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Background: Detecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge., Results: Here, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs., Conclusions: Here, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP-SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer., Availability and Implementation: https://github.com/scutdy/SSO/blob/master/SEEI.zip ., (© 2024. The Author(s).)

With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals' physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detection algorithm that monitors the state of human activity. This work designs a human fall detection algorithm based on hierarchical decision making. First, this work proposes a dimensionality reduction approach based on feature importance analysis (FIA), which optimizes the feature space via feature importance. This procedure reduces the dimension of features greatly and reduces the time spent by the model in the training phase. Second, this work proposes a hierarchical decision-making algorithm with an XGBoost model. The algorithm is divided into three levels. The first level uses the threshold approach to make a preliminary assessment of the data and only transfers the fall type data to the next level. The second level is an XGBoost-based classification algorithm to analyze again the type of data which remained from the first level. The third level employs a comparison method to determine the direction of the falling. Finally, the fall detection algorithm proposed in this paper has an accuracy of 98.19%, a sensitivity of 97.50%, and a specificity of 98.63%. The classification accuracy of the fall direction reaches 93.44%, and the algorithm can efficiently determine the fall direction.

Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search. To test the performance of the proposed approach, the basic benchmark suites, CEC2013 and CEC2014, and a parameter optimization problem of SVM are used to compare 15 well-known algorithms. Experimental results demonstrate that the performance of the proposed algorithm is better than that of the other heuristic algorithms., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2021 Deyu Tang et al.)

Recently, in most existing studies, it is assumed that there are no interaction relationships between drugs and targets with unknown interactions. However, unknown interactions mean the relationships between drugs and targets have just not been confirmed. In this paper, samples for which the relationship between drugs and targets has not been determined are considered unlabeled. A weighted fusion method of multisource information is proposed to screen drug-target interactions. Firstly, some drug-target pairs which may have interactions are selected. Secondly, the selected drug-target pairs are added to the positive samples, which are regarded as known to have interaction relationships, and the original interaction relationship matrix is revised. Finally, the revised datasets are used to predict the interaction derived from the bipartite local model with neighbor-based interaction profile inferring (BLM-NII). Experiments demonstrate that the proposed method has greatly improved specificity, sensitivity, precision, and accuracy compared with the BLM-NII method. In addition, compared with several state-of-the-art methods, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) of the proposed method are excellent., Competing Interests: The authors declare no conflicts of interest regarding the publication of this paper., (Copyright © 2021 Shuaiqi Liu et al.)

Reconstruction of neuronal populations from ultra-scale optical microscopy (OM) images is essential to investigate neuronal circuits and brain mechanisms. The noises, low contrast, huge memory requirement, and high computational cost pose significant challenges in the neuronal population reconstruction. Recently, many studies have been conducted to extract neuron signals using deep neural networks (DNNs). However, training such DNNs usually relies on a huge amount of voxel-wise annotations in OM images, which are expensive in terms of both finance and labor. In this paper, we propose a novel framework for dense neuronal population reconstruction from ultra-scale images. To solve the problem of high cost in obtaining manual annotations for training DNNs, we propose a progressive learning scheme for neuronal population reconstruction (PLNPR) which does not require any manual annotations. Our PLNPR scheme consists of a traditional neuron tracing module and a deep segmentation network that mutually complement and progressively promote each other. To reconstruct dense neuronal populations from a terabyte-sized ultra-scale image, we introduce an automatic framework which adaptively traces neurons block by block and fuses fragmented neurites in overlapped regions continuously and smoothly. We build a dataset "VISoR-40" which consists of 40 large-scale OM image blocks from cortical regions of a mouse. Extensive experimental results on our VISoR-40 dataset and the public BigNeuron dataset demonstrate the effectiveness and superiority of our method on neuronal population reconstruction and single neuron reconstruction. Furthermore, we successfully apply our method to reconstruct dense neuronal populations from an ultra-scale mouse brain slice. The proposed adaptive block propagation and fusion strategies greatly improve the completeness of neurites in dense neuronal population reconstruction.

In order to eliminate the influence of motion artifacts, high-frequency noise and baseline drift on photoplethysmographic (PPG), and to obtain the accurate value of heart rate in motion state, this paper proposed a de-noising method of PPG signal based on normalized least mean square (NLMS) adaptive filtering combining ensemble empirical mode decomposition(EEMD). Firstly, the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts. Secondly, the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function (IMF) according to the frequency from high to low. The threshold range of the signal is judged by the permutation entropy (PE) criterion, thereby filtering out the high frequency noise and the baseline drift. The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram (ECG) signal is 0.731 and the average absolute error percentage is 6.10% under different motion states, which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.

