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Connected-component labeling (CCL) is indispensable for pattern recognition.Many connected-component labeling algorithms have been proposed.The state-of-the-art CCL algorithms presented in the last decade are reviewed. This article addresses the connected-component labeling problem which consists in assigning a unique label to all pixels of each connected component (i.e., each object) in a binary image. Connected-component labeling is indispensable for distinguishing different objects in a binary image, and prerequisite for image analysis and object recognition in the image. Therefore, connected-component labeling is one of the most important processes for image analysis, image understanding, pattern recognition, and computer vision. In this article, we review state-of-the-art connected-component labeling algorithms presented in the last decade, explain the main strategies and algorithms, present their pseudo codes, and give experimental results in order to bring order of the algorithms. Moreover, we will also discuss parallel implementation and hardware implementation of connected-component labeling algorithms, extension for n-D images, and try to indicate future work on the connected component labeling problem.

Propagation characteristics of low-frequency radio waves at short ranges and with high propagation angles are analyzed by the finite-difference time-domain and one-way/two-way parabolic equation (PE) methods. Both narrow-angle (NA) and wide-angle (WA) PE expressions are considered. It is observed that the NAPE method is hard to be applied in the region where the propagation angle is above 15°, whereas the WAPE method can obtain a higher accuracy so long as the earth surface slope is not very big. If the terrain slope changes dramatically, results of the two-way WAPE are more reliable with an interference pattern. However, diffraction effects of the waves over the mountain are not given full consideration.

Table S1 Summary of RNA-seq data. (DOCX 15 kb)

Figure S1 Top 30 significantly enriched GO terms in L6vsL0. (DOCX 1233 kb)

Figure S3 Meteorological data recorded during the period of wheat growth (October to June). (DOCX 708 kb)

Table S2 List of selected genes for KEGG pathways in L6vsL0. (DOCX 14 kb)

Table S3 List of selected genes for KEGG pathways in T6vsT0. (DOCX 15 kb)

Figure S2 Significantly enriched GO terms in T6vsT0. (DOCX 1122 kb)

Table S5 Primers used for qRT-PCR. (DOCX 14 kb)

Deformation twins in cast uranium strained to 4.2% and 6.2% by quasi static compression were investigated using electron backscattered diffraction and transmission electron microscopy. Twin types of {130}, ‘{172}’, {112} and ‘{176}’ were observed in present experiment. All the operative twin variants in each twin type have the highest Schmid factor among the equivalent variants. Some {130} twins in cast uranium were inclined to disappear during subsequent loading through the re-twinning processes with Schmid factor values greater than 0.4. The ‘(−176)’ variant was identified by indexing the electron diffraction pattern combining with the stereographic projection analysis. Twin pairs of ‘(−176)’–‘(−17−2)’ occurred in the adjacent grains were well matched with the geometric compatibility factor value of 0.933.

The basic shape features of an object in a binary image, i.e., the area, perimeter, circularity, and centroid, are important for image analysis and pattern recognition. In conventional algorithms, to calculate the basic shape features of objects in a binary image, it is usually necessary to first perform connected-component labeling to generate a labeled image (intermediate image), in which every image object is assigned a unique label so that it may be distinguished. Using the labeled image, the basic shape features of the object corresponding to each label can then be calculated. When a two-scan labeling algorithm is used, three scans are necessary. This paper proposes an efficient algorithm for calculating the shape features of objects in a binary image. Instead of a labeled image, our proposed algorithm calculates the basic shape features of objects using the image and the representative label table generated by the first scan of an efficient two-scan labeling algorithm. Thus, we can compute shape features using two scans. Experiments demonstrate that our proposed algorithm is much more efficient than conventional algorithms for calculating the basic shape features of objects in a binary image.

A multiple access chaos shift keying cooperative communication (MA-CSK-CC) system over Rayleigh fading channels and in the presence of Gaussian white noise is investigated in depth in this paper. The simplified half-duplex with a single relay decode-and-forward (DF) cooperative strategy is proposed to illustrate and analysis as an example. Exact and approximation bit-error-ratio (BER) performance expressions are derived. Simulation results demonstrate that the proposed system has a prominent advantage of high BER performance over the conventional multiple access CSK systems that have a single path to transmit signal at the same data rate over Rayleigh fading channels with additive Gaussian white noise. Moreover, based on the BER performance analysis, some useful results are also derived such as the relationships between BER versus spread spectrum factors, BER versus the number of users, etc.

The Euler number is an important topological property in a binary image, and it can be computed by counting certain bit-quads in the binary image. This paper proposes a further improved bit-quad-based algorithm for computing the Euler number. By scanning image rows two by two and utilizing the information obtained while processing the previous pixels, the number of pixels to be checked for processing a bit-quad can be decreased from 2 to 1.5. Experimental results demonstrated that our proposed algorithm significantly outperforms conventional Euler number computing algorithms.

