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High-efficiency narrowband emission is always in the central role of organic optoelectronic display applications. However, the development of organic afterglow materials with sufficient color purity and high quantum efficiency for hyperafterglow is still great challenging due to the large structural relaxation and severe non-radiative decay of triplet excitons. Here we demonstrate a simple yet efficient strategy to achieve hyperafterglow emission through sensitizing and stabilizing isolated fluorescence chromophores by integrating multi-resonance fluorescence chromophores into afterglow host in a single-component copolymer. Bright multicolor hyperafterglow with maximum photoluminescent efficiencies of 88.9%, minimum full-width at half-maximums (FWHMs) of 38 nm and ultralong lifetimes of 1.64 s under ambient conditions are achieved. With this facilely designed polymer, a large-area hyperafterglow display panel was fabricated. By virtue of narrow emission band and high luminescent efficiency, the hyperafterglow presents a significant technological advance in developing highly efficient organic afterglow materials and extends the domain to new applications.

The main bottleneck faced by total variation methods for image fusion is that it is difficult to design a novel optimization model that can be solved by numerical methods. This paper proposes a general framework of total variation optimized by deep learning for infrared and visible image fusion, which combines the advantages of deep convolutional neural networks. Under this framework, any arbitrary convex or non-convex total variation model for image fusion can be designed, and its optimization solution can be obtained through neural network learning. The core idea of the proposed framework is to transform the designed variational model into a loss function of a deep convolutional neural network, and then use the initial fused image of a source image and the output fused image to represent the data item, use the output image and the source image to represent the regularization term, and finally use a deep neural network learning method to obtain the optimal fused image. Based on the proposed framework, further research on pre-fusion, network model and regularization item can be carried out. To verify the effectiveness of the proposed framework, we designed a specific non-convex total variational model and performed experiments on the infrared and visible image datasets. Experimental results show that the proposed method has strong robustness, and compared with the fused images obtained by current state-of-art algorithms in terms of objective evaluation metrics and visual effects, the fused image obtained by the proposed method has more competitive advantages. Our code is publicly available at https://github.com/gzsds/globaloptimizationimagefusion .

Objective. To investigate the changes in blood D-dimer (D-D), high-sensitivity troponin I (hs-cTnI), and N-terminal B-type brain natriuretic peptide (NT-proBNP) levels in patients with acute aortic dissection (AAD) and its clinical significance. Methods. Forty patients with AAD diagnosed in our hospital from January 2018 to December 2019 were selected as the observation group, and 40 patients with chest pain and non-AAD treated in our hospital during the same period were included in the control group. The patients were subdivided into a death group and a survival group as per the prognosis. The clinical symptoms and signs of the two groups of patients upon admission were observed, and the levels of D-D, hs-cTnI, and NT-proBNP were determined. The differences in clinical data, plasma D-D, hs-cTnI, and NT-proBNP levels between the two groups of patients were analyzed. Results. The clinical data and physical signs were homogeneous between the two groups ( P > 0.05 ), while a significant elevation in the level of hs-cTnI in the control group was observed 24 h after admission ( P < 0.05 ). The observation group showed significantly higher levels of D-D, NT-proBNP, and hs-cTnI than the control group ( P < 0.05 ). The prevalence and surgical cure rate of Stanford A in the survival group were significantly lower in contrast with the death group, with an obvious higher intervention cure rate in the survival group. Higher D-dimer and NT-proBNP levels were identified at 24 h after admission versus upon admission, and the death group had a greater increase of D-dimer and NT-proBNP levels. Conclusion. Clinical symptoms and signs are insufficient to constitute a diagnosis of AAD, whereas the elevated expression levels of D-D, hs-cTnI, and NT-proBNP demonstrated great potential for the diagnosis and prognosis of AAD.

The fusion of infrared and visible images is difficult because of their different modalities. Current fusion methods are difficult to maintain both complementary information and good visual effects, such as methods of region discrimination based on visual saliency and methods based on total variation (TV). Among them, methods of region discrimination based on visual saliency for fusion have better complementary information but poor visual consistency, while methods based on total variation for fusion have good visual consistency, but there is no proper regularization to ensure sufficient selection and fusion of complementary information. In this paper, an improved infrared and visible image fusion method via visual saliency and fractional-order total variation is proposed. First, the infrared and visible images are fused through the saliency map to obtain a fused image, and then the fused image and an selected original image are fused by the fractional-order total variation to obtain the final fused image. In this paper, visual saliency map-based fusion makes the fused image contain as much complementary information as possible from the source image, while fractional-order total variation-based fusion makes the fused image have better visual effects. Compared with the state-of-the-art image fusion algorithm, the experimental results show that the proposed method is more competitive in retaining image texture details and having visual effects.

