Prior Algorithms Research Articles

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

In this paper, the Gram matrix is used to calculate the correlation of the filter response sets under different scale kernels learned by each layer of the network in the deconvolution, and the loss between the corresponding feature response correlations in the multilayer network is calculated. Linear summation is used to obtain a stable, multiscale image model representation. This paper extracts the contours of the salient areas of the image and adjusts the parameters of the deconvolution network to learn the salient area patterns of the image. At the same time, for the image to be generated, a shape template is used to limit the range of the area to be generated in order to obtain a shape image with similar patterns. When the spatial relative relationship characteristics of the image constituent objects are obvious, we appropriately add high-level semantic feature activation values for reinforcement. This paper solves the estimation of the unknown blur kernel by using image prior knowledge, filtering and gradient domain algorithms and other different technologies to obtain image jitter or scene movement information and estimate the size, location, and density of the blur kernel. This paper studies a relatively robust deconvolution model, which is insensitive to random noise, has stable effects, and can overcome the water ripple effect caused by the usual convolution process. This paper attempts to study the fuzzy model with variable space. The usual blur is a spatial invariant model; that is, a single kernel is used to describe the motion of all pixels on the image. By selecting different characteristic parameters, this paper conducts experimental research on some existing hydrophobic indicator function methods and calculates the relationship between characteristic parameters and hydrophobicity when different hydrophobic indicator functions are adopted. One characteristic of the hydrophobic image of composite insulators is low contrast. The traditional method of converting color images to grayscale images cannot improve the image contrast. This paper analyzes the hydrophobic image of the composite insulator, and the extracted B channel component image of the hydrophobic image improves the contrast of the image and facilitates the subsequent segmentation of water traces and background. In this paper, the water repellent image's watermark area is counted, and connected-domain wave processing is used to limit the area of water droplets retained, thereby improving the efficiency of filtering water droplets without having a big impact on the image as a whole. The problem of uneven illumination is an unavoidable problem in the field of image processing, and the resulting reflection problem brings difficulties to image processing. This article regards the reflective area of the watermark as a “hole” and uses the idea of “hole filling” to eliminate the reflective point, which weakens the reflection problem to a certain extent.

Computing the Covering Radius of a Polytope with an Application to Lonely Runners

We are concerned with the computational problem of determining the covering radius of a rational polytope. This parameter is defined as the minimal dilation factor that is needed for the lattice translates of the correspondingly dilated polytope to cover the whole space. As our main result, we describe a new algorithm for this problem, which is simpler, more efficient and easier to implement than the only prior algorithm of Kannan (1992). Motivated by a variant of the famous Lonely Runner Conjecture, we use its geometric interpretation in terms of covering radii of zonotopes, and apply our algorithm to prove the first open case of three runners with individual starting points.

RecoXplainer: A Library for Development and Offline Evaluation of Explainable Recommender Systems

Since recommender systems play an important role in our online experience today and are involved in a wide range of decisions, multiple stakeholders are requesting explanations for the corresponding algorithmic predictions. These demands—together with the benefits of explanations (e.g., trust, efficiency, and sometimes even persuasion)—have triggered significant interest from researchers in academia and in industry. Nonetheless, to the best of our knowledge, no comprehensive toolkit for development and evaluation of explainable recommender systems is available to the community yet. Instead, researchers are frequently faced with the challenge of re-implementing prior algorithms when creating and evaluating new approaches. This paper introduces recoXplainer, an easy-to-use, unified and extendable library that supports the development and evaluation of explainable recommender systems. recoXplainer includes several state-of-the-art black box algorithms, model-based and post-hoc explainability techniques, as well as offline evaluation metrics in order to assess the quality of the explanation algorithms.