Background: Protein complexes are the cornerstones of many biological processes and gather them to form various types of molecular machinery that perform a vast array of biological functions. In fact, a protein may belong to multiple protein complexes. Most existing protein complex detection algorithms cannot reflect overlapping protein complexes. To solve this problem, a novel overlapping protein complexes identification algorithm is proposed., Results: In this paper, a new clustering algorithm based on overlay network chain in quotient space, marked as ONCQS, was proposed to detect overlapping protein complexes in weighted PPI networks. In the quotient space, a multilevel overlay network is constructed by using the maximal complete subgraph to mine overlapping protein complexes. The GO annotation data is used to weight the PPI network. According to the compatibility relation, the overlay network chain in quotient space was calculated. The protein complexes are contained in the last level of the overlay network. The experiments were carried out on four PPI databases, and compared ONCQS with five other state-of-the-art methods in the identification of protein complexes., Conclusions: We have applied ONCQS to four PPI databases DIP, Gavin, Krogan and MIPS, the results show that it is superior to other five existing algorithms MCODE, MCL, CORE, ClusterONE and COACH in detecting overlapping protein complexes.

Background: Heart rate variability (HRV) can reflect the relationship between heart rhythm and sleep structure., Objective: In order to study the effect of support vector machine (SVM) on the results of automatic sleep staging and improve the effectiveness of heart rate variability (HRV) as a sleep structure biomarker, thereby realize long term and non-contact monitoring of sleep quality., Methods: Two kinds of parameter optimization methods are applied to stage sleep experiments when the known SVM can be used for automatic sleep staging. By factor analysis of the time domain, frequency domain, and nonlinear dynamic characteristics of subjects' HRV signals, the accuracy of the cross-validation method (K-CV) is used as the fitness function value in genetic algorithm (GA) and particle swarm optimization (PSO). Furthermore, GA and PSO are used to optimize the SVM parameters., Results: The results show that the accuracy rate of sleep stage is 64.44% when parameters are not optimized, the accuracy rate based on PSO is improved to 78.89% and the accuracy rate based on GA is improved to 84.44%., Conclusion: Both optimization algorithms can improve the accuracy of SVM for sleep staging and better results based on GA in the experiment.

An accurate method is proposed to design low-backscattering metasurfaces efficiently using an iterative Fourier transform algorithm, which avoids the large amount of time-consuming numerical simulations of complicated electromagnetic problems and provides satisfactory performance to reduce the backward scattering. As an example of the application, a broadband low-backscattering metasurface is designed, fabricated, and characterized. Both full-wave simulation and measured results reveal that the proposed method offers a rapid and efficient tool to manipulate the scattering behaviors of the metasurface, and thus realizes significant scattering reductions.

Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset.

To address the lack of 3D space information in the digital radiography of a patient femur, a pose estimation method based on 2D-3D rigid registration is proposed in this study. The method uses two digital radiography images to realize the preoperative 3D visualization of a fractured femur. Compared with the pure Digital Radiography or Computed Tomography imaging diagnostic methods, the proposed method has the advantages of low cost, high precision, and minimal harmful radiation. First, stable matching point pairs in the frontal and lateral images of the patient femur and the universal femur are obtained by using the Scale Invariant Feature Transform method. Then, the 3D pose estimation registration parameters of the femur are calculated by using the Iterative Closest Point (ICP) algorithm. Finally, based on the deviation between the six degrees freedom parameter calculated by the proposed method, preset posture parameters are calculated to evaluate registration accuracy. After registration, the rotation error is less than l.5°, and the translation error is less than 1.2 mm, which indicate that the proposed method has high precision and robustness. The proposed method provides 3D image information for effective preoperative orthopedic diagnosis and surgery planning.

Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively. A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper. The proposed ABC-IFC clustering model contains two parts: searching for the optimum cluster centers using ABC mechanism and forming clusters using intuitionistic fuzzy clustering (IFC) method. Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix. Then the cluster centers are updated through different functions of bees in ABC algorithm; then the clustering result is obtained through IFC method based on the new optimized cluster center. To illustrate its performance, the ABC-IFC method is compared with the traditional fuzzy C-means clustering and IFC method. The experimental results on MIPS dataset show that the proposed ABC-IFC method not only gets improved in terms of several commonly used evaluation criteria such as precision, recall, and P value, but also obtains a better clustering result.