The random noise and anisotropic motion of atmospheric turbulence can cause different degradation patterns, which make images of space targets observed from ground-based stations severely disturbed. In recent years, benefit from the development of convolutional neural networks (CNNs), a large number of effective end-to-end methods were proposed to restore images. However, a single-frame method whose input is just a single image can hardly achieve a further improvement for the restoration image due to the diversified degradation patterns of space-target images. In this paper, we proposed a multi-branch network with a multi-frame input to restore space-target images. The multi-frame input contains space-target images which own different degradation patterns at different moments. In this way, we can fully use the complementary information between input frames. And in this network, two effective technologies are introduced: one is the full resolution convolution module which extracts features by using convolutional layers with different dilation rates to keep feature information complete; the other is the branch-attention module which is used to pass effective information between different branches of the network. Furthermore, we demonstrated the effectiveness of our method by comparing it with those state-of-the-art methods.

Video-based anomalous human behavior detection is widely studied in many fields such as security, medical care, education, and energy. However, there are still some open problems in anomalous behavior detection, such as the large and complicated model is difficult to train, the accuracy of anomalous behavior detection is not high enough and the speed is not fast enough. A spatiotemporal representation learning model is proposed in this paper. Firstly, the spatial-temporal features of the video are extracted by the constructed multi-scale 3D convolutional neural network. Then the scene background is modeled by the high-dimensional mixed Gaussian model and used for anomaly detection. Finally, the accurate position of anomalous behavior in the video data is achieved by calculating the position of the last output feature, that is, the position of the receptive field. The proposed model does not require specific training. Moreover, the proposed method has the advantages of high versatility, fast calculation speed and high detection accuracy. We validated the proposed algorithm on two representative surveillance scene datasets, the Subway and the UCSDSped2. Results show that proposed algorithm has achieved the detection rate of 18 FPS under the condition of common computing resources, and meet the real-time requirements. Moreover, compared the similar methods, the proposed method has achieved the competitive results in both frame-level accuracy and pixel-level accuracy.

Deep learning has achieved significant success in various applications due to its powerful feature representations of complex data. Financial time series forecasting is no exception. In this work we leverage Generative Adversarial Nets (GAN), which has been extensively studied recently, for the end-to-end multi-classification of financial time series. An improved generative model based on Convolutional Long Short-Term Memory (ConvLSTM) and Multi-Layer Perceptron (MLP) is proposed to effectively capture temporal features and mine the data distribution of volatility trends (short, neutral, and long) from given financial time series data. We empirically compare the proposed approach with state-of-the-art multi-classification methods on real-world stock dataset. The results show that the proposed GAN-based method outperforms its competitors in precision and F1 score.

Effective and concise feature representation is crucial for time series mining. However, traditional time series feature representation approaches are inadequate for Financial Time Series (FTS) due to FTS' complex, highly noisy, dynamic and non-linear characteristics. Thus, we proposed an improved linear segmentation method named MS-BU-GA in this work. The critical data points that can represent financial time series are added to the feature representation result. Specifically, firstly, we propose a division criterion based on the quantile segmentation points. On the basis of this criterion, we perform segmentation of the time series under the constraint of the maximum segment fitting error. Then, a bottom-up mechanism is adopted to merge the above segmentation results under the maximum segment fitting error. Next, we apply Genetic Algorithm (GA) to the merged results for further optimization, which reduced the overall segment representation fitting error and the integrated factor of segment representation error and number of segments. The experimental result shows that the MS-BU-GA has outperformed existing methods in segment number and representation error. The overall average representation error is decreased by 21.73% and the integrated factor of the number of segments and the segment representation error is reduced by 23.14%.

The characterization of pressure-sensitive paint (PSP) is affected by many physical and chemical factors, making it is difficult to analyze the relationship between characterization and influencing factors. An artificial neural network (ANN)-based method for predicting pressure sensitivity using paint thickness and roughness was proposed in this paper. The mean absolute percentage error (MAPE) for predicting pressure sensitivity is 6.5088%. The difference of paint thickness and roughness between sample and model surface may be a source of experimental error in PSP pressure measurement tests. The Stern-Volmer coefficients A and B are strongly linked. Pressure sensitivity is approximately equal to coefficient B, so coefficient A is predicted using pressure sensitivity based on the same ANN, and the MAPE of predicting A is 2.1315%. Then, we try to calculate the pressure by using the thickness and roughness on a model to predict pressure sensitivity and Stern-Volmer coefficient A. The PSP pressure measurement test was carried out at the China Aerodynamic Research and Development Center. Using the Stern-Volmer coefficient calculated by the in situ method, the method in this paper, and the sample calibration experiment, the root mean square errors (RMSE) of the pressure are 47.4431 Pa, 63.4736 Pa, and 73.0223 Pa, respectively.

