Nonhomogeneous Background Research Articles

On Instabilities Caused by Magnetic Background Fields

We review instabilities that appear from the coupling of spin-one fields to a magnetic background in a non-Abelian theory. Such coupling results, due to asymptotic freedom in a negative quantum, contribute to the effective potential. In QCD, the Savvidy vacuum results. However, due to the tachyonic mode, such a state is not stable, and the question about the true ground state of QCD is still open. In the electroweak model, the corresponding instability is postponed to very large background fields and may be of relevance in the early universe, at best. We start with an introduction to the topic and display the necessary formulas and methods. Then, we consider the one-particle spectra of the fields in a magnetic background and the related Euler–Heisenberg Lagrangians. In addition, we discuss the potential instability connected with the anomalous moment of the electron. The main part is on the quantum correction to the energy in non-Abelian fields, including massive ones. Here, the focus is on so-called electroweak magnetism and the search for a classical solution of the field equations and their approximations by a lattice of flux tubes. Finally, we review approaches with non-homogeneous background fields and the background of an A0-field.

Greedy Integration Based Multi-Frame Detection Algorithm in Radar Systems

In this paper, we address the problem of detection and tracking of dim targets in radar systems through the use of multi-frame detection (MFD) techniques. We first study the energy expansion problem during multi-frame integration for classical MFD methods, which shows that the extended energy peak envelope poses a great challenge for target detection in nonhomogeneous backgrounds and multi-target cases. Next, a greedy integration based MFD algorithm is proposed, which separates the overall multi-frame joint maximization into two processes of confidence building and extended energy suppression. The proposed algorithm can eliminate the energy expansion intrinsically and achieve better detection performance with a lower implementation complexity. In addition, we extend the proposed algorithm to radar systems by deriving the accurate nonlinear conversion relationships between target states of Cartesian coordinates and echo measurements of polar coordinates. In radar scenarios, the proposed algorithm is capable to make detection adaptively after multi-frame integration through the use of traditional constant false alarm rate (CFAR) procedures. Finally, numerical results and tests with real radar data are presented to demonstrate the effectiveness of the proposed algorithm.

High-boost-based local Weber contrast method for infrared small target detection

ABSTRACT High-precision detection of small targets with low signal-to-noise ratio in infrared (IR) images has great significance for IR precise guidance, video surveillance, etc. In this paper, we proposed a novel method called centre-surround high-boost filter-based improved local Weber contrast measure. First, centre-surround high-boost filter is proposed to fully utilize the grey difference information, which reduces the influence of the non-homogeneous background such as cumulus sky, sea-sky clutters and high brightness edge. Then, an improved local Weber contrast measure is used to construct the contrast operator of the ratio-difference joint form for further target contrast enhancement and arbitrary interference suppression. Finally, we apply an adaptive thresholding segmentation to find the true target. Experimental results on various cluttered background sequences demonstrate that our proposed method can detect small targets with higher detection rate and lower false alarm rate, and the performance is more stable than five comparable methods in diverse scenarios.

Deep diagnosis: A real-time apple leaf disease detection system based on deep learning

Background and objectiveDiseases and pests are one of the major reasons for low productivity of apples which in turn results in huge economic loss to the apple industry every year. Early detection of apple diseases can help in controlling the spread of infections and ensure better productivity. However, early diagnosis and identification of diseases is challenging due to many factors like, presence of multiple symptoms on same leaf, non-homogeneous background, differences in leaf colour due to age of infected cells, varying disease spot sizes etc. MethodsIn this study, we first constructed an expert-annotated apple disease dataset of suitable size consisting around 9000 high quality RGB images covering all the main foliar diseases and symptoms. Next, we propose a deep learning based apple disease detection system which can efficiently and accurately identify the symptoms. The proposed system works in two stages, first stage is a tailor-made light weight classification model which classifies the input images into diseased, healthy or damaged categories and the second stage (detection stage) processing starts only if any disease is detected in first stage. Detection stage performs the actual detection and localization of each symptom from diseased leaf images. ResultsThe proposed approach obtained encouraging results, reaching around 88% of classification accuracy and our best detection model achieved mAP of 42%. The preliminary results of this study look promising even on small or tiny spots. The qualitative results validate that the proposed system is effective in detecting various types of apple diseases and can be used as a practical tool by farmers and apple growers to aid them in diagnosis, quantification and follow-up of infections. Furthermore, in future, the work can be extended to other fruits and vegetables as well.

Development of a validated HPTLC-bioautographic method for evaluation of aromatase inhibitory activity of plant extracts and their constituents.