This paper introduces an algorithm for detecting T wave alternans (TWA) using Poincare mapping method, which is a technique for nonlinear dynamic systems to display periodic behavior. Vector angle index (VAI) was used to determine the presence or absence of TWA. Adopting MIT/BIH Arrhythmia database and European ECG ST-T database to simulate, we used VAI via poincare mapping method for correlation analysis with Vtwa by way of the spectral method. The cross-correlation coefficient between Vtwa and VAI is gamma = 0.860 1. The algorithm can identify the absence and presence of TWA accurately and provide idea for further study of TWA.

Optical networks with flexible bandwidth provisioning have become a very promising networking architecture. It enables efficient resource utilization and supports heterogeneous bandwidth demands. In this paper, two novel spectrum defragmentation approaches, i.e. Maximum Path Connectivity (MPC) algorithm and Path Connectivity Triggering (PCT) algorithm, are proposed based on the notion of Path Connectivity, which is defined to represent the maximum variation of node switching ability along the path in flexible bandwidth networks. A cost-performance-ratio based profitability model is given to denote the prons and cons of spectrum defragmentation. We compare these two proposed algorithms with non-defragmentation algorithm in terms of blocking probability. Then we analyze the differences of defragmentation profitability between MPC and PCT algorithms.

Near infrared (NIR) spectroscopy combined with pattern recognition was attempted to discriminate the freshness of eggs. The algorithm of one-class support vector machine (OC-SVM) was employed to solve the classification problem due to imbalanced number of training samples. In this work, 86 samples of eggs (71 samples of fresh eggs and 15 samples of unfresh eggs) were surveyed by Fourier transform NIR spectroscopy. Firstly, original spectra of eggs in the wave-number range of 10 000-4 000 cm(-1) were acquired. And then, principal component analysis (PCA) was employed to extract useful information from original spectral data, and the number of PCs was optimized. Finally, OC-SVM was performed to calibrate discrimination model, and the optimal PCs were used as the input eigenvectors of model. In order to obtain a good performance, the regularization parameter v and parameter sigma of the kernel function in OC-SVM model were optimized in building model. The optimal OC-SVM model was obtained with nu = 0.5 and sigma2 = 20.3. Experimental result shows that OC-SVM got better performance than conventional two-class SVM model under the same condition. The OC-SVM model was achieved with identification rates of 80 for both fresh eggs and unfresh eggs in the independent prediction set. The identification rates of fresh eggs were 100% in two-class SVM model. However, when the two-class SVM model was used to discriminate the unfresh eggs of, the identification rates were 0% in the independent prediction set. Compared with conventional two-class SVM model, the OC-SVM model showed its superior performance in discrimination of minority unfresh eggs samples. This work shows that it is feasible to identify egg freshness using NIR spectroscopy, and OC-SVM is an excellent choice in solving the problem of imbalanced number of samples in training set.

Based on chest images' features, this paper presents a retrieval method with domination color of partition, gray level co-occurrence matrix and gradient image shape invariant moments, as well as the fusion of them and the technique of relevance feedback. In Windows Vista environment with Visual C + +, a chest image retrieval prototype is implemented and the experiments have proved that this method has a better retrieval effect.

Because the factors of interference in R wave of normal QRS are few except for respiration, the algorithm uses supra ventricular beats in the ECG to extract respiratory signal (EDR) (ECG-derived respiratory signal). The method used here firstly transfers the original single lead ECG signal into the wavelet, then prolongs R amplitudes of supra ventricular beats in the proper scale approximation of wavelet, then the prolonged signal passes a low pass filter, then reduces sample rate to 5 Hz, finally through a band pass filter (0.1-0.4 Hz), the output of the band pass filter is the EDR.

An algorithm of detecting the TWA (T-wave alternans) based on the maximum of T waves transformed by FFT (Fast Fourier Transform) is described in the paper. By using the method we can reduce the sampling rate, which means "smaller amount of calculation", and solve the problem "the sampling point of T wave changes with the rate of heart beat". The 128 maximum values of T wave in series normal ST-T complex are used to carry out FFT transforms. The TWA is detected by using the frequency of power spectrum.

