, dual stream decoder enriches the representation of advanced features and avoids the dilution of information in the decoding stage. Suffering from the scarcity of defective data, an industrial RGB-D dataset NEU RSDDS-AUG is built. Finally, ablation studies verify the effectiveness of our proposed method. Compared with the existing nine state-of-the-art methods, CLANet has achieved improvements in all five parameters. Our method is also competitive on four public benchmark datasets.

Laboratory investigation on short design wave extreme responses for floating hinged-raft wave energy converters

In offshore renewable energy design procedures, accurate predictions of extreme responses are required in order to design for survivability whilst minimising associated costs. At present, the established method for predicting extreme responses is to conduct a large number of long-duration simulations, which is practical only in cases where the structural behaviour is captured by a computationally efficient linear approach. Many applications, however, will require a nonlinear approach, which significantly increases the computational cost, and hence the time required to analyse a problem. Should high-fidelity numerical approaches be the appropriate analysis tool, the long-duration simulations are likely to be impractical and in many cases infeasible. Laboratory testing can be utilised to address this to some extent, but this still time-consuming and expensive from a financial perspective. Consequently, there has been considerable interest in the use of short design waves as an alternative method for speeding up the design process. Currently, standards advise that short design waves can be utilised in the design of fixed offshore structures, but application to floating offshore structures needs verification before it becomes an established procedure. This study considers application of single and constrained short design waves to a floating hinged-raft wave energy converter using a 1:50 scale physical modelling approach, and compares with equivalent irregular sea states. The single wave approaches considered here are “NewWave” and the “Most Likely Extreme Response” wave, which are derived from the frequency content of the wave spectrum and response spectrum, respectively. The constrained approach considered in this study is the “Conditional Random Response Wave,” where the Most Likely Extreme Response wave is embedded within a random short irregular background. Results show that the single wave approaches under-estimate the extreme loading for the hinge-angle and mooring system compared with the irregular and constrained approaches. The discrepancy between single and constrained waves implies that memory effects are non-negligible, and hence it is critical that they are accounted for when utilising short design waves for floating applications.

Infrared Dim and Small Target Detection Based on Strengthened Robust Local Contrast Measure

Infrared small target detection is a popular issue in the field of computer vision. Traditional methods have been researched a lot and can basically deal with target detection in usual scenarios. However, small and dim targets do not have obvious characteristics and are easily interfered by clutter in a complex background. This letter studied common target detection methods such as low-rank and sparse representation as well as local contrast measure (LCM), a new method called strengthened robust local contrast measure (SRLCM) algorithm is proposed here. By solving non-convex problem more rigorously and analyzing small target features carefully, SRLCM can achieve a better detection and segmentation performance in an irregular background.

Emotion Recognition Positively Correlates with Steady-state Visual Evoked Potential Amplitude and Alpha Entrainment

Emotion recognition reflects the psychological and physiological status of humans. Numerous studies have investigated the neural mechanisms of emotion recognition based on electroencephalography (EEG) features. In the previous study, emotion target was presented under a static or irregular background, which made the response highly time-locked. As an oscillatory component of EEG, steady-state visual evoked potential (SSVEP) has distinctive frequency and phase properties, which provides more stable information than the other components of EEG. This study combined the emotion target with SSVEP to explore neural mechanisms of visual neurons under flickering background. Three basic emotions (delightfulness, sadness and, anger) were presented in 216 frequency-intensity conditions. Participants were asked to recognize the emotions and make judgments. The degree of alpha entrainment (valued as normalized Shannon entropy), SSVEP amplitude and recognition accuracy were calculated as response features. The results indicated that: SSVEP amplitude and recognition accuracy positively correlated with each other in frequency domain (7–15 Hz); alpha entrainment, and recognition accuracy had similar linear variation in intensity domain (level 1–4), and had a threshold around intensity 3; the three basic emotions had no clear relationship with each other in recognition. This study provided a new sight for neuroscience and would be an important reference to clinical psychology.

Bridging Single Neuron Dynamics to Global Brain States.

Biological neural networks produce information backgrounds of multi-scale spontaneous activity that become more complex in brain states displaying higher capacities for cognition, for instance, attentive awake versus asleep or anesthetized states. Here, we review brain state-dependent mechanisms spanning ion channel currents (microscale) to the dynamics of brain-wide, distributed, transient functional assemblies (macroscale). Not unlike how microscopic interactions between molecules underlie structures formed in macroscopic states of matter, using statistical physics, the dynamics of microscopic neural phenomena can be linked to macroscopic brain dynamics through mesoscopic scales. Beyond spontaneous dynamics, it is observed that stimuli evoke collapses of complexity, most remarkable over high dimensional, asynchronous, irregular background dynamics during consciousness. In contrast, complexity may not be further collapsed beyond synchrony and regularity characteristic of unconscious spontaneous activity. We propose that increased dimensionality of spontaneous dynamics during conscious states supports responsiveness, enhancing neural networks' emergent capacity to robustly encode information over multiple scales.