Aromatase is a CYP450 enzyme that catalyses the conversion of androgens into oestrogens, where the decrease in the production of oestrogens aided by aromatase inhibitors is considered a target in post-menopausal breast cancer therapy. TLC-bioautography is a technique employed for combining chromatographic separations on TLC plates with bioassays. This is the first report to evaluate aromatase inhibitory activity using this technique. The aim of this study is to develop and validate a new TLC-bioautographic method for determination of aromatase inhibitory activity in 14 plant extracts. Two quantitation methods, the peak area method and reciprocal iso-inhibition volume (RIV) method, were compared and investigated to attain reliable results. Factors affecting the enzymatic reaction (temperature, pH, enzyme and substrate concentrations … etc.) were also investigated to attain the optimum parameters. TLC assisted by digital image processing was implemented for quantitative estimation of the aromatase inhibition of 14 plant extracts using chrysin as positive control. The fluorometric substrate dibenzyl fluorescein (DBF) was utilised for the assay, where inhibitory compounds were visualised as dark spots against a blue fluorescent background. Two software programs, Sorbfil® videodensitometer (in the peak area method) and ImageJ® (in the RIV method), were thoroughly validated using the International Council on Harmonisation (ICH) guideline and used for quantitation. The RIV method showed superiority over the peak area method in the quantitation results of the tracks with non-homogenous background with %RSD values of 0.98 and 1.49 compared with 2.86 and 3.58, respectively. Further, the methods allow the comparison of the activity of different unknown inhibitory compounds without the need for a reference or a positive control. Using the TLC-bioautographic method by image processing combined with the RIV quantitation method, simultaneous separation and quantitation of aromatase inhibitory components could be applied to estimate the relative activity of various plant extracts.

Multi-focus Image Fusion for Confocal Microscopy Using U-Net Regression Map.

Characterizing the spatial relationship between blood vessel and lymphatic vascular structures, in the mice dura mater tissue, is useful for modeling fluid flows and changes in dynamics in various disease processes. We propose a new deep learning-based approach to fuse a set of multi-channel single-focus microscopy images within each volumetric z-stack into a single fused image that accurately captures as much of the vascular structures as possible. The red spectral channel captures small blood vessels and the green fluorescence channel images lymphatics structures in the intact dura mater attached to bone. The deep architecture Multi-Channel Fusion U-Net (MCFU-Net) combines multi-slice regression likelihood maps of thin linear structures using max pooling for each channel independently to estimate a slice-based focus selection map. We compare MCFU-Net with a widely used derivative-based multi-scale Hessian fusion method [8]. The multi-scale Hessian-based fusion produces dark-halos, non-homogeneous backgrounds and less detailed anatomical structures. Perception based no-reference image quality assessment metrics PIQUE, NIQE, and BRISQUE confirm the effectiveness of the proposed method.

Fuzzy CFAR Detectors for MIMO Radars in Homogeneous and Non-Homogeneous Pareto Clutter

In this paper, we propose to use fuzzy fusion rules to improve the performances of the CA-CFAR and OS-CFAR detectors for non-coherent MIMO radars in homogenous and non-homogenous Pareto background. First, the membership function for each individual detector is computed. At the fusion center, the membership functions are combined using four fuzzy fusion rules, namely; the “MIN”, “MAX”, “algebraic product” and the “algebraic sum” to yield a binary decision after defuzzification. The obtained results show that for a number of nodes equal to four, the performance is the best for a high number of receivers. In homogeneous background, the “algebraic product” fusion rule gives the best result when SCR > 4 dB whereas the “algebraic sum” is the best when SCR < 4 dB for the CA-CFAR. For the OS-CFAR, it is clearly shown that the “algebraic product” is the best. In non-homogenous case, the “algebraic sum” fusion rule gives the best results for the CA-CFAR, and the “algebraic product” for the OS-CFAR.

A Constrained Sparse-Representation-Based Spatio-Temporal Anomaly Detector for Moving Targets in Hyperspectral Imagery Sequences

At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal spectral information. Besides, multiple targets in complex motion situations, such as multiple targets at different velocities and dense targets on the same trajectory, are still challenges for moving target detection. To address these problems, we propose a novel constrained sparse representation-based spatio-temporal anomaly detection algorithm that extends AD from the spatial domain to the spatio-temporal domain. Our algorithm includes a spatial detector and a temporal detector, which play different roles in moving target detection. The former can suppress moving background regions, and the latter can suppress non-homogeneous background and stationary objects. Two temporal background purification procedures maintain the effectiveness of the temporal detector for multiple targets in complex motion situations. Moreover, the smoothing and fusion of the spatial and temporal detection maps can adequately suppress background clutter and false alarms on the maps. Experiments conducted on a real dataset and a synthetic dataset show that the proposed algorithm can accurately detect multiple targets with different velocities and dense targets with the same trajectory and outperforms other state-of-the-art algorithms in high-noise scenarios.