Many web-based pharmaceutical e-commerce platforms allow consumers to post open-ended textual reviews based on their purchase experiences. Understanding the true voice of consumers by analyzing such a large amount of user-generated content is of great significance to pharmaceutical manufacturers and e-commerce websites. The aim of this paper is to automatically extract hidden topics from web-based drug reviews using the structural topic model (STM) to examine consumers&rsquo, concerns when they buy drugs online. The STM is a probabilistic extension of Latent Dirichlet Allocation (LDA), which allows the consolidation of document-level covariates. This innovation allows us to capture consumer dissatisfaction along with their dynamics over time. We extract 12 topics, and five of them are negative topics representing consumer dissatisfaction, whose appearances in the negative reviews are substantially higher than those in the positive reviews. We also come to the conclusion that the prevalence of these five negative topics has not decreased over time. Furthermore, our results reveal that the prevalence of price-related topics has decreased significantly in positive reviews, which indicates that low-price strategies are becoming less attractive to customers. To the best of our knowledge, our work is the first study using STM to analyze the unstructured textual data of drug reviews, which enhances the understanding of the aspects of drug consumer concerns and contributes to the research of pharmaceutical e-commerce literature.

Due to the protection of data security and personal privacy in email networks, it is difficult to select the neighbor user using email user's personalized attributes and behavior data. In daily life, the more interactive the people are, the more similar they are. So many researchers use the interaction strength to select the neighbor users. But this method has not been experimentally verified. Receiving the same email is the most basic condition that a neighbor user can provide a valid recommendation to the target user. So, we build an email corpus using real email data to verify the selection effect of different methods. The emails come from Enron email data set, log files of a Chinese corporate email server, and volunteers. This corpus contains more than 870,000 users and more than 5.14 million emails. With this corpus, we conducted experiments on seven different methods of neighbor user selection. The experimental results show the effect of user interaction strength is far from being as good as expected, and far inferior to user social attributes. In addition, the recipient performs far better than the sender. This paper provides a useful reference for the research of spam filtering based on user-based collaborative recommendation filtering in mail network.

The aim of this study is to analyze the methylation levels of SPG20 promotor region and explore the association between the methylation levels and clinical features in hepatocellular carcinoma (HCC).We collected paired of HCC and adjacent non-cancerous tissues (ANT) from 160 HCC patients and analyze the methylation levels through MassARRAY Analyzer 4. The statistical calculations were performed using SPSS version 22.0. Real-time-quantification PCR was performed to assess expression levels of SPG20 in HCC cell lines. Wound healing assay and transwell assay was used to measure cell migration capacity.We found that mean methylation level of SPG20 in tumor tissues was significantly higher than that in ANT (7.3% vs. 16.2%, P0.0013). There was a significantly negative correlation between expression level and methylation level of SPG20 (P0.01). In addition, the methylation levels in HCC were correlated with age and HBV infection. Meanwhile, micro-satellite tumors (P = 0.016) and tumor number (P = 0.018) was found significantly associated with increased methylation levels of several CpG sites and the mean levels of SPG20 promotor in ANT. In addtion, the capacity of cell migration was significantly enhanced in SPG20 knock-down HCC cells.The hypermethylation status of SPG20 gene promoter is significantly associated with intra-hepatic metastasis and contribute to HCC metastasis.

With the rapic developments in most China cities, urban environment monitoring is very important, for example, for the subway safety, illegal drilling and construction in subway field should be quickly detected. Monitoring techniques with high precision and efficiency are vital to prevent the accidents and reduce losses, and UAV has great potential and advantages to conduct such task compared to human daily inspection. To quickly get the illegal operation information from UAV image, a method of change detection based on elevation difference and local binary pattern (LBP) is proposed to monitor the land surface along subway by unmanned aerial vehicle remote sensing (UAVRS). After accurate registrations without GCPs complete, the two DSMs and DOMS are mutually matched by the same points in their own model. Gaussian smoothing is used in gray-scale map to eliminating noise jamming before making change detection based on elevation difference. Pix4D is used to generate 2 DSMs of the study area, and texture feature is measured by LBP which is advanced in its rotational invariance and brightness invariance. Comparing with former researches, two DSMs are matched by invariant points in their own models instead of GCPs which are usually collected by GPS, with the registration precision less than 0.1m both in XY and Z directions, which meets the requirement of illegal operation detection in subway safety monitoring, and the adoption of LBP works well for images collected in different climate and illumination.

