Centroid Algorithm Research Articles

Crop Disease Source Location and Monitoring System Based on Diffractive Light Identification Airborne Spore Sensor Network

Traditional methods based on the Internet of Things (IoT) or IoT methods based on microscopic imaging are difficult to automatically realize early warning of crop diseases. In this article, a diffraction imaging IoT system based on spore detection is proposed to indirectly monitor crop diseases instead of directly taking crop disease images. Multiple NB-IoT nodes are deployed to build an IoT system to realize the judgment of spore diffraction image transmission, which is based on the detection of environmental temperature and humidity. The method of digital image processing is applied to filter out impurities and count microparticles with the accuracy of 85%. By obtaining the number of spores in different positions, the microparticles diffusion model is established to study the law of microparticles transmission in specific space. According to the diffusion model, the weighted centroid and particle filter algorithm are applied to locate the particle source in windless and windy conditions. Thirteen nodes are arranged in a 2 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 2 m laboratory to carry out the experiment. The maximum error in windless and windy conditions is 0.18 and 0.35 m. Compared with the traditional microscopic imaging-based IoT method, the detection limit of the proposed diffraction imaging method is 1/50. It provides inspiration for the IoT in the early detection and disease location of crop diseases.

Estimation and performance analysis of multiple incident beam misalignment in spatial diversity based FSO transmissions

A novel approximated misalignment model of an alignment error with multiple incident beams in the position detection of the pointing, acquisition, and tracking (PAT) system is developed for spatial diversity based free-space optical (FSO) communication system. A centroid algorithm used in position detection of the incident beam to align transceivers for the PAT system can make additional pointing errors when multiple beams are incident on the position detector. The centroid algorithm measures the alignment errors through the ratio of the segmented received powers from the position detector. Therefore, the distant arrangement of transmitters and the atmospheric fading affect the alignment error, and the pointing loss of FSO communication increases. We propose a statistical misalignment model with multiple incident beams and analyze the transmission performance using this model to achieve power-efficient FSO communication. The optimum beam width with the misalignment model can reduce the transmit power required to satisfy system demands and outage probability with redundant power for reliable FSO communication.

Kalman filter spot centroid detection algorithm in optical wireless communication

Atmospheric turbulence creates a light spot on the focal plane of the receiving end scintillation and angle of arrival fluctuation in optical wireless communication. The combination of a spot centroid detection algorithm and Kalman filter (KF) is proposed for spot tracking. The theoretical calculated value of the spot circle fit is filtered to obtain an optimal estimate of the spot center. The root mean square error of the detected spot centroids is used as an evaluation metric to verify the conventional algorithm and KF algorithm for the real-time tracking of the laser spot at transmission distances of 1.3, 4.1, and 10.3 km. The results show that the root mean square error of the spot in the pitch direction decreases from 0.740, 2.537, and 7.256 pixels to 0.683, 1.792, and 4.756 pixels, respectively, whereas the root mean square error of the spot in the horizontal direction decreases from 0.481, 2.131, and 5.932 pixels to 0.463, 1.471, and 4.362 pixels, respectively, compared with the conventional algorithm at three transmission distances. The envelope fluctuation of the baseband signal at the receiving end is significantly improved using KF algorithm compared with the conventional algorithm, which improves the stability of the optical wireless communication link.

Calculation of dynamic assembly and tensioning loads at multiple points of prefabricated structure

Prefabricated underground station structure is rapidly assembled on site with modular components manufactured in factory. The longitudinal assembly accuracy of the components along the station’s longitudinal direction determines the construction quality of the station. In order to solve the problems encountered in the longitudinal pre-tensioning and assembly of structural components for a prefabricated station, such as low efficiency of manual operation, difficulty in determining the dynamic tensioning load, and insufficient accuracy of assembly control, a dynamic pre-tensioning load calculation method is proposed. The method, also referred to as centroid tracking pre-tensioning load algorithm, is based on controlling the dynamic relationship among the geometric centroid of rubber gasket, the centroid of multipoint tensioning load and the centroid of the elastic reaction force of rubber gasket. By using the dynamic correspondence between the compression reaction force distribution of the rubber gasket and the seam width under multipoint tensioning load, a loading control strategy is established to realize the balanced compression of the seam width of the prefabricated structure by controlling the load at each tensioning point. Thus, the centroid of the tensioning load continues to track the symmetry point of the centroid of the rubber gasket’s elastic reaction force with the geometric centroid of the rubber gasket as the reference. In this way, the tension value at each point can be precisely configured to improve the assembly accuracy of the components and realize seam width control. Based on the proposed algorithm, a set of tensioning and assembly equipment and control systems for prefabricated components of underground prefabricated structures are developed to carry out the automatic configuration of multipoint tensioning loads. The assembly tests show that the seam gap of assembled components is less than 1 mm. The proposed method demonstrates that the fast and high-precision automated assembly can be achieved and is conducive to improving the construction quality of prefabricated stations.