Improved Byzantine fault tolerance with fast consensus

AbstractThis article presents an improved Byzantine fault tolerance algorithm to tolerate Byzantine faults rapidly by minimizing the load of the network with a minimum cost that provides a very simple methodology to design Byzantine fault tolerance (BFT) state machine replication protocol with the most favorable flexibility. Although, we have taken the initiative of fault tolerance from earlier works and conversely perform a different approach than them. Our protocol just requires replicas forffaults instead of in previous Byzantine protocols to mitigate the attacks by faulty primary and replicas. It also improves prior algorithms in terms of different parameters and requires fewer cryptographic techniques. Similar to previous BFT protocols, this protocol also guarantees correctness.

Analysis of Sedimentation towards the Changes of Mangrove Area Using Multitemporal Remote Sensing Technology (A Case Study in Gresik Regency, East Java, Indonesia)

Mangrove grows in coastal areas with the soil is resulted from the accumulation of mud substrate from the sedimentation process. Gresik Regency is a downstream area where the Bengawan Solo River flows, and there is the Java Sea that carries a lot of sediment material to the coast. As a result, sedimentation is forming new land increasingly that can be a place for mangroves to live. Therefore, in this study, the calculation of suspended sediment concentration and mangrove area in Gresik Regency during 2016-2019 uses Sentinel-2A satellite imagery. The purpose of these calculations is to determine the effect of sedimentation as suspended sediment on changes in the mangrove area. Mangrove area is obtained from the Maximum Likelihood supervised classification. While suspended sediment concentrations estimated from remote sensing data are obtained using four prior algorithms, they do not meet the specified accuracy requirement. This research shows that there have been changes in the mangrove area to increase and decrease during 2016-2019. The largest addition of area occurred in the period 2016-2017, which is 479.347 Ha, and the most reduction in the area occurred in the period 2018-2019, which is 534.087 Ha. The statistical test result proves that the suspended sediment as sedimentation affects the mangrove by 64.9% in the significance level of 5% or 95% confidence level.

Fog removal and enhancement method for UAV aerial images based on dark channel prior

The existing UAV aerial image de-fog methods have low image contrast after de-fog, the difference between light and dark image is not obvious, leading to poor de-fog effect. Therefore, an aerial image de-fog enhancement method based on dark channel a priori is proposed. The image variance and absolute gradient mean are combined to get the weight coefficients, and the edge pixels are smoothed by using the multiple decomposition form. The image intensity is calculated and the noise is reduced. A convolution neural network is introduced to calculate the atmospheric transmittance in haze. Based on this, dark channel prior algorithm is used to enhance the light and shade difference of aerial photography image and realise the de-fog enhancement of aerial photography image. To verify the performance of the proposed method, simulation experiments are designed which were compared with the existing methods results in better fog-removing effect, higher contrast and shorter time.

Spatio-temporal Model Combining VMD and AM for Wind Speed Prediction

This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and-excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of each component. Contrasting experiments for short-term prediction on the actual wind power dataset prove that VCGA is superior to prior algorithms.

UAV Remote Sensing Image Dehazing Based on Double-Scale Transmission Optimization Strategy

Current dehazing methods for unmanned aerial vehicle (UAV) remote sensing images often have texture detail loss and color distortion problems, especially in highlighted regions. This is mainly due to the rich texture and low intensity of UAV remote sensing images being ignored, which results in incorrect transmission estimation. In this paper, we propose a UAV remote sensing image dehazing method based on double-scale transmission optimization strategy. First, we propose a double-scale optimization strategy to estimate the transmission map with more accurate texture details and color preservation, especially in highlighted regions of hazy UAV images that are most severely distorted. Second, a UAV-adaptive haze-line prior algorithm is proposed to address the large scene depth and low intensity of UAV remote sensing images. Finally, we introduce a luminance-weighted frequency domain saliency model to avoid texture detail loss and color distortions for better transmission optimization, especially in highlighted regions. Compared with state-of-the-art methods, our method shows better detail performance and visual effects, especially for UAV images with highlighted regions.