Performance Amelioration of Standard Variants of Adaptive Schemes Operating in Heterogeneous Environment

The ruggedness of emerging single adaptive approach that performs well in all types of operating conditions has led to the development of composite adaptive strategy. In this regard, the fusion of particular decisions of single adaptive schemes through suitable fusion rules can provide a better final detection. Particularly, the fusion of cell-averaging (CA), ordered statistics (OS) and trimmed-mean (TM) procedures can enhance the overall detection performance. Our goal in this paper is to analyze this developed model when the operating environment is heterogeneous. A χ2-distribution with two- and four-degrees of freedom is assumed for the fluctuation of primary and secondary extraneous targets. A closed form processor performance is derived for single pulse detection. The results show that for the non-homogeneous background the new approach is more practical. Particularly in multitarget situations, it exhibits higher robustness as compared to the CA, OS, or TM architectures. Additionally, the novel strategy has a homogeneous performance that surpasses performance of the classical Neyman-Pearson (N-P) detector, which can be employed as a yardstick for the analysis of different techniques in the CFAR world.

Performance evaluation of RQ non‐parametric CFAR detector in multiple target and non‐uniform clutter

The performance analysis of the rank quantisation (RQ) non-parametric constant false alarm rate (CFAR) detector in homogeneous and non-homogeneous backgrounds is carried out. A closed-form expression of the false alarm rate of the RQ detector at clutter edges is derived. It is shown that the RQ detector pays an additional loss of about 3 dB in detection performance relative to the cell averaging (CA)-CFAR with incoherent integration in homogeneous background, but it can accommodate a number of strong interfering targets within the reference window. When both of the heavy and the weak clutters follow a Rayleigh distribution, the rise of false alarm rate for the RQ detector at clutter edges lies between that of the CA-CFAR and the ordered statistic (OS)-CFAR with incoherent integration. If the Weibull distributed clutter with a long tail moves into the reference window, the rise of the false alarm rate of the CA-CFAR, the OS-CFAR and the greatest-of-CFAR with incoherent integration reaches a peak of more than three orders of magnitude, and cannot return to the original design P fa. However, the RQ detector reveals its inherent virtue that it can maintain a CFAR despite the changes in the underlying distributions of observed data.

New Proposal for Size and Size-Distribution Evaluation of Nanoparticles Synthesized via Ultrasonic Spray Pyrolysis Using Search Algorithm Based on Image-Processing Technique.

Nanoparticle properties are correlated to their size, size distribution, and shape; it is essential to accurately measure these features in the field of nanoscience. In this study, silver nanoparticles (AgNPs) were synthesized with the ultrasonic-spray-pyrolysis (USP) method from a water solution of silver nitrate. The synthesized AgNPs were characterized by Dynamic Light Scattering (DLS) analysis and Scanning Electron Microscopy (SEM) to reveal their size and size distribution. A search algorithm based on an image-processing technique to obtain particle size and particle-size distribution from SEM micrographs is proposed. In order to obtain more quantitative information and data with respect to the morphology of particles synthesized under different process parameters, SEM micrographs with a nonhomogeneous background contrast were examined via image-processing techniques in MATLAB. Due to the inhomogeneous contrast of SEM micrographs, defining an overall threshold value was insufficient in the detection of whole nanoparticles. Thus, subimages were directly created according to the maximum and minimum particle size specified by the user to determine local threshold values. The obtained results were automatically combined to represent both particle dimension and location in the SEM micrographs. We confirmed that the results of our DLS analysis, theoretical calculation, and image-processing technique were correlated with our expected results.

Hyperspectral Anomaly Detection via Tensor- Based Endmember Extraction and Low-Rank Decomposition

Due to the limited resolution of hyperspectral sensors, anomalous targets expressed with subpixels are often mixed with nonhomogeneous backgrounds. This fact makes anomalies difficult to distinguish from the surrounding background. From this perspective, a novel hyperspectral anomaly detection (AD) algorithm based on endmember extraction and low-rank representation (LRR) is proposed. For the characteristics of pixels in hyperspectral images (HSIs), the proposed algorithm employs an endmember extraction technology to yield an abundance matrix for AD, thereby gathering more feature information compared with the direct use of a raw image. In addition, a dictionary construction strategy based on Tucker decomposition, and the ${k}$ -means++ clustering method is proposed to make the dictionary more stable and discriminative. An LRR method based on the dictionary is applied to obtain a sparse residual matrix. Finally, anomalies can be determined by the response of the residual matrix. Experiments on three hyperspectral data sets validate the performance of the proposed algorithm.