Background Hepatocellular carcinoma (HCC) is a malignancy with poor prognosis. Complex genetic and epigenetic alterations are the two primary causes of HCC. The aim of the study was mainly to explore the correlation between the methylation status of DNAH17 and HCC. Methods We evaluated the methylation levels of DNAH17 in 163 HCC samples and their paired normal tissue using Sequenom EpiTYPER assays and performed the TaqMan copy number assay to assess the copy number status of DNAH17 in HCC samples. Results The mean methylation levels were significantly decreased in the tumor tissues compared to the paired normal tissues in both selected regions of DNAH17 (amplicon 1:58.7% vs 84.5%, P

Deformed microstructure of the adiabatic shear bands in zirconium alloy impacted by split Hopkinson pressure bar at a strain rate of about 2300 s-1 was characterized systemically. Four different strains of zirconium alloy subjected to impact loading at same strain rate were designed by means of strain stopper rings. At a strain of 0.30, the transformed bands were distinguished at the tip of crack. The transformed bands mainly composed of the ultrafine and equiaxed grains with the mean diameter of about 100~300 nm were found by a dual beam FIB/TEM system. Besides ultrafine grains in the transformed bands, nanometer fine grain was observed and confirmed by HRTEM. Thus, the transformed bands mainly composed of the mixed microstructure including nanometer and ultrafine grains were confirmed, and n phase transformation and amorphization in transformed bands take place.

Hepatocellular carcinoma (HCC) is the sixth most common tumor worldwide. The relationship between the gene methylation accumulation and HCC has been widely studied. In our study, we used the Sequenom EpiTYPER assay to investigate the methylation levels of the RASA3 in 164 HCC samples and paired adjacent non-cancerous tissues, and the association between methylation level and clinicopathological features. The methylation level of the RASA3 in HCC samples was found significantly lower than that in the adjacent non-cancerous tissues (P

Extraction of regions of interest plays an important rule in computer aided lung nodules detection. However, because of the complex background and structure, accurate and robust extraction of ROIs in medical image still remains a problem. Aim at this problem, a two-stage operations joint filter: Hessian-LoB, is proposed. The first stage is blobs (which being taken as candidate ROIs) detection and the second stage is ROIs extraction. In the first stage, the derivatives of a Hessian matrix at multiple scales are convolved with input images to localize blobs. Then in the second stage, Laplacian of bilateral filter (LoB) is convolved with the detected blobs to extract the final ROIs. Experiments show that the proposed filter can deal with images with noise and low brightness contrast, and is effectively in ROI extraction for lung nodule detection.

This paper proposes a fast algorithm for integrating connected-component labeling and Euler number computation. Based on graph theory, the Euler number of a binary image in the proposed algorithm is calculated by counting the occurrences of four patterns of the mask for processing foreground pixels in the first scan of a connected-component labeling process, where these four patterns can be found directly without any additional calculation; thus, connected-component labeling and Euler number computation can be integrated more efficiently. Moreover, when computing the Euler number, unlike other conventional algorithms, the proposed algorithm does not need to process background pixels. Experimental results demonstrate that the proposed algorithm is much more efficient than conventional algorithms either for calculating the Euler number alone or simultaneously calculating the Euler number and labeling connected components.

The Euler number of a binary image is an important topological feature for many image processing, image analysis, pattern recognition, and computer vision applications. This paper proposes a new run-based Euler number computation algorithm. The conventional run-based algorithm processes rows of the given image one-by-one from top to bottom in a single phase. For each row, it finds the runs in the row and records the start and end locations of each run to compute neighbor runs. In contrast, our algorithm calculates the Euler number of an image in two phases. In the first phase, we process odd rows alternately to find runs and only record its end location. In the second phase, we process each of the remaining even rows to find runs and calculate neighboring runs between the current row and the rows immediately above and below using the recorded run data. Using this method, the number of accesses required to compute the Euler number decreases in almost all cases. Analysis of the time complexity and experimental results demonstrate that our algorithm outperforms conventional Euler number computation algorithms.

This paper proposes a new first-scan method for two-scan labeling algorithms. In the first scan, our proposed method first scans image lines three by three with a leaving line, and for foreground pixels among each three lines, assigns them provisional labels, and finds and resolves label equivalences among them. Then, it processes the leaving lines from top to bottom one by one, and for each line, assigns foreground pixels on the line provisional labels, finding and resolving label equivalences between the foreground pixels and those on the lines immediately above and below the current line. Experimental results demonstrated that our method is more efficient than conventional label-equivalence-based labeling algorithms.

The shape features of objects (connected components) in binary images are very important for image analysis, pattern (object) recognition, and computer vision. Conventional algorithms for calculating the shape features of objects in binary images can only calculate the shape features of objects without holes. This paper presents an algorithm or calculating the shape features of objects in binary images with holes. Based on a contour-tracing-based connected-component labeling algorithm, our proposed algorithm can calculate the number of objects, the number of holes, the Euler number in a binary image, extract the contours of objects, and calculate the area, perimeter, circularity, centroid of each object. Our proposed algorithm is able to make a contribution to image analysis, pattern recognition, and computer vision.

