Coherent backscattering (CBS) arises from complex interactions of a coherent beam with randomly positioned particles, which has been typically studied in media with large numbers of scatterers and high opacity. We develop a first-principles scattering model for scalar waves to study the CBS cone formation in finite-sized and sparse random media with specific geometries. The current study provides insights into the effects of density, volume size, and other relevant parameters on the angular characteristics of the CBS cone emerging from sparse and bounded random media for various types of illumination, with results consistent with well-known CBS studies which are typically based on samples with much larger number of scatterers and higher opacity. The enhancements are observed in scattering medium with dimensions between 10× and 40× wavelength and the number of particles as few as 370. This work also highlights some of the potentials and limitations of employing the CBS phenomenon to characterize disordered configurations. The method developed here provides a foundation for studies of complex electromagnetic fields beyond simple incident classical beams in randomized geometries, including structured wavefronts in illumination and quantized fields for investigating the effects of the quantum nature of light in multiple scattering, with no further numerical complications. The constructive interference between reciprocal scattering paths3, leads to an enhancement in the average intensity of the scattered light in the backscattering direction. While scattering from any specific realization of disorder leads to speckle, CBS may be observed by ensemble averaging over different realizations, ideally exhibiting a two-fold enhancement of the intensity in the exact backscattering direction compared to the diffuse background at large angles. Enhanced backscattering was first considered in studies of the electromagnetic reflection from a turbulent medium, looking at the large-scale interference effects4. The CBS effect has since been observed for multiple scattering of electromagnetic waves in several experiments including studies on concentrated aqueous suspension of dielectric spheres7, dielectric powders9, multimode optical fibers10, photonic crystals11, cold atoms13, and biological media15. In addition, the formation and behavior of this coherent effect have been studied in the presence of nonlinearity17, time variation18, and magnetic biases20. In principle, reciprocity must be maintained for CBS to appear, and any perturbation inducing nonreciprocal behavior may alter or eliminate such localization effects21. For instance, nonlinearity introduces additional phase shifts proportional to the wave intensity, which can transform CBS peak into a dip . Additionally, CBS peaks have been studied and observed for various types of waves besides electromagnetic waves26.The phenomenon of coherent backscattering (CBS) is an expression of the weak localization of waves in disordered media and stems from coherent effects among time-reversed scattering paths, which introduce identical phases in multiple scattering3. Inclusion of all ladder terms and cyclical terms becomes necessary if the optical thickness of the random medium becomes appreciable and for samples with high surface reflectivity27. Classical transport theory has also been used to estimate the Green’s function describing light transport in a random medium, following the diffusion approximation1. This technique has allowed for the estimation of the shape and width of the backscattering cone1. Furthermore, numerical simulations such as Monte Carlo and related approaches30, and T-Matrix methods32 have also been used to model the multiple scattering processes in disordered media.A wide variety of theoretical approaches have been taken to model light scattering in random media, employing microscopic or macroscopic descriptions of the scattering phenomena. Multiple scattering theory for discrete scatterers has been employed using Feynman diagrams. The backscattering enhancement, however, cannot be captured by using ladder terms in such analyses and it is necessary to use cyclical terms, as demonstrated in Ref.35, theoretical37 and experimental38 investigations. Many non-biological samples such as nematic liquid crystals, porous fibers, and porous semiconductors as well as biological samples such as muscle, bone, and skin tissues may exhibit such structural anisotropy, causing them to show non-symmetric near- and far-field scattering patterns or anisotropic transport of light. Transport of light in anisotropic samples is found to be adequately captured via an anisotropic diffusion model36 for cases where the propagation length is several times larger than the transport mean free path. Consequently, characterizing such diffuse transport can determine the diffusion constant, the mean free path tensor, and the extrapolation length38. In addition, the multiple scattering interference phenomenon in such systems can produce angularly anisotropic behaviors in weak localization of light, i.e., anisotropic enhanced backscattering40, which can be effectively captured considering enhanced backscattering models with generalized anisotropic mean free paths39. In this regard the CBS phenomenon has been used as a static and accessible technique to characterize the optical anisotropy of biological samples such as beetle scales41.Besides isotropic samples, the nature of light scattering and transport in disordered samples with structural anisotropy have been the subject of multiple numerical42. In addition to the emergence of CBS, we analyze the ensemble-averaged specular reflection in the scattering from random samples, a feature typically overlooked in previous numerical studies. We then develop a method to eliminate this coherent specular contribution, and hence enable a clean observation of the pure CBS peak under normally incident illumination.In this paper, we use a microscopic numerical scalar wave analysis to examine the formation of CBS peaks in scattering from finite-sized, wavelength-scale random media. The inherent simplicity of the calculation method allows us to systematically study how different parameters of the scattering object can affect the line shape of the backscattered enhancement, and specifically to understand the limits on the formation of the backscattering cone for small numbers of scatterers and sparse samples. Additionally, this approach allows a straightforward way to calculate the effects of density factor, sample size, absolute number of particles, and iteration factor. Here, we first consider scattering from small scatterers positioned within a rectangular space with different edge sizes ranging from 10 to 40 free-space wavelengths, with excitation in the form of a monochromatic plane wave. This approach allows us to easily extend the computation to the scattering of arbitrary input states of light, such finite-size excitation beams or other tailored excitations45 , it allows us to study more populated scattering volumes. Here, we analyze volumes consisting of up to 20,000 particles, much higher than previous studies which computed exact solutions of Maxwell’s equations46.While a scalar analysis does not capture polarization effects47. This approximation is accurate for cases of centimeter-sized samples under external optical illumination, as the mean free path length of the wave inside such disordered media is much smaller than the physical size of the scattering sample. However, such models do not provide insights into the behavior and physics of wave scattering in the case of smaller random configurations, i.e., those with dimensions in the order of multiple wavelengths or comparable to the mean free path length, as we