Exploration of the pathogenesis of Sjögren's syndrome via DNA methylation and transcriptome analyses.

Sjögren's syndrome (SS), a systemic autoimmune disorder, is characterized by dry mouth and eyes. However, SS pathogenesis is poorly understood. We performed bioinformatics analysis to investigate the potential targets and molecular pathogenesis of SS. Gene expression profiles (GSE157159) and methylation data (GSE110007) associated with SS patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially methylated positions (DMPs) and differentially expressed genes (DEGs) were identified by the R package limma. The potential biological functions of DEGs were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Key DMPs were selected by overlap and the shrunken centroid algorithm, and corresponding genes were identified as hub genes, with their diagnostic value assessed by receiver operating characteristic (ROC) curves. The potential molecular mechanisms of hub genes were analyzed by protein-protein interaction (PPI) networks and single-gene gene set enrichment analysis (GSEA). Peripheral blood mononuclear cells (PBMCs) were collected from control and SS patients atThe Affiliated Hospital of Southwest Medical Universityand Dazhou Central Hospital. The mRNA levels of hub genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR). We identified 788 DMPs and 2457 DEGs between the two groups. Functional enrichment analysis suggested that the DEGs were significantly enriched in T cell activation, leukocyte cell-cell adhesion, and cytokine-cytokine receptor interaction. TSS200, TSS1500, and 1stExon were identified as highly enriched areas of differentially methylated promoter CpG islands (DMCIs). In total, 61 differentially methylated genes (DMGs) were identified by the overlap of 2457 DEGs and 507 genes related to DMPs (DMPGs), of which 21 genes located near TSS200, TSS1500, and 1stExon were selected. Then, three key DMPs and the corresponding hub genes (RUNX3, HLA-DPA1, and CD6) were screened by the shrunken centroid algorithm and calculated to have areas under the ROC curve of 1.000, 0.931, and 0.986, respectively, indicating good diagnostic value. The GSEA results suggested that all three hub genes were highly associated with the immune response. Finally, positive mRNA expression of the three hub genes in clinical SS samples was verified by qRT-PCR, consistent with the GSE157159 data. The identification of three hub genes provides novel insight into molecular mechanisms and therapeutic targets for SS. Key Points • Hub genes were screened by DNA methylation and transcriptome analyses. • The relative expression of hub genes in peripheral blood samples was verified by qRT-PCR. • HLA-DPA1 was correlated with the pathogenic mechanism of SS.

Asymmetry robust centroid localization in confocal microscopy.

We present a centroid algorithm with asymmetry-robust error compensation for the peak position localization of asymmetrical axial response signals in confocal microscopy. Compared with the state-of-the-art algorithms, which are usually developed for symmetrical signals, our asymmetry robust centroid algorithm is found to have much smaller localization bias and higher precision for an asymmetrical confocal signal in numerical simulations and experiments.

A Fast Centroiding Algorithm for Digital Sun Sensors for Nanosatellite Applications

This letter proposes a fast centroiding algorithm for Euler angles calculation based on digital Sun sensors aiming at nanosatellite systems application. The two-step algorithm uses light-spot profiling to generate columns and rows vectors by processing the image as a pixel matrix. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> centroid coordinates are defined by searching for the vectors indexes with the largest values. The algorithm was compared to the conventional basic centroiding thresholding method (BCTM), the multiple-threshold averaging approach, and the double balance centroiding algorithm regarding the processing time and accuracy. The proposed algorithm reduces the processing time by around 50% with similar accuracy to the BCTM approach while overcoming the other algorithm results. Later, the algorithms were optimized by introducing an image segmentation procedure to improve the initial version, in which our proposal has proved a faster performance.