Towards a viable effective field theory of mimetic gravity

We discuss mimetic gravity theories with direct couplings between the curvature and higher derivatives of the scalar field, up to the quintic order, which were proposed to solve the instability problem for linear perturbations around the FLRW background for this kind of models. Restricting to homogeneous scalar field configurations in the action, we derive degeneracy conditions to obtain an effective field theory with three degrees of freedom. However, performing the Hamiltonian analysis for a generic scalar field we show that there are in general four or more degrees of freedom. The discrepancy is resolved because, for a homogeneous scalar field profile, ∂iφ≈ 0, the Dirac matrix becomes singular, resulting in further constraints, which reduces the number of degrees of freedom to three. Similarly, in linear perturbation theory the additional scalar degree of freedom can only be seen by considering a non-homogeneous background profile of the scalar field. Therefore, restricting to homogeneous scalar fields these kinds of models provide viable explicitly Lorentz violating effective field theories of mimetic gravity.

Aircraft Detection for HR SAR Images in Non‐homogeneous Background Using GGMD‐Based Modeling

In the problem of aircraft detection for High resolution (HR) Synthetic aperture radar (SAR) images, the background areas commonly contain multiple land cover types, such as runways and grassland. The conventional Constant false alarm rate (CFAR) detection in these non-homogeneous backgrounds with homogeneous assumption leads to unreliable detection results. This paper constructs a one-stage detection method based on the Generalized gamma mixture distribution (GGMD), which is regarded as a competitive and applicable model for combining the advantages of the Generalized gamma distribution (GGD) and the Finite mixture model (FMM). In order to evaluate the availability of the proposed algorithm, HR SAR images for aircraft detection from different product types and with various resolutions are examined. Compared with the CFAR algorithms based on the Gamma distribution, the GGD, and the gamma mixture distribution, the proposed algorithm demonstrates its availability and effectiveness for aircraft detection in HR SAR images in non-homogeneous background.

Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

Confidence-Based Hybrid Tracking to Overcome Visual Tracking Failures in Calibration-Less Vision-Guided Micromanipulation

This article proposes a confidence-based approach for combining two visual tracking techniques to minimize the influence of unforeseen visual tracking failures to achieve uninterrupted vision-based control. Despite research efforts in vision-guided micromanipulation, existing systems are not designed to overcome visual tracking failures, such as inconsistent illumination condition, regional occlusion, unknown structures, and nonhomogenous background scene. There remains a gap in expanding current procedures beyond the laboratory environment for practical deployment of vision-guided micromanipulation system. A hybrid tracking method, which combines motion-cue feature detection and score-based template matching, is incorporated in an uncalibrated vision-guided workflow capable of self-initializing and recovery during the micromanipulation. Weighted average, based on the respective confidence indices of the motion-cue feature localization and template-based trackers, is inferred from the statistical accuracy of feature locations and the similarity score-based template matches. Results suggest improvement of the tracking performance using hybrid tracking under the conditions. The mean errors of hybrid tracking are maintained at subpixel level under adverse experimental conditions while the original template matching approach has mean errors of 1.53, 1.73, and 2.08 pixels. The method is also demonstrated to be robust in the nonhomogeneous scene with an array of plant cells. By proposing a self-contained fusion method that overcomes unforeseen visual tracking failures using pure vision approach, we demonstrated the robustness in our developed low-cost micromanipulation platform. Note to Practitioners —Cell manipulation is traditionally done in highly specialized facilities and controlled environment. Existing vision-based methods do not readily fulfill the need for the unique requirements in cell manipulation including prospective plant cell-related applications. There is a need for robust visual tracking to overcome visual tracking failure during the automated vision-guided micromanipulation. To address the gap in maintaining continuous tracking for vision-guided micromanipulation under unforeseen visual tracking failures, we proposed a purely visual data-driven hybrid tracking approach. Our proposed confidence-based approach combines two tracking techniques to minimize the influence of scene uncertainties, hence, achieving uninterrupted vision-based control. Because of its readily deployable design, the method can be generalized for a wide range of vision-guided micromanipulation applications. This method has the potential to significantly expand the capability of cell manipulation technology to even include prospective applications associated with plant cells, which are yet to be explored.

