A spatially-aware algorithm for location extraction from structured documents

Compared with the ultrasonic measurement of fetal heart motion, the Fetal Electrocardiogram (FECG) provides clinically significant information. In this paper, novel FECG Blind Source Extraction (BSE) algorithm based on Blind Source Separation (BSS) in noise is presented. We first build the basic BSE model for FECG extraction and then based on the time structure of the FECG and maternal ECG, the FECG extraction algorithm is constructed using Second-Order Statistics (SOS). To reduce the noise influence, the algorithm using Discrete Wavelet Transformation (DWT) moves the conventional time-domain signals to the wavelet-domain and then the approximate source number is detected and the noise in FECG can be reduced. According this noise reduction procedure and SOS, the proposed robust FECG extraction algorithm is derived. The experimental outcomes demonstrate the performance of the proposed method and , in turn, verify the validity of the model in biomedical applications. The advantages and limitations of the proposed algorithm and its applications are analysed in the discussions and conclusions.

Rule extraction using Recursive-Rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset

In the process of nano-electronic circuit design, there are defects inevitably. Due to the variety of defects and nets on circuits, the extraction of the critical area leading to the short circuit fault has become the bottleneck of layout optimization and of integrated circuit yield estimation. This paper proposes a new model of short critical area and extraction algorithm based on short circuit faults caused by redundant defects, combining the redundant defect features and mathematical morphology theory. Firstly, defect feature sizes and the nets ranges of the circuit are extracted. Secondly, according to the definition of short critical area and utilizing the expansion operation of mathematical morphology a new model of short critical area of regular nets is derived. Finally, extraction algorithm of short critical area is designed and implemented in accordance with the new model. The new model and extraction algorithm have the characteristics of being more generic and being independent of the shape features of defects and nets. Compared with the existing models, the new model has the similar performance however the extraction algorithm has significantly raised time efficiency. Furthermore, the extraction algorithm is more efficient than the previous algorithm which is based on mathematical morphology and non-model method. The experimental results on the real layouts of synthesized OpenSparc circuit and MUSB L70 show that the proposed method is accurate and effective.

With the development of information technology, precision agriculture has also ushered in new development prospects. The use of farm robots to accurately identify the navigation path is of great significance for achieving accurate positioning of agriculture. In this study, when analysing the extraction algorithm of farm robot visual navigation based on dark primary colours, a method for pre-processing and edge detection of farmland images based on dark primary colours is proposed. At the same time, the least square method of linear fitting is used for the navigation path of agricultural robot, and then the fitting program is executed. On this basis, combined with the actual situation of outdoor farming and greenhouse cultivation of crops, the effectiveness of the robotic visual navigation extraction algorithm was verified. The research results show that for any form of farmland cultivation, image extraction technology based on dark primary colours can effectively distinguish between soil and crops, and the visual navigation path of farm robots fitted with least squares is basically linear, which is consistent with the commonly used crops for farm planting. The legal route is basically the same, and then the effectiveness of the extraction algorithm is verified. It is hoped that this study will provide a certain reference and reference for the analysis of the field navigation robot visual navigation extraction algorithm based on dark primary colours.

The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results.

Improvement of Automated Breast- region -extraction Algorithm in a Mammogram CAD System

Extraction is a generalization of fitting, and is sometimes given the name robust fitting. In ordinary fitting the assumption is made that all the points belong to the curve or surface being fit. In extraction, or robust fitting, this assumption does not hold. Thus an extraction routine must return not only the equation of the best primitive (curve or surface), but also which of the data points are described by this primitive. We give a short description of our extraction algorithm which is based on random sampling. Previously we have shown how our extraction algorithm can deal with curves and surfaces defined implicitly. In this paper we extend this algorithm to curves and surfaces defined parametrically. Being able to extract curves and surfaces defined both implicitly and parametrically makes this algorithm unique. We show a number of experimental results that demonstrate the extraction algorithm in operation.

Over the past decades, many studies have been published on the extraction of non-invasive foetal electrocardiogram (NI-FECG) from abdominal recordings. Most of these contributions claim to obtain excellent results in detecting foetal QRS (FQRS) complexes in terms of location. A small subset of authors have investigated the extraction of morphological features from the NI-FECG. However, due to the shortage of available public databases, the large variety of performance measures employed and the lack of open-source reference algorithms, most contributions cannot be meaningfully assessed.This article attempts to address these issues by presenting a standardised methodology for stress testing NI-FECG algorithms, including absolute data, as well as extraction and evaluation routines. To that end, a large database of realistic artificial signals was created, totaling 145.8 h of multichannel data and over one million FQRS complexes. An important characteristic of this dataset is the inclusion of several non-stationary events (e.g. foetal movements, uterine contractions and heart rate fluctuations) that are critical for evaluating extraction routines. To demonstrate our testing methodology, three classes of NI-FECG extraction algorithms were evaluated: blind source separation (BSS), template subtraction (TS) and adaptive methods (AM). Experiments were conducted to benchmark the performance of eight NI-FECG extraction algorithms on the artificial database focusing on: FQRS detection and morphological analysis (foetal QT and T/QRS ratio).The overall median FQRS detection accuracies (i.e. considering all non-stationary events) for the best performing methods in each group were 99.9% for BSS, 97.9% for AM and 96.0% for TS. Both FQRS detections and morphological parameters were shown to heavily depend on the extraction techniques and signal-to-noise ratio. Particularly, it is shown that their evaluation in the source domain, obtained after using a BSS technique, should be avoided. Data, extraction algorithms and evaluation routines were released as part of the fecgsyn toolbox on Physionet under an GNU GPL open-source license. This contribution provides a standard framework for benchmarking and regulatory testing of NI-FECG extraction algorithms.