Resolution enhancement of angular plasmonic biochemical sensors via optimizing centroid algorithm

In this paper, a simple removable post-processing method was employed to improve the pixel-angle resolutions for different surface plasmon resonance (SPR) biochemical sensors. Compared with conventional centroid algorithm, dip span and height proportion of our enhanced algorithm were optimized simultaneously. To improve sensing stability when resonance angle shifting, the calculation region varies with the resonance angle. Besides, FFT algorithm and adjacent-averaging smoothing algorithm were also introduced into our proposed algorithm. It was found that FFT algorithm could reduce standard deviation of resonance angle for dynamic baseline algorithm. Adjacent-averaging smoothing algorithm with an average number of 10 points could also reduce standard deviation of resonance angle. The refractive index (RI) resolution could reach 1.55 ​× ​10−6 RIU, 5.52 ​× ​10−7 RIU and 8.47 ​× ​10−7 RIU for conventional Au-based SPR sensor, MSF and PAA-based plasmonic waveguide resonance (PWR) sensors, respectively. In biochemical sensing experiments, the limit of detection (LOD) of Zn2+ ion for MSF-based PWR chemical sensor could obtain 14.86 ​nM. The LOD of BSA protein could obtain 5.68 pM for PAA-based PWR biosensor. Further, Ag-based SPR biosensor modified with rabbit IgG was used to detect goat anti-rabbit IgG and a LOD of 9.93 pM was achieved.

Deterministic Centroid Localization for Improving Energy Efficiency in Wireless Sensor Networks

Abstract Wireless sensor networks are an enthralling field of study with numerous applications. A Wireless Sensor Network (WSN) is used to monitor real-time scenarios such as weather, temperature, humidity, and military surveillance. A WSN is composed of several sensor nodes that are responsible for sensing, aggregating, and transmitting data in the system, in which it has been deployed. These sensors are powered by small batteries because they are small. Managing power consumption and extending network life is a common challenge in WSNs. Data transmission is a critical process in a WSN that consumes the majority of the network’s resources. Since the cluster heads in the network are in charge of data transmission, they require more energy. We need to know where these CHs are deployed in order to calculate how much energy they use. The deployment of a WSN can be either static or random. Although most researchers focus on random deployment, this paper applies the proposed Deterministic Centroid algorithm for static deployment. Based on the coverage of the deployment area, this algorithm places the sensors in a predetermined location. The simulation results show how this algorithm generates balanced clusters, improves coverage, and saves energy.

Different thresholding methods on Nearest Shrunken Centroid algorithm

This article considers the impact of different thresholding methods to the Nearest Shrunken Centroid algorithm, which is popularly referred as the Prediction Analysis of Microarrays (PAM) for high-dimensional classification. PAM uses soft thresholding to achieve high computational efficiency and high classification accuracy but in the price of retaining too many features. When applied to microarray human cancers, PAM selected 2611 features on average from 10 multi-class datasets. Such a large number of features make it difficult to perform follow up study. One reason behind this problem is the soft thresholding, which is known to produce biased parameter estimate in regression analysis. In this article, we extend the PAM algorithm with two other thresholding methods, hard and order thresholding, and a deep search algorithm to achieve better thresholding parameter estimate. The modified algorithms are extensively tested and compared to the original one based on real data and Monte Carlo studies. In general, the modification not only gave better cancer status prediction accuracy, but also resulted in more parsimonious models with significantly smaller number of features.

A combined algorithm for tunnel personnel localization based on error areal division

In a tunnel personnel positioning system, the average positioning error is typically used to reflect the precision and accuracy of the positioning algorithm. However, in practical applications, the error areal distribution is generally non-uniform. The positioning errors of some areas are greater than the average positioning errors, which lead to a low positioning accuracy. In this study, based on the simulation of the positioning error areal distributions of the quadrilateral centroid localization and maximum likelihood estimation algorithms, the causes for large positioning errors in upper and lower edges and four corners were analyzed. In addition, a combined localization algorithm based on error areal division was proposed to improve the average positioning accuracy. Thus, the quadrilateral centroid localization algorithm was used for the middle section of the tunnel personnel location area, the maximum likelihood estimation algorithm was used for the upper and lower edges, and the centroid algorithm was used for the four corners. The simulation results show that the average positioning errors of the combined localization algorithm were reduced by 5.56% and 44.76% when compared with those of the quadrilateral centroid localization and maximum likelihood estimation algorithms, respectively, while the maximum positioning error was reduced by 46.45% and 42.81%, respectively.