A Finite-Difference Approach for Plasma Microwave Imaging Profilometry

Plasma diagnostics is a topic of great interest in the physics and engineering community because the monitoring of plasma parameters plays a fundamental role in the development and optimization of plasma reactors. Towards this aim, microwave diagnostics, such as reflectometric, interferometric, and polarimetric techniques, can represent effective means. Besides the above, microwave imaging profilometry (MIP) may allow the obtaining of tomographic, i.e., volumetric, information of plasma that could overcome some intrinsic limitations of the standard non-invasive diagnostic approaches. However, pursuing MIP is not an easy task due to plasma’s electromagnetic features, which strongly depend on the working frequency, angle of incidence, polarization, etc., as well as on the need for making diagnostics in both large (meter-sized) and small (centimeter-sized) reactors. Furthermore, these latter represent extremely harsh environments, wherein different systems and equipment need to coexist to guarantee their functionality. Specifically, MIP entails solution of an inverse scattering problem, which is non-linear and ill-posed, and, in addition, in the one-dimensional case, is also severely limited in terms of achievable reconstruction accuracy and resolution. In this contribution, we address microwave inverse profiling of plasma assuming a high-frequency probing regime when magnetically confined plasma can be approximated as both an isotropic and weak penetrable medium. To this aim, we adopt a finite-difference frequency-domain (FDFD) formulation which allows dealing with non-homogeneous backgrounds introduced by unavoidable presence of plasma reactors.

SAR image target‐detection method based on two‐stage CFAR detector with G 0 distribution

CFAR detection algorithm is widely used in the typical target detection of SAR images. In SAR image target detection, the simple statistical distribution models, such as Gaussian distribution, normal distribution, Weibull distribution etc., are obviously not suitable for the fitting of the background clutter distribution of missile-borne SAR images, and the G 0 distribution is suitable for extremely unstable regions and its expression is relatively simple. A two-stage CFAR detector with G 0 distribution is designed and the distribution of ground SAR images is statistically analysed by using the CA-CFAR detector. A target in a relatively non-homogenous background usually has a long tail, and the detection results of the two-stage CFAR detector based on the G0 distribution are much better.

Nonstationary Shape Estimation in Electrical Impedance Tomography Using a Parametric Level Set-Based Extended Kalman Filter Approach

This paper presents a parametric level set-based reconstruction method for nonstationary applications using electrical impedance tomography (EIT). Due to relatively low signal-to-noise ratios in EIT measurement systems and the diffusive nature of EIT, the reconstructed images often suffer from low spatial resolution. In addressing these challenges, we propose a computationally efficient shape-estimation approach where the conductivity distribution to be reconstructed is assumed to be piecewise constant, and the region boundaries are assumed to be nonstationary in the sense that the characteristics of region boundaries change during measurement time. The EIT inverse problem is formulated as a state estimation problem in which the system is modeled with a state equation and an observation equation. Given the temporal evolution model of the boundaries and observation model, the objective is to estimate a sequence of states for the nonstationary region boundaries. The implementation of the approach is based on the finite-element method and a parametric representation of the region boundaries using level set functions. The performance of the proposed approach is evaluated with the simulated examples of thorax imaging, using noisy synthetic data and experimental data from a laboratory setting. In addition, robustness studies of the approach with respect to the modeling errors caused by inaccurately known boundary shape, non-homogeneous background and varying conductivity values of the targets are carried out and it is found that the proposed approach tolerates such kind of modeling errors, leading to good reconstructions in nonstationary situations.

A Moving Morphable Components Based Shape Reconstruction Framework for Electrical Impedance Tomography.

This paper presents a new computational framework in electrical impedance tomography (EIT) for shape reconstruction based on the concept of moving morphable components (MMC). In the proposed framework, the shape reconstruction problem is solved in an explicit and geometrical way. Compared with the traditional pixel or shape-based solution framework, the proposed framework can incorporate more geometry and prior information into shape and topology optimization directly and therefore render the solution process more flexibility. It also has the afford potential to substantially reduce the computational burden associated with shape and topology optimization. The effectiveness of the proposed approach is tested with noisy synthetic data and experimental data, which demonstrates the most popular biomedical application of EIT: lung imaging. In addition, robustness studies of the proposed approach considering modeling errors caused by non-homogeneous background, varying initial guesses, differing numbers of candidate shape components, and differing exponent in the shape and topology description function are performed. The simulation and experimental results show that the proposed approach is tolerant to modeling errors and is fairly robust to these parameter choices, offering significant improvements in image quality in comparison to the conventional absolute reconstructions using smoothness prior regularization and total variation regularization.