study in this work.To model the CBS formation, a random medium is typically approximated by a half space in order to simplify the calculationFigure mentclass2pt{minim48 and To calculate the scattered field, we solve for the total scalar field amplitude mentclass2pt{minim48. For a collection of discrete point-like scatterers , 49. The free space Green’s function 50. In the final step, the scattering amplitude of the outgoing spherical wave, in the far field domain, is extracted and studied . We model 51). The computation time and complexity of such analysis, however, drastically increases with increasing the number of the scatterers and the size of the simulation domain. On the other hand, the scalar approximation47 enables us to capture many important underlying physical properties of the system, while avoiding unrealistically long simulation times.The susceptibility parameter 2.Several physical factors must be considered in the choice of the material and the size of the scattering bodies. In particular, if the scattering elements are very far off resonance , one might expect that millions of particles are required to create a meaningful scattering response, associated with large mean free path lengthsAlong the same line of physical reasoning, we expect to observe meaningful coherent scattering responses and weak localization effects from a smaller number of scatterers when the scattering amplitude is higher. Using Mie theory, the extinction efficiency  for the dielectric spheres (scattering centers) modeled in this work is found to be 0.22, the amplitude of the electric dipole scattering coefficient is 0.19, and the amplitudes for all other higher order scattering coefficients are smaller than 0.03. We note that the response of each single particle is still well within the off-resonant dipolar approximation. For the lossless configurations examined here the extinction cross section is equal to the scattering cross section and represents the amount of power that the object extracts from the input wave normalized to the incident power intensity.In the following, we study the formation of the CBS cone from various finite-sized media based on the above formulation. In addition to the conventional parameters applicable in studying semi-infinite random media, such as density factor and angle of incidence, here we also study parameters specifically relevant to finite-sized systems including the absolute number of scatterers and the size of the domain. The ensemble-averaged specular reflection in multiple scattering and a technique to eliminate it is also discussed and the modified results are presented.We analyze several examples of random geometries , usually tens to hundreds of measurements are sufficient for the speckle to average to a diffuse background allowing the CBS cone to emerge55. This is consistent with our observations here  number of simulations is sufficient to reveal the coherent scattering response in these geometries. The CBS cone is still observable for averaging over 200 samples, however as Performing a statistical analysis over multiple implementations of the random medium provides insight into the coherent wave effects inside the random structure; at the same time, it is also insightful to investigate the scattering response for individual realizations. Here, to emphasize the effect of ensemble averaging, the angular distribution of scattering intensity for two sample realizations . When the medium is of finite size or finite thickness, the triangular line shape becomes rounded due to the omission of the higher-order multiple scatterings2.Here, 57 for the appearance of Anderson localization. We note that the shape of the backscattering cone for this finite, wavelength-scale geometry using the scalar wave analysis can still be closely captured by those analytical predictions .Figure To better understand the effects of sample size, number of particles, and density factor on the shape of the cone, in the following we investigate two specific cases where we fix the density factor while changing the box size Fig. , and numFigure 56, indicating longer path lengths for more sparse samples as long as the sample size is much larger than 54. In the case of constant density factor, increasing the sample volume  also significantly increases the enhancement ratio . As an example, illumination with structured profiles, such as those generated using spatial light modulators, can be implemented with no additional numerical complications. We also provide a quantitative study of the behavior of CBS cone emergence, information that is valuable in experimental studies. The number of realizations sufficient to converge the enhancement and the behavior of convergence is not a quantity accessible through techniques relying solely on averaged quantities. Additionally, the fact that the CBS enhancement is present for very small samples and for very low densities suggest the possibility of characterizing such samples using CBS measurements. This technique is already widely in use for characterizing optically thick samples mostly with large opacity, and our results indicate the possibility of extending this technique to study other structures such as thin films . The level of disorder or porosity of such films can degrade the performance of the device or on occasion be utilized to gain a benefit, and thus must be carefully characterized. Studies of such samples with finite illumination and collection apertures is effectively equivalent to studying sparse and small structures . Therefore, our studies on CBS cone for finite-sized geometries suggest a useful technique to characterize the spatial inhomogeneity of samples.In this work we have provided a comprehensive study of the coherent backscattering from finite-sized sparse scattering medium based on a scalar wave analysis. Relying on a simplified form of Maxwell’s equations in a multi-scattering medium, we investigated the effect of density factor, number of particles, and size of the scattering medium on different characteristics of the CBS line shape. The density factor was shown to be the prominent factor in determining the width of CBS cone, while both the density factor and the number of particles influence the degree of enhancement in the backscattering direction. The angular distribution and the enhancement factor of the scattering intensity hold valuable information to characterize random samples. The cone shapes are found to be in very good agreement with previous theoretical calculations. Finally, we have devised a technique to exclusively eliminate the specular reflection component from the ensemble-averaged backscattered intensities, enabling the observation of pure CBS line shapes even for normal illuminations of the structure.emergence of the main features of the CBS light. A relevant situation is that of coherent backscattering enhancement in single scattering phenomenon. This effect has been recently studied58 by making connections between the solution of Maxwell’s equations in scattering from single particles and an order-of-scattering formalism. Interference effects, analogous to the ones discussed here, between the conjugate pairs of sequences result in enhancements. It has been shown that the angular width of coherent backscattering enhancement-induced peak is significantly dependent on the particle size and is inversely proportional to this parameter. Similar behavior has been observed in our work (see Fig. While the results of this work are entirely consistent with the known properties of CBS in various systems, it is valuable in designing and interpreting experiments on practical, finite-size systems to be able to compute the detailed scattered field and observe the see Fig. . To makeSupplementary Information 1.