Objective: To develop a wavelet noise canceller that cancels muscle electricity and power line hum in wide range of frequency., Method: According to the feature that the QRS complex has higher frequency components, and the T, P wave have lower frequency components, the biorthogonal wavelet was selected to decompose the original signals. An interference-eliminated signal ECG was formed by reconstruction from the changed coefficients of wavelet., Result: By using the canceller, muscle electricity and power line interference between 49 Hz and 61 Hz were eliminated from the ECG signals., Conclusion: This canceller works well in canceling muscle electricity, and basic and harmonic frequencies of power line hum. The canceller is also insensitive to the frequency change of power line, the same procedure is good for both 50 and 60 Hz power line hum.

Under the foggy environment, lane line images are obscured by haze, which leads to lower detection accuracy, higher false detection of lane lines. To address the above problems, a multi-layer feature fusion dehazing network based on CycleGAN architecture is proposed. Firstly, the foggy image is enhanced to remove the fog in the image, and then the lane line detection network is used for detection. For the dehazing network, a multi-layer feature fusion module is used in the generator to fuse the features of different coding layers of U-Net to enhance the network's recovery of information such as details and edges, and a frequency domain channel attention mechanism is added at the key nodes of the network to enhance the network's attention to different fog concentrations. At the same time, to improve the discriminant effect of the discriminator, the discriminator is extended to a global and local discriminator. The experimental results show that the dehaze effect on Reside and other test data sets is better than the comparison method. The peak signal-to-noise ratio is improved by 2.26 dB compared to the highest GCA-Net algorithm. According to the lane detection of fog images, it is found that the proposed network improves the accuracy of lane detection on foggy days. [ABSTRACT FROM AUTHOR]

Arc faults are the main cause of electrical fires according to national fire data statistics. Intensive studies of artificial intelligence-based arc fault detection methods have been carried out and achieved a high detection accuracy. However, the computational complexity of the artificial intelligence-based methods hinders their application for arc fault detection devices. This paper proposes a lightweight arc fault detection method based on the discrimination of a novel feature for lower current distortion conditions and the Adam-optimized BP neural network for higher distortion conditions. The novel feature is the pulse signal number per unit cycle, reflecting the zero-off phenomena of the arc current. Six features, containing the novel feature, are chosen as the inputs of the neural network, reducing the computational complexity. The model achieves a high detection accuracy of 99.27% under various load types recommended by the IEC 62606 standard. Finally, the proposed lightweight method is implemented on hardware based on the STM32 series microcontroller unit. The experimental results show that the average detection accuracy is 98.33%, while the average detection time is 45 ms and the average tripping time is 72–201 ms under six types of loads, which can fulfill the requirements of real-time detection for commercial arc fault detection devices. [ABSTRACT FROM AUTHOR]

Objective: To compare the diagnostic power among various machine learning algorithms utilizing multi-sequence magnetic resonance imaging (MRI) radiomics in detecting anterior cruciate ligament (ACL) tears. Additionally, this research aimed to create and validate the optimal diagnostic model. Methods: In this retrospective analysis, 526 patients were included, comprising 178 individuals with ACL tears and 348 with a normal ACL. Radiomics features were derived from multi-sequence MRI scans, encompassing T1-weighted imaging and proton density (PD)-weighted imaging. The process of selecting the most reliable radiomics features involved using interclass correlation coefficient (ICC) testing, t tests, and the least absolute shrinkage and selection operator (LASSO) technique. After the feature selection process, five machine learning classifiers were created. These classifiers comprised logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). A thorough performance evaluation was carried out, utilizing diverse metrics like the area under the receiver operating characteristic curve (ROC), specificity, accuracy, sensitivity positive predictive value, and negative predictive value. The classifier exhibiting the best performance was chosen. Subsequently, three models were developed: the PD model, the T1 model, and the combined model, all based on the optimal classifier. The diagnostic performance of these models was assessed by employing AUC values, calibration curves, and decision curve analysis. Results: Out of 2032 features, 48 features were selected. The SVM-based multi-sequence radiomics outperformed all others, achieving AUC values of 0.973 and 0.927, sensitivities of 0.933 and 0.857, and specificities of 0.930 and 0.829, in the training and validation cohorts, respectively. Conclusion: The multi-sequence MRI radiomics model, which is based on machine learning, exhibits exceptional performance in diagnosing ACL tears. It provides valuable insights crucial for the diagnosis and treatment of knee joint injuries, serving as an accurate and objective supplementary diagnostic tool for clinical practitioners. [ABSTRACT FROM AUTHOR]