Method for assessing the characteristics of the quality of service of networks

Variational quantum algorithms are at the forefront of modeling fault-tolerant quantum devices of the near future. While most variational quantum algorithms use only continuous optimization variables, their representative power can sometimes be greatly increased by adding certain discrete optimization variables, as illustrated by the example of the generalized quantum approximation optimization algorithm. However, the hybrid discrete-continuous optimization problem poses an optimization problem. The paper proposes a new algorithm that combines the Monte Carlo tree search method with an improved gradient solver to optimize discrete and continuous variables in a quantum circuit, respectively. The algorithm has been found to have excellent noise tolerance properties and is superior to prior algorithms in the field.

Magnetic Resonance Imaging Evaluation of Hemangioma Resection for Encephalofacial Angiomatosis (Sturge-Weber Syndrome) in Children under Intelligent Algorithm.

This study was aimed to evaluate the clinical efficacy of hemangioma resection in the treatment of infantile encephalofacial angiomatosis (Sturge–Weber syndrome, SWS) through magnetic resonance imaging (MRI) images, and intelligent algorithms were employed to process MRI images. A retrospective study of 45 children diagnosed with facial hemangioma admitted to hospital was conducted. Then, MRS images were acquired, and a mathematical model for MRI image denoising and reconstruction was constructed based on nonlocal similar block low-rank prior algorithms. The processing effect was assessed regarding the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Finally, MRI images were collected to analyze the difference between the metabolites of N-acetylaspartic acid (NAA), creatine (Cr), choline (Cho), and their ratios in the lesions of the children before and after treatment. The improvement rate was analyzed through a twelve-month follow-up. The algorithm test results showed that compared with the classic K-singular value decomposition (K-SVD) denoising algorithm and the Sparse MRI reconstruction algorithm, the proposed algorithm processed MRI images more clearly and had more detailed information. The quantitative results showed that the PSNR and SSIM in the image processed by the algorithm proposed were remarkably large. The clinical treatment results showed that compared with those before treatment, the nCho level after treatment, the ratio of Cho/Cr and Cho/NAA were remarkably reduced, and the difference was remarkable (P < 0.05). The follow-up results showed that the considerable improvement rate was 88.89%, the postoperative organ remodeling rate was 17.78%, and the probability of reoperation was only 6.67%. In summary, the introduction of intelligent algorithms for denoising and reconstruction of MRI images can remarkably improve image quality and help doctors use image information to diagnose diseases and evaluate treatment effects. The hemangioma resection for the treatment of pediatric SWS had a high treatment improvement rate and was worthy of clinical adoption.

Research on Haze Image Enhancement based on Dark Channel Prior Algorithm in Machine Vision.

According to the characteristics of foggy images, such as high noise, low resolution, and uneven illumination, an improved foggy image enhancement method based on dark channel priority is proposed. First, the new algorithm refines the transmittance and optimizes the atmospheric light value and converts the restored image to HSV space. Second, the brightness V component is enhanced by MSRCR algorithm improved by bilateral filtering, and the saturation S is improved by adaptive stretching algorithm. Finally, the image is converted from HSV space to RGB space to complete image enhancement. The new method solves the problems of that the color of large area is uneven and the overall color of the image is dark when the traditional dark channel prior method is used to remove fog. The experimental results show that from subjective evaluation and quantitative analysis the new algorithm overcomes the shortcomings of noise amplification and edge blur when the conventional enhancement algorithm enhances the image. It can improve image darkening and avoid image distortion in JPEG, BMP, GIF, PNG, PSD, and TIFF formats. By comparing with other image enhancement algorithms, the improved algorithm performs better than DCP, SSR, MSR, MSRCR, and CLAHE algorithm in PSNR, SSIM, and IE evaluation indexes. It has a good effect on preserving the edge information and has good adaptability and stability for heavily polluted haze image enhancement.