High-precision displacement measurement algorithm based on a depth fusion of grating projection pattern.

Displacement measurement based on an image recognition algorithm has more advantages in the field of displacement measurement. Due to the uncertainty of image location, the accuracy of the displacement measurement algorithm studied in the previous stage is limited. To improve the accuracy of image-type displacement measurement, a high-precision displacement measurement algorithm based on depth fusion of the grating projection pattern is proposed. First, a line-scan image sensor and parallel light source are placed on both sides of the calibration grating. The marking pattern on the calibration grating is projected onto the image sensor through illumination of the parallel light source to realize the projection imaging. Then, the centroid algorithm is used to calculate the centroid of each line in the collected image, and the mean value of the centroid group is determined. Finally, a linear function is used to fit multiple centroids to calculate the endpoints of the subdivision region, and the subdivision operation is carried out according to a ratio algorithm. To test the performance of the proposed algorithm, a linear displacement measurement device with a 250 mm range and an angular displacement measurement device with a 100 mm diameter grating are both used for experiments. After testing, the linear measurement accuracy of the proposed algorithm is improved from 4.63 to 1.26 µm, and the angular displacement measurement accuracy is enhanced from 8.87 to 2.88''. The research in this paper provides a theoretical basis for high-performance displacement measurement.

Dizajn i razvoj sistema robotskih ruku za klasifikaciju i sortiranje primenom mašinskog vida

The main focus of this paper is to design and develop a system of two robot arms for classifying and sorting objects based on shape and size using machine vision. The system uses a low-cost and high-performance hierarchical control system including one master and two slaves. Each slave is a robot controller based on a microcontroller that receives commands from the master to control the robot arm independently. The master is an embedded computer used for image processing, kinematic calculations, and communication. A simple and efficient image processing algorithm is proposed that can be implemented in real-time, helping to shorten the time of the sorting process. The proposed method uses a series of algorithms including contour finding, border extraction, centroid algorithm, and shape threshold to recognize objects and eliminate noise. The 3D coordinates of objects are estimated just by solving a linear equation system. Movements of the robot's joints are planned to follow a trapezoidal profile with the acceleration/deceleration phase, thus helping the robots move smoothly and reduce vibration. Experimental evaluation reveals the effectiveness and accuracy of the robotic vision system in the sorting process. The system can be used in the industrial process to reduce the required time to achieve the task of the production line, leading to improve the performance of the production line.

Performance Comparison of Machine Learning Algorithms for Albanian News articles

This paper presented a methodology to classify news articles in Albanian languages with machine learning algorithms and NLP. In this proposed work Multinomial Naïve Bayes, Logistic Regression, k-nearest neighbors, Bernoulli, Centroid, SVM, SGD, Perceptron, Passive Aggressive, Decision Tree, and Random Forest machine learning Classification algorithms are trained to classify Albanian news articles and compared in term of accuracy, training time and testing time. We have used 70% of the dataset for training and 30% for tests. The execution time of training and testing algorithms is recorded and presented for different input sizes of data. For experimental we used different numbers of inputs, starting from 8000 articles, and continuing in increasing order by 8000 until the level of 80000 in 8 categories. Experimental results indicate that the Passive Aggressive algorithm shows the best performance in terms of accuracy for Albanian news articles. While the classifier with the lowest accuracy was Random Forest. Logistic Regression, SVM, and Decision Tree have a high delay in training data. SVM also has the longest testing time compared to other classifiers.