The present paper introduces an optimization-oriented method here practiced for designing high performance single antennas in a fully automated environment. The proposed method comprises two sequential major steps. The first one devotes configuring the shape of antenna and determining the feeding point by employing the bottom-up optimization (BUO) method. In this algorithm, the number of microstrip transmission lines (TLs) used to model the radiator is increased consecutively and the shape of the antenna is revised up to finding the initial satisfying results. Secondly, for determining the best design parameters of the configured antenna shape in the first step , deep neural network (DNN) that is based on Thompson sampling efficient multi-objective optimization (TSEMO) is applied. The recommended optimization method is successfully attracted as a problem solver for designers to tackle the subject for antenna design such as the complexity and large dimensions of structures. Hence, the main advantage of the implemented optimization method in this article is to noticeably decrease the required designer’s involvement automatically generating valid layouts. For validating the suggested method, two wideband antennas are designed, prototyped and subjected to experiment. The first optimized antenna covers the frequency band 8.8–10.1 GHz (13.75 % bandwidth) characterized by a maximum gain of 7.13 dB while the second one covers the frequency band 11.3–13.16 GHz (15.2 %) which exhibits a maximum gain of 7.8 dB. As world population enjoys a noticeable rise, that in turn increases the need of mobile and hand held devices, improvement of antenna performances has been sensed in order to provide the appropriate BW and nearly flat and constant gain performance in the operational frequency band(s)2.Antennas become an important part in any hi-tech content device. The technology development has increased the need of various types of antennas subject to different constrains set by various considerations extending from electromagnetic to thermal going through mechanical or space limits. Due to their remarkable advantages such as cost-effective, consolidated effortless fabrication process, reduce space occupation, conformable, satisfactory bandwidth (BW), and adequate gain performance, microstrip patch antennas represent the most favorable class of antennas which are mostly used in communication systems3, particle swarm optimization4, spider monkey optimization5, genetic algorithm optimization7, and K-nearest neighbor algorithm8. These algorithms are successful set of rules; however, due to the complexity of antennas more accurate and advanced multi-objective optimization techniques are required for optimizing designs. Recently, neural networks have proved their reliable performance and competence in modeling high dimensional radio frequency (RF) designs14. Deep neural networks (DNNs) in opposite to the shallow neural networks (SNNs), include multi layers in their structures that results in more accurate performance in the complex and high-dimensional designs9.One of the major critical design challenges consists of fulfilling the design goals in the considered operation BW. Hence, designing and optimizing antennas considering conventional electronic design automation (EDA) tools for achieving proper BW and very nearly constant gain would be rather difficult due to the complexity of antenna structures. During the last decade, a variety of optimization methods have absorbed the attention of designers to tackle the drawbacks of EDA tools in this sense. Some of the various reported optimization methods are: surrogate-based optimization15 where the number of microstrip transmission lines (TLs) are increasing sequentially and the suitable feeding point is optimized. And the second step of optimization devotes to sizing the antennas and augmenting antenna performances using the DNN-based Thompson sampling efficient multi-objective optimization (TSEMO) algorithm16. This algorithm is selected in this work due to the successful reductions in the hypervolume calculations16. The regression DNN is applied for accurately modeling and enhancing the overall performance and is devoted to synthesizing the final post-layout of antenna by applying electromagnetic (EM)-verified fabrication rules.To the best of authors’ knowledge, it is for a very first time in the literature where a fully automated optimization method is presented for configuring the antenna’s structure and sizing the design parameters, respectively. The automated algorithm includes two main optimization procedures. The first step optimization is for configuring the initial antenna shape using the bottom-up optimization (BUO) methodThe present paper is organized as follow: in “This section provides brief details of the two employed optimization methods for modeling the antenna structure and sizing the design parameters of the configured antenna, respectively. The design goals are Determining the initial antenna shape is critical which paves the way of designers. For this case, the BUO method is employed for configuring the primary antenna shape and also for determining the suitable feeding point. In this part, we explain the theory of BUO method in detail which automatically generates the initial antenna geometry with feeding point. The summary of employed BUO method is provided in Algorithm 1 and Fig. spectra.raw17 and does the mathematical analysis.For minimizing the designers’ interruptions, fully automated optimization environment is created by the combination of EDA tool such as keysight ADS and the numerical analyzer as MATLAB. In this phase, the ADS software works in the background and the MATLAB tool handles the generated output data from the file namely as 15. Providing the starting point of optimization that represents the general geometry of the single antenna is a significant role. Hence, this optimization method is employed where both the single antenna configuration with feeding point are determined and optimized. The BUO algorithm considers the fine-tuning exploiting the sensitivity of the resonance to the geometrical dimensions. With the help of this method, the initial configuration of antenna is generated through BUO method that the number of transmission lines are increasing sequentially. All the data (including the width and length of transmission lines) is saved in the output file namely as ‘spectra.raw’ file.The BUO method is a combination of lower-level components where the higher-level design is decomposed into several hierarchical blocks similarly as it is done in a general domain decompositionnetlist file is created from ADS and then the optimization process is initialized. In this algorithm, the number of TLs is increasing continuously and various shape of TLs are examined up to achieving primary responses in terms of gain and band frequency. Beside, the suitable biasing point is also determined by the help of this algorithm. The output responses are achieved in the spectra.raw file and MATLAB is analyzing these generated outcomes. As presented in “16gain within the BW. Hence, advanced multi-objective optimization methods are required to deal with multi-objective specifications. In this work, we employ the TSEMO algorithm by using the regression DNN. The TSEMO algorithm is selected in this works due to the fast optimization process by building the Gaussian process surrogate models and also the ability to evaluate different simulations, in parallel. For applying the TSEMO algorithm the DNN, includes multi-layers and optimal neurons, is used which results in accurate antenna modeling. In this section, firstly the theory of the TSEMO method is explained and then the procedures for constructing and using the DNN in the multi-objective optimization is described, briefly.After determining the suitable antenna configuration, it is time to obtain the optimized design parameters as length (L) and width (W) of included TLs. The target of this part is to enhance the flatness of After constructing the initial configuration of the single antenna, the optimized values for design parameters must be achieved. The brief definitions for this algorithm is as following:x is the decision vector and G is a vector of m objective functions ((x)).The TSEMO algorithm as a multi-objective optimization is defined in :1\documeg approach that builds Gaussian process (GP). The single objective method of TSEMO in BO is to find the accurate global minimizer entclass1pt{minima18 is needed for initializing GPs. The detailed description of GP is described below up to  for inferring is used. The MAP hyperestimation is given in  where \dm sample functions are obtained as Secondly, a set of candidate for sampling must be specified. The inputs of the data collected and the corresponding responses of each