Infrared small target detection is widely used for early warning, aircraft monitoring, ship monitoring, and so on, which requires the small target and its background to be represented and modeled effectively to achieve their complete separation. Low-rank sparse decomposition based on the structural features of infrared images has attracted much attention among many algorithms because of its good interpretability. Based on our study, we found some shortcomings in existing baseline methods, such as redundancy of constructing tensors and fixed compromising factors. A self-adaptive low-rank sparse tensor decomposition model for infrared dim small target detection is proposed in this paper. In this model, the entropy of image block is used for fast matching of non-local similar blocks to construct a better sparse tensor for small targets. An adaptive strategy of low-rank sparse tensor decomposition is proposed for different background environments, which adaptively determines the weight coefficient to achieve effective separation of background and small targets in different background environments. Tensor robust principal component analysis (TRPCA) was applied to achieve low-rank sparse tensor decomposition to reconstruct small targets and their backgrounds separately. Sufficient experiments on the various types data sets show that the proposed method is competitive.

Dim and small target detection based on passive millimeter wave or infrared imaging is of great value in both security and military fields and has been studied extensively. The problems of weak distinction between small targets and backgrounds and of less extractable features of targets have always been a technical bottleneck for accurate detection of dim and small targets. For dim and small targets with few pixel-based features on complex and diverse backgrounds, we propose a high-precision detection algorithm based on feature mapping deep neural networks with a spindle network structure. Firstly, the features of low-dimension dim and small target blocks are mapped to a higher-dimensional space. An encoded neural network is then used to extract high-discriminant features to complete the background and target recognition. Background suppression and target enhancement is realized according to the intensity (the distinguished output of the network). Finally, a detection method based on the constant false alarm rate is used to detect dim and small targets. The experimental results show that, compared with several popular algorithms for millimeter-wave and infrared image detection in different scenarios, the proposed algorithm has a lower false alarm rate, higher detection accuracy and stronger robustness. Statistics for experiments on under various false alarm rates and signal-to-noise ratios show that the detection rate of the proposed method is about 15% higher than that of the compared algorithms. In experiments on real data, the detection rate of our algorithm is more than 25% higher than that of the suboptimal algorithm.

A railway track model with the tuned rail damper (TRD) is developed to simulate the system's dynamic response. Two criteria are proposed to estimate the inherent performance and practical effects of the TRD on railway rolling noise. Analysis and simulation results demonstrate that the inherent property of the TRD does not change, but its practical effects may vary with environmental conditions. The theoretical predictions are validated by in-situ measurements. The proposed methodology, which combines the simulation results with the measurement data, is straightforward and easy to use for estimation of the practical effects on rolling noise reduction of the TRD to be used.

Atmospheric turbulence-degraded images in typical practical application scenarios are always disturbed by severe additive noise. Severe additive noise corrupts the prior assumptions of most baseline deconvolution methods. Existing methods either ignore the additive noise term during optimization or perform denoising and deblurring completely independently. However, their performances are not high because they do not conform to the prior that multiple degradation factors are tightly coupled. This paper proposes a Noise Suppression-based Restoration Network (NSRN) for turbulence-degraded images, in which the noise suppression module is designed to learn low-rank subspaces from turbulence-degraded images, the attention-based asymmetric U-NET module is designed for blurred-image deconvolution, and the Fine Deep Back-Projection (FDBP) module is used for multi-level feature fusion to reconstruct a sharp image. Furthermore, an improved curriculum learning strategy is proposed, which trains the network gradually to achieve superior performance through a local-to-global, easy-to-difficult learning method. Based on NSRN, we achieve state-of-the-art performance with PSNR of 30.1 dB and SSIM of 0.9 on the simulated dataset and better visual results on the real images.

FAST is designed, developed, manufactured and constructed by Chinese scientists. It took 22 years from the pilot study in 1994 to its completion in 2016. Its design and construction witnessed three major independent and endogenous innovations.

While looking up to the sky, humans always wondering who we are, where we come from and whether we are alone. In the vast universe, are there other civilizations? For thousands of years, man has merely observed the universe through the visible spectrum, while the radiation from celestial bodies covers the entire electromagnetic spectrum.