In this work, a fast second-order finite volume element (FVE) algorithm is proposed to solve the nonlinear time-fractional coupled diffusion model based on the time two-mesh (TT-M) computing method. In this algorithm, the integer and Riemann-Liouville fractional derivatives are approximated by the second-order backward difference formula and the WSGD formula respectively, the time interval is divided into coarse and fine meshes, then the three steps TT-M FVE algorithm is constructed by using the interpolation operator. The existence and uniqueness for the TT-M FVE algorithm are analyzed in detail, the asymptotically optimal a priori error estimates for variables u and v in the discrete L ∞ (L 2 (Ω)) and L2(H1(Ω) norms are obtained. It is shown that when time coarse and fine mesh sizes satisfy τ c = O (τ f 1 / 2) , the fast algorithm can achieve the same accuracy as the FVE algorithm, and reduce more computational cost. Finally, some numerical results are given to demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET) is a popular research topic. People are becoming more interested in PET images as they become more widely available. However, the partial volume effect (PVE) in PET images remains one of the most influential factors causing the resolution of PET images to degrade. It is possible to reduce this PVE and achieve better image quality by measuring and modeling the point spread function (PSF) and then accounting for it inside the reconstruction algorithm. In this work, we examined the response characteristics of the MetisTM PET/CT system by acquiring 22Na point source at different locations in the field of view (FOV) of the scanner and reconstructing with small pixel size for images to obtain their radial, tangential, and axial full-width half maximum (FWHM). An image-based model of the PSF model was then obtained by fitting asymmetric two-dimensional Gaussians on the 22Na images. This PSF model determined by FWHM in three directions was integrated into a three-dimensional ordered subsets expectation maximization (3D-OSEM) algorithm based on a list-mode format to form a new PSF-OSEM algorithm. We used both algorithms to reconstruct point source, Derenzo phantom, and mouse PET images and performed qualitative and quantitative analyses. In the point source study, the PSF-OSEM algorithm reduced the FWHM of the point source PET image in three directions to about 0.67 mm, and in the phantom study, the PET image reconstructed by the PSF-OSEM algorithm had better visual effects. At the same time, the quantitative analysis results of the Derenzo phantom were better than the original 3D-OSEM algorithm. In the mouse experiment, the results of qualitative and quantitative analyses showed that the imaging quality of PSF-OSEM algorithm was better than that of 3D-OSEM algorithm. Our results show that adding the PSF model to the 3D-OSEM algorithm in the MetisTM PET/CT system helps to improve the resolution of the image and satisfy the qualitative and quantitative analysis criteria. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms are often leveraged to solve complicated engineering optimization and scientific problems. Salp swarm algorithm is one of the most useful meta-heuristic algorithms in recent years. To alleviate the slow convergence speed of the salp swarm algorithm, as well as the tendency to fall into local minima, we have proposed an efficient salp swarm algorithm called E-SSA, which combines the effective evolutionary strategies of basic salp swarm algorithm and two efficient mechanisms named self-adaption weight and scale-free network. These two mechanisms have been integrated into the follower evolution process of the algorithm to achieve the balance of exploration and exploitation. The performance of the E-SSA is benchmarked against a suit of CEC'2019 series functions and 23 commonly used international benchmarks. The algorithm is further validated via three engineering application problems. The experimental results indicate that the improved algorithm has clear advantages in optimization performance compared with other existing heuristic algorithms. [ABSTRACT FROM AUTHOR]

With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals' physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detection algorithm that monitors the state of human activity. This work designs a human fall detection algorithm based on hierarchical decision making. First, this work proposes a dimensionality reduction approach based on feature importance analysis (FIA), which optimizes the feature space via feature importance. This procedure reduces the dimension of features greatly and reduces the time spent by the model in the training phase. Second, this work proposes a hierarchical decision-making algorithm with an XGBoost model. The algorithm is divided into three levels. The first level uses the threshold approach to make a preliminary assessment of the data and only transfers the fall type data to the next level. The second level is an XGBoost-based classification algorithm to analyze again the type of data which remained from the first level. The third level employs a comparison method to determine the direction of the falling. Finally, the fall detection algorithm proposed in this paper has an accuracy of 98.19%, a sensitivity of 97.50%, and a specificity of 98.63%. The classification accuracy of the fall direction reaches 93.44%, and the algorithm can efficiently determine the fall direction. [ABSTRACT FROM AUTHOR]

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. • CA-based framework enhances efficiency, accuracy for incremental feature selection. • Each component of Tri-accelerator contributes to improving efficiency. • CA-based significance measure well improves accuracy, especially for large datasets. • The CD-accelerator filters out insignificant samples with a theoretical lower bound. [ABSTRACT FROM AUTHOR]