function For the output data set as For constructing an accurate regression DNN, some requirements must be set that are: suitable amount of data set, number of hidden layers with number of neurons, and also input and output layer features. Figure After configuring the initial antenna shape, optimal design parameters must be determined. All the optimization process is performed automatically in the created platform described in “Training set: This set is used for training the model such as antenna and to encourage the model to learn the hidden features. In this case, the model is trained for all diverse data and will be able to predict the unseen future data. As the neural network is trained with these data, large ratio of data set  layers and one fully connected layer as Fig. As explained before, DNNs consist of multiple layers that each layer include some neurons. Determining optimal hyperparameters  is not straightforward and requires optimization techniques as well. In this work, we apply the BO for predicting the optimal hyperparameters of the employed regression DNN as it is more accurate than grid search and randomized searchA summary of the proposed algorithm for obtaining the best component values is the following: 20 are applied during the optimization process for passing the EM simulations successfully. As Figs. This section provides a sequential construction of the proposed optimization strategy that is performed automatically. In the first phase, the general antenna structure with location of the feeding point are determined using the BUO method. For this case, the BUO method is employed where Fig. In our proposed method, the LSTM layers are used for optimizing antennas in a large frequency band. In this method, gain and To verify the proposed method of optimization, two microstrip patch antennas have been designed and fabricated as illustrated in Figs. Figure For validating the proposed optimization strategy, another single microstrip antenna is optimized. Figure Figure The purpose of this study is to apply an automated optimization method by the means of the BUO and DNN based multi-objective optimization to obtain desired BW and flat gain performance. Proposed optimization methodology in this paper is presented for the very first time in the literature and includes two phases, which are applied sequentially. The first phase is responsible for configuring the initial single antenna configuration and also for determining the suitable feeding point. The second step of the optimization corresponds to applying the multi-objective algorithm as TSEMO method through regression DNN to determine the design values, that in turn gives rise to achieve the design goals. The regression DNN helps designers to model the complex antenna designs accurately where it provides a regression environment for applying multi-objective algorithms. The advantage of our proposed method is to reduce the designer intervention and prepare ready-to-fabrication layouts by applying fabrication rules and constrains. To verify the effectiveness of the proposed method, two single microstrip antennas in the frequency band of 8.8–10.1 GHz and 11.3–13.16 GHz are designed, fabricated, and measured where the measurement results show well-agreement convergence to the simulated results. In our method, two specifications as gain and


Fine motion recognition is a challenging topic in computer vision, and it has been a trendy research direction in recent years. This study combines motion recognition technology with dance movements and the problems such as the high complexity of dance movements and fully considers the human body's self-occlusion. The excellent motion recognition content in the dance field was studied and analyzed. A compelling feature extraction method was proposed for the dance video dataset, segmented video, and accumulated edge feature operation. By extracting directional gradient histogram features, a set of directional gradient histogram feature vectors is used to characterize the shape features of the dance video movements. A dance movement recognition method is adopted based on the fusion direction gradient histogram feature, optical flow direction histogram feature, and audio signature feature. Three components are combined for dance movement recognition by a multicore learning method. Experimental results show that the cumulative edge feature algorithm proposed in this study outperforms traditional models in the recognition results of HOG features extracted from images. After adding edge features, the description of the dance movement shape is more effective. The algorithm can guarantee a specific recognition rate of complex dance movements. The results also verify the effectiveness of the movement recognition algorithm in this study for dance movement recognition. Motion recognition is one of the most popular research directions in the field of computer vision. Its application range covers intelligent human-computer interaction, virtual reality, and motion-aided analysis . Many acMultimodal machine learning (mmml)  aims to D is used to characterize the shape features of video dance movements. A dance motion recognition method based on histogram feature, optical flow direction histogram, and audio feature is proposed. This study solves the problem of heterogeneous feature fusion. The multimode environmental monitoring method is adopted to organically integrate three features for dance movement recognition research.Based on the above methods, this study proposes an effective feature extraction method for the dance video dataset. Its main mechanism is to segment the video equally and perform edge feature operations on the segmented video at the same time. The edge features of all video images in each segment are added to one embodiment, and the direction elements of the gradient histogram are extracted. Finally, through the experiment, a set of directional gradient histogram feature vectors A local feature is a distinguishable description extracted from the region of interest of the task, as far as the human motion recognition task is concerned . The recResearchers widely recognize the application histogram of directional gradient (HOG) feature in visual retrieval tasks. This feature was first proposed by Dalal et al. in 2005 and achieved excellent results in pedestrian detection tasks . The HOGKlaser et al. proposed the 3D orientation gradient histogram HOG3D feature for human motion recognition for the first time to solve the problem of insufficient quantization of HOG feature direction . As showFour-dimensional average vector direction histogram (HON4D) is shown. As shown in The above researchers put forward new research ideas from the application of the oriented gradient (HOG) feature in visual retrieval tasks and the first proposed 3D-oriented gradient histogram HOG3D feature for human motion recognition. Optical flow direction histogram features and audio features are from the appearance and shape of the dance movements in the video to the movements of the human dance movements, and with the help of audio features, the characteristics of the dance movements are described.Given the specific ability of a single element, this study uses the linear weighted combination method of the multikernel learning method to fuse the HOG feature, optical flow direction histogram feature, and audio feature to complement each other and improve the recognition ability of the classifier . The spea and b of the support vector machine classifier. Based on the SimpleMKL algorithm idea introduced by Rakotomamonjy et al. [In support vector machines based on multikernel learning, the task of the multikernel learning model training stage is to learn and solve the weight order of each kernel function and the parameters y et al. , in the a and b by giving the weight order of the kernel function. Then, obtain a new kernel weight order by providing a and b. Therefore, show the classification function based on a multikernel learning support vector machine as follows [According to the idea of the implemented algorithm, use the gradient descent algorithm to minimize the objective function of learning and solve the optimal parameters . The spe follows (2)y=Fx=∑One versus one: N class classification problems require N (n-1)/2 classifiers, each of which trains two class samples. When classifying the unknown samples, counter the votes of N categories in all classifiers and the category with the most votes in the category of the location samples.One versus rest: one versus rest strategy for all samples, group the instances of one category into one category, and group the representatives of all remaining types into another category. For N class classification problems, this strategy requires training N classifiers. When training each classifier, assign a class