White Channel Prior: 개선된 Dark Channel Prior를 이용한 안개 제거 연구

This paper proposes a new dehaze method to improve the contrast value and color intensity degradation of the result image that occurs when the haze is removed through Dark Channel Prior, a representative algorithm of the prior based dehaze method. Due to the estimate and restores by defined a dark channel based on the minimum values of the R, G, and B channels of the local patch, the existing dehaze method decrease the reconstruction image in contrast and color intensity. Therefore, to improve this, this paper proposes White Channel Prior that improve the reduced contrast and color intensity by reprocessing the image processed by the Dark Channel Prior algorithm in the opposite concept to DCP. The proposed method improves the image quality by reconstructing the contrast and color intensity degradation problem of the existing method. As a result of a comparison experiment between the proposed method and the conventional method, the dehaze image to which White Channel Prior method is applied shows a subjective quality improvement compared to the existing method results.

An improved dark channel defogging algorithm based on the HSI colour space

When the traditional dark channel prior (DCP) algorithm is used for image defogging, it faces some problems such as unsatisfactory restoration of the target edge details, distortion in large-scale and high-brightness sky regions. To solve these problems, this paper proposes an improved dark channel defogging algorithm based on the HSI colour space, named the IDCP algorithm. First, an asymmetric mean filter window is used to obtain the modified dark channel image to improve the grey value mutation in the traditional dark channel image. Based on this work, an improved global atmospheric transmittance calculation method is proposed, which effectively improves the inapplicability of the dark channel prior in large-scale and high-brightness sky regions. Finally, the Laplacian operator is used to sharpen the image to enhance the target edge information. Simulation results show that the proposed algorithm can effectively solve the distortion phenomenon caused by the traditional DCP algorithm when handling large-scale and high-brightness sky regions, improve the target edge details, and restore the true colour of the image scene.

Critically appraising the 2018 United Network for Organ Sharing donor allocation policy: adding life boats or rearranging the deck chairs?

Due to the growing mismatch between donor supply and demand as well as unacceptably high transplant waitlist mortality, the heart organ allocation system was revised in October 2018. This review gives an overview of the changes in the new heart organ allocation system and its impact on heart transplant practice and outcomes in the United States. The 2018 heart allocation system offers a 6-tiered policy and therefore prioritizes the sickest patients on the transplant waitlist. Patients supported with temporary mechanical circulatory support devices are prioritized as Status 1 or Status 2, resulting in increased utilization of this strategy. Patients supported with durable left ventricular assist devices have been prioritized as Status 3 or 4, which has resulted in decreased utilization of this strategy. Broader geographic sharing in the new heart allocation system has resulted in prolonged donor ischemic times. Overall, the new heart allocation system has resulted in significantly lower candidate waitlist mortality, shorter waitlist times, and higher incidence of transplantation. The new United Network for Organ Sharing allocation policy confers significant advantages over the prior algorithm, allowing for decreased waitlist times and improved waitlist mortality without major impact on posttransplant survival.

Research on underwater image enhancement algorithm based on improved DCP

All for underwater images, there are some drawbacks, such as low definition, serious color bias, dark brightness, etc. On the basis of in-depth analysis of common image enhancement algorithms, This paper uses the improved dark channel priority algorithm to enhance the underwater image, Improving the contrast of underwater images and color correction of underwater images. Color correction is added based on dark channel prior algorithm; Make the image look more even, higher contrast, more acceptable. The improved algorithm model has a higher transfer rate; PSNR is more balanced and has better contrast to meet the requirements of underwater image observation.

Electrocardiography of cardiac resynchronization therapy: Pitfalls and practical tips.