On-off intermittency and hard turbulence in the flow of fluid in the magnetic field.

This work provides an analysis of experiments in which various modes of mercury flow in a constant external magnetic field were observed; we examined the temperature oscillations in the mercury flow in a heated pipe at various Reynolds and Hartmann numbers. In some modes, the temperature oscillations have specific forms of strong aperiodic "bursts" over the weak irregular background, which are specific to the developed turbulent flow. To determine the nature of these temperature oscillations and the characteristics of the fluid flow, we examined them through the apparatus of nonlinear dynamics. The totality of all the results (autocorrelation function, correlation integral, maximum Lyapunov exponents, and Fourier transform) provide evidence of the chaotic nature of the observed flow modes despite the relative weakness of high-frequency harmonics in comparison to low-frequency ones. In the case of separate bursts of turbulence, the duration of the laminar phase τ follows the known distribution ∼τ-3/2, derived from the theory of on-off intermittency.

Development of the optimal gamma-ray imaging system design

This article covers solutions in the field of the gamma-ray imaging systems, as well as proposes an optimal solution for design of such system.Gamma-ray imaging systems are proving their utility as a useful tool for radio-ecological research, investigation of isotope mishandling, nuclear incidents liquidation and so on. They allow viewing gamma-ray fields as if they were visible. For example, this kind of system is capable of pointing to radiation sources on top of video stream, which allows localization of radiation sources without the need of getting close to them with a dosimeter.Overview of the existing solutions covers directional imagers, collimator-based imagers, coded aperture imagers and rotation modulation systems. Each section of this paragraph outlines main advantages and disadvantages of the given solution.Based on a solution overview, a proposition of an optimal solution for this kind of system is made. Proposition covers a creation of the one-dimensional coded aperture system with modified uniformly-redundant array mask, scintillation detectors and FPGA-based data acquisition system. This kind of system uses a thick screen, also called mask, with holes. This screen casts a gamma-ray shadow onto an array of detectors. Then a data acquisition system is used to run a correlation algorithm, which finds a shadow pattern in detector’s readouts. This allows determining from what angle this shadow has been cast. The mask is formed by a modified uniformly-redundant array pattern to minimize the chance of incorrect correlation algorithm result and to use it’s ability to turn coded aperture into an inverse one. Performing measurements with and inverse mask allows compensation for irregular background radiation and irregularity between detector sensitivity in the array of sensors. It should also be noted, that using of such ability requires to physically turn the mask around it’s vertical axis.This paragraph outlines a number of design principles and requirements, such as avoidance of irregularities in radiation transparency of the mechanical construction, benefits of making the whole system remotely operated, overall requirements for supply and readout electronics and a proposal to use programmable logic integrated circuits as a data acquisition measure. This section also covers possible steps to improve system performance, via turning it around vertical axle, rotation of the viewing frame to create 2-dimensional images and performing correlation algorithm on the spectral data to distinguish different nuclides amongst gamma-ray sources.Overall conclusions of this article suggests an implementation of the test device to verify and field-test proposed technical solutions and to promote further research and development in this field.

Development of Method of Matched Morphological Filtering of Biomedical Signals and Images

Formalized approach to the analysis of biomedical signals and images with locally concentrated features is developed on the basis of matched morphological filtering taking into account the useful signal models that allowed generalizing the existing methods of digital processing and analysis of biomedical signals and images with locally concentrated features. The proposed matched morphological filter has been adapted to solve such problems as localization of the searched structural elements on biomedical signals with locally concentrated features, estimation of the irregular background aimed at the visualization quality improving of biological objects on X-ray biomedical images, pathologic structures selection on mammogram. The efficiency of the proposed methods of matched morphological filtration of biomedical signals and images with locally concentrated features is proved by experiments.

S176. A Mean field model to study the triphasic waves in acute hepatic encephalopathy

Introduction Triphasic waves (TWs) are a distinctive electroencephalogram (EEG) pattern consisting of consisting ‘triphasic’ epileptiform discharges repeating at relatively regular intervals, most commonly in patients with acute, severe toxic/metabolic encephalopathy (TME). TWs share some similarities with EEG patterns seen in non-convulsive status epilepticus (NCSE), and show similar responses to benzodiazepine administration. Nevertheless the mechanisms underlying TWs are poorly understood, and their relationship to NCSE debated. Here we explore the conditions in a biologically plausible computational model of the EEG that lead to patterns resembling TWs, and attempt to relate these to mechanisms operative in one type of TME: acute hepatic encephalopathy (AHE). Methods Our work builds on an existing neural mean field model (NMFM) developed for how periodic discharges arise in severe cerebral anoxia (an adaptation of the bursting Liley model), through three types of processes: (a) metabolic failure leading to impaired synaptic transmission; and (b) increased neuronal excitability. In addition, the model accounts for effects of (c) GABA-ergic modulation by anesthetics. We identify pathophysiologic mechanisms involved in AHE which can lead to each of these effects, and relate these mechanisms to changes in the parameters of the NMFM. In this way our model relates “microscopic” (biochemical) mechanisms to “macroscopic” observations (EEG patterns). Then, we relate the simulated EEG patterns to clinical EEG data of patients with AHE through by constructing a comparison function, on the basis of different feature extraction methods, similarity measurements and optimization algorithms. Results The model is able to reproduce key qualitative features of EEG data from patients with AHE, including the approximate shape, irregular and slowed background, and semi-periodic pattern of TWs. In addition, we find that the dynamic evolution of EEG activity during AHE can be characterized through the change of four key parameters of proposed NMFM ((recovery time constants), (potentiation factor of EPSP), (decay rate of IPSP)), which reflect gradual changes in underlying physiological mechanisms. Conclusion Known alterations in cerebral physiology in acute hepatic encephalopathy are relatable to parameters of a biologically plausible mean-field model of the EEG. Our model represents a starting point for exploring the dynamical mechanisms underlying the EEG of severe encephalopathy.