of samples a positive label and give all the harmful brands.In addition, equation  is a binP class SVM classifiers to assume P class dance movements. Therefore, the following equation shows the objective function of the multiclass classification.This study chooses the second strategy in classification. Transform the multiclass problems in this study into multiple joint dichotomies. For each category in the dataset, all dance movements in this category are marked as positive and keep other dance movements as unfavorable . The SimJP is the machine binary classifier of the P support vector. Output the P dance movement. A negative sample is a category that is not a P dance movement. Finally, the algorithm in this study obtains action categories according to the following formula when conducting multicategory classification:In equation , JP is tThe research on the combination of motion recognition technology and dance has just started. There are still few available dance datasets, including the motion capture dataset of Carnegie Mellon University and the DanceDB dataset of Virtual Reality Laboratory of the University of Cyprus. But the public dataset contains very little dance data—the data used for dance movement recognition research. The FolkDance dataset made by the team laboratory is used in this experiment. The FolkDance dataset is divided into four groups of dances, each of which contains several subdivided dance movements with rich movement categories. Each group of dance movements is relatively complex and challenging.The motion capture device Vicon is used to collect professional dance movements in the FolkDance dataset. During making the dataset, I designed four groups of FolkDance movements after discussion with dance experts according to the dataset making plan.  Two-flower combination with step  One-flower combination with inside  Paper towel  One-flower combinationThe production of FolkDance datasets takes into account the situation of solo dances, regardless of changing stage backgrounds and props. Record 84 dance videos, and set the environment and camera angle in each video as fixed. Set the frame rate of images in the video as 20fps, and the size of each frame is 480 × 360. This dataset contains many dance movements that meet the recognition research requirements in the dance video movement. Use this dataset to verify the effectiveness of the dance movement recognition algorithm proposed in this study. FolkDance dataset mainly includes four groups of dances:The specific dance movement classification and sample frames for each group are provided.There are seven dance movements in the combination of double flowers in heel step: double flowers in heel step, small dance on the head, front kick of key position flower, cut flower in a circle, cut flower in the circle from low to high, squat and cross-step, and two drums. There are five dance movements in the combination of the inside scene, namely, the kick after the inside location, the cross move, the head move, the pull in the following step, and the cross move. Towel flower combination includes eight dance movements: towel flower four drum, point kicking step, head flower cross, forearm flower cover cross, large alternating flower cross, shoulder flower, breaking and winding flower forward and backward, towel flower six drum.The combination includes eight dance movements: point stand step, press and kick step 1, double hand chip turn simultaneously, double hand chip press and kick step, broken step circle chip left, broken step circle chip right, press and kick step 2, jump, and kick step double hand chip. It notes that there are two pressing back kick movements in this combination. The above two movements are not the same but similar. Here, we mark them by serial number to distinguish them. This combination is shown in R) Core (TM) 15-4460@3.20GHZ, 8 GB. Operating system used is Ubuntu, 64-bit.The experimental environment used in this study is as follows: CPU: Intel (Development environment: MATLAB 2012b, SimpleMKL, OpenCV 2.4.8. SimpleMKL multicore learning open-source library, the implementation of the multicore learning algorithm; OpenCV is an open-source library for computer vision, which is mainly written by C and C++ languages, and can be used across platforms, realizing many commonly used algorithms in computer vision.In the specific experimental design of this study, we verify the algorithm and the recognition effect of all single features on two dance datasets . Considefps and the error of the segmented video is about 10 seconds, through the analysis of the dance, the movement difference is relatively small within a second, that is, the dance movement shape changes very little. So, we set the bisection value for each video segment to 10. The divide extraction process of audio signature features into two parts: first, extract the audio stream from the dance video; second, remove the 32-dimensional audio signature features from each frame of the audio stream. The literature constructs audio dictionaries according to the word bag model's ideal for audio signature features and sets the audio dictionary size to 50. The kernel functions used in this study are the Gaussian kernel and histogram cross kernel.The experiment extracts three features: histogram feature of direction gradient, straight optical flow direction, square feature, and audio signature feature . For theEvaluation of different characteristics: this experiment gave the recognition results of a single feature on two dance datasets and the recognition results of the feature fusion method using the multikernel learning method, compared them, and analyzed the influence of the three features on the experimental resultsCompare the HOG extracted from the accumulated edge feature image and the original dance imageThe recognition effect of the algorithm in this study on two dance datasetsComparison between the proposed algorithm and the benchmark algorithmThis study refers to the previous research methods of motion recognition multifeature fusion , and basThe experimental results of the proposed algorithm and the benchmark algorithm on two dance datasets are analyzed and compared.First, this study compares and analyzes HOG feature values extracted from feature membership images. The effect of action recognition in this study is different from the existing action, and the difference in the development is related to the extraction mode and calculation accuracy. The HOG advantages of the four groups of activities are given in The results in The comparison of the commonly used centralized dance action recognition modes in the existing literature shows that the recognition rate of the new model in this study is higher than that of the traditional model. The highest accuracy is increased by 12.6%. The towel flower combination dance action selected in this study is very complex, and the accuracy is improved by 4.39%. The dance movement exists appropriately, and the accuracy is improved by 10.09. This result shows that the new fusion model in this study can not only improve the recognition accuracy in the fundamental recognition effect but also reflect the advantages of the particular action, complex action, and staggering occlusion action recognition.Propose an effective method to extract the features of dance movements and divide dance movement videos equally. Accumulate the edge features of segmented videos. The edge features of all video images in each segment are added to one embodiment and extracted from the histogram features of the direction gradient. Aiming at the problem of heterogeneous feature fusion, three kinds of features are organically fused for dance action recognition through a multicore learning method, and the proposed fusion method can improve the dance recognition rate. Overall, the algorithm proposed in this study is more efficient than traditional methods.In dance movement recognition, the direction gradient histogram feature, optical flow histogram feature, extracted audio element, and carry dance movement recognition are out by multifeature fusion. Aiming at the problem of heterogeneous feature fusion, organically fuse three kinds of features by a multicore learning method for dance movement recognition.Make a FolkDance dataset. Develop a detailed dataset recording scheme. The Vicon motion capture system invites different dance majors to record dance videos according to dance group movement design. In the study for dance movement recognition research, the dataset concluded three people and four groups for 84 dance movement videos.This work mainly studies the selection and representation of features in dance movement recognition research and the fusion method of multimodal environmental monitoring data. The decisions are as follows:


Background: Radiomics is an emerging field that translates medical images into quantitative data to enable phenotypic profiling of human disease. In this retrospective study, we asked whether it is possible to use image-based phenotyping to describe and determine prognostic factors in the aging population.Methods: A radiomic frailty cohort with 101 patients was included in the analysis . A total of 44 texture features were extracted from the segmented muscle area of the ultrasound images of the anterior thigh. Univariate and multivariate analyses were performed to assess the image data sets and clinical data.Results: Our results showed that the heterogeneity of muscle was associated with an increased incidence of hearing impairment, stroke, myocardial infarction, dementia/memory loss, and falls in the following two years. Regression analysis revealed a muscle radiomic model with 87.1% correct predictive value with good sensitivity and moderate specificity (p = 0.001).Conclusion: It is possible to develop and identify image-based phenotypes in the elderly population. The muscle radiomic model needs to further be validated. Future studies correlated with biological data  will give further insights into the biological basis and molecular processes of the developed radiomic model. Medical imaging provides the ability to detect and localize many anatomical or physiological changes that are important to determine whether a disease is present or if therapy is effective , but to The field of radiology is going through a revolution, starting with an increased number of pattern recognition tools. These advances have facilitated the development of “Radiomics”, a precision tool that extracts intensity, shape, size, or texture from medical images using data-characterization algorithms . This meOur research group has focused on researching and developing quantitative imaging biomarkers for the diagnosis, treatment, follow-up, and risk assessment of frailty. In this Brief Research Report, we would like to highlight the possible application of Radiomics as a research method in the aging population. We believe it can also be used to take a fresh look at other pathophysiological processes, such as frailty. In this retrospective study, we asked whether it is possible to use image-based phenotyping to describe and determine prognostic factors in the aging population. We will also explore and explain the usefulness and applicability of such methodology, as well as its current limitations.This was an exploratory study of previously acquired ultrasound data (November 2014—February 2015) and its corresponding follow-up clinical data (March 2017). The cohort consisted of 112 patients between 20 and 90 years old that previously participated in a cross-sectional frailty study  . We only excluded participants if image data was missing from the original study. Radiomic features were extracted from the segmented muscle area of the ultrasound images of the anterior thigh. The clinical data mining included obtaining physical characteristics, muscle performance data, frailty phenotype, and quality of life. We also recorded about 30 comorbidities and risk factors from the patient’s medical history at baseline and its incidence two years after the ultrasound scanning. Univariate and multivariate analyses were performed to assess associations of the radiomics features with the clinical data . All resWe defined 44 radiomic image features that are used to describe and characterize tissues and can be extracted in an automated way. Four features were extracted from the intensity histogram (first-order statistics) and the other 40 features were extracted from second-order and higher-order statistical methods: from the gray-level co-occurrence matrix (GLCM) 9 features were derived, from the gray-level run-length matrix (GLRLM) 13 features, from the gray-level size zone matrix (GLSZM) 13 features and from the neighborhood gray-tone difference matrix (NGTDM) only 5. The features were extracted using the MATLAB toolbox Radiomics implemented by Vallières and others . The fulrectus femoris muscle. The segmented ROI excluded bone, fatty tissue, muscle fascia, and the internal tendon of the rectus femoris muscle.We applied a radiomic analysis to one previously acquired image data set . The datAt the time of the ultrasound scanning the following information was collected: 1) physical characteristics that included patients age, sex, weight, height, and body mass index (BMI); 2) muscle performance that included muscle strength and gait speed; 3) the Frailty Criteria ; 4) qualIn texture analysis, the discriminative power of the predictive model is dependent on having sufficient data. Radiomic analysis can be performed with as few as 100 patients . Our stuDescriptive data are presented with mean ± standard deviation (SD) with the distribution of the data verified by the Kolmogorov-Smirnov normality test. The evaluations of the different variables of the study according to frailty phenotype and control group were determined using ANOVA for the parametric variables, and the non-parametric Kruskal-Wallis test was used. Correlations were performed to investigate the relationship between physical characteristics, muscle performance data, frailty phenotype, quality of life, the incidence of comorbidities, and/or risk factors, with the radiomic features. We used the coefficient of Pearson (r) for parametric data, Spearman’s Rank for non-parametric data, and Tau B of Kendal used for ordinal variables. Heatmaps and Manhattan plots were used to graphically represent the data and to better visualize the value of the associations within the data. Hierarchical and K-Mean cluster analysis was performed to identify the most relevant grouping of radiomic features for the radiomic signature. Once a radiomic phenotype and corresponding features were identified a multiple logistic regression analysis was performed. We applied a false discovery method by Benjamini and Hochberg as multiple comparisons were performed during the study. All statistical analyses were performed with SPSS version 24.0 for Windows .The sample was composed of 101 patients  and there were 24 controls, 22 robust, 30 pre-frail, and 25 frail patients . The phyThe analysis of comorbidities according to frailty phenotype , shows hWe used multiple correlations of all the variables in this study with the purpose to perform data mining, which refers to the process of discovering patterns in large data sets  associatp = 0.001). The complete list of radiomic features corresponding to each cluster is found in the supplemental materials . The models explained 34.80–63% (Nagelkerke R2) of the variance in the radiomic models. Although the data showed correct predictions ranging from 78.1 to 87.1%, the regression analysis plots revealed that only Model 1 was predictive of the outcome with good sensitivity and moderate specificity. The other two models were good at excluding disease or identifying individuals without this radiomic phenotype. This is corroborated by the high sensitivity and low specificity of both models.The muscle radiomic signature or phenotype identified a total of 22 subjects that developed 29 newly diagnosed comorbidities or events  in the following two years. For the full characteristics of the sample according to identified muscle radiomic phenotype see p < 0.05) and had the strongest explanatory weight on the model. The second block assessed the value of the strong correlated clinical variables with the radiomic model 1, but because of the high amount of variables included, all variables lose statistical significance. For this reason, we recalibrate the model excluding the radiomic feature with a Wald Statistic close to cero or cero and evaluated Block 3 with the clinical variables again. Muscle thickness, subcutaneous fat thickness, age, and gait speed had a strong explanatory weight with the model, but no statistical significance. The frailty phenotype had neither. This could be due to the suppression effect due to the close nature between the variables . Therefore, block 4 evaluated the effect of only age and frailty phenotype on the model given the big range of ages in the sample. Frailty phenotype had a higher weight with the model and was statistically significant, yet age was not statistically significant to the model. And although blocks 3 and 4 were statistically significant to the overall model, they did not improve substantially the predictive capacity of the models.A stepwise logistic regression analysis was applied to the best model see  to test This study tested the significance of thousands of variables, which creates serious concerns over the accumulated Type 1 error. Many of the significant developments within the field of so-called “large-p, small-n” data analysis problems are robust methods for the accommodation of multiple testing