With the continuous development of multimedia social networks, online public opinion information is becoming more and more popular. The rule extraction matrix algorithm can effectively improve the probability of information data to be tested. The network information data abnormality detection is realized through the probability calculation, and the prior probability is calculated, to realize the detection of abnormally high network data. Practical results show that the rule-extracting matrix algorithm can effectively control the false positive rate of sample data, the detection accuracy is improved, and it has efficient detection performance. [ABSTRACT FROM AUTHOR]

Geological sampling data often need to be regionally segmented to mark blocks with different geological properties in mathematical geosciences. This paper uses the directed graph model of graph theory to model sampling data and calculate the segmentation of the directed graph by the improved maximum flow minimum cut (max-flow min-cut) algorithm. The proposed energy expressions reflect the continuity of geological properties and the smoothness of segmentation boundaries. A major advantage of this method is that it allows geological experts to share prior knowledge regarding the partitioning of a mineralization area as the seeds of the directed graph. Incorporating this knowledge in the segmentation result via a very intuitive and simple outline operation greatly improves the interactivity and editability of the algorithm. For the integrity of the process framework, this paper also proposes incorporating the characteristic scale of the Hilbert–Huang transformation (HHT) framework as the characteristic parameter to calculate the initial seed, which can achieve automatic regional segmentation. [ABSTRACT FROM AUTHOR]

In recent years, image super-resolution (SR) based on deep learning technology has made significant progress. However, most methods are difficult to apply in real life because of their large parameters and heavy computation. Recently, residual learning has been widely applied to the problem of super-resolution. It can make the shallow features extracted from the input image act on each middle layer through long and short connection. Therefore, residual learning can be focused on processing high-frequency feature information, which significantly improves the SR performance of the network. However, with the improvement of network depth, the features that can be effectively utilized are still the shallow ones extracted from the input image. In this paper, we propose the feature separation and fusion network(FSFN). We further enrich the high-frequency feature information by separating and fusing the extracted and unextracted features in the internal shallow layer of each feature separation and fusion module. As the depth of the network increases, the shallow features extracted from the input image can be updated in a direction closer to those extracted from the real high-resolution image. A large number of experimental results show that this method has a strong performance compared with the existing SR algorithm with similar parameters and computation. [ABSTRACT FROM AUTHOR]

The identification of influential nodes in complex networks is an open issue. To address it, many centrality measures have been proposed, among which the most representative iteration algorithm is the PageRank algorithm. However, it ignores the correlation between nodes and assumes that the jumping probability from a node to its adjacent nodes is the same. To make up it, we proposed a method to improve the PageRank based on the structural similarity of nodes calculated by Kullback–Leibler divergence. The Susceptible-infected (SI) model was used in six real networks, and the results of comparison experiments demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Network transmission over millimeter-wave (mmW) bands has a big potential to provide orders of higher bandwidth. However, beamforming is generally needed to compensate for the high path loss. As mmW antennas have a potentially large number of candidate beamforming directions, to achieve high network throughput, the finding of a high gain direction between a base station and each mobile in the mmW network may involve a large overhead if training signals are directly sent along all possible directions or according to a large volume of codebook. Taking advantage of the block sparse characteristics of the mmW channel and coexistence of legacy antennas, we propose a comprehensive design for more efficient beam direction finding. Different from existing compressive-sensing-based schemes which just take a random subset of directions to measure, taking advantage of the path clustering feature of the mmW channel, we develop a self-adaptive block sparse algorithm which can benefit from preliminary channel estimation during each iteration of the problem solving to significantly improve the overall channel estimation accuracy thus the beam alignment gain. We also explore two methods to exploit co-located legacy antennas to provide further guidance for transmission direction finding. Simulation results indicate that our proposed beam alignment scheme outperforms the baseline and peer schemes in terms of the beamforming gain and training cost. By taking advantage of the block sparse properties of mmW channel, our proposed design is able to achieve the transmission throughput comparable with the exhaustive direction search at much lower overhead. [ABSTRACT FROM AUTHOR]