Cardiac resynchronization therapy (CRT) has been established as an effective mode of therapy in patients with heart failure and concurrent cardiac dyssynchrony, principally in the form of left bundle branch block (LBBB). The widespread use of CRT has ushered in a new landscape in 12-lead electrocardiography (ECG). ECG readings in these patients are most important to guide troubleshooting and also appropriate device programming, as well as discerning and managing nonresponders. A set of four ECG recordings need to accompany each patient with a CRT device, including a baseline ECG and recordings from monochamber (right and left ventricular) and biventricular pacing, which can be compared against a new recording to facilitate the evaluation of proper versus problematic biventricular pacing. Precordial ECG leads V1/2 acquired at the fourth intercostal space and limb leads, I and III, together with a quick assessment of perpendicular leads I and aVF to determine the quadrant of the QRS axis in the hexaxial diagram, may provide the framework for proper ECG interpretation in these patients. This important issue of 12-lead ECG in CRT patients is herein reviewed, pitfalls are pointed out and practical tips are provided for ECG reading to help recognize and manage problems with CRT device function. Furthermore, several pertinent ECG recordings and tabulated data are provided, and an algorithm is suggested that integrates prior algorithms and relevant information from current literature.

OF-VO: Efficient Navigation Among Pedestrians Using Commodity Sensors

We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our system uses commodity visual sensors, including a mono-camera and a 2D Lidar, to explicitly predict the velocities and positions of surrounding obstacles through optical flow estimation, object detection, and sensor fusion. A key aspect of our work is coupling the perception (OF: optical flow) and planning (VO) components for reliable navigation. Overall, our OF-VO algorithm using learning-based perception and model-based planning methods offers better performance than prior algorithms in terms of navigation time and success rate of collision avoidance. Our method also provides bounds on the probabilistic collision avoidance algorithm. We highlight the realtime performance of OF-VO on a Turtlebot navigating among pedestrians in both simulated and real-world scenes. A demo video is available at https://gamma.umd.edu/ofvo/

139 Creating an Automated Artificial Intelligence Left Ventricular Ejection Fraction Estimator for Parasternal Long Axis Scans from Unrelated Apical Window Echo Videos

A major hindrance to point-of-care ultrasound (POCUS) deep learning (DL) algorithm development is lack of large publicly available image databases, like ones found in traditional imaging. Our objective was to test the potential of using only apical 4-chamber (A4C) images from a large publically available dataset to train a DL algorithm to visually estimate left ventricle (LV) Ejection Fraction (EF) from a parasternal long axis (PSL) window. Researchers embedded a VGG16 convolutional neural network inside a long short term memory (LSTM) algorithm for ultrasound video analysis. We obtained access to the Stanford EchoNet-Dynamic database of approximately 10, 000 echo A4C videos. All echo examinations were read during comprehensive echocardiography examination and included calculated EF results. Ninety percent of the data were used for algorithm training and 10% for validation during training. The LV in A4C images take on a similar appearance to that in PSL images when flipped horizontally, across the vertical axis, and rotated 90o counter clockwise (CCW) and could potentially be used to simulate PSL data. As part of DL algorithm training, researchers tested 3 training options by applying image manipulation to Stanford A4C echo videos. Tested were: training the DL algorithm only on unaltered Stanford A4C videos, training on unaltered videos and those rotated 90o CCW and finally training on unaltered then rotated 90o CCW and then horizontally flipped A4C videos. As a real-world test, we obtained 569 echo examinations from a different medical center (UCLA) showing PSL window videos from comprehensive echo examinations, with respective EF, and tested the DL algorithm variants’ performance on these actual PSL videos. We calculated mean absolute error (MAE) on algorithm EF results per field standards for evaluating algorithm accuracy compared to Echo Lab calculated EF and Bland-Altman analyses. MAE for skilled echo techs ranges from 4 to 5%. Prior algorithm training with Stanford A4C videos and testing only on Stanford A4C videos achieved an MAE of 8.08% (95% CI 7.60 to 8.55). In this study, the DL algorithm trained only on unaltered A4C videos and tested on unrelated PSL achieved MAE of 27.03% (95% CI 25.59 to 28.46) for visual EF estimation. Training on unaltered A4C videos and then on 90o CCW rotated videos achieved an MAE of 17.41 (95% CI 16.26 to 18.19). Training on unaltered, then 90o CCW rotated and then horizontally flipped videos achieved an MAE of 16.18 (95% CI 15.21 to 17.14). The Bland-Atlman plots showed the vast majority of points falling within the 95% CI for the third and best training iteration. Our study results indicate the potential for POCUS AI researchers to use non-POCUS image data in algorithm development by adapting it for training purposes via video rotation and manipulation designed to simulate the desired imaging window. This may be important for future POCUS algorithm development which may benefit from adaptation of traditional imager data, and help overcome the paucity of POCUS databases.