A Hybrid Algorithm for the Laser Stripe Centreline Extraction

Centreline extraction is one of the critical processes of structured light systems that play an increasingly important role in industry 4.0. This paper proposes a novel algorithm to extract the centreline by combining an improved skeleton thinning algorithm to extract the initial centreline and an improved grey centroid algorithm to establish the final centreline. It consists of several steps: Firstly, an image difference operation and a thresholding operation are undertaken, and the Canny edge detection algorithm is applied to locate the approximate position of the laser stripe to reduce the image matrix calculation cost; Secondly, an initial centreline is extracted via the use of an image morphology operation and a skeleton thinning algorithm; Finally, a high-power grey centroid method is employed to acquire the centreline at the sub-pixel level. Experiments together with a comparison with other mainstream methods is conducted to test the validity and practicability of the proposed hybrid algorithm. The experimental results show that the speed of the proposed algorithm is 50% faster than the Steger algorithm, and the root-mean-square error is 40% lower than the grey centroid method.

Low-Velocity Impact Localization of Cfrp Laminates Combined with Correlation Distance Method and Weighted Centroid Algorithm

Low-Velocity Impact Localization of Cfrp Laminates Combined with Correlation Distance Method and Weighted Centroid Algorithm

Improved Centroid Algorithm for Beam Stability Control System

In order to eliminate the beam jitter caused by the air disturbance and the vibrating devices, the error of light spotted jitter must be detected firstly. The beam jitter error can be corrected by a compensator to realize the beam stability control system. In this paper, an Improved Centroid Algorithm (ICA) combining with the Gaussian Fitting Algorithm (GFA), the Bilinear Interpolation and the Weighted Center of Mass (WCOM) was proposed. A Gaussian template is used to roughly locate the spot image in order to improve the efficiency and stability of the algorithm. The light spot image is interpolated by the Bilinear Interpolation to improve the accuracy of centroid identification. The identification accuracy and efficiency of the algorithm were verified by 100 sets of light spot experiments. The experimental results showed that the identification accuracy and efficiency of the Improved Centroid Algorithm were higher than those of the other algorithms. Our algorithm was of great significance for improving the performance of the beam stability control system.

Median based algorithm for sub-pixel estimation of extrema positions of diffuse light reflection signal

It has been proposed to utilize the median algorithm for determination of the extrema positions of diffuse light reflectance intensity distribution by a discrete signal of a photodiode linear array. The algorithm formula has been deduced on the base of piecewise-linear interpolation for signal representation by cumulative function. It has been shown that this formula is much simpler for implementation than known centroid algorithm and the noise immune Blais and Rioux detector algorithm. Also, the methodical systematic errors for zero noise as well as the random errors for full common mode additive noises and uncorrelated noises have been estimated and compared for mentioned algorithms. In these terms, the proposed median algorithm is proportionate to Blais and Rioux algorithm and considerably better then centroid algorithm.

The modelling and implementation of the virtual 3D animation scene based on the geometric centre-of-mass algorithm

Abstract In the process of modernisation, the essence of virtual 3D animation scene modelling is to enhance the rendering effect of animation, so designers need to understand and skilfully use virtual reality technology. During the scene modelling, the system perception module will obtain the perception information on the basis of understanding the behaviour state of the role, and thus provide the data basis for the emotion module and the behaviour module. Among them, the emotion module is mainly used to adjust the emotional changes of the role, and the behaviour module is mainly used to build the motion database. So this article after understanding the current situation on the basis of the virtual 3D animation scene modelling, use the geometric centroid algorithm for virtual 3D animation scene modelling technology to realise in-depth understanding, and the final results prove that the build system can not only clear intuitive present animation image, but can be in effective connection also, on the basis of multiple motion clips, to maintain the original movement in detail.

High-precision Shack-Hartmann wavefront sensing with a multi-focal diffraction Taiji-lenslet array

A Hartmann wavefront sensor is a type of wavefront detection instrument that has been widely used in various fields. Traditional Hartmann wavefront sensors usually comprise a monofocal refraction lenslet array to segment the wavefront at the entrance pupil. Each wavelet is focused at the focal plane along the projection of the lenslet, forming the foci array. Unlike the multifocal self-interference Taiji-lenslet array, a type of multifocal diffraction Taiji-lenslet array was proposed in this study to improve the measurement accuracy using the weighted centroid location algorithm of these multifocal spots, where the latter is more easily designed than the former. An optical experiment was implemented using the multifocal diffraction Taiji-lenslet array to verify its effectiveness. As a type of diffractive lens, a large-aperture Taiji-lenslet array can be easily fabricated via lithography, which has great potential for application in the measurement of large-scale laser beams and optical elements.