The modification of commercial ultra-stable Y zeolites using citric acid and phosphoric acid was investigated systematically via a L18(38) orthogonal experiment. The pore structure, acid property and crystal structural of modified USY zeolites were characterized by a variety of means such as N2 adsorption–desorption, Fourier transform infrared spectroscopy, NH3-temperature programmed desorption and X-ray diffraction. The optimal modification condition is found to be that the volume ratio of citric acid (0.3 mol/L) and phosphoric acid (0.3 mol/L) is 1.0, and the operation is performed at 100 °C for 6 h. The as-synthesized sample presents an increased secondary pore volume up to 0.207 cm3/g which accounts for 42.9 % of the total pore volume, and appropriate acidity distribution as well as good crystallinity. In addition, the USY obtained by 1.0 L scale-up modification possesses a secondary pore volume of 0.210 cm3/g which accounts for 42.4 % of the total pore volume, showing no obvious scale-up effects. Furthermore, the hydrothermal stability of the modified samples meets the requirements of commercial catalysts for hydrocracking. Performance evaluation was carried out on a 200 mL fixed-bed single stage hydrogenation unit using Daqing VGO as feedstock. The 140–370 °C middle distillate yield is 66.09 %, and middle distillate selectivity can reach up to 80.45 %. Compared with commercial catalyst, the yield and selectivity are increased by 5.67 and 4.07 %, respectively.

According to the characteristics of photovoltaic cell output power curve, this paper analyzed and explained the principle of Maximum Power Point Tracking (MPPT) and both advantages and disadvantages of constant voltage tracking method & perturbation observation method. Further, in combination with the advantages of existing maximum power tracking methods, this paper comes up with an improved tracking method which is recognized as maximum power point tracking combined with constant voltage tracking method & variable step-size perturbation observation method. The Simulink simulation results have proven this enhanced tracking method has a better performance in System response and steady state characteristics.

Connected-component labeling and Euler number computing are two essential processing tasks for extracting objects' features in a binary image for the pattern recognition, image analysis, and computer (robot) vision. In general, the two processing tasks are usually executed independently by different algorithms in different scans. This paper proposes a combinational algorithm for labeling connected components in a binary image and computing the Euler number of the image simultaneously. In our algorithm, for the current pixel, the two processing tasks use the same information obtained from its neighbor pixels in the same scan. Moreover, the information obtained during processing the current pixel will be used for processing the next pixel. Our method is simple in principle and powerful in practice. Experimental results demonstrated that our method is much more efficient than conventional methods on various kinds of images, either in the case where the Euler number is calculated alone or in the case where both connected-component labeling and the Euler number computing are necessary.

With the complex of the tunnel construction, tunnel construction become more and more difficult, in order to ensure the safety of tunnel construction, the paper introduced a kind of tunnel construction monitoring system based on sensor. The system achieves the real- time monitoring of tunnel construction environment including temperature and humidity, gas concentration, dust concentration, location tracking for construction workers through the wireless communication technology, to control of the real-time status of the tunnel, and ensure timely rescue when the accident occurred.

One of the major problems for computer-aided pulmonary nodule detection in chest radiographs is that a high false-positive (FP) rate exists. In an effort to overcome this problem, a new method based on the MTANN (Massive Training Artificial Neural Network) is proposed in this paper. An MTANN comprises a multi-layer neural network where a linear function rather than a sigmoid function is used as its activity function in the output layer. In this work, a mixture of multiple MTANNs were employed rather than only a single MTANN. 50 MTANNs for 50 different types of FPs were prepared firstly. Then, several effective MTANNs that had higher performances were selected to construct the MTANNs mixture. Finally, the outputs of the multiple MTANNs were combined with a mixing neural network to reduce various different types of FPs. The performance of this MTANNs mixture in FPs reduction is validated on three different versions of commercial CAD software with a validation database consisting of 52 chest radiographs. Experimental results demonstrate that the proposed MTANN approach is useful in cutting down FPs in different CAD software for detecting pulmonary nodules in chest radiographs.

In the optical measurement of burst multi-targets such as the rocket, missile, bullet and so on, in order to solve the poor real-time reliability problem of the multi-targets ballistic recognition caused by the invalid ballistic recognition algorithm variable and less movement feature quantity, a multi-targets ballistic recognition method based on movement multiple feature correlation has been proposed, and the mathematical model algorithm and multi-targets real-time ballistic recognition strategy has been established at the same time. It has been proved that the method can improve the adaptability, real-time performance and reliability of multi-targets real-time ballistic recognition, to provide technical support to real-time multi-targets optical measurement and processing.