Extensions of Granger Causality Calculations on Brain Networks for Efficient and Accurate Seizure Focus Identification via iEEGs.

Current epilepsy surgery planning protocol determines the seizure onset zone (SOZ) through resource-intensive, invasive monitoring of ictal events. Recently, we have reported that Granger Causality (GC) maps produced from analysis of interictal iEEG recordings have potential in revealing SOZ. In this study, we investigate GC maps’ network connectivity patterns to determine possible clinical correlation with patients’ SOZ and resection zone (RZ). While building understanding of interictal network topography and its relationship to the RZ/SOZ, we identify algorithmic tools with potential applications in epilepsy surgery planning. These graph algorithms are retrospectively tested on data from 25 patients and compared to the neurologist-determined SOZ and surgical RZ, viewed as sources of truth. Centrality algorithms yielded statistically significant RZ rank order sums for 16 of 24 patients with RZ data, representing an improvement from prior algorithms. While SOZ results remained largely the same, this study validates the applicability of graph algorithms to RZ/SOZ detection, opening the door to further exploration of iEEG datasets. Furthermore, this study offers previously inaccessible insights into the relationship between interictal brain connectivity patterns and epileptic brain networks, utilizing the overall topology of the graphs as well as data on edge weights and quantity of edges contained in GC maps.

Even More Efficient Quantum Computations of Chemistry Through Tensor Hypercontraction

We describe quantum circuits with only O~(N) Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of N arbitrary (e.g., molecular) orbitals. With O(λ/ϵ) repetitions of these circuits one can use phase estimation to sample in the molecular eigenbasis, where λ is the 1-norm of Hamiltonian coefficients and ϵ is the target precision. This is the lowest complexity shown for quantum computations of chemistry within an arbitrary basis. Furthermore, up to logarithmic factors, this matches the scaling of the most efficient prior block encodings that can work only with orthogonal-basis functions diagonalizing the Coloumb operator (e.g., the plane-wave dual basis). Our key insight is to factorize the Hamiltonian using a method known as tensor hypercontraction (THC) and then to transform the Coulomb operator into an isospectral diagonal form with a nonorthogonal basis defined by the THC factors. We then use qubitization to simulate the nonorthogonal THC Hamiltonian, in a fashion that avoids most complications of the nonorthogonal basis. We also reanalyze and reduce the cost of several of the best prior algorithms for these simulations in order to facilitate a clear comparison to the present work. In addition to having lower asymptotic scaling space-time volume, compilation of our algorithm for challenging finite-sized molecules such as FeMoCo reveals that our method requires the least fault-tolerant resources of any known approach. By laying out and optimizing the surface-code resources required of our approach we show that FeMoCo can be simulated using about four million physical qubits and under 4 days of runtime, assuming 1-μs cycle times and physical gate-error rates no worse than 0.1%.13 MoreReceived 12 December 2020Revised 7 April 2021Accepted 24 May 2021DOI:https://doi.org/10.1103/PRXQuantum.2.030305Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum computationQuantum simulationPhysical SystemsStrongly correlated systemsTechniquesElectronic structureCondensed Matter, Materials & Applied PhysicsQuantum InformationAtomic, Molecular & Optical