Active power filters are effective in mitigating line current harmonics and compensating for the reactive power in the line. Many papers comparing the performance of active power filter extraction algorithms are found in the literature, but there is no explicit mention about the suitability of any particular extraction algorithm under all conditions. Hence this paper is an attempt to compare the performance of direct methods of compensating current extraction algorithms for three-phase active power filters under different conditions of mains supply using Matlab/Simulink™. Both transient and steady state conditions are considered. The compensating techniques under analysis are: synchronous detection methods, synchronous reference frame method, i d -i q method, instantaneous active reactive power theory (p-q theory), modified p-q theory, adaptive noise cancellation, notch filtering and Fourier based sinusoidal subtraction algorithms. Under balanced conditions all the compensating techniques give proper results, but under unbalanced conditions only few of the compensating techniques work satisfactorily. In fact the equal resistance synchronous detection method and Fourier based sinusoidal subtraction algorithms perform better under extreme conditions. With proper implementation, sinusoidal subtraction algorithm performs better under both transient and steady state conditions.

This paper introduces techniques from combinatorial and algebraic topology to help in explaining and measuring the performance of modern logic minimizers. The concepts of simple and the Euler-Poincare characteristic of a logic cover are defined and analyzed. In particular, simple is related to the minimization algorithms Espresso-EXACT and Roth's Extraction Algorithm. Experimental results on the Euler-Poincare characteristic, along with a new measure, the Euler Ratio are related to the function complexity concepts of cyclic constraints in Espresso_EXACT, the CyclicKernel in Roth's Extraction Algorithm, and cubical homotopy introduced in this paper.

In order to develop a new recognition method for cracked hen egg with high accuracy and adoption, images with different crack width of the hen eggs were captured. After that, according to the change regulation of the local gray value of the crack area in the egg image, a sequenced wave signal extraction and identification algorithm was developed. This algorithm composed of wave signal extraction, wave signal connection, and sequenced wave signal identification. Results showed that, the sequenced wave signals extracted from the egg image were due to cracks and translucent areas of the egg shell. The highest length-width ratio of the segmented area (R max) and gray value change index (D) were two parameters extracted from the sequenced wave signal, and could be used to identify the sequenced wave signals caused by the cracks from other sequenced wave signals. A hen egg image with at least one crack-caused sequenced wave signal was considered as a cracked egg image. Using this method, recognition accuracy of hen eggs with a crack of 0.06–1.13 mm width was 98.9%, and that of intact eggs was 96%. The sequenced wave signal extraction and identification algorithm may have application potential to recognize cracked hen eggs on-line.

Modern radio pulsar surveys produce a large volume of prospective candidates, the majorityof which are polluted by human-created radio frequency interference or other forms of noise. Typically, large numbers of candidates need to be visually inspected in order to determineif they are real pulsars. This process can be labour intensive. In this paper, we introducean algorithm called Pulsar Evaluation Algorithm for Candidate Extraction (PEACE) whichimproves the efficiency of identifying pulsar signals. The algorithm ranks the candidates basedon a score function. Unlike popular machine-learning-based algorithms, no prior training datasets are required. This algorithm has been applied to data from several large-scale radiopulsar surveys. Using the human-based ranking results generated by students in the AreciboRemote Command Center programme, the statistical performance of PEACE was evaluated. It was found that PEACE ranked 68 per cent of the student-identified pulsars within the top0.17 per cent of sorted candidates, 95 per cent within the top 0.34 per cent and 100 per centwithin the top 3.7 per cent. This clearly demonstrates that PEACE significantly increases thepulsar identification rate by a factor of about 50 to 1000. To date, PEACE has been directlyresponsible for the discovery of 47 new pulsars, 5 of which are millisecond pulsars that maybe useful for pulsar timing based gravitational-wave detection projects.

A practical algorithm for subsurface defect extraction (blobs) in infrared or in low spatial content images is proposed. It first locates potential defects by a spatial sorting of the pixels, placed in decreasing order, based on their brightness. Labeling of the pixels is based on the distance. Defect shape is grown by gradually decreasing the threshold until a sudden increase in the number of pixels agglomerates together or an image boundary is encountered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Boundary extraction is a key task in many image analysis operations. This paper describes a class of constrained clustering algorithms for object boundary extraction that includes several well-known algorithms proposed in different fields (deformable models, constrained clustering, data ordering, and traveling salesman problems). The algorithms belonging to this class are obtained by the minimization of a cost function with two terms: a quadratic regularization term and an image-dependent term defined by a set of weighting functions. The minimization of the cost function is achieved by lowpass filtering the previous model shape and by attracting the model units toward the centroids of their attraction regions. To define a new algorithm belonging to this class, the user has to specify a regularization matrix and a set of weighting functions that control the attraction of the model units toward the data. The usefulness of this approach is twofold: it provides a unified framework for many existing algorithms in pattern recognition and deformable models, and allows the design of new recursive schemes.

Recent advances in high-power, high-repetition-rate laser systems are driving the adoption of data-driven experimental approaches in high-energy density science. To fully realize the potential of these methodologies, automated and high-throughput analysis of key diagnostics is essential for effective feedback and real-time optimization. We present a novel algorithm, ARISE (algorithm for rapid ion spectrum extraction), developed for fast and reliable extraction of laser-accelerated ion spectra from Thomson parabola spectrometers, capable of operating at repetition rates exceeding 20 Hz. ARISE enables real-time, data-driven experimentation through features including background subtraction, automatic identification of the zero-deflection reference point and automated determination of maximum ion energy. We validate the accuracy of ARISE in spectrum extraction and energy detection, and demonstrate its integration within a Bayesian optimization framework during a proof-of-concept experiment conducted using the 350 TW SCAPA laser, enabling real-time optimization of laser-accelerated ion beam parameters.