With the receiving area of approximately 30 soccer fields, the 500-m aperture FAST will maintain its status as the world-class equipment in the next 10–20 years. As it has been completed and put into operation, it will provide opportunities for astronomical development.

Due to the favorable denoising performance of the discriminative learning model, a large number of discriminative learning models have been designed to remove noise. However, most discriminative learning models can only deal with the noise that already exists in the training data. In this paper, a multi-scale trainable deep residual convolutional neural network (DCMSNet) based on dilated convolution is proposed. DCMSNet consists of a chain of dilated convolution layers, convolution layers, normalized multi-scale convolution blocks (BNMCBlock), multi-scale convolution blocks (MCBlock) and multi-scale dropout convolution blocks (MCDBlock). The use of dilated convolution can avoid over-parameterization problems caused by too deep networks. In order to capture more feature information during feature extraction and image reconstruction, we designed novel BNMCBlock and MCBlock. In order to reduce the degree of coupling between image features and improve the generalization ability of the network, we also designed a MCDBlock. Meanwhile, residual learning, batch normalization and dropout are utilized to speed up the training process and boost the denoising performance. Unlike existing denoising models, DCMSNet is able to remove different degrees of noise. Compared with state-of-the-art image denoising methods, DCMSNet has achieved relatively competitive results.

Most existing image restoration methods based on deep neural networks are developed for images which only degraded by a single degradation mode and imaging under an ideal condition. They cannot be directly used to restore the images degraded by multi-factor coupling. A complex task decomposition regularization optimization strategy (TDROS) is proposed to solve the problem. The restoration of images degraded by multi-factor coupling is a complex task that can be solved by separating these multiple factors, that is, breaking the complex task into numbers of simpler tasks to make the entire complex problem be overcome more easily. Motivated by this idea, the TDROS decomposes the complex task of image restoration into two sub-task: the potential task constrained by regularization and the main task for reconstructing high-definition images. In TDROS, the front of the neural network is focused on the restoration of images degraded by additive noise, while the other part of the network is focused mainly on the restoration of images degraded by blur. We applied the TDROS to an 11-layer convolutional neural network (CNN) and compared it with initial CNNs from the aspects of restoration accuracy and generalization ability. Based on these results, we used TDROS to design a novel network model for the restoration of atmospheric turbulence-degraded images. The experimental results demonstrate that the proposed TDROS can improve the generalization ability of the existing network more effectively than current popular methods, offering a better solution for the problem of severely degraded image restoration. Moreover, the TDROS concept provides a flexible framework for low-level visual complex tasks and can be easily incorporated into existing CNNs.

Dim and small targets detection in a cluttered background requires high discrimination features to separate them from the noisy environment. Previous local contrast measure based methods lack scale invariance and also cannot fully learn the features of target and target's living environment. Semantic segmentation based methods are helpless for very dim and small targets. A 3D-based convolutional network for analyzing and reconstructing dim and small targets is proposed in this paper. To separate targets from their living environment effectively, A 3D tensor is extracted around the target and used as the input of the proposed analytical network. Then, comprehensive features and environmental information of the targets are extracted by a serial of 3D convolution layers. To improve the reconstruction ability of the proposed model, a trick of skip-connection is adopted to fuse the abstract features and detailed features. The block attention module and dilated convolution can extract important and high-level features. After the dim and small targets are reconstructed, a detection method based on the constant false alarm rate (CFAR) is adopted to detect and locate these targets. Experiments on simulated data and real data with diverse information demonstrate that the proposed approach outperforms the state-of-the-art methods.

The blind restoration of a scene distorted by atmospheric turbulence remains a challenging problem for space target video surveillance. As there are multiple factors coupling the degradation of space target images, traditional methods based on a single, simplified image blind restoration model have difficulty achieving the desired results. In this paper, a new convolutional auto-encoder deep neural network is proposed for modeling the degradation and restoration process of spatial target images. The whole network consists of two parts, convolution and deconvolution, which are used to achieve the purpose of degraded feature learning and blind image restoration, respectively. Simulation image training data are constructed by a series of 3D space target models and combined with the turbulence multi-factor degradation model. The neural network model is then trained with this data. Contrast experiments are conducted using the simulation image data and real image data, and the results show that the proposed method is robust to noise and the reconstructed images have clearer edge details. The output images also have better continuous consistency and superior visual effects.