Early weak fault feature extraction is difficult due to the interference by wideband random noise and other vibration sources in the same frequency band. In this paper, a weak fault feature extraction method of rotating machinery based on the double-window spectrum fusion enhancement (DWSFE) is proposed to address this issue. This method is established through combining fast Kurtogram algorithm and spectrum fusion enhancement. In this scheme, the fast Kurtogram algorithm is utilized to suppress the interference of weak fault feature extraction by noise signals outside the optimal resonant band and to achieve the signal envelope demodulation in the passband of the optimal resonant region. Subsequently, discrete short-time Fourier transforms (DSTFTs) with long- and short-time windows are constructed to obtain the time–frequency spectra with different time–frequency resolutions, respectively. By integrating the advantages of the time–frequency resolutions of these spectra, the spectrum correlation analysis denoising and the spectrum amplitude association enhancement are proposed to suppress the random interference in the passband and to highlight the signal characteristic frequency. Furthermore, the weak fault features are separated from other interference of vibration sources in the same frequency band, and finally, the weak fault feature extraction of rotating machinery can be achieved. The simulation and application results show that this method can enhance weak fault features while denoising and achieve the weak fault feature extraction of rotating machinery under strong noise. [ABSTRACT FROM AUTHOR]

• Simplicity of principle improves computational efficiency. • Methods to effectively assess reliability. • Incorporate engineering realities to ensure accuracy. With the rise of large-scale energy DC transmission system, more and more projects use UHVDC transmission system for long-distance and large-capacity power transmission, in which the converter station is the core of the whole UHV DC transmission system. In this paper, a reliability assessment method based on improved Dijkstra's algorithm is proposed for the main wiring system of the converter station of the UHVDC transmission system and for the components in the main wiring. In order to fully consider the different states of components in actual operation, a four-state model is adopted in the study. The model improves the accuracy of the model by comprehensively considering the component's state characteristics, switching sequence, operation mode and other factors. Based on the state model of the components, the main wiring system is calculated in accordance with the theory of complex distribution networks in order to reduce the computational complexity. Finally, the main wiring system of the double 12-pulse converter station is traversed through the minimum paths based on the improved Dijkstra search method, and the reliability indexes of the obtained minimum paths are calculated according to the theory of the minimum path cut set. A typical bipolar dual 12-pulse series connection is used to verify the validity of the method, and some guidance is given for practical engineering. [ABSTRACT FROM AUTHOR]

Background: Stroke is the most prevalent neurological disease and often leads to disability. Stroke can affect a person's daily life, for example, its typical feature is the decline in the patient's upper limbs. In order to reduce the sports injury of stroke patients, the best method is to carry out certain rehabilitation training.Objective: In this paper, inverse kinematic analysis and trajectory planning of a modular upper limb rehabilitation exoskeleton are proposed.Methods: The reverse coordinate system method is applied to solve inverse kinematics of the exoskeleton with a non-spherical joint in the wrist. For verifying the effectiveness of the algorithms, the smooth round-trip trajectory movement in joint place is designed and simulated.Results: The reverse coordinate system method can simplify the calculation process compared with the normal coordinate system. Smooth round-trip trajectory planning is simulated to generate a smooth trajectory curve.Conclusions: The developed inverse kinematics algorithm and trajectory planning method are effective. [ABSTRACT FROM AUTHOR]

As speckle seriously restricts the applications of remote sensing images in many fields, the ability to efficiently and effectively suppress speckle in a coherent imaging system is indispensable. In order to overcome the over-smoothing problem caused by the speckle suppression algorithm based on classical sparse representation, we propose a non-local speckle suppression algorithm that combines the non-local prior knowledge of the image into the sparse representation. The proposed algorithm first applies shearlet to sparsely represent the input image. We then incorporate the non-local priors as constraints into the image sparse representation de-noising problem. The denoised image is obtained by utilizing an alternating minimization algorithm to solve the corresponding constrained de-noising problem. The experimental results show that the proposed algorithm can not only significantly remove speckle noise, but also improve the visual effect and retain the texture information of the image better. [ABSTRACT FROM AUTHOR]

This study proposes a new liver segmentation method based on a sparse a priori statistical shape model (SP-SSM). First, mark points are selected in the liver a priori model and the original image. Then, the a priori shape and its mark points are used to obtain a dictionary for the liver boundary information. Second, the sparse coefficient is calculated based on the correspondence between mark points in the original image and those in the a priori model, and then the sparse statistical model is established by combining the sparse coefficients and the dictionary. Finally, the intensity energy and boundary energy models are built based on the intensity information and the specific boundary information of the original image. Then, the sparse matching constraint model is established based on the sparse coding theory. These models jointly drive the iterative deformation of the sparse statistical model to approximate and accurately extract the liver boundaries. This method can solve the problems of deformation model initialization and a priori method accuracy using the sparse dictionary. The SP-SSM can achieve a mean overlap error of 4.8% and a mean volume difference of 1.8%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 0.8 mm and 1.4 mm, respectively. [ABSTRACT FROM AUTHOR]