issues . We applRadiomics aims to capture the informative content hidden in medical images, overcoming the limitations of the human eyes and human cognitive patterns . These pThis study shows that it is possible to use image-based phenotyping or radiomics, to describe and determine prognostic factors in the aging population . First, The prognostic model we propose is simple and can be built solely from data extracted from a routine muscle ultrasound examination. Radiomic signature consisted of 26 radiomic features, 4 focused on the variance of the pixels, 8 focused on the distribution of pixels with high gray-level, and 14 focused on the amount of heterogeneity the distribution of the pixels had. Not all features were evenly weighted; as some had higher Walt statistics concerning the model . In biolWhen aspects of the mitochondrial theory of aging were examined in single muscle fibers instead of whole muscle homogenates, it was discovered that some segments of fibers were completely deficient for electron transport chain (ETC) activity. There was a near-complete absence of normal mtDNA in these regions, suggesting that ETC activity was closely linked to the status of mtDNA in the region. Furthermore, fibers displaying these mtDNA deletions and ETC deficiencies were also significantly atrophied. These observations suggest that mtDNA deletions may appear in a stochastic manner in small segments of single fibers leading to the death of a segment of a fiber . These fp-value change erratically in response to small changes in the model from one block to the other one. This could be because of multicollinearity of the variable, as the Fried frailty phenotype involves muscle dysfunction at its core and increased echo-intensity has been reported associated with muscle strength, muscle thickness, and gait speed—markers with strong relationships with frailty status in previous research (Given the range of different ages (range from 20 to 90 years old) included in the study, one might question the strong predictive power of radiomics phenotype is because of the number of younger patients included in the control and robust group. Others might also think as odd to include a control group in a non-experimental design, but the presence of control groups allows researchers to confirm that study results are due to either manipulation of independent variables or inherent characteristics of the patients being studied rather than extraneous variables. For this reason, we conducted an analysis of the age and frailty phenotype effect had on the radiomic model . The resresearch . UltimatThese results thus lend further credence to earlier studies. One is that radiological images can detect structural muscle changes in the aging population and that these changes can predict important clinical outcomes, implying the importance of detecting muscle quality in radiological images . NotwithOne of the main benefits is that medical imaging data acquired in routine healthcare can be used in a new way to inform clinicians about the biology of a disease and provide potential prognostic or predictive information . With a This study has two major strengths. One that is a longitudinal study in comparison to one-time measures studies, which are lacking in the frailty literature, which allows for a better understanding of the adverse outcome prediction . And secOur study must also be interpreted with caution as the findings are based on a limited sample size from a single center. As with other radiomics studies, there is an inherent need for a high number of patients, otherwise, it leads to overfitting of data or finding relationships where there are none, also called false discovery . The limAnother limitation of this methodology is that unfortunately, neither the computational feature by traditional radiomics nor the use of machine learning or deep network learning can explain the function of radiomics for predicting clinical events. The interpretability of this method is not strong, and in particular, explanations of the underlying biological and molecular mechanisms are lacking to none existent . AlthougFurthermore, radiomics in general still faces many challenges, including the stability and reproducibility of the developed phenotypes or predictive models, as well as the interpretability before it can be translated to clinical applications . To accoIt is hypothesized that phenotype similarities of different disorders may indicate biological relationships of the underlying genes . MitochoIn the future, we hope to connect our radiomics data with other data such as genetics, transcriptomics, proteomics, or metabolomics to achieve better identification of actual pathways of disease progression and to investigate evolving physiology in this field . Data seAlthough this study has several limitations, including its retrospective nature, a small number of events and patients, and the lack of biological correlation, it demonstrates the feasibility and usefulness of radiomics methodology for building prognostic image-based phenotyping in the elderly and frail population. The muscle radiomic model needs to further be validated. Future studies correlated with biological data  will give further insights into the biological basis and molecular processes of the developed radiomic model.


Reaction wheels (RW), the most common attitude control systems in satellites, are highly prone to failure. A satellite needs to be oriented in a particular direction to maneuver and accomplish its mission goals; losing the reaction wheel can lead to a complete or partial mission failure. Therefore, estimating the remaining useful life (RUL) over long and short spans can be extremely valuable. The short-period prediction allows the satellite’s operator to manage and prioritize mission tasks based on the RUL and increases the chances of a total mission failure becoming a partial one. Studies show that lack of proper bearing lubrication and uneven frictional torque distribution, which lead to variation in motor torque, are the leading causes of failure in RWs. Hence, this study aims to develop a three-step prognostic method for long-term RUL estimation of RWs based on the remaining lubricant for the bearing unit and a potential fault in the supplementary lubrication system. In the first step of this method, the temperature of the lubricants is estimated as the non-measurable state of the system using a proposed adjusted particle filter (APF) with angular velocity and motor current of RW as the available measurements. In the second step, the estimated lubricant’s temperature and amount of injected lubrication in the bearing, along with the lubrication degradation model, are fed to a two-step particle filter (PF) for online model parameter estimation. In the last step, the performance of the proposed prognostics method is evaluated by predicting the RW’s RUL under two fault scenarios, including excessive loss of lubrication and insufficient injection of lubrication. The results show promising performance for the proposed scheme, with accuracy in estimation of the degradation model’s parameters around 2–3% of root mean squared percentage error (RMSPE) and prediction of RUL around 0.1–4% error. Over the past four decades, condition monitoring and, in particular, fault diagnosis and prognosis (FDP) for condition-based maintenance (CBM) and safety assurance have been fields of interest ,2. The pIn aerospace, the low cost of developing and launching modular and nano/microsatellites has contributed to their rapid growth in the past few years. These satellites are usually used for educational or monitoring purposes  and haveThe attitude control system (ACS), as one of the essential parts of a satellite, is prone to faults , and itsIn the past decades, many researchers have worked on determining and potentially extending the lifespan of these critical satellite components, namely, RWs . CreatinA combination of a backpropagation neural network with a similitude method is presented in  for RUL Another data-based method using a long short-term memory (LSTM) neural network is proposed in  to estims HI. In , the autMuthusamy and Kumar  present The accuracy of all the reviewed data-driven and hybrid methods is highly dependent on the amount of labeled run-to-failure data sets from the same type of system and under the same operating conditions , both ofThe model-based prognostics methods are generally associated with an estimation method for the desired system states. The Kalman filter (KF) is one of the most widely used stochastic estimation methods . KF-baseDespite EKF’s ability to estimate parameters of nonlinear degradation models, errors in Jacobian matrices with higher-order terms can majorly affect EKF’s accuracy . Rahimi ater, in , Rahimi ater, in ,18 conduater, in , which wAs a rotary system, the bearing unit of an RW requires proper lubrication for its entire operational life. Typically, the lubricant stored in the bearing housing is sufficient for 3–4 years of operation under normal conditions, and adding a supplementary lubrication system will extend the life of the system . HoweverIn , the autAs shown in Step 1. Since direct measurement of the lubricant’s temperature under the operation condition is not practical, in the first step of this research, we investigate the estimation of the lubricant temperature using angular velocity  The cumulative consumption of lubricant reaches the amount stored in the system. (2) Deficiencies in lubrication injection and supplemental lubrication systems lead to bearing unit dry-out. In the first scenario, the fault occurs during normal operating conditions and will be accelerated by lubrication bleeding or RW heating. However, a fault in the supplementary lubrication system causes an unbalanced injection of lubrication in the second scenario (see ario see .