Atrial fibrosis influences atrial fibrillation (AF) development by transforming growth factor beta 1 (TGF-β1)/Smad pathway. Although microRNAs are implicated in the pathogenesis of various diseases, information regarding the functional role of microRNAs in atrial dysfunction is limited. In the present study, we found that microRNA-27b (miR-27b) was the dominant member of miR-27 family expressed in left atrium. Moreover, the expression of miR-27b was significantly reduced after angiotensin II (AngII) infusion. Masson's trichrome staining revealed that delivery of miR-27b adeno-associated virus to left atrium led to a decrease in atrial fibrosis induced by AngII. The increased expression of collagen I, collagen III, plasminogen activator inhibitor type 1 and alpha smooth muscle actin was also inhibited after miR-27b upregulation. In isolated perfused hearts, miR-27b restoration markedly attenuated AngII-induced increase in interatrial conduction time, AF incidence and AF duration. Furthermore, our data evidence that miR-27b is a novel miRNA that targets ALK5, a receptor of TGF-β1, through binding to the 3' untranslated region of ALK5 mRNA. Ectopic miR-27b suppressed luciferase activity and expression of ALK5, whereas inhibition of miR-27b increased ALK5 luciferase activity and expression. Additionally, miR-27b inhibited AngII-induced Smad-2/3 phosphorylation without altering Smad-1 activity. Taken together, our study demonstrates that miR-27b ameliorates atrial fibrosis and AF through inactivation of Smad-2/3 pathway by targeting ALK5, suggesting miR-27b may play an anti-fibrotic role in left atrium and function as a novel therapeutic target for the treatment of cardiac dysfunction.

Ultra-thin layered double hydroxides (LDHs) with small particle sizes were synthesized using a deep eutectic solvent (DES). Products were characterized and applied towards boron removal. The results showed that the LDHs were successfully prepared in a DES (I-LDH) with a narrow diameter distribution (10–40 nm) and a monolayer of around 0.7 nm. Due to the reduced size, the interaction between the carbonate and metal layers was weak; therefore, I-LDH was found to have a higher sorption capacity to boron than that of LDH prepared through the urea method. The calcinated products (I-CLDH) also exhibited excellent sorption efficiency. The sorption mechanisms of I-LDH, I-CLDH, and U-CLDH were studied. Ion exchange was found to be the predominant mechanism of boron removal by I-LDH. However, for samples after calcination, two stages were present; the first stage included surface complexation and electrostatic attraction, and the second stage included immobilizing boric acid into Mg(OH)2 and attracting borate as an interlayer anionic species into the newly formed LDHs. In addition, pH was found to have a slight influence on sorption performance, although co-existing anions, especially carbonates, played a role in hindering sorption. Base on the Langmuir sorption isotherm, the sorption density of I-CLDH showed a good capacity for removing borate due to the ultrathin structure and high surface area. In addition, the influence of temperature, shaking speed, and dosage on borate sorption by I-CLDH has been studied. I-CLDH was also tested for borate removal from actual wastewater.

Multi-modal image fusion is necessary for describing a target abundantly. With the consideration of the correlations between multi-source signals and the sparse characteristics of image, this paper proposed a novel fusion rule of multi-modal image fusion scheme based on the joint sparsity model. First, the source image was represented as a shared sparse component and an exclusive sparse component with an over-complete dictionary. Second, the designed novel fusion rule acts on the shared and exclusive sparse coefficients to obtain the fused sparse coefficients. Finally, fused image was reconstructed by the fused sparse coefficients and the dictionary. The proposed approach was tested on the infrared and visual images, medical images. The results were compared with those of traditional methods, such as the multi-scale transform based methods, sparse representation based methods and joint sparsity representation based methods. Experimental results demonstrated that the proposed method outperforms the existing state-of-the-art methods, in terms of better texture clarity. Moreover, the fused image shows better edge consistence and visual effect.

This paper reports on the time and frequency standard system for the Five-hundred meter Aperture Spherical radio Telescope (FAST), including the system design, stability measurements and pulsar timing observations. The stability and drift rate of the frequency standard are calculated using 1-year monitoring data. The UTC-NIM Disciplined Oscillator (NIMDO) system improves the system time accuracy and stability to the level of 5 ns. Pulsar timing observations were carried out for several months. The weighted RMS of timing residuals reaches the level of less than 3.0 μs.