Superconducting quantum circuits are promising candidates for realizing quantum computation due to their intrinsic scalability. Here, we report the implementation of the refined Deutsch-Jozsa algorithm in a 3D superconducting transmon qutrit system. Such 3D superconducting systems usually preserve a relatively long coherence time. A qutrit can take advantage of the third level of a superconducting artificial atom and extends the Hilbert space. Compared with the two-qubit system used for realizing Deutsch-Jozsa algorithm, the employment of qutrit simplifies the hardware design. This simplification and the intrinsic long coherence time jointly lead to a higher fidelity than in previous transmon implementation [DiCarlo et al., Nature 460 (7252), 240-244 (2009)]. [ABSTRACT FROM AUTHOR]

This paper presents a synthetic algorithm for tracking a moving object in a multiple-dynamic obstacles environment based on kinematically planar manipulators. By observing the motions of the object and obstacles, Spline filter associated with polynomial fitting is utilized to predict their moving paths for a period of time in the future. Several feasible paths for the manipulator in Cartesian space can be planned according to the predicted moving paths and the defined feasibility criterion. The shortest one among these feasible paths is selected as the optimized path. Then the real-time path along the optimized path is planned for the manipulator to track the moving object in real-time. To improve the convergence rate of tracking, a virtual controller based on PD controller is designed to adaptively adjust the real-time path. In the process of tracking, the null space of inverse kinematic and the local rotation coordinate method (LRCM) are utilized for the arms and the end-effector to avoid obstacles, respectively. Finally, the moving object in a multiple-dynamic obstacles environment is thus tracked via real-time updating the joint angles of manipulator according to the iterative method. Simulation results show that the proposed algorithm is feasible to track a moving object in a multiple-dynamic obstacles environment. [ABSTRACT FROM AUTHOR]

With the development of location-based services, more moving objects can be traced and a great deal of trajectory data can be collected. Periodicity is very commonly used to analyse the habits of moving objects, so finding objects’ periodic patterns can aid in understanding their behaviour. However, objects’ periodic patterns are always unknown previously, and describing their periods with different granularities will create some surprised findings. This article proposes a multi-granularity periodic activity discovery (MPAD) approach for moving objects. First, a multi-granularity model is introduced to describe the spatial and temporal information of an object’s activities. Then, two algorithms, namely, spatial first and temporal first multi-granularity activity discovery algorithms, are provided to transfer objects’ activities into different granularities. Finally, a novel periodic discovery algorithm is described to find the periodicities of objects’ activities. Experiments on both synthetic and real datasets demonstrate both the efficiency and effectiveness of the proposed work and its notably improved running performance compared to the same algorithms. Additionally, the discovered periodic patterns are more practically significant. [ABSTRACT FROM PUBLISHER]

Clustering is an efficient way to group data into different classes on basis of the internal and previously unknown schemes inherent of the data. With the development of the location based positioning devices, more and more moving objects are traced and their trajectories are recorded. Therefore, moving object trajectory clustering undoubtedly becomes the focus of the study in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object clustering and analyze typical moving object clustering algorithms presented in recent years. In this paper, we firstly summarize the strategies and implement processes of classical moving object clustering algorithms. Secondly, the measures which can determine the similarity/dissimilarity between two trajectories are discussed. Thirdly, the validation criteria are analyzed for evaluating the performance and efficiency of clustering algorithms. Finally, some application scenarios are point out for the potential application in future. It is hope that this research will serve as the steppingstone for those interested in advancing moving object mining. [ABSTRACT FROM AUTHOR]

To fulfill the applications on robot vision, the commonly used stereo matching method for depth estimation is supposed to be efficient in terms of running speed and disparity accuracy. Based on this requirement, Delaunay-based stereo matching method is proposed to achieve the aforementioned standards in this paper. First, a Canny edge operator is used to detect the edge points of an image as supporting points. Those points are then processed using a Delaunay triangulation algorithm to divide the whole image into a series of linked triangular facets. A proposed module composed of these facets performs a rude estimation of image disparity. According to the triangular property of shared vertices, the estimated disparity is then refined to generate the disparity map. The method is tested on Middlebury stereo pairs. The running time of the proposed method is about 1 s and the matching accuracy is 93%. Experimental results show that the proposed method improves both running speed and disparity accuracy, which forms a steady foundation and good application prospect for a robot’s path planning system with stereo camera devices. [ABSTRACT FROM AUTHOR]