(1)Investigation of the temperature and lubrication effects on RWs deterioration and its impact on RWs remaining useful life that has not been previously addressed in the literature;(2)A three-step process to determine the RUL of RWs undergoing temperature and lubrication malfunctions that include a novel particle filter resampling approach to improve the accuracy and efficiency of estimation results.In addressing the identified gap in the literature, this study offers the following novelties:The remainder of this paper is organized as follows: In This section provides background and theories used in this study to help the reader follow the content easier and for the paper to be self-contained. This includes satellite RW modeling, degradation modeling, and the state estimation theory used in this work.The two primary sources of lubrication loss in RW’s bearing unit are evaporation and creeping. They are both functions of the lubricant’s temperature. As a result, monitoring the temperature is crucial to developing an HI based on cumulative lubrication loss. It is generally not possible to directly measure the lubricant’s temperature. Therefore, in this study, an estimation approach is employed that uses angular velocity  torque limiting loop:The Coulomb friction is created by the dry friction within bearings. This loss torque is independent of the angular velocity or the temperature;The negative feedback viscous friction:The negative feedback speed limiter loop prevents the wheel from accelerating to unsafe speeds. This loop is triggered by exceeding the speed threshold, The motor torque control unit is a voltage-controlled current source with a gain Several essential loops in the RW model ensure precision. The nonlinear mathematical model of the RW in The For the numerical solution of the nonlinear model, the discontinuous functions are needed to be approximated. In , an apprTherefore, the proposed Considering lubricant evaporation and creeping due to temperature gradient, Jin et al.  suggest This concludes the introduction of the degradation model in this study. In the next section, some background on the estimation theory will be provided for the state and parameter estimations in this study.The particle filter is a recursive Bayesian estimation technique based on the Monte Carlo simulation . This meTo illustrate how PF works, a general nonlinear system can be written as:The PF process consists of two steps: (i) the prediction step and (ii) the update step. In the prediction step, base000000000d on the posterior PDF Based on the obtained The original formulation of the particle filter does not include a resampling stage, which can impose some issues on the filter’s performance. Hence, in the next section, resampling is discussed.One issue with particle filtering is that after several iterations of particle propagation, the weight distribution among the particles becomes skewed, with few particles having a significant weight and most particles having very little weight. Resampling can address this degeneracy, but it may also lead to sample impoverishment, where only a small number of particles have significant weights while the majority of particles with low weights are discarded. To address this trade-off, resampling can be applied at predetermined intervals only when the variance of the non-normalized weights exceeds a certain threshold, indicating sample degeneracy. A method for determining the effective sample size  Resampling only happens when a certain threshold  shows an adapted logistic function with a maximum value of 1, and its growth rate is a function of Once the estimation is complete, the next step is to use the obtained information and propagate it in time to obtain the RUL, which is discussed next.The proposed parameter estimation method has been developed to address the estimation confusion error resulting in cases where the number of measurements is less than the number of to-be-estimated parameters.The general parameter estimation problem in a nonlinear system can be written as:Assuming the parameter vector of the model consists of Having Equation (23), the sub-nonlinear system equation for As can be seen with the estimation of In the second step, an online parameter tracking of the lubricant consumption model, as well as the degradation model, is conducted using the estimated lubricant’s temperature and the measured amount of injected lubricant.The last step is to predict the RUL of RW based on two scenarios of failure: (1) The cumulative consumption of lubricant reaches the amount stored in the system. (2) Deficiencies in lubrication injection and supplemental lubrication systems lead to bearing unit dry-out. In the first scenario, the fault occurs during normal operating conditions and will be accelerated by lubrication bleeding or RW heating. However, a fault in the supplementary lubrication system causes an unbalanced lubrication injection in the second scenario see .RUL of a system at time In general, the In which In the next section, the preliminary material and the proposed methodology are put together to show results and discussions for a case study on a RW’s remaining useful life estimation based on its residual lubricant level for the bearing unit and its impact on the unit’s life expectancy.(1)Normal or excessive lubricant loss. Under this scenario, the RW will operate until it runs out of lubricant. In this case, the cumulative loss of lubricant acts as the HI of the system, and the maximum amount of lubricant it can carry is the threshold;(2)Insufficient lubrication caused by a fault in the supplementary lubrication system. This scenario results in bearings drying out while there is still lubricant in the supplementary lubrication system. For this scenario, HI is computed as the difference between the normal lubricant loss and the injection amount, and the failure threshold is the amount of lubrication loss the bearing unit can tolerate without additional lubrication.In this study, the RUL of RWs, based on the residual lubricant level for the bearing unit, is studied under two incipient fault scenarios to evaluate the proposed method’s effectiveness:The simulations were set up, as shown in In this section, the performance of the first step of the proposed method is examined by following a temperature path introduced in The objective of the state estimation block is to estimate the temperature of the lubricant , the degradation model comes with two parameters .As it is impractical to measure lubrication consumption over 0.05 s, which is the time step necessary for estimating the temperature, e filter  is formee filter . As can The first PF block uses the ratio of three consecutive measurements of lubrication consumption to estimate Using Equation (32), the sub-nonlinear system equation in Equation (24) can be written as:Having the estimated This section evaluates the performance of the proposed three-step prognosis method using estimated degradation model parameters for both fault scenarios. As a result of the first fault scenario, the rate at which the lubricant is injected is greater than normal, resulting in a faster consumption of resources than expected. In contrast, the second fault scenario causes less lubrication injection, which cannot compensate for normal lubrication loss. The bearing unit dries out due to this scenario over time.The failure time  the new adaptive resampling method for the particle filter detailed in A combination of fault diagnostic and failure prognostic methods can ensure the validity of the system’s model parameter and the reliability of the prognostic process. A potential path for future works is to benefit from the proposed multi-step online parameter estimation method to monitor other parameters of the RW model, particularly the motor torque constant (