Atmospheric turbulence can change the path and direction of light during the imaging of a target in space due to the random motion of the turbulent medium, resulting in severe image distortion. To correct geometric distortion, and reduce spatially and temporally varying blur, this paper proposes a convolutional network for blind deblurring atmospheric turbulence (BDATNet) that includes a feature extraction noise suppression block (FENSB), an asymmetric U-net, and an image reconstruction subnetwork (IRSubnetwork). A deblurring noise suppression block (DNSB) is used instead of the traditional convolution layer for the U-net. The core principle of this model is to suppress noise before deblurring. During convolutional encoding, the FENSB and DNSB can suppress noise and capture rich feature maps. To fuse information obtained from low-level and high-level features, the FENSB and IRSubnetwork are skip-connected to ensure the integrity of the former during image reconstruction. Moreover, the method of gradually increasing the difficulty of data to train the network is used to cause it to gradually converge from simple to complex, so that it can deal with images severely degraded by turbulence. The experimental results of real data and simulation data show that the BDATNet can restore details of the image and sharpen its edges, and can suppress noise.

It is well known that the point targets observed by the space-based platform are extremely difficult to track due to the interference of a large number of stellar targets and background noise. The traditional GM-PHD, due to its probability accumulation, is easy to mistakenly identify the star as a derived new target when the real target passes closely to the star. In order to achieve precise tracking of multiple moving point targets for space-based observations in a complex background environment such as a starry sky background, this paper proposes a simple and effective combined penalized weights based GM-PHD for filtering. First, a unique label is given for each target to determine which real target the estimate came from. Next, the moving region of target is discretized into 10 irregular intervals to construct ten different motion templates, numbered one by one, for discretization of the direction of the target motion. Next, the true target at time k − 1 and all the estimated values having the same label at time k are used to calculate the direction penalty factor and the speed penalty factor to obtain the penalty strength matrix and penalized weight matrix, respectively. Finally, the penalized weight matrix is used to output the target with the highest weight in each label as the real target at time k. And a given penalty threshold is used to remove some fuzzy weights with greater penalty in the penalty strength matrix, so as to selectively filter out some less relevant targets to further avoid the stars participating in the next iteration. In order to verify the effectiveness of the proposed algorithm, we used the Tycho-2 catalog to simulate four kinds of starry-sky data sets with different complexity and conducted comparative experiments on each data set. The experimental results show that the proposed algorithm can effectively track multiple space-based infrared weak point targets in complex starry-sky scenarios.

The structural memory effect of layered double hydroxides (LDHs) is one of the important reasons for their extensive use in environmental remediation. In this study, humic acid (HA) was extracted from black soil and sediments and characterized to determine their structures. The regeneration mechanisms of calcinated LDHs (CLDHs) including different divalent metals (Mg-CLDH and Zn-CLDH) in deionized water and different HA solutions were carefully elucidated, and the reasons for the behavior differences in the two materials were explained. The presence of the HAs significantly increased the dissolution rate of Mg2+ ions from Mg-CLDHs and subsequent regeneration of Mg-LDH. Because of the diverse functional groups in the HAs, these groups were complexed with metallic ions such as Mg2+ on the surface of Mg-CLDH in the beginning. During the process, the HAs adsorbed the regenerated LDHs on the surfaces. Therefore, the crystallinity, morphology, and specific surface area of the regenerated Mg-LDH significantly ch...

It is an important basis for spectrum management to predict accurately the change trend of parameters in electromagnetic environment, which can help decision makers to develop the optimal action for reducing risk and improving the economic and social benefits. Markov prediction model as a heuristic method is widely used in nonlinear systems. For two shortages in traditional Markov model, this paper puts forward a new fuzzy Markov model based on FCM, in which, FCM algorithm is used to divide time series into a serial fuzzy states, the membership function is employed to calculate membership vector for each object about every fuzzy state. Then, using membership vector of predictive point as the weights, calculating predictive value by clustering center. Finally, the prediction model proposed in this paper is applied to predict indicate parameters in electromagnetic environment. The experimental results showed that the prediction model proposed in this paper is not only reasonable, effective, and also the prediction accuracy significantly improved.

P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive tok-means algorithm and several evolutionary clustering algorithms recently reported in the literature.

The parameters of a radio environment map play an important role in radio management and cognitive radio. In this paper, a method for estimating the parameters of the radio environment map based on the sensing data of monitoring nodes is presented. According to the principles of radio transmission signal intensity losses, a theoretical variogram model based on a propagation model is proposed, and the improved theoretical variation function is more in line with the attenuation of radio signal propagation. Furthermore, a weight variogram fitting method is proposed based on the characteristics of field strength parameter estimation. In contrast to the traditional method, this method is more closely related to the physical characteristics of the electromagnetic environment parameters, and the design of the variogram and fitting method is more in line with the spatial distribution of electromagnetic environment parameters. Experiments on real and simulation data show that the proposed method performs better than the state-of-the-art method.