Study on the segmentation of abrasive grains in diamond tools

Segmentation of abrasive images of diamond tools is the most important part of computer vision, which is the basis for detecting the state of diamond tools, such as the characteristics of diamond number, distribution, size and state. As diamond images have rich irregular background textures and reflective features, it is a major challenge to detect and segment these diamond images. The present work demonstrates an invalid edge elimination method for such images to segment and mat the diamond grains, which mainly consists of histogram, edge detection, and morphology. Based on the method, the part of diamond in the original images can be accurately and effectively segmented and matted. Then, the method is also used to segment and mat the diamond grains in other images of different diamond tools, and it also has a good segmenting and matting effect. Finally, the segmented and extracted diamond grains are used to analyse the diamond number, density and its wear state, the result indicates that the proposed method can be used in diamond tool detection.

Use of constrained focused waves to measure extreme loading of a taut moored floating wave energy converter

This paper concerns experimental measurements of the interaction of a taut moored floating body, representing a point absorbing wave energy converter in survivability mode, with extreme waves. The extreme waves are modelled in four ways. NewWave theory is first used to generate focused wave groups of varying steepness. Steepness is shown to have negligible effect on peak mooring loads, but causes significant differences in the resulting motion. The NewWave group is then constrained into both regular and irregular background wave trains so that the floating body has a load history caused by previous waves when interacting with the focused wave group. It is shown that an independent focused wave group is insufficient to properly model the extreme response of the floating body. However differences between the target and measured constrained time series due to non-linear wave-wave interaction limited the potential benefits of this approach. Finally the results from these tests are compared with measurements taken using irregular waves without any deterministic focused wave groups present. This comparison found cases where the floats response was greater than during any of the constrained NewWave tests, indicating that the assumption made that NewWave will generate the largest response was incorrect in this case.

Ultrasound variants of autoimmune thyroiditis in children and adolescents and their clinical implication in relation to papillary thyroid carcinoma development

BackgroundThe prevalence of autoimmune thyroiditis (AIT) and papillary thyroid carcinoma (PTC) is rising in children and adolescents, and the coincidence of AIT and PTC is as high as 6.3–43%.ObjectiveTo investigate the ultrasound manifestation of AIT in relation to PTC development in paediatric patients.Patients179 paediatric patients (133 females), mean (SD) age: 13.9 (3.03) years diagnosed with AIT and referred for ultrasound evaluation. Eight patients were diagnosed with PTC (6 females).MethodsRetrospective analysis of thyroid ultrasound scans of patients diagnosed with AIT. Thyroid and autoimmune status was assessed based on TSH, fT4, fT3 and increased aTPO and/or aTG and/or TRAB levels. In patients with PTC, total thyroidectomy was performed.ResultsAnalysis of thyroid US scans revealed that the following five ultrasound variants of AIT were observed in 179 patients: the most common in 35.2%—diffuse thyroiditis with hypoechogenic background and normoechogenic parenchyma, in 30.2%—diffuse thyroiditis with irregular background, in 18.9% nodular variant with normoechogenic background, in 11.7%—micronodulations and in 3.9%—diffuse hypoechogenic background. Eight cases of PTC were diagnosed in nodular variant of AIT with normoechogenic irregular background.ConclusionPatients with AIT and nodular variant with normoechogenic irregular background of the thyroid gland on US scans are in the risk group of developing PTC and should be followed up with regular neck US assessment.

Spasmophilia.

Sixty eight cases of spasmophilia were studied, consisting of 44 girls and 24 boys, the age ranging from 7 to 12 years. The most prominent symptom was cephalgia. Other symptoms included cramps, fainting attacks, abdominal pains, myalgia, breathing difficulties, palpitation, precordial pain, tremor and learning problems. Laboratory examination showed that most of them were hypocalcemic with prolonged Qo - Tc interval in electrocardiogram. BEG showed a certain pattern of increased thetha waves with irregular background activities and nonspecific changes. Treatment with calcium showed good clinical results. There seems to be no correlation between clinical improvement of symptoms and EMG findings.