The Support Vector Machine (SVM) is a widely used tool in classification problems, but the classification performance of Support Vector Machine (SVM) largely depends on the choice of its relevant parameters. This paper proposes a model of Support Vector Machine (SVM) classification based on Cell-like Membrane computing Optimization algorithm (CMO-SVM). In the model, the parameters of Support Vector Machine (SVM) (cost parameter C and RBF kernel parameter ) are optimized by cell-like membrane computing optimization algorithm for the sake of getting the best combination parameters of SVM for classification. This method overcomes the insufficiency of the conventional method which converged to local optimum, at the same time also has the advantages of good robustness, fast convergence speed and obtains the global optimal solution. Finally, to show the applicability and superiority of the proposed algorithm, the method is employed to identify abnormal signal of c-band radio (including radar, jammer, single carrier and single frequency point). Compared with Genetic Algorithm-based SVM (GA-SVM), Simulated Annealing algorithm-based SVM (SA-SVM), Ant Colony algorithm-based SVM (AC-SVM), the proposed model performs best for the four abnormal signal.

A multimodal image fusion method based on the joint sparse model (JSM), multiscale dictionary learning, and a structural similarity index (SSIM) is presented. As an effective signal representation technique, JSM is derived from distributed compressed sensing and has been successfully employed in many image-processing applications such as image classification and fusion. The highly redundant single dictionary always has difficulty satisfying the correlations between images in traditional JSM-based image fusion. Therefore, the proposed fusion model learns a more compact multiscale dictionary to effectively combine the multiscale analysis used in nonsubsampled contourlet transformation with the single-scale joint sparse representation used in image domains to solve the issues of single-scale sparse fusion and to improve fusion quality. The experimental results demonstrate that the proposed fusion method obtains the state-of-the-art performances in terms of both subjective visual quality and objective metrics, especially when fusing multimodal images.

An adaptive joint sparsity model (JSM) is presented for multimodal image fusion. As a multisignal modeling technique, JSM, which is derived from distributed compressed sensing, has been successfully employed in multimodal image fusion. In traditional JSM-based fusion, a single dictionary learned by K-singular value decomposition (SVD) has higher coherence yet may result in potential visual confusion and misleading. In the proposed model, we first learn a plurality of subdictionaries and use a supervised classification approach based on gradient information. Then, one of the learned subdictionaries is adaptively applied to JSM to obtain the common and innovative sparse coefficients.. Finally, the fused image is reconstructed by the fused sparse coefficients and the adaptive dictionary. Infrared-visible images and medical images were selected to test the proposed approach. The results were compared with those of traditional methods, such as the multiscale transform-based methods, JSM-based method, and adaptive sparse representation (ASR) model-based method. Experimental results on multimodal images demonstrate that the proposed fusion method can obtain better performance than the conventional JSM-based method and ASR-based method in terms of both visual quality and objective assessment.

High precision detection of small targets in complex background is a challenging task, which has not been well resolved. In this paper, the improve sparse representation(ISR) algorithm is proposed based on the characteristics of passive millimeter wave imaging as well as the difference of the priori characteristics between the small target and background clutter. Firstly, the algorithm constructs a over-complete dictionary based on the content of the image itself, and then improves the original sparse representation method to complete precise classification of target and background dictionaries. After background suppression and target enhancement, we can easily extract the target. The millimeter wave images of different scenes are detected and the results show that compared with some other mainstream algorithms, the ISR algorithm has lower false alarm rate, higher detection accuracy and stronger robustness.

This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction(τ,m)and least squares support vector machine (LS-SVM)(γ,σ)by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Optical and infrared imaging is often used in ground-based optical space target observation. The fusion of the two types of images for a more detailed observation is the key problem to be solved. A space target multimodal image fusion scheme based on the joint sparsity model, which takes the correlations among the native sparse characteristics of the image, clarity features of the image, and multisource images into consideration, is proposed. First, using an overcomplete dictionary, the source images are represented as a combination of a shared sparse component and exclusive sparse components. Second, a method for image clarity feature extraction is proposed to design the fusion rules of exclusive sparse components to obtain the fused exclusive sparse components. Finally, the fused image is reconstructed with the fused sparse components and overcompleted dictionary. The proposed method was tested on the space target image and nature scene image data sets. Compared with traditional methods such as the multiscale transform-based methods, sparse representation-based methods, and joint sparsity representation-based methods, the final experimental results demonstrated that our method outperforms the existing state-of-the-art methods on the human visual effect and the objective evaluation indexes. In particular, for the evaluation indexes Q A B / F and Q E , the scores increase to nearly 10% more than those for traditional methods, which indicates that the fused image of our method has better edge clarity.

The distribution of water between toluene and the ionic cores of sodium bis(2-ethylhexyl) sulfosuccinate (AOT) and those of block ionomer reverse micelles was evaluated by 1H chemical shift measurements of water at different temperatures. The block ionomer reverse micelles investigated were composed of a nonionic polystyrene (PS) block attached to an ionic block consisting of either poly(sodium methacrylate) (PMANa), poly(sodium acrylate) (PANa), poly(cesium acrylate) (PACs), or poly((4-vinylpyridinium)methyl iodide) (P4VPMeI). The water content is described by the ratio R, defined as the molar ratio of water either to the number of moles of surfactant for AOT or to the number of moles of ionic repeat units for the block ionomers. It was found that for R = 6 and at 25 °C, the distribution coefficient of water (K) decreases in the following order for the different ionic groups in the micelle cores: COO-Cs+ > SO3-Na+ ≈ COO-Na+ ≫ Npy+(Me)I-, where Npy+(Me)I- represents pyridine quarternized with methyl iodi...

Publised version of an article from the journal:Eurasip Journal on Image and Video Processing. Also available on SpringerLink:http://dx.doi.org/10.1186/1687-5281-2013-20. Open Access Co-segmentation aims at segmenting common objects from a group of images. Markov random field (MRF) has been widely used to solve co-segmentation, which introduces a global constraint to make the foreground similar to each other. However, it is difficult to minimize the new model. In this paper, we propose a new Markov random field-based co-segmentation model to solve co-segmentation problem without minimization problem. In our model, foreground similarity constraint is added into the unary term of MRF model rather than the global term, which can be minimized by graph cut method. In the model, a new energy function is designed by considering both the foreground similarity and the background consistency. Then, a mutual optimization approach is used to minimize the energy function. We test the proposed method on many pairs of images. The experimental results demonstrate the effectiveness of the proposed method.

In this paper, we present a linguistic interval 2-tuple representation model and new linguistic interval 2-tuple aggregation operators, i.e., linguistic interval 2-tuple power average (LI2TPA) operator, linguistic interval 2-tuple weighted power average (LI2TWPA) operator and linguistic interval 2-tuple power ordered weighted average (LI2TPOWA) operator. Some desired properties of the developed operators are also studied. Moreover, we use these aggregation operators to deal with multiple attribute group decision making problems under linguistic interval 2-tuple environment. In the situations where the weighting vector of the decision makers is known, we use the LI2TWPA operator to make multiple attribute group decision. In the situations where the weighting vector of the decision makers is unknown, we use the LI2TPOWA operator to deal with multiple attribute group decision making. A numerical example is provided to show the effectiveness of our method.

It is found that crew-cut micelles with very narrow size distribution, stable in water, can be prepared from monodisperse amphiphilic styrene-vinylpyridinium methyl iodide block ionomers synthesized by anionic polymerization

A methods is proposed for the determination of the critical micelle concentration (cmc) of block copolymer micelles fromstatic light scattering measurements, which is based on a recent model of micellization of block copolymers. The method considers the polydispersity of the block copolymers, the variation of the total single chain concentration with total concentration, and the relationship between the cmc and the length of the insoluble block. One family of block ionomers, polystyrene(660)-b-poly(sodium acrylate) with a low polydispersity index and ionic block lengths varying from 2.6 to 14 units, was investigated by light scattering near the cmc in THF

The effect of LiCl on the anionic polymerization of tert-butyl methacrylate (tBuMA) initiated with monofunctional alkali metal-based carbanionic species was investigated at [minus]78 C in THF. The propagation rate of the polymerization process was determined by gas chromatography and by gravimetry. It was found that, in the presence of LiCl in a molar ratio of 5 with respect to the initiator, the rate constant, k[sub p], is 20 times lower than that in the absence of LiCl. The polymers and oligomers were analyzed by size exclusion chromatography (SEC) and [sup 13]C NMR spectroscopy. The polymers obtained in the absence of LiCl had broad molecular weight distributions and contained significant amounts of oligomers, while those obtained in the presence of LiCl were monodisperse, without any noticeable oligomers. [sup 13]C NMR showed that the isotactic content of the polymers increased significantly when the molar ratio of LiCl to the initiator was higher than 2. The effect of LiCl on the rate constant and on the microstructure of the polymers was attributed to complex formation between LiCl and the living polymer chains.